Essentialism written by Greg Mckeown is a book centered me to
essentialism from my minimalism mindset and lifestyle. Dan recommended
it to me in our first coffee chat :)
What Ela Bhatt said reminds me how I became a minimalism -
Out of all virtues simplicity is my most favorite virtue. So much so
that I tend to believe that simplicity can solve most of the problems,
personal as well as the world problems. If the life approach is simple
one need not lie so frequently, nor quarrel nor steal, nor envy, anger,
abuse, kill. Everyone will have enough and plenty so need not hoard,
speculate, gamble, hate. When character is beautiful, you are beautiful.
That is the beauty of simplicity.
What is the Core
Mindset of an Essentialist?
The way of the Essentialist means living by design, not by default.
Instead of making choices reactively, the Essentialist deliberately
distinguishes the vital few from the trivial many, eliminates the
nonessentials, and then removes obstacles so the essential things have
clear, smooth passage. In other words, Essentialism is a disciplined,
systematic approach for determining where our highest point of
contribution lies, then making execution of those things almost
effortless.
It is a discipline you apply each and every time you are faced with a
decision about whether to say yes or whether to politely decline. It’s a
method for making the tough trade-off between lots of good things and a
few really great things. It’s about learning how to do less but better
so you can achieve the highest possible return on every precious moment
of your life.
A Nonessentialist approaches every trade-off by asking, “How can I do
both?” Essentialists ask the tougher but ultimately more liberating
question, “Which problem do I want?” An Essentialist makes trade-offs
deliberately. She acts for herself rather than waiting to be acted upon.
As economist Thomas Sowell wrote: “There are no solutions. There are
only trade-offs.”
It’s not only about mental discipline.
Explore:
Discerning The Trivial Many from the Vital Few
Essentialisms systematically and deliberately explore and evaluate a
broad set of options at first to ensure that they pick the right one
later. This exploration requires time and space and this can be seen as
trivial and unnecessary by nonessentialists.
We need to look for the lead rather than being distracted by minor
details. we need to pay attention to those not explicitly stated rather
than everything. We not only capture the dots, but also connect them to
see the trends.
He (Frank O’Brien) wrote: “I think it’s critical to set aside time to
take a breath, look around, and think. You need that level of clarity in
order to innovate and grow.” Furthermore, he uses the meeting as a
litmus test to alert him if employees are spending too much time on the
nonessential: “If somebody can’t make the meeting because of too much
going on, that tells me either we’re doing something inefficiently or we
need to hire more people.” If his people are too busy to think, then
they’re too busy, period.
Being a journalist of your own life will force you to stop
hyper-focusing on all the minor details and see the bigger picture. You
can apply the skills of a journalist no matter what field you are in-you
can even apply them to your personal life. By training yourself to look
for “the lead,” you will suddenly find yourself able to see what you
have missed. You’ll be able to do more than simply see the dots of each
day: you’ll also connect them to see the trends. Instead of just
reacting to the facts, you’ll be able to focus on the larger issues that
really matter.
We need to play to relieve stress and expand minds in ways that allow
us to generate new ideas. We also need enough sleep hours to allow new
neural connections to be made.
Play expands our minds in ways that allow us to explore: to germinate
new ideas or see old ideas in a new light. It makes us more inquisitive,
more attuned to novelty, more engaged.
The best asset we have for making a contribution to the world is
ourselves. If we underinvest in ourselves, and by that I mean our minds,
our bodies, and our spirits, we damage the very tool we need to make our
highest contribution. One of the most common ways people-especially
ambitious, successful people-damage this asset is through a lack of
sleep.
Sleep Is the New Status Symbol for Successful Entrepreneurs.
Eliminate: Cutting Out
the Trivial Many
Clarity is the key.
When there is a serious lack of clarity about what the team stands
for and what their goals and roles are, people experience confusion,
stress, and frustration. When there is a high level of clarity, on the
other hand, people thrive.
When there is a lack of clarity, people waste time and energy on the
trivial many. When they have sufficient levels of clarity, they are
capable of greater breakthroughs and innovations- greater than people
even realize they ought to have- in those areas that are truly
vital.
Creating an essential intent is hard. It takes courage, insight, and
foresight to see which activities and efforts will add up to your single
highest point of contribution. It takes asking tough questions, making
real trade-offs, and exercising serious discipline to cut out the
competing priorities that distract us from our true intention. Yet it is
worth the effort because only with real clarity of purpose can people,
teams, and organizations fully mobilize and achieve something truly
excellent.
We need to make our choices about where to focus our energy and time
purposefully and deliberately. This is necessary because “If you don’t
prioritize your life, someone else will”. And this is not easy because
sometime we are going against social expectation and we are under social
pressure. We need courage and grace to navigate some hard moments.
The only way out of this trap is to learn to say no firmly,
resolutely, and yet gracefully. Because once we do, we find, not only
that our fears of disappointing or angering others were exaggerated, but
that people actually respect us more. Since becoming an Essentialist I
have found it almost universally true that people respect and admire
those with the courage of conviction to say no.
How do we learn to say no gracefully?
Separate the decision from the relationship
Saying “No” gracefully doesn’t have to mean using the word
No
Focus on the trade-off
Remind yourself that everyone is selling something
I am simply saying everyone is selling something-an idea, a
viewpoint, an opinion-in exchange for your time. Simply being aware of
what is being sold allows us to be more deliberate in deciding whether
we want to buy it.
Make your peace with the fact that saying “No” often requires
trading popularity for respect
When the initial annoyance or disappointment or anger wears off, the
respect kicks in. When we push back effectively, it shows people that
our time is highly valuable. It distinguishes the professional from the
amateur.
Becoming an Essentialism requires us to eliminate things that are
really good in order to save time and space for something better.
The Latin root of the word decision-cis or cid-literally means “to
cut” or “to kill.” Since ultimately, having fewer options actually makes
a decision “easier on the eye and the brain,” we must summon the
discipline to get rid of options or activities that may be good, or even
really good, but that get in the way. Yes, making the choice to
eliminate something good can be painful. But eventually, every cut
produces joy-maybe not in the moment but afterwards, when we realize
that every. additional moment we have gained can be spent on something
better. That may be one reason why Stephen King has written, “To write
is human, to edit is divine.”
Condensing doesn’t mean doing more at once, it simply means less
waste. It means lowering the ratio of words to ideas, square feet to
usefulness, or effort to results. Thus to apply the principle of
condensing to our lives we need to shift the ratio of activity to
meaning. We need to eliminate multiple meaningless activities and
replace them with one very meaningful activity.
Becoming an Essentialist means making cutting, condensing, and
correcting a natural part of our daily routine-making editing a natural
cadence in our lives.
Setting boundaries is not having limits of life nor evidence of
weakness. It’s a way of avoiding being distracted by something that is
essential to others rather than that is essential to ourselves. We need
to articulate our boundaries and set them up in advance.
After all, if you don’t set boundaries-there won’t be any. Or even
worse, there will be boundaries, but they’ll be set by default-or by
another person-instead of by design.
Essentialists, on the other hand, see boundaries as empowering. They
recognize that boundaries protect their time from being hijacked and
often free them from the burden of having to say no to things that
further others’ objectives instead of their own. They know that clear
boundaries allow them to proactively eliminate the demands and
encumbrances from others that distract them from the true
essentials.
Whoever it is that’s trying to siphon off your time and energies for
their own purpose, the only solution is to put up fences. And not at the
moment the request is made, you need to put up your fences well in
advance, clearly demarcating what’s off limits so you can head off time
wasters and boundary pushers at the pass.
The simple reality is, if you can’t articulate these to yourself and
others, it may be unrealistic to expect other people to respect them or
even figure them out.
Execution:
Removing Obstacles and Making Execution Effortless
Time and space are required as a buffer to reduce friction and ensure
success.
The way of the Essentialist is different. The Essentialist looks
ahead. She plans. She prepares for different contingencies. She expects
the unexpected. She creates a buffer to prepare for the unforeseen, thus
giving herself some wiggle room when things come up, as they inevitably
do.
Essentialists accept the reality that we can never fully anticipate
or prepare for every scenario or eventuality; the future is simply too
unpredictable. Instead, they build in buffers to reduce the friction
caused by the unexpected.
Essentialists produce more by removing more instead of doing
more.
Essentialists don’t default to Band-Aid solutions. Instead of looking
for the most obvious or immediate obstacles, they look for the ones
slowing down progress. They ask, “What is getting in the way of
achieving what is essential?” While the Nonessentialist is busy applying
more and more pressure and piling on more and more solutions, the
Essentialist simply makes a one-time investment in removing obstacles.
This approach goes beyond just solving problems; it’s a method of
reducing your efforts to maximize your results.
Essentialists make progress by making small and concrete wins. It’s
harder to make achievement when you set big, lofty, and impossible
goals.
The way of the Nonessentialist is to go big on everything: to try to
do it all, have it all, fit it all in. The Nonessentialist operates
under the false logic that the more he strives, the more he will
achieve, but the reality is, the more we reach for the stars, the harder
it is to get ourselves off the ground.
The way of the Essentialist is different. Instead of trying to
accomplish it all and all at once, and flaring out, the Essentialist
starts small and celebrates progress. Instead of going for the big,
flashy wins that don’t really matter, the Essentialist pursues small and
simple wins in areas that are essential.
A popular idea in Silicon Valley is “Done is better than perfect.”
The sentiment is not that we should produce rubbish. The idea, as I read
it, is not to waste time on nonessentials and just to get the thing
done. In entrepreneurial circles the idea is expressed as creating a
“minimal viable product.” The idea is, “What is the simplest possible
product that will be useful and valuable to the intended customer?”
Personalizing patterns of action (i.e. routine) allows us to pay more
attention to matters that count rather than to other’s expectations.
The way of the Nonessentialist is to think the essentials only get
done when they are forced. That execution is a matter of raw effort
alone. You labor to make it happen. You push through.
The way of the Essentialist is different. The Essentialist designs a
routine that makes achieving what you have identified as essential the
default position. Yes, in some instances an Essentialist still has to
work hard, but with the right routine in place each effort yields
exponentially greater results.”
Be present - Focus on what is important now. Don’t pretend that you
can multifocus.
As he tells his players: “There is a difference between losing and
being beaten. Being beaten means they are better than you. They are
faster, stronger, and more talented.” To Larry, losing means something
else. It means you lost focus. It means you didn’t concentrate on what
was essential. It is all based on a simple but powerful idea: to operate
at your highest level of contribution requires that you deliberately
tune in to what is important in the here and now.
The ancient Greeks had two words for time. The first was chronos. The
second was kairos. The Greek god Chronos was imagined as an elderly,
gray-haired man, and his name connotes the literal ticking clock, the
chronological time, the kind we measure (and race about trying to use
efficiently). Kairos is different. While it is difficult to translate
precisely, it refers to time that is opportune, right, different.
Chronos is quantitative; kairos is qualitative. The latter is
experienced only when we are fully in the moment-when we exist in the
now.
Nonessentialists tend to be so preoccupied with past successes and
failures, as well as future challenges and opportunities, that they miss
the present moment. They become distracted. Unfocused. They aren’t
really there.
The way of the Essentialist is to tune into the present. To experience
life in kairos, not just chronos. To focus on the things that are truly
important-not yesterday or tomorrow, but right now.
We can easily do two things at the same time. What we can’t do is
concentrate on two things at the same time. When I talk about being
present, I’m not talking about doing only one thing at a time. I’m
talking about being focused on one thing at a time. Multitasking itself
is not the enemy of Essentialism; pretending we can “multifocus” is.
Related to Leadership
Again, clarity is the key.
When there was a high level of clarity of purpose, the teams and the
people on it overwhelmingly thrived. When there was a serious lack of
clarity about what the team stood for and what their goals and roles
were, people experienced confusion, stress, frustration, and ultimately
failure. As one senior vice president succinctly summarized it when she
looked at the results gathered from her extended team: “Clarity equals
success.”
The Nonessentialist disempowers people by allowing ambiguity over who
is doing what. Often this is justified in the name of wanting to be a
flexible or agile team. But what is actually created is a counterfeit
agility. When people don’t know what they are really responsible for and
how they will be judged on their performance, when decisions either are
or appear to be capricious, and when roles are ill-defined, it isn’t
long before people either give up or, worse, become obsessed with trying
to look busy and therefore important instead of actually getting any
real work done.
The Nonessentialist leader communicates in code, and as a result
people aren’t sure what anything really means. Nonessentialist
communication usually is either too general to be actionable or changes
so quickly that people are always caught off guard. Essentialist
leaders, on the other hand, communicate the right things to the right
people at the right time. Essentialist leaders speak succinctly, opting
for restraint in their communication to keep the team focused. When they
do speak, they are crystal clear. They eschew meaningless jargon, and
their message is so consistent it seems almost boring to their ears. In
this way, teams are able to pick up the essential through all the
trivial noise.
Apply “less but better” in hiring people.
And the cost of hiring too many wrong people (and one wrong hire
often leads to multiple wrong hires because the wrong person will tend
to attract more wrong people) is what Guy Kawasaki called a “Bozo
explosion”—a term he uses to describe what happens when a formerly great
team or company descends into mediocrity.
An Essentialist, on the other hand, is ridiculously selective on
talent. She has the discipline to hold out for the perfect hire-no
matter how many résumés she has to read, or interviews she has to
conduct, or talent searches she has to make-and doesn’t hesitate to
remove people who hold the team back. The result is a team full of
all-star performers whose collective efforts add up to more than the sum
of their parts.
Some
interesting observations or research results mentioned
This is how after you become a “go to” person and gain a lot of
opportunities, you started to diffuse your efforts and be distracted by
a lot of options.
The pursuit of success can be a catalyst for failure. Put another
way, success can distract us from focusing on the essential things that
produce success in the first place.
We are unprepared in part because, for the first time (in human
history), the preponderance of choice has overwhelmed our ability to
manage it. We have lost our ability to filter what is important and what
isn’t. Psychologists call this “decision fatigue”: the more choices we
are forced to make, the more the quality of our decisions
deteriorates.
If you are an overachiever thinking you can do anything, how about
taking the challenge of saying no to an opportunity and taking a
nap.
Stuart Brown, the founder of the National Institute for Play, has
studied what are called the play histories of some six thousand
individuals and has concluded that play has the power to significantly
improve everything from personal health to relationships to education to
organizations’ ability to innovate.
“Play,” he says, “leads to brain plasticity, adaptability, and
creativ-ity.” As he succinctly puts it, “Nothing fires up the brain like
play.”
In a Harvard Business Review article called “Sleep Deficit: The
Performance Killer,” Charles A. Czeisler, the Baldino Professor of Sleep
Medicine at Harvard Medical School, has explained how sleep deprivation
undermines high performance. He likens sleep deficit to drinking too
much alcohol, explaining that pulling an all-nighter i.e., going
twenty-four hours without sleep) or having a week of sleeping just four
or five hours a night actually “induces an impairment equivalent to a
blood alcohol level of 0.1%. Think about this: we would never say, ‘This
person is a great worker! He’s drunk all the time!’ yet we continue to
celebrate people who sacrifice sleep for work.”
The researchers explained that while we sleep our brains are hard at
work encoding and restructuring information. Therefore, when we wake up,
our brains may have made new neural connections, thereby opening up a
broader range of solutions to problems, literally overnight.
Clarity is critical.
In my work, I have noticed two common patterns that typically emerge
when teams lack clarity of purpose.
Playing politics
In the first pattern, the team becomes overly focused on winning the
attention of the manager. The problem is, when people don’t know what
the end game is, they are unclear about how to win, and as a result they
make up their own game and their own rules as they vie for the manager’s
favor. Instead of focusing their time and energies on making a high
level of contribution, they put all their effort into games like
attempting to look better than their peers, demonstrating their
self-importance, and echoing their manager’s every idea or sentiment.
These kinds of activities are not only nonessential but damaging and
counterproductive.
It’s all good (which is bad)
In the second pattern, teams without purpose become leaderless. With
no clear direction, people pursue the things that advance their own
short-term interests, with little awareness of how their activities
contribute to (or in some cases, derail) the long-term mission of the
team as a whole.
‘Uncommit’ is a way to minimize loss and win big.
Sunk-cost bias is the tendency to continue to invest time, money, or
energy into something we know is a losing proposition simply because we
have already incurred, or sunk, a cost that cannot be re-couped. But of
course this can easily become a vicious cycle: the more we invest, the
more determined we become to see it through and see our investment pay
off. The more we invest in something, the harder it is to let go.
The power of small wins.
Research has shown that of all forms of human motivation the most
effective one is progress. Why? Because a small, concrete win creates
momentum and affirms our faith in our further success. In his 1968
Harvard Business Review article entitled “One More Time: How Do You
Motivate Employees?” among the most popular Harvard Business Review
articles of all time, Frederick Herzberg reveals research showing that
the two primary internal motivators for people are achievement and
recognition for achievement.
Indeed, today Zimbardo is attempting a grand social experiment along
those lines called the “Heroie Imagination Project.” The logic is to
increase the odds of people operating with courage by teaching them the
principles of heroism. By encouraging and rewarding heroic acts,
Zimbardo believes, we can consciously and deliberately create a system
where heroic aets eventually become natural and effortless.
Be present and focus on one thing.
Thich Nhat Hanh, the Vietnamese Zen Buddhist monk who has been
called the “world’s calmest man,” has spent a lifetime exploring how to
live in kairos, albeit by a different name. He has taught it as
mindfulness or maintaining “beginner’s mind.” He has written:
“Mindfulness helps you go home to the present. And every time you go
there and recognize a condition of happiness that you have, happiness
comes.”
“This incident shows clearly that Windows must prioritize change
and innovation in the area of end-to-end resilience. […] Examples of
innovation include the recently announced VBS enclaves, which provide an
isolated compute environment that does not require kernel mode drivers
to be tamper resistant.” - John Cable, Microsoft VP of Program
management
Microsoft Azure decelerated by 1 point sequentially to 30% YoY, while
Google Cloud accelerated.
Recent business highlights: 1) global IT outage caused by a faulty
update by CrowdStrike affected 8.5 M Windows PCs, 2) Microsoft facing
investigation by UK’s CMA over hiring former Inflection AI Staff and the
partnership with the startup.
According to Nielsen,
Prime Video captured 3.1% of US TV Time in June (a
decline of 0.1 points Y/Y). Prime Video captures just over a third of
Netflix’s market share (and more than Disney+ and Paramount+
combined).
As Amazon continues to invest in live sports and expand its
content catalog, Prime members may find themselves spending more time
with the service they already pay for. Prime Video may have started as a
loss leader, but if it can become the go-to streaming platform for
ad-supported content, it could evolve into a significant revenue driver,
even for a behemoth like Amazon.
― Amazon: This Team is Cooking - App Economy
Insights [Link]
Portfolio rebalancing: Apple stock surged 24%
between May 1st and June 30th. As a result, Buffett would have seen AAPL
take up nearly 60% of Berkshire’s portfolio. A stake reduction is a
typical move to rebalance a portfolio and lower its risk
profile.
Valuation: Apple is valued above 30 times
forward earnings. That makes it less likely to deliver alpha for
shareholders. It’s possible Buffett felt like the odds of market-beating
returns at this level were subpar.
Taxes matter: Buffett told shareholders in May
that he finds the current tax rate on capital gains relatively low,
potentially prompting him to realize his significant AAPL gains while
the rate is reasonable.
No place to hide: Buffett is building up his
cash pile and waiting for a “fat pitch.”
The Buffett Indicator:* This ratio compares
the total market capitalization of US stocks to the country’s GDP. It’s
often used to gauge whether stock valuations in the US are overinflated.
It reached 138% during the dot-com bubble, which was
considered high at the time. Low and behold, the indicator hit
190% at the end of June.*
― Berkshire Slashes Apple Stake - App Economy
Insights [Link]
Factors of today’s macro environment: 1) The AI Bubble, 2) The Yen
Carry Trade, 3) Potential Recession.
Llama 3.1’s Impact on China, Kuaishou’s AI Video Generator
Goes Global, and Alibaba Backs $2.8B AI Firm - Recode China AI
[Link]
Highlights key AI news in China: 1) Kuaishou’s global launch of its
AI video generator, Kling AI, and Zhipu AI’s introduction of Ying, show
China’s progress in AI video generation. 2) Alibaba, Tencent, and
state-backed AI funds poured $690 M into the $2.8 B AI firm Baichuan
AI.
State of AI in Venture Capital 2024 - AI Supremacy
[Link]
“AI CapEx” is a euphemism for building physical data centers with
land, power, steel and industrial capacity. There’s been a lot of
investment in data centers and AI chips, but not AGI in sight. You can
buy all the shovels you want, but if the mine ain’t making money, we
have a problem. If there’s no gold in the mine, the shovels aren’t worth
very much. BigTech hyperscalers and VCs might have gotten this all
wrong.
― OpenAI’s SearchGPT and the Impossible Promises of AI - AI
Supremacy [Link]
This article points out that industry is facing immense financial
pressures and strategic uncertainties. The concerns are as follows: 1)
OpenAI’s operating costs exceed $ 8 B, with a projected loss of $5 B in
2024, 2) annual AI revenue to justify the investment in data centers and
chips is unlikely to be achieved by 2025, 3) integrating SearchGPT into
ChatGPT is a risky bet because users don’t use ChatGPT frequently enough
for it to be a successful search tool, 4) competitive market has pushed
many AI startups out of the market, AI innovation cannot compete with
market dominance (e.g. Microsoft’s attempts to integrate AI into Bing),
5) Big tech companies have accepted that they are possibly
over-investing in AI due to FOMO (fear of missing out), leading to
unsustainable financial practices, 6) Nvidia’s revenue is risky since it
comes majorly from a few tech giants.
The Morningstar framework: The framework is built on 5 “moat
sources”:
Intangible assets (Coca-Cola)
Switching Costs (Oracle)
Network Effects (CME Group)
Cost Advantages (UPS)
Efficient Scale (Kinder Morgan)
― 5 Wide Moat Businesses - Invest in Quality [Link]
Intangible assest: Coca-Cola, SANOFI, Unilever, Johnson &
Johnson.
AI: Are we in another dot-com bubble? - AI Musings by
Mu [Link]
A comprehensive analysis comparing current AI cycle to the
internet/telecom cycle of the 90s. The author examines the
technological, economic, and capital differences between the two eras
and concludes that while a bubble may be inevitable in the long run, we
are still far from reaching that point.
Key points:
Similarities between AI cycle since Nov 2022 and internet cycle of
the 90s: 1) Both cycles have similar ecosystem structures, with
companies providing infrastructure, enablement, and applications. 2)
Occur amid equity bull markets, driven by favorable economic conditions.
3) Require significant infrastructure investments. 4) Attract
significant VC interest, leading to high valuations.
Differences between AI cycle since Nov 2022 and internet cycle of the
90s: 1) AI companies are generating revenue much earlier than dot-com
companies did, with more sustainable business models. 2) The current
economic environment is less robust than in the 90s, leading to a more
cautious investment climate. 3) AI investments are primarily
equity-funded by big tech, unlike the debt-financed dot-com boom. 4)
Valuations of AI companies, while high, are more grounded in near-term
earnings than those during the dot-com era.
Bubble Likelihood: The article argues that while there are risks, the
current AI cycle is less likely to be in a bubble compared to the
dot-com era. The more cautious investment environment, sustainable
business models, and the structured flow of capital contribute to this
conclusion.
Lessons from Dot-Com Bubble: 1) Infrastructure buildouts take time.
2) Being a first mover can be a disadvantage, as seen with early
internet companies that were later overtaken by more successful
competitors. 3) The importance of being critical and not getting swept
up in the hype, learning from the past to navigate the present.
To recap the above post, they do the new normal,
including:
Human preference data and HelpSteer style grading of
attributes for regularization.
High-quality reward models for filtering.
Replacement of human demonstrations with model completions in
some domains.
Multi-round RLHF — “We iterate data and model qualities jointly
to improve them in a unified flywheel.”
A very large suite of data curation techniques, including prompt
re-writing and refining for expansion of costly datasets, filtering math
and code answers with outcomes (correctness or execution), filtering
with LLMs-as-a-judge, and other new normal stuff.
― A recipe for frontier model post-training - Nathan Lambert,
Interconnects [Link]
Recent papers and reports (Llama 3.1, Nemotron 340B, and Apple
foundation model) have made it clear that a new default recipe exists
for high-quality RLHF. It has a few assumptions:
Synthetic data can be of higher quality than humans, especially for
demonstrations on challenging tasks.
Reinforcement learning from human feedback (RLHF) can scale far
further than instruction tuning.
It takes multiple rounds of training and generation to reach your
best model.
Data filtering is the most important part of training.
It becomes clear that the post training is highly correlated with the
style and robustness gains.
The new normal seems to be converged as follows:
post-training
OpenAI and Generative AI are at a Crossroads - AI
Supremacy [Link]
Views of AI landscape.
At least five interesting things for your weekend (#45) -
Noahpinion [Link]
GPT-5: Everything You Need to Know - The Algorithmic
Bridge [Link]
New LLM Pre-training and Post-training Paradigms - Ahead of
AI [Link]
You don’t have to be a manager - Elena’s Growth
Scoop [Link]
Eric Schmidt’s AI prophecy: The next two years will shock you
- Exponential View [Link]
Former Google CEO Eric Schmidt predicts rapid advancements in AI,
where large language models and agent-based systems with text-to-action
capabilities could converge, causing tremendous economic and
technological disruption.
Meta: Better Sorry Than Safe - App Economy Insights
[Link]
Judge Amit Mehta handed down the ruling, finding that the tech
giant has been using its dominance in the search market to favor its own
products and services, making it harder for rivals to gain a
foothold.
The Justice Department had sued Alphabet over its multi-billion
dollar deals with smartphone manufacturers and wireless providers to be
the default search engine on mobile devices.
Based on the $87 billion in Services revenue for Apple in 2023,
Alphabet accounted for 25% of Apple Services, and over 20% of the
Apple’s net profit
― Google’s Antitrust Loss - App Economy Insights [Link]
Potential remedies are 1) ending exclusivity deals: prohibit Alphabet
from securing exclusive agreements with partners like Apple, 2)
behavioral remedies: restrict Alphabet’s conduct e.g. bundling some of
its services together, 3) structural remedies: divest some of Alphabet’s
businesses e.g. search advertising, YouTube, smartphone business.
A critical component of AMD’s recent strategy is to go beyond
advanced chips and offer software (for developers to
access the capabilities through applications) and now tailored
system solutions (to optimize the data center for
performance).
― AMD Acquires ZT Systems - App Economy Insights [Link]
In AMD presentation, they mentioned that 1) system design and
enablement engineers in ZT systems will enable AMD to design world-class
AI infrastructure delivered through an ecosystem of OEM and ODM
partners, 2) ZT system’s extensive cloud solutions experience will help
accelerate the deployment of AMD-powered AI infrastructure at scale with
cloud customers, 3) AMD will deliver optimized solutions to market with
our CPU, GPU, networking, and now systems solutions.
Uber’s continued growth and expanding margins today are
encouraging.
More users and more trips in mature markets.
Strong advertising performance showing product-market fit.
Multi-product adoption, improving churn, and lowering acquisition
costs
― Uber’s Big Autonomy Plan - App Economy Insights
[Link]
Kind of interested in how Uber’s long-term vision differs from that
of Tesla or Waymo.
Business highlights: Autonomous vehicles (AV): the ongoing
collaborations with 10 AV companies across all segments fueled the surge
of Uber’s AV trip growth. AVs on Uber is a win-win because of Uber’s
massive network.
Looking forward: 1) The advertising business reached a revenue run
rate of over $1 billion, compared to $650 million a year ago, 2) Uber
has made many strategic investments, representing over $6 billion in
equity stakes today.
The Corporate Life Cycle: Managing, Valuation and Investing
Implications - Musings on Markets [Link]
52 Reasons to Fear that Technological Progress Is Reversing -
The Honest Broker [Link]
Very good article summarizing concerning warnings happened recently.
Warning signs that all of us have observed. For example, 1) people
refuse to upgrade their operating system, probably because the risk
started to overweight performance increase, 2) Scientific journals are
now filled with thousands of fake AI-generated papers, very concerning,
3) education degree starts to have less value because the tuition now
outweights perceived benefits, 4) Google overwhelms search results with
affiliate-driven content, often of low quality. Its search results have
become dominated by ads, thinly veiled as recommendations, and content
designed to maximize affiliate commissions rather than genuinely help
users, leading to a degraded user experience, 5) people are addicted to
their phones, because tech companies have conducted comprehensive and
rigorous research studies, finding way to design and improve products,
so that people can stick to them.
Instead of pursuing truth, new technologies aim to replace it
with mimicry and fantasy.
This has empowered shamming, scamming & spamming at
unprecedented levels.
Users are not the real customers—so billions of people must
suffer to advance the interests of a tiny group of
stakeholders.
Real people become inputs in a profit-maximization scheme which
requires that they are constantly controlled and manipulated.
In this environment, everything gets viewed as a resource or
input and the natural world (including us) is ruthlessly
exploited.
The groundwork for this was laid by theorists who replaced truth
with power.
In the past, governments controlled huge technologies (nuclear
power, spaceships, etc.) so they were somewhat accountable to citizens,
but now the most powerful new tech is in private hands, and the public
good is no longer even considered.
So much wealth is concentrated in the hands of the winners in
these processes, that they literally become more powerful than nation
states.
With this shift in power, even the most independent politicians
turn into controlled agents working for the technocracy — making a
mockery of democracy.
If you oppose this command-and-control tech you can be
theoretically (and often literally) erased, suspended, deplatformed,
shadow-banned, surveilled, de-banked, digitally faked, etc. —so who will
dare?
― 10 Reasons Why Technological Progress Is Now Reversing -
Ted Gioia [Link]
Good thinking and perspectives. We human are all adapting to a
constantly changing life due to the advancement of technology. Ethical
boundaries become blur. Truth and false are re-defined. We are farther
away from nature and closer to artificiality. We are farther away from
truth and closer to hallucination / scam. This is inevitable. The point
is, how can we maximize the chance of correcting ourselves in the way of
development.
“If you want to be a good evaluator of businesses,” said Buffett,
“you really ought to figure out a way — without too much personal damage
— to run a lousy business for a while. You’ll learn a whole lot more
about business by actually struggling with a terrible business for a
couple of years than you learn by getting into a very good one where the
business itself is so good that you can’t mess it up.”
― The Lessons of a Lousy Business - Kingswell [Link]
This is about one of Warren Buffett stories — his investment in
Dempster Mill Manufacturing Company. Three lessons:
Don’t throw good money after bad
Avoid the mistake of continuing to invest in something that is not
working, hoping to recover the losses. Warren Buffett learned from his
experience to avoid reinvesting in failing operations. Instead of
pouring more money into trying to revive the company’s subpar
operations, Buffett used the temporary profits from cost-cutting and tax
advantages to invest in other, more promising ventures. This approach
allowed him to build a successful business empire rather than wasting
resources on a losing cause.
Look for .400 hitters
Warren Buffett prioritizes finding exceptional managers—like those
who are as rare and skilled as a .400 hitter in baseball—when acquiring
companies. By securing top-tier managers, Buffett can delegate the
day-to-day operations with confidence, allowing him to focus on what he
does best: allocating capital. This approach simplifies his role and
ensures that the companies he acquires are in capable hands, which is a
crucial aspect of his investment strategy.
Some things are worth more than money
This is the value Warren Buffett places on the impact of his business
decisions on people and communities. Despite the financial losses from
Berkshire Hathaway’s textile operations, Buffett kept them running for
many years because they were a major source of employment for a
struggling region. This decision reflects his consideration of the
social and human aspects of business, prioritizing the well-being of the
community over pure financial gain.
Pandemic Darlings That Never Bounced Back - Investment
Talk [Link]
This article is a reminder that highly speculative situations rarely
turn out well in the long run. Many of the speculative stocks that were
supposedly going to take advantage of “permanent” societal changes in
2020 and 2021 collapsed and remain far below their highs.
My intuitive explanation is that business that is able to benefit
from the sudden covid impact on society is not necessarily stable or
invulnerable. It’s probably because the business only works in such
abnormal situation where covid was widespread. But the thing is that
situation won’t last long and people don’t enjoy it. Business that is
sensitive to this societal change is testified by this change to show
that normal society sticks to or prefers the business. And you can never
underestimate this stickiness or preference, which is like a moat.
We have lots of data, but none of it means much until you attach
a story to it about what you think it means and what you think people
will do with it next. That seems obvious to me. But ask forecasters if
they think the majority of what they do is storytelling and you’ll get
blank stares. At best. It never seems like storytelling when you’re
basing a forecast in data.
[History] cannot be interpreted without the aid of imagination
and intuition. The sheer quantity of evidence is so overwhelming that
selection is inevitable. Where there is selection there is art. Those
who read history tend to look for what proves them right and confirms
their personal opinions. They defend loyalties. They read with a purpose
to affirm or to attack. They resist inconvenient truth since everyone
wants to be on the side of the angels.
― A Number From Today and A Story About Tomorrow - Morgan
Housel @ Collabfund [Link]
AI for Non-Techies: Top Tools for Search, Agent Building,
Academic Paper Reviews & Sales Automation - AI Supremacy
[Link]
China’s Humanoid Robots, Former Huawei Genius‘
Needle-Threading Robot, and Big Tech Reap AI Rewards - Tony
Peng [Link]
AI applications will ultimately determine the revenue created
across the AI value chain. The primary question in AI is this: “What
problems is AI solving? How large are the scale of those problems? What
infrastructure needs to be in place to support those
applications?”
It’s important to think about where value accrues along the AI Value
Chain:
ai-value-chain
New Grad or L3
Stay hungry
Listen to team meetings to identify areas.
Ask your team leads where you can help.
Self-nominate
Review your team’s backlog and identify “nice to have”
items.
Come forward when someone is looking for assistance.
Be curious
Shadow a senior’s coding practices.
Clarify tasks given to you and understand the context of the
larger goals.
Mid-level or L4
Take ownership
“How can others benefit from this work?”.
Pick up anything dropped on the floor, don’t complain, and take
it to the finish line.
Assist your teammates
Align with the next level
Find projects to collaborate with key ICs in your
organization.
Become an expert on a specific area for your team.
Senior or L5
Delegate
Create space for others to grow.
Scale yourself by delegating work and grow your
impact.
Clear Communication
Invest in mastering concise communication.
❌ “The functionality of the module should be enhanced to provide
increased flexibility and adaptability for the end user, with a focus on
streamlining the overall workflow process and enhancing the overall user
experience.”
✅ “We need to improve the module so it’s easier to use and can
handle a wider variety of tasks, making the user’s workflow
smoother.”
Be intentional with everything you say.
❌ “I think if we decide to make this change, our downstream systems
might suffer.”
✅ “This change increases latency by 20%, breaking service X.”
― Build Your Credibility As You Grow - Leadership
Letters [Link]
Harris makes a big mistake by embracing price controls -
Noahpinion [Link]
The main supporting points:
Ineffectiveness of Price Controls: The article argues that price
controls on groceries are likely to be either ineffectual or harmful. If
implemented, they could lead to shortages as grocery stores operate on
razor-thin profit margins. If these stores are forced to sell goods at a
loss due to government-mandated price controls, they might reduce
supply, leading to empty shelves and potential scarcity, similar to
situations seen in the Soviet Union and Venezuela.
Inflation is Already Under Control: The article points out that
grocery inflation has already stabilized, with prices having flatlined
since early 2023. This suggests that the problem Harris is trying to
solve with price controls—rising grocery prices—doesn’t actually exist
anymore. Implementing price controls now could be seen as a solution in
search of a problem, leading to unnecessary economic distortions.
Legal and Political Risks: Harris’s proposal involves using the
Federal Trade Commission (FTC) to enforce price controls, which the
article argues is beyond the agency’s current legal authority. This
could require new legislation, potentially face legal challenges, and
expand the FTC’s powers in ways that could be problematic. The risk is
that this could lead to a slippery slope of further government
intervention in the economy.
Historical Precedents: Historical examples of price controls, such
as in Argentina and the U.S. during the 1970s, show that they often lead
to short-term price reductions followed by inflation and shortages once
the controls are lifted. The article argues that these historical
precedents suggest that the best-case scenario for Harris’s proposal is
negligible impact, while the worst-case scenario is significant economic
disruption.
Misguided Focus on Grocery Stores: The article highlights that
grocery stores are not the main culprits behind price increases, as
their profit margins are extremely low. The real issue may lie with
other parts of the supply chain, such as food processors, where there is
more evidence of monopoly power. Thus, targeting grocery stores with
price controls may not address the root causes of price increases.
Articles and Blogs
In the Age of A.I., What Makes People Unique? - The New
Yorker [Link]
How To Get Promoted (Without Getting Lucky) - The Developing
Dev [Link]
How to Get Rich (without getting lucky) - Naval @ X
[Link]
Key takeaways: 1) know what you organization considers impactful, 2)
learn to sell your ideas, set directions, grow and help others, 3) build
your brand by embracing accountability and sharing your results, 4)
become a good collaborator and be transparent to your manager about
goals and gaps, 5) protect your focus time - “what you work on
is more important than how hard you work“, do work that you
enjoy and has impact.
Is Consistency Hurting Your Sustainability? - Leadership
Letters [Link]
Key takeaways: 1) it’s ok to be inconsistent sometimes, you should
update your plan that respects flexibility, balance priorities, or
adjust your expectations, 2) Life is not a sprint, taking a pause and
pushing goals to the future is not always bad, 3) consistency is about
never giving up, 4) don’t set consistency as a goal, find out what is
your real goal, so that accepting and developing “bounce-back” plan is
possible
McKinsey’s 2024 annual book recommendations [Link]
Have selected some books and added them into my read list: 1) God,
Human, Animal, Machine: Technology, Metaphor, and the Search for Meaning
by Meghan O’Gieblyn, 2) Outlive: The Science & Art of Longevity by
Peter Attia, 3) The Journey of Leadership: How CEOs Learn to Lead from
the Inside Out by Dana Maor, Hans-Werner Kaas, Kurt Strovink, and Ramesh
Srinivasan, 4) Slow Productivity: The Lost Art of Accomplishment Without
Burnout by Cal Newport, 5) How Legendary Leaders Speak: 451 Proven
Communication Strategies of the World’s Top Leaders by Peter D.
Andrei.
Paid Advertising 101: A Guide for Startup Founders -
Kaya [Link]
Building A Generative AI Platform - Chip Huyen [Link]
This blog post outlines common themes in building generative AI
systems. It covers many of the building blocks a company should consider
when deploying its models to production.
DeepMind research scientist shares practical techniques to augment
your work with LLMs.
FlexAttention: The Flexibility of PyTorch with the
Performance of FlashAttention - PyTorch [Link]
Use FlexAttention in PyTorch to achieve 90% of FlashAttention2
Speed.
Deploy open LLMs with Terraform and Amazon SageMaker -
Philschmid [Link]
Sonnet 3.5 for Coding - System Prompt - reddit [Link]
How Product Recommendations Broke Google And ate the internet
in the process. - Intelligencer [Link]
Health.com’s Purifier Reviews: The article mentions that Health.com
claims to have tested 67 air purifiers but provides no actual test data.
This example supports the point that many product recommendations are
not based on rigorous testing, undermining their credibility.
HouseFresh’s Critique: HouseFresh published a critical assessment of
its competitors, pointing out issues like subpar products being
recommended due to brand recognition. This supports the argument that
the affiliate marketing model is corrupting the integrity of product
recommendations.
Time Stamped and AP Buyline: These brands, operated by Taboola, are
presented as examples of how even reputable organizations are now
involved in affiliate marketing, blurring the lines between independent
journalism and commercial content. This supports the point that the
distinction between quality content and affiliate-driven recommendations
is becoming increasingly unclear.
SGE (Search Generative Experience): Google’s AI-powered search
results, which aggregate and recommend products directly, are criticized
for being misleading and overly simplistic. This supports the idea that
even Google’s attempts to solve the problem are falling short, further
complicating the search landscape.
These examples illustrate how the proliferation of affiliate-driven
content has compromised the quality of online information, leading to a
search experience that is more about driving sales than helping users
make informed decisions.
The Berkshire Hathaway MBA - The Rational Walk [Link]
Pathway to a great investor.
Syntopical Reading is the highest form of reading because it
involves reading a number of books on the same topic analytically and
then placing the books in context in relation to one another and the
overall subject. This level of reading has the potential to bring about
insights that are not found in any one of the books when considered in
isolation.
As an example relevant to investors, one might want to conduct an
analytical reading of Benjamin Graham’s The Intelligent Investor and Philip
Fisher’s Common Stocks and Uncommon
Profits and then come to grips with the underlying themes expressed
in both volumes while drawing conclusions on investing that might not
appear in either book in isolation. This approach can, of course, be
applied to other forms of literature including biographies. It is quite
possible than a thorough analytical reading of Roger Lowenstein’s Buffett: The Making of an American
Capitalist and Alice Schroeder’s The Snowball: Warren Buffett and the
Business of Life could lead to insights about Warren Buffett that
one could not achieve by reading one of these books in
isolation.
― How to Read a Book: The Classic Guide to Intelligent
Reading - The Rational Walk [Link]
Do Not Use LLM or Generative AI For These Use Cases -
Christopher Tao on TowardsAI [Link]
genai-usecase-
YouTube and Podcasts
Elon Musk: Neuralink and the Future of Humanity | Lex Fridman
Podcast [Link]
Eight hours interview..
Kamala surges, Trump at NABJ, recession fears, Middle East
escalation, Ackman postpones IPO - All-in Podcast [Link]
AI and The Next Computing Platforms With Jensen Huang and
Mark Zuckerberg - NVIDIA [Link]
Nvidia CEO Jensen and Zuckerberg discuss the future of AI.
There’s a famous quote from an economist Simon Kuznets who said
there’s four kinds of countries in the world there’s developed countries
undeveloped countries Japan and Argentina. And I think the reason he
said that is that Japan has been in the state since the 90s so they had
a massive property and Equity bubble collapse. And they’ve not had to
deal with anything that looked like typical economic issues since then
and part of it is because the Govern plays a very big hand in the
Japanese economy, there’s a lot of price controls there. So I don’t know
I’m not sure what it is that we can learn there that you can extrapolate
to the rest of the world. - Chamath Palihapitiya
When you have massive amounts of debt it definitely limits your
flexibility. It’s just arithmetic, you are going to pay for it with
either economic contraction, higher taxes, or inflation. Those are the
three places it goes. - David Sacks & David Friedberg
Well so it looks like since the start of the year they’ve sold
55% of their Holdings in apple. And if you look at the end of the year,
this is what berkshire’s stock Holdings were in their non-majority owned
businesses. So businesses that they don’t own the business outright and
50% of their portfolio was in Apple at $174 billion. We obviously saw
Apple’s stock price Peak highest level ever just a few days ago, but it
has since come down as it was reported that since the start of the year.
Now Berkshire sold 55% of this position, so some people are arguing that
they’ve got a point of view on the company strategy and comp competitive
kind of landscape. Some folks have argued that the valuation multiple
has gotten too high trading at nearly 30 times earnings the stock has
risen 900% since Berkshire bought the stock in 2016. Bagger nicely done
yeah and some people would argue that the percent of the portfolio is
too high at over 50%, as you can see here at the start of the year. But
you know I’ll kind of provide some of the counterarguments you know
Warren Buffett does not do much analysis on corporate strategy when he
provides reviews of the stocks that he’s picked he often finds and talks
a lot about great managers that generate great returns. And he sticks
with them and he sticks with them sometimes for many many decades. The
management in this company has not changed the return profile on cash
invested and cash returned has only improved since he put money in.
They’re generating more cash flow they’re offering more dividends
they’re doing more stock BuyBacks and he’s happy to be concentrated over
the years he’s made large bets on single companies to the point that
sometimes he just outright buys the entire company like he did with.
Geico in 1996 he always talks a lot about finding a company that is run
by great managers that has a premium product with a nice high margin and
a durable moat strong brand value. As I look at kind of what’s really
gone on here it feels to me like the difference between Apple and some
of the other big Holdings in its portfolio is that many of those other
businesses are regulated monopolies. So BNSF Railway is regulated by the
Federal Railroad Administration Berkshire energy which owns mid americ
is a regulated utility. The prices that they charge consumers are set by
the government so they have a market that’s locked in the prices are set
they have locked in distribution they have locked in utility value and
the same is true in the insurance business. Geico’s rates are approved
and set effectively by state Regulators Berkshire has a moat because
they’ve got the largest Capital base and they’ve got this machine that
just keeps generating cash and the rates are publicly set by government
Apple. However is not regulated and it is very clear that apple is
facing very deep and severe Financial impact from the regulatory
authorities that are overseeing the business so if you look at the
Google antitrust we’re going to get into the Google deal in a second.
There’s a real regulatory risk there because Google’s paying Apple $20
billion a year to be the default search engine. Apple also has a very
deep relationship with China they have a lot of manufacturing being done
in China and they sell a lot of product into China. So as Regulators
start to take a harder look as they said they’re going to at companies
relationships with China that’s a real risk to Apple. Advertising
tracking users and then the subscription fees that are charged to
Consumers and most importantly we’ve talked a lot about the 30% Vig that
Apple takes on their App Store and how Regulators are now stepping in
and take a look at this. So because this business is not yet a regulated
Monopoly it may be a monopoly in many senses of the world it’s not
regulated yet. And that transition could be financially painful for
Apple once they get to the other side it starts to look a lot more like
a large scale Burkshire type business. So that that’s my kind of summary
take on what’s going on with apple. - David Friedberg
There’s very little kind of editorialization going on with
respect to showing the rankings of the new sources. The ranking of the
new sources is typically set by some ranking algorithm. The algorithm is
usually around click-throughs views popularity of the sites, how many
visitors there are, so there are other metrics that drive the order. So
for example if NBC CNN Fox News all have kind of higher rankings than
some smaller publication, they’re going to end up Hing the the ranking
algorithm, because they have a higher quality score. There’s also
measures on how often people click through and come back, the bounceback
rate, so if they click through an article and then come back that can
actually reduce the ranking versus if they click through and stay on the
site. So there’s a lot of factors that go into the ranking algorithm.
The thing that probably upsets people is that there isn’t any
transparency into this, so there’s no understanding on how these things
are ranked, how they’re set, and it’s probably very good guidance and
feedback that there should be more transparency and openness. And I’m
not necessarily trying to defend anyone’s product or behavior, I’m just
saying that there’s a certainly a lack of understanding on why one thing
is being shown versus another. I’ll also say Sach there’s probably the
case or there might be the case that there’s many more sites potentially
putting out pro Harris articles, and there are putting out pro Trump
articles which can start to overweight the the algorithm as you know or
overweight the rankings that are showing up. So that might also be
feeding into this that that the general news media bias is what you’re
actually seeing versus a Google bias. - David Friedberg
I just want to show you one chart because important for you to
understand the number of people in journalism. This is from 1971 to 2022
who say the identifying Republican has just absolutely plummeted. I know
this and this is what I’m trying to explain to you Sacks, is a
incredible opportunity for your party since you know you’re passionate
about this is to invest in more journalism, invest in more journalists,
because I don’t buy this Independence the fact that they’re claiming
they’re independent in journalism. I believe that’s cap I believe they
say that the Gap is 33% now between people who say they’re Democrats and
people who say they’re Republican in journalism that is a key piece to
this problem. And layered on top of it, I agree with you that Google is
filled with liberal people, and I agree with you Chamath, that they need
to intervene and put at the top of the search results in news. These are
the you know this is what we’re indexing, this is the percentage that’s
left leaning, this is the percentage that’s right leaning, and there are
a lot of organizations that examine and rate Publications on their bias
left and right, And that’s something that Google could do that’s very
unique and that could move the whole show that you’re saying which is
they could showcase that up top. So to my friends at Google who are
listening do a better job of just being more transparent, so we don’t
have this tension in society. - Jason Calacanis
― Yen Carry Trade, Recession odds grow, Buffett cash pile,
Google ruled monopoly, Kamala picks Walz - All-in Podcast [Link]
Interviewing Ross Taylor on LLM reasoning, Llama fine-tuning,
Galactica, agents - Interconnects AI [Link]
Interviewing Sebastian Raschka on the state of open LLMs,
Llama 3.1, and AI education - Interconnects AI [Link]
Here you can see that their (Starbucks) net revenue growth was
only 1% year-over-year, but their operating margin’s been on the
decline, so they have not really been able to boost their operating
margin very much in the past 5 years. So while they’ve raised prices,
they’ve had a really hard time making more money and that’s because the
cost of food and the cost of Labor and the cost of rent, and the capital
expenditures needed to upgrade stores has far exceeded the ability for
them to grow revenue and compete. And now revenue is flatlining because
consumers are getting tapped out with respect to how much they can spend
and there’s only so much Innovation you can really do to charge more,
get people to come in the store more and drive up revenue. - David
Friedberg
Brian Nickel has an incredible reputation prior to Chipotle. He
ran Taco Bell and he ran Taco Bell for several years and made it one of
the most profitable Quick Serve Restaurants (QSR) in the world. He did
this by focusing on every nickel. He is notorious for being a Cost
Cutter, for being an efficiency driver, for being a productivity Hound.
He goes into the business and he figures out every step in the supply
chain, every step of the operating activities of the employee in the
stores. So he was recruited heavily. I don’t know if you guys remember
Chipotle’s founder was running Chipotle and at the time there was a lot
of investor activism around Chipotle because they were wasting money
like no one’s business. The guy had a private Jet, he was flying his
management team back and forth between Denver and New York. They were
spending money on crazy projects. And the board fired the CEO founder of
Chipotle, brought in Nickel. Nickel came in and made Chipotle an
incredibly profitable growing business. And the expectation is he’ll
come and do the same here that maybe over the years Starbucks’s success
has bred laziness. Starbucks’s success has bred fat slowness
productivity decline, and that this guy is the right guy to come in and
find all the nickels. And Brian Nichol is probably the right guy which
is why you’re seeing the stock kind of rally as hard as it has. - David
Friedberg
But the thing about Starbucks is they realized early on that when
you can customize a consumer experience, the consumer comes back more
frequently. So when you see your name written on that cup, you feel like
you’re getting your product, you’re not buying an off-the-shelf product,
you’re getting a custom personalized experience. What that led to is
people customizing their drinks and what did they find that they liked
when they customized their drinks sugary sweet add-ons. And then that
became more and more of the standard menu and then that just kept
evolving. And that’s just the consumer feedback mechanism working which
is to Chamath’s point, led to 60 gram sugar drinks that are now the
standard product at Starbucks, not an espresso or a cappuccino which is
how they started, and it’s really unfortunate. - David
Friedberg
So arguably I would say that trying to step in and cap prices
will reduce competition, and as a result will reduce investment in
improving productivity. And we have seen this countless times with every
socialist experiment in human history has started with caps on food, and
it has resulted in spread lines like you see in the image behind me
today as we can see in Soviet Russia. This is a mistake, it is a
problem, it is anti-American, it is anti-free Market, it is anti-
innovation, it is anti- productivity, and ultimately it’s anti- liberty
and I cannot stand it. - David Friedberg
To support your point, and what Chamath was messaging on our
chat, look at Walmart stocks up 7% today, because they offer lower
priced solutions to consumers, and Dollar General and Dollar Tree are
rallying as well, when the market competes, consumers benefit, and there
are companies that will win. And the companies that try to price gouge,
and the companies that try to charge too much will lose. Starbucks has
been trying to charge too much for sugar water, they have a real problem
they are now tackling. Walmart is trying to bring value to consumers,
they are winning. That is how free markets work. When the government
steps in and says here’s how much margin you can make or here’s how much
prices should be, it ruins everything, and the entire incentive
structure goes away, and you end up with breadlines. - David
Friedberg
― Break up Google, Starbucks CEO out, Kamala’s price
controls, Boeing disaster, Kursk offensive - All-in Podcasts
[Link]
I think the thing that matters more than anything else is to make
sure that the people that they are letting in are in love and obsessed
with the things that MIT is supposed to be great at. … You should not be
going to MIT because you think it’s a check mark. You should be going
there because you think that there are professors in organic Chemistry,
in physics, in these disciplines that are really important who are
experts in their fields that you can learn from and become an expert
yourself. And I think the problem with all of this other stuff is once
you make it a credential, there are some folks that are only going to
MIT because they could get in and because it’s a great credential in
their minds and they shouldn’t go there either. So I think the thing is
you have to get back to what matters which is there are all of these
industries that have not progressed that much. And in order for those
Industries to advance you need really talented young people who can
learn an apprentice and then take over. And I think MIT is one of these
rare places that focuses on this part of the physical world that hasn’t
had as much progress. And so I just want to make sure that the people
that go there actually want to be there for that reason gender race all
that other stuff shouldn’t matter. - Chamath Palihapitiya
Kamala Harris and Tim Waltz have only ever work for government,
Trump and Vance have worked in Private Industry. It’s not just their
perspective being colored by the the lack of participation in the
private economy, but the lack of employment in the private economy,
they’ve never worked for a private business, they’ve never been
employees of a private business, they’ve never built a private business.
I’m not trying to be disparaging but I do think I’m just trying to
underline the point here Chamath which is the voter’s choice is do you
want candidates that are not typically government operatives, or do you
want candidates that have spent their whole career as government
operatives. And that is effectively what the voters are going to be
voting for. And they’re going to make a decision they may want to have
someone that’s going to lead the biggest government in history, because
they’ve spent their whole careers in government. Or they’re going to say
you know what the biggest government in history needs to be
significantly altered, and we want to bring someone in from the outside
that’s worked in Private Industry. And that is the voter’s choice.
That’s one way to view the voter’s choice here. - David
Friedberg
I think just because someone has served in Government doesn’t
mean that they truly even understand what the problems are, or that
they’re even the master of government. I mean you saw this over the past
week we talked about the 88,000 jobs that didn’t exist. They asked Gina
Rundo the Secretary of Commerce about this and she just said that’s a
Trump lie, and they said no actually it’s the Bureau of Labor Statistics
report that like is under US Secretary of Commerce. She said I’m not
familiar with that, so you have people running the government who don’t
even know what their own departments are doing. Now I think it’s just a
function of the fact that the government is so big and out of control
that no one even understands what it does. I think it’s more important
to have someone who at least has some experience in the private sector
who truly understands how jobs are created, how wealth is created, what
causes inflation, okay. We’ve talked about this before, what causes
inflation is the printing of too much money, it’s government spending
too much, it is not corporate greed, because corporate greed as a
constant, it’s not price gouging. - David Sacks
And how the free market incentivizes the creation of improved
productivity which over time translates into improved prosperity for the
society within which that is taking place that is so critical and we saw
that happen even in China in the last 30 years when the government
allowed entrepreneurship to flourish in certain parts of the country. As
a result there were significant productivity gains and they brought a
billion people out of poverty - they created a middle class. - David
Friedberg
― Massive jobs revision, Kamala wealth tax, polls vs
prediction markets, end of race-based admissions - All-in
Podcasts [Link]
Why We Don’t Own Coupang Stock (CPNG) - Chit Chat Stock
Podcast [Link]
Good analysis of CPNG’s business, advantages and disadvantages,
future and expectation.
Track Record and Risk w/ Guy Spier - We Study
Billionaires [Link]
Gradient-Boosting RL (GBRL) brings the advantages of GradientBoosting
Trees (GBT) to reinforcement learning.
SpreadsheetLLM: Encoding Spreadsheets for Large Language
Models [Link]
Microsoft releases SpreadsheetLLM, a model designed to optimize LLMs’
powerful understanding on spreadsheets. It’s a great paper that outlines
how you can turn a spreadsheet into a representation that is useful to a
modern LLM. This can be used for Q/A, formatting, and other data
operations.
The core innovation in SpreadsheetLLM is the SheetCompressor module,
which efficiently compresses and encodes spreadsheets. It includes 1)
Structural-anchor-based compression, 2) Inverse index translation, 3)
Data-format-aware aggregation.
Controlled study finds MLP generally outperforms KAN across various
tasks. MLP outperformed KAN in machine learning (86.16% vs. 85.96%),
computer vision (85.88% vs. 77.88%), NLP (80.45% vs. 79.95%), and audio
processing (17.74% vs. 15.49%). KAN excelled only in symbolic formula
representation (1.2e-3 RMSE vs. 7.4e-3).
NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million
Context Window? [Link]
The problem of current evaluation methods is that they are inadequate
for assessing LLM performance on long context, however reasoning on long
texts becomes more and more demanded. So they present a framework called
NeedleBench for evaluating the long-context capabilities of LLMs across
extensive text lengths. By some experiments, they find that current LLMs
are struggling with complex reasoning tasks when it comes to long texts,
showing a potential improvement room for LLMs.
Retrieval Augmented Generation or Long-Context LLMs? A
Comprehensive Study and Hybrid Approach [Link]
This study investigates how large language models handle
question-answering tasks under two conditions: when they receive
comprehensive context information (long-context) versus when they are
given only selected chunks of the necessary information (RAG). It shows
that long context surpasses RAG significantly for Gemini-1.5-Pro, GPT-4O
and GPT-3.5-Turbo.
A Survey of Prompt Engineering Methods in Large Language
Models for Different NLP Tasks [Link]
This paper summarizes 38 prompt engineering techniques for LLM
reasoning and lists the types of problems and datasets they have been
used with.
Can Long-Context Language Models Subsume Retrieval, RAG, SQL,
and More? [Link]
This paper explores the capabilities of long-context language models
(LCLMs) in handling tasks traditionally dependent on external tools like
retrieval systems, RAG (Retrieval-Augmented Generation), and SQL
databases. It reveals that LCLMs, such as Gemini 1.5 Pro, GPT-4o, and
Claude 3 Opus, can perform competitively with specialized models in
tasks like retrieval and RAG. In particular, at the 128k token context
length, LCLMs rival the performance of state-of-the-art retrieval
systems and even surpass some multi-modal retrieval models. However,
LCLMs struggle significantly with more complex tasks requiring multi-hop
compositional reasoning, such as SQL-like tasks. The findings also
highlight the importance of prompt design, as performance can vary
greatly depending on the prompting strategies used.
Apple Intelligence Foundation Language Models -
Apple [Link]
This report describes the architecture, the data used to train the
model, the training process, how the models are optimized for inference,
and the evaluation results, for the foundation language model developed
to power Apple Intelligence features.
Notice that Apple includes the fundamentals of their RL methods,
including a different type of soft margin loss for the reward model,
regularizing binary preferences with absolute scores, their rejection
sampling algorithm (iTeC) that is very similar to Meta’s approach, and
their leave-one-out Mirror Descent RL algorithm, MDLOO.
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories,
Applications and Opportunities [Link]
This survey provides an in-depth review of model merging techniques,
an increasingly popular method in machine learning that doesn’t require
raw training data or expensive computation.
Interactive visualization tool designed for non-experts to learn
about Transformers.
The AI Scientist: Towards Fully Automated Open-Ended
Scientific Discovery - Sakana.AI [Link]
Transformers in music recommendation - Google
Research [Link]
A Comprehensive Overview of Large Language Models
[Link]
A Survey on Benchmarks of Multimodal Large Language
Models [Link]
This paper provides an extensive review of 180 benchmarks used to
evaluate Multimodal Large Language Models.
Qwen2-VL: To See the World More Clearly - QwenLM
[Link]
The model comes in three sizes : 2B and 7B (open-sourced under Apache
2.0 license), and 72B (available via API).
Performance:
Qwen2-VL demonstrates state-of-the-art performance on visual
understanding benchmarks. The 72B model surpasses GPT-4o and Claude
3.5-Sonnet on most metrics.
The 7B model achieves top performance in document understanding and
multilingual text comprehension. Even the 2B model shows strong
performance in video-related tasks and document understanding.
Key Capabilities:
Processing videos over 20 minutes long
Complex reasoning for device operation (e.g., mobile phones,
robots)
Multilingual text understanding in images (European languages,
Japanese, Korean, Arabic, Vietnamese)
Function calling for integration with external tools
Document understanding and general scenario question-answering
Integrates with Hugging Face Transformers and vLLM.
Supports various tools for quantization, deployment, and
fine-tuning, making it accessible for ML engineers and researchers to
implement and customize.
Two key architectural innovations:
Naive Dynamic Resolution support allows Qwen2-VL to
handle arbitrary image resolutions by mapping them to a dynamic number
of visual tokens. This ensures consistency between input and image
information.
Multimodal Rotary Position Embedding (M-ROPE)
enables concurrent capture of 1D textual, 2D visual, and 3D video
positional information.
LlamaIndex releases notebook implementation of Microsoft’s
GraphRAG.
Step-By-Step Tutorial: How to Fine-tune Llama 3 (8B) with
Unsloth + Google Colab & deploy it to Ollama - reddit [Link]
Notebooks: Using Mistral Nemo with 60% less memory -
Nvidia [Notebook1]
[Notebook2]
Nvidia has released two free notebooks for Mistral NeMo 12b, enabling
2x faster finetuning with 60% less memory. Mistral’s latest free LLM is
the largest multilingual open-source model that fits in a free Colab
GPU.
Google has an illegal monopoly on search, judge rules. Here’s
what’s next - CNN [Link]
Berkshire Hathaway sells off large share of Apple and
increases cash holdings - The Guardian [Link]
Having accurate, reliable benchmarks for AI models matters, and
not just for the bragging rights of the firms making them. Benchmarks
“define and drive progress”, telling model-makers where they stand and
incentivising them to improve, says Percy Liang of the Institute for
Human-Centred Artificial Intelligence at Stanford University. Benchmarks
chart the field’s overall progress and show how AI systems compare with
humans at specific tasks. They can also help users decide which model to
use for a particular job and identify promising new entrants in the
space, says Clémentine Fourrier, a specialist in evaluating LLMs at
Hugging Face, a startup that provides tools for AI developers.
― GPT, Claude, Llama? How to tell which AI model is best -
The Economist [Link]
Current benchmark MMLU (massive multi-task language understanding)
has a few problems: 1) too easy for today’s models leading to the
problem of ‘saturation’. New alternatives are developed such as
MMLU-Pro, GPQA, MUSR, etc, 2) training data comes from internet which is
a source of questions and answers for MMLU, resulting in a problem
called “contamination”, 3) answers in MMLU tests are sometimes wrong or
correct answers are more than one, 4) small changes in the way questions
are posed to models can significantly affect their scores.
There are some trustworthy automated testing systems other than
ChatBotArena leaderboard: HELM (holistic evaluation of language models)
built by Dr Liang’s team at Stanford, and EleutherAI Harness uses by Dr
Fourrier’s teams at Hugging Face for open source models.
As model gain new skills, new benchmarks are being developed to
assess them. For example, GAIA tests model on real world problem
solving, NoCha provides novel challenge, etc. However, new benchmarks
are expensive to develop because they require human experts to create a
detailed set of questions and answers. Dr Liang is working on project
AutoBencher, Anthropic started funding the creation of benchmarks with a
focus of AI safety.
GPT-4o is small and intelligent. It’s probably distilled from current
or unreleased version of OpenAI’s models, similar to what Claude did
with Claude Haiku and Google with Gemini Flash.
Made by Google 2024: Pixel 9, Gemini, a new foldable and
other things to expect from the event - TechCrunch [Link]
MIT releases comprehensive database of AI risks -
VentureBeat [Link]
Apple will let other digital wallets into Apple Pay, and even
be the default - ars technica [Link]
Apple Aiming to Launch Tabletop Robotic Home Device as Soon
as 2026 With Pricing Around $1,000 [Link]
This is one of my notes of the online Causal Inference Course in
Columbia University, taught by Michael E. Sobel who is a professor in
the Department of Statistics. It would be good to have this overview of
causal inference regarding the framework in mind before getting into
statistical and theoretical details, especially for beginners. A clear
approach framework is very important that it’s not only because rigorous
experiment and analysis methods could be developed with this
well-defined framework, but also because it can guide you to deal with
challenging situations in a correct way, while being clear about the
limitations and assumptions at the same time. This is why it became a
Science.
Modern Approach for Causal
Inference
The modern dominant approach for causal inference, significantly
influenced by Donald Rubin’s contributions, primarily revolves around
the following key ideas:
Potential Outcomes Framework:
Potential Outcomes Notation: Introduced by Neyman
and further developed by Rubin, this framework involves conceptualizing
the outcomes that would occur both with and without the treatment for
each unit. Each unit has a potential outcome under treatment and a
potential outcome under control, but only one of these outcomes is
observed for each unit.
Average Treatment Effects: The focus is on
estimating the average causal effect of a treatment across a population.
This involves comparing the average outcomes of treated and untreated
groups, taking into account the potential outcomes framework.
Randomization and Its Analogs:
Role of Randomization: In experimental studies,
random assignment of treatments is crucial for ensuring that the
treatment groups are comparable, allowing for unbiased estimation of
causal effects.
Randomization-like Conditions in Observational
Studies: Rubin extended the framework to observational studies
by arguing that causal inferences can be made if these studies fulfill
conditions similar to randomization. This involves controlling for
confounding variables that influence both the treatment and the outcome,
often through methods like matching, regression adjustment, or
instrumental variables.
Counterfactual Reasoning:
Counterfactual Conditionals: Causal relationships
must satisfy counterfactual conditions. This means that for a cause to
be deemed responsible for an effect, it should be demonstrable that if
the cause had not occurred, the effect would not have occurred. This is
formalized through the potential outcomes framework.
Key Features of Rubin’s
Approach:
Application to Both Experimental and Observational
Studies: Rubin’s framework is versatile and can be applied to
both types of studies, providing a unified approach to causal
inference.
Focus on Estimating Causal Effects: The primary
goal is to estimate the causal effect of treatments or interventions,
rather than simply identifying associations.
Use of Statistical Methods: The approach leverages
statistical methods to control for confounding variables and to estimate
causal effects, emphasizing the importance of rigorous statistical
analysis.
Two Key Criteria of
Modern Causal Inference
The modern dominant approach to causal inference primarily builds on
two key criteria:
Causation at the Singular Level:
This criterion allows for the possibility that causation can be
specific to individual subjects or units, acknowledging effect
heterogeneity. It means that a cause may produce an effect in one
individual but not necessarily in another, depending on various
conditions.
Satisfaction of Counterfactual Conditionals:
A causal relationship must sustain a counterfactual conditional.
This means that for a cause to be deemed responsible for an effect, it
should be demonstrable that if the cause had not occurred, the effect
would not have occurred. This criterion is essential for defining and
reasoning about causal relationships in both experimental and
observational studies.
Impact on Empirical Research:
Rubin’s contributions have led to more careful and precise inferences
about causal effects in various disciplines, particularly in the social
sciences. Researchers now more rigorously design studies and analyze
data to ensure that their conclusions about causality are well-founded
within this robust statistical framework.
Challenges in Randomized
Studies:
Non-compliance with Treatment Assignments:
Example: In a study by the University of Michigan
in the 1990s, unemployed persons were assigned to receive or not receive
assistance in job searching. A significant percentage of those assigned
to the treatment group did not actually take the treatment, complicating
the comparison between groups and potentially overestimating the
treatment’s effectiveness.
Intermediate Variables:
Example: An educational researcher wants to know
the effect of encouragement to study on test scores. While the
researcher can estimate the effect of encouragement, estimating the
direct effect of study time is more complicated because it involves
intermediate variables (encouragement affecting study time, which in
turn affects test scores).
Breakdown of Random Assignment:
Example: If a subject’s treatment adherence is
influenced by their perception of treatment benefits, comparing only
those who comply can lead to biased estimates.
Challenges in Observational
Studies:
Identifying and Measuring All Covariates:
Example: When studying the effect of education on
earnings, researchers must account for various covariates that affect
both education levels and earnings. Failure to identify or measure all
relevant covariates can lead to biased estimates.
Estimating Average Treatment Effects:
Example: In observational studies, various methods
like matching, weighting, and regression are used to estimate treatment
effects. Each method has its own set of practical issues and assumptions
that need to be carefully managed.
Longitudinal Observational Studies:
Example: When treatments administered in different
periods depend on previous treatments and outcomes, analysis becomes
more complicated.
Interference:
Example: In a housing experiment conducted by the
U.S. government, participants assigned to move from housing projects to
suburbs knew each other. If the treatment assignment of one participant
influenced the decision or outcome of another, traditional analysis
methods might not be adequate.
These examples illustrate that both randomized and observational
studies require careful consideration of various factors to ensure
accurate and reliable causal inferences.
Potential Outcomes,
Unit, and Average Effect
Potential Outcomes Framework
Potential Outcomes:
Each unit (e.g., individual) has two potential outcomes: one if
treated and one if not treated. However, only one outcome can be
observed for each unit.
This leads to the “fundamental problem of causal inference,” where
we cannot observe both potential outcomes for a single unit.
Notation and Unit Effects:
For a unit \(i\), denote the
outcome as \(Y\_i (1)\) if treated and
\(Y\_i (0)\) if not treated.
The unit effect is defined as the difference \(Y\_i (1)−Y\_i (0)\).
The observed outcome \(Y_i\) is
determined by the treatment assignment \(Z\_i\), where \(Z\_i=1\) if the unit is treated and \(Z\_i=0\) if not.
Randomized vs. Observational Studies:
In randomized experiments, treatment assignment \(Z\_i\) is random.
In observational studies, subjects choose their treatment,
introducing potential biases.
Average Treatment Effects
Sample Average Treatment Effect (SATE):
The average of the unit effects for the sample.
Finite Population Average Treatment Effect (FATE):
The average treatment effect for a finite population from which the
sample is drawn.
Average Treatment Effect (ATE):
The average treatment effect in an infinite or large population.
This is treated as an expectation of the potential outcomes.
Estimands of Interest:
Various estimands depend on the marginal distributions of potential
outcomes, such as ATE and Average Treatment Effect on the Treated
(ATT).
Challenges and Assumptions:
Estimating these effects requires assumptions like the Stable Unit
Treatment Value Assumption (SUTVA), which ensures that the potential
outcomes are well-defined and not affected by other units’
treatments.
Practical Implications
Decision-Making:
Knowledge of average treatment effects aids decision-making in
contexts like medical treatments and policy implementations.
Ignorability Conditions:
Under certain conditions, known as ignorability or unconfoundedness,
it is possible to use observed data to estimate causal effects
reliably.
Extensions and Assumptions:
The framework extends to multiple treatments and continuous
treatments, though additional assumptions may be required.
SUTVA assumes no alternative representations of treatment and no
interference between units, which may need adjustments in certain
studies.
Conditions
Allow Average Effects be Unbiasedly/ Consistently Estimated
Key Concepts
Average Treatment Effect (ATE) Estimation:
Random Sampling: Drawing random samples of treated
(\(y\_1\)) and untreated (\(y\_0\)) units to estimate their respective
means.
Sample Means: The means of treated (\(\\bar{y}\_1\)) and untreated (\(\bar{y}\_0\)) samples serve as unbiased and
consistent estimators of the population means.
Unconfoundedness:
Definition: Treatment assignment \(z\) is independent of potential outcomes
(\(y\_0\) and \(y\_1\)).
Intuition: In randomized experiments, treatment
assignment is blind to potential outcomes, ensuring unconfoundedness. In
observational studies, treatment assignment might depend on factors
related to potential outcomes, potentially confounding the
estimates.
Examples
Randomized Experiment vs. Observational Study:
Randomized Experiment: Treatment assignment is
random (e.g., coin flip), ensuring \(z\) is independent of \(y\_0\) and \(y\_1\).
Observational Study: Treatment assignment may
depend on patient characteristics, potentially leading to biased
estimates.
Age and Treatment Example:
Scenario: Older patients might forego treatment
believing it’s less beneficial, while younger patients might opt for
treatment believing it’s more beneficial.
Consequence: Naive comparison between treated and
untreated groups might overestimate the treatment effect due to
confounding by age.
Ignorability Condition
Condition: \(y\_0\) and \(y\_1\) are independent of \(z\) given covariates \(x\) (e.g., age).
Stratified Analysis: In both randomized experiments
and observational studies, stratifying on covariates like age can help
achieve conditional unconfoundedness.
Adjusting for Covariates:
Randomized Experiment: Can stratify on covariates
either before or after the experiment.
Observational Study: Treat it as a stratified
randomized experiment by conditioning on covariates related to treatment
status and potential outcomes.
Practical Implications
Comparison of Groups:
In a stratified randomized experiment, compare treated and control
groups within each stratum (e.g., age group) to estimate ATE.
In observational studies, stratify on covariates to reduce bias and
estimate ATE as if it were a stratified randomized experiment.
Challenges in Observational Studies:
Unknown Assignment Mechanism: Unlike randomized
experiments, the assignment mechanism in observational studies is not
controlled, making it harder to ensure unconfoundedness.
Measurement of Confounders: It’s crucial to measure
and account for all relevant confounders, though it may not always be
possible.
I’ve never looked so deeply into my feelings inside until I met this
book written by Brené Brown. I fell in love with it immediately when I
saw the quote from Theodore Roosevelt at the very beginning:
It is not the critic who counts; not the man who points out how the
strong man stumbles, or where the doer of deeds could have done them
better. The credit belongs to the man who is actually in the arena,
whose face is marred by dust and sweat and blood; who strives valiantly;
who errs, who comes short again and again… who at the best knows in the
end the triumph of high achievement, and who at the worst, if he fails,
at least fails while daring greatly.
Part 1. Rumbling with
Vulnerability
Definition of the courage to be vulnerable:
The courage to be vulnerable is not about winning or losing, it’s
about the courage to show up when you can’t predict or control the
outcome. The only thing I know for sure after all of this research is
that if you’re going to dare greatly, you’re going to get your ass
kicked at some point. If you choose courage, you will absolutely know
failure, disappointment, setback, even heartbreak. That’s why we call it
courage. That’s why it’s so rare.
Definition of rumbling with vulnerability. It’s a foundational and
core skill of courage building, and “our ability to be daring leaders
will never be greater than our capacity for vulnerability”.
A rumble is a discussion, conversation, or meeting defined by a
commitment to lean into vulnerability, to stay curious and generous, to
stick with the messy middle of problem identification and solving, to
take a break and circle back when necessary, to be fearless in owning
our parts, and, as psychologist Harriet Lerner teaches, to listen with
the same passion with which we want to be heard.
Section 1. The Moment and
The Myths
Good practices:
Have the courage to show up when you can’t control the outcome
The definition of vulnerability is the emotion that we experience
during times of uncertainty, risk, and emotional exposure. Vulnerability
is not winning or losing. It’s having the courage to show up when you
can’t control the outcome.
Step over cheap-seat feedback and keep daring
If you are not in the arena getting your ass kicked on occasion, I’m
not interested in or open to your feedback. There are a million cheap
seats in the world today filled with people who will never be brave with
their lives but who will spend every ounce of energy they have hurling
advice and judgment at those who dare greatly. Their only contributions
are criticism, cynicism, and fearmongering. If you’re criticizing from a
place where you’re not also putting yourself on the line, I’m not
interested in what you have to say.
Don’t grab hurtful comments and pull them close to you by rereading
them and ruminating on them. Don’t play with them by rehearsing your
badass comeback. And whatever you do, don’t pull hatefulness close to
your heart.
Don’t shield ourselves from all feedback
Again, if we shield ourselves from all feedback, we stop growing. If
we engage with all feedback, regardless of the quality and intention, it
hurts too much, and we will ultimately armor up by pretending it doesn’t
hurt, or, worse yet, we’ll disconnect from vulnerability and emotion so
fully that we stop feeling hurt. When we get to the place that the armor
is so thick that we no longer feel anything, we experience a real death.
We’ve paid for selfprotection by sealing off our heart from everyone,
and from everything-not just hurt, but love.
The six misguided myths of vulnerability:
Vulnerability is weakness
I don’t do vulnerability
Choosing to own our vulnerability and do it consciously means
learning how to rumble with this emotion and understand how it drives
our thinking and behavior so we can stay aligned with our values and
live in our integrity. Pretending that we don’t do vulnerability means
letting fear drive our thinking and behavior without our input or even
awareness, which almost always leads to acting out or shutting down.
I can do it alone
You can engineer uncertainty out of vulnerability
Trust comes before vulnerability
We need to trust to be vulnerable, and we need to be vulnerable in
order to build trust.
Trust is the stacking and layering of small moments and reciprocal
vulnerability over time. Trust and vulnerability grow together, and to
betray one is to destroy both.
And I like this marble jar approach:
We trust the people who have earned marbles over time in our life.
Whenever someone supports you, or is kind to you, or sticks up for you,
or honors what you share with them as private, you put marbles in the
jar. When people are mean, or disrespectful, or share your secrets,
marbles come out. We look for the people who, over time, put marbles in,
and in, and in, until you look up one day and they’re holding a full
jar. Those are the folks you can tell your secrets to. Those are the
folks you trust with information that’s important to you.
Vulnerability is disclosure
Section 2. The Call to
Courage
Leaders must either invest a reasonable amount of time attending to
fears and feelings, or squander an unreasonable amount of time trying to
manage ineffective and unproductive behavior.
Hunt treasures
This is when I remember Joseph Campbell’s quote, which I believe is
one of the purest calls to courage for leaders: “The cave you fear to
enter holds the treasure you seek.
When we are in fear or in self-protection, these are the patterns of
how we assemble our armor. And they will NOT lead us to anywhere.
I’m not enough
If i’m honest about what’s happening, they will think less of me
or maybe use it against me
No one else is going to be honest about what’s happening and so
no way am I going to do that
They are not honest about what scares them and they’ve got a lot
of issues
This is their fault and they are trying to blame me
I’m better than them
Serve people
When you find the courage to enter that cave, you’re never going in
to secure your own treasure or your own wealth; you face your fears to
find the power and wisdom to serve others.
Good practices:
Have a one-on-one discussion
Stop talking. Leave long white pauses and empty space so that we can
start peeling and going deep.
When they start talking. Really listen.
When we are in tough rumbles with people, we can’t take
responsibility for their emotions.
When rumbles become unproductive, give everyone minutes to walk
around outside or catch their breath.
Section 3. The Armory
The problem is that when we imprison the heart, we kill courage. In
the same way that we depend on our physical heart to pump life-giving
blood to every part of our body, we depend on our emotional heart to
keep vulnerability coursing through the veins of courage and to engage
all of the behaviors we talked about in the prior section, including
trust, innovation, creativity, and accountability.
You got to put down the weapons and show up.
As children we found ways to protect ourselves from vulnerability,
from being hurt, diminished, and disappointed. We put on armor; we used
our thoughts, emotions, and behaviors as weapons; and we learned how to
make ourselves scarce, even to disappear. Now as adults we realize that
to live with courage, purpose, and connection to be the person who we
long to be-we must again be vulnerable. We must take off the armor, put
down the weapons, show up, and let ourselves be seen.
Forms of armored leadership with top three as perfectionism,
foreboding joy, numbing:
Driving Perfectionism (armored leadership) vs encouraging for
healthy striving (daring leadership).
Perfectionism is not the same thing as striving for excellence.
Perfectionism is not about healthy achievement and growth. Perfectionism
is a defensive move.
Perfectionism is not the self-protection we think it is. It is a
twenty-ton shield that we lug around, thinking it will protect us, when
in fact it’s the thing that’s really preventing us from being seen.
Perfectionism is not self-improvement. Perfectionism is, at its core,
about trying to earn approval. Most perfectionists grew up being praised
for achievement and performance (grades, manners, rule following, people
pleasing, appearance, sports). Somewhere along the way, they adopted
this dangerous and debilitating belief system: I am what I accomplish
and how well I accomplish it. Please. Perform. Perfect. Prove. Healthy
striving is self-focused: How can I improve? Perfectionism is
other-focused: What will people think? Perfectionism is a hustle.
Perfectionism is not the key to success. In fact, research shows that
perfectionism hampers achievement. Perfectionism is correlated with
depression, anxiety, addiction, and life paralysis, or missed
opportunities. The fear of failing, making mistakes, not meeting
people’s expectations, and being criticized keeps us outside the arena
where healthy competition and striving unfolds.
Last, perfectionism is not a way to avoid shame. Perfectionism is a
function of shame.
Squandering opportunities for joy and recognition (armored
leadership) vs practicing gratitude and celebrating milestones (daring
leadership).
Numbing (armored leadership) vs setting boundaries and finding
real comfort (daring leadership)
We cannot selectively numb emotion. If we numb the dark, we numb the
light. If we take the edge off pain and discomfort, we are, by default,
taking the edge off joy, love, belonging, and the other emotions that
give meaning to our lives.
Propagating the false dichotomy of victim or viking (armored
leadership) vs practicing integration - strong back, soft front, wild
heart (daring leadership)
The opposite of living in a world of false binaries is practicing
integration the act of bringing together all the parts of ourselves, as
we talked about earlier. We are all tough and tender, scared and brave,
grace and grit. The most powerful example of integrationa practice that
I wrote about in Braving the Wilderness and that I try to live by-is
strong back, soft front, wild heart.
How can we give and accept care with strongback, soft-front
compassion, moving past fear into a place of genuine tenderness? I
believe it comes about when we can be truly transparent, seeing the
world clearly-and letting the world see into us.
Being a knower and being right (armored leadership) vs being a
learner and getting it right (daring leadership)
Having to be the “knower” or always being right is heavy armor. It’s
defensiveness, it’s posturing, and , worst of all, it’s a huge driver of
bullshit.
Hiding behind cynicism (armored leadership) vs modeling clarity,
kindness, and hope (daring leadership)
Using criticism as self-protection (armored leadership) vs making
contributions and taking risks (daring leadership)
Using power over (armored leadership) vs using power with, power to,
and power within (daring leadership)
Hustling for your worth (armored leadership) vs knowing your value
(daring leadership)
When people don’t understand where they’re strong and where they
deliver value for the organization or even for a single effort, they
hustle. And not the good kind of hustle. The kind that’s hard to be
around because we are jumping in everywhere, including where we’re not
strong or not needed, to prove we deserve a seat at the table. When we
do not understand our value, we often exaggerate our importance in ways
that are not helpful, and we consciously or unconsciously seek attention
and validation of importance.”
Zigzagging and avoiding (armored leadership) vs talking straight and
taking action (daring leadership)
Zigzagging is a metaphor for the energy we spend trying to dodge the
bullets of vulnerability whether it’s conflict, discomfort,
confrontation, or the potential for shame, hurt, or criticism.
When we find ourselves zigzagging-hiding out, pretending, avoiding,
procrastinating, rationalizing, blaming, lying-we need to remind
ourselves that running is a huge energy suck and probably way outside
our values. At some point, we have to turn toward vulnerability and make
that call.
Section 4. Shame and Empathy
The definition of shame:
First, shame is the fear of disconnection. As we talked about in the
myths of vulnerability, we are physically, emotionally, cognitively, and
spiritually hardwired for connection, love, and belonging. Connection,
along with love and belonging, is why we are here, and it is what gives
purpose and meaning to our lives. Shame is the fear of
disconnection-it’s the fear that something we’ve done or failed to do,
an ideal that we’ve not lived up to, or a goal that we’ve not
accomplished makes us unworthy of connection.
Shame is the intensely painful feeling or experience of believing
that we are flawed and therefore unworthy of love, belonging, and
connection.
Retreating into our smallness becomes the most seductive and easiest
way to stay safe in the midst of the shame squeeze. But, as we’ve talked
about, when we armor and contort ourselves into smallness, things break
and we suffocate.
Behavioral cues that shame has permeated a culture:
Perfectionism; Favoritism; Gossiping; Back-channeling; Comparison;
Self-worth tied to productivity; Harassment; Discrimination; Power over;
Bullying; Blaming; Teasing; Cover-ups.
Shame resistance is not possible as long as we care about connection,
but shame resilience is possible, learnable by all of us. We need to be
empathy and self-compassion.
Shame resilience is the ability to practice authenticity when we
experience shame, to move through the experience without sacrificing our
values, and to come out on the other side of the shame experience with
more courage, compassion, and connection than we had going into it.
Ultimately, shame resilience is about moving from shame to empathy the
real antidote to shame.
it’s important to understand that if we share our story with someone
who responds with empathy and understanding, shame can’t survive.
Self-compassion is also critically important, but because shame is a
social concept-it happens between people it also heals best between
people. A social wound needs a social balm, and empathy is that balm.
Self-compassion is key because when we’re able to be gentle with
ourselves in the midst of shame, we’re more likely to reach out,
connect, and experience empathy.
The definition of empathy:
Empathy is not connecting to an experience, it’s connecting to the
emotions that underpin an experience.
Empathy is a choice. And it’s a vulnerable choice, because if I were
to choose to connect with you through empathy, I would have to connect
with something in myself that knows that feeling. In the face of a
difficult conversation, when we see that someone’s hurt or in pain, it’s
our instinct as human beings to try to make things better. We want to
fix, we want to give advice. But empathy isn’t about fixing, it’s the
brave choice to be with someone in their darkness-not to race to turn on
the light so we feel better.
If struggle is being down in a hole, empathy is not jumping into the
hole with someone who is struggling and taking on their emotions, or
owning their struggle as yours to fix. If their issues become yours, now
you have two people stuck in a hole. Not helpful. Boundaries are
important here. We have to know where we end and others begin if we
really want to show up with empathy.
Empathy is at the heart of connection-it is the circuit board for
leaning into the feelings of others, reflecting back a shared experience
of the world, and reminding them that they are not alone.
Empathy skills:
From practical perspective, empathy is first to take the perspective
of another person, second to stay out of judgment, third to understand
their emotion, and fourth to communicate my understanding of their
emotion.
To see the world as others see it, or perspective taking
Perspective taking requires becoming the learner, not the knower.
Again, it’s only when diverse perspectives are included, respected,
and valued that we can start to get a full picture of the world, who we
serve, what they need, and how to successfully meet people where they
are.
I love what Beyoncé said in her first-person essay in the September
2018 issue of Vogue: ”If people in powerful positions continue to hire
and cast only people who look like them, sound like them, come from the
same neighborhoods they grew up in, they will never have a greater
understanding of experiences different from their own. They will hire
the same models, curate the same art, cast the same actors over and over
again, and we will all lose. The beauty of social media is it’s
completely democratic. Everyone has a say. Everyone’s voice counts, and
everyone has a chance to paint the world from their own
perspective.”
To be unjudgmental
Based on research, there are two ways to predict when we are going to
judge: We judge in areas where we’re most susceptible to shame, and we
judge people who are doing worse than we are in those areas.
To understand another person’s feelings
To communicate your understanding of that person’s feelings
Fluency in emotional conversation means being able to name at least
thirty of them.
One reason emotion is difficult to identify and name is the iceberg
effect.
Many of the emotions that we experience show up as pissed off or shut
down on the surface. Below the surface, there’s much more nuance and
depth. Shame and grief are two examples of emotions that are hard to
fully express, so we turn to anger or silence.
The vast majority of us find it easier to be mad than hurt. Not only
is it easier to express anger than it is to express pain, our culture is
more accepting of anger. So the next time you’re shutting down or angry,
ask yourself what lies beneath.
Mindfulness / Paying attention
Self-compassion skills
Maintain clear line
Do not take responsibility and ownership for the words of other
people-just own your part.
Jumping into the hole with no way out is enmeshment-jumping into
struggle with someone while maintaining clear lines about what belongs
to whom is empathy.
Stop beating yourself
Talk to yourself the way you’d talk to someone you love.
Four elements of shame resilience:
Recognizing shame and understanding its triggers
When we have understanding and awareness around shame, we are less
likelyy to default to our shame shields or the following three
strategies of disconnection:
Moving away: Withdrawing, hiding, silencing ourselves, and keeping
secrets. Moving toward: Seeking to appease and please. Moving against:
Trying to gain power over others by being aggressive, and by using shame
to fight shame.
Practicing critical awareness
Reaching out
Speaking shame
Section 5.
Curiosity and Grounded Confidence
Dheeraj explained to me that when leaders don’t have the skills to
lean into vulnerability, they’re not able to successfully hold the
tension of the paradoxes that are inherent in entrepreneurship. His
examples of the paradoxes that elicit vulnerability in leaders align
with what we heard from the research participants: • Optimism and
paranoia • Letting chaos reign (the act of building) and reining in
chaos (the act of scaling) • Big heart and tough decision making •
Humility and fierce resolve • Velocity and quality when building new
things • Left brain and right brain • Simplicity and choice • Thinking
global, acting local • Ambition and attention to detail • Thinking big
but starting small • Short-term and long-term • Marathons and sprints,
or marathon of sprints in business-building Dheeraj told me, “Leaders
must learn the skills to hold these tensions and get adept at balancing
on the ‘tightrope’ of life. Ultimately, leadership is the ability to
thrive in the ambiguity of paradoxes and opposites.”
How to build skills to hold tensions of the paradoxes:
Rumble skills: easy learning does not build strong skills
The reality is that to be effective, learning needs to be effortful.
That’s not to say that anything that makes learning easier is
counterproductive-or that all unpleasant learning is effective. The key
here is desirable difficulty. The same way you feel a muscle “burn” when
it’s being strengthened, the brain needs to feel some discomfort when
it’s learning. Your mind might hurt for a while-but that’s a good
thing.
Curiosity
In his book Curious: The Desire to Know and Why Your Future Depends
on It, Ian Leslie writes, “Curiosity is unruly. It doesn’t like rules,
or, at least, it assumes that all rules are provisional, subject to the
laceration of a smart question nobody has Yet thought to ask. It
disdains the approved pathways, preferring diversions, unplanned
excursions, impulsive left turns. In short, curiosity is deviant.”
Practice vulnerability, become self-aware, and engage in tough
conversations
There’s an old saying that I lead by now: “People don’t care how much
you know until they know how much you care.” I’ve learned one way to
help people understand how much you care is to share your story.
Part 2. Living into Our
Values
Values and living into our values:
A value is a way of being or believing that we hold most important.
Living into our values means that we do more than profess our values, we
practice them. We walk our talk-we are clear about what we believe and
hold important, and we take care that our intentions, words, thoughts,
and behaviors align with those beliefs.
More often than not, our values are what lead us to the arena
door-we’re willing to do something uncomfortable and daring because of
our beliefs. And when we get in there and stumble or fall, we need our
values to remind us why we went in, especially when we are facedown,
covered in dust and sweat and blood. Here’s the thing about values:
While courage requires checking our armor and weapons at the arena door,
we do not have to enter every tough conversation and difficult rumble
completely empty-handed.
Three steps to help you know more about yourself and how to live into
your values:
We can’t live into values that we can’t name
Taking values from BC to behavior
Empathy and self-compassion: the two most important seats in the
arena
Regardless of the values you pick, daring leaders who live into their
values are never silent about hard things.
“You first listen about race. You will make a lot of mistakes. It
will be super uncomfortable. And there’s no way to talk about it without
getting some criticism. But you can’t be silent.” To opt out of
conversations about privilege and oppression because they make you
uncomfortable is the epitome of privilege.
Silence is not brave leadership, and silence is not a component of
brave cultures. Showing up and being courageous around these difficult
conversations is not a path you can predetermine. A brave leader is not
someone who is armed with all the answers. A brave leader is not someone
who can facilitate a flawless discussion on hard topics. A brave leader
is someone who says I see you. I hear you. I don’t have all the answers,
but I’m going to keep listening and asking questions. We all have the
capacity to do that. We all have the ability to foster empathy. If we
want to do good work, it’s imperative that we continue to flesh out
these harder conversations, to push against secrecy, silence, and
judgment. It’s the only way to eradicate shame from the workplace, to
clear the way for a performance in the arena that correlates with our
highest values and not the fearmongers from the stands.
The biggest challenge we face when it comes to values is the
necessity to give feedback and receive feedback. You have to know when
you are ready to give feedback and be good at receiving feedback.
Understand their values
You don’t really know people until you take the time to understand
their values.
Daring leaders assume the best about people’s intention and assume
they are doing the best they can. Leaders struggling with ego, armor,
and/or a lack of skills do not make that assumption.
What is the foundational skill of assuming the best in people?
Setting and maintaining boundaries. What’s the fundamental belief
underpinning the assumption of positive intent? That people are doing
the best they can.
The people who are the most generous in their assumptions of others
have the clearest boundaries. The most compassionate and generous people
I’ve interviewed in my career are the most boundaries. It turns out that
we assume the worst about people’s intentions when they’re not
respectful of our boundaries: It is easy to believe that they are trying
to disappoint us on purpose. However, we can be very compassionate
toward people who acknowledge and respect what’s okay and what’s
not.
In addition to boundaries, an assumption of positive intent relies on
the core belief that people are doing the best they can with what
they’ve got, versus that people are lazy, disengaged, and maybe even
trying to piss us off on purpose. Sure, we’re all capable of change and
growth, but assuming positive intent requires the belief that people are
really trying in that moment.
Assuming positive intent does not mean that we stop helping people
set goals or that we stop expecting people to grow and change. It’s a
commitment to stop respecting and evaluating people based solely on what
we think they should accomplish, and start respecting them for who they
are and holding them accountable for what they’re actually doing. And
when we’re overwhelmed and struggling, it also means turning those
positive assumptions toward ourselves: I’m doing the very best I can
right now.
Part 3. Braving Trust
Importance of talking about trust:
Because talking about trust is tough, and because these conversations
have the potential to go sideways fast, we often avoid the rumble. And
that’s even more dangerous. First, when we’re struggling with trust and
don’t have the tools or skills to talk about it directly with the person
involved, it leads us to talk about people instead of to them. It also
leads to lots of energy-wasting zigzagging.
To measure individual level of trustworthiness, you can refer to the
following seven behaviors - BRAVING inventory:
Boundaries: You respect my boundaries, and when you’re not clear
about what’s okay and not okay, you ask. You’re willing to say no.
Reliability: You do what you say you’ll do. At work, this means staying
aware of your competencies and limitations so you don’t overpromise and
are able to deliver on commitments and balance competing priorities.
Accountability: You own your mistakes, apologize, and make amends.
Vault: You don’t share information or experiences that are not yours to
share. I need to know that my confidences are kept, and that you’re not
sharing with me any information about other people that should be
confidential. Integrity: You choose courage over comfort. You choose
what is right over what is fun, fast, or easy. And you choose to
practice your values rather than simply professing them. Nonjudgment: I
can ask for what I need, and you can ask for what you need. We can talk
about how we feel without judgment. We can ask each other for help
without judgment. Generosity: You extend the most generous
interpretation possible to the intentions, words, and actions of
others.
Unpacking Vault:
When I walk into a co-worker’s office and spill, there might be a
moment of connection, but it’s counterfeit connection. The second I walk
out, that colleague is likely thinking, “I should be careful about what
I tell Brené; she’s got no boundaries.”
Unpacking nonjudgment:
We are afraid of being judged for a lack of knowledge or lack of
understanding, so we hate asking questions.
We asked a thousand leaders to list marble earning behaviors-what do
your team members do that earns your trust? The most common answer:
asking for help. When it comes to people who do not habitually ask for
help, the leaders we polled explained that they would not delegate
important work to them because the leaders did not trust that they would
raise their hands and ask for help.
Trust is built in small moments. If you struggle with reliability,
make small and doable promises to yourself that are easy to fulfill,
until you get a flywheel of reliability going again. If you struggle
with boundaries, set small ones with your partner-like you will not be
responsible for both cooking and cleaning up dinner-until you are adept
at putting boundaries into action in a more meaningful way. That’s how
you fill your own marble jar. And never forget-we can’t give people what
we don’t have.
Part 4. Learning to Rise
We can’t expect people to be brave and risk failure if they’re not
prepped for hard landings.
Here’s the bottom line: If we don’t have the skills to get back up,
we may not risk falling. And if we’re brave enough often enough, we are
definitely going to fall. The research participants who have the highest
levels of resilience can get back up after a disappointment or a fall,
and they are more courageous and tenacious as a result of it. They do
that with a process that I call Learning to Rise. It has three parts:
the reckoning, the rumble, and the revolution.
Three steps process for learning to rise:
When we have the courage to walk into our story and own it, we get to
write the ending. And when we don’t own our stories of failure,
setbacks, and hurt-they own us.
The Reckoning
The reckoning is as simple as that: knowing that we’re emotionally
hooked and then getting curious about it.
The ego doesn’t own stories or want to write new endings; it denies
emotion and hates curiosity. Instead, the ego uses stories as armor and
alibi. The ego says “Feelings are for losers and weaklings.”
The most effective strategy for staying with emotion instead of
offloading it is something I learned from a yoga teacher. And from a few
members of the military Special Forces. It’s breathing.
Breathing is also the key to another strategy for reckoning with
emotion, and one of the most underrated leadership superpowers:
practicing calm.
I define calm as creating perspective and mindfulness while managing
emotional reactivity.
Calm is a superpower because it is the balm that heals one of the
most prevalent workplace stressors: anxiety.
Rumble: conspiracies, confabulations, and shitty first drafts
If the reckoning is how we walk into a tough story, the rumble is
where we go to the mat with it and own it.
The rumble starts with this universal truth: In the absence of data,
we will always make up stories. It’s how we are wired. Meaning making is
in our biology, and when we’re in struggle, our default is often to come
up with a story that makes sense of what’s happening and gives our brain
information on how best to self-protect. And it happens a hundred times
a day at work.
In our SFDs, fear fills in the data gaps. What makes that scary is
that stories based on limited real data and plentiful imagined data,
blended into a coherent, emotionally satisfying version of reality, are
called conspiracy theories. Yes, we are all conspiracy theorists with
our own stories, constantly filling in data gaps with our fears and
insecurities.
Confabulation has a really great and subtle definition: A
confabulation is a lie told honestly. To confabulate is to replace
missing information with something false that we believe to be true.
Confabulation shows up at work when we share what we believe is
factual information, but it’s really just our opinion.
Gottschall writes, “Conspiracy is not limited to the stupid, the
ignorant, or the crazy. It is a reflex of the storytelling mind’s
compulsive need for meaningful experience.” The problem is that rather
than rumbling with vulnerability and staying in uncertainty, we start to
fill in the blanks with our fears and worst-case-scenario planning. I
love this line from Gottschall: “To the conspiratorial mind, shit never
just happens.”
The three most dangerous stories we make up are the narratives that
diminish our lovability, divinity, and creativity.
The reality check around our lovability: Just because someone isn’t
willing or able to love us, it doesn’t mean that we are unlovable.
The reality check around our divinity: No person is ordained to judge
our divinity or to write the story of our spiritual worthiness.
The reality check around our creativity: Just because we didn’t
measure up to some standard of achievement doesn’t mean that we don’t
possess gifts and talents that only we can bring to the world. And just
because someone failed to see the value in what we can create or achieve
doesn’t change its worth or ours.
When we own a story and the emotion that fuels it, we get to
simultaneously acknowledge that something was hard while taking control
of how that hard thing is going to end. We change the narrative. When we
deny a story and when we pretend we don’t make up stories, the story
owns us. It drives our behavior, and it drives our cognition, and then
it drives even more emotions until it completely owns us.
The Revolution
I’m not afraid of the word revolution, I’m afraid of a world that’s
becoming less courageous and authentic. I’ve always believed that in a
world full of critics, cynics, and fearmongers, taking off the armor and
rumbling with vulnerability, living into our values, braving trust with
open hearts, and learning to rise so we can reclaim authorship of our
own stories and lives is the revolution. Courage is rebellion.
Revolution might sound a little dramatic, but in this world, choosing
authenticity and worthiness is an absolute act of resistance. Choosing
to live and love with our whole hearts is an act of defiance. You’re
going to confuse, piss off, and terrify lots of people-including
yourself. One minute you’ll pray that the transformation stops, and the
next minute you’ll pray that it never ends. You’ll also wonder how you
can feel so brave and so afraid at the same time. At least that’s how I
feel most of the time … brave, afraid, and very, very alive.
Own the fear, find the cave, and write a new ending for yourself, for
the people you’re meant to serve and support, and for your culture.
Choose courage over comfort. Choose whole hearts over armor. And choose
the great adventure of being brave and afraid. At the exact same
time.
Playing defense: How to control the narrative if your work is
being questioned - Wes Kao’s Newsletter [Link]
It’s normal that people will misunderstand and disagree with you.
what we need to do is to 1) learn to explain your ideas better and 2)
stay calm and share your thought process in the most objective way
possible.
Defending your thinking means to share logic, evidence and rationale
that explains why you believe your conclusion is the right one. It’s not
to try to protect your ego by refusing to acknowledge a good argument,
and being delusional about the strength of your claim. Being able to
play defense is important because it’s about your credibility. If you do
it well you are building more trust, otherwise you will be diminishing
trust.
Some suggestions mentioned in this article:
Have a rationale of every small decision you made.
Try to anticipate questions.
Embrace “show, not tell”
React as positively as possible. e.g. “Ah! I’m so glad you
asked”.
Consider the question behind the question.
Be happy that the person voiced their concern.
Beware of insecure vibes.
If you overcompensate, you’ll come across as defensive. This
decreases your credibility too. You’ll need to use your judgment and
read the situation. An open, curious, and almost playful attitude shows
you’re not afraid of hard questions.
Many people underestimate the daily moments where your credibility
can either be reinforced or eroded. This might sound dramatic, but it’s
quite banal: Every interaction folks have with you gets added to their
subconscious cumulative repository of data points about you.
Insecure vibes are subconscious clues and signals that you might
be giving off when you’re feeling anxious, nervous, or uncertain. Get
rid of insecure vibes—and your writing, meetings, presentations,
negotiations, and pitches will become stronger.
― “Insecure vibes” are a self-fulfilling prophecy - Wes Kao’s
Newsletter [Link]
In the following situations, insecure vibes happen:
When other person touched on your sore spot and you feel
threatened
You assume the person will say no before you even start
This can make you talk fast - showing you enter the conversation
already playing defense. You don’t give the person a chance to say yes
because you’ve already said no to yourself.
You insist on email or slack when you know a phone call is
better
Explaining your point in writing is a sign of lacking confidence and
avoiding confrontation.
You over-explain because you expect the other person to be
skeptical
You bring up counterpoints to arguments no one has mentioned. Not a
good time to do so. It looks like you intentionally bring up something
new to surprise others rather than having a reasonable justification of
your point.
To avoid being doubtful if you actually feel confident:
Don’t preface your idea with too many caveats. Speak in complete
sentences. Remove “ands” and “buts” that create never-ending sentences,
which can sound less authoritative.
Notice if you start to ramble. Try to prepare the first few lines of
what you want to say to kick off a meeting, so you start strong.
Practice your actual script so you get comfortable saying those
words.
To get rid of insecure vibes, ask yourself
Could this be interpreted as sounding defensive?
Am I overcompensating or overexplaining?
How would I respond on my best day?
Would I say this if I felt secure?
Strategy, not self-expression: How to decide what to say when
giving feedback - Wes Kao’s Newsletter [Link]
Ask yourself “Is this strategy or self expression?” before giving
feedback.
Do not self-express your feeling or complain. Do not say anything
that does not motivate them to change. Instead, say things that get you
closer to changing the person’s behavior.
How to focus on strategy rather than self-expression:
Mentally forgive this person
Identify what is most likely to motivate them to change,
Say only 10% that will actually change behavior, thinking
about:
How does this make them even more effective?
How will this allow them to work even better with the people around
them?
How does this get them closer to their goals?
How is this a skill they can apply now and in all future roles?
Don’t trigger the defensiveness in the first place
The minute your recipient gets defensive, it becomes a lot harder to
undo the defensiveness and get them to accept what you’re
saying.
Let the other person talk, e.g. “I’d love to hear what you think.
What parts are resonating most with you?”
The other benefit of letting the other person talk is cognitive
dissonance: if they say out loud what they are committed to doing
differently, they are reinforcing the idea in their own mind.
Keep your eyes on the person’s behavior change
Always be framing
“Strategy, not self-expression” applies to many more situations
too.
The more controversial the idea, the higher the burden of
proof.
Update your assumptions about how you add value.
Share where your hunch is coming from—because it’s coming from
somewhere.
Describe why the problem matters, so people understand why
you’re speaking up.
Don’t rely on your credentials. Your idea should make sense on
its own.
Use language that accurately reflects your level of
certainty.
― How to share your point of view (even if you’re afraid of
being wrong) - Wes Kao’s Newsletter [Link]
Every week, we make business cases at work. I’m defining a
business case as any recommendation to pursue a business opportunity or
solve a problem. A business case can be a 5-page document, 5 sentences
in Slack, or a 5-minute phone call. The larger the project, the more you
may need to make a comprehensive business case. But the underlying
premise is the same: If you don’t explain why a problem matters, your
colleagues won’t have the necessary information to decide how to support
you.
― The #1 question every business case should answer - Wes
Kao’s Newsletter [Link]
Skilled immigration is a national security priority -
Noahpinion [Link]
Skilled immigration should be supported while illegal immigration
should be avoided. Also, avoid US education system to become corrupt
system for immigration.
The low road, the high road, and the way the wind blows -
Silver Bulletin [Link]
Paramount Merges With Skydance - App Economy
Insights [Link]
PARA agreed to merge with Skydance. The new CEO is Skydance Media CEO
David Ellison whose father Larry Ellison is the founder of Oracle.
Old paramount businesses: 1) filmed entertainment (Paramount Pictures
and Nickelodeon movie), 2) TV media (Paramount’s broadcast) and cable
television networks, like CBS and MTV, 3) Direct to consumer streaming
services like Paramount+ and Pluto TV. Its current problems: 1) flat
revenue, 2) streaming services are losing, 3) high long-term debt but
low cash.
New Paramount Plan: 1) unify marquee rights, 2) reorganize finance,
3) transition into a tech-media hybrid.
For the #3, the vision is to better position Paramount on the front
end (DTC apps) and the back end (cloud infrastructure, cloud-based
production, and AI tools). Specifically, they are going to 1) rebuild
DTC into a differentiated platform, 2) build studio-in-the-cloud, 3)
leverage Gen AI.
Apple Pay unveiled a new peer-to-peer (P2P) feature called “Tap to
Cash”, which is a natural evolution of the existing “Tap to Pay”. This
is not the only case where big tech offers features that directly
compete with financial institutions: Google Wallet, Amazon’s lending
program for sellers, etc, competing with PayPal’s Venmo, Block’s Cash
App, etc. Big tech’s move not only intensifies competition within P2P
payment, but also raises questions about the future of the payment
industry.
Highlights:
Visa and Mastercard both face mobile wallet threats to card-based
business model.
American Express renowned for its premium card offerings, targeting
high spending high credit quality customers, continuing to attract
millennials and Gen Z customers. The recent strategic acquisitions are
Tock (a reservation platform for high end restaurant and events) and
Rooam (a mobile payment and ordering platform for restaurants and
venues). However, it’s sensitive to economic downturns and facing
competitions for other premium card issuers and digital payment
platforms.
Fiserv’s merchant solutions and financial solutions look positive,
it’s actively investing in digital transformation, and it recently
acquired BentoBox (a digital marketing and commerce platform for
restaurants).
Adyen serves large enterprise clients with its unified commerce
platform
PayPal lowered its FY 2024 guidance. It’s facing a decline in active
accounts. To solve this, it focuses on strategic partnerships such as
collaboration with Apple. It’s facing competitive pressure from Big Tech
payment solutions like Apple Pay and Google Pay. It’s currently
undergoing significant restructuring such as layoffs and leadership
change. And it’s initiating AI-powered personalized ads platform and
strengthening relationships with SME customers.
Block’s growth is driven by its momentum of Cash App ecosystem,
square ecosystem, and significant investment in Bitcoin. However, it’s
facing challenges of regulatory scrutiny on cryptocurrency activities
and compliance practices, competition from established competitors and
fintech startups, needs of balancing growth and profitability given FY
2024 guidance.
What to watch for the shift in payment landscape:
Facing legal battle from Big Tech, can Visa and Mastercard find
innovation or new solutions to maintain their revenue streams from swipe
fee?
Will Digital Wallet dominant payment industry? Will traditional
cards remain or be replace?
New possibilities of payments have been developed such as Buy Now
and Pay Later (BNPL). Will innovations gain mainstream adoption in
payment industry?
Can the challenges of cryptocurrencies be overcome? Will
cryptocurrencies be mainstream adoption?
As consumers are demanding seamless, secure, cheaper, and
personalized payment experiences, companies that can provide such
services will become successful in the future.
Nike: Losing Its Swoosh? - App Economy Insights [Link]
Nike’s facing challenges of 1) shifting consumer preferences to newer
brands (On and Hoka), 2) softer traffic and lower sales of classic
footwear franchises in direct-to-consumer channel, 3) macroeconomic
headwinds.
Nike Q4 2024 Highlights: 1) Nike is reducing supply of classic
footwear franchises to create space for newness and innovation, 2)
focusing on performance and innovation, 3) Jordan Brand is still growing
YoY.
Other observation: Nike brand value declined by 4% YoY, while Adidas
and Lululemon gain brand values.
Broadcom now operates across two primary segments: 1) semiconductor
solutions (chips for networking, server storage, broadband, wireless
communication, and industrial applications), and 2) infrastructure
software (a explosive leap with the acquisition of VMware).
Highlights: VMware acquisition brings significant revenue to
Broadcom; AI as a great growth driver; jumbo acquisition resulted in
gigantic net debt; strong cash generation; 10-for-1 stock split on July
15.
Future: Next-generation products include Tomahawk and Jericho; supply
chain disruption and inflation resulted from macroeconomic environment;
regulatory scrutiny into VMware acquisition.
Starbucks: A Brewing Crisis - App Economy Insights
[Link]
[LinkedIn]
Three main issues:
the boycott impact: losing 1.5M loyal customers, as a result of the
fact that in October 2023, Starbucks became embroiled in a controversy
related to the ongoing violence in the Middle East,
significant loss in traffic from non-Rewards members due to
additional reasons such as awareness of daily drink price, gourmet
coffee boom, health-conscious consumers, changing work habits, and
competitors with coffee offerings in lower prices. Solutions on this are
new initiatives such as physical and digital enhancements like updated
POS system, siren system speedup, and opening mobile orders beyond its
loyalty programs.
Price war in Chinese coffee market e.g. Luckin Coffee.
7 Mindsets That Are Slowing Down Your Career Growth - The
Caring Techie Newsletter [Link]
Solo Contributor Mindset -> Prioritize get thing done with
others
That’s not my job -> willing to do things outside of my
scope
My work will speak for itself -> do the work and say that I did
the work
If I do what I’m told, I will get promoted -> I need to sit in
the driver’s seat of my career growth
If I’m not getting any feedback, it means I’m doing good -> I
need to actively seek feedback
I’m not ready for the next level -> I might be ready for a
promotion despite my doubts
Picking the devil you know -> next promotion might come from
joining another company
Mark Zuckerberg and Peter Thiel - Internal Tech
Emails [Link]
Peter Thiel and Mark Zuckerberg on Facebook, Millennials, and
predictions for 2030.
Google owes its stable position as much to Generative AI’s slow
progress as its own innovations. While OpenAI, Anthropic, Meta, and
others have built more powerful AI models into their chatbots, people
haven’t substituted those bots for traditional search. As of February,
Bing still had less than 4% of search market share worldwide compared to
Google’s 91%. ChatGPT, for context, debuted nearly two years
ago.
This week, when OpenAI introduced its own search engine, called
SearchGPT, it didn’t exactly strike fear in the halls of Mountain
View.
― Surprisingly, Google Is Thriving In The GenAI Era - Big
Technology [Link]
Netflix: Ad Tech Focus - App Economy Insights [Link]
Tesla’s revenue comes from three main sources 1) automative (78%
revenue), 2) services and other (12% revenue), 3) energy generation and
storage (10% revenue).
Production and Deliveries are the two main metrics.
Tesla’s margins have historically been ahead of other car
manufacturers thanks to three critical leverages: 1) Economies of scale
(though gigafactories), 2) Direct-to-consumer (online and via its
showrooms), 3) Low marketing costs (Tesla barely spends on
advertising).
Highlights:
Tesla missed earnings expectations for the fourth consecutive
quarter.
Elon Musk pushed the Robotaxi announcement from August 8 to October
10.
Profits fell for the second straight quarter, driven by slower
demand, competition, and price cuts. Price cut remain a double-edged
sword.
Operating margin declined by 3% YoY and was at its lowest in years.
Negative impacts are from 1) price cuts, 2) delivery decline, 3) AI
projects, 4) restructuring costs. Positive impacts are from 1) lower
cost per vehicle, production ramp of 4680 cells, higher regulatory
credits, and non-auto segments.
Energy generation and storage doubled.
Future:
Humanoid Robots (Optimus): Tesla will begin producing humanoid
robots for internal use next year and plans to sell to other companies
in 2026.
Market Share and BYD: Tesla outsold BYD in Q2 2024, but the gap
between the two companies was only 18K deliveries. Tesla had a 50%
market share in BEV sales in the US, with 164K deliveries in Q2. As
expected, the market share of BEVs has consistently declined, reflecting
the continued adoption of all-electric cars.
More than 1.5 million developers are now using Gemini across our
developer tools.
Waymo’s served more than 2 million trips to-date and driven more
than 20 million fully autonomous miles on public roads. Waymo’s now
delivering well over 50,000 weekly paid public rides, primarily in San
Francisco and Phoenix.
Our AI-driven profit optimization tools have been expanded to
performance max and standard shopping campaigns. Advertisers use profit
optimization and smart bidding see a 15% uplift in profit on average
compared to revenue-only bidding.
Soon we’ll actually start testing Search and Shopping ads in AI
overviews for users in the US, and they will have the opportunity to
actually appear within the overview in a section clearly labeled as
sponsored.
― Google: AI Spending Spree - App Economy Insights
[Link]
Highlights of Q2 FY24: 1) Revenue growth slowed down, 2) search
advertising showed no slowdown, 3) YouTube Ads growth slower than Q1, 4)
subscriptions decelerated from Q1 due to YouTubeTV increased its price
in Q2 FY23, 5) cloud accelerated, 6) margin improved YoY but are about
to compress due to AI investments etc, 7) Capex were up and expected to
continue being up, 8) Alphabet committed \(\$5\) B to the ongoing operations of Waymo,
9) The company returned \(\$18.2\) B to
shareholders, including \(\$15.7\) B in
buybacks, showing their confidence in stock value.
Highlights of Cookies, Cloud, and YouTube: 1) planned to phase out
third-party cookies from Chrome to address privacy concerns regarding
tracking but reversed its decide to let users choose their tracking
preferences, 2) AI boost continues to accelerate cloud revenue growth
especially in GCP and Workspace, 3) YouTube gains market share, 9.9% in
Jun, up from 9.2% in prior year.
Future: 1) Project Astra, 2) SearchGPT competition: Search is
critical for Alphabet because it contributes 57% revenue. SearchGPT
could shake up the market but challenges are ensuring accuracy and
avoiding hallucination. OpenAI doesn’t have either user engagement or ad
performance which are required by a successful search business.
American Express had that network because of its legacy
traveler’s check business so it was able to leverage that network to
create and establish its credit card business. Without such a network,
it’s impossible to operate a closed loop system.
― I Am Buying American: American Express - Capitalist
Letters [Link]
Why American Express is superior than Visa and Mastercard? It’s
business model.
Visa is a typical payment processor. It connects the merchant to the
issuer bank. It’s an open loop. American Express, on the other hand, is
a closed loop system which makes it a money printing machine. It uses
two strategies: 1) set stricter standards to issue cards, 2) provides
travel privileges to attract frequent travelers who have higher net
worth.
Why good investment: 1) Giant moat due to closed loop system, 2)
inflation proof: customer base are those with stronger purchasing power,
3) it’s expanding internationally and among younger people: in 2023, 60%
of new consumer accounts were Gen-Z and Millennial, international
businesses billed for card services grew 14% YoY last quarter,
accounting for 35% overall growth.
How Github grows and makes money - Productify by
Bandan [Link]
Github’s culture values: 1) Customer-obsessed, 2) Ship to learn 3)
Growth mindset, 4) Own the outcome, 5) Better together, 6) Diverse and
inclusive.
How does Github make money: 1) Al powered tools - Github CoPilot, 2)
Subscription Plans, 3) Enterprise solutions, 4) Marketplace and
additional services - Github Marketplace, Github Actions, Github
Packages.
Revenue Breakdown: Major contributors are Github CoPilot and
Enterprise solutions, Steady contributors are subscription plans,
growing segments are marketplace and additional services.
Github’s product and engineering culture: 1) Open source, 2) Remote
first prioneers - pull requests, 3) Octocat obsession, 4) Continuous
learning and growth, 5) Al integration - Github Copilot, 6) Hackathons
and innovation time, 7) Inclusive design.
Key Takeaways from Github’s growth strategy: 1) Unwavering
developer-centric focus and positioning, 2) Building relevant products
for its user problems, 3) strong cultural values and community
engagement
No Rules Rules - The secret sauce of Netflix - Tech
Books [Link]
How to win at Enterprise AI - A playbook - Platforms, AI, and
the Economics of BigTech [Link]
YouTube and Podcasts
Hot Swap growing, donors revolt, President Kamala? SCOTUS
breakdown: Immunity, Chevron, Censorship - All-in Podcast [Link]
they’re probably two of the key things that I would look at to
determine are we looking at a a true luxury business and then you can go
into all um uh kinds of detail um but I think they would be the two ones
I’d look at um in terms of the experience from the customer point of
view I think it’s also important to remember that there needs to be a
social (06:37) element um to the product or service for it to be a
luxury in in the in a commercial sense and in a sort of financial um you
know investor sense um and so the idea there is if you look at many
Artisans or makers of high quality bespoke Goods um you know that they
may very well be high quality product but there’s no social Dimension
right so there’s no element of showing off if you will to use a slightly
sort of negative connotation and so therefore I think in a in in this
for the purposes of our discussion The Artisans and the and the (07:14)
sort of small independent bespoke makers would not be considered luxury
businesses right um so there would probably be two or three things I
would look at clay to figure out if I’m looking at a true luxury
business um and then finally the the fact that these businesses and the
market overall tends to be driven more by the offering than the demand
side so in some sense these companies create you know their own Market
they create their own Demand by offering things to the consumer that the
consumer may not realize they they need or desire or even um or or dream
of right so there there’s a number of unique characteristics to these
businesses which I think you know make them very interesting to study
sometimes it’s difficult to determine if you’re looking at a true luxury
business or not and sometimes Within These large groups take an lbmh for
instance you know some of their offering for some of their brands take
Cosmetics or perfumes some of that offering I probably wouldn’t classify
as a luxury business right but there are still part (08:16) of um the
group and and they generate some revenues at group level um and then you
have some parts of the business say say the LV or Dior Brands where it’s
and especially leather goods and apparel where it’s much easier to say
that this is a this operates as a true luxury business so you know you
can attempt to draw these distinctions but I think sometimes the lines
are blurred。
The Luxury Strategy | Why LVMH & Hermès have Outperformed
the Market w/ Christian Billinger (TIP643) [Link]
Simple Diffusion Language Models (15min video) [Link]
You cannot spend this kind of money and show no incremental
revenue potential. So while this is incredible for NVIDIA, the chicken
is coming home to roast, because if you do not start seeing revenue flow
to the bottom line of these companies that are spending 26 B dollars a
quarter, the market cap of NVIDIA is not what the market cap of NVIDIA
should be, and all of these other companies are going toe get punished
for spending this kind of money. Where are all these new fangled things
that we are supposed to see that justifies a hundred billion dollar of
chip spend a year, two hundreds billion dollars of energy spend, a
hundred billion dollars of all this other stuff, we are now spending 750
billion dollars. This is on the order of a national transfer payment,
and we’ve seen nothing to show for it except that you can mimic
somebody’s voice. It doesn’t all hang together yet. - Chamath
Palihapitiya
There’s gaps in the quality of the products that can be created
to not have hallucination. Those gaps are too large right now for them
to be used reliably in production settings unless you have a very
defined scope. If you have a defined scope though, the implementation
costs are not nearly what needs the level of spend to support. So there
is just a big mismatch. Second is that we have a huge problem with
NVIDIA, which is you can’t spend this kind of money to have tech lock-in
to one hardware vendor, and that makes no sense. And what you are seeing
now is that Amazon Google Microsoft AMD Intel, a plethora of startups
Grok, everybody trying to make now different hardware solution. The
problem though is that we have this massive lock-in right now, because
the code is littered with all these NVIDIA specific ways of implementing
access to GPUs, that’s not sustainable. So we are in an existential
thrash and I think the only way that we are going to get around this is
to do a little bit of a reset. And I think that’s going to touch a lot
of startups that have already taken down way too much money at really
insane valuations. I think we are in a bit of a reckoning right now it’s
going to be complicated couple of quarters to at a minimum and probably
a complicated year to sort out who’s actually real. - Chamath
Palihapitiya
There is ton of capital that was raised during the covid bubble
era, and the ZIRP (Zero Interest Rate Policy) era, that needed a place
to go. And a lot of traditional business model, traditional in the
technology sense - SAS and a lot of biotech stuff, it became
uninvestable. Then there is a lot of money in the public markets that
was sitting on the sidelines, that was sitting in treasuries and so on.
So every dollar is looking for growth and there is a lot of dollars
still sitting around out there from the ZIRP era and the coming into
this kind of post ZIRP era, looking for a place to growth. And there is
very little growth as we talked about with the S&P 479 not being
very performative with respect to growth and revenue and having great
outlook for the next five years. So then when there is a glimmer of
upside there is a glimmer of opportunity, even if it’s just painting a
picture of a growth story, all the capital drives into it. And we’ve all
heard stories about these series a startups in AI, getting bit up to a
100M valuation. I’ve seen a couple of these where people have pitch me
things on like protein modeling AI startups, and it’s literally like two
guys from meta and openai that left and started this company, and they
raised 30 on a 12 per year or something, and it’s just two guys building
a model. That’s because that capital needs to find a place where it can
tell itself a growth story. So I think we are still dealing with the
capital hangover from ZIRP. And the fact there is an area to invest for
real growth that has allowed the AI bubble to grow as quickly as it has.
- David Friedberg
Now as Chamath points out we are kind of rationalizing that back
and I do think that there is going to be a reset. Now I’ll also say that
the Goldman report which I read and some of the other analyses that have
been done. I think there was some commentary or some analysis that hey
it costs me six times as much as having an analyst do this work. The
energy cost of the AI is still so high, the actual performance of the
model is not good. What that fails to write it’s right and wrong. It’s
right in the sense that yes it’ s more expensive today and ROI is not
there today. It’s wrong in that it ignores the performative model
improvements that we’re seeing in nearly any metric over the past couple
of months. Every few months as we know we see new models, new
improvements, new architectures, new ways to leveraging the chips to
actually drive a lover token cost, to drive lower energy cost per
answer, lower energy cost per run. Every metric that matters is
improving, so if you fast forward another 24 or 36 months, I do think
that there is a great reason to be optimistic that there is going to be
extraordinary ROI based on the infrastructure that’s being built. It’s a
question of are you going to get payback before the next cycle of
infrastructure needs to be made and everything comes back in. We saw
this during the dotcom boom where a lot of people built out data centers
and by the time they were able to actually able to make money on those
data centers, it was like hey all the new Telco equipment, all the new
servers needs to be put in, and everything got written off. So there is
a big capex kind of question mark here, but I do think that the
fundamental economics of AI will be proven over the next couple of
quarters. - David Friedberg
I’m much more bullish than you guys about this investment that’s
being made. Remember that when the internet got started in the 90s, it
was via dialup. I mean you literally had to have a modem and you would
dial up the internet and it was incredibly slow. Photo sharing didn’t
even work, so social networking wasn’t possible. And basically what
happened next was that the Telecom company spent a ton of money building
out broadband and people started upgrading to broadband. Then we had the
Doom crash everyone thought that telecom companies had wasted billions
of dollars investing in all this Broadband infrastructure. And it turned
out that no they were right to do that, it just took a while for that to
get used and this is a pretty common pattern with technological
revolutions is that you can have a bubble in the short term but then it
gets justified in the mid to long term. The build out of the railroads
in the United States another example of this we had huge railroad
bubbles but it turned out that that investment was all worthwhile. -
David Sacks
― Biden chaos, Soft landing secured? AI sentiment turns
bearish, French elections - All-in Podcast [Link]
Project 2025: The Radical Conservative Plan to Reshape
America Under Trump | WSJ [Link]
Trump assassination attempt, Secret Service failure, Inside
the RNC, VC liquidity problem - All-in Podcast [Link]
Trump’s VP pick JD Vance SPEAKS at 2024 RNC (FULL SPEECH) -
NBC Chicago [Link]
You have to put one foot in front of the other every day, and you
have to focus on tangible progress. And where that fails is when most
people and I do it a lot and I’ve tried to get better as I’ve gotten
older, is when I get comparative and I compare myself to the other
person, the other company, the other funding round, there’s so many
reasons for you to feel like you’re less than something else. And the
reality is that has nothing to do with you, you’re not in control of
that, but it’s so hard. And then if I don’t take that medicine, I become
insecure, and then I make mistakes that are entirely avoidable. So it’s
just tangible progress the things that I can control. That’s probably
the most useful piece of advice that I try to remind myself of every
day. - Chamath Palihapitiya
― The Besties Take Napa | All-In Special - All-in
Podcast [Link]
Sharing good insights about AI, David’s amazing story with Poker,
some great career advice. And happy birthday to David Friedberg!
We talked a little bit about it with Jonathan height. There’s
some great studies that have shown in the past that the change in income
is a better predictor of happiness than absolute income. Eventually
everything normalizes so I think UBI makes no sense for three reasons.
The first is this normalization of spending level. So once you’ve kind
of had this increase, you have a moment of happiness, and then you
actually start spending differently or spending more. And effectively
every human has one innate trait desire. And desire is what drives
humanity. It’s what drives progress. It’s what pushes us forward because
no matter what our absolute condition, it’s our relative condition that
matters relative to others or relative to ourselves in the past or
perspectively in the future. And so we always want to improve our
condition. So a UBI based system basically gives a flat income so the
only way for it to really work is if you increase the income
automatically by say 10% a year. So in a UBI world, no amount of money
will actually make someone satisfied or meet their minimum thresholds
because those minimum thresholds will simply shift. And you know the
second issue is just the net economic effect if we gave 350 million
Americans 1000 bucks a month, that’s \(\$350\) billion a month, that’s \(\$4\) trillion a year. Our prospective
budget for next year is 7.3 trillion at the federal level, so you know
that’s already more than 50% of the total projected federal budget next
year finding the mechanism for funding this at scale is not what this
study actually looked at. Because if you look at it the net effect would
be inflationary. And that’s the third major reason is that ultimately
this would have an inflationary effect anytime. We’ve stimulated the
economy with outside money. With government-driven money, we see many
bubbles emerge and we see an inflationary effect. So look at covid,
there were all these little bubbles that popped up in the financial
markets, we had NFTs, we had crypto, we had all these sort of new places
that money found its way to and then we had an aggregate inflationary
effect food prices are still up 30 40% since covid. And so I think that
the study provides an interesting insight into the micro effect the
psychological effects, the social effect, but macro effects are what is
so like simply arithmetically obvious, which is inflation and an
inability to actually fund us at scale. And fundamentally people want to
work so they’ll take that money, and then they’ll go find ways to work
and generate more money, and you have this inflationary effect so I
think UBI does not make sense. - David Friedberg
That’s not UBI right and what you’re describing I think exist and
there are incentives and programs and opportunities out there people can
sign up with Roth IRAs they can contribute some percentage of their
paycheck to a 401k. If they have a job that has a 401k setup for them
there’s a lot of systems and mechanisms out there and you get tax breaks
for doing that. So there’s mechanisms and incentives out there to do
that sort of thing the concept with UBI is can you pay people a flat
amount of money so that they don’t have to work, and then they end up
being able to explore and do other things with their life as the robots
and AI does everything for them. And I’ve just always been of the belief
that I don’t think that there’s this natural border that we hit beyond
which humans don’t work. I think that AI based tools and automation
tools are the same as they’ve always been. When we developed a tractor
people didn’t stop farming. They could get much more leverage using the
tractor and farm more. And new jobs and new Industries emerged. And I
expect that the same thing will happen with this next evolution of
technology and human progress. Humans will find ways to create new
things to push themselves forward to drive things forward. And for the
natural market-based incentives that fundamentally are rooted in this
internal system of Desire will create new opportunities that we’re not
really thinking about so I don’t believe in this idea of UBI in some
utopian world where everyone’s happy not working and letting machines do
everything for them I think that the fundamental sense of a human is to
find purpose, and to realize that purpose to drive themselves forward
and progress themselves. And I think that that’s always going to be the
case. - David Friedberg
A year later, what Threads could learn from other social
networks - TechCrunch [Link]
Though Threads has reached 175 million monthly active users in its
first year and has made some progress such as integrating fediverse,
there are a lot of things need to be improved by learning from other
social medias.
Custom Feeds: Learning from Bluesky: Threads should implement
advanced custom feed features to allow users to easily follow specific
topics and events without relying solely on tags.
Third-Party Apps: Learning from Mastodon and Bluesky: Meta should
consider opening up Threads to third-party developers to create diverse
client applications, allowing for a broader range of user experiences
and features.
Algorithm Improvement: Improving “For You” Feed: Threads needs to
refine its algorithm to ensure that users receive more relevant and
personalized content, avoiding random or irrelevant posts that can
detract from user experience.
Handling News and Political Content: Learning from X and
Mastodon: Threads should develop mechanisms to handle news and political
content more effectively, balancing visibility without suppressing
important information, and potentially integrating features like
context-providing notes or bylines.
Local Content Engagement: Learning from Instagram and Twitter:
Threads should enhance its focus on local content by developing
partnerships and features that cater to regional interests and events,
like live scores for popular sports in specific regions.
Separation from Instagram: Developing Independent Profiles:
Threads should work on allowing users to create and manage profiles
independent of Instagram accounts, offering more flexibility and
autonomy in account management.
If “product-market-fit” means that you’ve found the right kind of
product that the market wants… “Position-market-fit” means that you’ve
found the right combination of
product/brand/marketing/pricing/go-to-market/sales/etc in a given
domain.
― Product-market fit is not enough anymore. You need
position-market fit - Aakash Gupta on X [Link]
Product-market fit is about having the right product for the market,
while position-market fit is about effectively positioning that product
within the market to stand out and meet specific customer
expectations.
A discussion of discussions on AI bias - Dan Luu [Link]
How to build a valuable tech company - Jason Shen on
X [Link]
Jensen’s Mindmap about his secrets to building the mos tvaluable tech
company in the world
jensen-mindmap
As a general rule, don’t let your company start doing the next
thing until you’ve dominated the first thing. No great company I know of
started doing multiple things at once—they start with a lot of
conviction about one thing, and see it all the way through. You can do
far fewer things than you think. A very, very common cause of startup
death is doing too many of the wrong things. Prioritization is critical
and hard.
While great founders don’t do many big projects, they do whatever
they do very intensely. They get things done very quickly. They are
decisive, which is hard when you’re running a startup—you will get a lot
of conflicting advice, both because there are multiple ways to do things
and because there’s a lot of bad advice out there. Great founders listen
to all of the advice and then quickly make their own decisions.
Please note that this doesn’t mean doing everything
intensely—that’s impossible. You have to pick the right things. As Paul
Buchheit says, find ways to get 90% of the value with 10% of the effort.
The market doesn’t care how hard you work—it only cares if you do the
right things.
Fire quickly. Everyone knows this in principle and no one does
it. But I feel I should say it anyway. Also, fire people who are toxic
to the culture no matter how good they are at what they do. Culture is
defined by who you hire, fire, and promote.
― Startup Playbook by Sam Altman - Sam Altman [Link]
The primary battleground was data and Al governance.
Snowflake fired the first shot by open-sourcing Polaris, its catalog
for Apache Iceberg, a popular open-source table format that’s compatible
any compute engine. Databricks countered by announcing its acquisition
of Tabular, a managed solution for Iceberg created by the project’s
founders, right in the middle of Snowflake’s conference. The tollowing
week, at their own summit, Databricks further upped the ante by
open-sourcing its Unity catalog in front of a live audience.
Data has gravity, so It’s far more efficient to bring applications
and services to data rather than vice versa.
Both Databricks and Snowflake are now vying to build the ultimate
enterprise AI platform: one capable of serving as the foundation for
this “small-but-mighty” vision of AI. Their shared goal is to become the
single source of truth for all of an organization’s data and use this
position to power intelligent applications across every business
function.
Databricks emerged from the open-source Apache Spark project and
initially focused on serving the needs of data scientists and ML
engineers. Its big data processing capabilities made it a natural fit
for AI and data science workloads. Snowflake, by contrast, built its
early success around a SQL-centric architecture and tight integration
with BI tools, catering to data analysts and traditional IT departments
with a closed, “it just works” solution.
― Databricks vs. Snowflake: What their rivalry reveals about
AI’s future - Foundation Capital [Link]
Databricks and Snowflake are fighting for the future of enterprise
Al. This article discussed four key concepts that shed light on the
competitive dynamics: data gravity, the convergence of analytics and Al,
the strategic importance of open source, and the rise of compound Al
systems.
How to Interview and Hire ML/ AI Engineers -
eugeneyan [Link]
Interviewing Meta CTO Andrew Bosworth on the Metaverse,
VR/AR, AI, Billion-Dollar Expenditures, and Investment Timelines -
MatthewBall.co [Link]
Spotify is no longer just a streaming app, it’s a social
network - TechCrunch [Link]
Gen AI: too much spend, too little benefit? - Goldman
Sachs [Link]
On July 19, 2024 at 04:09 UTC, as part of ongoing operations,
CrowdStrike released a sensor configuration update to Windows systems.
Sensor configuration updates are an ongoing part of the protection
mechanisms of the Falcon platform. This configuration update triggered a
logic error resulting in a system crash and blue screen (BSOD) on
impacted systems. The sensor configuration update that caused the system
crash was remediated on Friday, July 19, 2024 05:27 UTC. This issue is
not the result of or related to a cyberattack.
― Technical Details: Falcon Content Update for Windows
Hosts [Link]
In any massive failure there are a host of smaller errors that
compound; in this case, CrowdStrike created a faulty file, failed to
test it properly, and deployed it to its entire customer base in one
shot, instead of rolling it out in batches. Doing something different at
each one of these steps would have prevented the widespread failures
that are still roiling the world
The real issue, though, is more fundamental: erroneous
configuration files in userspace crash a program, but they don’t crash
the computer; CrowdStrike, though, doesn’t run in userspace: it runs in
kernel space, which means its bugs crash the entire computer — 8 million
of them, according
to Microsoft. Apple and Linux were not impacted, for a very obvious
reason: both have long since locked out 3rd-party software from kernel
space.
― Crashes and Competition - Ben Thompson on
Stratechery [Link]
The Munger Series - Learning from Benjamin Franklin -
Investment Master Class [Link]
How Benjamin Graham Survived World Panic on Wall Street (#17)
- Beyond Ben Graham [Link]
Introducing Llama 3.1: Our most capable models to date - Meta
AI Blog [Link]
Meta is releasing Llama 3.1 405B, the first frontier-level
open-source AI model. Along with Llama 3.1 70B and 8B models, they offer
superior cost / performance and are open for Fien-tuning and distilling.
And they are collaborating with companies such as Amazon, Databricks,
NVIDIA, Grow, etc, to support developers in fine-tuning and distilling
models.
Open Source AI Is the Path Forward - Meta News [Link]
In this letter, Zuckerberg emphasizes Meta’s commitment to open
source AI. Similar to Unix and Linux, Zuckerberg believes AI development
will eventually go to open source. Open source AI has several benefits:
1) it benefits developers in customization, control and security, cost
efficiency, and long-term standards, 2) it benefits Meta in avoiding
being locked into competitor’s ecosystems, allowing for freedom in
innovation and product development, enhancing its competitiveness, and
building a community of partnerships and developers, 3) it benefits the
world in providing wide spread access to AI benefits, ensuring safety
and security, and avoiding monopoly in AI power.
This study suggests the importance of optimizing both cost and
accuracy in benchmarking and evaluation of AI agents. Since the issues
of inadequate hold-out sets, absence of standardized evaluation
practices, etc, the authors also suggests a principled framework that
emphasizes the development of agents effective especially in practical
scenarios rather than on benchmarks.
Scaling Synthetic Data Creation with 1,000,000,000
Personas [Link]
This team generated 1B personas based on web info and stored them
into a Persona Hub. They introduced a synthetic data generation method
called ‘persona-driven data synthesis’. These personas can be
potentially used to 1) generate personalized content, 2) support LLM
prompting, 3) enhance product research, 4) create NPCs in games. The
compression perspective is more interesting and helpful for
understanding the approach: Persona Hub can be seen as the compressed
form of the world knowledge into distributed carriers. And the public
web text can be seen as the decompressed content created by these
personas with their knowledge and experiences.
This is a comprehensive survey on LLM MoE technique. MoE stands out
for enabling model scaling with minimal additional computation. This
survey as a systematic MoE literature review, covers MoE’s structure,
taxonomy, core designs, open-source resources, applications, and future
research directions.
An Extremely Opinionated Annotated List of My Favourite
Mechanistic Interpretability Papers v2 [Link]
Magic Insert: Style-Aware Drag-and-Drop - Google [Link]
FlashAttention-3: Fast and Accurate Attention with Asynchrony
and Low-precision [Link]
FlashAttention-3: achieves a 1.5-2x speedup and reaching up to 740
TFLOPS on FP16 and nearly 1.2 PFLOPS on FP8. This increases GPU
utilization to 75% of the theoretical maximum on H100 GPUs, up from
35%.
FlashAttention-3 introduces three main techniques to boost
performance:
Overlapping computation and data movement
Interleaving matrix multiplications (matmul)
Softmax operations, and using low-precision FP8
Mobility VLA: Multimodal Instruction Navigation with
Long-Context VLMs and Topological Graphs [Link]
Beyond Euclid: An Illustrated Guide to Modern Machine
Learning with Geometric, Topological, and Algebraic Structures
[Link]
AI achieves silver-medal standard solving International
Mathematical Olympiad problems - Google Research [Link]
This is one of the most surprising breakthrough in this AI and LLM
year. AlphaProof got a silver medal in IMO. It’s a neurosymbolic system
- a combination of Google’s Gemini LLM and DeepMind’s Alpha Zero, so it
uses LLM to generate plausible solutions and uses self-play style to
search for the right one. It opens a research direction of AI use cases,
which has been discussed about by many AI frontier experts and
companies, which is “scientific discovery”. Mastering math can be the
first step of expanding frontier of our knowledge. OpenAI’s Strawberry
project seems to have the same ambitions.
Gen AI: Too Much Spend, Too Little Benefit? - Goldman Sachs
Research Newsletter [Link]
As money’s flooded into GenAI projects, people started to question
whether or when the investment would net a return. Though the bubble may
or may not be bursting, a healthy discussion like this is worth a
read.
News
Prices fell in June for the first time since the start of the
pandemic - CNN [Link]
CPI dropped 0.1% from May. Odds of Fed cutting the rate are
increasing. Effect of high inflation is expected to be long lasting.
Here’s how far the Dow has fallen behind the S&P 500 so
far in 2024 - Morningstar [Link]
Tech Giants Face Tough Task to Sustain Second Half Stock
Rally - Bloomberg [Link]
Magnificent 7 stocks have accounted for majority of the S&P 500
growth this year. If this projection of AI optimism fails to
materialize, it could trigger a massive decline of the index.
Apple Poised to Get OpenAI Board Observer Role as Part of AI
Pact - Bloomberg [Link]
Microsoft, Apple Drop OpenAI Board Plans as Scrutiny Grows -
Bloomberg [Link]
This is Big Tech’s playbook for swallowing the AI industry -
The Verge [Link]
Amazon Hires Top Executives From AI Startup Adept for AGI
Team - Bloomberg [Link]
Big Tech companies are finding new ways to integrate AI startups into
their operations without triggering antitrust scrutiny - ‘reverse
acquihire’, an approach where actual acquisitions are masked by
employment and licensing agreements. This is highlighted by Microsoft
hiring inflection’s team and licensing of its AI tech, and Amazon hiring
roughly 2/3 of Adept’s personnel and securing a deal to license its AI
tech.
What happened to the artificial-intelligence revolution? -
The Economics [Link]
Silicon Valley companies are investing heavily in AI while the
revenue from AI products is still far from the projected figures.
Humanoid robots powered by AI turn heads at the World
Artificial Intelligence Conference - AP News [Link]
Record 300,000 visitors attend World AI Conference
[Link]
The World AI Conference and High-level Meeting on Global AI
Governance (WAIC) 2024 closed in Shanghai on Saturday, covering
investment plans, cooperation projects, city-level organizations, and
development plans for AI. Robotic tech such as humanoid models is
capturing the attention of attendees.
Fame, Feud and Fortune: Inside Billionaire Alexandr Wang’s
Relentless Rise in Silicon Valley - The Information [Link]
Robinhood snaps up Pluto to add AI tools to its investing app
- TechCrunch [Link]
The AI tool Pluto will allow Robinhood to add tools for quicker
identification of trends and investment opportunities, help guide users
with their investment strategies, and offer real-time portfolio
optimization.
“The algorithm is looking at traditional economic indicators that
you would normally look at. But then inside of our proprietary
algorithm, we’re ingesting the behavioral data and transaction data of
240 million Americans, which nobody else has,” said David Steinberg,
co-founder, chairman and CEO of Zeta Global.
The eight verticals the economic index uses include automotive
activity, dining and entertainment, financial services such as credit
line expansion, health care, retail sales, technology and
travel.
― A new index is using AI tools to measure U.S. economic
growth in a broader way - CNBC [Link]
The Zeta Economic Index uses Gen AI to analyze “trillions of
behavioral signals” to score growth of US economy.
OpenAI Hires Zapier Revenue Chief to Lead Sales Strategy -
The Information [Link]
OpenAI has recently hired new CFO and CPO to enhance its focus on
both consumer and enterprise products. It appointed Giancarlo Lionetti
(former CRO at Zapier, worked at Atlassian, Confluent, and Dropbox) to
lead its sales strategy in OpenAI’s sales team.
Tesla’s Share of U.S. Electric Car Market Falls Below 50% -
The New York Times [Link]
Tesla’s Upcoming Model Y, Project Juniper, Spotted with Front
Bumper Camera; Coming in 2025 [Link]
Persona’s founders are certain the world can use another
humanoid robot - TechCrunch [Link]
Thermonuclear Blasts and New Species: Inside Elon Musk’s Plan
to Colonize Mars - The New York Times [Link]
OpenAl says there are 5 ‘levels’ for AI to reach human
intelligence - it’s already almost at level 2 [Link]
The reason we decided to do the 100k H100 and next major system
internally was that our fundamental competitiveness depends on being
faster than any other AI company. This is the only way to catch up.
Oracle is a great company and there is another company that shows
promise also involved in that OpenAI GB200 cluster, but, when our fate
depends on being the fastest by far, we must have our own hands on the
steering wheel, rather than be a backseat driver. - Elon Musk @
X
― xAI Appears to Confirm Ended Talks With Oracle Over
Expanded AI Chips Agreement - WSJ [Link]
[X]
Elon’s business strategy - being completely vertical integrated, on
many of his companies (Tesla, SpaceX, etc) are working very well over
the years.
Venture capital firm A16z stashing GPUs, including Nvidia’s,
to win AI deals: report - Seeking Alpha [Link]
A16z has purchased thousands of GPUs including Nvidia’s H100, in an
effort to win deals for AI startups. They store those H100s and give
them to companies they invest in. It’s hard for startups to get vast
amounts of computing power. So this practice can make them more
competitive in these VC deals.
OpenAI and Los Alamos National Laboratory announce bioscience
research partnership - OpenAI [Link]
OpenAI and LANL are working together on evaluating how frontier
models like GPT-4o can assist humans in physical lab setting through
multimodal capabilities to support bioscience research.
In response to the fourth question in the investor call
transcript, Furukawa said the following (obtained via machine
translation and edited for clarity):
“In the game industry, AI-like technology has long been used to
control enemy character movements, so I believe that game development
and AI technology have always been closely related.
Generative AI, which has been a hot topic recently, can be more
creative [in its use], but I also recognize that it has issues with
intellectual property rights.
Our company has [had] the know-how to create optimal gaming
experiences for our customers for decades.
While we are flexible in responding to technological
developments, we would like to continue to deliver value that is unique
to us and cannot be created simply by technology alone.”
― Nintendo becomes the biggest company in the games industry
- and maybe the world - to say ‘no, thank you’ to using generative AI -
PC Gamer [Link]
Most gaming companies would like to incorporate AI in some sense but
Nintendo as the biggest company in the game industry said no thank you
to Gen AI. This sounds counter to what other game companying are aiming
for, but it’s also reasonable because Nintendo has built incredible IP
and they just want to be classic and they want everything to be their
own.
However, many people have imagined the future of video game would be
powered by AI with contents dynamically created for players in real
time.
Watch a robot navigate the Google DeepMind offices using
Gemini - TechCrunch [Link]
Google DeepMind Robotics developed a robot navigation system powered
by Gemini 1.5 Pro. It responds to human language commands, navigates the
office environment. It uses “Multimodal Instruction Navigation with
demonstration Tours (MINT)” to familiarize itself with the office and
hierarchical Vision-Language-Action (VLA) for understanding and
reasoning. The ability of recalling environment is boosted by 1M token
context length of Gemini 1.5 Pro
OpenAI tiers range from the kind of AI that can interact in
conversational language with people (lvl 1) to AI that can do the work
of an organization (lvl 5). The OpenAI executives believes that they are
at stage one and reaching towards the second tier. The third tier on the
way to AGI would be ‘Agents’ - AI systems which can spend several days
taking actions on a user’s behalf. Tier 4 would be the kind of AI that
can come up with innovations. And the tier 5 would be called
‘organization’.
Samsung’s Jam-Packed Galaxy Unpacked: Galaxy Ring, Z Fold 6
and All the New Products Announced [Link]
Among the 35 companies approved to test by the California DMV,
seven are wholly or partly China-based. Five of them drove on California
roads last year: WeRide,
Apollo, AutoX, Pony.ai, and DiDi Research America. Some Chinese
companies are approved to test in Arizona and Texas as well.
― Chinese self-driving cars have quietly traveled 1.8 million
miles on U.S. roads, collecting detailed data with cameras and lasers -
Fortune [Link]
Since 2017, self-driving cars owned by Chinese companies have
traverse 1.8M miles of California alone. They captured video of their
surroundings and map the state’s roads to within 2 cm of precision.
These information have been transferred to data centers and been used to
train their self-driving systems.
Evaluate prompts in the developer console - Anthropic
News [Link]
Anthropic releases some new features every week. Now they allow users
to generate, test, and evaluate prompts in the Anthropic Console.
Fine-tune Claude 3 Haiku in Amazon Bedrock -
Anthropic [Link]
Customers can now fine-tune Claude 3 Haiku in Amazon Bedrock to
customize model for vertical business usage.
Shooting at Trump Rally Comes at Volatile Time in American
History - The New York Times [Link]
This is crazy but legendary.
Insurers Pocketed $50 Billion From Medicare for Diseases No
Doctor Treated - The Wall Street Journal [Link]
UnitedHealth Group committed a $50 billion fraud over the three years
of 2019, 2020, and 2021. Though treating doctors say “no treatment or
minimal treatment necessary for this diagnosis”, UnitedHealth overrides
the docstors’ judgment, generates its own diagnosis code, bills medicare
with this new code.
Thousands of Windows machines are experiencing a Blue Screen of
Death (BSOD) issue at boot today, impacting banks, airlines, TV
broadcasters, supermarkets, and many more businesses worldwide. A faulty
update from cybersecurity provider CrowdStrike is knocking affected PCs
and servers offline, forcing them into a recovery boot loop so machines
can’t start properly. The issue is not being caused by Microsoft but by
third-party CrowdStrike software that’s widely used by many businesses
worldwide for managing the security of Windows PCs and servers.
― Major Windows BSOD issue hits banks, airlines, and TV
broadcasters - The Verge [Link]
That from Christopher Thornberg who heads a California-based
consulting firm called Beacon Economics. He says moving a main office
like this out of state would likely mean anywhere from dozens of lost
jobs to a couple hundred, not thousands of jobs lost.
Governor Newsom’s press office took to X after Musk made the
announcement comparing California to Texas saying, “The last time Elon
Musk moved an HQ, Tesla ended up expanding in California, even
relocating their Global Engineering and AI headquarters to California
because of diverse, world leading talent.”
― What Elon Musk’s Texas relocation plan for SpaceX, X HQs
could mean for CA - ABC7 News [Link]
Salesforce: Worst Day in 20 Years - App Economy
Insights [Link]
PayPal hired Mark Grether who was head of Uber’s ad business to lead
the initiative of an ad network.
Costco’s membership fees declined from 86% to 50% of the its
operating profit. It has shown economies of scale and benefits from a
more favorable revenue mix.
Salesforce’s revenue grew 11% to \(\$9.1\)B, missing Wall Street estimates by
\(\$20\)M. Current Remaining
Performance Obligations - the best indicator of future growth - rose
10%, missing estimates of 12%. The slowing growth is partially due to
broader macroeconomic challenges and internal execution issues.
Salesforce Data Cloud is contributing to 25% of the deals valued above
$1M, indicating it’s well-positioned to benefit from AI boom.
Live Nation has caused such widespread outrage in 2022 Taylor Swift
Eras Tour ticket sales because fans faced technical glitches and
exorbitant fees. Live Nation was accused of locking venues into
exclusive deals and bullying artists into using its services, which
caused higher ticket prices through service and convenience fees. Live
Nation is under scrutiny by the government. It is forced to divest
Ticketmaster (acquired in 2020). Fans / artists are expecting increased
competition in live music industry and lower prices, and a smoother
ticket buying experience.
Online Travel: AI is Coming - App Economy Insights
[Link]
AI agents as the next frontier could make traveling personalized. The
key metrics to define their success are 1) gross bookings, 2) nights
booked, 3) average daily rate (ADR), 4) revenue per available room
(RevPAR), customer acquisition cost (CAC).
The largest travel companies (online travel agencies and rentals and
hotel chains) are Booking Holdings, Airbnb, Expedia, Marriott, and
Hilton.
Highlights: 1) Booking Holdings (operating as an OTA): CEO Glenn
Fogel envisions AI enhancing connected trips (single booking that
include multiple travel elements such as flights, accommodations, car
rentals, etc), 2) Airbnb exceeded expectations on both revenue and
profitability in Q1 due to its robust international expansion, while
slowing down the growth in North America. Airbnb is aiming to create an
AI powered concierge to elevate the overall Airbnb experiences, 3)
Expedia (operating as an OTA): Expedia is currently facing challenges of
transition and adjustment: Vrbo vacation rental platform integration
into Expedia platform is slower than expected. And it’s also facing
challenges in attracting and retaining customers in its B2C segment. A
new CEO Ariane Gorin was recently appointed to help navigate through
these challenges. 4) Marriott (operating as booking platform): Marriott
has developed Homes & Villas tool that allows users to search for
vacation rentals using language. A slow-down RevPAR in North America has
been observed which indicates a shift in consumer preferences towards
international destinations and alternative accommodations. Its brand
reputation, loyalty program and focus on group/business travel remain
strong. 5) Hilton: has strong emphasis on personalization and loyalty
programs though facing headwinds in the US. CEO Chris Nassetta envisions
AI-powered tools to address guest concerns in real time.
From graphics rendering, gaming and media, cloud computing and
crypto, Nvidia’s chips have led the way in each of these past innovation
cycles as it saw its GPU applications expand over the last 2 decades.
And now it is getting ready to advance the next industrial revolution,
that will be powered by AI.
Some industry experts believe that 20%
of the demand for AI chips next year will be due to model inference
needs, with “Nvidia deriving about 40% of its data center revenue just
from inference.”
― NVIDIA’s chips are the tastiest AI can find. It’s stock
still has ways to go - The Pragmatic Optimist [Link]
This is a good summary of Nvidia’s strategies towards computing, path
to AI domination, tailwinds of efficiency, position in the future.
Nvidia is “at the right place at the right time”:
During 2000-2010 where the world successfully emerged from the
dot-com bust, demand of Nvidia’s GPUs increased as the proliferation of
games and multimedia applications. By 2011, Nvidia had already begun to
reposition the company’s strategy for GPU chips towards mobile
computing. At the same time, the concept of cloud computing,
crypto-mining, and data center started to form.
Nvidia has built grounded relationship with academics and
researchers. According to the paper published by Andrew Ng to show the
power of NVIDIA GPU. During 2011-2015, Ng had been working as the Chief
Scientist at many big tech firms and deployed data center architectures
based on Nvidia’s GPUs. During 2010-2014, data center and HPC grew at a
compounded growth rate of 64%. This period of time was one of the
moments that set Nvidia on the course to dominate AI.
In semiconductor industry, there are two different ways of
manufacturing chips at scale:
Designing and manufacturing your own chip - what Intel was doing.
Manufacturing chips can be very expensive and hard. Today, chip
foundries such as Taiwan’s TSMC and South Korea’s Samsung are able to
maintain leading edge.
Designing and producing powerful chips at a quicker pace by
partnering with chip foundries like TSMC - what Nvidia and AMD fabless
companies are doing.
2024 could be the first year that Huang’s Nvidia could cede some
market share to AMD. AMD launched their own MI300-series and Intel
launched their Gaudi3 AI Accelerator chip, aiming to get back share from
Nvidia’s H100/H200 chips. However Huang looks ahead:
Huang believes Nvidia must turn its attention to the next leg of
AI - Model Inference.
Tech companies spend more on AI data center equipment over years,
Nvidia’s revenue won’t slow down.
Nvidia’s executives also believe that company can benefit from
demand from specific industry verticals, such as automotive.s
Tesla, for example, is leading self driving cars.
Automotive AI and Sovereign AI are two future areas of growth
where enterprises continue to spend on data centers for model training
and inferencing.
The authors also assessed Nvidia’s valuation and believe that:
Between 2023 and 2026, Nvidia’s sales should be growing at a
compounded annual growth rate of 43–45%.
Over the next 3 years, they expect operating profit to grow in line
with revenue growth, with operating profit margins remaining relatively
flat in 2025 and 2026.
The Coming Wave of AI, and How Nvidia Dominates - Fabricated
Knowledge [Link]
Nvidia is the clear leader in 1) System and Networking, 2) Hardware
(GPUs and Accelerators), and 3) Software (Kernels and Libraries) but
offers the whole solution as a product.
Amazon drives tremendous savings from custom silicon which are
hard for competitors to replicate, especially in the standard CPU
compute and storage applications. Custom silicon drives 3 core benefits
for cloud providers.
Engineering the silicon for your unique workloads for higher
performance through architectural innovation.
Strategic control and lock-in over certain workloads.
Cost savings from removing margin stacking of fabless design
firms.
The removal of these workloads from server CPU cores to the
custom Nitro chip not only greatly improves cost, but also improves
performance due to removing
noisy neighbor problems associated with the hypervisor, such as
shared caches, IO bandwidth, and power/heat budgets.
― Amazon’s Cloud Crisis: How AWS Will Lose The Future Of
Computing - Semianalysis [Link]
A good overview of Amazon’s in-house semiconductor designs (Nitro,
Graviton, SSDs, Inferentia, and Trainium). It includes how Microsoft
Azure, Google Cloud, Nvidia Cloud, Oracle Cloud, IBM Cloud, Equinix
Fabric, Coreweave, Cloudflare, and Lambda are each fighting Amazon’s
dominance across multiple vectors and to various degrees.
Amazon’s custom silicon efforts - Nitro:
AWS worked with Cavium on developing the custom SoC on a discrete
PCIe card and associated software, named “Nitro System”. It removes
workloads from server CPU cores to the custom Nitro chips.
Annapurna Labs was acquired by Amazon in 2015. It focuses on server
SOCs for networking and storage. Amazon was trying to continue its
efforts on storage, and Nitro is the main enabler of a competitive
advantage in these storage and databases.
Nitro provides services such as virtual disk to the tenant’s virtual
machines and enables customers to dynamically grow and shrink high
performance storage at low cost.
Amazon worked with Marvell to co-design the AWS Nitro SSD
controller. The focus was on avoiding latency spikes and latency
variability, and maximizing the lifetime of the SSD.
Other two clouds (Google and Microsoft) are years behind Amazon, both
required a partner, and both were stuck with 1st or 2nd generation
merchant silicon for the next few years.
James Hamilton, an engineer in Amazon, had and look at two key ways
in which using AWS-designed, Arm-based CPUs could offer advantages
compared to their external counterparts.
Using Arm’s scale in mobile by using the arm-designed Neoverse cores
or
TSMC’s manufacturing scale. Both would reduce costs and offer better
value to customers.
In-house CPUs enables Amazon to design CPUs to maximize density and
minimize server and system level energy which helps reduce costs. The
tremendous scale of Amazon especially regarding general-purpose compute
and storage-related verticals will continue to drive a durable advantage
in the cloud for many years.
Ultrafusion is Apple’s marketing name for using a local silicon
interconnect (bridge die) to connect the two M2 Max chips in a package.
The two chips are exposed as a single chip to many layers of software.
M2 Ultra utilizes TSMC’s
InFO-LSI packaging technology. This is a similar concept as TSMC’s
CoWoS-L that is being adopted by Nvidia’s Blackwell and future
accelerators down the road to make large chips. The only major
difference between Apple and Nvidia’s approaches are that InFO is
chip-first vs CoWoS-L is chip-last process flow, and that they are using
different types of memory.
― Apple’s AI Strategy: Apple Datacenters, On-device, Cloud,
And More - Semianalysis [Link]
Apple’s purchases of GPUs are minuscule and Apple is not a top 10
customer of Nvidia. The production of M2 Ultras can be consistent with
the fact that Apple is using their own silicon in their own data centers
for serving AI to Apple users. And Apple has expansion plans for their
own data center infrastructure. Furthermore, Apple made a number of
hires including Sumit Gupta who joined to lead cloud infrastructure at
Apple in March.
One of the best known non-bank banks is Starbucks – “a bank
dressed up as a coffee shop”. Trung Phan, rates
the misperception up there alongside “McDonald’s is a real estate
company dressed up as a hamburger chain” and “Harvard is a hedge fund
dressed up as an institution of higher learning”.
Today, more than 60% of the company’s peak morning business in
the US comes from Starbucks Rewards members who overwhelmingly order via
the app. The program has 33 million users, equivalent to around one in
ten American adults.
Starbucks had offered a gift card since 2001 and started to pair it
with a new loyalty program “Starbucks Rewards” in 2008. Consumers are
allowed to access free wifi and refillable coffee by paying with a
reloadable card. The card was put onto an app in 2010 and expanded to
over 9000 locations. It quickly became the largest combined mobile
payments in loyalty program in the US. Uses load or reload around \(\$10\) B of value onto their cards each
year and so about \(\$1.9\)B of stored
card value sat on the company’s balance sheet, just like customer
deposits. There are several advantages: 1) the company does not pay
interest on customer funds, and 2) sweep customer funds into company’s
own bank account when it concludes customers may have forgotten about
them - this is called ‘breakage’. In late 2023, Starbucks was accused of
making it impossible for consumers to spend down their stored value
cards by only allowing funds to be added in \(\$5\) increments and requiring a \(\$10\) minimum spend. Although the company
has to pay rewards to customers, it saves on merchant discount fees and
receives a lots of free and valuable personal information about
customers.
Delta’s SkyMiles scheme is one of the largest globally with 25 M
active members. There are two ways points schemes generate money: 1)
when scheme member buy a regular ticket, they also buy mileage credit
they can redeem in the future, and 2) they make money from card
companies (such as American Express) and other partners.
VC Says “Chaos” Coming for Startups, Ads, and Online Business
as Generative AI Eats Web - Big Technology [Link]
The main point is that, as generative AI is ingested into the web, a
decades-old system of online referrals and business building will be
reshaped. The business model of every existing business (online travel,
ecommerce, online advertising, etc) on the internet are impacted due to
the transformation of online search by AI. It decreases the number of
customer’s impressions on the internet thus reduce advertiser’s chance
of being charged. And it also reduces the chance for startups to be
observed and build brands.
The four titans of accounting industry - Deloitte, PwC, EY, and KPMG.
They make money from 1) audit: verifying financial statement, 2)
assurance: including processes, internal control, cybersecurity
assessments, and fraud investigations, 3) consulting: offering advice on
everything from M&A to digital transformation, especially in helping
enterprise software sales, 4) risk adn tax advisory: navigating
compliance, regulations, and tax laws.
Insights:
Deloitte: 1) fastest growing in revenue, 2) heavily investing in AI
and digital transformation, 3) acquisition as a growth strategy.
PwC: 1) heavily investing $1B in Gen AI initiative with Microsoft, 2)
will become the largest customer and 1st reseller of OpenAI’s enterprise
product, 3) leader of financial services sector, serving most global
banks and insurers, 4) has faced scrutiny over its audit of the failed
cryptocurrency exchange FTX, raising concerns about its risk management
practices.
EY: 1) invested $1.4B to create EY.ai EYQ, an AI platform and LLM, 2)
abandoned its “Project Everest” plan to split it audit and consulting
businesses in 2023, 3) growing business through strategic acquisitions,
4) has faced criticism for an about $2B hole in its accounts, raising
concerns about its audit practices and risk management, 5) was fined
$100M because hundreds of employees cheated on ethics exams.
KPMG: 1) focusing on digital transformation (data analytics, AI, and
cybersecurity), 2) has faced regulatory scrutiny and fines due to audit
practices, raising concerns about its audit quality and
independence.
I think the release highlights something important happening in
Al right now: experimentation with four kinds of models - Al models,
models of use, business models, and mental models of the future. What is
worth paying attention to is how all the Al giants are trying many
different approaches to see what works.
This demonstrates a pattern: the most advanced generalist Al
models often outperform specialized models, even in the specific domains
those specialized models were designed for.
That means that if you want a model that can do a lot - reason
over massive amounts of text, help you generate ideas, write in a
non-robotic way - you want to use one of the three frontier models:
GPT-40, Gemini 1.5, or Claude 3 Opus.
― What Apple’s AI Tells Us: Experimental Models⁴ - One Useful
Thing [Link]
Al Models: Apple does not have frontier model like Google and
Microsoft do, but they have created a bunch of small models that are
able to run on Al-focused chips in their products. The medium-sized
model that can be called by iPhone in the cloud. The model that’s
running on iPhone and the model that’s running in the cloud are as good
as Mistral’s and ChatGPT.
Models of Use: However, larger, less constrained models are the
potential gains to Al, the productivity boosts and innovation. You would
prefer to use GPT-4o to do nuanced tasks such as helping with your
interviews rather than use Apple Al-powered Siri.
Business Models: Apple sounds like they will start with free
service as well, but may decide to charge in the future. The truth is
that everyone is exploring this space, and how they make money and cover
costs is still unclear (though there is a lot of money out there. People
don’t trust Al companies because they are concerning about privacy.
However Apple makes sure models cannot learn about your data even if it
wanted to. Personal data on your iPhone are only accessed by local
Al.
And those handed to the cloud is encrypted. For those data given to
OpenAl, it’s anonymous and requires explicit permission. Apple is making
very ethical use of Al. Though we should still be cautious about Apple’s
training data.
Models of the Future: Apple and OpenAl have different goals.
Apple is building narrow Al systems that can accurately answer questions
about your personal data. While OpenAl is building autonomous agents
that would complete complex tasks for you. In comparison, Apple has a
clear and practical vision of how Al can be applied, while the future
OpenAl’s AGI remains to be seen.
Elon has been spreading significant FUD by threatening to
prohibit Apple devices at his companies. The truth is Apple at no point
will be sending any of your data to OpenAI without explicit user
permission. Even if you opt for “Use ChatGPT” for longer questions,
OpenAI isn’t allowed to store your data.
According to Counterpoint Research, smartphone makers who have
launched AI features on their smartphones have seen a revival in sales.
Look at Samsung for example, where its S24 series grew 8% compared to
S23 in 2024 with sales for its mid-range premium model growing 52% YoY.
With Apple having a larger market share, along with receding
expectations for an economic recession, this could be the start of a new
growth chapter for the Cupertino darling once again.
Pete Huang at The
Neuron explains in a step by step process of what really goes down
when you ask Siri with AI a question.
For almost all questions, Siri uses AI that lives on the
device, aka it won’t need to hit up the cloud or ChatGPT, aka your
question won’t ever leave the phone.
These on-device models are decent (they’re built on top of
open-source models) and outperform Google’s on-device model, Gemma-7B,
70% of the time.
For more complex questions like “Find the photo I took at the
beach last summer,” Siri will consult a smarter AI model that runs on
Apple’s servers.
When Siri sends your question to Apple’s servers, your data is
anonymized and not stored there forever.
Now, for longer questions like “Can you help me create a
weekly meal plan?” or “Rewrite this email using a more casual tone,”
Siri will use ChatGPT only if you give it permission to.
Even if you opt for “Use ChatGPT,” OpenAI isn’t allowed to store
your data.
― The Real Test For Consumer’s AI Appetite Is About To Begin
- The Pragmatic Optimist [Link]
Interested to know how it actually works when you ask Siri with AI a
question.
5 Founder-Led Businesses - Invest in Quality [Link]
Three research findings:
Founder-led businesses outpaced other companies by a wide margin.
(Researched by Ruediger Fahlenbrach in 2009).
Family-owned businesses ignored short-term quarterly numbers to
focus on the long-term value creation, which lead to a major
outperformance because of 1) lower risk-taking in the short term, and 2)
greater vision and investment for the long term. (Researched by Henry
McVey in 2005).
Businesses managed by billionaires outperformed the market by 7%
annually from 1996 to 2011. (researched by Joel Shulman in 2012).
Insights behind the findings above:
Founders and owners often have their life savings invested in the
shares of the business, so they have the incentive aligned.
Bureaucracy reduces business performance. They will almost never
make a radical shift, because politicians care more about their job
title than the long term prospects of the business. Founders on the
other hand are able to make radical decisions and overrule the
bureaucracy, therefore they can take the business in a direction to
fulfill long term vision.
Founders and billionaires are exceptional people to run
business.
The article listed five examples of founder-led businesses:
MercadoLibre, Adyen, Fortinet, Intercontinental Exchange, and
Paycom.
This move positions Apple as an AI aggregator, offering users a
curated selection of the best AI tools while keeping their data private.
It’s a win-win. Apple gets to enhance its user experience with powerful
AI capabilities. At the same time, OpenAI gains access to Apple’s
massive user base for brand recognition and potential upsell to ChatGPT
Plus. There is no detail available on the exact terms of the
partnership.
― Apple: AI for the Rest of Us - App Economy
Insights [Link]
Integrating ChatGPT alongside Apple Intelligence features is a smart
move that allows Apple to focus on their strengths (privacy,
personalization) while leveraging general knowledge AI from OpenAI. This
will enable Apple to blame any wrong answers and hallucinations on the
LLMs the company partners with and stay out of PR trouble.
The Other Side of the Trade - The Rational Walk [Link]
An ethical implication about taking advantage of a glitch caused by
software malfunction.
I have noticed the proliferation of a different type of species
in academia: what I call The Failed Corporatist. This is someone who
stumbles upon academia not so much out of a love for The Truth, as due
to an inability to thrive in corporate settings for various other,
unrelated reasons. But the Failed Corporatist has a very conventional,
corporate like mindset anyway. It usually loves process, admin and
adding more admin and adding more process and METRICS and social
conformity. This skill set enables them to ascend the ranks of academic
administration, often gaining significant influence and control over The
Weird Nerd. Confronted with this altered habitat, The Nerd often finds
itself in a state of distress and confusion. Its intrinsic motivation
clashes with the newly imposed corporate-like order and the demand for
conformity, leading to frantic efforts to assert its natural tendencies.
Unfortunately, these efforts are often met with resistance or outright
rejection, not only from The Failed Corporatist but also from the
broader world that the academic reserve is a part of. All in all, I
think this disturbance means the remaining Nerds are further driven
away.
― The flight of the Weird Nerd from academia - Ruxandra’s
Substack [Link]
A couple of months ago I wrote a piece called “The
flight of the Weird Nerd from academia”, in which I argued there is
a trend wherein Weird Nerds are being driven out of academia by the
so-called Failed Corporatist phenotype. Katalin Karikó is a perfect
example of a Weird Nerd. I recently argued that many Weird Nerds (I
called them autistics, but people really hated that2),
have found
a refuge on the Internet, where their strengths are amplified and
their weaknesses are less important.
And I believe the conversation here starts with accepting a
simple truth, which is that Weird Nerds will have certain traits that
might be less than ideal, that these traits come “in a package” with
other, very good traits, and if one makes filtering or promotion based
on the absence of those traits a priority, they will miss out on the
positives. It means really internalizing the existence of trade-offs in
human personality, in an era where accepting trade-offs is deeply
unfashionable, and structuring institutions and their cultures while
keeping them in mind.
Everything comes at a cost: spend more time worrying about
politics, there will be less time for science. What’s more, the kind of
people who really care about science or truth to the extent that Karikó
did, are not the same people that get motivated by playing politics or
being incredibly pleasant. There is a strong anti-correlation between
these interests (that of course does not mean there is no one who is
good at both.) Selecting future intellectuals based on traits like
Agreeableness or Extraversion might not be only unnecessary, but
actually harmful. We might be actively depleting the talent pool of the
kind of people we do want to see in academic institutions.
― The Weird Nerd comes with trade-offs - Ruxandra’s
Substack [Link]
The intersection of AI with the ocean of mass surveillance data
that’s been building up over the past two decades is going to put truly
terrible powers in the hands of an unaccountable few. - Edward
Snowden
The former head of the NSA may be a great guy. But you don’t put
the former head of the NSA on your board (as OpenAI just did) because
he’s nice. You put him there to signal that you’re open to doing
business with the IC and DoD. - Matthew
Green
OpenAI hired retired US Army general Paul M. Nakasone to its board of
directors. This fact raises an issue of trust and makes people question
where this leads to.
Adobe is one of the companies potentially most disrupted by Gen AI
but it turns out to be one of the fastest to capitalize on the
technology. Adobe benefits the most from AI as they incorporate it in
all layers of their existing stack.
Revenue has three main segments: 1) 74% digital media (creative cloud
including Adobe express, document cloud including adobe acrobat), 2) 25%
digital experience, 3) publishing and advertising (1%).
New AI powered product: 1) GenStudio platform is a new Gen AI powered
tool aiming to streamline the entire content creation process. It’s
announced at Adobe Summit 2024 in March and expected to launch in Q3
2024. It will be integrated into Adobe Experience Cloud plans or offered
as a standalone product. 2) Adobe Experience Platform AI Assistant is a
natural language chatbot. 3) Adobe Experience Manager is a tool to
deliver right content to users at the right time. 4) Adobe Content
Analytics gives access to tools to measure the marketing performance of
AI created content, 4) Acrobat AI Assistant was integrated into Adobe
Acrobat Reader. Others: 1) Firefly Services, 2) Adobe Express on mobile,
etc.
One thing is clear: the future of fast food will be shaped by
brands that can adapt to the changing landscape and evolve through savvy
marketing, new menus, and the boost of tech to prepare and deliver your
favorite meal.
― Fast Food Economics - App Economy Insights [Link]
Quick-service restaurant (QSR) industry is undergoing a shift. This
article helps to understand how QSR giants navigates a landscape of
soaring inflation, labor shortages, and ever-changing consumer
tastes.
Giants:
McDonald: primary as a real estate company with majority of revenue
from franchised restaurants paying rent and royalties. Working on
offering more compelling value deals, menu innovation, digital sales and
MyMcDonald’s rewards program, growth plan of reaching 50000 restaurants
globally by 2027 and doubling sales from its loyalty program.
Chipotle: digital platform works well, great menu that worths
growing prices, rewards program members shows an impressive loyal and
boosting sales, testing its new automated digital makeline and food prep
robot ‘Autocado’.
YUM! (KFC, Taco Bell, Pizza Hut, Habit Burger Grill): Taco Bell is
proved resilient and popular. KFC and Pizza Hut are experiencing sales
decline. Digital delivers and sales are bright, proving their investment
in online ordering, delivery, and AI-powered drive-thru tech are
successful.
Restaurant Brand International (RBI) (Tim Hortons, Burger King,
Popeyes, Firehouse Subs): Burger King’s investment in store renovations,
menu innovations, marketing campaigns since 2022 are paying off. The
coffee and donut chain performs reliably especially in its home market
of Canada. Popeyes continues strong store sales growth. The main
component of RBI’s growth strategy is digital transformation.
Broadcom operates across two primary segments: 1) semiconductor
solutions, which has traditionally been Broadcom’s core strength, and 2)
infrastructure software, which was propelled since acquisition of VMware
in 2023 Nov.
Highlights: 1) $3.1B (roughly 26% of all) in revenue is from AI
products. AI alone contributed to a $2.2B revenue increase year over
year. 2) Margins were down year over year significantly, primarily due
to expenses related to VMware integration. 3) Broadcom has a gigantic
net debt position of $62B. 4) strong cash generation - $18 B in past 12
months. 5) 10-for-1 stock split will happen in July 15. 6) it’s known
for regular cadence of product introduction - Tomahawk and Jericho
switching products.
AI is both a threat and an opportunity for software leaders. But
for cybersecurity giants, AI means business. New technology means new
threats, with Large Language Models (LLMs) dealing with vast amounts of
data in the cloud and on devices.
AI tech stack has 3 layers: 1) top: Apps or enterprise software, 2)
middle: LLMs, 3) bottom: compute hardware and chips. The bottom layer
(NVIDIA, AMD, ASML, TSMC) and middle layer (AWS, Azure, Google Cloud)
have already benefitted from AI revenue boost. However it usually takes
longer time to manifest because companies take time to adapt to new ways
of optimizing processes, particularly in ERP, CRM, and BI verticals.
There is sentiment around enterprise software saying that AI would
make the cost of software go to zero. But there are also some
counterarguments: 1) the main expense for most software companies is not
R&D but sales and marketing, 2) switching cost is high enough that
even freemium software is not able to disrupting existing solutions, 3)
resources needed to develop new features will decline, 4) implementing
and maintaining a software solution is costly.
This article shows how some of the cybersecurity companies are
navigating current environment.
Highlights:
Palo Alto Networks
Strong strength and rapid growth in Next-Gen Security (NGS)
offerings. Its platformization focus and one-stop shop for security
needs include cloud-delivered security services like Prisma Access
(SASE), Prisma Cloud (cloud security), and Cortex (security operations).
It’s facing billing issues: it’s slashing its FY24 billings guidance by
$600M.
CrowdStrike
Strong Q1 performance, lower Total Cost of Ownership (TCO) due to
lightweight agent and unified approach, $4B in ARR growing at over 30%
YoY.
Fortinet
It specializes in network security appliances, secure SD-WAN, and
operational tech security. It’s hardware-centric. And it’s currently
under competitive pressure.
Zscaler
It specializes in Zero Trust solutions, a security model that assumes
no user or device should be trusted by default. It has strong growth and
optimistic outlook. It’s riding the wave of increasing cybersecurity
demand. And it current has a rumor of Broadcom acquisition.
Cloudflare
Descipte beating earnings estimates in Q1 FY24, stock price has
dropped due to concerns about its conservative guidance. Although there
is short term headwind, long term growth is still promising.
AI may take longer to monetize than most expect. How long
will investor optimism last? - The Pragmatic Optimist [Link]
The Dark Stain on Tesla’s Directors - Lawrence Fossi
[Link]
2,596 - How to make the most out of Google’s leaked ranking
factors [Link]
Today, foundries manufacture supermajority of the chips produced
in the world, and Taiwan Semiconductor Manufacturing Company
(TSMC) alone has ~60% market share in the global
foundry market.
Perhaps more astonishingly, TSMC has a de-facto monopoly with
~90% market share in the leading edge nodes
(manufacturing processes with the smallest transistor sizes and highest
densities). Leading edge nodes are crucial for applications requiring
the highest computing performance like supercomputers, advanced servers,
high-end PCs/laptops, smartphones, AI/machine learning, and
military/defense systems. As a result, the very basic tenet of modern
life is essentially standing on the shoulders of one company based in
Taiwan.
― TSMC: The Most Mission-Critical Company on Earth
[Link]
This is a deep dive report of Taiwan Semiconductor Manufacturing
Company (TSMC).
In terms of next steps, Google has “limited the inclusion of
satire and humor content” as part of “better detection mechanisms for
nonsensical queries.” Additionally:
“We updated our systems to limit the use of user-generated
content in responses that could offer misleading advice.”
“We added triggering restrictions for queries where AI Overviews
were not proving to be as helpful.”
“For topics like news and health, we already have strong
guardrails in place. For example, we aim to not show AI Overviews for
hard news topics, where freshness and factuality are important. In the
case of health, we launched additional triggering refinements to enhance
our quality protections.”
― Google explains AI Overviews’ viral mistakes and updates,
defends accuracy [Link]
It remains questions whether AI could become commoditized, whether it
has potential to produce revenue and profits, and whether a new economy
is actually being born.
According to Anshu Sharma, the future of AI startups (OpenAI and
Anthropic) could be dim, and big tech companies (Microsoft and Google)
will make profits from existing users and networks but need to spend a
lot of money for a long time, which would leave the AI startups unable
to compete. This is true that AI startups are already struggling right
now, because at current stage AI is hard to commoditized, and it
requires a lot of investments.
The improvement of AI is slowing down because they exhausted all
available data on the internet. Regarding the adoption of AI in
enterprise, time is required to make sure that chatbots can replace the
specialized knowledge of human experts due to technical challenges.
One thing we’ve learned: the business goal must be paramount. In
our work with clients, we ask them to identify their most promising
business opportunities and strategies and then work backward to
potential gen AI applications. Leaders must avoid the trap of pursuing
tech for tech’s sake. The greatest rewards also will go to those who are
not afraid to think big. As we’ve observed, the leading companies are
the ones that are focusing on reimagining entire workflows with gen AI
and analytical AI rather than simply seeking to embed these tools into
their current ways of working. For that to be effective, leaders must be
ready to manage change at every step along the way. And they should
expect that change to be constant: enterprises will need to design a gen
AI stack that is robust, cost-efficient, and scalable for years to come.
They’ll also need to draw on leaders from throughout the organization.
Realizing profit-and-loss impact from gen AI requires close partnership
with HR, finance, legal, and risk to constantly readjust the resourcing
strategies and productivity expectations. - Alex Singla
Although it varies by industry, roughly half of our survey
respondents say they are using readily available, off-the-shelf gen AI
models rather than custom-designed solutions. This is a very natural
tendency in the early days of a new technology—but it’s not a sound
approach as gen AI becomes more widely adopted. If you have it, your
competitor probably has it as well. Organizations need to ask
themselves: What is our moat? The answer, in many cases, likely will be
customization. - Alexander Sukharevsky
― The state of AI in early 2024: Gen AI adoption spikes and
starts to generate value - McKinsey [Link]
Industries are struggling with budgeting for Gen AI. There are some
areas where investments are paying off, such as meaningful cost
reductions in HR and revenue increases in supply chain management from
Gen AI.
There are varies risks of Gen AI usage: data privacy, bias,
intellectual property (IP) infringement, model management risks,
security and incorrect use, etc. Among all, inaccuracy and intellectual
property infringement ar eincreasingly considered relevant risks to
organizations’ Gen AI use.
According to the three archetypes for implementing Gen AI solutions
(takers, shapers, and makers), survey has found that in most industries,
organizations are finding off-the-shelf offerings applicable to their
business needs, about half of reported Gen AI uses publicly available
models or tools with little or no customization. Respondents in energy
and materials, technology, and media and telecommunications are more
likely to report significant customization or tuning of publicly
available models or developing their own proprietary models to address
specific business needs.
The time required to put Gen AI to production for most of the
respondents is around 1-4 months.
Gen AI high performers are excelling. Some common characteristics or
practices: 1) They are using Gen AI in more business functions (an
average of 3) compared to others average 2, 2) They are more likely to
use Gen AI in marketing and sales and product or service development
like others, but they are more likely than other s to use Gen AI
solutions in risk, legal, and compliance; in strategy and corporate
finance; and in supply chain and inventory management, 3) They are three
times as likely as others to be using Gen AI in activities ranging from
processing of accounting doc and risk assessment to R&D testing and
pricing and promotions, 4) They are less likely to use those
off-the-shelf options than to either implement significantly customized
version to develop their own proprietary foundation models, 5)
encountered challenges with their operating model.
― Stop Overlooking the Leadership Potential of Asian
Employees - Harvard Business Review [Link]
This article talked about the reasons why Asian employees’ careers
stagnate, solutions for the organization to help employees move past the
roadblock, and reasons of investment in Asian employees.
Introducing Apple’s On-Device and Server Foundation
Models [Link]
This article provides details about how Apple developers trained
models, fine-tuned adapters for specific user needs, and evaluated model
performance.
The analogy here is to Search, another service that requires
astronomical investments in both technology and infrastructure; Apple
has never built and will never need to build a competitive search
engine, because it owns the devices on which search happens, and thus
can charge Google for the privilege of making the best search engine the
default on Apple devices. This is the advantage of owning the device
layer, and it is such an advantageous position that Apple can derive
billions of dollars of profit at essentially zero cost.
First, with regards to the title of this Article, the fact it is
possible to be too early with AI features, as Microsoft seemed to be in
this case, implies that not having AI features does not mean you are too
late. Yes, AI features could differentiate an existing platform, but
they could also diminish it. Second, Apple’s orientation towards
prioritizing users over developers aligns nicely with its brand promise
of privacy and security: Apple would prefer to deliver new features in
an integrated fashion as a matter of course; making AI not just
compelling but societally acceptable may require exactly that, which
means that Apple is arriving on the AI scene just in time.
― Apple Intelligence is Right On Time - Stratechery
[Link]
This article worths a read. It famously talks about Aggregation
Theory as applied to the internet, and predicted much of how the
Google/Facebook age unfolded through that lens. This is one main reason
why winners are still old players in AI era at least for now.
OpenAI presents new approach to interpret concepts captured by
GPT-4’s neural networks. They used sparse autoencoder to make sense of
neural activity within LLMs and found 16 million features in GPT-4.
Limitations are 1) hard to interpret, 2) no good way to check the
validity of interpretations, 3) not all behaviors are captured, 4)
challenging to scale to frontier LLMs.
YouTube and Podcasts
In an H100 GPU, every second we can move at most 3.35 terabytes
of RAM in and out of memory registers. And in the same second, we can
multiply 1.98 quadrillion 8bit floating point numbers. This means that
it can do 591 floating point operations in the time it takes to move one
byte. In the industry this is known as a 591:1 ops:byte ratio. In other
words, if you are going to spend time moving an entire gigabyte around
you should do at least 591 billion floating point operations. If you
don’t, you are just wasting GPU and potential compute. But if you do
more than that, you are just waiting around on memory bandwidth to get
your data in there. In our models, the amount of memory we need to move
around is relatively fixed, it’s roughly the size of our model. This
means that we do have some control over on how much math that we can do
by changing our batch size.
In reality, we’ve discovered that bottleneck can arise from
everywhere from memory bandwidth, network bandwidth between GPUs,
between nodes, and other areas. Furthermore the location of those
bottlenecks will change dramatically on the model size, architecture,
and usage patterns.
― Behind the scenes scaling ChatGPT - Evan Morikawa at
LeadDev West Coast 2023 [Link]
This is a behind the scenes look at how OpenAI scaled ChatGPT and the
OpenAI APIs. But also a very good talk to show how hard it is to scale
infrastructure for model architecture etc, and how important it is to
master these skills and knowledge in chip manufacture and design
industry and in LLM development industry. The talk covers 1) GPU RAM and
KV Cache, 2) batch size and ops:bytes, 3) scheduling in dozens of
clusters, 4) autoscaling (and the lack thereof).
Key facts to consider when developing metrics for compute
optimization and model scaling:
GPU memory is valuable. But it is frequently a bottleneck, not
necessarily compute.
Cache misses are non linear on compute, because we suddenly need to
start recomputing all stuff.
When scaling ChatGPT, we need to:
Look at KV cache utilization and maximize all the GPU RAM we have,
and
Monitor batch size - the number of concurrent requests we run to the
GPU at the same time, to ensure the GPUs are fully saturated. These are
two main metrics used to determine how loaded our servers were.
In reality, there are more bottlenecks (memory bandwidth, network
bandwidth between GPUs, between nodes, and other areas) and the location
where they arise can change according to the model size, architecture,
and usage patterns. The variability has made it very hard for AI model
developer and chip manufactures to design chips to get that balance
right. Future ML architectures and sizes have been very difficult to
predict. But overall we need to be tweaking this math as the models
evolve.
The third challenge is to find enough GPUs. Note that the time of a
response is dominated by the GPU streaming out one token at a time, as a
result, it’s been more important to just get capacity and optimized a
well balanced fleet, over putting things geographically close to
users.
The fourth challenge is the inability to scale up this fleet. OpenAI
has delayed some feature of ChatGPT due to the limitation of compute
resources.
Some lessons they have learned in solving GPU challenges:
It’s important to treat this as a system engineering challenge as
opposed to a pure research project.
It’s important to adaptively factor in the novel constraints of
these systems.
Every time model architecture shifts, a new inference idea is
proposed or a product decision is changed, we need to adapt and rerun a
lot of this math again. Diving really deep has been important. This low
level of implementation details matter.
The final challenge is abuse on the system and AI safety
challenges.
For many years, particularly following the original SARS
pandemic, there was a lot of conversations around how do we get in front
of the next pandemic, how do we figure out what’s coming and how do we
prepare for it. And there was a lot of research that was launched to try
and resolve that key question. It’s like does the effort to try and stop
the problem cause the problem. I think that from my point of view there
is a very high probability that there was some leak that meant that the
work that was going on to try and get in front of the next pandemic and
understand what we could do to prepare ourselves, and what vaccines
could be developed and so on, actually led to the pandemic. So then when
that happens how do you respond when you are sitting in that seat,
that’s the key question that I think this committee is uncovering. -
David Friedberg
The TED AI Show: What really went down at OpenAI and the
future of regulation w/ Helen Toner [Link]
In the interview, Toner revealed that the reason of firing Altman is
his psychological abuse and being manipulative in different situation.
Looking at Altman’s track record prior to OpenAI, it seems those are not
new problems of Sam.
How Walt Mossberg Built Relationships With Jobs, Gates, and
Bezos - Big Technology Podcast [Link]
Nvidia’s 2024 Computex Keynote: Everything Revealed in 15
Minutes [Link]
What really works when it comes to digital and AI
transformations? - McKinsey [Link]
… but what I do take offense at is labeling millions and millions
of ordinary Americans as somehow lacking in empathy, lacking in caring,
not being a good parents, because you don’t like their support for
Trump. And I think that that is a statement that frankly reeks of being
cocooned in an elite bubble for way too long. Let me just explain. If
you look at where Trump’s support is strongest. It’s really in Middle
America and sort of the heartland of America, basically the part of
America that the Coastal at least dismissively refer to as flyover
country. It’s a lot of the industrial midwest and frankly that part of
the country had not had the same type of economic experience that we’ve
had in Silicon Valley. They have not been beneficiaries of
globalization. If you are in a handful of export industries in America
and I’m talking about if you are in Hollywood or you are in Big Finance
or you are in Software, then globalization has been great for you,
because it has created huge global markets for our products. However, if
you are in an industry that has to compete with global exports, then
it’s been very bad with you and blue collar workers have been hurt,
labor’s been hurt, people who work with their hands have been hurt. They
have not benefitted in the same way from the system we’ve had in this
country for the last 30 years. So you can understand why they would not
be so enchanted with elite thinking. I think to then label those people
as lacking in caring or empathy or not being good parents because they
haven’t had the same economic ride that you had for the last 30 years
and then you are the one who is fighting a legal battle to kick some of
those people off the public beach in front of your beach house, and then
you are saying they are the ones lacking in empathy, dude, look in the
mirror. - David Sacks
This is the future of how smart reasonable moderate people should
make decisions. It is an example. Talking to somebody you disagree with
does not make your opinion bastardized, it actually makes your opinion
valuable. There are these simple truths to living a productive live that
if you want to embrace, you need to find friends that you can trust,
even on issues when you disagree, you can hear them out. - Chamath
Palihapitiya
There is no law that defines why you should or shouldn’t buy a
security, with respect to the diligence you have individually done, to
determine whether the underlying business is worth the price you are
paying. The law says, that the business that are listing their
securities for public trading have an obligation to make disclosures on
their financials and any other material events to the public and they do
that through the SEC filing process. That’s all out there. And then what
you as an individual will do with it is up you to. - David
Friedberg.
This four hours video guides you to create a fully functional GPT-2
model from scratch. It includes details about model construction, speed
optimization, hyperparameter setup, model evaluation, and training.
Building open source LLM agents with Llama 3 [Link]
LangChain and Meta uploads new recipes/tutorials to build agents that
runs locally using LangGraph and Llama 3.
Apple took a shortcut to get here, they partnered with open ai.
And this is something that I don’t think they’ve ever really done before
at the operating system level. Apple is famous for being vertically
integrated, for being a walled garden, for being end to end. They
control everything from the chips to the hardware to the operating
system, and they don’t let anybody else in, until you are at the App
Store Layer. This is allowing somebody in beneath the level of the App
Store. This is allowing someone OpenAI to get access to your data, and
to control your apps, at the operating system level. Elon pointed out
wait a sec what are the privacy implications here. And I think there are
major privacy implications. There is simply no way that you are going to
allow an AI on your phone to take. Remember Apple in the past has been
the advocate for consumer privacy. There is a whole issue of the San
Bernardino terrorist where the FBI went to Apple and said we want you to
give us back door access to their phone and Apple refused to do it and
went to court to defend user privacy. And furthermore, one of Apple’s
defenses to the antitrust arguments for allowing sideloading and
allowing other apps to get access to parts of the operating system,
they’ve always said we can’t do this because it would jeopardize user
privacy and user security. Well here they are opening themselves up to
OpenAI in a very deep and fundamental way in order to accelerate the
development of these features… I think this is going to open Pandora’s
box for Apple, because again they’ve proven that they can open up the
operating system to a third party now, and who knows what the privacy
implications of this are going to be. - David Sacks
I think there are three numbers that matter: the inflation rate,
the growth in GDP, and the cost to borrow. The growth in GDP in the
first quarter of 2024 was a lousy - 1.3% on the annualized basis. And
even if the rate of inflation came down, we are still inflating the cost
of everything by north of 3%. So the economy is only growing by 1.3% and
it costs more than 3% each year to buy stuff. So that means everyone’s
spending power is reducing, and our government’s ability to tax is
declining, because the economy is only growing by 1.3%. And the most
important fact is that the interest rates are still between 4-5% (4.7%).
That means that borrowing money costs 4.7%, but the business the economy
on average is only growing 1.3%. So just think about that for a second.
We have tremendous amount of leverage on businesses on economy on the
federal government. That leverage, the cost to pay for that debt is more
than 4-5% but you are only growing your revenue by 1.3%. So at some
point you cannot make your payments. That is true for consumers, it’s
true for enterprises, and it’s true for federal government. The whole
purpose of raising rates is to slow the flow of money through the
economy. And by slowing the flow of money through the economy, there is
less spending which means that you are reducing the demand relative to
the supplies, so the cost of things should come down, you should reduce
the rate of increasing in the cost of things… There is certainly a shift
in the market because what this tells us is that the timeline at which
the fed will cut rates is coming is a little bit. So the market is
saying okay let’s adjust to lower rates, the 10 year treasury yield has
come down a little bit, but we are still in a difficult situation for
people, and for businesses. - David Friedberg
If the revenue of everything combined which is GDP isn’t going
faster than the increase in the cost of everything, people, businesses,
and government can’t afford their stuff. And that’f fundamentally what
is going on right now. What we need to see is a normalization where GDP
growth is greater than inflation rate. And as soon as that happens then
we have a more normalized and stable economy. So right now things are
not stable. There is a lot of difficulty and strain in the system. -
David Friedberg
You had 1.3% GDP growth rate with a 6% of GDP deficit by the
government. If the government wasn’t printing so much money, wasn’t over
spending, and you were to have a balanced budget, it would be a
recession. It would be negative GDP growth if not for the government’s
program stimulating the economy. And a lot of jobs you are talking about
are government jobs. The government is creating jobs like crazy, not in
the private sector but in the public sector, because it is an election
year. So there is a lot of political forces proping things up, and I
wonder what happens after the election. - David Sacks
Energy is high at the beginning with a blackjack! Went through
several news and topics: 1) Elon’s comp package approved by
shareholders, besties criticized some reneging people, who are really
not good ones to do business with, 2) Apple announces “Apple
Intelligence” and ChatGPT deal at WWDC, first time of opening up OS to
the third party, raising data privacy concerns, 3) OpenAI reportedly hit
a $3.4B revenue run rate, 4) state of US economy.
Leopold Aschenbrenner - 2027 AGI, China/US Super-Intelligence
Race, & The Return of History [Link]
Private Cloud Compute: A new frontier for AI privacy in the
cloud - Apple Security Research [Link]
What I learned from all of that, if I look at his mistakes and
successes, I learned a couple things. The first is most of the money
he’s made by holding onto things, not the momentum trading that he was
known for in, in public equities. And number two, he made most of his
money buying quality, like quality was very important to his success.
And that’s something I’ve taken from him. And I truly believe, like the
environment in which you grow up dictates the kind of person and
mentality you will have in life. So let me just quickly expand on that.
If you grow up during the Great Depression, I would presume you’re
focused on saving every penny and looking for cheap. And I think I’m
just taking a guess that you’re gonna be much more focused on buying
cigar butts in life than you might be on buying the highest quality
asset you can find and maybe paying up for. Somehow my father figured
out that the real money is in the best businesses and the higher quality
assets. And he instilled that in me very early. And sometimes these
things look expensive. And so my point earlier is that he taught me very
early not to dismiss something that might look expensive on the surface
before you do the deep work and really understand what you’re buying and
what it’s worth.
And the problem that a lot of value investors have, I think, is
that they all screen for low p ratios, high dividend yields, you know,
low ev to cash flows, what whatever it is. They’re screening and they
will miss because the kind of any value in that area is gonna get
competed away. And so I’m more interested in businesses that might look
expensive on the surface, but actually aren’t, and you have to be
careful because a lot actually are expensive, right? And there’s no
margin safety there. But there is Peter Kaufman said there’s margin
where there’s mystery. I think that’s so true. There’s margin where
there’s mystery. And so sometimes the best investments are those that
are misunderstood and might appear expensive, and they’re often quality
kinds of businesses. So I gravitate toward quality partly as a result of
that influence he had on me, if that makes sense.
Tesla company is definitely one of the most misunderstood
companies that I’ve seen in my 25 years or so of managing money for
others. It’s such an interesting company and it, and it’s so
misunderstood and I think it’s misunderstood for a few reasons. One, you
have this kind of overarching personality where people start to
formulate opinions based on what Elon is. And people think about it as a
car company. And third, most people haven’t actually dived in, right?
They’ve not spent the hundreds and hundreds of hours on this company and
they haven’t really gone through the financials to understand the
economics of the business. So there are a few things coming together
where I think that this company still remains misunderstood, but there’s
a reason why it’s up whatever 15000% or so since the IPO. And there is a
reason why it continues to go higher over time because there are plenty
of people that do get it right and that it’s just getting more and more
concentrated into what I would probably call hands of smart
money.
I’d say it’s not just a car company, it is very much an EV
company, but it’s not just a car company… Why would the future look any
different? And of course it, it it did. Why were people skeptical? While
there were no paved roads, supply chains were very limited. There were
very few fueling stations, there was very little manufacturing capacity.
Kind of sound familiar right to today in EV terms. But ice vehicles,
Henry Ford disrupted the horse and carriage very quickly within 20
years, which is happened to be pretty much the timeframe during which
curves take formation is a 20 year disruption period with respect to
pretty much all these transformational technologies.
Going back to the guttenberg printing press and the steam engine
and the spinning wheel, it’s all about 20 years. So my point is that
there’s all of this skepticism and you had horse and carriage competing
against this noisy ice vehicle. Both were forms of transportation, both
gotten you from point A to point B, but one was fundamentally different.
It was fundamentally different because it was a much more efficient
process of getting you from point A to point B. And that is the lens
from a kind of very high top down level that I look at Tesla and
electric vehicles in general, they’re just a much more efficient way to
get you from point A to point B than ice vehicles. And what I mean by
that is the cost of ownership and cost per mile is just much lower. And
so then it’s a question of what are the risks? What are the competitive
advantage does Tesla have over the rest of the competition in EV and how
will Tesla survive and thrive against ice vehicles? Which to me are
going the way of the dinosaur. That is a big assumption that I believe
is true because I think that EV adoption is following the traditional S
curve adoption phase and there’s not a lot of time left for ice vehicles
to exist.
The real voyage of discovery is not in seeking new landscapes,
but in having new eyes.
I gave you the first kind of like lens at which I’m looking at
Tesla as competing as an EV company against ice vehicles and ev as a
whole being much more efficient than ice. But the other lens I should
share is that I don’t think you can understand this company if you don’t
understand. I could be totally wrong, but as assuming that I’m right, I
don’t believe you can understand the company if you don’t understand
that - to me it’s an advanced electronics manufacturer and software
company competing against a traditional automobile manufacturing
company.
This is super important. It’s an electronics slash software
company competing against traditional auto. It’s super important to
understand that because there’s certain things that kind of like come
into play. There was an aeronautical engineer by the name of Theodore
Wright, he devised this concept called the rights law, which states that
for every doubling of cumulative production, that costs fall by a
constant percentage. And when you understand that Tesla is an electronic
software company, you understand like where is this company along this
rights law curve.
Tesla is so much further along the curve than any of the ice
vehicles that are constrained because they’re not electronic in software
companies, they’re traditional auto companies. And it’s Tesla’s so much
further along the curve with respect to other EV companies. And so as
ice vehicles traditional auto catches up, Tesla just moves much further
along the curve. And so the spread between the competitive advantage of
Tesla, the other EV companies and traditional auto is actually widening.
It’s not getting more narrow, it’s widening because of its massive
scale, which allows it to push itself out along the, the cost curve
further than anybody else.
We actually did buy more around current prices and these
arguments, most of them at least except for the 50 billion compensation
package, most of these arguments sound like the same arguments that you
could go back and read since the company went public. They’re probably
less negative articles today than there were around 2011, 2012, 2013.
But they seem very similar. And yet the stock continues to go higher up
15,000 or so percent since it’s IPO. And what’s the case with pretty
much every growth company from Amazon to Microsoft? Any great growth
company, there are always periods when the stock is not going up. There
have been so many massive drawdowns in Tesla since it’s IPO, like
there’s gotta be a couple dozen, at least 40% drawdowns since it’s IPO
or at least 30% drawdowns. And that’s just part of investing in growth
companies. No businesses. And I wrote a paper called Power and
Challenges of Compounding, which is on our website, but growth companies
just don’t go in straight lines. They move more like in a step
formation. And if you look at the kind of longer term chart of Tesla,
Tesla’s just kind of in a step formation just like Amazon was and
Microsoft was.
I totally focus on the business fundamentals. I really don’t, I
don’t let the stock prices dictate like what I’m thinking about the
business. And when I’m looking at a business, a potential company to
buy, I try my very best like not to pay any attention to the stock price
and just come up with my own idea of what I think the business is worth.
And so the key is really to focus on the fundamentals. And if the
fundamentals are moving in the right direction, then the stock price
will take care of itself over time. The the problem is that if you have
all your money in one or even five companies or maybe even 10 companies,
it’s really, really hard to deal with that emotionally.
Mr. Rogers was not just a TV host who was the central figure for
Mr. Rogers neighborhood, but he was also a Presbyterian minister. And I
think that that upbringing education and that way of life influenced how
he dealt with people. And he had this wonderful expression. He said that
there are three ways to ultimate success. The first is to be kind, the
second is to be kind, and the third is to be kind. I thought that was
really interesting and very powerful and it meshed with how I wanted to
try to live life. And it also me meshes with, you know, this idea of
reciprocity, which is deep rooted human condition, right? If we give
kindness to the world and bringing kindness back, I truly believe that.
And, and so he was, and has been influential to how I think about life
and how I try my best. I don’t always succeed, but I do try my best to
live my life according to Mr. Rogers’ values.
Peter said that, look, you need to look at your life as one
ladder. And there’s seven steps to the ladder. These seven steps are
pretty much in this order. Health and then family and friends, career,
community, spirituality and hobbies. The most important, those of those
seven steps is health. Because health is multiplicative, right? If you
take health and you multiply it by zero, everything else goes to zero,
not good. So you wanna focus on that as first and foremost.
― Real Success w/Christopher Tsai - We Study Billionaires
Podcast [Link]
In this episode Christopher talked about his family history,
investment in Tesla, Microsoft, Visa, and Mastercard, and some other
personal development and investment tips. This is what I’ve learned:
It completely changed my mind in understanding Tesla. I thought
Tesla’s main business is traditional automobile, while EV is a new
leading branch in the frontier. But it turns out that it’s wrong. Tesla
is actually an electronic software company with a smart undercover of
traditional automobile. As traditional auto vehicle companies catching
up, Tesla is just moving further and further. Also being aware of what’s
happening around Tesla: stock price was falling, sales growth is
slowing, BMW and BYD are flooding the market with EV, unfocused CEO Elon
Musk, no new models of the car since 2020, etc, I was already starting
questioning Tesla, but in fact, the truth is Elon beat almost every
milestone, he kept almost every promise, and he deserves his $50B pay
package. What Tesla is current going through is as normal as what any
other growth companies have experienced.
The key of being a value investor and picking valuable stocks is to
focus on the business fundamentals but not stock price.
Diversifying portfolio is a good way to deal with pain and emotions,
and stop you from interrupting the compounding process.
In conversation with President Trump - All-In
Podcast [Link]
They brought President Donald Trump to the show! They asked great
questions and they have 40 min high-quality recap, it’s very impressive.
Btw, I do feel President Trump is a really engaging person - you can
feel it by just listening to him speaking out.
Data + AI Summit Keynote Day 1 - Full - Databricks
[Link]
Experts, researchers and open source contributors — from Databricks
and across the data and AI community gathered in San Francisco June 10 -
13, 2024 to discuss the latest technologies in data management, data
warehousing, data governance, generative AI for the enterprise, and data
in the era of AI.
Notes of “Ali Ghodsi, Co-founder and CEO,Databricks”:
There are three problem from AI practitioners:
Everyone wants AI
Organization don’t care about MMLU performance, they care about using
model to do well on their data for their use cases and businesses.
According to a survey, 85% of the use cases have not made it into
production. This indicates that getting AI on your data into production
is hard - people want high quality, low cost, and privacy.
Security and privacy are under pressure
It’s under intense pressure. People care about AI regulation, data
privacy, and cyberattacks.
Data estate is fragmented.
Lots of complexity, huge costs, and proprietary lock-in.
Databricks solution to these three problems is data intelligence
platform. The idea is: don’t give your data to vendors, instead, own
your own data, store them in data lake in a format that’s standard.
These are what Databricks is doing:
They acquired Tabular because they want the data to be stored in a
standard format so that every engine can access to.
They also launched project UniForm, which aims to make sure that
UniForm has 100% full compatibility and interoperability for both of
Delta Lake and Iceberg projects.
Unity Catalog allows you to do governance to ensure access, control,
and security, and also discovery, lineage, auditing, data and model
quality monitoring. And They have just open-sourced Unity Catalog.
Data in data lake combining with Mosaic AI’s AI models is called
Data Intelligence Platform. This platform trains Gen AI models on your
data in isolation for each customer, and leverages that throughout the
platform for everything it does.
Data Intelligence is democratized data plus democratized AI.
Democratized data means everyone in your organization should be able to
access to the data directly. They, including those don’t know how to
speak sql, should be able to access to data or get insights from data by
spending languages. Democratized AI means everyone should be able to
create AI models that understand your data in your organization.
All the Databricks now are available in 100% serverless.
Note of “Brian Ames, General Motors”:
Their mission is zero crashes, zero emissions, and zero congestion.
In order for GM to be part of the future, they need to become a software
company. They started from building data silos, on-prem infrastructure,
and keeping pace of innovation. Their vision and strategy are to change
the culture, move to the cloud, create a data insights factory cloud,
and build upon Databricks.
The GMs data insights factory today includes single source of truth
(where big data are ingested), trusted data (with functions of GenAI
platform, ETL and orchestration, and data warehousing), open ecosystem
(with Meta’s LLMs, AI models, and data governance ), and react front
end. Majority of the factory is supported by Databricks.
Morgan Housel: Get Rich, Stay Rich - The Knowledge
Project [Link]
The Founder of Rolex: Hans Wilsdorf - Founders [Link]
[Transcript]
E156｜自动驾驶领域的GPT时刻来了？聊聊特斯拉V12、FSD入华与RoboTaxi
- The Silicon Valleyer with Jane [Link]
Good discussion about Tesla V12, FSD in China, and RoboTaxi. I learnt
new views about end-to-end technology, Musk’s vision, overview of EV
market, EV competitors, etc.
It makes so much sense for them so I think they should do it as
quickly as possible. We are in the first inning of what should probably
be an enormous tectonic shift in technology. And I think if whoever wins
in the first inning usually isn’t the one that’s winning by the ninth
inning. And so I would encourage anybody that’s winning right now to
monetize, get secondaries, take money off the table as fast as possible.
Because the future is unknown and the more disruptive the technology is,
the more entropy there is, which means that there is going to be more
changes not less. And again I would just look at search as an example, I
would look at social networking as an example. When you look 20 years
later the people who captured all the value were not the one that at the
beginning who everybody thought was going to win. And so I think If it
plays out similarly, it’s important for the people that are in the lead
today, to recognize that it’s too early, and they should monetize their
perceived success as quickly as they can to the largest magnitude as
possible. - Chamath Palihapitiya
I think OpenAI is running a very strategic game plan to become
part of the tech establishment as quickly as they can, so that they are
in the inside looking out as opposed to the outside looking in. They
were able to add the former head of the NSA to their board of directors.
It’s how you become part of the establishment. Do you think the former
head of the NSA no long has a security clearance or knows people in the
NSA? No of course not. And I think that there is a group of people that
want to make sure that these kinds of technologies and capabilities are
firmly within the hands of the US apparatus and not anybody else. And so
I think that that pulls them closer to the kinds of folks that could
otherwise give them a hard time or regulate them, etc. So now what
happens is when you have senate hearing about this stuff it’s more
likely that it’s confidential behind closed door, it’s under the purview
of National Security. All these things are beneficial to OpenAI. And
secondly they were able to get Elon to drop his lawsuit. So the next
logical step is now to create Capital Market distribution, which is
really about syndicating ownership of the company to all the big deep
pools of money, so that they are also rowing in the same direction in
support of OpenAI. That’s what a lot of people don’t get, it’s not about
valuations or this and that, this is about creating a highlevel game
theory of how to create an international apparatus that supports your
corporate objectives. There are a few companies that have done this
well, and they are now one of them, the only thing left is to get shares
into the hands of the BlackRocks, the t-rows, all the big mutual fund
apparatuses of the world that then syndicate to all the individual
investors of the world. You have everything, you have government
connections, you have no real legal overhang, then the likelihood that
an IRS agent all of a sudden decides to audit OpenAI is basically zero.
It’s a smart business strategy. - Chamath Palihapitiya
“Microsoft excels with bundling. It’s their not so secret weapon
for dominating new markets. We know the playboo: Office + Teams, Windows
+ Explorer, Azure + Visual Studio, 365 + OneDrive, & Xbox + Game
Pass. - Marc Benioff @X”
― Presidential Debate Reaction, Biden Hot Swap?, Tech
unemployment, OpenAI considers for-profit & more [Link]
Last time of watching this series of lectures it was 3 years ago.
Happy to see these two old guys again (Trevor Hastie and Robert
Tibshirani). I’m planning to review the whole series in my spare
time.
This is an unusual (to me) ML course with a strong focus on theory.
It’s taught from a very different perspective that’s supplementary to
what I have learned from my ML courses. Definitely going to watch it
once I have time.
EfficientML.ai Lecture, Fall 2023, MIT 6.5940 - MIT HAN
Lab [Link]
[Website]
I have to watch this. Feed me knowledge please! ヾ(◍°∇°◍)ﾉﾞ
Papers and Reports
SimPO: Simple Preference Optimization with a Reference-Free
Reward [Link]
Deep Learning Interviews: Hundreds of fully solved job
interview questions from a wide range of key topics in AI [Link]
Best preparation book for AI/ML job seekers and students.
The economic potential of generative AI: The next
productivity frontier - McKinsey [Link]
Better & Faster Large Language Models via Multi-token
Prediction [Link]
How Can Recommender Systems Benefit from Large Language
Models: A Survey [Link]
Sparser is Faster and Less is More: Efficient Sparse
Attention for Long-Range Transformers [Link]
Efficient data generation for source-grounded
information-seeking dialogs: A use case for meeting transcripts - Google
Research [Link]
This open source dataset “Meeting Information Seeking Dialogs” is
unique with an aim of improving interaction with meeting recordings
through conversational AI models. It allows users to query and engage
with transcript content efficiently through a developed agent.
Meta Large Language Model Compiler: Foundation Models of
Compiler Optimization - Meta Systems Research [Link]
Meta LLM Compiler is a family of models built on Meta Code Llama with
additional code optimization and compiler capabilities.
ESM3: Simulating 500 million years of evolution with a
language model - EvolutionaryScale Research [Link]
ESM3 is an AI model capable of understanding and predicting the
sequence, structure and function of proteins, simulating evolutionary
processes, and generating new proteins with specific traits.
Alibaba released new open-source LLM called Qwen2, which outperforms
Meta’s Llama 3 in specialized tasks. It’s accessible via HuggingFace,
with weights available and five model sizes (0.5B, 1.5B, 7B, 57B-14B
(MoE), and 72B). Qwen2 has been trained on data in 29 languages, and can
handle up to 128K tokens in context length. It has been benchmarked
against Meta’s Llama 3 and OpenAI’s GPT-4, achieving top scores. The
primary innovation of Qwen2 is its long-context understanding.
Introducing llama-agents: A Powerful Framework for Building
Production Multi-Agent AI Systems - LlamaIndex [Link]
News
Amazon to expand drone delivery service after clearing FAA
hurdle [Link]
Amazon’s drone delivery services “Prime Air” was laid out more than a
decade ago but has struggled since then. In 2022, Amazon said it would
begin testing deliveries in College Station, Texas. In 2023, Prime Air
was hit by layoffs. But recently Amazon said it would expand drone
operations to Phoenix, Arizona, etc. And it’s expected to expand to
other cities in 2025.
Salesforce’s stock suffers its biggest drop in two
decades [Link]
NASA’s James Webb Space Telescope Finds Most Distant Known
Galaxy [Link]
Saudi fund joins $400m funding round of Chinese AI startup
Zhipu [Link]
A PR disaster: Microsoft has lost trust with its users, and
Windows Recall is the straw that broke the camel’s back [Link]
As Microsoft has done a lot of things (obtrusive ads, full-screen
popups, ignoring app defaults, forcing Microsoft Accounts, etc) to
degrade the Windows user experience over the last few years, it lost the
trust relationship between Windows users and Microsoft, therefore a tool
like Recall is described as literal spyware or malware by users no
matter how well you communicate the features to the world.
Apple Made Once-Unlikely Deal With Sam Altman to Catch Up in
AI [Link]
The deal between Apple and OpenAI will give OpenAI access to hundreds
of millions of Apple users, and bring Apple the hottest technology of
the AI era - that can pair with its own services.
Nvidia is now more valuable than Apple at $3.01
trillion [Link]
Mark this today, on Jun 5, 2024, Nvidia’s market cap is higher than
Apple becomes the second most valuable company in the world.
SpaceX’s Starship Rocket Successfully Completes 1st Return
From Space [Link]
Woman Declared Dead Is Found Alive at Funeral Home
[Link]
BYD Launches Hybrids With 1,300-Mile Driving Range
[Link]
China’s plan to dominate EV sales around the world
[Link]
Nvidia emails: Elon Musk diverting Tesla GPUs to his other
companies [Link]
Next week, at Apple’s annual Worldwide Developers Conference, the
company is set to join an AI arms race - announce an array of generative
AI upgrades to its software products, including Siri.
Tesla’s $450 lightning-shaped bottle of mezcal is its most
expensive liquor yet [Link]
Apple’s Upcoming AI Reveal, Pika Labs Raises $80 Million,
Twelve Labs, $50 Million [Link]
Among the biggest spenders on sovereign AI is Singapore, whose
national supercomputing center is being upgraded with Nvidia’s latest AI
chips and where state-owned telecom Singtel is
pushing an expansion of its data center footprint in Southeast Asia in
collaboration with Nvidia. The country is also spearheading a large
language model that is trained on Southeast Asian languages.
Other big projects are taking place in Canada, which last month
pledged $1.5 billion as part of a sovereign computing strategy for the
country’s startups and researchers, and Japan, which said it is
investing about $740 million to build up domestic AI computing power
this year following a visit from Huang.
Similar pushes are spreading across Europe, including those in
France and Italy, where telecom companies are building AI supercomputers
with Nvidia’s chips to develop local-language large language models.
French President Emmanuel Macron last month called on Europe to create
public-private partnerships to buy more graphics processing units, or
the core chips used to train AI, to push its share of those deployed
globally from 3% currently to 20% by 2030 or 2035.
― Nvidia’s New Sales Booster: The Global Push for National AI
Champions [Link]
Cloud-computing giants and big tech companies have been a great
source of revenue for NVIDIA, now Sovereign Al is another lever.
Governments demand sovereign clouds for their AI infrastructure and
sensitive data, and US tech companies such as NVIDIA are eager to build
those for them. Question would be how long can they keep this momentum
in generating high revenue.
Do you best creating, thinking, learning, brainstorming,
note-taking - Google NotebookLM [Link]
Google upgraded its NotebookLM powered by Gemini 1.5.
There’s one other way Apple is dealing with privacy concerns:
making it someone else’s problem. Apple’s revamped
Siri can send some queries to ChatGPT
in the cloud, but only with permission after you ask some really tough
questions. That process shifts the privacy question into the hands of
OpenAI, which has its own policies, and the user, who has to agree to
offload their query. In an
interview with Marques Brownlee, Apple CEO Tim Cook said that
ChatGPT would be called on for requests involving “world knowledge” that
are “out of domain of personal context.”
Apple’s local and cloud split approach for Apple Intelligence
isn’t totally novel. Google has a Gemini Nano model that can work
locally on Android devices alongside its Pro and Flash models that
process on the cloud. Meanwhile, Microsoft Copilot Plus PCs can process
AI requests locally while the company continues to lean on its deal with
OpenAI and also build
its own in-house MAI-1 model. None of Apple’s rivals, however, have
so thoroughly emphasized their privacy commitments in
comparison.
― Here’s how Apple’s AI model tries to keep your data
private [Link]
Introducing Apple Intelligence, the personal intelligence
system that puts powerful generative models at the core of iPhone, iPad,
and Mac [Link]
Ilya Sutskever Has a New Plan for Safe
Superintelligence [Link]
AI Employees Should Have a “Right To Warn” About Looming
Trouble - Big Technology [Link]
Ilya left OpenAI in mid-May and started Safe Superintelligence Inc.
in mid-Jun, with a goal of creating a safe powerful AI system. Daniel
Gross (former Apple Inc. AI lead) and Daniel Levy (former AI engineer at
OpenAI).
To me, it’s not a bad thing to let OpenAI safety employees leave and
start their own business. It’s actually a good thing for both. OpenAI
led by Sam is eager to stay the leading AI company and develop AGI as
quickly as possible. Having colleagues concerning about AI safety would
only slow down the progress. So the goals of OpenAI vision & mission
and OpenAI safety team don’t align. Not saying AI safety is not
important. I’m saying the AI pioneers and AI safety team should both
exist individually and separately so that they are restricting each
other in an official, public, and even way, and not conflicting each
other from inside.
Musk’s xAI supercomputer will get server racks from Dell and
Super Micro [Link]
Dell and Super Micro Computer will provide server racks for the
supercomputer being developed by xAI. The supercomputer aims to power
the next iteration of xAI’s chatbot Grok, which requires a vast number
of Nvidia GPUs for training. Musk plans to have the supercomputer
operational by fall 2025.
Releasing New AI Research Models to Accelerate Innovation at
Scale - Meta News [Link]
Meta Chameleon: 7B & 34B language models
Open source model is catching up GPT-4o. This is the first open
source base model that is able to take multimodal inputs and generate
outputs. Unfortunately it currently only has a research
license.
Meta Multi-Token Prediction LLM
Meta released a language model for code completion using multi-token
prediction. The approach was newly proposed in a paper aiming to build
better and faster LLMs by using multi-token prediction. [Paper]
Meta JASCO: text-to-music models
They released generative text to music models able to accept various
conditioning inputs for greater controllability.
Meta AudioSeal: audio watermarking model
This is the first designed specifically for the localized detection
of AI-generated speech, available under a commercial license. This would
be very useful to detect deep fakes.
Additional RAI artifacts
To ensure geographical and cultural diversity in the capability of
text to image models, they developed automatic indicators to evaluate
potential geographical disparities in text to image models. And now they
released this evaluation code and annotations. [Paper]
Claude 3.5 Sonnet achieves higher performance in various key metrics
and tasks, outperforms competitor models as well, and performs at twice
the speed of Claude 3 Opus and at 1/5 the cost. Also, Anthropic
introduced a new feature called Artifacts on Claude.ai to expand how
users can interact with Claude.
AI tools are coming to Gmail, Google Drive, and
Firefox [Link]
Google is integrating AI side panels powered by Gemini into Gmail,
Docs, Sheets, Slides, and Google Drive, enhancing writing assistance,
summarization, and content creation.
Firefox starts letting you use AI chatbots in the
sidebar [Link]
Mozilla is incorporating AI chatbots into Firefox. ChatGPT, Google
Gemini, HuggingChat, or Le Chat Mistral are options for users to choose
in the sidebar.
Meet Sohu, the fastest AI chip of all time. - Etched @X [Link]
Sohu is building the fastest specialized chip for transformer
models.
Gemma 2 is now available to researchers and developers -
Google Developers [Link]
What's true about New York City: People come and go, they don't
stay.
Back to the topic:
When we talk about investment, we talk about economic values. The
current situation of AI is very similar to Cisco’s in 2000. Cisco as an
internet company spread the capacity of the World Wide Web, but sooner
people realized that there is no economic value in internet company,
instead, opportunities are in e-commerce etc. AI is a tool very similar
to web tech. Currently, with heightened expectations, people are
allocating investments and capital expenditure in AI model development,
however, end-user demand is unclear and revenue is relatively minimal.
This situation makes AI look like a bubble from a very long term
perspective.
Stepping closer to it, there is still room in the market to party.
GPUs for training and inference are increasingly on demand. First round
of beneficiaries are Cloud and Ad. Second round could be hardware or
something else. Although it looks like a Capitalism’s scam which is
getting more money to the big tech, as small open-source models are
released, moats are expected to be disintegrated and distributed. I’ve
seen more and more enterprises going to have Gen AI integrated to their
business or operation now. Enterprise is going to be continuously
transformed to be more efficient and productive, as well as human life
with this long lasting attention on AI. This kind of long lasting
attention and consistent innovation are something different from
internet tech in 2000 and will probably create a momentum against
bubble.
To me, the best model going forward is going to be based on the
weighted performance per parameter and training token
count. Ultimately, a model keeps getting better the longer you
train it. Most open model providers could train longer, but it hasn’t
been worth their time. We’re starting to see that change.
The most important models will represent improvements in
capability density, rather than shifting the frontier.
In some ways, it’s easier to make the model better by training
longer compared to anything else, if you have the data.
The core difference between open and closed LLMs on these charts
is how undertrained open LLMs often are. The only open
model confirmed to be trained on a lot of tokens is DBRX.
― The End of the “Best Open LLM” - Interconnects [Link]
Good analysis of the direction of open LLM development in 2023 and
2024. In 2023, models were progressing in MMLU by leveraging more
compute budgets to handle scaled active parameters and training tokens.
In 2024, the progressing direction is slightly changed to be orthogonal
to previous - which is improving on MMLU while keeping compute budgets
constant.
The companies that have users interacting with their models
consistently have moats through data and habits. The models themselves
are not a moat, as I discussed at the end of last year when I tried to
predict machine
learning moats, but there are things in the modern large language
model (LLM) space that open-source will really struggle to replicate.
Concretely, that difference is access to quality and diverse training
prompts for fine-tuning. While I want open-source to win out for
personal philosophical and financial factors, this obviously is not a
walk in the park for the open-source community. It’ll be a siege of a
castle with, you guessed it, a moat. We’ll see if the moat
holds.
― Model commoditization and product moats -
Interconnects [Link]
The goal of promoting scientific understanding for the betterment
of society has a long history. Recently I was pointed to the essay The
Usefulness of Useless Knowledge by Abraham Flexner in 1939 which
argued how basic scientific research without clear areas for profit will
eventually turn into societally improving technologies. If we want LLMs
to benefit everyone, my argument is that we need far more than just
computer scientists and big-tech-approved social scientists working on
these models. We need to continue to promote openness to support this
basic feedback loop that has helped society flourish over the last few
centuries.
The word openness has replaced the phrase open-source among most
leaders in the open AI movement. It’s the easiest way to get across what
your goals are, but it is not better in indicating how you’re actually
supporting the open ecosystem. The three words that underpin the one
messy word are disclosure (the details),
accessibility (the interfaces and infrastructure), and
availability (the distribution).
― We disagree on what open-source AI should mean -
Interconnects [Link]
The report of Google Search’s death is exaggerated so far. In fact,
search advertising has grown faster at Google than at Microsoft. User
searching behavior is harder to change than people expected. Also,
Google is leading the development of AI powered tools for Search: 1)
“circle to search” is feature allowing a search from an image, text, or
video without switching apps. 2) “Point your camera, ask a question” is
a feature allowing for multisearch with both images and text for complex
questions given an image to the tool. Overall, SGE (Search Generative
Experience) is revolutionizing search experience (“10 blue links”) by
introducing a dynamic AI-enhanced experience. So far from I observed AI
powers Google Search rather than weakens it.
Amazon’s margin expansion: AWS hit $100 B run rate with a 38%
operating margin; Ads is surging; delivery costs have been reduced.
The biggest risk is not correctly projecting demand for end-user
AI consumption, which would threaten the utilization of the capacity and
capital investments made by tech firms today. This would leave them
exposed at the height of the valuation bubble, if and when it bursts,
just like Cisco’s growth story that began to
unravel in 2000.After all, history may not repeat, but it
often rhymes.
At the Upfront Ventures confab mentioned earlier, Brian
Singerman, a partner at Peter Thiel’s Founders Fund, was asked about
contrarian areas worth investing in given the current landscape. His
response: “Anything not AI”.
― AI’s Bubble Talk Takes a Bite Out Of The Euphoria - AI
Supremacy [Link]
When we talk about investment, we talk about economic values. Current
situation of AI is very similar to Cisco’s in 2000. Cisco as an internet
company spread the capacity of the World Wide Web, but sooner people
realized that there is no economic value in internet company, instead,
opportunities are in e-commerce etc. AI is a tool very similar to web
tech. Currently, with heightened expectations, people are allocating
investments and capital expenditure in AI model development, however,
end-user demand is unclear and revenue is relatively minimal. This
situation makes AI look like a bubble from a very long term
perspective.
Steve Jobs famously said that Apple stands at the intersection of
technology and liberal arts. Apple is supposed to enhance and improve
our lives in the physical realm, not to replace cherished physical
objects indiscriminately.
― Apple’s Dystopian iPad Video - The Rational Walk
Newsletter [Link]
Key pillars of the new strategy (on gaming):
Expanding PC and cloud gaming options.
Powerful consoles (still a core part of the vision).
Game Pass subscriptions as the primary access point.
Actively bringing Xbox games to rival platforms (PS5,
Switch).
Exploring mobile gaming with the potential for handheld
hardware.
Microsoft’s “every screen is an Xbox” approach is a gamble and
may take a long time to pay off. But the industry is bound to be
device-agnostic over time as it shifts to the cloud and offers
cross-play and cross-progression. It’s a matter of when not if.
― Microsoft: AI Inflection - App Economy Insights
[Link]
Highlights: Azure’s growth accelerated sequentially thanks to AI
services and was the fastest-growing of the big three (Amazon AWS,
Google Cloud, Microsoft Azure). On Search, Microsoft is losing market
share to Alphabet. Capex on AI grows roughly 80% YoY. On gaming, it’s
diversifying approaches from selling consoles. Copilot and the Office
succeed with Enterprise customers.
To founders, my advice is to remain laser-focused on building
products and services that customers love, and be thoughtful and
rational when making capital allocation decisions. Finding
product-market fit is about testing and learning from small bets before
doubling down, and it is often better to grow slower and more
methodically as that path tends to lead to a more durable and profitable
business. An axiom that doesn’t seem to be well understood is that the
time it takes to build a company is also often its half-life.
― 2023 Annual Letter - Chamath Palihapitiya [Link]
This is a very insightful letter about how economic and tech trends
of 2023 have shaped their thinking and investment portfolio. What I have
learned from this letter:
Tech industry has shifted their focus from unsustainable “growth
at any cost” to more prudent forms of capital allocation. This results
in laying off employees and slashing projects that are not relevant to
the core business.
Rising of interest rate is one of the reasons of bank crisis.
During zero interest rate decade, banks sought higher rates of return by
purchasing longer duration assets while the value of them are negatively
correlated to interest rate. As those caused losses are known by the
public, a liquidity crisis ensued.
The advancement of Gen AI has lowered the barriers of starting a
software company, and lowered capital requirement in Bio Tech and
material sciences, and changed the process of building companies
fundamentally, and empowered new entrants to challenge established
businesses.
The key question is: where will value creation and capture take
place? when and where should capital be allocated and company should be
started? Some author’s opinions:
It’s premature to declare winners now. Instead, author suggested
people should deeply understand the underlying mechanisms that will be
responsible for value creation over next few years.
There are at least two areas of value creation now
Proprietary data
Example: recent partnership between Reddit and Google
Infrastructure used to run AI application
For apps built on top of language models, responsiveness is a
critical lynchpin. However GPUs are not well-suited to run
inference.
Example: Author’s investment in Groq’s LPU for inference
Heightened geopolitical tensions due to Russia-Ukraine conflict,
Israel and Hamas, escalating tensions between China and Taiwan, resulted
in a de-globalization trend and also a strategic shift in the US. US
legislative initiatives aims to fuel a domestic industrial renaissance
by incentivizing reshoring and fostering a more secure and resilient
supply chain. They include CHIPS Act, Infrastructure Investment, Job
Act, Inflation Reduction Act, etc.
The author highlights the opportunity for allocators and founders:
companies can creatively and strategically tap into different pools of
capital-debt, equity, and government funding.
OpenAI’s strategy to get its technology in the hands of as many
developers as possible — to build as many use cases as possible — is
more important than the bot’s flirty disposition, and perhaps even new
features like its translation capabilities (sorry).
If OpenAI can become the dominant AI provider by delivering quality
intelligence at bargain prices, it could maintain its lead for some
time. That is, as long as the cost of this technology doesn’t drop near
zero.
A tight integration with Apple could leave OpenAI with a strong
position in consumer technology via the iPhone and an ideal spot in
enterprise via its partnership with Microsoft.
― OpenAI Wants To Get Big Fast, And Four More Takeaways From
a Wild Week in AI News - Big Technology [Link]
As GPT-4o is 2x faster and 50% cheaper, this discourages competitors
to develop LLMs to compete and encourages companies to build with
OpenAI’s model for their business. This shows that OpenAI wants to get
big fast. However, making GPT-4o free disincentivizes users from
subscribing the Plus version.
There is a tight and deep bond between OpenAI and Apple. The desktop
app has been debuted on Mac and Apple will build OpenAI’s GPT Tech into
mobile iOS.
“You can borrow someone else’s stock ideas but you can’t borrow
their conviction. True conviction can only be obtained by trusting your
own research over that of others. Do the work so you know when to sell.
Do the work so you can hold. Do the work so you can stand
alone.”
Investing isn’t about blindly following the herd. It’s about
carving your own path, armed with knowledge, patience, and a relentless
pursuit of growth and learning.
― Hedge Funds’ Top Picks in Q1 - App Economy
Insights [Link]
As I’ve dug into this in more detail, I’ve become convinced that
they are doing something powerful by searching over language
steps via tree-of-thoughts reasoning, but it is much smaller of
a leap than people believe. The reason for the hyperbole is the goal of
linking large language model training and usage to the core components
of Deep RL that enabled success like AlphaGo: self-play and look-ahead
planning.
To create the richest optimization setting, having the ability to
generate diverse reasoning pathways for scoring and learning from is
essential. This is where Tree-of-Thoughts comes in. The
prompting from ToT gives diversity to the generations, which a policy
can learn to exploit with access to a PRM.
Q seems to be using PRMs to score Tree of Thoughts reasoning data
that then is optimized with Offline RL. This wouldn’t look too different
from existing RLHF toolings that use offline algorithms like DPO or ILQL
that do not need to generate from the LLM during training. The
‘trajectory’ seen by the RL algorithm is the sequence of reasoning
steps, so we’re finally doing RLHF in a multi-step fashion rather than
contextual bandits!
― The Q* hypothesis: Tree-of-thoughts reasoning, process
reward models, and supercharging synthetic data - Interconnects
[Link]
It’s well known on the street that Google DeepMind has split all
projects into three categories: Gemini (the large looming model),
Gemini-related in 6-12months (applied research), and fundamental
research, which is oddly only > 12 months out. All of Google
DeepMind’s headcount is in the first two categories, with most of it
being in the first.
Everyone on Meta’s GenAI technical staff should
spendabout 70% of the time directly on incremental
model improvements and 30% of the time on ever-green
work.
A great read
from Francois Chollet on links between prompting LLMs, word2vec, and
attention. One of the best ML posts I’ve read in a while.
Slides
from Hyung Won Chung’s (OpenAI) talk on LLMs. Great summary of
intuitions for the different parts of training. The key point: We can
get further with RLHF because the objective function is
flexible.
― The AI research job market shit show (and my experience) -
Interconnects [Link]
No concern about Apple’s earnings potential, make sense to take some
profits as value is now too high.
Right way to look at share buybacks
A business should pay dividends only if it cannot make good use of
the excess capital it has. Good use capital means the Return of Equity,
which is on average 12% for American companies. If the company is able
to allocate capital better than shareholders themselves and provide them
with above average returns, it should retain the earnings and allocate
capital itself.
Buybacks only makes sense at the right price and buying back shares
just to support stock price is not the best action ti take for
shareholders. All investment decisions should be price
dependent.
How would he invest small sums of money?
At the time of market crashes or economic downturns, you find
exceptional companies trading at ridiculously cheap prices and that’s
your opportunity, When you find those companies fairly priced or
overvalued and you look for special situations while holding onto your
positions in those exceptional companies.
Views on capital allocation
Study picking businesses, not stocks.
Investing in foreign countries
America has been a great country for building wealth and capitalist
democracy is the best system of governance ever invented.
Advice on job picking
Remember Steve Jobs’ famous words in the Stanford Commencement speech
he gave before his death: “Keep looking, don’t settle!”
On the importance of culture
In Berkshire culture, shareholders feel themselves as the owners of
the businesses. Greg Abel will keep the culture alive in the
post-Buffett period and this will automatically attract top talent to a
place where they are given full responsibility and trust.
When to sell stocks
A bigger opportunity comes up, 2. something drastically changes in
the business, and 3. to raise money
Effects of consumer behavior on investment decisions
Two types of businesses have durable competitive advantage: 1) Lowest
cost suppliers of products and services, 2) suppliers of unique products
and services.
How to live a good life? “I’ve written my obituary the way I’ve
lived my life”‘ - Charlie Munger
Primary drivers of Data Center Revenue: 1) Strong demand (up 29%
sequentially) for the Hopper GPU computing platform used for training
and inferencing with LLMs, recommendation engines, and GenAl apps, 2)
InfiniBand end-to-end solutions (down 5% sequentially due to timing of
supply) for networking. NVIDIA started shipping the Spectrum-X Ethernet
networking solutions optimized for Al.
In the earning call, three major customer categories are provided: 1)
cloud service providers (CSPs) including hyperscalers Amazon Microsoft
and Google. 2) enterprise usage: Tesla expanded training Al cluster to
35000 H100 GPUs and used NVIDIA Al for FSD V12. 3) consumer internet
companies: Meta’s Llama 3 powering Meta Al was trained on a cluster of
24000 H100 GPUs.
Huang explained in the earning call that AI is not a chip problem
only but also a systems problem now. They build AI factories.
For further growth, Blackwell platform is coming, Spectrum-X
networking is expanding, new software tools like NIMs is developing.
A lot of current research focuses on LLM architectures, data
sources prompting, and alignment strategies. While these can lead to
better performance, such developments have 3 inter-related critical
flaws-
They mostly work by increasing the computational costs of
training and/or inference.
They are a lot more fragile than people realize and don’t lead
to the across-the-board improvements that a lot of Benchmark Bros
pretend.
They are incredibly boring. A focus on getting published/getting
a few pyrrhic victories on benchmarks means that these papers focus on
making tweaks instead of trying something new, pushing boundaries, and
trying to address the deeper issues underlying these
processes.
― Revolutionizing AI Embeddings with Geometry
[Investigations] - Devansh [Link]
Very few AI research work don’t have # 1 and # 3 flaws and they are
really good hard-core work. Time is required to verify whether they are
generalizable, widely applicable or not. Especially nowadays the process
of scientific research is very different from previous years where there
was usually a decade between starting your work and publishing it.
This article highlights some publications in complex embedding and
looked into how they improved embeddings by using complex numbers.
Current challenges in embedding are 1) sensitivity to outliers 2)
limited capacity in capture complex relationship in unstructured text,
3) inconsistency in pairwise rankings of similarities, and 4)
computational cost. The next generation complex embedding is benefitting
from the following pillars: 1) complex geometry provides richer space to
capture nuanced relationships and handle outliers, 2) orthogonality
allows each dimension to be independent and distinct, 3) contrastive
learning can be used to minimize the distance between similar pairs and
maximize the distance between dissimilar pairs. Complex embeddings have
a lot of advantages: 1) increasing representation capacity with two
components (real and imaginary) of complex numbers, 2) complex geometry
allows for orthogonality and thus improves generalization, and also
allows use to reach stable convergence quickly, 3) robust features can
be captured which improves robustness, and 4) solved limitation of
cosine similarity (saturation zones which lead to vanishing gradients
during optimization) by angle optimization in complex space.
Llama 3 8B might be the most interesting all-rounder for
fine-tuning as it can be fine-tuned no a single GPU when using
LoRA.
Phi-3 is very appealing for mobile devices. A quantized version
of it can run on an iPhone 14.
― How Good Are the Latest Open LLMs? And Is DPO Better Than
PPO? [Link]
Good paper review article. Highlights key discussions:
Mixtral 8x22B: The key idea is to replace each feed-forward
module in a transformer architecture with 8 expert layers. It achieves
lower active parameters (cost) and higher performance (MMLU).
Llama 3: The main difference between Llama 3 and Llama 2 are 1)
vocab size has been increased, 2) used grouped-query attention, 3) used
both PPO & DPO. The key research finding is that the more data the
better performance, no matter what model size is.
“Llama 3 8B might be the most interesting all-rounder for fine-tuning
as it can be fine-tuned no a single GPU when using LoRA.”
Phi-3: Key characteristics are 1) it’s based on Llama
architecture, 2) trained on 5x fewer tokens than Llama 3, 3) used the
same tokenizer with a vocab size of 32064 as Llama2, much smaller than
Llama 3 vocab size, 4) has only 3.8B parameters, less than half the size
of Llama 3 8B, 5) secret sauce is dataset quality over quantity - it’s
trained on heavily filtered web data and synthetic data.
“Phi-3 is very appealing for mobile devices. A quantized version of
it can run on an iPhone 14.”
OpenELM: key characteristics are 1) 4 relatively small sizes:
270M, 450M,1.1B, and 3B, 2) instruct version trained with rejection
sampling and DPO, 3) slightly better than OLMo in performance, even
though trained on 2x fewer tokens, 4) main architecture teak - a
layer-wise scaling strategy, 5) sampled a relatively smaller subset of
1.8T tokens from various public datasets, but no clear rationale for
subsampling, 6) one main research finding is that there is no clear
difference between LoRA and DoRA for parameter efficient
fine-tuning.
About the layer-wise scaling strategy: 1) there are N transformer
blocks in a model, 2) layers are gradually widened from the early to the
later transformer blocks, so for each block: a) number of heads are
increased, b) dimension of each layer is increased.
DPO vs PPO: The main difference between DPO and PPO is that “DPO
does not require training a separate reward model but uses a
classification-like objective to update LLM directly”.
Key findings of the paper and best practices suggested: 1) PPO is
generally better than DPO if you use it correctly. DPO suffers from
out-of-distribution data, which means instruction data is different from
preference data. The solution could be to “add a supervised instruction
fine-tuning round on the preference dataset before following up with DPO
fine-tuning.”, 2) If you use DPO, make sure to perform SFT on preference
data first, 3) “iterative DPO which involves labeling additional data
with an existing reward model is better than DPO on existing preference
data.”, 4) “If you use PPO, the key is to use large batch sizes,
advantage normalization, and parameter update via exponential moving
average.”, 5) though PPO is generally better, DPO is more
straightforward and will still be a popular go-to option, 6) both can be
used. Recall the pipeline behind Llama3: pretraining -> SFT ->
rejection sampling -> PPO -> DPO.
Google I/O AI keynote updates 2024 - AI Supremacy
[Link]
Musings on building a Generative AI product - LinkedIn
Engineering Blog [Link]
This is a very good read about developing Gen AI product for business
by using pre-trained LLM. This article elaborates how this product is
designed, how each part works specifically, what works and what does not
work, what has been improving, and what has been struggling. Some
takeaways for me are
Supervised fine tuning step was done by embedding-based retrieval
(EBR) powered by an in-memory database to inject response examples into
prompts.
An organizational structure was designed to ensure communication
consistency: one horizontal engineering pod for global templates and
styles, and several vertical engineering pods for specific tasks such as
summarization, job fit assessment, interview tips, etc.
Tricky work:
Developing end to end automatic evaluation pipeline.
Skills in dynamically discover and invoke APIs / agents.
This requires input and output to be ‘LLM friendly’ - JSON or YAML
schemes.
Supervised fine tuning by injected responses of internal
database.
As evaluation becoming more sophisticated, prompt engineering needs
to be improved to reach high quality/evaluation scores. The difficulty
is that quality scores shoot up fast then plateau so it’s hard to reach
a very high score in the late improvement stage. This makes prompt
engineering more like an art rather than science.
Tradeoff of capacity and latency
Chain of Thoughts can improve quality and accuracy of responses, but
increase latency. TimeToFirstToken (TTFT) & TimeBetweenTokens (TBT)
are important to utilization but need to be bounded to limit latency.
Besides, they also intend to implement end to end streaming and async
non-blocking pipeline.
The concept of open source was devised to ensure developers could
use, study, modify, and share software without restrictions. But AI
works in fundamentally different ways, and key concepts don’t translate
from software to AI neatly, says Maffulli.
But depending on your goal, dabbling with an AI model could
require access to the trained model, its training data, the code used to
preprocess this data, the code governing the training process, the
underlying architecture of the model, or a host of other, more subtle
details.
Which ingredients you need to meaningfully study and modify
models remains open to interpretation.
both Llama 2 and Gemma come with licenses that restrict what
users can do with the models. That’s anathema to open-source principles:
one of the key clauses of the Open Source Definition outlaws the
imposition of any restrictions based on use cases.
All the major AI companies have simply released pretrained
models, without the data sets on which they were trained. For people
pushing for a stricter definition of open-source AI, Maffulli says, this
seriously constrains efforts to modify and study models, automatically
disqualifying them as open source.
― The tech industry can’t agree on what open-source AI means.
That’s a problem. ― MIT Technology Review [Link]
This article argues that the definitions of open-source AI are
problematic. ‘Open’ models either have restriction on usage or don’t
release details of training data. This does not fit traditional
definition of ‘open source’. However, people argue that for the special
case of AI, we need different definition of open source. As long as the
definition remains vague, it’s problematic, because big tech will define
open-source AI to be what suits it.
A backdoor in xz-utils (used for lossless compression) was recently
revealed by Andres Freund (Principle SDE at Microsoft). The backdoor
only shows up when a few specific criteria are met at least: 1) running
a distro that uses glibc, 2) have version 5.6.0 or 5.6.1 xz installed or
liblzma installed. There is a malicious script called
build-to-host.m4 which checks for various conditions like
the architecture of the machine. If those conditions check, the payload
is injected into the source tree. The intention of payload is still
under investigation. Lasse Collin, one of the maintainer of the repo,
has posted an update and
is working on carefully analyzing the situation. The author Evan Boehs
in the article present a timeline of the attack and online
investigators’ discoveries of Jia Tan identity (from IP address,
LinkedIn, commit
timings, etc), and raises our awareness of the human costs of open
source.
Having a crisp mental model around a problem, being able to break
it down into steps that are tractable, perfect first-principle thinking,
sometimes being prepared (and able to) debate a stubborn AI — these are
the skills that will make a great engineer in the future, and likely the
same consideration applies to many job categories.
― Why Engineers Should Study Philosophy ― Harvard Business
Review [Link]
Human comes into a new stage of learning: smartly asking AI questions
to get answers as accurate as possible. So prompt engineering is a very
important skill in AI era. In order to master prompt engineering, we
need to have divide and conquer mindset, perfect first-principle
thinking, critical thinking, and skepticism.
If we had infinite capacity for memorisation, it’s clear the
transformer approach is better than the human approach - it truly is
more effective. But it’s less efficient - transformers have to store so
much information about the past that might not be relevant. Transformers
(🤖) only decide what’s relevant at recall time. The
innovation of Mamba (🐍) is allowing the model better ways of forgetting
earlier - it’s focusing by choosing what to discard using
Selectivity, throwing away less relevant information at
memory-making time.
A very in-depth explanation of Mamba architecture. So the main
difference between Transformer and Mamba is that Transformer stores all
past information and decides what is relevant at recall time. While
Mamba uses Selectivity to decide what to discard earlier. Mamba ensures
both efficiency and effectiveness (space complexity reduces from O(n) to
O(1), time complexity reduces from O(n^2) to O(n)). If Transformer has
high effectiveness and low efficiency due to large state, and RNN has
high efficiency and low effectiveness due to small state, Mamba is in
between - Mamba selectively and dynamically compress data into the
state.
The Power of Prompting ― Microsoft Research Blog [Link]
Basically this study demonstrates that GPT-4 is able to outperform a
leading model that was fine-tuned specifically for medical application
by Medprompt - a composition of several prompting strategies. This shows
that fine-tuning might not be necessary in the future though it can
boost performance, it is resource-intensive and cost-prohibitive. Simple
prompting strategies could serve to transform generalist models into
specialists and extending benefits of models to new domains and
applications. Similar study was also done in finance domain by JP Morgan
with similar results.
Previously, we made some progress matching patterns of neuron
activations, called features, to human-interpretable concepts. We used a
technique called “dictionary learning”, borrowed from classical machine
learning, which isolates patterns of neuron activations that recur
across many different contexts.
In turn, any internal state of the model can be represented in
terms of a few active features instead of many active neurons. Just as
every English word in a dictionary is made by combining letters, and
every sentence is made by combining words, every feature in an Al model
is made by combining neurons, and every internal state is made by
combining teatures.
The features are likely to be a faithful part of how the model
internally represents the world, and how it uses these representations
in its behavior.
― Mapping the Mind of a Large Language Model -
Anthropic [Link]
This is an amazing work towards AI safety by Anthropic. The main goal
is to understand the inner workings of AI models and identify how
millions of concepts are represented inside Claude Sonnet, so that
developers can better control AI safety. Previous progress of this work
was to match pattern of neuron activations (“features”) to
human-interpretable concepts by technique called “dictionary learning”.
Now they are scaling up the technique to the vastly larger AI language
models. Below is a list of key experiments and findings.
Extracted millions of features from the middle layer of Claude 3.0
Sonnet. Features have a depth, breadth, and abstraction reflecting
Sonnet’s advanced capabilities.
Find more abstract features - responding to bugs in code, discussion
of gender biases in professions, etc.
Measure a “distance” between features based on which neurons
appeared in their activation patterns. They find that features with
similar concept are close to each other. This demonstrates internal
organization of concepts in AI model correspond to human notions of
similarity.
By artificially amplifying or suppressing features, they see how
Claude’s responses change. This shows that features can be used to
change how a model acts.
For the purpose of AI safety, they find features corresponding to
the capabilities with misuse potential (code backdoors, developing
bio-weapons), different forms of biases (gender discrimination, racist
claims about crime), and potentially problematic AI behavior
(power-seeking, manipulation, secrecy)
For previous concern about sycophancy, they also find a feature
associated with sycophantic praise.
This study proposed a good approach to ensure AI safety: use the
technique described here to monitor AI systems for dangerous behaviors
and to debias outcomes.
To qualify as a “Copilot+ PC” a computer needs distinct CPUs,
GPUs, and NPUs (neural processing units) capable of >40 trillion
operations per second (TOPS), and a minimum of 16 GB RAM and a 256 GB
SSD.
All of those analysts who assumed Wal-Mart would squish Amazon in
e-commerce thanks to their own mastery of logistics were like all those
who assumed Microsoft would win mobile because they won PCs. It turns
out that logistics for retail are to logistics for e-commerce as
operating systems for a PC are to operating systems for a phone. They
look similar, and even have the same name, but require fundamentally
different assumptions and priorities.
I then documented a few seminal decisions made to demote windows,
including releasing Office on iPad as soon as he took over, explicitly
re-orienting Microsoft around services
instead of devices, isolating the Windows organization from the rest
of the company, killing Windows Phone, and finally, in the decision that
prompted that Article, splitting up Windows itself. Microsoft was
finally, not just strategically but also organizationally, a services
company centered on Azure and Office; yes, Windows existed, and still
served a purpose, but it didn’t call the shots for the rest of
Microsoft’s products.
That celebration, though, is not because Windows is
differentiating the rest of Microsoft, but because the rest of Microsoft
is now differentiating Windows. Nadella’s focus on AI and the company’s
massive investments in compute are the real drivers of the business,
and, going forward, are real potential drivers of Windows.
This is where the Walmart analogy is useful: McMillon needed to
let e-commerce stand on its own and drive the development of a
consumer-centric approach to commerce that depended on centralized
tech-based solutions; only then could Walmart integrate its stores and
online services into an omnichannel solution that makes the company the
only realistic long-term rival to Amazon.
Nadella, similarly, needed to break up Windows and end Ballmer’s
dreams of vertical domination so that the company could build a
horizontal services business that, a few years later, could actually
make Windows into a differentiated operating system that might, for the
first time in years, actually drive new customer acquisition.
Chatbot Arena results are in: Llama 3 dominates the upper and
mid cost-performance front (full analysis) ― Reddit [Link]
Efficiently fine-tune Llama 3 with PyTorch FSDP and
Q-Lora [Link]
YouTube and Podcasts
I don’t have an answer to peace in the Middle East, I wish I did,
but I do have a very strong view that we are not going to get to peace
when we are apologizing or denying crimes against humanity and crime
mass rape of women. That’s not the path to peace, the path to peace is
not saying this didn’t happen, the path to peace is saying this happened
no matter what side of the fence you are on no matter what side of the
world you are on, if you are the far right the far left, anywhere on the
world, we are not going to let this happen again and we are going to get
to peace to make sure. - Sheryl Sandberg
― In conversation with Sheryl Sandberg, plus open-source AI
gene editing explained - All-In Podcast [Link]
U.N. to Study Reports of Sexual Violence in Israel During Oct. 7
Attack [Link]
Western media concocts ‘evidence’ UN report on Oct 7 sex crimes
failed to deliver [Link]
It’s crazy that what is happening right now in some of the colleges
is not to protest sexual violence as a tool of war by Hamas. This kind
of ignorance or denial of sexual violence is horrible. People are so
polarized to black and white that if something does not fit into their
view, they are going to reject it. There are more than two sides to the
Middle East story, one of them is sexual violence - mass rape, genital
mutilation of men and women, women tied to trees naked bloody leg
spread…
There is a long history of the involvement of women’s bodies in Wars.
It’s only 30 years ago, people started to say rape is not a tool of War
and should be prosecuted as a war crime against humanity. The feminist,
human rights, and civil rights groups made this happen. Now it happened
again in Gaza according to the report released by U.N., however there
are a lot difficulties in proving and testifying the truth e.g. they
couldn’t locate a single victim, or they don’t have the victim rights to
take pictures. But victims are dead and they cannot speak up. Denying
the fact of sexual violence is just unacceptable. And there is such a
great documentary
shedding lights on the unspeakable sexual violence committed on Oct 7,
2023 that I think everyone should watch.
Good news is that the testimony of eyewitness meets the criteria of
any international or global court. So crimes can be proven by any
eyewitness for sure.
John Schulman - Reinforcement Learning from Human Feedback:
Progress and Challenges [Link]
John Schulman is a research scientist and cofounder of OpenAI,
focusing on Reinforcement Learning (RL) algorithms. He gave a talk on
making AI more truthful on Apr 24, 2023 in UCB. The ideas and
discussions are still helpful and insightful today.
In this talk, John discussed the issue of hallucination with large
language models. He claims that behavior cloning or supervised learning
is not enough to fix the hallucination problem, instead, reinforcement
learning from human feedback (RLHF) can help improve the model’s
truthfulness by 1) adjusting output distribution so model is allowed to
express uncertainty, challenge premise, admit error, and 2) learning
behavior boundaries. In his conceptual model, fine-tuning leads the
model to hallucinate when it lacks knowledge. Retrieval and citing
external sources can help improve verifiability. John discusses models
that can browse the web to answer technical questions, citing relevant
sources.
John mentioned three open problems in LLM: 1) how to train models to
express uncertainty in natural language, 2) go beyond what human
labelers can easily verify (“scalable oversight”), and 3) optimizing for
true knowledge rather than human approval.
The 1-Year Old AI Startup That’s Rivaling OpenAI — Redpoint’s
AI Podcast [Link]
A great interview with the CEO of Mistral Arthur Mensch on the topic
of sovereignty and open models as a business strategy. Here are some
highlighted points from Arthur:
Open-source is going to solidify in the future. It is an
infrastructure technology and at the end of the day it should be
modifiable and owned by customers. Now Mistral has two offerings, open
source one and commercial one, and the aim is to find out the business
model to sustain the open source development.
The things that Mistral is best at 1) training model, and 2)
specializing models.
The way they think about partnership strategy is to look at what
enterprises would need, where they were operating, where the developers
were operating, and figure out the channels that would facilitate
adoption and spread. To be a multiplatform solution and to replicate the
solution to different platforms is a strategy that Mistral is
following.
There is still an efficiency upper bound to be pushed. Other than
compute to spend on pre-training, there is still research to do on
improving model efficiency and strength. On architecture side, we can be
more efficient than plain Transformer which spends same amount of
compute on every token. Mistral is making model faster. By making model
faster, we open up a lot of applications that involve an LLM as a basic
brick and then we can figure out how to do planning, explorations, etc.
By increasing efficiency, we open up areas of research.
Meta has more GPUs than Mistral do. But Mistral has a good
concentration of GPU (number of GPU per person). This is the way to be
as efficient as possible to come up with creative ways of training
models. Also unit economics need to be considered to make sure that
\(\$1\) that you spend on training
compute eventually accrues to more than \(\$1\) revenue.
Transformer is not an optimal architecture. It’s been out there for
7 years now. Everything is co-adapted to it such as training methods,
debug methods, the algorithms, and hardware. It’s challenging to find a
better one and also beat the baseline. But there are a lot of research
on modification of attention to boost memory efficiencies and a lot of
things can be done in that direction and similar directions.
About AI regulations and EU AI Act, Arthur states that it does not
solve the actual problem of how to make AI safe. Because making AI safe
is a hard problem (stochastic model), different from the way we evaluate
software before. It’s more like a product problem rather than a
regulation problem. We need to rethink continuous integration,
verifications, etc and make sure everything is happening as it should
be.
Mistral recently released Le Chat to help enterprise start
incorporating AI. It gives an assistant that is contextualized on their
enterprise data. It’s a tool to be closer to the end user to get
feedback for the developer platform and also a tool to get the
enterprise into GenAI.
Open Source AI is AI we can Trust — with Soumith Chintala of
Meta AI [Link]
Synthetic data is the next rage of LLM. Soumith pointed out that
synthetic data is where we as humans already have good symbolic models
off, we need to impart that knowledge to neural networks, and we figured
out the synthetic data is a vehicle to impart this knowledge to it.
Related to synthetic data but in an unusual way, there is new research
on distilling GPT-4 by creating synthetic data from GPT-4, creating mock
textbooks inspired by Phi-2 and then fine tuning open source models like
Lambda.
Open source means different things to different people and we haven’t
had a community norm definition yet at this very early stage of LLM.
When being asked about open source, people in this field are used to
highlight the definition of it in advance. In the open source topic,
Soumith pointed out that the most beneficial value of open is it makes
the distribution very wide and available with no friction so that people
can do transformative things in a way that is very accessible.
Berkshire Hathaway 2024 Annual Meeting Movie: Tribute to
Charlie Munger [Link]
First year that the annual meeting movie is made public. First year
that the annual meeting is without Charlie. Already started to miss his
jokes.
I think the reason why the car could have been completely
reimagined by Apple is that they have a level of credibility and trust
that I think probably no other company has, and absolutely no other tech
company has. I think this was the third Steve Jobs story that I left out
but in 2001, I launched a 99 cent download store and Steve Jobs just ran
total circles around us, but the reason he was able to is he had all the
credibility to go to the labels and get deals done for licensing music
that nobody could get done before. I think that is an example of what
Apple’s able to do which is to use their political capital to change the
rules. So if the thing that we could all want is safer roads and
autonomous vehicles, there are regions in every town and city that could
be completely converted to level 5 autonomous zones. If I had to pick
one company that had the credibility to go and change those rules, it’s
them. Because they could demonstrate that there was a methodical safe
approach to doing something. So the point is that even in these
categories that could be totally reimagined, it’s not for a lack of
imagination, again it just goes back to a complete lack of will. I
understand because if you had 200B dollars of capital on your balance
sheet, I think it’s probably easy to get fat and lazy. - Chamath
Palihapitiya
― In conversation with Sam Altman — All-In Podcast
[Link]
If you are a developer, the key thing to understand is where does
model innovation end and your innovation begin, because if you get that
wrong you will end up doing a bunch of stuff that the model will just
obsolete in a few months. - David Sacks
The incentive for these folks is going to be push this stuff into
the open source. Because if you solve a problem that’s operationally
necessary for your business but it isn’t the core part of your business,
what incentive do you have to really keep investing in this for the next
5 to 10 years to improve it. You are much better off release it in the
open source, let the rest of the community take it over so that it’s
available to everybody else, otherwise you are going to be stuck
supporting it, and then if and when you ever wanted to switch out a
model, GPT-4o, Claude, Llama, it’s going to be costly. The incentive to
just push towards open source in this market if you will is so much
meaningful than any other market. - Chamath Palihapitiya
I think the other thing that is probably true is a big measure at
Google on the search page in terms of search engineer performance was
the bounceback rate, meaning someone does a search, they go off to
another site and they come back because they didn’t get the answer they
wanted. Then one box launched which shows a short answer on the top,
which basically keeps people from having a bad search experience,
because they get the result right away. So a key metric is they are
going to start to discover which vertical searches will provide the user
a better experience than them jumping off to a third party page to get
the same content. And then they will be able to monetize that content
that they otherwise were not participating in the monetization of. So I
think the real victim in all this is that long tale of content on the
internet that probably gets cannibalized by the snippet one box
experience within the search function. And then I do think that the
revenue per search query in some of those categories actually has the
potential to go up not down. You keep people on the page so you get more
search volume there, you get more searches because of the examples you
gave. And then when people do stay, you now have the ability to better
monetize that particular search query, because you otherwise would have
lost it to the third party content page. Keeping more of the experience
integrated they could monetize the search per query higher and they are
going to have more queries, and then they are going to have the quality
of the queries go up. Going back to our earlier point about precision vs
accuracy, my guess is there’s a lot of hedge fund type folks doing a lot
of this Precision type of analysis trying to break apart search queries
by vertical and try to figure out what the net effect will be of having
better AI driven box and snippets. And my guess is that is why there is
a lot of buying activity happening. I can tell you Meta and Amazon do
not have an Isomorphic Lab and Waymo sitting inside their business, that
suddenly pops to a couple hundred billion of market cap and Google does
have a few of those. - David Friedberg
One thing I would say about big companies like Google or
Microsoft is that the power of your monopoly determines how many
mistakes you get to make. So think about Microsoft completely missed
iPhone, remember they screwed up the whole smartphone era and it didn’t
matter. Same thing here with Google, they completely screwed up AI. They
invented the Transformer, completely missed LLMs. Then they had that
fiasco where they have black George Washington. It doesn’t matter, they
can make 10 mistakes but their monopoly is so strong, that they can
finally get it right by copying the innovator, and they are probably
going to be come 5T dollar company. - David Sacks
― GPT-4o launches, Glue demo, Ohalo breakthrough,
Druckenmiller’s bet, did Google kill Perplexity? — All-In
Podcast [Link]
Great conversations and insightful discussions as usual. Love it.
When you are over earning so massively, the rational thing to do
for other actors in the arena is to come and attack that margin, and
give it to people for slightly cheaper slightly faster slightly better
so you can take share. So I think what you’re seeing and what you will
see even more now is this incentive for Silicon Valley who has been
really reticent to put money into chips, really reticent to put money
into hardware. They are going to get pulled into investing this space
because there is no choice. - Chamath Palihapitiya
Why? It’s not that intel was a worse company, but it’s that
everything else caught up. And the economic value went to things that
sat above them in the stack, then it want to Cisco for a while right,
then after Cisco, it went to the browser companies for a little bit,
then it went to the app companies, then it went to the device companies,
then it went to the mobile companies. So you see this natural tendency
for value to push up the stack over time. For AI, we’ve done the step
one which is now you’ve given all this value to NVIDIA and now we are
going to see it being reallocated. - Chamath Palihapitiya
The reason why they are asking these questions is that if you go
back to the doom dot come boom in 1999, you can see that Cisco had this
incredible run. And if you overlay the stock price of Nvidia, it seems
to be following that same trajectory. And what happened with Cisco is
that when the doc come crash came in 2000, Cisco stock lost a huge part
of its value. Obviously Cisco is still around today and it’s a valuable
company, but it just hasn’t ever regained the type of market cap it had.
The reason this happened is because Cisco got commoditized. So the
success and market cap of that company attracted a whole bunch of new
entrance and they copied Cisco’s products until they were total
commodities. So the question is whether that happened to Nvidia. I think
the difference here is that at the end of the day Network equipment
which Cisco produced was pretty easy to copy, whereas if you look at
Nvidia, these GPU cores are really complicated to make. So it’s a much
more complicated product to copy. And then on top of that, they are
already in the R&D cycle for the next chip. So I think you can make
the case that Nvidia has a much better moat than Cisco. - David
Sacks
I think Nvidia is going to get pulled into competing directly
with the hyperscalers. So if you were just selling chips, you probably
wouldn’t, but these are big bulky actual machines, then all of a sudden
you are like well why don’t I just create my own physical plant and just
stack these things, and create racks and racks of these machines. It’s
not a far stretch especially because Nvidia actually has the software
interface that everybody uses which is CUDA. I think it’s likely that
Nvidia goes on a full frontal assault against GCP and Amazon and
Microsoft. That’s going to really complicate the relationship that those
folks have with each other, but I think it’s inevitable because how do
you defend an enormously large market cap, you are forced to go into
businesses that are equally lucrative. Now if I look inside of compute
and look at the adjacent categories, they are not going to all of a
sudden start a competitor to TikTok or a social network, but if you look
at the multi hundred billion revenue businesses that are adjacent to the
markets that Nvidia enables, the most obvious ones are the hyperscalers.
So they are going to be forced to compete otherwise their market cap
will shrink and I don’t think they want that, and then it’s going to
create a very complicated set of incentives for Microsoft and Google and
Meta and Apple and all the rest. And that’s also going to be an
accelerant, they are going to pump so much money to help all of these
upstarts. - Chamath Palihapitiya
Economy is bad without recognizing that it is an inflationary
experience whereas economists use the definition of “economic growth”
being gross product, and so if gross product or gross revenue is going
up they are like oh the economy is healthy we are growing. But the truth
is we are funding that growth with leverage at the national level the
federal level and at the household a domestic level. We are borrowing
money to inflate the revenue numbers , and so the GDP goes up but the
debt is going higher, and so the ability for folks to support themselves
and buy things that they want to buy and continue to improve their
condition in life has declined if things are getting worse… The average
American’s ability to improve their condition has largely been driven by
their ability to borrow not by their earnings. - David
Friedberg
Scarlett Johansson vs OpenAI, Nvidia’s trillion-dollar
problem, a vibecession, plastic in our balls [Link]
It’s a fun session and it made my day :). Great discussions about
Nvidia’s business, America’s negative economic sentiment, harm of
plastics, etc.
This is a must-read paper if you would like to have a comprehensive
overview of SOTA LLMs, technical details, applications, datasets,
benchmarks, challenges, and future directions.
Little Guide to Building Large Language Models in 2024 -
HuggingFace [Link]
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial
Text Analytics? A Study on Several Typical Tasks [Link]
Bloomberg fine-tuned GPT-3.5 on their financial data only to find
that GPT-4 8k, without specialized finance fine-tuning, beat it on
almost all finance tasks. So there is really a moat? Number of
parameters matters and data size matters, and they all require compute
and money.
Jamba: A Hybrid Transformer-Mamba Language Model [Link] [Link]
Mamba paper
has been rejected while fruits are reaped fast: MoE-Mamba, Vision Mamba, and Jamba.
It’s funny to see the asymmetric impact in ML sometimes, e.g.
FlashAttention has <500 citations and is used everywhere. Github
repos used by 10k+ has <100 citations, etc.
This is a mathematically beautiful idea. The main difference between
traditional MLP and KAN is that KAN has learnable activation function on
weights, so all weights in KAN are non-linear. KAN outperforms MLP in
accuracy and interpretability. Whether in the future KAN is able to
replace MLP depends on whether there could be suitable learning
algorithms like SGD, AdamW, etc and whether it will be GPU friendly.
Interesting paper to read if you like philosophy. This paper argues
that there is a platonic representation as a result of convergence of AI
models towards a shared statistical model of reality. They show that
there is a growing similarity in data representation across different
model architectures, training objectives, and data modalities, as the
model size, data size, and task diversity are growing. They also
proposed three hypothesis for the representation convergence: 1) The
multitask scaling hypothesis, 2) The capacity hypothesis, and 3) The
simplicity bias hypothesis. And it definitely worths reading the
counterexamples and limitations.
Frontier Safety Framework - Google DeepMind [Link]
DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Model [Link]
One main improvement: Multi-head latent attention via compressed
latent KV requires smaller amount of KV cache per token but achieves
stronger performance. Heads can be compressed differently (taking
different portion of compressed latent states), and keys and values can
be compressed differently.
What matters when building vision-language models
[Link]
The Unreasonable Ineffectiveness of the Deeper
Layers [Link]
RecurrentGemma: Moving Past Transformers for Efficient Open
Language Models [Link]
This paper published by Google DeepMind proposes language model
called RecurrentGemma
that can match or exceed the performance of transformer-based models
while being more memory efficient.
Towards Responsible Development of Generative AI for
Education: An Evaluation-Driven Approach - Google’s Tech Report of
LearnLM [Link]
Chameleon: Mixed-Modal Early-Fusion Foundation
Models [Link]
This paper published by Meta proposed a mixed model which uses
Transformer architecture under the covers but applies some innovations
such as query-key normalization to fix the imbalance between the text
and image tokens and other innovations as well.
Simple and Scalable Strategies to Continually Pre-train Large
Language Models [Link]
Tricks for successful continued pretraining:
Re-warming and re-decaying the learning rate.
Adding a small portion (e.g., 5%) of the original pretraining data
(D1) to the new dataset (D2) to prevent catastrophic forgetting.
Note that smaller fractions like 0.5% and 1% were also effective.
Cautious about their validity on model with larger sizes.
Is DPO Superior to PPO for LLM Alignment? A Comprehensive
Study [Link]
Intel Corp has committed \(\$28\)B
to build a “mega fab” called Ohio One which could be the biggest chip
factory on Earth. The Biden administration has agreed to provide Intel
with \(\$19.5\)B in loans and grants to
support finance the project.
EveryONE Medicines: Designing Drugs for Rare Diseases, One at
a Time [Link]
Startup EveryONE Medicine aims to develop drugs designed based on
genetic information for individual children who have rare,
life-threatening neurological diseases. Since the number of patients
with diseases caused by rare mutation is significant, the market share
is large if EveryONE can scale its process. Although the cost won’t be
the same as a standard drugmaker that runs large clinical trials, the
challenge is safety without a standard clinical-testing protocol. To be
responsible to patients, the initial drugs will have a temporary effect
and a wide therapeutic window, so the potential toxicity will be
minimized or stopped if there is.
Voyager 1’s Communication Malfunctions May Show the
Spacecraft’s Age [Link]
In Nov 2023, NASA’s over 46-year-old Voyager 1 spacecraft started
sending nonsense to Earth. Voyager 1 was initially intended to study
Jupiter and Saturn and was built to survive only 5 years of flight,
however the trajectory was forged further and further into space and so
the mission converted from a two-planet mission to an interstellar
mission.
In Dec 2023, the mission team restarted the Flight Data Subsystem
(FDS) but failed to return the subsystem to functional state. On Mar 1
2023, they sent a command “poke” to the probe and received a response on
Mar 3. On Mar 10, the mission team finally determined the response
carried a readout of FDS memory. By comparing the readout with those
received before the issue, the team confirmed that 3% of FDS memory was
corrupted. On Apr 4, the team concluded the affected code was contained
on a computer chip. To solve the problem, the team decided to divide
these affected code into smaller sections and to insert those smaller
sections into other operative places in the FDS memory. During Apr
18-20, the team sent out the orders to move some of the affected code
and received responses with intelligible systems information.
Berkeley based startup Profluent Bio used an AI based protein
language model to create and train on an entirely new library of Cas
proteins that do not exist in nature today and eventually find one
called ‘OpenCRISPR-1’ that is able to replace or improve the ones that
are on the market today. The goal of this AI model is to learn what
sequence of DNA generated what structure of protein that’s really good
at gene editing. The new library of Cas proteins is created by
simulation of trillions of letters. They made ‘OpenCRISPR-1’ publicly
available under an open source license so anyone can use this particular
Cas protein.
Sony and Apollo in Talks to Acquire Paramount [Link]
Paramount’s stock declined 44% in 2022 and another 12% in 2023. It’s
experiencing declining revenue as consumers abandon traditional pay-TV
and it’s losing streaming business. Berkshire sold its entire Paramount
shares in March 2023 and soon Sony Pictures and Apollo Globals
Management reached out to Paramount board expressing interest of
acquisition. Now Paramount decided to open negotiation with them after
exclusive talks with Hollywood studio Skydance. This deal would break
the Paramount and potentially transform the media landscape if
successful. Otherwise an office of the CEO as the replacement of CEO Bob
Bakish will be preparing a long term plan for the company.
AlphaFold 3 predicts the structure and interactions of all of
life’s molecules [Link]
Previously, Google DeepMind AlphaFold project took 3D images of
proteins and the DNA sequence that codes for those proteins and then
they built a predictive model that predicted the 3D structure of protein
base on DNA sequence. What is difference in AlphaFold 3 is that all
small molecules are included. The way how small molecules are bind
together with the protein is part of the predictive model. This is a
breakthrough in that off target effect could be minimized by taking
consideration of other molecules’ interactions in the biochemistry
environment. Google has a drug development subsidiary called Isomorphic
Labs. They kept all of IP for AlphaFold 3. They published a web viewer
for non-commercial scientists to do fundamental research but only
Isomorphic Labs can make it for commercial use.
Introducing GPT-4o and making more capabilities available for
free in ChatGPT [Link]
I missed the live announcement but watched the recording. GPT-4o is
amazing.
One of the interesting technical difference made is tokenizer delta.
GPT-4 and GPT-4-Turbo both had a tokenizer with a vocabulary of 100k
tokens. GPT-4o has a tokenizer with 200k tokens to work better for
native multimodality and multilingualism. The more tokens the more
efficient in generating characters.
“Our goal is to make it effortless for people to go anywhere and
get anything,” said Dara Khosrowshahi, CEO of Uber. “We’re excited that
this new strategic partnership with Instacart will bring the magic of
Uber Eats to even more consumers, drive more business for restaurants,
and create more earnings opportunities for couriers.”
― Uber
Eats to Power Restaurant Delivery on Instacart [Link]
Project Astra: Our vision for the future of AI
assistants [Link]
This developer conference is about Google’s AI related product
updates. Highlighted features: 1) AI Overview for search 2) Ask Photos,
3) 2M context window, 4) Google Workspace, 5) NotebookLM, 6) Project
Astra, 7) Imagen 3, 8) Music AI Sandbox, 9) Veo, 10) Trillium TPU, 11)
Google Serach, 12) Asking Questions with Videos, 13) Gemini interacting
with Gmail and data, 14) Gemini AI Teammate, 15) Gemini App, and
upgrades, 16) Gemini Trip Planning.
Leike went public with some reasons for his resignation on Friday
morning. “I have been disagreeing with OpenAI leadership about the
company’s core priorities for quite some time, until we finally reached
a breaking point,” Leike wrote in a series of posts on X. “I believe
much more of our bandwidth should be spent getting ready for the next
generations of models, on security, monitoring, preparedness, safety,
adversarial robustness, (super)alignment, confidentiality, societal
impact, and related topics. These problems are quite hard to get right,
and I am concerned we aren’t on a trajectory to get there.”
― OpenAI created a team to control ‘superintelligent’ AI —
then let it wither, source says [Link]
Other News:
Encampment Protesters Set Monday Deadline for Harvard to
Begin Negotiations [Link]
Israel Gaza war: History of the conflict explained
[Link]
Cyber Stuck: First Tesla Cybertruck On Nantucket Has A Rough
Day [Link]
Music teacher never answered my question: why should Triangle be
included in a piece of music while there are already 10+ instruments and
sounds loud in there? I accidentally got the answer from my dance
teacher. She said: “different people have different hearing capabilities
and thus different understanding of music, what dancers are doing is
actually to interpret or reproduce music.”
Back to the topic:
There is always a lot to learn about strategic thinking from Zuck.
Here are some of his smart strategies behind open source I’ve
learned:
According to this interview, Zuck’s
point of open source is to avoid concentration of AI while he didn’t
ignore the harmful consequences of open source saying that it’s our
responsibility to do a good job of reducing harm. There are several
benefits of open source, one is that people could figure out cheaper
ways to develop models so it won’t cost too much resource. The other
benefit is that they could enable more efficient developments and
vertical use cases in a lot of different systems. Take Google and Apple
for example, their mobile ecosystems restricted what developers could
build or what features they could launch on them.
For companies like Meta with well-established network effect,
they really don’t need to have the best model. AI’s content creation
potential benefits Meta’s platforms, even if the models are not
exclusively theirs. This is the most reasonable reason from business
perspective and was stated in an earnings
call.
By open-sourcing models, Meta started developer communities which
can contribute to whatever the ecosystem Meta built and help solidify
the advantage of it. Most recent example is the open model of Horizon OS
which powers its VR headsets. It allows developers and creators to take
advantage of these technologies to create MR experiences and grow
business on it. Then Meta Quest Store can be quickly
established.
Models themselves are not a moats. Moats are built through data
and habits. Open source eventually makes economic value of foundational
model disintegrated. There will be no value in foundational model
economically and there is probably less point for VC to plow billions of
dollars into a foundational model development startup. The potential
economic values are in 100K+ developers iteratively and quickly training
and deploying the open source models for specific business use cases.
Inference will be way more important than training. So attention and
money will be less concentrated to products like OpenAI GPT series and
Nvidia training GPU but more on Cloud platforms with inference GPU for
personal and business usage.
I have read a lot of news and articles, and watched a few interviews
regarding AI safety these days. The future of AI is promising. People
are working towards more powerful AI and personalized AI assistants or
agents. At the same time, AI causes problems. A very obvious downside of
AI is people could use AI to do harmful things, but I think we are good
to work together and prevent that from different angles such as legal
aspect or open source software. I worry more about things that are not
easily avoided and cannot be seen at least in these years when AI is
still immature - which is being too early to rely on AI and thus
deviating from truth. For example, it is too soon for us to lose faith
in jobs like teachers, historians, journalists, writers, etc, but I’m
concerned we are already losing faith in those jobs because of the
development of AI and some violations of copyrighted work. As we have
seen AI could have a wrong understanding of facts, have biased opinions,
and make things up that don’t exist, we could fight for the truth but
the dead cannot speak for themselves. It would be pathetic if humans
lived in hallucinations in the future, and I don’t know if there’s any
good practice to prevent it. It’s like Pandora’s box is opened and
complications cannot be stopped. But we should at least think seriously
about the potential impact of AI on society and human consciousness and
possible unexpected consequences.
