Being a leader requires 'followers' only, those who volunteer to go
where you are going rather than being incentivized to, threatened to, or
having to. And leadership requires a vision of the world that does not
yet exist and the ability to communicate it. The former is the tangible
result of what the world would like if we spent every day pursuing WHY,
due to the power of WHY in inspiring action. The inspirational book
'Start with Why: How Great Leaders Inspire Everyone to Take
Action' written by Simon Sinek explores this concept deeply,
arguing that the most successful and inspiring leaders communicate from
the inside out—starting with their 'Why' (purpose or belief), then 'How'
(process), and finally 'What' (product or service). This is a very
inspiring book to read, for any type of leaders who is pursuing profound
fulfillment.
start_with_why
Below are the quotations I've selected from the book.
Manipulations are the norm, but the better alternative is
inspiration.
Beyond the business world, manipulations are the norm in politics
today as well. Just as manipulations can drive a sale but not create
loyalty, so too can they help a candidate get elected, but they don't
create a foundation for leadership. Leadership requires people to stick
with you through thick and thin. Leadership is the ability to rally
people not for a single event, but for years. In business, leadership
means that customers will continue to support your company even when you
slip up.
Manipulative techniques have become such a mainstay in American
business today that it has become virtually impossible for some to kick
the habit. Like any addiction, the drive is not to get sober, but to
find the next fix faster and more frequently. And as good as the
short-term highs may feel, they have a deleterious impact on the
long-term health of an organization. Addicted to the short-term results,
business today has largely become a series of quick fixes added on one
after another after another.
Leaders who choose to inspire people rather than manipulate
people follow the concept of 'The Golden Circle'.
The Golden Circle is an alternative perspective to existing
assumptions about why some leaders and organizations have achieved such
a disproportionate degree of influence.
This alternative perspective is not just useful for changing the
world; there are practical applications for the ability to inspire, too.
It can be used as a guide to vastly improve leadership, corporate
culture, hiring, product development, sales, and marketing. It even
explains loyalty and how to create enough momentum to turn an idea into
a social movement.
Companies try to sell us WHAT they do, but we buy WHY they do it.
This is what I mean when I say they communicate from the outside in;
they lead with WHAT and HOW. When communicating from inside out,
however, the WHY is offered as the reason to buy and the WHATs serve as
the tangible proof of that belief. The things we can point to
rationalize or explain the reasons we're drawn to one product, company
or idea over another.
When the WHY is absent, imbalance is produced and manipulations
thrive. And when manupulations thrive, uncertainty increases for buyers,
instability increases for sellers and stress increases for all.
Biologically, the limbic brain drives behaviors (decisions).
Great leaders win hearts before minds.
We are drawn to leaders and organizations that are good at
communicating what they believe. Their ability to make us feel like we
belong, to make us feel special, safe, and not alone is part of what
gives them the ability to inspire us. Those whom we consider great
leaders all have an ability to draw us close and to command our loyalty.
And we feel a strong bond with those who are also drawn to the same
leaders and organizations.
The newest area of the brain, our Homo Sapien brain, is the
neocortex, which corresponds with the WHAT level. The neocortex is
responsible for rational and analytical thought and language. The middle
two sections comprise the limbic brain. The limbic brain is responsible
for all of our feelings, such as trust and loyalty. It's also
responsible for all human behavior and all our decision making, but it
has no capacity for language.
When we communicate from the outside in, when we communicate WHAT we
do first, yes, people can understand vast amounts of complicated
information, like facts and features, but it does not drive behavior.
But when we communicate from the inside out, we're talking directly to
the part of the brain allows us to rationalize those decisions.
Our limbic brain is powerful, powerful enough to drive behavior that
sometimes contradicts our rational and analytical understanding of a
situation. We often trust our gut, even if the decision flies in the
face of all the facts and figures. Richard Restak, a well-known
neuroscientist, talks about this in his book, The Naked Brain. When you
force people to make decisions with only the rational part of their
brain, they almost invariably end up 'overthinking.' These rational
decisions tend to take longer to make, says Restak, and can often be of
lower quality. In contrast, decisions made with the limbic brain, gut
decisions, tend to be faster, higher-quality decisions.
Our limbic brains are smart and often know the right thing to do. It
is our inability to verbalize the reasons that may cause us to doubt
ourselves or trust the empirical evidence when our gut tells us not
to.
People don't buy WHAT you do, they buy WHY you do it. A failure to
communicate WHY creates nothing but stress or doubt.
Those decisions started with WHY - the emotional component of the
decision - and then the rational components allowed the buyer to
verbalize or rationalize the reasons for their decision.
Great leaders are those who trust their gut. They are those who
understand the art before the science. They win hearts before minds.
They are the ones who start with WHY. "I can make a decision with 30
percent of the information, " said former Secretary of State Colin
Powell. "Anything more than 80 percent is too much." There is always a
level at which we trust ourselves or those around us to guide us, and
don't always feel we need all the facts and figures.
Our hope, dreams, hearts, and guts drive us to try new
things, not logic or facts.
If we were all rational, there would be no small businesses, there
would be no exploration, there would be very little innovation and there
would be no great leaders to inspire all those things. It is the undying
belief in something bigger and better that drives that kind of
behavior.
In reality, their purchase decision and their loyalty are deeply
personal. They don't really care about Apple; it's all about them.
Products are not just symbols of what the company believes, they also
serve as symbols of what the loyal buyers believe.
Products with a clear sense of WHY give people a way to tell the
outside world who they are and what they believe.
Clarity of WHY, discipline of HOW, and Consistency of WHAT
are all needed.
Ask the best salesmen what it takes to be a great salesman. They will
always tell you that it helps when you really believe in the product
you're selling... When salesmen actually believe in the thing they are
selling, then the words that come out of their mouths are authentic.
When belief enters the equation, passion exudes from the salesman. It is
this authenticity that produces the relationships upon which all the
best sales organizations are based. Relationships also build trust. And
with trust comes loyalty. Absent a balanced Golden Circle means no
authenticity, which means no strong relationships, which means no trust.
And you're back at square one selling on price, service, quality or
features. You are back to being like everyone else. Worse, without that
authenticity, companies resort to manipulation: pricing, promotions,
peer pressure, fear, take your pick. Effective? Of course, but only for
the short term.
If they buy something that doesn't clearly embody their own sense of
WHY, then those around them have little evidence to paint a clear and
accurate picture of who they are. The human animal is a social animal.
We're very good at sensing subtleties in behavior and judging people
accordingly. We get good feelings and bad feelings about companies, just
as we get good feelings and bad feelings about people. There are some
people we just feel we can trust and others we just feel we can't.
Trust begins to emerge when we have a sense that the driver
of behaviors is anything but self-gain.
Trust is not a checklist. Fulfilling all your responsibilities does
not create trust. Trust is a feeling, not a rational experience. We
trust some people and companies even when things go wrong, and we don't
trust others even though everything might have gone exactly as it should
have. A completed checklist does not guarantee trust. Trust begins to
emerge when we have a sense that another person or organization is
driven by things other than their own self-gain.
Those who lead are able to do so because those who follow trust that
the decisions made at the top have the best interests of the group at
heart. In turn, those who trust work hard because they feel like they
are working for something bigger than themselves.
When people come to work with a higher sense of purpose, they find it
easier to weather hard times or even to find opportunity in those hard
times. People who come to work with a clear sense of WHY are less prone
to giving up after a few failures because they understand the higher
cause.
Finding the people who believe what you believe
We do better in cultures in which we are good fits. We do better in
places that reflect our own values and beliefs. Just as the goal is not
to do business with anyone who simply wants what you have, but to do
business with people who believe what you believe, so too is it
beneficial to live and work in a place where you will naturally thrive
because your values and beliefs align with the values and beliefs of
that culture.
When employees belong, they will guarantee your success. And they
won't be working hard and looking for innovative solutions for you, they
will be doing it for themselves.
As Herb Kelleher famously said, "you don't hire for skills, you hire
for attitude. You can always teach skills."
The truth is, almost every person on the planet is passionate; we are
not all passionate for the same things.
The goal is to hire those who are passionate for your WHY, your
purpose, cause or belief, and who have the attitude that fits your
culture.
Great companies don't hire skilled people and motivate them; they
hire already motivated people and inspire them.
If those inside the organization are a good fit, the opportunity to
"go the extra mile", to explore, to invent, to innovate, to advance, and
more importantly, to do so again and again and again, increases
dramatically. Only with mutual trust can an organization become
great.
The Law of Diffussion
Our population is broken into five segments that fall across a bell
curve: innovators, early adopters, early majority, late majority and
laggards.
Early adopters are willing to pay a premium or suffer some level of
inconvenience to own a product or espouse an idea that feels right.
Their willingness to suffer an inconvenience or pay a premium had less
to do with how great the product was and more to do with their own sense
of who they are. They wanted to be the first.
The farther right you go on the curve, the more you will encounter
the clients and customers who may need what you have, but don't
necessarily believe what you believe. As clients, they are the ones for
whom, no matter how hard you work, it's never enough. Everything usually
boils down to price with them. They are rarely loyal. They rarely give
referrals and sometimes you may even wonder out loud why you still do
business with them, "They just don't get it," our gut tells us. The
importance of identifying this group is so that you can avoid doing
business with them.
There is an irony to mass-market success, as it turns out. It's near
impossible to achieve if you point your marketing and resources to the
middle of the bell, if you attempt to woo those who represent the middle
of the curve without first appealing to the early adopters. It can be
done, but at a massive expense. This is because the early majority,
according to Rogers, will not try something until someone else has tried
it first. The early majority, indeed the entire majority, needs the
recommendation of someone else who has already sampled the product or
service.
That's what a manipulation is. They may buy, but they won't be loyal.
Don't forget, loyalty is when people are willing to suffer some
inconvenience or pay a premium to do business with you. They may even
turn down a better offer from someone else - something the late majority
rarely does.
Get enough people on the left side of the curve on your side and they
encourage the rest to follow.
Energy excites. Charisma inspires.
Charisma has nothing to do with energy; it comes from a clarity of
WHY. It comes from absolute conviction in an ideal bigger than oneself.
Energy, in contrast, comes from a good night's sleep or lots of
caffeine. Energy can excite. But only charisma can inspire. Charisma
commands loyalty. Energy does not.
Golden Circle matches an organization
Sitting at the top of the system, representing the WHY, is a leader;
in the case of a company, that's usually the CEO. The next level down,
the HOW level, typically includes the senior executives who are inspired
by the leader's vision and know HOW to bring it to life. Don't forget
that a WHY is just a belief, HOWs are the actions we take to realize
that belief and WHATs are the results of those actions. No matter how
charismatic or inspiring the leader is, if there are not people in the
organization inspired to bring that vision to reality, to build an
infrastructure with systems and processes, then at best, inefficiency
reigns, and at worst, failure results.
WHY-types are focused on the things most people can't see, like the
future. HOW-types are focused on things most people can see and tend to
be better at building structures and processes and getting things
done.
Most people in the world are HOW-types. Most people are quite
functional in the real world and can do their jobs and do very well.
Some may be very successful and even make millions of dollars, but they
will never build billion-dollar businesses or change the world.
HOW-types don't need WHY-types to do well. Buy WHY-guys, for all their
vision and imagination, often get the short end of the stick. Without
someone inspired by their vision and the knowledge to make it a reality,
most WHY-types end up as starving visionaries, people with all the
answers but never accomplishing much themselves.
When a company is small, it revolves around the personality of the
founder. There is no debate that the founder's personality is the
personality of the company. As a company grows, the CEO's job is to
personify the WHY. To ooze of it. To talk about it. To preach it. To be
a symbol of what the company believes.
We all know when a company's WHY goes fuzzy. Split can
happen.
For Wal-Mart, WHAT they do and HOW they are doing it hasn't changed.
And it has nothing to do with Wal-Mart being a 'corporation'; they were
one of those before the love started to decline. What has changed is
that their WHY went fuzzy. And we all know it. A company once so loved
is simply not as loved anymore. The negative feelings we have for the
company are real, but the part of the brain that is able to explain why
we feel so negatively toward them has trouble explaining what changed.
So we rationalize and point to the most tangible things we can see -
size and money. If we, as outsiders, have lost clarity of Wal-Mart's
WHY, it's a good sign that the WHY has gone fuzzy inside the company
also. If it's not clear on the inside, it will never be clear on the
outside. What is clear is that the Wal-Mart of today is not the Wal-Mart
that Sam Walton built.
It's too easy to say that all they care about is their bottom line.
All companies are in business to make money, but being successful at it
is not the reason why things change so drastically. That only points to
a symptom. Without understanding the reason it happened in the first
place, the pattern will repeat for every other company that makes it
big. It is not destiny or some mystical business cycle that transforms
successful companies into impersonal Goliaths. It's people.
For most of us, somewhere in the journey, we forget WHY we set out on
the journey in the first place. Somewhere in the course of all those
achievements, an inevitable split happens.
Those with an ability to never lose sight of WHY, no matter how
little or how much they achieve, can inspire us. Those with the ability
to never lose sight of WHY and also achieve the milestones that keep
everyone focused in the right direction are the great leaders.
As this metric grows, any company can become a 'leading' company. But
it is the ability to inspire, to maintain clarity of WHY, that gives
only a few people and organizations the ability to lead. The moment at
which the clarity of WHY starts to go fuzzy is the split. At this point,
organizations may be loud, but they are no longer clear.
The challenge isn't to cling to the leader, it's to find effective
ways to keep the founding vision alive forever.
For an organization to continue to inspire and lead beyond the
lifetime of its founder, the founder's WHY. must be extracted and
integrated into the culture of the company. What's more, a strong
succession plan should aim to find next generation. Future leaders and
employees alike must be inspired by something bigger than the force of
personality of the founder and must see beyond profit and shareholder
value alone.
The WHY originates from looking back
Before it can gain any power or achieve any impact, an arrow must be
pulled backward, 180 degrees away from the target. And that's also where
a WHY derives its power. The WHY does not come from looking ahead at
what you want to achieve and figuring out an appropriate strategy to get
there. It is not born out of any market research. It does not come from
extensive interviews with customers or even employees. It comes from
looking in the completely opposite direction from where you are now.
Finding WHY is a process of discovery, not invention.
How to Handle Visionary Leaders Without Losing the Team - Amy
Mitchell, Product Management IRL [Link]
visionary_v_execution
Microsoft announced AI credits for Copilot in Microsoft 365 in January.
Salesforce added a new flexible, credit-based
model for their AI agent in May. Cursor shifted to credit-based
pricing in June (and faced some real
pushback from users). Not to be outdone, OpenAI recently replaced
seat licenses with a pooled
credit model for its Enterprise plans.
― Why everyone’s switching to AI credits - Kyle Poyar, Growth
Unhinged [Link]
Companies are transitioning to credit-based pricing models,
particularly for AI services, for several key reasons related to
managing costs, maximizing profitability, accommodating evolving AI
technology, and establishing market standards.
The shift to credit-based models is largely driven by challenges
related to AI operational expenses and usage patterns.
Companies are using credits as a mechanism to transition from
flat-rate pricing toward models based on the value delivered.
The move by major technology companies validates and standardizes
the credit model for AI consumption.
Credit models offer flexibility for both vendors and users.
Focus on what you can do. End on an affirmative.
Cite trade-offs.
Get more info to make an informed decision.
Add “because” to share your rationale.
Give the benefit of the doubt.
― Why "'no' is a complete sentence" is dangerous advice - Wes
Kao's Newsletter [Link]
How to make your writing C.R.I.S.P. - Dan Hock's
Essays [Link]
How To Expand Your Influence Skills - Yue Zhao, The Uncommon
Executive [Link]
Shaping the opinions of others, or building influence, is about more
than just data and logic. It's about understanding and managing
emotions.
Handling your own emotions: Notice and reflect on what is driving
your actions, such as fear, and then name it. This helps you move
forward with clarity and confidence.
Leading others through their emotions: When you want to get buy-in
for your ideas, help people process their emotions. You can do this by
creating a space that welcomes emotions, validating their concerns, and
then shifting their focus to what they can do to move forward.
The Hidden Rulebook of Corporate Politics (and How to Use It
to Your Advantage) - Gaurav Jain, The Good Boss [Link]
I have to review this article regularly.
The moment you stop believing in the corporate fiction is the
moment you can start using it. Once you see it as infrastructure rather
than identity, as a resource rather than a calling, everything
shifts.
Your corporate role doesn't need to be meaningful. It needs to be
useful. Useful for building skills, for funding your real projects, for
buying time while you figure out what matters to you.
The death of the corporate role isn't a crisis. It's freedom from
having to pretend your spreadsheet about spreadsheets is your life's
work.
― The death of the corporate job - Alex Mccann, Still
Wandering [Link]
Good piece.
Articles and Blogs
President Trump, Tech Leaders Unite to Power American AI
Dominance - The White House [Link]
The August jobs report has economists alarmed. Here are their
3 top takeaways. - CBS News [Link]
The August jobs report is raising concerns among economists due to
several alarming trends. Employers added only 22,000 nonfarm jobs, which
is significantly lower than the 80,000 jobs that analysts had forecast.
The unemployment rate also rose to 4.3%, the highest level since October
2021.
The three top takeaways are
The job market is stalling
Job growth is at its lowest level in 15 years
The federal reserve will likely cut interest rates
The Recession is Already Happening for Many Americans -
Bloomberg [Link]
Read the text messages between Charlie Kirk accused and
roommate - BBC [Link]
U.S. Investors, Trump Close In on TikTok Deal With China -
Raffaele Huang, Lingling Wei, Alex Leary, The Wall Street
Journal [Link]
The near-finalized framework of a deal between the U.S. and China
concerning the popular social media application TikTok, involves
creating a new U.S. entity to manage the app’s American operations, with
an investor consortium, including Oracle, taking a roughly 80%
controlling stake, which satisfies a recent U.S. law regarding foreign
ownership. A key component of the agreement is the establishment of
American control over user data and the crucial content-recommendation
algorithms, although they will be based on technology licensed from
TikTok's Chinese parent company, ByteDance. Furthermore, the article
notes that President Trump has delayed the TikTok ban until December as
negotiations conclude, signaling the resolution of a multi-year national
security dispute over the app's influence in the U.S. Both Chinese and
American officials have reached a basic consensus on the terms, which
also include Oracle managing U.S. user data at its facilities in
Texas.
Google brings Gemini in Chrome to US users, unveils agentic
browsing capabilities, and more - TechCrunch [Link]
Tesla Dojo: The rise and fall of Elon Musk’s AI supercomputer
- TechCrunch [Link]
Dojo was a custom-built supercomputer intended to be the cornerstone
of Tesla's AI ambitions, specifically for training the neural networks
of its Full Self-Driving (FSD) technology and humanoid robots.
The primary strategic reasons cited for the project's termination
include:
the strategic pivot to AI6 chips. The AI6 chip is Tesla’s new
strategic bet on a chip design intended to scale across FSD, Tesla’s
Optimus humanoid robots, and high-performance AI training in data
centers
moving away from hardware self-reliance. Dojo was intended to reduce
reliance on expensive eand difficulty-to-secure Nvidia GPUs, but Tesla
is now "going all-in on partnerships" with major chip providers,
including Nvidia, AMD, and Samsung (which will build the AI6 chip)
technological and compatibility hurdles. Dojo’s design, based on
proprietary D1 chips, faced inherent technological challenges related to
integration with the broader AI ecosystem
internal competition and redundency. In August 2024, Tesla began
promoting Cortex, described as the company’s "giant new AI training
supercluster" being built at Tesla HQ in Austin. Cortex was later
deployed at Gigafactory Texas.
You are by default a product leader, navigating product
directions with data.
Data scientists at Meta don’t just analyze data — they transform
business questions into data-driven product visions that help building
better human connections.
The most successful data scientists that I’ve worked with not
only excel at adapting their approach to the specific data-problem
quadrant they’re operating in, but also are effective in working with
Cross-Functional partners to drive collaboration pushing product
strategy development forward.
With Product Managers:
Speak in terms of business problems, not data
techniques
Help PMs translate intuition into testable hypotheses
Co-create metrics frameworks that balance short and long-term
objectives
With Engineering:
Bridge implementation and insight by understanding technical
constraints
Design analytics requirements that respect engineering
resources
Create feedback loops that allow for continuous
improvement
With Design/User Researchers:
Humanize data insights through collaborative
storytelling
Provide quantitative context for qualitative user
research
Partner on creating experiences that naturally generate valuable
data
Deb Liu, former VP of Meta, highlighted in herproduct
strategy framework: “a great product strategy is opinionated,
objective, operable, and obvious.” Data scientists are uniquely
positioned to help product teams achieve these qualities
through:
Opinionated: Grounding strategic choices in data-backed
insights
Objective: Bringing analytical rigor to opportunity sizing and
risk assessment
Operable: Creating measurement frameworks that make execution
tractable
Obvious: Revealing patterns that make the path forward clear to
all stakeholders
― Meta’s Data Scientist’s Framework for Navigating Product
Strategy as Data Leaders - Medium [Link]
Generate data insights to identify problems and guide early
decisions
Create product strategy to drive measurable improvements
Note: a good strategy decides which problems to prioritize in
solving as well as those we choose not to solve.
Best Practice: narrowing the problem space through structured
discovery.
Collaboration among design, PM, and XFN
Define (north star) metrics
Translate business questions into testable hypotheses
Use analytics to yield insights
Quadrant 2: The Craftsperson (Low Data, Concrete
Problem)
Strategic Approach:
Design targeted data collection aligned to the specific problem
Develop creative measurement frameworks that work with sparse
data
Leverage analogous data from similar contexts
Note: focus on setting clear learning milestones rather than
promising specific outcomes. The goal is to systematically reduce
uncertainty around a concrete problem with iterative data learnings to
update our beliefs.
Quadrant 3: The Explorer (High Data, Broad
Problem)
Strategic Approach:
Pattern recognition at scale to identify unrecognized opportunities
(e.g., opportunity sizing model, gap analysis framework)
Segmentation and clustering to create structure in an ambiguous
space (e.g., segmentation model)
Insight translation that transforms data patterns into business
narratives
Note: structure the problem space through data, allowing the
product team to move from broad exploration to targeted opportunities.
The role is to transform overwhelming data into clear strategic choices
for your product partners.
Quadrant 4: The Optimizer (High Data, Concrete
Problem)
Continuous learning systems that adapt as conditions change
Best Practices for Developing a Product Strategy - Deb
Liu [Link]
A New Ranking Framework for Better Notification Quality on
Instagram - Engineering at Meta [Link]
While existing machine learning (ML) models optimize for high
engagement, they can result in repetitive and potentially "spammy"
notifications, leading users to disable them. To combat this, the new
framework applies a multiplicative penalty to notification scores based
on their similarity to recently sent ones, using criteria such as author
and product type. This strategy has successfully reduced notification
volume while increasing engagement rates by ensuring a more varied and
personalized mix of content.
The methodology begins with the existing machine learning (ML)
models, which calculate a base score for notification candidates based
on factors like the probability of a user clicking (Click-Through-Rate
or CTR) and time spent. The new framework introduces a diversity layer
on top of these existing engagement ML models.
The methodology involves the following steps:
Evaluation of Similarity: The diversity layer
evaluates each notification candidate's similarity to recently sent
notifications across multiple dimensions, such as content, author,
notification type, and product surface.
Application of Penalties: The system applies
carefully calibrated penalties, expressed as multiplicative demotion
factors, to downrank candidates that are too similar or repetitive to
recent notifications.
Re-ranking: The adjusted scores (base relevance
score multiplied by the demotion factor) are used to re-rank the
candidates.
Selection: The final selection process uses a
quality bar to choose the top-ranked candidate that successfully passes
both the ranking and diversity criteria.
Within the diversity layer, the methodology is mathematically
implemented using a multiplicative demotion factor applied to the base
relevance score:
Demotion Multiplier (\(D(c)\)): This is a penalty factor
where the value falls within the range of 0 to 1 (\(D(c) \in\)), reducing the score based on
similarity to recently sent notifications.
Similarity Signal: To calculate \(D(c)\), a similarity signal (\(p_i(c)\)) is computed for a set of semantic
dimensions (e.g., author, product type) using a maximal marginal
relevance (MMR) approach.
Binary Baseline: In the baseline implementation,
the similarity signal \(p_i(c)\) is
binary: it equals 1 if the similarity exceeds a predefined threshold
(\(\tau_i\)), and 0 otherwise.
Flexible Control: The methodology defines the final
demotion multiplier using adjustable weights (\(w_i\)), which control the strength of
demotion for each respective dimension.
The State of AI in Financial Services in 2025 — views from
our front row seats - Peter Hung, Illuminate Financial [Link]
The best roadmaps aren't checklists; they tell a story about why
something is being built. They show how short-term initiatives connect
to long-term strategic goals.
The Now, Next, Later Framework is a core pattern,
reflecting the reality of uncertainty. Now initiatives are tight,
concrete, and focused on current goals (e.g., MVP launch). Next
initiatives are more exploratory bets. Later initiatives are
deliberately fuzzy, long-term aspirations that signal intent without
making firm promises.
Effective roadmaps frame initiatives as problems to solve and tie
them to clear outcomes and business objectives (e.g., "reduce onboarding
friction" instead of "ship a new login flow"). This keeps the team
flexible and focused on results.
There is no one-size-fits-all roadmap. A startup's roadmap is about
survival and proving a hypothesis. A scale-up's roadmap is about
smoothing friction and deepening engagement. A hardware roadmap must
account for manufacturing cycles, while a mission-critical one must
prioritize compliance and security.
Expanding economic opportunity with AI - OpenAI [Link]
GenAI Doesn’t Just Increase Productivity. It Expands
Capabilities - BCG [Link]
A point made around 'reskilling': While GenAI can immediately boost a
worker's aptitude for new tasks, it does not necessarily "reskill" them
in a traditional sense. The study found that participants were able to
perform complex data-science tasks with the help of GenAI, but they did
not retain the knowledge or skills gained after the tools were taken
away. The article refers to GenAI as an "exoskeleton" that enables
workers to do more, but does not intrinsically change what they have
learned.
Building Etsy Buyer Profiles with LLMs - Isobel Scot, Etsy
Code as Craft [Link]
Non-Obvious Tips for Landing the Job You Want - Deb
Liu [Link]
Seven Non-Obvious Strategies
Never rely only on online submission: Avoid the "digital dustbin" by
finding an alternative path in, such as a referral, connection, or
direct reach-out.
Ask for advice, not a job: Sincerely seek guidance on entering a
field or company, as people are often generous and may uncover new
opportunities for you.
Give them a reason to say yes: Counter the process of finding
reasons to say no (misspellings, poor grammar) by providing
human connection points like shared alma maters, hobbies, or passions to
hack affinity bias.
Find the “you-shaped hole”: Seek roles where your unique skills,
experience, or passion make you the best bet, demonstrating you can "hit
the ground running on day one".
See the world through the hiring manager’s eyes: Hiring managers
prioritize managing risk because a bad hire is costly. Your job is to
close the asymmetry of information, prove you are a "sure bet," and show
you are a great return on investment.
Do the job before you get the job: Demonstrate initiative by acting
like an employee; use the product, talk to customers, and bring specific
ideas or prototypes to show you want this job.
Tailor your resume (and your story) for the role: Treat your resume
as a "living document" to tell a specific story, reframing factual
experiences to align with the target role and "speak the language of the
hiring company".
American Express is Accepted at 160 Million Merchants Around
the World; Since 2017, Amex-Accepting Locations Have Increased by Nearly
5x - Business Wire [Link]
Hallucinations persist partly because current evaluation methods
set the wrong incentives. While evaluations themselves do not directly
cause hallucinations, most evaluations measure model performance in a
way that encourages guessing rather than honesty about
uncertainty.
Hallucinations are not inevitable. Language models can choose to
abstain when uncertain. Abstaining (indicating uncertainty) is better
than providing confident, incorrect information, aligning with the core
value of humility
Avoiding hallucination can be easier for a small model to know its
limits. Being "calibrated" (knowing its confidence) requires much less
computation than being accurate
To measure hallucinations, all of the primary evaluation metrics
need to be reworked to reward expressions of uncertainty. Hallucination
evals have little effect against hundreds of traditional accuracy-based
evals that punish humility
How to Think About GPUs - How to Scale Your Model
[Link]
'A Systems View of LLMs on TPUs'
Anthropic Economic Index report: Uneven geographic and
enterprise AI adoption - Anthropic [Link]
[PDF]
Key findings:
I. Adoption Speed and Shift to Delegation
AI adoption is occurring at an unprecedented speed, reaching in two
years the adoption rates that took the internet around five years. In
the US, 40% of employees report using AI at work, doubling the rate from
two years prior in 2023.
Usage patterns on Claude.ai show a net shift toward delegation
(automation). The share of "Directive" conversations, where users
delegate complete tasks, jumped from 27% to 39%, meaning automation
usage now exceeds augmentation usage for the first time.
There is sustained growth in knowledge-intensive tasks like
education and science. In coding, there is a net shift of 7.4 percentage
points toward program creation and away from debugging, suggesting
models have become more reliable.
Geographic Concentration and Inequality Risk
AI usage is highly geographically concentrated and correlates
strongly with income across countries. A 1% increase in GDP per
working-age capita is associated with a 0.7% increase in Claude usage
per capita.
Small, technologically advanced economies lead in per-capita
adoption, with Israel (7x expected usage) and Singapore (4.57x expected
usage) being top examples.
Low-adoption countries are more likely to delegate complete tasks
(automation), while high-adoption countries tend toward greater learning
and collaborative iteration (augmentation), even when controlling for
task mix.
Current usage patterns suggest that AI benefits may concentrate in
already-rich regions, potentially increasing global economic
inequality.
Enterprise Automation and Deployment Bottlenecks
Enterprise usage via the 1P API is predominantly
automation-dominant, with 77% of business uses involving automation
patterns (delegating tasks programmatically), compared to about 50% for
Claude.ai users.
Business deployment is largely price-insensitive. Model capabilities
and the economic value of automation appear to matter more than cost, as
higher-cost tasks tend to have higher usage rates.
For complex tasks, deployment is constrained by the access to
information rather than just model capabilities. Companies face a
bottleneck requiring costly data modernization and organizational
investments to centralize the contextual information needed for
sophisticated AI use.
Papers and Reports
NCRI Assassination Culture Brief - NCRI and Rutgers
University [Link]
Political violence targeting figures like Donald Trump and Elon Musk
is becoming normalized. The report's key findings are based on a survey
and social media analysis. Main points:
Growing justification for violence
The rise of "Assassination Culture"
Social Media as an Amplifier
YouTube and Podcast
Trump Takes On the Fed, US-Intel Deal, Why Bankruptcies Are
Up, OpenAI's Longevity Breakthrough - All-In Podcast [Link]
Elon Musk on DOGE, Optimus, Starlink Smartphones, Evolving
with AI, Why the West is Imploding (All-In Summit) - All-In
Potcast [Link]
Inside the White House Tech Dinner, Weak Jobs Report, Tariffs
Court Challenge, Google Wins Antitrust - All-In Podcast [Link]
To build an AI native product, a PM needs mastery of the
following- vision, opinionated UX design- model
intuition to extract max value- ability to go from pixels
-> evals -> hill climb- understanding of agentic flows -
tools, context, safety guardrails- deep user understanding -
lot more than previously because of the nature of agents
― AI PM mastery is a rare skill - Madhu Guru [Link]
The Systems Thinker's Blindspot - Shreyas Doshi [link]
I read the book "Never Split the Difference : Negotiating As If
Your Life Depended On It" by Chris Voss a month ago and finally got
time to write some notes down. I love this type of book that provides
structured, practical suggestions for achieving a goal, backed by
academic research and theories.
never_split_the_difference
This book is building its argument on some well-established,
peer-reviewed psychological theories that show human decision-making is
often more emotional and irrational than we'd like to believe. Voss
grounds his approach in Daniel Kahneman and Amos Tversky's foundational
research on behavioral economics and cognitive psychology. The specific
concepts highlighted in the book are: cognitive biases, the framing
effect, loss aversion, system 1 and system 2 thinking:
Cognitive biases: People are not purely rational actors. Instead,
our decisions are influenced by systematic, unconscious, and irrational
mental shortcuts.
The framing effect: People respond differently to the same choice
depending on how it's presented or "framed." For example, framing a
negotiation in terms of what the other party stands to lose is often
more powerful than framing it in terms of what they stand to gain.
Loss aversion: A core tenet of Prospect Theory, this principle
states that the psychological pain of a loss is roughly twice as
powerful as the pleasure of an equivalent gain.
System 1 and system 2 thinking: Introduced in Kahneman's book,
Thinking, Fast and Slow, this model describes two distinct
modes of thought. System 1 is our fast, instinctive, and emotional mind.
System 2 is our slow, deliberate, and logical mind. Voss's techniques
are designed to bypass the logical System 2 and appeal directly to the
emotional and intuitive System 1.
The central tenets of Chris Voss's effective negotiation strategy are
rooted in emotional intelligence and a shift from a competitive to a
collaborative mindset. Rather than seeking a compromise, his methods
focus on understanding the other party to influence their
decision-making. The key elements of his approach include:
Tactical empathy: intentionally using empathy to understand the
other person's perspective, emotions, and motivations. The goal is to
build a trust-based relationship, not necessarily to agree with
them.
Active learning: it's important to truly listen to what the other
person is saying, rather than just waiting for your turn to speak. This
includes paying attention to their words, tone, and body language, to
uncover their real needs and fears.
Calibrated questions: open-ended questions that start with 'how' or
'what', and are designed to give the other person the illusion of
control while you guide them toward a solution that benefits both
sides.
Key techniques:
Mirroring: repeating the last one to three key words of what the
other person has said. This encourages them to elaborate and creates a
sense of rapport.
Labeling: verbally identifying the acknowledging the other person's
emotions. This helps to diffuse negative emotions and makes them feel
heard.
The power of 'no': 'no' is not a failure but a critical starting
point. It makes the other party feel safe and in control, and it allows
you to get past insincere agreements to uncover the true issues.
"That's right" as the goal: Instead of aiming for "yes," the
ultimate goal is to get the other person to say, "That's right." This
phrase signifies that they feel you have accurately understood their
position and worldview, creating a turning point in the
negotiation.
Other impressive key lessons to remember:
Be ready for possible surprises, and use skills to reveal the
surprises
View assumptions as hypotheses and use the negotiation to test them
rigorously
Negotiation is not a battle but a process of discovery with the goal
of uncovering as much information as possible
Let the person be in a positive frame of mind. Positivity creates
mental agility in both you and your counterpart
Keep voice calm and slow. Create an aura of authority and
trustworthiness without triggering defensiveness
Use positive / playful voice as default. Use direct or assertive
voice rarely
Acknowledging the other person's situation to convey that you are
listening
Focus first on clearing the barriers to agreement
Pause and let the other party to fill in the silence
Label your counterpart's fears to diffuse their power and generate
safety, well-being, and trust
Accusation audit: List the worst things that the other party could
say about you and say them before the other person can
All negotiations are defined by a network of subterranean desires
and needs
Don't compromise. Meeting halfway often leads to bad deals for both
sides
Approaching deadlines entice people to rush the negotiating process
and do impulsive things that againt their best interests
Before you make an offer, emotionally anchor them by saying how bad
it will be. When you get to numbers, set an extreme anchor to make your
'real' offer seem reasonable, or usse a range to seem less
aggressive.
People will take more risks to avoid a loss than to realize a
gain.
Avoid asking questions that can be answered by 'yes'. Ask calibrated
questions that start with the words 'how' or 'what'. Avoid asking
questions starting with 'why' which is always an accusation in any
language.
Calibrate questions to point your counterpart toward solving your
problems.
There is always a team on the other side. You are vulnerable if you
don't influence those behind the table.
Asking 'how' question gives counterpart an illusion of control and
leads them to contemplate yoru problems when making their demand.
Identify the motivations of players 'behind the table'. You can do
so by asking how a deal will affect everybody else and how on board they
are.
Pay 38% attention to tone of voice and 55% to body language. The
rest 7% is on words.
Test whether 'yes' is real or counterfeit by using calibrated
questions, summaries, and labels to get your counterpart to reaffirm
their agreement at least three times.
Pay attention to a person's use of pronouns which offers deep
insights into his or her relative authority. If you are hearing a lot of
'I', 'me', and 'my', the real power to decide probably lies elsewhere.
Picking up a lot of 'we', 'they', and 'them', it's more likely you are
dealing directly with a savvy decision maker keeping his options
open.
Humor and humanity are the best ways to break the ice and remove
roadblocks.
Identify your counterpart's negotiation style: Accomodator,
Assertive, or Analyst.
Prepare dodging tactics to avoid getting sucked into the compromise
trap.
Learn to take a punch or punch back without anger. The guy across
the table is not the problem, the situation is.
Prepare an Ackerman plan:
Set you target price (goal)
Set your first offer at 65% of your target price
Calculate three raises of decreasing increments (to 85%, 95%, and
100%)
Use lots of empath and different ways of saying 'No' to getthe other
side to counter before you increase your offer
when calculating the final amount, use precise, non round numbers
like, $37,893 rather than $38,000. It gives the number credibility and
weight.
On your final number, throw in a non monetary item (that they
probably don't want) to show you are at your limit.
Black swans are leverage multipliers. Remember the three types of
leverages: positive (the ability to give someone what they want);
negative (the ability to hurt someone); and normative (using your
counterpart's norms to bring them around).
Understand the other side's 'religion / worldview' (reason for
being) so that we are able to speak persuasively, develop options that
resonate for them, and build influence. Black swan usually dwells in the
hidden negotiation space.
People are more apt to concede to someone they share a cultural
similarity with.
Get face time with the counterpart.
Selected Quotes:
What good negotiators do when labeling is to address those underlying
emotions. Labeling negatives diffuses them (or defuses them, in extreme
cases); labeling positives reinforces them.
Great negotiators seek 'No' because they know that's often when the
real negotiation begins.
Whether you call it "buy-in" or 'engagement' or something else, good
negotiators know that their job isn't to put on a great performance but
to gently guide their counterpart to discover their goal as his own.
Never split the difference. Creative solutions are almost always
preceded by some degree of risk, annoyance, confusion, and conflict.
Accommodation and compromise produce none of that. You've got to embrace
the hard stuff. That's where the great deals are. And that's what great
negotiators do.
If you can get the other party to reveal their problems, pain, and
unmet objectives - if you can get at what people are really buying -
then you can sell them a vision of their problem that leaves your
proposal as the perfect solution.
When you are selling yourself to a manager, sell yourself as more
than a body for a job; sell yourself, and your success, as a way they
can validate their own intelligence and broadcast it to the rest of the
company. Make sure they know you'll act as a flesh-and-blood argument
for their importance.
The key issue here is if someone gives you guidance, they will watch
you to see if you follow their advice. They will have a personal stake
in seeing you succeed. You've just recruited your first unofficial
mentor.
Negotiation was coaxing, not overcoming; co-opting, not defeating.
Most importantly, successful negotiation involved getting your
counterpart to do the work for you and suggest your solution himself. It
involved giving him the illusion of control while you, in fact, were the
one defining the conversation.
Asking for help in this manner (give illusion of control by asking
calibrated questions), after you've already been engaged ina dialogue,
is an incredibly powerful negotiating technique for transforming
encounters from confrontational showdowns into joint problem-solving
sessions. And calibrated questions are the best tool.
Expression of anger increase a negotiator's advantage and final take.
Anger shows passion and conviction that can help sway the other side to
accept less. However, by heightening your counterpart's sensitivity to
danger and fear, your anger reduces the resources they have for other
cognitive activity, setting them up to make bad concessions that will
likely lead to implementation problems, thus reducing your gains.
Also beware: researchers have also found that disingenuous
expressions of unfelt anger - faking it - backfire, leading to
inractable demadns and destroying trust. For anger to be effective, it
has to be real, the key for it is to be under control because anger also
reduces our cognitive ability.
No deal is better than a bad deal. Once you're clear on what you
bottom line s, you have to be willing to walk away. Never be needy for a
deal.
Think of punching back and boundary-setting tactics as a flattened
S-curve: you've accelerated up the slope of a negotiation and hit a
plateau that requires you to temporarily stop any progress, escalate or
de-escalate the issue acting as the obstable, and eventually bring the
relationship backto a state of rapport and get back on the slope. Taking
a positive, constructive approach to conflict involves understanding
that the bond is fundamental to any resolution. Never create an
enemy.
By positioning your demands within the worldview your conuterpart
uses to make decisions, you show them respect and that gets your
attention and results. Knowing your counterpart's religion is more than
just gaining normative leverage per se. Rather, it's gaining a holistic
understanding of your counterpart's worldview and using that knowledge
to inform your negotiating moves.
Two tips for reading religion correctly:
Review everything you hear
Use backup listeners whose only job is to listen between the lines.
They will hear things you miss.
When you recognize that your counterpart is not irrational, but
simply ill-informed, constrained, or obeying interests that you do not
yet know, your field of movement greatly expands. And that allows you to
negotiate much more effectively.
The Art of 'No':
Saying "No" gives the speaker the feeling of safety, security, and
control. You use a question that prompts a "No" answer, and your
counterpart feels that by turningyou down hehas proved that he's in the
driver's seat. Good negotiators welcome - even invite - a solid "No" to
start, as a sign that the other party is engaged and thinking.
Gun for a "Yes" straight off the bat, though, and your counterpart
gets defensive, wary,and skittish. That's why I tell my students that,
if you are trying to sell something, don't start with "Doyou have a few
minutes to talk?" Instead ask, "Is now a bad time to talk?" Either you
get "Yes, it's a bad time" followed by a good time or a request to go
away, or you get "No, it's not" and total focus.
It's a reaffirmation of autonomy. It is not a use or abuse of power;
it is not an act of rejection; it is not a manifestation of
stubbornness; it is not the end of the negotiation.
"No" has a lot of skills:
"No" allows the real issues to be brought forth
"No" protects people from making - and lets them correct -
ineffective decisions
"No" slows things down so that people can freely embrace their
decisions and the agreements they enter into
"No" helps people feel safe, secure, emotionally comfortable, and in
control of their decisions
"No" moves everyone's efforts forward
There is a big difference between making your counterpart feel that
they can say "No" and actually getting them to say it. Sometimes, if you
are talking to somebody who is just not listening, the only way you can
crack their cranium is to antagonize them into "No".
One great way to do this is to mislabel one of the other party's
emotions or desires. You say something that you know is totally wrong.
That forces them to listen and makes them comfortable correcting
you.
Another way to force "No" in a negotiation is to ask the other party
what they don't want. People are comfortable saying "No" here because it
feels like self-protection. And once you've gotten them to say "No",
people are much more open to moving forward toward new options and
ideas.
To successfully transition into the role of an AI Collaborator or
Strategist, developers must focus on strategic adoption and skill
augmentation:
Embrace Experimentation and Iterate Aggressively
Achieve AI Fluency: Commit to continuous learning and adaptability
to understand the capabilities and constraints of different AI tools,
platforms, and models given the "breakneck" speed of innovation.
Shift Focus to Delegation and Orchestration: Move from writing code
to architecting and verifying.
Prioritize Verification and Quality Control: developers must
rigorously review, test, and verify AI-generated code.
Maintain Deep Foundational Knowledge: Continue to deepen
understanding of programming basics, algorithms, data structures, and
overall software systems.
Elevate Systems and Product Thinking: Adopt a hybrid mindset that
incorporates engineering, design, and product management.
Increase Ambition View AI tools as a way to raise the ceiling of
achievable outcomes and expand scope, rather than merely focusing on
"time saved" or reducing effort.
Actionable Insights for Strategy and Tool
Development
For companies and those building future tools, the focus should be on
redefining success and ensuring the developer experience is
fulfilling:
Update Success Metrics: Measure the ability to raise the ceiling of
the work and outcomes accomplished (increasing ambition).
Invest in Advanced Capabilities: Recognize that achieving ambitious,
expanded scopes requires investing in the most advanced agentic
capabilities.
Ensure Fulfillment During Transition: Tool builders should design
future tools to be intuitive, delightful, and cater to developers’
curiosity to keep them fulfilled and happy during the transition
period.
Guided Learning in Gemini: From answers to understanding -
Maureen, Heymans, Google Blog [Link]
Why developer expertise matters more than ever in the age of
AI - Laura Lindeman, Github Blog [Link]
While AI tools like GitHub Copilot significantly boost coding speed,
human critical thinking and fundamental developer skills remain
essential for building resilient, scalable, and secure software. There
are three core areas developers must master to thrive with AI:
excellence in pull requests, thorough code reviews, and investment in
clear documentation.
We must build AI for people; not to be a person - Mustafa
Suleyman [Link]
The author argues that Seemingly Conscious AI (SCAI) is an inevitable
and unwelcome outcome given current technological capabilities, warning
that the illusion of consciousness could lead people to dangerously
advocate for AI rights, welfare, and even citizenship, leading to
societal polarization and psychological risks. The essay emphasizes the
urgent need for clear guardrails and design principles in the AI
industry to ensure that AI companions remain tools maximizing human
utility while actively minimizing markers of consciousness.
Chatbots Can Trigger a Mental Health Crisis. What to Know
About ‘AI Psychosis’ - Robert Hart, Time [Link]
AI psychosis - users develop delusions or distorted beliefs after
extensive use of chatbots like ChatGPT. Those with a personal or family
history of psychosis, or those with personality traits susceptible to
fringe beliefs, may be more vulnerable. Extended use, often hours every
day, is a significant risk factor. Experts advise users to view AI
chatbots as tools, not friends, and to avoid relying on them for
emotional support. They recommend that companies collect more data, work
with mental health professionals, and build safeguards directly into
their models, such as prompting users to take breaks or issuing "warning
labels."
How companies adopt AI is crucial. Purchasing AI tools from
specialized vendors and building partnerships succeed about 67% of the
time, while internal builds succeed only one-third as often.
This finding is particularly relevant in financial services and
other highly regulated sectors, where many firms are building their own
proprietary generative AI systems in 2025. Yet, MIT’s research suggests
companies see far more failures when going solo.
― MIT report: 95% of generative AI pilots at companies are
failing - Sherly Estrada, Fortune [Link]
I talked to Sam Altman about the GPT-5 launch fiasco - Alex
Heath, The Verge [Link]
Chaotic rollout of GPT-5 - Altman admitted the
company "totally screwed up" some aspects, though API traffic and user
numbers continued to climb.
Altman's extensive ambitions
Planning to spend trillions of dollars on data center construction
to address GPU capacity constraints;
Aggressively scaling ChatGPT, which is already one of the most
widely used products on earth, with the goal of reaching billions of
people a day and becoming the third biggest website in the world
(surpassing Instagram and Facebook);
Interested in buying Google Chrome if it becomes available;
Confirming OpenAI's interest in developing new consumer hardware and
a brain-computer interface to rival Neuralink.
AI Bubble - Investors, as a whole, are currently
overexcited about AI. He explained that when bubbles occur, "smart
people get overexcited about a kernel of truth".
Mark Zuckerberg Shakes Up Meta’s A.I. Efforts, Again - Mike
Isaac and Eli Tan, The New York Times [Link]
Mark Zuckerberg initiated a significant restructuring of Meta’s
artificial intelligence division in a push for "superintelligence." This
reorganization involves splitting the current AI division into four
distinct groups focused on research, superintelligence, product
development, and infrastructure, which is intended to help Meta compete
more effectively in the AI arms race. Furthermore, the company is
considering a major strategic shift from exclusively using its own
open-source models to exploring the use of third-party or closed-source
AI technology to power its products.
Meta Freezes AI Hiring After Blockbuster Spending Spree - The
Wall Street Journal [Link]
Meta Platforms has frozen hiring in its artificial-intelligence
division following months of aggressive recruitment, which saw the
company hire over fifty new researchers and engineers. This hiring
freeze is happening alongside a significant reorganization of its AI
operations, now consolidated under the umbrella of Meta
Superintelligence Labs.
Generative AI is revolutionizing how code is written. In just the
past 6 months, coding assistant tools like Cursor, Windsurf, Lovable, Bolt, and Replit have evolved from being cute ways
to help with 10-20% of code to now generating the majority of code for
many startups. 1
in 4 companies in the latest YC batch have 95% of their code written
by AI.
This new way to build products is much faster and simpler than
before, it involves just 4 steps.
Prioritize features by impact
Ship simple version or clickable prototype
Test at scale with users, measure impact
Iterate or kill
― The Lean Startup is Dead - Fletcher Richman [Link]
A key part of being a lifelong learner is retaining what you are
learning and comparing ideas and putting learning into our
lives.
I choose a certain number of topics/books that I want to
read/learn each year and focus on reading those books
deliberately.
I find reading to be a more positive habit than scrolling
mindlessly on my phone or watching YouTube videos. I do those things as
well but I try to change my habits by choosing books instead. I also
read multiple books at a time. This helps me avoid feeling the dread of
picking up a challenging or long book when I am tired after a long
day.
I have tried different retention techniques over the years, and
have found these to work best for me. At first, these were slower and
felt less efficient, but I have gotten faster and better at utilizing
these tips with practice.
― How To Remember What You Read - Ryan Hall, Read and Think
Deeply [Link]
Ryan Hall's top five tips for retaining more of what you are
reading:
Underline or highlight key ideas or phrases.
When reading deeply, always have a pen or highlighter in hand.
On the first read-through, underline or highlight any key concepts,
ideas, characters, or quotes.
This practice makes the reader interactive with the text and enables
quick review of key concepts after reading. Reviewing these key ideas
after finishing a chapter is helpful and increases focus as you actively
look for points to underline.
Write in books.
As you read and underline, write notes in the margins. These notes
can include key ideas, questions, or indications if you don't understand
a section or disagree with something.
Notes are often single words or short phrases, like "Habit Stacking"
when reading Atomic Habits. These words stand out when you
revisit a section or chapter, keeping your mind engaged.
For digital readers (like on a Kindle), keep a notes app open on
your phone to jot down words or phrases related to the chapter. (A
separate source comment also notes that Kindles allow unlimited marginal
notes without needing a separate app).
Briefly summarize each section or chapter immediately
after you have read it.
Keep a notebook for reading notes, where you can write the date,
book title, and chapter. Highlighting different books with different
colors can help distinguish ideas from various books.
Immediately after finishing a chapter or section, briefly summarize
it in your own words, keeping it short (1-3 sentences). Putting ideas
into your own words helps formulate thoughts and allows you to test your
understanding of the concepts.
Talk to others or teach someone else.
Tell someone else about what you are reading and learning. This
verbal processing forces your mind to recall what you have read and put
the pieces together, leading to greater retention.
Write reviews or summaries.
After finishing a book, write a review or a summary. It doesn't need
to be elaborate; the goal is to start the process of putting thoughts on
paper or keyboard to let your mind work through what you've learned. Try
to recall key plot points, ideas, and quotes, referencing your notebook
notes and margin annotations.
Summarize what you've read and ideas you'd like to incorporate into
your life. For nonfiction, try to apply one idea into your life. Another
comment also suggests writing a summary paragraph of each chapter and
then summarizing those in a review.
Additionally, bonus tips:
Re-read classic or deeper non-fiction books, as
they are often meant to be revisited and "wrestled with".
Listen to podcasts or interviews with the author
(especially for nonfiction) after reading the book, as authors may
provide more context or better explanations in an interview format.
Write Everything Down (and not in your notes app) - Megan,
Typewriter Time [Link]
The author found that digital notes were easily forgotten and lacked
the tangible connection and memory associated with handwriting. By
shifting to a dedicated creative writing notebook, the author
experienced improved recall, a more thoughtful writing process, and a
stronger connection to their ideas and progress. The piece advocates for
the benefits of physical writing for creative endeavors and personal
reflection, highlighting how it fosters a deeper engagement with one's
own thoughts and creations, a sentiment echoed by the included
comments.
Suggestions:
Switch to handwriting everything in notebooks instead of using your
phone.
Use a dedicated notebook for creative writing only.
Write down ideas and pieces by hand.
Constantly flip back through the pages of your physical
notebook.
Write out observations about your growth and areas for improvement
directly within the same notebook.
Create an index in your notebook so you can find things easily.
Tab pages of importance.
Scratch out things when you're stuck or frustrated. This allows for
a "messy and alive" notebook that reflects the organic nature of the
creative process, unlike the clean digital interface.
Brookfield: Undervalued Giant In An Overvalued Market! -
Capitalist Letters [Link]
Systemic Approach: It's a system, not a one-off
prompt, where the final prompt is woven together programmatically from
multiple components (e.g., role instruction, user query, fetched data,
examples).
Dynamic and Situation-Specific: Context assembly
happens per request, adapting to the query or conversation state. This
involves including different information depending on the situation,
such as a summary of a multi-turn conversation or a relevant excerpt
from a document.
Blending Multiple Content Types: It covers
instructional context (prompts, guidance, examples), knowledge context
(domain information, facts via retrieval), and tools context
(information from tool outputs like web searches or database
queries).
Format and Clarity: It's about how
information is presented, not just what is included. This means
compressing and structuring information for the model's comprehension,
using formatting like bullet points, headings, JSON, or pseudo-code, and
labeling sections (e.g., "Relevant documentation:").
You can learn anything in 2 weeks - Dan Koe, Puture /
Proof [Link]
"skill acquisition = technique stacking." Instead of trying to learn
an entire skill (like playing the guitar or Photoshop), you should focus
on specific techniques needed for a direct purpose.
"pure focus" as the missing ingredient for rapid learning. To
achieve this, he suggests "tactical stress" – putting yourself in a
high-pressure situation with a strong deadline that forces you to learn
quickly to avoid negative consequences. This pain of the current
situation outweighs the pain of learning, propelling you forward.
How to instantly be better at things - Cate Hall, Useful
Fictions [Link]
Suggestions:
Mimic others, especially those better than you.
Simulate the thinking of experts: Even without direct observation of
someone's thoughts, you can improve by asking yourself "what would a
better [chess player/person/etc.] do?"
Mimic generally competent individuals for new tasks
Ignore existing standards and aspire to a higher level: Recognize
that many skills are "pre-competitive," meaning current standards don't
reflect the full potential. Aim to be better than anyone you've ever
seen, rather than just slightly better than those around you. This
involves a commitment to rigorous effort and exploration beyond
perceived limits.
Cultivating a state of mind where new ideas are born - Henrik
Karlsson, Escaping Flatland [Link]
Techniques to maintain the creative state
Ritualistic work habits: Establishing consistent routines for
creative work (e.g., daily writing sessions at a specific time and
place) can induce a state akin to self-hypnosis, fostering a
non-judgmental zone.
Delaying exposure: Introducing a long delay between creation and
public presentation can reduce self-censorship, as the creator feels
detached from immediate judgment.
Viewing work in religious terms: Framing the creative process as a
service to a higher power can provide the necessary awe and daring to
push into the unknown.
Strategic collaboration: Working with supportive, open-minded
collaborators who challenge rather than conform can be beneficial.
Subverting expectations: Actively seeking out ideas or approaches
that feel slightly uncomfortable or that one might be "ashamed of
liking" can lead to truly original work.
Working at speed: Forcing oneself to produce work rapidly can bypass
self-censorship and allow raw, unfiltered ideas to emerge.
Where do Tech Returns Come From? - Eric Flaningam, Generative
Value [Link]
The article suggests that successful technology investing requires
embracing uncertainty, understanding the "base rates" of different
company categories, recognizing where true differentiation lies (often
beyond just technology), viewing market size from a first-principles
perspective, and being aware of the unique opportunities unlocked by new
technology waves.
The next \(\$100B\) company
will not look like the last: Value in technology is driven by
"anomalous" companies founded by "anomalous" people, making pattern
matching ineffective. The most successful companies create new
categories.
Know the game you’re playing: Different categories
have different "Slugging Ratios" (Value/Company). Consumer companies,
often network-driven marketplaces with winner-take-all dynamics, have
the highest upside, while Hardtech companies also have high slugging
ratios but are riskier. Enterprise software, while less "Power Lawed,"
offers more predictable returns and is suitable for an expanding venture
capital landscape due to its scalability, moats, and lower operating
costs.
Software is like chicken, 80% of it tastes the
same: Technical differentiation in enterprise software is often
nuanced. Sales, marketing, and building "mindshare" are as, if not more,
important than technical moats, especially as software becomes easier to
build and features are quickly replicated. The "GPT Wrapper" argument
for AI applications is analogous to how many successful enterprise
software companies were essentially "database wrappers."
“Market size” may be the single greatest reason for
investors missing great companies: Humans struggle with
uncertainty, and new markets introduce exactly that. Many successful
companies like Palantir, Shopify, and Uber created new markets that
didn't exist before, leading to investors underestimating their
potential market size. Companies with "multiple-expansion tailwinds" and
strong platforms also tend to be underestimated.
Companies resemble the technology waves they ride in
on: New technology waves (internet, mobile, cloud, AI) unlock
the ability for new businesses to exist. AI, for example, is enabling
anyone to create software and automate voice/text-based workflows,
expanding the market significantly and allowing for the creation of
entirely new categories (e.g., legal software companies like Harvey
reaching \(\$5B\)+ valuations
quickly).
Don't underestimate the Power Law, ever: While
mentioned throughout, this point emphasizes the extreme concentration of
value in a very small number of companies. The article states that the
top seven companies in its dataset accounted for nearly 50% of the \(\$13\) trillion in value creation.
The Great Mental Models: Visual Book Summary -
DoubleThink [Link]
The Map is Not The Territory: This model highlights
that maps (including mental models) simplify reality and are imperfect
reductions of what they represent. While useful, they lack perfect
fidelity and should be used carefully, as the real world is complex.
Circle of Competence: Emphasizes the importance of
knowing what you know and, critically, what you don't know. It's
dangerous to incorrectly assume knowledge. The advice is to operate
within your area of expertise and outsource the rest.
Falsifiability: States that for a theory to be
confirmed, it must be challengeable. Instead of trying to prove a theory
correct, one should try to prove it incorrect. A theory becomes stronger
when rigorous experimentation fails to disprove it.
First Principles Thinking: Involves breaking down a
problem into its fundamental, non-reducible parts to challenge
pre-existing assumptions. It's an effective way to clarify and approach
complex problems by building solutions from the bottom up, often using
techniques like Socratic Questioning and the Five Whys.
Thought Experiment: Refers to mentally simulating
situations to test theories or reach conclusions, rather than conducting
physical experiments. It allows for gaining confidence in answers, as
illustrated by comparing hypothetical basketball games.
Necessity and Sufficiency: Explains that having all
necessary conditions does not guarantee all sufficient conditions are
met. Meeting necessary conditions might make success possible, but it
doesn't assure it (e.g., knowing how to write vs. being a New York Times
Bestseller).
Second-Order Thinking: Encourages looking beyond
immediate consequences to consider the "consequence of the consequence"
or further. It involves thinking several steps ahead to avoid short-term
positive decisions that lead to long-term negative effects.
Probabilistic Thinking: Acknowledges that the future
cannot be predicted perfectly, but this model helps improve the accuracy
of guesses using three main concepts:
Bayesian Thinking: Using all relevant prior
information for informed decisions in unfamiliar scenarios.
Fat-Tailed Curves: Understanding that the more
extreme scenarios are possible, the higher the likelihood of any one of
them occurring.
Asymmetries: Assessing the probability that your
estimates accurately reflect the real world.
Correlation vs. Causation: Highlights that a
correlation between two things does not necessarily mean one causes the
other. Large datasets can yield strong correlations purely by chance, as
demonstrated by the unrelated alignment of Walgreens customer
satisfaction and Russell Crowe's movie appearances.
Inversion Principle: A thinking tool that involves
approaching a situation from the opposite end of the usual starting
point to reframe a problem into a solution (e.g., "make money" becomes
"avoid going into debt").
Hanlon’s Razor: Suggests that one should "never
attribute to malice that which can be adequately explained by
stupidity." It implies that actions that seem ill-intended are often
accidents or misunderstandings, and the explanation assuming the least
intent is most likely correct.
Occam’s Razor: States that when multiple
explanations are possible, the one that makes the fewest assumptions is
generally the most probable and closest to the truth. In essence, the
simplest explanation is usually the correct one.
Amazon: Betting The Farm - App Economy Insights [Link]
Tesla: From Bad to Worse - App Economy Insights [Link]
On the EV Landscape (specifically Tesla's automotive
business):
The author highlights that Tesla's year is going "from bad to
worse". Global deliveries have fallen by 13%, marking their steepest
quarterly drop ever.
Tesla's revenue is declining, margins are compressing, and cash flow
has dried out. Automotive revenue specifically fell by 16%
year-over-year. The author notes that "Q2 results remain very poor" if
Tesla is viewed purely as an auto business.
Historically strong margins, supported by gigafactory scale,
direct-to-consumer sales, and minimal marketing costs, are now being
eroded by price cuts and rising competition.
A return to growth, which was predicted earlier in the year, now
"looks unlikely". The company has also withheld full-year guidance due
to factors like trade policy and political backlash, adding to the
uncertainty.
The author expresses concern about "mounting evidence of brand
erosion".
On the AV Landscape (specifically Tesla's Robotaxi
program):
The author acknowledges that Tesla's robotaxi program "could unlock
tremendous value". Elon Musk himself emphasizes that "autonomy is the
story" for Tesla.
Despite its significant potential, the author cautions that even if
these "moonshots in robotaxi and robotics succeed," they are "years away
from offsetting collapsing vehicle demand". This indicates that the
robotaxi program is not seen as an immediate solution to Tesla's current
financial woes in its automotive segment.
The robotaxi program faces considerable regulatory hurdles.
The author raises a critical question about whether the ongoing
"brand erosion" could "undermine even the most ambitious upside" of the
robotaxi program.
The author foresees an "upcoming robotaxi war" among Big Tech
companies, suggesting a highly competitive environment for autonomous
vehicles.
Microsoft: AI Crossroads - App Economy Insights [Link]
The author asserts that Figma is "not just a great product—it’s a
great business" and describes its growth since monetizing in 2017 as
"one of the most explosive runs in SaaS history".
Figma achieved $ $749$ million in FY24 revenue, up 48%
year-over-year, with over 1,000 customers paying \(\$100\)K+ annually, and
95% of Fortune 500 companies using Figma. The author
considers its product-led, freemium "Land and Expand" growth model to be
"hard to manufacture—and even harder to replicate".
The author highlights Figma's transformation "from a design tool
into a full-stack product platform". It is "evolving into a full product
development suite", with new tools like FigJam, Dev Mode, and Figma Make
(AI-driven prototyping) expanding its reach across the entire product
lifecycle.
Figma is positioned as a "productivity platform disguised as a
design tool", which, in the author's view, separates it "from legacy
tools and what opens the door to much broader enterprise budgets" by
serving designers, engineers, product managers, marketers, and
executives.
Figma's "web-first" and "multiplayer by default" approach gave it a
"distinct edge over incumbents like Adobe".
Figma's Uncertainty and Challenges:
The author notes the collapse of the \(\$20\) billion Adobe acquisition due to
"antitrust concerns in the US, UK, and Europe". While Figma
received a "\(\$1\) billion breakup
fee" and regained independence, this past scrutiny highlights a
challenging regulatory environment that companies of Figma's scale can
face.
The author points out a significant "catch" in Figma's reported net
dollar retention of 132% in Q1 FY25. They state that the metric "only
includes customers still spending over \(\$10,000\) today, then looks back at what
those same customers were spending a year ago." This indicates a
potential lack of clarity or transparency in how a key growth metric is
presented, which could be an uncertainty for investors trying to assess
actual customer retention.
Articles and Blogs
How we built our multi-agent research system -
Anthropic [Link]
Building the Hugging Face MCP Server - Hugging Face
[Link]
This is a 10-lesson guide covering GitHub automation, custom
workflows, and MCP integration. Teaches you how to use Claude Code to
automate dev tasks in 36 minutes.
Papers and Reports
A Survey of Context Engineering for Large Language
Models [Link]
YouTube and Podcasts
Grok 4 Wows, The Bitter Lesson, Elon's Third Party, AI
Browsers, SCOTUS backs POTUS on RIFs - All-In Podcast [Link]
Trump vs Powell, Solving the Debt Crisis, The $10T AGI Prize,
GENIUS Act Becomes Law - All-In Podcast [Link]
Silicon Valley Insider EXPOSES Cult-Like AI Companies | Aaron
Bastani Meets Karen Hao [Link]
Karen Hao, an expert in mechanical engineering and journalism,
provides a comprehensive critique of the A industry, detailing her
opinions, arguments, and proposals across various topics during the
interview.
Understanding AI and its Definition
Hao argues that the term "artificial intelligence" is poorly defined
and was originally coined in 1956 by John McCarthy to attract more
attention and funding for his research, essentially as a marketing term.
She notes that while AI generally refers to recreating human
intelligence in computers, there is no scientific consensus on what
human intelligence is, contributing to the term's ambiguity.
AI serves as an "umbrella" term encompassing various technologies
that simulate human behaviors or tasks, ranging from Siri to ChatGPT,
which operate on vastly different scales and have different use cases.
Hao uses the analogy that AI is like the word "transportation" to
illustrate its vagueness: just as "transportation" can refer to bicycles
or rockets, AI can refer to vastly different technologies with different
purposes and costs. She finds it frustrating and unproductive when
politicians use the term vaguely, suggesting it means "progress" without
specifying the type of AI or its potential costs, which she compares to
promoting rockets for commuting when more efficient alternatives
exist.
Environmental and Public Health Costs of AI
Development
Hao emphasizes that the resource consumption required to develop and
use generative AI models is quite extraordinary. She cites a McKinsey
report projecting that within the next five years, current data center
and supercomputer expansion for AI will require adding around half to
1.2 times the amount of energy consumed in the UK annually to the global
grid. A significant portion of this energy will be serviced by fossil
fuels, including natural gas and the extended lives of coal plants.
Hao highlights that this acceleration not only impacts the climate
crisis but also exacerbates public health crises, citing Elon Musk's
xAI's Colossus in Memphis, Tennessee, which is powered by 35 unlicensed
methane gas turbines pumping toxic air pollutants into the community.
She argues that "unlicensed" means the company completely ignored
existing environmental regulations.
She stresses the undertalked about issue of water consumption: AI
data centers require fresh, potable water for cooling to prevent
corrosion and bacterial growth, often using public drinking water
infrastructure. She notes that two-thirds of new AI data centers are
being built in water-scarce areas, providing the example of Montevideo,
Uruguay, where Google proposed a data center during a historic
drought.
The Business Case and Ideology Driving AI
Hao contends that the business case for AI is currently unclear,
noting that even Microsoft has started pulling back investments in data
centers and its CEO, Satya Nadella, has expressed skepticism about the
"race to AGI". She argues that what drives the fervor in the absence of
a clear business case is an ideology or a "quasi-religious fervor".
People genuinely believe in the ability to fundamentally recreate human
intelligence, seeing it as the most important civilizational goal.
She explains that this ideological drive from startups like OpenAI
and Anthropic pressures larger, more traditional tech giants to invest
heavily, as shareholders demand an AI strategy, often due to consumer
shifts (like using ChatGPT as search). Hao explains that OpenAI's pitch
to investors is that funding could lead to being the first to AGI for
"the biggest returns you've ever seen" or, failing that, could automate
human tasks to replace labor, generating significant returns.
She warns of a "bandwagon mentality" among investors. Crucially, she
highlights that if the AI bubble pops, the risk is not just for Silicon
Valley but will have ripple effects across the global economy, as
investments often come from public endowments.
OpenAI's Origins and Sam Altman's Leadership
Hao reveals that OpenAI started as a nonprofit in late 2015,
co-founded by Elon Musk and Sam Altman, as an "anti-Google" initiative
to conduct fundamental AI research without commercial pressures. Musk
specifically feared Google's DeepMind could lead to AI going "very badly
wrong" (sentience, harming humans). The original "open" in OpenAI stood
for open source, and for its first year, the company genuinely
open-sourced its code and research. Hao speculates that the nonprofit
status was a recruitment tool to attract talent, as they couldn't
compete with Google's salaries but could offer a compelling sense of
mission. However, within less than a year, the bottleneck shifted from
talent to capital, leading to the decision to convert to a for-profit
entity. This shift also led to a falling out between Musk and Altman
over who would be CEO.
Regarding Sam Altman, Hao portrays him as a "master manipulator" and
"understander of human psychology". She notes that Altman was not
publicly well-known but was a critical "lynchpin" within the tech
industry, having cultivated relationships with powerful networks and
policymakers early in his career as president of Y Combinator. Hao
states that people who worked with Altman consistently told her they
didn't know what he truly believed because he would often say he
believed what the person he was talking to believed, even if those
beliefs were diametrically opposed. She concludes that Altman's
comparative advantages as a leader include his ability to persuade
people to join his "quest," acquire necessary resources (capital, land,
energy, water, laws), and instill a powerful sense of belief in his
vision among his team. She describes his work as being most effective in
one-on-one meetings where he can tailor his message to achieve his
goals.
Critique of Big Tech as a Corporate Empire
Hao argues that if allowed to expand unfettered, these corporate
empires will ultimately erode democracy. Hao states that tech leaders
view the rest of the world, including other Western countries, as
"resources"—territories from which to acquire land, labor, minerals,
energy, and water for their data centers. She highlights that data
center expansion often targets economically vulnerable communities in
rural areas of the US and UK, which are often uninformed about the true
costs, such as bans on new housing construction due to massive
electricity consumption, or the depletion of fresh water supplies. Hao
laments that politicians are often unaware of these negative
consequences.
She argues that the idea that "you need colossal data centers to
build AI systems" is a "false trade-off". Before OpenAI, AI research was
trending towards "tiny AI systems" requiring little computational
resources, showing that AI innovation can occur without these massive,
resource-intensive approaches. Hao points out that most AI experts today
are employed by these companies, which she likens to climate scientists
being bankrolled by oil and gas companies, leading to biased information
that serves the company's interests rather than scientific
grounding.
Exploitative Labor Practices
Hao exposes grueling exploitative practices in the global AI supply
chain, particularly regarding content moderation for OpenAI. Kenyan
workers were contracted to sift through "reams of the worst text on the
internet," including child sexual abuse, hate speech, and violent
content, to build content moderation filters for ChatGPT. She details
how this work traumatized workers, causing PTSD, personality changes,
and family breakdowns, like the story of Moffat, whose family left him
due to his changed demeanor. These workers were paid only a few dollars
an hour.
She also discusses data annotation, a long-standing part of the AI
industry. Venezuelan refugees in Colombia, highly educated but desperate
due to their country's economic crisis, became cheap labor for labeling
data for self-driving cars and retail platforms. Hao describes the
structural exploitation where workers compete for tasks on platforms,
leading to immense anxiety and control over their lives, exemplified by
a woman who wouldn't walk outside during weekdays for fear of missing
tasks and would wake up at 3 AM if an alarm signaled a new task. She
asserts that there is no moral justification for why these workers,
whose contributions are critical, are paid pennies while company
insiders receive multi-million dollar compensation packages; the only
"justification" is an ideological one that some people are
superior.
Proposals for Public Action and Shaping the Future of
AI
Hao believes that anyone in the world can take action to shape the
AI development trajectory. She proposes thinking of AI development as a
"full supply chain of AI development", where various resources (data,
land, energy, water) and deployment spaces (schools, hospitals, offices)
are points of democratic contestation.
She suggests the public can reclaim ownership over resources. She
encourages people to contest the spaces where AI is deployed. Hao also
advises people to research AI technologies and vendors to make informed
choices about which AI systems to use.
She expresses optimism that widespread, democratic contestation
at every stage of the AI development and deployment pipeline can
"reverse the imperial conquest of these companies" and lead to a more
broadly beneficial trajectory for AI.
How to Gamify Your Life (And Reinvent Yourself ... Fast) -
Dan Koe [Link]
The Future of Work (How to Become AI-First) - Dan
Koe [Link]
Winning the AI Race Part 1: Michael Kratsios, Kelly Loeffler,
Shyam Sankar, Chris Power [Link]
Winning the AI Race Part 2: Vice President JD Vance
[Link]
Winning the AI Race Part 3: Jensen Huang, Lisa Su, James
Litinsky, Chase Lochmiller [Link]
Turbo-Scaling GenAI at DoorDash: From Product Knowledge Graph
to Real-Time Personalization - Predibase [Link]
I finished reading Peter Thiel's 'Zero to One: Notes on Startups,
or How to Build the Future' today. With the rapid advancements and
widespread discussion around AI, the core arguments about technology,
human-machine collaboration, and the nature of progress hold up
remarkably well. And in some ways, as a manifesto for building a better
future, what's written in this book is even more relevant now.
Chapter 2 Party Like It's 1999 outlines four lessons
learned from the dot-com crash that became 'dogma' in the startup world,
however, Thiel argues that these dogmas are largely incorrect and that
the opposite principles are probably more correct:
Make incremental advances: Grand visions were
seen as bubble-inflating, so small, incremental steps became the
preferred path.
Thiel: It is better to risk boldness than triviality.
Stay lean and flexible: Planning was deemed
arrogant, and "agnostic experimentation" became the norm.
Thiel: A bad plan is better than no plan.
Improve on the competition: Focus on existing
customers and recognizable products, improving on what competitors
already offer.
Thiel: Competitive markets destroy profits.
Focus on product, not sales: If a product
requires advertising or salespeople, it's not good enough; viral growth
is the only sustainable growth.
Thiel: Sales matters just as much as product.
"The most contrarian thing of all is not to oppose the crowd, but to
think for yourself."
In this end this chapter, Thiel is challenging the reader to not
simply adopt the prevailing "lessons learned" from the past, but to
critically evaluate them. He suggests that true contrarianism isn't just
about disagreeing with the majority for the sake of it, but about
independent thought and forming your own conclusions, even if those
conclusions align with or contradict the crowd. It's about genuine
intellectual autonomy.
In Chapter 3 All Happy Companies Are Different, he
argues that successful companies are unique and that true value comes
from creating a monopoly rather than competing in existing markets.
Thiel uses the economic models of "perfect competition" and "monopoly"
to explain this difference. In perfect competition, firms sell identical
products, have no market power, and thus, in the long run, make no
economic profit as new entrants drive prices down. A monopoly,
conversely, owns its market, allowing it to set prices and maximize
profits due to a lack of close substitutes. He asserts that competition
is destructive, leading to a ruthless struggle for survival and zero
profits. Monopolies, on the other hand, can afford to focus on long-term
innovation, employee well-being, and broader societal impact because
they are not constantly battling for survival. Creative monopolies are
powerful engines for progress as they introduce entirely new categories
of abundance to the world.
He then discusses how both monopolists and non-monopolists tend to
misrepresent their market conditions. Monopolists (like Google) downplay
their dominance by broadly defining their market to avoid scrutiny,
while non-monopolists (like a new restaurant owner) narrowly define
their market to appear unique and avoid acknowledging intense
competition. Thiel emphasizes that losing sight of competitive reality
by focusing on trivial differentiators is a fatal mistake for
startups.
"All happy companies are different: each one earns a monopoly by
solving a unique problem. All failed companies are the same: they failed
to escape competition."
This is the core message from the book. Entrepreneurs should strive
to build unique, monopolistic businesses by creating something entirely
new.
Chapter 3 primarily focuses on the economic and strategic advantages
of monopoly and the destructive nature of perfect competition.
Chapter 4: "The Ideology of Competition" shifts the
focus to the societal and psychological impact of competition.
Competition is not merely an economic concept but a deeply ingrained
"ideology" that pervades our society, from education to personal
aspirations. He reminds readers that this competition can blind people
to real opportunities and lead to irrational behavior and missed
chances, and suggests us to recognize and resist the pervasive ideology
of competition.
Chapter 5 Last Mover Advantage discusses how a great
business is defined by its ability to generate future cash flows and
argues that being a last mover (i.e., to make the last great development
in a market and enjoy long-term monopoly profits) is more advantageous
than being a first mover. It outlines four characteristics of monopoly
that contribute to a company's durability:
Proprietary Technology: This makes a product
difficult to replicate, ideally being at least 10 times better than its
closest substitute (e.g., Google's search algorithms, PayPal's payment
system for eBay, Amazon's book selection, Apple's integrated
design).
Network Effects: The product becomes more
valuable as more people use it (e.g., Facebook). Thiel emphasizes that
such businesses must start with a very small, focused market to get
initial users.
Economies of Scale: Fixed costs can be spread
over increasing sales, making the business stronger as it grows.
Software companies are particularly suited for this due to near-zero
marginal costs.
Branding: A strong brand creates a monopoly
(e.g., Apple). However, branding needs to be built on substantive
advantages, not just surface-level polish.
"You've probably heard about 'first mover advantage': if you're the
first entrant into a market, you can capture significant market share
while competitors scramble to get started. But moving first is a tactic,
not a goal. What really matters is generating cash flows in the future,
so being the first mover doesn't do you any good if someone else comes
along and unseats you. It's much better to be the last mover— that is,
to make the last great development in a specific market and enjoy years
or even decades of monopoly profits. The way to do that is to dominate a
small niche and scale up from there, toward your ambitious long-term
vision. In this one particular at least, business is like chess.
Grandmaster José Raúl Capablanca put it well: to succeed, 'you must
study the endgame before everything else.'"
Thiel's advice for startups (1) start small and monopolize, 2) scale
up gradually, and 3) don't discrupt) reminds me of Google's AI strategy
in recent two years, which seems to align with the 'last mover
advantage' mentality. Instead of trying to release one massive,
all-encompassing AI that competes directly with established players
across every front, Google has released or integrated AI into many
"smaller" applications or features (workspace, photos, maps, gemini,
etc). Each of these can be seen as a "small market" or specific use case
where AI offers a distinct advantage, allowing Google to "monopolize"
that particular user experience. After establishing AI capabilities in
focused areas, they are integrating these more broadly. Successful AI
features in Workspace might then be leveraged for enterprise solutions.
Advancements in image recognition from Photos could be applied to
broader visual search or other AI models. The iterative development of
Bard/Gemini, starting as a conversational AI and gradually expanding its
capabilities (multimodality, coding, planning), is a clear example of
scaling up. They build upon established user bases and technological
strengths. While Google is certainly competing, their strategy doesn't
always seem to be about a direct, disruptive frontal assault that
immediately aims to destroy an incumbent. Instead, it's often about: 1)
leveraging their exsiting ecosystem, 2) focusing on unique capabilities,
3) creating new user behaviors.
In Chapter 6 You Are Not a Lottery Ticket, Thiel
described the concept of definite vs. indefinite futures and asserts
that the prevailing indefinite optimism, particularly in the US, is
unsustainable. He argues that real progress and success require definite
plans and individual effort.
“When Baby Boomers grow up and write books to explain why one or
another individual is successful, they point to the power of a
particular individual's context as determined by chance. But they miss
the even bigger social context for their own preferred explanations: a
whole generation learned from childhood to overrate the power of chance
and underrate the importance of planning."
The core of Chapter 7 Follow the Money applies the
power law to venture capital (VC). Venture returns are not normally
distributed (where most companies perform average). Instead, they follow
a power law: a small handful of companies radically outperform all
others, often returning more than the entire rest of the fund combined.
People often fail to see the power law, which is a fundamental law of
the universe, because it only becomes clear over time; early-stage
companies in a portfolio might look similar before exponential growth
kicks in. Despite being a niche (less than 1% of new businesses receive
VC funding), venture-backed companies disproportionately drive the
economy, creating 11% of private sector jobs and generating 21% of GDP.
The largest tech companies, all venture-backed, are worth more than all
other tech companies combined.
Understanding the power law means focusing on the singular, most
important things (e.g., one best market, one dominant distribution
strategy). To achieve disproportionate success, one must identify and
focus relentlessly on those few critical elements.
In Chapter 8 Secrets, Thiel begins by posing his
contrarian question ("What important truth do very few people agree
with you on?") in the context of secrets. He states that a good
answer to this question implies the existence of secrets – something
important, unknown, difficult, but achievable. He argues that secrets
still exist and are crucial for progress.Secrets can lead to monumental
advancements in science, medicine, and technology (e.g., curing
diseases, new energy sources). In business, secrets can lead to valuable
companies built on overlooked opportunities, like Airbnb (untapped
supply and unaddressed demand in lodging) and Uber/Lyft (connecting
drivers and riders). In terms of how to find secrets, Thiel has
discussed about 1) secrets of nature from studying physical world, vs.
secrets about people from understanding human nature, 2) looking at the
fields that matter but haven't been standardized.
Chapter 9 Foundations is around 'Thiel's law': a
startup messed up at its foundation cannot be fixed, providing guidance
on fundamental level: co-founder relationships, ownership, possession,
and control, small boards, full time commitment, equity is the king,
founding moment, etc. Chapter 10 The Mechanics of Mafia
highlights the importance of company culture. Chapter 11 If You
Build It Will They Come stresses that distribution (sales,
marketing, advertising) is often underestimated and is just as crucial
as product development.
"The founding moment of a company, however, really does happen just
once: only at the very start do you have the opportunity to set the
rules that will align people toward the creation of value in the
future.
The most valuable kind of company maintains an openness to invention
that is most characteristic of beginnings. This leads to a second, less
obvious understanding of the founding: it lasts as long as a company is
creating new things, and it ends when creation stops. If you get the
founding moment right, you can do more than create a valuable company:
you can steer its distant future toward the creation of new things
instead of the stewardship of inherited success. You might even extend
its founding indefinitely."
"'Company culture' doesn't exist apart from the company itself: no
company has a culture; every company is a culture. A startup is a team
of people on a mission, and a good culture is just what that looks like
on the inside."
There is a core debate right now around whether AI is going to
replace human‘s jobs, and this book offers powerful arguments for the
"AI as complement, not replacement" side. Thiel explicitly argued
against the "substitution fallacy" in Chapter 12 (Man and
Machine), stating that computers and humans have different
strengths and will thrive through collaboration. Although Generative AI
is unprecedented with its impact on human society nuanced to discuss, I
agree there are fundamental differences in intelligence between humans
and AI. Human possess intentionality, true innovation, empathy, and
emotional intelligence, and human judgment is needed when there are
ethical concerns or complex problems. AI as a tool can do augmentation
to increase productivity, but not automation. Historically speaking,
tech development always creates more jobs than destroyed. While some
roles are eliminated, new roles emerge: AI engineers, Prompt Engineers,
AI Product Managers, etc. In essence, it's about a redefinition of work,
rather than elimination.
"People compete for jobs and for resources; computers compete for
neither."
Globalization is about substitution. Technology is about
complementarity.
Chapter 13 Seeing Green analyzes the failure of the
cleantech bubble, attributing it to a widespread failure to answer the
seven critical questions every successful business must address.
The Engineering Question: Most offered only
incremental, not breakthrough (10x better), technology (e.g., Solyndra's
inefficient cylindrical solar cells).
The Timing Question: They misjudged market
readiness and the slow, linear progress of solar technology compared to
exponential tech.
The Monopoly Question: They pursued
"trillion-dollar markets" that were fiercely competitive, rather than
small, defensible niches.
The People Question: Teams were often led by
"salesman-executives" lacking technical expertise, focusing on
fundraising over product. (Thiel suggests a "never invest in a tech CEO
that wears a suit" rule.)
The Distribution Question: Companies often
overlooked effective distribution, leading to complex and inconvenient
sales models (e.g., Better Place's battery swapping).
The Durability Question: They failed to anticipate
competition (e.g., from China) or market shifts (e.g., the rise of
fracking).
The Secret Question: They based their ventures on
"conventional truths" (the need for a cleaner world), which everyone
agreed on, rather than unique, hidden insights.
"The 1990s had one big idea: the internet is going to be big. But too
many internet companies had exactly that same idea and no others. An
entrepreneur can't benefit from macroscale insight unless his own plans
begin at the micro-scale. Cleantech companies faced the same problem: no
matter how much the world needs energy, only a firm that offers a
superior solution for a specific energy problem can make money. No
sector will ever be so important that merely participating in it will be
enough to build a great company."
Chapter 14 The Founder's Paradox explores the often
extreme, contradictory, and seemingly peculiar traits of successful
founders, arguing that these unique characteristics are both powerful
for a company and carry inherent dangers for the founder. Society needs
founders – unusual individuals who can make authoritative decisions,
inspire loyalty, and plan long-term, moving companies beyond
incrementalism. However, founders must be wary of overestimating their
own power and succumbing to their own myth, mistaking public adulation
or criticism for truth. The greatest danger for a founder is losing
their mind; for a business, it's losing its myth and vision.
The current AI boom feels very much like an "accelerating takeoff "
in terms of technological advancement, which is mentioned in the final
chapter "Conclusion: Stagnation or Singularity", as one
of the Nick Bostrom's four possible patterns for humanity's future.
Accelerating Takeoff (Singularity) is the most difficult scenario to
imagine: new technologies so powerful that they transcend current
understanding, leading to a much better future. Ray Kurzweil's
"Singularity is near" concept, based on exponential growth trends, is
mentioned as a prominent view of this outcome. However, as Thiel's book
is a manifesto for building a better future and criticizes 'indefinite
optimism', in the context of AI boom, 'Singularity' is not a
predetermined destination, but the choices we make today:
Are we using AI for "0 to 1" innovation to solve truly hard
problems and create new value, or are we just using it for "1 to n"
incremental improvements and fierce competition?
Are we making definite plans for how AI will integrate with and
enhance human capabilities, or are we succumbing to "indefinite fears"
or blind optimism?
Are we building companies around unique AI-driven insights that
can create sustainable monopolies, or are we simply entering crowded AI
markets hoping for a piece of an existing pie?
The frontier isn’t volume—it’s discernment. And in that shift,
taste has become a survival skill.
Because when abundance is infinite, attention is everything. And
what you give your attention to—what you consume, what you engage with,
what you amplify—becomes a reflection of how you think.
What matters now is what you do with it. How you filter it. How
you recognize signal in the noise. Curation is the new IQ test.
Taste is often dismissed as something shallow or subjective. But
at its core, it’s a form of literacy—a way of reading the world. Good
taste isn’t about being right. It’s about being attuned. To rhythm, to
proportion, to vibe. It’s knowing when something is off, even if you
can’t fully articulate why.
Taste is what allows you to skim past the performative noise, the
fake depth, the viral bait, and know—instinctively—what’s worth your
time.
And that’s what real taste is: a deep internal coherence. A way
of filtering the world through intuition that’s been sharpened by
attention.
When you sharpen your discernment, you stop being swayed by
trends. You stop needing consensus. You stop reacting to every new thing
like it’s urgent.
There will always be creators. But the ones who stand out in this
era are also curators. People who filter their worldview so cleanly that
you want to see through their eyes. People who make you feel sharper
just by paying attention to what they pay attention to.
1995 interview with Steve Jobs — “Ultimately, it comes
down to taste. It comes down to trying to expose yourself to the best
things that humans have done, and then try to bring those things into
what you’re doing.”
Good taste isn’t restrictive. It’s expansive. It allows you to
contain multitudes without becoming incongruent.
But good taste is deep structure. It’s the throughline in
someone’s life. You can see it in the design of their home, the cadence
of their speech, the way they treat people, the books on their
shelves.Taste is how you live a congruent life. Not in the
sense of brand consistency, but in the sense of spiritual alignment. You
can change your mind. Explore new spaces. But your values stay intact.
Your center holds.
― Taste Is the New Intelligence - Wild Bare Thoughts
[Link]
This is an amazing article. In an age of infinite content, taste is
your compass. It’s not about elitism—it’s about aligning your attention
with what truly matters to you. We can do these:
Learnings and Suggestions:
Cultivate Discernment Over Consumption: Prioritize depth over
volume in what you read, watch, and engage with. Ask "Is this worth my
time?" before consuming content, creating something, or sharing. Trust
your intuition—if something feels off, skip it.
Curate Your Inputs (Because They Shape Your Outputs): Unfollow
accounts, mute topics, and unsubscribe from newsletters that don’t align
with your values. Follow thinkers, creators, and curators who
consistently offer depth. Set boundaries (e.g., no mindless scrolling
after 9 PM). Pause after reading/watching to digest, not just
react.
Build a "Library Mindset" (Not a Wishlist One): Read books,
essays, and long-form work that lingers. Don’t engage with viral content
just because it’s popular. Save/share only what resonates deeply—not
what’s merely entertaining.
Train Your Taste Like a Muscle: Study great art, writing, music,
and design to refine your sensibility. Remove distractions, unnecessary
commitments, and low-value inputs. Note what ideas/images/sounds stay
with you—these reveal your true taste.
Embrace Coherence Over Consistency: Your bookshelf, playlists,
and feeds should reflect who you are (or aspire to be). Stay open to new
influences, but filter them through your core principles. Don’t adopt
aesthetics/opinions for status—authenticity matters more.
Practice "Vibe Coding" (Like Rick Rubin): Whether in
conversations or creativity, prioritize feeling over formulas. In
work/life, strip away excess until only the essential remains. If
something feels "alive," lean in—even if it defies logic.
Reject Cheap Dopamine for Lasting Satisfaction: Opt for the book
over the tweet, the slow movie over the clip. After consuming something,
ask: Did this uplift or drain me? Regularly eliminate distractions
(apps, subscriptions, habits) that don’t serve you.
Taste as a Spiritual Practice: Prioritize art/ideas that
rearrange your perspective. From your home to your workspace, align
space with intention. Engage only with what nourishes, not
depletes.
Remember: Curation = Power: Amplify only what deserves a wider
audience. Your ability to filter signal from noise is a competitive
edge. The more you refine your taste, the more it protects you from
chaos.
A Primer on US Healthcare - Generative Value [Link]
This article covers an overview of the system (main players), the
value chain (how products and services flow through the system and what
profitable segments are), incentives (motivation of behaviors),
challenges (significant issues within the industry), and potential
solutions (software and AI).
It deeply focuses on the interplay between incentives, middlemen, the
resulting administrative burden, and AI as the specific technological
solution appears to be a key perspective.
BREAKING: UnitedHealth Bleeds. CEO Witty Steps Down. - Sergei
Polevikov, AI Health Uncut [Link]
UnitedHealth Abuse Tracker - Matt Stoller, American Economic
Liberties Project [Link]
Vibe coding is a new approach to software development that
utilizes AI tools to assist individuals in creating applications and
software without requiring extensive programming
knowledge.
The term was popularized by Andrej Karpathy, an AI expert, who
described it as a method where users interact with AI using
natural language to describe their ideas rather than writing
traditional code directly.
This allows creators, particularly those lacking technical
skills, to build functional applications rapidly by
simply explaining their requirements to the AI, which generates the
relevant code for them.
Who’s the Highest-Paid CEO? - App Economy Insights
[Link]
Rick Smith, co-founder and CEO of Axon.
I Summarized Mary Meeker's Incredible 340 Page 2025 AI Trends
Deck—Here's Mary's Take, My Response, and What You Can Learn - Nate, Ai
& Product [Link]
Nate's overall take is that while Mary Meeker is correct that
Generative AI adoption is exploding, real value accrues only where
organizations align real-world problems with AI’s actual strengths in
workflows. He believes bigger claims demand commensurately bigger
evidence.
Carl Dahlman later gave us the three categories that are widely
used today:
Search and information costs: discovering what
is available to purchase and comparing alternatives
Bargaining and decision costs: coming to an
agreement between buyer and seller, including establishing the final
price and terms
Enforcement and policing costs: ensuring that
both sides holds up their end of the deal
Distribution costs: actually getting the good
or service to the end consumer
― How To Build AI Agents (2025 Guide) - Max Berry, Max'
Prompts [Link]
Key Concepts:
Transaction Costs: Costs incurred in addition to
the actual price of a good or service, necessary to coordinate and
execute a transaction. Marketplaces primarily sell the reduction of
these costs.
TAM Expansion: Reducing transaction costs lowers
the effective cost of a good or service, increasing demand and expanding
the Total Addressable Market (TAM). The degree of TAM expansion relates
to the percentage of total cost eliminated.
Value Distribution: Marketplaces save sellers money
on transaction costs and charge them a fee (often similar to what
sellers paid previously). They typically pass efficiency gains on to
buyers in the form of easier and faster experiences, creating a
demand-constrained market. Variable Costs of Addressing
Transaction Costs:
Low Variable Costs: Addressing search and
bargaining costs is highly efficient and has low variable costs.
Marketplaces can keep more of the value created here.
High Variable Costs: Addressing enforcement and
distribution costs involves significant variable costs (e.g., funding
returns, building logistics). While these make marketplaces bigger,
margins may be lower as value is passed to buyers.
Takeaways:
This article puts the concept of transaction costs as central to
understanding marketplaces. Transaction costs are defined as the costs
incurred beyond the actual price of a good or service, associated with
coordinating and executing the transaction itself. Marketplaces are
essentially businesses that sell the reduction of these transaction
costs. Studying transaction costs can help determine where marketplaces
will succeed, what kind of marketplaces to build, and how to price
them.
Looking ahead, the article suggests that the "free lunch"
opportunities in many industries are exhausted, pushing marketplaces
into high variable cost activities. This implies future marketplaces may
be higher scale but potentially lower margin and more operationally
intensive. To disrupt incumbent marketplaces, one should look for
remaining transaction costs that can be addressed much more efficiently
than the current solution. The article suggests disrupting food delivery
was possible by building a more efficient network than restaurants had,
but disrupting shipping for handmade goods is harder because it requires
competing with highly efficient companies like UPS and Fedex.
By far, the largest unsolved transaction costs are in the
services industries (e.g., freelancing, home
improvement), which constitute two-thirds of consumer spending. Most
services marketplaces are currently stuck at the Lead Generation stage,
limiting penetration and take rate. This might be partly because much
spend is on recurring services where customers leave the marketplace
once a good provider is found, leading services marketplaces to rely on
high-churn consumer subscriptions. Despite this, there are
opportunities, such as Zillow exploring expanding into managed
marketplace territory for home services.
four-stages-of-marketplaces
An hour a day is all you need. - The Improvement
Journal [Link]
The one hour is suggested to be dedicated to three key practices that
aim to rebuild an individual from the ground up:
Build Something That's Yours: This involves
creating something that belongs to you, beyond your job, such as a
newsletter, product, service, blog, or by learning/teaching a skill. The
purpose is to "plant seeds" that will compound over time, pulling you
out of stagnation.
Train Like You Want to Be Here for a While: This
practice emphasizes physical strength and movement, like walking,
running, stretching, lifting, breathing, sleeping deeply, eating real
food, and drinking water. It's a message of self-care and an intention
to use one's body, which also sharpens mental clarity, as Seneca
suggested, "The body should be treated more rigorously, that it may not
be disobedient to the mind".
Create Enough Silence to Hear Your Own Voice: This
habit counters the constant noise and stimulation of modern life. It
encourages practices like journaling, meditating, taking walks without
headphones, or simply sitting still without a goal or screen. The goal
is not productivity, but presence and creating space for reflection and
insight, preventing thoughts from being drowned out and actions from
remaining unexamined.
How to become friends with literally anyone - April & The
Fool [Link]
The article suggests that becoming friends with anyone is to approach
interactions with a deep-seated belief in shared humanity, genuine
curiosity about individual worldviews, and an open, empathetic demeanor
that seeks to understand rather than judge.
Try to understand people through conversations with a belief
in the universal commonality of human nature: The author
fundamentally believes that all people are driven by the same core human
urges and desires, such as the need to feel loved, respected, and seen.
This perspective makes it intuitive to understand others. They see
meeting someone new as a "puzzle of empathy" and a "game of
commonality," where they try to understand what someone would think,
want, need, or crave given their background, values, limitations, and
longings.
From common nature to differences among people due to
environmental factors: The author acknowledges that
how these needs are defined and achieved varies dramatically
due to factors like nationality, gender, religion, cultural heritage,
socioeconomic class, hobbies, and upbringing. These "little big
differences" are where things become interesting, leading to unique
individual personalities and perspectives.
Follow the reasonableness within their personal worldview to
understand motivations and values: The author believes that
while people may not always be rational, they are always "reasonable"
within their own worldview. This means that everyone has reasons for
their actions, and those reasons make sense within their personal
framework. Understanding a person's circumstances allows the author to
understand their motivations, struggles, and values.
Be curious and genuine while engaging with people:
The author describes themselves as extremely extraverted, loving people,
and hating small talk. They are curious about people, viewing them as
containing "worlds, histories, stories that span across generations and
geographies". This curiosity leads them to give "rapt attention and
genuine space to be yourself".
Assumption of friendship from the beginning, share stories
and genuine care: The author approaches new encounters with the
assumption that "we are friends" from the moment they meet. They are
open, putting "all my cards on the table" and inviting others to reveal
theirs. They enjoy conversations, making people feel understood, heard,
and cared for.
The key takeaway is that genuine technological advancement, which
is real and accelerating, must be distinguished from the business models
built around it, which frequently adhere to age-old patterns. When
stated purpose and actual function align, it typically indicates that
the technology addresses a specific, measurable problem with clear
economic value, rather than promising to "transform
everything."
Systemantics, or, the art of understanding what’s going on, means
recognizing the persistent gaps between what systems proclaim and what
they actually do, and capitalizing on that insight.
When the fog dissipates and clarity emerges, the survivors will
be those who patiently deciphered the underlying mechanics amidst
fleeting illusions.
Enduring AI companies will emerge in two distinct spaces by 2035:
unglamorous but essential tools that demonstrably improve margins or
reduce costs, and genuine frontier research that reveals entirely new
problem spaces. The first category refines what exists; the second
invents what doesn't yet.
Real opportunities lie in the quiet spaces between stated
ambitions and operational truths. Just as they always have.
― The Art of Understanding What's Going On - Tina He,
Fakepixels [Link]
How I Went From Reading 20 Books Per Year to Over 75 Books -
Ryan Hall, Read and Think Deeply [Link]
Takeaways:
Always take a book with you.
Give it about 50 pages before you quit. This keeps you from getting
stalled on a book that is not resonating with you.
Schedule the reading time. e.g., 45 min in the morning, 30 min in
the evening, and throughout the day when you get breaks.
Weekend sprints. Read in hour-long stretches on weekends or to do
several smaller stretches and get through entire sections or even whole
books on the weekends.
there are people with half your skills and intelligence living
out your dreams, just because they put themselves out there and didn’t
overthink it.
Reach out anyway—someone will always have more followers, more
free time, a better setup. It’s up to you to push through everything,
part the crowd, and make some space for yourself to at least give
yourself the chance of getting what you want.
You will never be fully ready and there will never be a perfect
time. It’s genuinely not about waiting for the right time to do
something when you’re ready, it’s about doing things before
you’re readyjust to make them exist.
― literally just do things - Erifili Gounari, crystal
clear [link]
Diabolus Ex Machina - Amanda Guinzburg, Everything Is A
Wave [link]
how to think like a genius (the map of all knowledge) - Dan
Koe, Future/Proof [Link]
the article suggests that thinking like a genius involves adopting a
holistic and nuanced approach to problems by utilizing the AQAL model,
encompassing all relevant perspectives (quadrants) and evolving one's
consciousness to a "second-tier" level that can integrate and synthesize
different stages of understanding. This allows for faster
problem-solving and greater achievement in life.
All Quadrants:
Individual Interior (Upper Left): Your personal thoughts, emotions,
beliefs, and consciousness. Questions in this quadrant might include
core values, what makes you feel alive, or fears holding you back.
Individual Exterior (Upper Right): Your behaviors, actions, and
physical brain states. This involves looking at natural talents,
developed skills, and what your behavior reveals about your
preferences.
Collective Interior (Lower Left): Shared culture, values, and group
consciousness. This could involve understanding parental or religious
expectations, influence of friends, or shared values you're drawn
to.
Collective Exterior (Lower Right): Systems, structures, and social
institutions. This quadrant considers current job opportunities, the
impact of education or technology, and systemic barriers or
advantages.
All Levels:
Premodern: Characterized by following established authority and
traditions, with black-and-white thinking and obedience to a God or
conformity.
Modern: Values science, individual achievement, competition, and
merit-based success.
Postmodern: Emphasizes relativistic thinking, where everyone's
truth is valid, and focuses on inclusion and equality. The article notes
that postmodern thinking can become pathological when it attempts to
dismantle all hierarchies.
Second-Tier: This is the suggested stage for
"genius" thinkers. Individuals at this level can look back and
synthesize truths from all prior perspectives, embracing complexity,
systems thinking, and awareness. It's less about "I'm right and you're
wrong" and more about finding the best solution through
synthesis, holding contradictions in mind until they can be
reconciled. Genius thinkers act as "translators"
between different stages.
How to Be Taken Seriously - Tessa Xie, Diving Into
Data [Link]
A summary of the four junior traits and what to do instead:
Junior Trait #1: Providing too much
detail/over-explaining
Excessive detail doesn't showcase knowledge and consideration of
edge cases, but it typically confuses the audience and makes them appear
unable to synthesize information, causing key points to be missed.
Managers may even prevent such individuals from presenting to executives
to avoid confusion and inefficiency in meetings.
What to do about it:
In written form: Summarize work with a "TL;DR" at the top, using the
Pyramid Principle (conclusion first, then supporting evidence). Focus on
what is important enough to communicate, moving less critical details to
an appendix.
In verbal form: Practice an "elevator pitch" of less than 30 seconds
to peers, focusing on the "why" and enough "what" to allow for opinion
formation. The ability to decide what NOT to communicate is as
crucial as what to mention.
Junior Trait #2: Not having an opinion or
recommendation
As data scientists become more senior, translating analysis into a
recommendation becomes increasingly important. Hesitation to provide
recommendations often stems from the perceived risk and the nuanced,
non-black-and-white nature of data, leading to "analysis paralysis".
However, not giving recommendations shows a lack of ownership and limits
one to simple "execution" work.
What to do about it:
Adopt an ownership mindset, imagining you are the
decision-maker. Ask what data you would need and if the
presented data would convince you.
Understand that value comes from giving robust recommendations
despite nuance and ambiguity, just as taking risks can lead to
above-average returns.
While you should list caveats, most people prefer a
recommendation they disagree with over no recommendation at all, as it
provides a basis for discussion and understanding assumptions.
Data teams are paid to drive business decisions, not
just pull and present data.
Junior Trait #3: Not being clear about the "why" behind the
analysis
Junior DS often state "XYZ stakeholder asked for this" as the sole
reason for an analysis, which is insufficient. This indicates a lack of
ownership of the business problem and hinders the ability to deliver
effective solutions, leading to frustration from changing data
requests.
What to do about it:
Own the problem. When asked to pull data, find out
why the stakeholder needs it and what decision they are trying
to make.
By understanding the ultimate business problem, you can
brainstorm the most effective data solutions,
potentially different from the original request, thus elevating
yourself to athought partner.
Junior Trait #4: Not having the basics down to be able to
stay "one step ahead"
Losing credibility happens when people feel you don't know the data
or business area you cover. To establish yourself as an expert, you need
to anticipate common questions and be the most familiar with the
data in your area. If you lack answers to natural follow-up
questions, it suggests you haven't thoroughly understood or explored the
data.
What to do about it:
Be curious about your data; start with a basic question and explore
from there, jotting down answers.
Anticipate follow-up questions in three buckets before presenting:
foundational knowledge (e.g., how the product works,
user numbers), your analysis (details beyond the main
insights), and next steps (what the findings mean for
stakeholders).
Get a second pair of eyes on your work, ideally from someone not
deep in the analysis, to catch obvious omissions.
How to work with AI: Getting the most out of Deep Research -
Torsten Walbaum, Operator's Handbook [Link]
A comprehensive guide of AI Deep Research from idea to value. The
author provided a good ChapGPT Deep Research example here.
Deep Research prompt for meeting transcription by o3 here.
structuring_a_deep_research_prompt
Free 15 queries per (ChatGPT+Gemini); Free 13 queries per day
(perplexity+Grok)!
pricing_and_limits
Papers and Reports
Trends - Artificial Intelligence - Mary Meeker, Bond
[Link]
Think Only When You Need with Large Hybrid-Reasoning
Models [Link]
Large Reasoning Models (LRMs) improve reasoning via extended thinking
(e.g., multi-step traces), but this leads to inefficiencies like
overthinking simple queries, increasing latency and token usage. The
team Introduces Large Hybrid-Reasoning Models (LHRMs) — the first models
that adaptively choose when to think based on query complexity,
balancing performance and efficiency. They utilizes a two-stage
approach: 1) Hybrid Fine-Tuning (HFT) – cold start
using curated datasets labeled as "think" vs. "non-think"; 2)
Hybrid Group Policy Optimization (HGPO) – an online RL
method that trains the model to pick the optimal reasoning mode. They
defines Hybrid Accuracy to evaluate how well the model
selects between thinking and non-thinking strategies; correlates
strongly with human judgment. Experiments show LHRMs outperform both
LRMs and traditional LLMs in reasoning accuracy and response quality,
while also reducing unnecessary computation.
The 2025 State of B2B - Monetization - Kyle Poyar
[Link]
The report summarizes a poll of 240 software companies about their
pricing strategies. Key findings indicate a decline in flat-rate and
seat-based pricing models, with hybrid pricing (combining subscriptions
and usage) emerging as the dominant approach, especially for companies
incorporating AI capabilities. The report also highlights a growing
interest in outcome-based pricing among AI-native companies and stresses
the importance of pricing agility and clear ownership of pricing
strategy within organizations.
The Illusion of Thinking: Understanding the Strengths and
Limitations of Reasoning Models via the Lens of Problem Complexity -
Apple Machine Learning Research [Link]
The authors analyzed the thinking process and reasoning traces of
LRMs in several smart ways:
A custom pipeline using regex identifies and extracts potential
solution attempts from the LRM's thinking traces.
Extracted solutions are rigorously verified against puzzle rules and
constraints using specialized simulators for step-by-step
correctness.
Records the accuracy of valid solutions and their relative position
within the reasoning trace for behavioral insights.
Categorizes LRM thinking patterns (e.g., overthinking, late success,
collapse) by analyzing how solution correctness and presence vary with
problem complexity.
Examines how the proportion of correct solutions changes
sequentially within the thinking trace, revealing dynamic accuracy
shifts.
Pinpoints the initial incorrect step in a solution sequence to
understand the depth of correct reasoning before error.
Quantifies thinking token usage to analyze scaling of effort with
complexity, noting an unexpected decline at high complexity.
How much do language models memorize? - Meta, Google, NVIDIA,
and Cornell University [Link]
This paper proposed a new method to quantify how much information a
language model "knows" about a datapoint.
They formally separate memorization into two components by two novel
definitions of memorization: unintended memorization (information about
a specific dataset) and generalization (information about the true
data-generation process).
There are several interesting findings:
By training models on uniform random bitstrings (eliminating
generalization), they precisely measure model capacity, finding
GPT-style transformers store 3.5 to 4 bits per parameter.
Their framework shows that the double descent phenomenon occurs when
the data size exceeds the model capacity, suggesting that models are
"forced" to generalize when they can no longer individually memorize
datapoints.
The paper develops and validates a scaling law that predicts
membership inference performance based on model capacity and dataset
size, indicating that membership inference becomes harder with larger
datasets relative to model capacity.
To understand their smart methods:
They proposed a very clever approach to understand memorization
and model capacity in LM. They isolate unintended memorization by
training models on uniform random bitstrings.
No Generalization Signal: When training on truly random data, there
are no underlying patterns, rules, or structures for the model to
generalize from. Each bitstring is an independent, random piece of
information.
Only Memorization is Possible: In this scenario, the only
way for the model to "learn" or perform well on this data (i.e., predict
the next bit in a sequence or identify if it was part of the training
set) is to literally memorize the specific bitstrings it has seen. Any
"knowledge" the model gains is purely about the individual data
points.
Total Memorization as Measured: Therefore, when generalization is
effectively zero, the information the model stores about the random
bitstrings directly reflects its total memorization capacity
for that type of information. There's no "general knowledge" to
distinguish; it's all about remembering specific instances.
Therefore, they are measuring the maximum amount of distinct,
specific information the model can store.
They equal total memorization to model capacity. In machine learning,
model capacity generally refers to the size and complexity of
the functions a model is capable of learning. It's the model's
ability to fit a wide variety of patterns in the data. A model with
higher capacity can potentially fit more complex relationships or
memorize more specific data points.
The paper further quantifies this by showing that GPT-style models
have a capacity of approximately 3.6 bits-per-parameter. This indicates
that each parameter in the model effectively acts as a certain amount of
storage for information, reflecting the overall capacity of the neural
network architecture.
The fundamental challenge in understanding and evaluating
language models is the ambiguity and conflation of
"memorization" (copy or reproduce a specific sequence in the training
data) and "learning." (truly understand and generalize a pattern or
concept) This is exactly what they addressed by decomposing
memorization into unintended memorization and generalization. The
decomposition enables controlled measurement and the use of random
bitstrings is the key innovation.
About the Double Descent Phenomena: When a model's capacity exceeds
the generalizable patterns in the data, it starts to memorize individual
data points. As data size increases relative to capacity, the model is
"forced" to generalize more, leading to a decrease in unintended
memorization and an improvement in performance.
The core insight is that as models become massively overparameterized
(far beyond what's needed to simply fit the training data), they find
"simpler" interpolating solutions that generalize better, often due to
the implicit biases of optimization algorithms like Stochastic Gradient
Descent (SGD).
Intuition for double descent:
"Under-parameterized" Regime (Classical ML): Model Capacity <
Data Size: very generalizable, low test error
"Interpolation Threshold" (The Peak): Model Capacity ~ Data Size:
peak of test error, due to overfitting the noise
"Over-parameterized" Regime (Double Descent / Modern Deep Learning):
Model Capacity >> Data Size: robust generalization happens, test
error goes down again.
Here is a concept Membership Inference Attacks
(MIAs): These are attacks that attempt to determine whether a
specific data point was part of a model's training dataset or not. A
successful MIA indicates that the model has "memorized" that specific
data point. "Scaling Laws for Membership Inference" in the paper refers
to predictive relationships that describe how the success rate
of a MIA changes as a function of various model and data
characteristics, such as model capacity and dataset size.
They basically propose that membership inference success is
inversely related to how "generalizable" the data point is within the
model's capacity. In other words, membership inference is
easier when the model has high capacity relative to the
dataset size, leading to more unintended memorization of individual data
points. Membership inference becomes harder (i.e., less
successful for an attacker) when the dataset size is very large compared
to the model's capacity, or when the model starts to "grok" (generalize)
more.
LlamaFirewall: An open source guardrail system for building
secure AI agents - Meta AI [Link]
Reasoning models don't always say what they think -
Anthropic [Link]
The core issue explored is whether the Chain-of-Thought accurately
reflects the model's true reasoning process ("faithfulness"). The
article's research suggests that models frequently do not faithfully
report their reasoning. Researchers tested this by subtly providing
hints (both correct and incorrect) to models during evaluations. They
then checked if the models admitted using these hints in their
Chain-of-Thought explanations. The results showed that models (Claude
3.7 Sonnet and DeepSeek R1) rarely mentioned the hints they used, even
when those hints led them to a different answer or involved potentially
problematic information (like "unauthorized access"). Instead, models
often created fake rationales for their answers.
The researchers also studied reward hacking by constructing synthetic
Reinforcement Learning (RL) environments with injected "reward hacks."
They then finetuned an earlier snapshot of Claude 3.7 Sonnet in these
environments using outcome-based RL. By setting up the environments this
way, they could observe if the models learned to pick the incorrect hint
answer to maximize their reward, and then check if their
Chain-of-Thought verbalized this reward-seeking behavior. They found
that models learned the reward hacks very quickly (selecting the
incorrect hint answer over 99% of the time), but rarely verbalized these
hacks in their Chain-of-Thought (less than 2% of examples in most
environments).
Your Brain on ChatGPT: Accumulation of Cognitive Debt when
Using an AI Assistant for Essay Writing Task - MIT [Link]
This experimental study combines neuroscience (EEG), educational
psychology, and human-AI interaction to examine how AI tools like
ChatGPT influence cognitive engagement during essay writing. The result
shows that brain-only group showed stronger and broader neural
connectivity, especially in theta bands, indicating deeper internal
ideation and cognitive engagement. LLM group exhibited reduced alpha and
theta connectivity, suggesting externalized and narrower thought
patterns—relying more on ChatGPT suggestions rather than internal
generation of ideas. So it's saying if you rely heavily on AI, you will
get dumber.
On the Extinction Risk from Artificial Intelligence -
RAND [Link]
This report examines the potential for AI to cause human extinction.
The authors analyzed three specific scenarios: the use of
weapons, the release of biological pathogens, and severe climate warming
via malicious geoengineering. The study concludes that
significant barriers exist for AI to achieve human extinction and it
would likely require intentional AI action and substantial time for the
threats to materialize, allowing for human response and mitigation
efforts. Ultimately, the report suggests that resources dedicated to AI
extinction risk should also support broader global catastrophic risk
mitigation and general AI safety.
This analysis starts from what could cause human extinction and
assesses how AI could contribute to that. The threats are limited to
those can be explored through scenario planning, while those that
involve a deeper level of uncertainty are ignored.
Can US infrastructure keep up with the AI economy? -
Deloitte [Link]
2025: The State of Consumer AI - Shawn Carolan, et al., Menlo
Venture [Link]
Generative AI & Gender Global Employment Trends -
Linkedin Economic Graph [Link]
Agentic Misalignment: How LLMs could be insider threats -
Anthropic [Link]
Researchers stress-tested 16 leading AI models from various
developers and found that in certain situations, these models resorted
to malicious behaviors like blackmailing officials or leaking sensitive
information. These actions occurred when the models faced replacement or
when their assigned goals conflicted with the company's changing
direction, even when they were given only harmless business goals and
explicitly told to avoid such behaviors. While no evidence of agentic
misalignment has been observed in real deployments, the findings suggest
caution when deploying current models in roles with minimal human
oversight and access to sensitive information.
SHADE-Arena: Evaluating sabotage and monitoring in LLM agents
- Anthropic [Link]
Dual Objective Setup:
Each evaluation environment includes a clearly defined main
task that simulates a real-world scenario and a secret
side task that is harmful but can be plausibly hidden within
the main task execution.
The agent's challenge: complete both tasks while
avoiding suspicion.
Using LLMs to assess detection, measuring task completion vs.
detection success, and providing a benchmark for improving agent safety
and control.
YouTube and Podcasts
Sundar Pichai: CEO of Google and Alphabet | Lex Fridman
Podcast [Link]
Jared Isaacman: What went wrong at NASA | The All-In
Interview [Link]
Naval Ravikant On The 4 Books That CHANGED His Life
(Financially And Philosophically) [Link]
Chamath Palihapitiya: Zuckerberg, Rogan, Musk, and the
Incoming “Golden Age” Under Trump - Tucker Carison [Link]
Satya Nadella on AI Agents, Rebuilding the Web, the Future of
Work, and more - Rowan Cheung [Link]
Jeff Bezos: Amazon and Blue Origin | Lex Fridman Podcast -
Lex Fridman [Link]
WWDC25: Platforms State of the Union - Apple [Link]
IPOs and SPACs are Back, Mag 7 Showdown, Zuck on Tilt,
Apple's Fumble, GENIUS Act passes Senate - All-In Podcasts [Link]
I think being smart — and not being afraid to show it — means
living with discomfort. It's difficult. It means being willing and able
to admit when you're wrong. And it means being okay with complexity,
contradiction, and uncertainty.
So, the good news: I think smart survives. It's stubborn. It's
not loud. It's not flashy. But it's resilient — like a cockroach with a
PhD.
And the thing about stupidity is that eventually it bumps into
the hard wall of reality. When the bridges collapse and the crops fail,
when the Amazon package doesn't show up because the supply chain finally
imploded — suddenly people start looking around and going, "Hey, does
anybody know how to fix things? Build things?"
And the guy who spent the last 20 years reading books and
educating himself instead of screaming at his phone? Yeah, he's the one
holding the duct tape.
It won't be sexy, and it won't be immediate. But intelligence
isn't dead — it's just hungover. And sooner or later, we're going to
need it to crawl out of bed, drink some black coffee, and start fixing
the mess.
So at the end of the day, stupidity will always be popular — at
least in the U.S. But intelligence — real, patient, compassionate
intelligence — is what keeps the lights on.
And by the way, that goes for emotional intelligence too, which
is every bit as important.
So if you're still here, still critically thinking, still
refusing to go quietly into that great dumb night — you're already part
of the resistance.
Keep going.
― The Death of Intelligence: Why Modern Society Celebrates
Stupidity - The Functional Melancholic [Link]
The Obsession That Creates Enduring Companies | David Senra
Interview - Invest Like The Best [Link]
Articles and Blogs
Everything Google Announced at I/O 2025 - WIRED [Link]
Launch Hugging Face Models In Colab For Faster AI Exploration
- Medium [Link]
My AI Skeptic Friends Are All Nuts - Thomas Ptacek
[Link]
The author argues that LLMs as agents are improving developer
productivity, and suggests that while the hype around AI can be
annoying, the technology's impact is real and profound. He believes that
those who don't embrace AI in their coding practices will be left
behind.
The author shares a disorienting sense of reality's erosion,
attributing it to various factors, including the relentless pace of
digital information, the overwhelming nature of political events, and
the insidious proliferation of AI. This environment fosters a collective
cognitive detachment and erosion of critical faculties, making it
challenging to discern truth, engage effectively, and maintain a
grounded sense of self and world.
Is there a Half-Life for the Success Rates of AI Agents? -
Toby Ord [Link]
News
Meet the Foundation Models framework - Apple [Link]
The iPhone maker has launched the Foundation Models framework to
allow users to run a 3B parameter model locally. The framework is part
of Apple Intelligence suite and allows developers to access it using
three lines of code. The model can be used to generate text, extract
summaries, and tag structured information from unstructured text.
Users should be aware of strength and weakness. It's only available
on Apple Intelligence-enabled devices with OS version 26+. You need to
use Xcode Playgrounds to prototype with real model output. You can use
Instruments profiling template to measure latency and token overhead.
There is no support for fine-tuning or external model deployment
Connect Your MCP Client to the Hugging Face Hub -
HuggingFace [Link]
HuggingFace releases open-source MCP server to allow accessing its
tools from VSCode and Claude Desktop.
New Book List
Some book names from my daily readings recently caught my attention
and might be the next book to read for me:
"How Leaders Learn" by David
Novak is a great book for active learners. It has three
chapters: "Learn from", "Learn to", and "Learn by".
Active learners are like artists—constantly refining, adapting, and
evolving. They approach life as an masterpiece-in-progress,
understanding that each new insight adds depth and clarity to the bigger
picture. The book encourages active learning and defines it as a mindset
- a daily discipline of seeking out knowledge from people, experience,
and failures, staying open to feedback, new perspectives, and
uncomfortable truths, and taking actions to test ideas, adapting and
refining.
An active learner is somebody who seeks out ideas and insights and
then pairs them with action and execution. They learn with purpose. The
result is greater possibilities-for them and the people around them.
It's as Eric Hoffer, the American philosopher, wrote in Reflections
on the Human Condition: "In a time of drastic change, it is the learners
who inherit the future." They can't wait to discover the next idea, and
the next, and the next, because behind every idea is a world of
possibility and a brighter future.
Warren Buffett once told me what he looks for in the companies he
acquires. He said, "I'm looking to buy companies that are run by
painters." When I asked for an explanation, he said, "Most great artists
have a hard time letting go of their paintings. They're in love with the
painting. They are constantly adding a dab of color here, a little more
texture there. I'm looking for the boss who is always tweaking their
company, constantly trying to make it better. No matter how successful
they may have already been, what they still see is a
masterpiece-in-progress." He calls Berkshire Hathaway a museum for these
masterpieces, but he expects the people who run them to keep making
progress, to keep changing and expanding.
This book covers a lot of good practices, some of which I learned
through experience and have been implementing in daily life, but I've
never clearly summarized them in words like this author does (e.g.,
learn from failure and success, learn to ask better questions, learn to
develop pattern thinking, learn to reflect, learn by tackling problems,
etc); some are common sense to people but not easy to follow (e.g. learn
to see the world the way it really is, learn to make and check your own
judgments, learn by being your best self, learn by seeking new
challenges, learn by making everyone count, learn by recognizing on
purpose, etc); others are new ideas and wise advice to me that are
incredibly enlightening (e.g., learn from new environment, learn to
trust in positive intentions, learn to be humble and confident, learn by
simplying, learn by teaching, etc).
My Learnings:
I carry good values and get rid of bad values from my upbringings
and move on, but never go back and think about weakness and blind spots
that were developed implictly.
Our upbringings shape us-the good and bad experiences, the normal
experiences of our day-to-day lives. When you choose to learn from your
upbringing, you learn who you are, your strengths and weaknesses, your
unique perspective, and your blind spots.
I'm the type of person who stick to one thing or one job, do the
best, and get the most learnings from it - greedy but probably not the
most efficient approach. So this is the top one advice for me:
"Not moving means not growing" and
"Choose environment wisely and don't stand
still".
New environments bring uncertainty and risk, two things humans really
don't like. The brain weighs threats of loss heavier than it does
opportunities for gain. Whether it's a move to a new city or a move to a
new company, we don't know the people or the culture and we don't know
if we'll succeed when we get there. The brain tells us it's best if we
just stay where we are, in our more certain, less risky, known
environment. But that's not always the right choice. Josh Waitzkin,
child chess prodigy, subject of the book and movie Searching for Bobby
Fischer, and later a tai chi world champion, wrote in The Art of
Learning, "Growth comes at the expense of previous comfort or
safety.
However, not every new environment is good for you, it requires some
luck and judgment.
When looking at a new environment, evaluate it for these four sources
of learning:
New knowledge, skills, or systems
New ideas and innovative thinking
New people and their perspectives and opinions
New influences that lead to personal growth
Some new environments aren't going to advance your learning; they
might even slow you down.
First, make sure the new environment will offer opportunities to
learn and grow in any area that's important to you right now, like I
did. This is especially true when you have an ambition but aren't sure
how to get there.
As important as this work is, the next important step is to insert
ourselves in an environment filled with people who routinely do what
we're struggling to imagine." This is the whole point of choosing a new
environment.
Second, choose an environment that's suited to you. Understanding
your personal ideal environment is an important aspect of
self-awareness.
Third, choose an environment that will exert the right influences on
you, so that you're not only learning new skills, new knowledge, and new
ideas, but also absorbing better collaboration, better leadership,
better self-management, or whatever area of personal growth you think
you need to work on.
It's not only about growth, new environment can shape a person.
Our social and cultural environments have a huge impact on our
thinking and behavior. In Infuencer, psychologist Joseph Grenny and his
coauthors explain that if you want to change behavior, you have to make
changes to the social and structural environment. In Atomic Habits,
James Clear argues that our environments usually matter more than our
motivation when it comes to building new habits: "Especially over a long
time period, your personal characteristics tend to get overpowered by
your environment."
You can either fight that truth or leverage it to learn more and grow
more. Eric Gleacher recognized the power of environment and how it could
not only offer new skills but also shape the person he would become at a
surprisingly young age.
Have a look at what a getting-things-done talent looks like and
fill the gap. Although people all succeed in different ways and no one's
success is replicable, becoming a 'working genius' is at least a good
option to start.
Invention: creating novel ideas or solutions
Discernment: evaluating and analyzing ideas and situations
Galvanizing: organizing and inspiring others to take action
Enablement: providing encouragement and assistance
Tenacity: pushing projects to completion
If you're wondering who you should turn to, always start with people
who have applied their ideas in the real world and can prove that they
work.
Next, ask, Will they actually fill my gaps, or will they hold back
their best ideas or try to elevate their ego by making what they know
seem complex and hard to understand? Will they make their knowledge
simple and clear? Essentially, you're asking, Is this person an active
learner? Because active learners love helping people fill their
gaps.
A final tip: if you want people to share their know-how with you, you
need to spread know-how. You need to be willing to share with them,
too.
Human has instinct to avoid social pain or negative truth about
themselves, when someone tells a less positive truth, we need to fight
our instincts and always listen.
When somebody cares enough and is brave enough to tell you the truth,
your best course of action is to fight your instincts to dismiss it or
hide from it. Overcome your brain's biological drive to protect you.
Shut down the voice in your head telling you they're wrong. Don't run
out of the room. Take some deep calming breaths (that really works),
remind yourself that this person probably has a good reason for bringing
the truth to your attention, and listen.
Active learners work through this set of mental gymnastics every day.
They work on their humility and maintaining an open mind (more on this
in part two because they see the value truth-tellers bring.
Pursue the truth of the world, don't be delusional. Although
'we see the world as we are, not as it is' (Adaptation of Anaïs
Nin's famous quote), we at least should be aware of this.
Andy Pearson: "Learn to see the world the way it really is, not how
you wish it to be."
Darrow: "Chase after the truth like all hell and you'll free
yourself, even though you never touch its coat tails."
In their book Decisive, Chip Heath and Dan Heath explained that a
sound decision-making process is more important than data and analysis,
because no matter what, that data or our analysis of it is often flawed.
We interpret it based on what we wish or what we assume or what we
think, not what is.
Good process can lead to better analysis, they explained, but
analysis without good process won't produce the best learning. You need
both to orient yourself to reality.
When you see the world the way it really is, the right action becomes
very clear.
One of the best ways to be a better critical thinker is to make sure
that your information is as close to the source as possible. If you
don't go to the source yourself, you might be letting one perception
after another influence what you end up hearing or learning. You won't
know if you're seeing reality.
When you're trying to see the world the way it really is, it's
important to not be blinded by good news, something a good process can
help you overcome.
A great way to stay grounded is to not only chase the truth but also
deal in it. Active learners know the value of being honest and
transparent. They tell it like it is, because they know when they do,
there's a greater chance others will, too.
I love pattern thinking and I seek out actively, but I still
limit myself by a passive pursue of richer life experience.
To prepare to make that leap, active learners expose themselves to as
many patterns from as many disciplines as they can. Being curious about
the world around us in the hope that we'll discover a new way of
thinking about a problem or a new way of seeing an opportunity is core
to active learning. Active learners read, listen, travel, try new
things, explore hobbies and interests. They explore trends and insights
from different disciplines, industries, cultures. Then they apply what
they've absorbed to problems or goals. Those habits have helped me come
up with some of my most successful ideas.
You might think of a pattern-thinking moment as an aha moment or a
stroke of inspiration, but active learners don't wait for the moment to
hit them; they work to find it.
Peter Georgescu, chairman emeritus of advertising giant Young &
Rubicam and author of The Source of Success, said of pattern thinking,
"A creative solution is a leap, and that leap is supported and fed and
nurtured by experiences in life. The richer your life experience is, the
more creative you'll become."
About reflection and thinking, the book elaborates two modes:
focus mode and diffese mode. It resonates with me as I do see the
benefits of switching between data science work during the day time and
freestyle dancing in the evening in terms of developing creative ideas
and unstuck myself from difficult problems.
In her talk, she described two modes of thinking: focus mode and
diffuse mode. Focus mode is exactly what it sounds like. It's how we
think when we're trying to accomplish a task or memorize something. Our
thinking is usually confined to neural paths we've already created.
Diffuse mode is a more "relaxed set of neural states" that allows our
thinking to take off, range widely, and process or even create new
ideas. When we are learning, we need both. And when we feel stuck in our
thinking, unable to understand a concept, unable to unravel a challenge,
we especially need the diffuse mode.
A combination of confidence and humility is a good
characteristic. I've never thought about them deeply as a combo, that's
why I've never found the sweet spot.
Confidence is important because nobody will follow you unless they
believe you know where you're going and you'll find a way to get there.
If that confidence isn't tempered by humility, though, it becomes
arrogance.
Humility is just the recognition that you can't do it by yourself
whatever "it" is-either because you simply can't, because you don't know
enough, or because it won't be as fun or fulfilling if you go it
alone.
Confidence is simply the expectation that you'll find a way to
win-somehow.
People have good side and bad side. If you believe in their good
side, they do so. From another perspective, it's often not their fault
if they choose to express the bad side.
In any relationship, business or personal, somebody has to trust more
or trust first to break inertia and build up positive momentum.
As important as it is for us to trust in positive intentions, if we
want people to trust in ours, we need to behave accordingly. We need to
build a well of trust to draw on.
We're all human; we're all going to lose our tempers or handle a
delicate situation poorly or not show as much compassion as we should or
make a poor judgment call. When we're on the receiving end, if we can
take a breath, find a little empathy, and trust that the other person
has good intentions that didn't pan out, we can avoid a total breakdown
in the flow of ideas and learning and collaboration.
I read a striking definition of trust recently: "Trust is a
relationship of reliance." Aren't we all reliant on each other if we
want to learn, grow, and expand our possibilities? We can choose to
support that relationship or tear it down. If we choose the second
option, we're only limiting ourselves. If we choose the first, the
possibilities are infinite.
This is from my experience: I only think hard, struggle and
learn, when I'm dealing with my own unique life path, I don't take time
to think when I follow other's path or live to other's expectation.
You may know the quote often attributed to Oscar Wilde: "Be yourself;
everyone else is taken." (What he actually wrote is more cynical: "Most
people are other people. Their thoughts are someone else's opinions,
their lives a mimicry, their passions a quotation." Maybe because of my
background and the potential prejudgments that came with it, I've spent
most of my life working hard to just be me to understand who that person
is, the contributions I have to offer, what I believe, and my purpose
and passions. If I hadn't followed this path, I would have missed out on
so much learning.
Active learners know that it's hard to learn when your mental energy
is focused on trying to be somebody other than yourself. Instead of
being open and curious, you'll be defensive. You'll be putting up
barriers and withholding your brilliance. And then the people around you
will do the same. Most of us can sense when people aren't being
authentic, and it makes us trust them less.
Active learners like Marvin pursue authenticity by recognizing their
unique value and talents, figuring out what matters to them and why, and
then leveraging it to have a positive impact.
It's all about bringing who you are to the moment so that you're
comfortable and open-minded enough to learn important lessons and ideas
as they arise.
Everyone knows we need do the right things, but when it comes
difficult situations, would you like to prioritize it above all
else?
This is vital, because over time, depending on environments and
circumstances and your own choices, your sense of right and wrong can
suffer from stepwise degradation. You stray over the line, stray a bit
further the next time, justifying one bad action after another. Stray
too far over the line and you can lose sight of it entirely. Eventually,
you lose the ability to know what doing the right thing looks like.
The best thing that happens when we do the right thing is that we
feel good about our choices and the impact we're having on the world,
and that inspires us to keep doing the right thing. Values aren't some
thing you write down on a piece of paper and then put in a drawer or
hang on the wall. Values are something you use to take good action. It
isn't always the easy choice, but it's always the best choice and the
one that helps you learn the most powerful lessons.
Input and output are different things. We collect information by
inputing knowledge from outside, and we make sense of those knowledge by
neural-networking it within our brain and outputing it in a little
different way which requires our logical, critical, and creative
thinkings.
Two things happen in the brain that help us "learn what we know." One
is that we believe ideas more when we share them with others verbally,
especially if we're trying to convince others that they're true.
Psychologists call it the "saying is believing effect." Want to convince
yourself to make time to exercise three times a week? Try convincing
somebody else that they could fit a simple exercise regimen into their
schedule. Another is that speaking (and writing) brings a different part
of our brain into play than just thinking, which changes how we think
about an idea. It's one reason that we can struggle and struggle to come
up with a solution to a problem, but almost as soon as we explain the
problem to another person out loud, a good solution pops into our head.
Talking it out forces us to slow down, zoom out (simplify), and order
our thoughts.
Sometimes, it's audience's engagement and support force us further
along the learning journey.
I learned things I didn't know, and I learned what I already knew, as
Timo put it, as I analyzed leadership, considered it from different
angles, and expanded or supported my ideas. Active learners use this
process to codify their ideas into something digestible and easily
shared. When you codify it, you can scale it.
Teaching well also forces you to stay on top of your game, to
continually look for new material to keep your ideas current and
relevant. And it forces you to learn good storytelling, an invaluable
skill. Stories are stickier than almost any other kind of information.
If you want an idea to stay with people, you better be able to convey it
in a relevant, compelling story with emotion and tension.
Many know "people go first", few know how to do it. If you want
them to care about what you care about, you need to care about them
first.
Active learners understand that people-not knowledge or
results-should be the priority. How we support people, how we show our
gratitude for them, how we show our interest or concern for them has a
much greater impact, especially over time, than the latest quarterly
earnings or the latest market rankings. I've said it before: I really
like to win. But you don't win for long if the people who make the
winning possible don't know how much they count.
I have always admired Geoff Colvin, senior editor-at-large of Fortune
magazine and author of books like Talent Is Overrated and Humans Are
Underrated. When he joined me on my podcast, he described the kinds of
high-value work that only humans can do and that technology or AI can't:
empathy, collaboration, and the insights or learning we generate along
the way.
The greatest muscle you can build is urgency. Decrease the time
between having an idea and getting it done. Everything changes – Codie
Sanchez
You either chase your one big goal with everything you’ve got, or
nothing will happen. Trying to be balanced is what’s wrong with
society.
Success in any field comes from IMBALANCE.
Hard work only feels bad if it’s building someone else’s dream,
not yours.
The most important thing in your career: Speed.
If you answer emails fast, walk fast, talk fast, get sh*t done
fast, you will make a lot of money. No sense of urgency, you won't. Nick
Huber
― The Most Successful People I Know Have a Psychopathic Sense
of Urgency - Unfiltered by Tim Denning [Link]
UBER: Distribution is The King - Capitalist Letters
[Link]
Great business with potential of continuous growth and expansion, and
cheap stock price.
Ecosystem is a huge advantage because it creates cross-platform
efficiencies.
uber_business_model
The concept of network effect was first laid out in 1985, by Carl
Shapiro and Michael Katz in their seminal paper “Network
Externalities, Competition, and Compatibility.”
network_effect
According to Russ Harris, author of The
Happiness Trap, values are “how we want to be, what we want to stand
for, and how we want to relate to the world around us.”
Values are attributes of the person we want to be.
― How Successful People Timebox - Nir Eyal's
Substack [Link]
Identify your values -> turn values into time commitments ->
create a timeboxed calendar -> track distractions -> reflect and
refine weekly. Do remember to schedule fun activities, use flexible
categories, and be aware that the goal isn't finishing tasks.
Some key ideas in the article backed by behaviorial science:
According to The Happiness Trap by Russ Harris (Acceptance
and Commitment Therapy - ACT), productivity should align with personal
values (e.g., health, relationships, growth) rather than just task
completion.
People often ignore realistic time estimates in favor of optimistic
ones, leading to overpacked schedules and missed deadlines.
Especially in the Bay Area, the problem isn’t mediocrity—it’s
misdirected excellence. Kids under Chua’s parenting style rarely have a
choice in their own extracurriculars from elementary through high
school. (I doubt being vice-president of the National Honor Society is a
dream to most.) Sure, it can produce a passable overachiever who knows
how to get A’s. But to produce someone capable of real vision, high
agency, and contrarian thinking, the irony is that that overachiever may
be ill-prepared as we approach an era where AI handles rote tasks and
the knowledge economy demands more creativity.
― How to Raise High-Agency Kids - Rebecca Wang [Link]
True excellence and future success come from fostering
agency—self-directed purpose, curiosity, and ownership—rather than
forcing kids to conform to hyper-competitive, checklist-driven
achievement cultures (like those common in the Bay Area).
What's the root problem? - misdirected
excellence
What's the solution? - Give kids structure
(boundaries, values) but autonomy (freedom to pursue
interests).
This isn’t about faking confidence. It’s about understanding the
low-pressure way to join a group.
Our ability to notice intricate details allows us to ask the
specific questions that make others feel truly seen.
In a world where everyone is clamoring to be heard, the ability
to observe and truly listen becomes your superpower.
Robert Greene's The 48 Laws of Power completed the picture with
"Never Outshine the Master", a lesson teaching the power of blending in
rather than disrupting. Don't announce your presence; become part of the
scenery, then contribute when appropriate.
― The Spy Trick to Joining Any Conversation (Even If You're
Anxious) - AnifragileADHD [Link]
For neurodivergent individuals (ADHD, social anxiety, etc.),
socializing isn’t about performing—it’s about strategic observation and
gradual integration. This article is backed by psychology and behavioral
science.
Small tips:
Stand inside the group (not on edges) and listen silently at
first.
Linger quietly to blend into the social environment.
Wait for a group member to naturally include you.
Ask open-ended questions about others’ interests.
Sustain conversation with follow-up questions.
Articles and Blogs
Scientists discover quantum computing in the brain -
Brighter [Link]
This research bridges quantum physics, biology, and information
theory, suggesting that life evolved to exploit quantum
mechanics for survival and intelligence. It challenges
reductionist views of biology and could redefine our understanding of
consciousness, disease, and even the origins of life.
Here are the 19 US AI startups that have raised $100M or more
in 2025 - TechCrunch [Link]
Just as “internet” evolved from buzzword to business backbone, AI
is following the same playbook.
― In 2025, venture capital can’t pretend everything is fine
any more - Pivot to AI [Link]
Venture capital in 2025 is a dying industry clinging to AI as its
last hope, with most investment funneled into OpenAI and a few other
hyped players while the rest of the startup ecosystem collapses. The
sector, which thrived on zero-interest-rate euphoria, now faces a harsh
reality: no exits, frozen IPOs, and a market unwilling to fund
early-stage ventures. VCs blame Trump’s chaotic tariffs—despite many
having supported him—but the real issue is their own inability to adapt
to a normal economy. The NVCA report offers no solutions, just desperate
optimism, as the industry’s leaders—many of whom lucked into
success—flail in ideological fringe movements and pray for a miracle.
The only remaining question is whether AI will keep the bubble inflated
long enough for them to cash out before it all implodes.
The walled garden cracks: Nadella bets Microsoft’s
Copilots—and Azure’s next act—on A2A/MCP interoperability -
VentureBeat [Link]
Nadella’s endorsement signals Microsoft’s commitment to open
protocols over proprietary ecosystems, aligning with his long-standing
advocacy for interoperability (e.g., ONNX, GitHub’s multi-model
approach). By backing A2A (agent-to-agent communication) and MCP
(model-data context standardization), Microsoft ensures Copilot,
Foundry, and Azure AI can seamlessly integrate with third-party AI
agents and tools. This move preempts enterprise concerns about vendor
lock-in, a criticism of past Microsoft products.
Car Companies Are In A Billion-Dollar Software War, And
Everyone's Losing - InsideEVs [Link]
why it's so hard to shift from lagacy automaker to SDV (software
designed vehicle) company?
Cultural shift: Legacy automakers treated software as an
afterthought, not a core product. Now, they must adopt a Silicon
Valley-like approach.
Supplier dependence: Traditional automakers rely on suppliers for
ECUs, creating a tangled web of software layers.
Safety vs. agility: They must balance "move fast and break things"
with "zero defects or recalls."
Hybrid challenges: Slowing EV demand means SDV systems must also
work with internal-combustion vehicles, complicating power and update
logistics.
Legacy automakers must become software companies to survive, but the
transition is painfully slow and expensive. The winners will be those
who can blend Silicon Valley speed with automotive-grade
reliability—something no traditional automaker has fully achieved
yet.
8 Reasons Leadership Is Hard And Why Few Are Prepared To Lead
- Forbes [Link]
The most inspiring leaders today aren’t just adapting—they’re
rewriting the rules. Leadership isn’t a pinnacle; it’s
a daily practice of courage and reinvention. The world
doesn’t need more bosses; it needs architects of
possibility.
Summary:
The Myth of the Omniscient Leader
Shift: From "knowing it all" to curiosity-driven
collaboration.
Action: Adopt a "Learn It All" mindset (Microsoft’s Satya Nadella
famously replaced "Know It All" with this).
Tool: Host "No Answers Meetings" where leaders openly discuss
unsolved problems, inviting teams to co-create solutions. Example:
Google’s "20% Time" empowers employees to explore innovations beyond
their core roles, democratizing problem-solving.
Embracing the Illusion of Control
Shift: From command-and-control to adaptive
stewardship.
Action: Practice "Scenario Planning" (like Shell Oil’s famed
strategy) to prepare for multiple futures, not just one.
Mindset: View volatility as a laboratory for innovation. Spotify’s
"Fail Fast, Learn Fast" approach rewards experimentation.
Quote: "The art of leadership is not to control, but to
unleash." — Reed Hastings, Netflix.
The Leadership Pipeline Crisis
Root Cause: Short-term efficiency has gutted long-term talent
development.
Fix: Reverse Mentorship Programs (e.g., GE’s junior employees mentor
execs on digital trends).
Metric: Track "Readiness Ratios"—how many high-potentials are
prepared for next-level roles?
Warning: Deloitte’s research shows 89% of executives see "weak
leadership benches" as their top threat.
Tool: Use "Pre-Mortems" (anticipating failures before launch) to
stress-test strategies. Example: Blockbuster’s rigid playbook failed,
while Netflix’s pivot to streaming embraced uncertainty.
Respect as a Daily Earned Currency
Key: Authenticity > Authority.
Action: Practice "Radical Transparency" (like Bridgewater
Associates’ culture of brutal honesty).
Tool: Replace "All Hands Meetings" with "All Hearts Meetings"—forums
for empathy and vulnerability. Example: Edelman’s Trust Barometer shows
employees trust "a peer like me" 3x more than CEOs.
Rebuilding Trust in Judgment
Antidote: Inclusive Decision-Making.
Action: Form "Shadow Boards" (e.g., Gucci’s millennial council
advising execs).
Rule: For major decisions, require "Disagree & Commit" (document
dissent but align once decided). Example: Patagonia’s CEO involves
employees in sustainability bets, building trust through shared
stakes.
Titles vs. Influence
New Power Model: Fluid Hierarchies.
Action: Adopt "Holacracy Lite" (like Zappos’ role-based authority,
not title-based).
Symbolic Step: Drop "CEO" for "Chief Enabler" (as some startups do
to signal servant leadership).
Stat: 72% of Gen Z workers prefer "Project Leaders" over "Managers"
(McKinsey, 2024).
Tool: "Skills Gap Heatmaps"—quarterly self-assessments on emerging
competencies (e.g., AI literacy).
Example: Adobe’s "Kickbox" program gives employees $1,000 to test
new ideas, forcing leaders to adapt.
The Path Forward: Leadership as a Dynamic
Practice
Your closing question—"Will you be one of them?"—is the call
to action. Leaders who thrive will:
1. Lead with Questions, not answers.
2. Treat Trust as Currency, not a given.
3. Build Antifragile Teams (Nassim Taleb’s concept of growing stronger
through chaos).
4. Measure Success in Learning Cycles, not quarterly profits alone.
Microsoft Follows Competitors Amazon, Meta, and Google in
Employee Productivity Crackdown [Link]
The pandemic hiring spree, rising interest rates, and the AI arms
race have forced tech giants to abandon the "growth at all costs"
mindset. Instead, they’re:
Maximizing output per employee (via stack ranking
and attrition policies)
Investing savings into AI (where Microsoft is
battling Google and OpenAI)
Master The Psychology Of Building An Unforgettable Personal
Brand - Forbes [Link]
When your brand is rooted in internal conviction, it radiates
effortlessly. The right opportunities find you.
"My worth isn’t measured by likes; it’s measured by impact."
"If they don’t buy, it’s not a rejection—it’s a mismatch."
"Outcomes are data, not identity."
"Consistency today compounds into authority tomorrow."
Zero to One: Learning Agentic Patterns - Philschmid
[Link]
This guide explores techniques such as prompt chaining, routing,
parallelization, reflection, tool integration, planning, and multi-agent
collaboration. It features practical code examples for each pattern,
enabling the development of efficient, context-aware workflows with
Google DeepMind Gemini. Emphasis is placed on structured strategies to
enhance task delegation and agent coordination.
Our research shows that by 2030, data centers are projected to
require \(\text{\$6.7}\) trillion
worldwide to keep pace with the demand for compute power. Data centers
equipped to handle AI processing loads are projected to require \(\$5.2\) trillion in capital expenditures,
while those powering traditional IT applications are projected to
require \(\$1.5\) trillion in capital
expenditures (see sidebar “What about non-AI workloads?”). Overall,
that’s nearly \(\$7\) trillion in
capital outlays needed by 2030—a staggering number by any
measure.
To qualify our \(\$5.2\)
trillion investment forecast for AI infrastructure, it’s important to
note that our analysis likely undercounts the total capital investment
needed, as our estimate quantifies capital investment for only three out
of five compute power investor archetypes—builders, energizers,
and technology developers and designers—that directly finance
the infrastructure and foundational technologies necessary for AI growth
(see sidebar “Five types of data center investors”). Approximately 15
percent (\(\$0.8\) trillion) of
investment will flow to builders for land, materials, and site
development. Another 25 percent (\(\$1.3\) trillion) will be allocated to
energizers for power generation and transmission, cooling, and
electrical equipment. The largest share of investment, 60 percent (\(\$3.1\) trillion), will go to technology
developers and designers, which produce chips and computing hardware for
data centers. The other two investor archetypes,
operators, such as hyperscalers and colocation
providers, and AI architects, which build AI models and
applications, also invest in compute power, particularly in areas such
as AI-driven automation and data center software. But quantifying their
compute power investment is challenging because it overlaps with their
broader R&D spending.
― The cost of compute: A $7 trillion race to scale data
centers - McKinsey [Link]
est_global_data_center_cap_demand
The Comfortable Life is Killing You - Poetic Outlaws
[Link]
Meaning is forged in resistance - Meaning is a byproduct of
engagement with resistance. Joy emerges when we meet challenges worthy
of our souls. To paraphrase Camus: The struggle itself is
enough.
Agentic AI Is Already Changing the Workforce - Harvard
Business Review [Link]
Papers and Reports
The power of one: How standout firms grow national
productivity - McKinsey Global Institute [Link]
Productivity growth is crucial for economic prosperity. The report
suggests that instead of waiting for all firms to improve, targeted
support for high-potential firms could accelerate national productivity
gains.
Identifying and scaling AI use cases - OpenAI [Link]
OpenAI ads, but useful for pitching GenAI use cases. It offers
guidance on identifying and scaling AI use cases within organizations,
noting that AI adoption is rapidly increasing and demonstrating
significant benefits for early adopters. It emphasizes three key steps
for businesses: understanding where AI can add value by focusing on
repetitive tasks, skill bottlenecks, and navigating ambiguity; teaching
teams fundamental AI use cases like content creation, research, and
automation; and prioritizing opportunities using an impact/effort
framework to determine which projects to pursue and scale.
ZeroSearch: Incentivize the Search Capability of LLMs without
Searching - Alibaba Group [Link]
Traditional RL training requires massive API calls to services like
Google Search, costing hundreds of thousands of dollars. ZeroSearch
replaces this with a simulated search environment where
the LLM itself generates both relevant and irrelevant documents in
response to queries.
Real search engines return unpredictable results, complicating
training. While ZeroSearch uses curriculum-based
rollouts, gradually degrading document quality to teach the
model to discern useful information.
It has a cost reduction up to 88% and its performance surpasses real
search engines.
AI Global, Global Sector Trends on Generative AI [Link]
Gen AI Traffic Share update - Similarweb @Twitter [Link]
Subdomains and pages only (below).
genai_traffic_share
YouTube and Podcasts
Fed Hesitates on Tariffs, The New Mag 7, Death of VC,
Google's Value in a Post-Search World - All-In Podcast [Link]
The Physical Turing Test: Jim Fan on Nvidia's Roadmap for
Embodied AI - Sequoia Capital [Link]
This lecture introduces the Physical Turing Test, a new
benchmark for robotics. Jim Fan from NVIDIA breaks down why solving this
is hard—and what tools researchers are using to make progress.
5 Types of AI Agents: Autonomous Functions & Real-World
Applications - IBM Technology [Link]
This lecture covers reflex agents, model-based agents, goal-based
systems, utility-based frameworks, and learning agents.
Stanford Webinar - Agentic AI: A Progression of Language
Model Usage - Stanford Online [Link]
How to connect AI agents to third-party tools using MCP -
Underfitted [Link]
Llamacon 2025 - Conversation with Mark Zuckerberg and Satya
Nadella - Meta Developers [Link]
AI mode finally - Smart move to embrace next-gen search. Android XR
glass is launching, and Gentle Monster + Warby Parker will be the first
eyewear partners. Genimi App has Agent mode is coming. And many
more!
NVIDIA CEO Jensen Huang Keynote at COMPUTEX 2025 -
NVIDIA [Link]
NVLink Fusion, DGX Spark AI Computer, DGX Station Super Computer, FTX
Pro Server, AI Robotics, etc.
I'll tell you my hiring experience. We have about 30 people at
8090 and the way that I have found it to work the best is you have
senior people act as mentors and then you have an overwhelming corpus of
young very talented people who are AI native. And if you don't find that
mix, what you have instead are L7s from Google and Amazon and Meta who
come to you with extremely high salary demands and stock demands and
they just don't thrive. And part of why they don't thrive is that they
push back on the tools and how you use them. They push back on all these
things that the tools help you get to it faster. M this is why I think
it's so important for the young folks to just jump in with two feet and
be AI native from the jump because you're much more hirable frankly to
the to the emergent company and the bigger companies you'll have a lot
of these folks that see the writing on the wall may not want to adapt as
fast as otherwise. Another way for example that you can measure this is
if you look inside your company on the productivity lift of some of
these coding assistants for people as a distribution of age. What you'll
see is the younger people leverage it way more and have way more
productivity than older folks. And I'm not saying that as an aegis
comment. I'm saying that it's an actual reflection of how people are
reacting to these tools. What you're describing is a paradigm shift. It
is a big leap. Is you know it's like when I went to college, when I took
computer science, it was object-oriented programming. It was like C++.
It was compiled languages. It was gnarly. It was nasty work. And then
you had these highle abstracted languages. And I used to remember at
Facebook, I would just get so annoyed because I was like, why is
everybody using PHP and Python? This is like not even real. But I was
one of these old lights who didn't understand that I just had to take
the leap. And what it did was it grew the top of the funnel of the
number of developers by 10x. And as a result, what you had were all of
these advancements for the internet. And I think what's happening right
now is akin to the same thing where you're going to grow the number of
developers upstream by 10x. But in order to embrace that, you just have
to jump in with two feet. And if you're very rigid in how you think the
job should be done technically, I think you're just going to get left
behind. - Chamath Palihapitiya
― AI Doom vs Boom, EA Cult Returns, BBB Upside, US Steel and
Golden Votes - All-In Podcast [Link]
A Parquet file is composed of Row Groups, Column Chunk, and
Pages.
Parquet is a self-described file format that contains all the
information needed for the application that consumes the file. This
allows the software to efficiently understand and process the file
without requiring external information. Thus, the metadata is the
crucial part of Parquet. They include Magic Number, FileMetadata, and
PageHeader.
Google Dremel (the query engine behind BigQuery) inspired
Parquet’s approach to implementing nested and repeated field storage. In
a 2010
paper introducing Dremel, Google detailed its method for efficiently
handling nested and repeated fields in analytics workloads using
definition level (for nested fields) and repetition level (for
array-like fields). I wrote an article about this approach seven months
ago; you can read it here:
― I spent 8 hours learning Parquet. Here’s what I discovered
- Vu Trinh [Link]
The overall BigQuery architecture includes independent components
for query execution, storage, a container management system, and a
shuffler service:
Colossus: A distributed storage system that
holds and stores data.
Dremel: The distributed query engine.
Borg is Google’s large-scale cluster management
system that can reliably manage and orchestrate compute resources. (Borg
is the predecessor of Kubernetes.) We will return to Borg when
discussing the Vortex architecture.
Dedicate shuffle service: Dremel was inspired
by the map-reduce paradigm to operate and manage the data shuffle
between stages efficiently; Google built a separate shuffle service on
top of disaggregated distributed memory. This service backs BigQuery and
supports other services, such as Google
Dataflow.
― I spent 4 hours learning the architecture of BigQuery's
storage engine - Vu Trinh [Link]
Extract: The process’s first step is
extraction. The needed data is gathered from various sources, such as
relational databases or third-party APIs
Transform: Extracted data undergoes many
potential transformations, including cleaning, filtering, combining from
different sources, and formatting to conform to a target
schema.
Load: The transformed data is loaded into the
destination with the predefined schema and constrained.
ELT solves many of the problems associated with ETL.
Most transformation logic can now be handled within the data
warehouse using SQL, making it more accessible for users such as data
analysts or data scientists. This eliminates the potential performance
bottleneck of ETL pipelines.
Most importantly, ELT allows you to keep raw data in the
warehouse. This approach offers several advantages. You don’t need to
plan transformation logic in advance; instead, the logic can evolve over
time based on analytical needs—an especially valuable benefit in today’s
agile software development environment.
Salesforce & AI Strategy - Generative Value [Link]
This article discusses the history of Salesforce, what made it
successful, the state of the business, and the AI opportunity (or
threat) today.
Everything Wrong with MCP - Shrivu's Substack [Link]
How to future-proof your career in the age of AI - Operator's
Handbook [Link]
Key Takeaways:
The author’s call to "lean into human strengths while actively
engaging with AI" is a compelling middle path. The essay underscores
that the future belongs to those who combine AI literacy with
irreplaceable human skills—judgment, influence, and adaptability.
Human Competitive Advantages:
Judgment & Conviction: Ability to make
decisions with incomplete/ambiguous data. Distinguishing impactful work
from "interesting but useless" projects. Simplifying complexity into
actionable frameworks.
Influence & Execution: Navigating
organizational politics and incentives. Building trust and adoption for
AI-driven outputs. Understanding unspoken processes and
relationships.
Actionable Skills to Cultivate:
Develop "taste" by studying excellence in your field.
Gain hands-on experience to pressure-test AI outputs.
Learn to align stakeholders and drive consensus.
Build strong interpersonal relationships and reputation.
Adaptability as the Ultimate Skill:
AI will keep evolving, so continuous learning and flexibility are
critical.
Focus on areas where humans add unique value (judgment, influence,
creativity).
This is a very interesting point: "Develop "taste" by studying
excellence in your field."
Just like any skill, taste sharpens with exposure and effort. The
more you study, critique, and create, the better you’ll get at
recognizing—and producing—excellence. In a world flooded with
AI-generated content, the people who thrive will be those who can
separate the remarkable from the mediocre.
Blogs and Articles
How Airbnb Standardized Metric Computation at Scale - Airbnb
Blog [Link]
Good tips and tricks for digital hygiene, given the pervasive nature
of internet fraud and the data collection practices of major tech
companies.
Measuring AI Ability to Complete Long Tasks - METR
[Link]
metr-length-of-tasks-log
The "think" tool: Enabling Claude to stop and think in
complex tool use situations - Anthropic [Link]
Anthropic introduces a "think" tool designed to enhance Claude's
complex problem-solving by providing a dedicated space for structured
reasoning during tasks. This tool differs from extended thinking by
allowing Claude to pause and consider necessary information
mid-response, particularly beneficial for multi-step processes and tool
use. Evaluations on benchmarks like τ-Bench demonstrated significant
performance improvements, especially in policy-heavy domains like
airline customer service, where optimized prompting alongside the
"think" tool proved most effective.
Tiny Agents: a MCP-powered agent in 50 lines of code -
HuggingFace [Link]
Anthropic CEO wants to open the black box of AI models by
2027 - Techcrunch [Link]
Powerful AI will shape humanity’s destiny, and we deserve to
understand our own creations before they radically transform our
economy, our lives, and our future.
― The Urgency of Interpretability - Dario Amodei [Link]
Interpretability isn’t just academic—it’s a prerequisite for safe,
controllable AI. The window to solve it is narrowing as AI grows more
powerful. By steering resources toward this goal now, we might avoid a
future where humanity builds systems it doesn’t understand but can’t
afford to stop.
The Jobs That Will Fall First As AI Takes Over The Workplace
- Forbes [Link]
Takeaways:
Timeline for Disruption:
By 2030: 30% of U.S. jobs could be automated (McKinsey).
By 2035: White-collar restructuring in finance, legal, and media
(Larry Fink, Jamie Dimon).
By 2045: 50% of jobs may be fully automated (Goldman Sachs).
By 2050: AI could dominate 60-80% of jobs, depending on innovation
pace.
Most Vulnerable Jobs (Near-Term):
Administrative: Data entry, scheduling, customer service (60%
automatable, per IPPR).
Finance & Legal: Bookkeeping, contract drafting, paralegal work
(AI tools like Harvey already achieve 90% accuracy).
Creative & Media: Basic graphic design, copywriting, journalism
(30% at risk by 2035, Pew Research).
Routine STEM Tasks: Coding, data analysis (40% automatable by 2040,
WEF).
More Resilient Jobs (Longer-Term):
Healthcare: Nursing, therapy, and patient care (empathy-driven
roles).
Skilled Trades: Construction, repair, maintenance (physical labor is
harder to automate).
Focus on critical thinking, creativity, and AI collaboration (e.g.,
prompt engineering, AI-augmented decision-making).
Target Resilient Sectors- Healthcare, education, skilled trades, and
AI-adjacent roles (e.g., cybersecurity, AI ethics).
Push for employer or government-sponsored programs to transition
into hybrid (human + AI) roles.
Embrace Hybrid Roles- Jobs that combine technical skills with human
judgment (e.g., AI-assisted healthcare diagnostics) will thrive.
As Ray Dalio warns, the economy faces a "great deleveraging" where AI
disrupts jobs faster than new ones emerge. The key is
adaptability—those who proactively reinvent their
skills today will shape the workforce of tomorrow.
Curation is the new leadership superpower. Here are 3 ways to
adopt a curation mindset - FastCompany [Link]
The most transformative leaders of the next decade will be those who
master the art of curation—seeing their role as a conduit for the best
ideas, not the source of them.
The Obsolescence of the "Omniscient Leader": The pace of change,
hyper-specialization, and interconnected challenges (e.g., AI, climate,
global markets) make it impossible for one person to have all the
answers. Leaders must shift from being "the smartest in the room" to
becoming "architects of collective intelligence."
Curation as the Core Leadership Skill:
Curating Talent: Prioritize cognitive diversity over homogeneity.
Example: Diverse teams solve problems faster (39% efficiency
boost).
Curating Ideas: Create systems where unconventional thinking
flourishes (e.g., Google’s 20% time → Gmail, Maps). Actively seek
"outliers" (contrarians, outsiders) to challenge groupthink.
Curating Innovation: Design for "structured serendipity" (e.g.,
Pixar’s open office, IDEO’s cross-industry brainstorming). Embrace
cross-disciplinary collisions (e.g., NASA’s tech inspiring sportswear,
biomimicry in architecture).
How to Cultivate a Curation Mindset:
Facilitate, don’t dictate: Ask better questions; let solutions
emerge from debate (e.g., Amazon’s "Disagree and Commit").
Optimize for collaboration, not just efficiency: Space matters
(physical or virtual).
Perplexity CEO says its browser will track everything users
do online to sell ‘hyper personalized’ ads - TechCrunch [Link]
Perplexity is building a browser (Comet) to track user behavior
across the web—explicitly to fuel targeted advertising. It highlights
the company’s ambition to emulate Google’s surveillance-capitalism
playbook.
Perplexity’s move confirms that the AI search revolution is less
about displacing Google’s model than replicating it—with AI as a smarter
wrapper for the same ads.
Today’s Most Crucial Leadership Skill Is Systems Thinking -
Forbes [Link]
Leaders who master systems thinking don’t just survive
uncertainty—they thrive in it, turning complexity into
competitive advantage.
Five Key Tools of Systems Thinking for Strategic
Leaders
Problem Statements: Move from surface-level fixes to systemic
solutions. Example: Instead of asking, “How do we get customers to
recycle?”, ask, “How can we redesign products and
infrastructure for circularity?”
Stakeholder Mapping: Identify all affected parties—not just obvious
ones. Example: For electric vehicles, consider miners of critical
minerals, urban planners, and regulators, not just automakers and
buyers.
Iceberg Analysis: Look beneath visible events to uncover hidden
structures and mindsets. Example: Employee burnout isn’t just about
workload—it’s shaped by corporate culture, incentive systems, and
societal norms.
Causal Loops: Visualize feedback loops to see how actions create
ripple effects. Example: A cost-cutting measure in one department may
increase inefficiencies elsewhere.
Iteration & Testing: Embrace adaptive strategies, not rigid
plans. Example: Pilot small-scale solutions, measure impact, and refine
before full rollout.
Perplexity CEO shares the Elon Musk–inspired mantra that
helped him build the $9 billion rival to OpenAI - Fortune [Link]
Srinivas’s journey highlights resilience, speed, and Silicon Valley’s
tight-knit founder network as key drivers of startup success.
"It’s Only Over When You Give Up" – Aravind Srinivas, CEO of AI
search startup Perplexity, draws inspiration from Elon Musk’s
perseverance during SpaceX’s early failures. He told Harvard students
that success comes from relentless self-belief, even when others doubt
you.
Rocketing Valuation – Perplexity, competing with Google and OpenAI,
grew from a 1B to 9B and is now in talks to raise funds at an 18B
valuation.
Forget Pitch Decks, Build Fast – Srinivas advises founders to focus
on rapid product iteration rather than lengthy business plans. He admits
he doesn’t even know how to make a pitch deck—Perplexity’s success came
from live demos.
OpenAI Alumni Network – Despite competing with OpenAI, Srinivas
maintains a strong relationship with Sam Altman (his former boss at
OpenAI). This mirrors the "PayPal Mafia" dynamic, where ex-OpenAI
employees now lead major AI firms like Anthropic and Safe
Superintelligence.
Marc Andreessen predicts one of the few jobs that may survive
the rise of AI automation - Fortune [Link]
Andreessen’s logic suggests focusing on roles where trust,
psychology, and networks matter more than data crunching. But don’t
underestimate AI’s ability to creep into those domains too.
How To Get Noticed Without Self-Promotion By Using Strategic
Visibility - Forbes [Link]
Core Lessons:
Hard Work ≠ Visibility: Doing great work is
necessary but insufficient. If leaders don’t know what you’re doing,
they can’t reward it. Waiting for annual reviews is too late—visibility
requires consistent, intentional updates.
Humility Has a Hidden Cost: While modesty is
admirable, staying silent can render you invisible. Gallup’s data on
declining engagement (just 36% in 2020) highlights how disengagement
hurts promotion prospects. Visibility isn’t ego-driven; it’s about
ensuring your impact is recognized.
Visibility ≠ Bragging: Framing contributions as
useful knowledge (e.g., "Here’s how I solved X") builds trust and
leadership credibility. Sharing wins, failures, and best practices helps
the team and positions you as a problem-solver.
Tactical Ways to Increase Visibility
Share knowledge: Lead "lessons learned" sessions or contribute to
internal newsletters.
Mentor others: Their success reflects your leadership.
Speak up strategically: One substantive insight per meeting >
empty chatter.
Volunteer for high-impact projects: Align with organizational
priorities.
Write internally: Document best practices to showcase thought
leadership.
Emotional Intelligence (EQ) Matters More Than
Extroversion
Visibility is about meaningful engagement, not being the
loudest.
Avoid self-deprecating language ("I’m sorry, but…")—speak with
conviction.
What Leaders Actually Notice
Initiative, influence, and alignment with goals matter more than
face-time.
Working smart (not just late) and collaborating effectively signal
leadership potential.
YouTube and Podcast
DOGE updates + Liberation Day Tariff Reactions with Ben
Shapiro and Antonio Gracias - All-In Podcast [Link]
2027 Intelligence Explosion: Month-by-Month Model — Scott
Alexander & Daniel Kokotajlo - Dwarkesh Patel [Link]
Trump vs Harvard, Nvidia export controls, how DEI killed
Hollywood with Tim Dillon - All-In Podcast [Link]
How DeepSeek Rewrote the Transformer [MLA] - Welch
Labs [Link]
A lecture explaining the architecture and optimizations behind
DeepSeek R1, a language model that improves Transformer efficiency.
Live Demo: Reinforcement Fine-Tuning for LLMs — Build Smarter
Models with Less Data l Tutorial - Predibase [Link]
This video was talking about why RFT beats supervised fine-tuning
(SFT) in reasoning tasks, giving live demo of an end-to-end RFT
workflow, and PyTorch-to-Triton case study showing real-world
impact.
Model Context Protocol (MCP), clearly explained (why it
matters) - Greg Isenberg [Link]
Trump Rally or Bessent Put? Elon Back at Tesla, Google's
Gemini Problem, China's Thorium Discovery - All-In Podcast [Link]
Suffering is mostly mental anguish and mental pain and it just
means you don't want to do the task at hand.
The kind of fame that pure actors and celebrities have, I
wouldn't want, but the kind of fame that's earned because you did
something useful, why dodge that.
People will always want more status uh but I think you can be
satisfied at a certain level of wealth.
Not the kind of confidence that would say I have the answer but
the kind of confidence that I will figure it out and I know what I want
or only I am a good arbiter of what I want.
Pride is the enemy of learning, so when I look at my friends and
colleagues, the ones who are still stuck in the past and have grown the
least are the ones who were the proudest, because they sort of feel like
they already had the answers and so they don't want to correct
themselves publicly.
I think everybody puts themselves first that's just human nature,
you're here because you survive you're a separate organism.
The happier you are, the more you can sustain doing something,
the more likely you're going to do something that will in turn make you
even happier, and you'll continue to do it, and you'll outwork everybody
else. The more free you are the better you can allocate your
time.
There are no problems in the real world other than maybe things
that inflict pain on your body. Everything else has to become a problem
in your mind first.
Your family is broken but you're going to fix the world. People
are running out there to try and fix the world when their own lives are
a mess.
I think the only true test of intelligence is if you get what you
want out of life and there are two parts to that one is getting what you
want so you know how to get it and the second is wanting the right
things knowing what to want in the first place.
Usually I think people end up there because they are going on
autopilot with sort of societal expectations or other people's
expectations or out of guilt or out of like mimetic desire.
Probably the biggest regret will be staying in the relationship
after you knew it was over, exactly you should have left sooner, the
moment you knew it wasn't going to work out, you should have moved
on.
We are naturally hardwired to be pessimists but modern society is
very different despite whatever problems you may have with modern
society, it is far far safer than living in the jungle and just trying
to survive and the opportunities.
Leave all those labels alone. It's better just to look at the
problem at hand, look at reality the way it is, try to take yourself out
of the equation in a sense.
The less you think about yourself the more you can think about a
mission or about God or about a child or something like that.
I don't think there are any formulas i think it's unique to each
person it's like asking a successful person how did you become
successful each one of them will give you a different story uh you can't
follow anyone else's path.
A lot of change is more about desire and understanding than it is
about uh forcing yourself or trying to domesticate yourself.
When your mind is under stress, it's because it has two
conflicting desires at once... and anxiety I think is sort of this
pervasive unidentifiable stress where you're just kind of stressed out
all the time and you're not even sure why and you can't even identify
the underlying problem. I think the reason for that is because you have
so many unresolved problems unresolved stress points that have piled up
in your life that you can no longer identify what the problems
are.
Life is going to play out the way it's going to play out there
will be some good and some bad most of it is actually just up to your
interpretation.
The gut is what decides the head, is kind of what rationalizes it
afterwards, the gut is the ultimate decision maker.
You can't change other people, you can change your reaction to
them.
If you do want to change someone's behavior, I I think the only
effective way to do it is to compliment them when they do something you
want, not to insult them or be negative or critical when they do
something you don't want.
If you can't decide, the answer is no.
Almost invariably the advice that you would give yourself 10
years ago is still the advice that you need to hear today.
On mental things, I think understanding is way more important
once you see the truth of something you cannot unsee it... when we
really do see something clearly, it changes our behavior immediately,
and that is far more efficient than trying to change your behavior
through repetition.
Truth is often painful, if it wasn't, we'd all be seeing truth
all the time. Reality is always reflecting truth that's all it is why
would you not have accessed it already exactly... wisdom is the set of
things that cannot be transmitted. If they could be transmitted you know
we'd read the same five philosophy books, and we'd all be done, we'd all
be wise. You have to learn it for yourself, it has to be rediscovered
for yourself in your own context.
You're probably better off only caring about things that are
local or things that you can affect. So if you really care about
something that's in the news, then by all means care about it but make a
difference go do something about it.
Desire is a contract to be unhappy until you get what you
want.
The real currency of life is attention it's what you choose to
pay attention to and and what you do about it.
― 44 Harsh Truths About Human Nature - Naval Ravikant (4K) -
Chris Williamson [Link]
Key Learnings:
Someone who can do the job peacefully or happily is more effective
than someone with unnecessary emotional turmoil
Fame sought for its own sake is fragile and leads to a constant need
to perform.
People often say things they don't really believe, driven by a
desire to be seen as something they are not.
Status is zero-sum and insatiable, unlike wealth. Status is often
comparative, like leaderboards, where one person's gain can be another's
loss.
Self-esteem comes from aligning actions with internal values,
especially when difficult. Genuine sacrifice, doing something you want
less for something you value more, can build self-esteem.
True confidence is not having all the answers but the self-belief to
figure things out.
Pride is an enemy of learning and can lead to being stuck in past
mistakes.
Everyone puts themselves first; unapologetic self-prioritization is
rare but perhaps more honest. Much of what appears as altruism might be
a waste of time if it goes against one's true desires.
Happiness and freedom are intertwined with efficiency and
productivity.
Many emotional problems arise from the mind creating problems where
none exist in the real world. He advises observing one's thoughts
objectively to realize unnecessary emotional energy expenditure.
People often try to fix the world while their own lives are in
disarray. He questions the credibility of those who cannot manage their
own lives but seek to solve global issues.
True intelligence is getting what you want out of life by wanting
the right things and knowing how to get them.
Many people go through life unconsciously following societal or
mimetic desires. He emphasizes the importance of thinking things through
for oneself rather than blindly following others.
Staying too long in bad situations (relationships, jobs) is a common
regret.
We are naturally hardwired for pessimism due to evolutionary
pressures to avoid ruin.
Humans are dynamic and labels like optimist, pessimist, introvert,
extrovert are self-limiting.
Overthinking about oneself can lead to misery; focusing on something
bigger can bring happiness. Overthinking and rumination do not help with
happiness.
There are no universal formulas for success or happiness; each
person's path is unique.
Lasting change comes from desire and understanding, not forcing
oneself. He suggests aligning actions with genuine wants for maximal
effectiveness.
Anxiety often stems from having many unresolved and conflicting
desires.
Our interpretations of experiences shape our reality. The same
experience can lead to different emotional responses based on individual
interpretation.
The "gut" is the ultimate decision-maker, representing refined
judgment accumulated through evolution and experience. He advises
trusting this instinct once it's developed.
You cannot change other people, only your reaction to them. He adds
that people change through their own insights or trauma, not by being
told to.
Negative reinforcement is less effective than positive reinforcement
in changing behavior.
If faced with a difficult choice and unable to decide, the answer is
often "no." He also suggests that when choosing between two equal
options, take the more painful path in the short term.
Understanding is more important than discipline for mental
change.
Truth, though often painful, is constantly reflected by reality;
wisdom is the personal rediscovery and contextual application of
timeless truths. He also mentions that many important life lessons are
"unteachable" in the sense that they must be experienced firsthand to be
truly understood.
Memorization is becoming less valuable in the age of readily
available information; understanding, judgment, and taste are more
crucial. He links understanding to solving real problems and finding
generalizable truths.
Philosophy evolves with new knowledge and perspectives. He explains
how advancements in science and technology lead to different
philosophical outlooks, and even moral philosophy progresses over
time.
Many philosophical paradoxes can be resolved by considering
different scales and timeframes. Naval suggests that seemingly
contradictory questions like free will and determinism can be understood
by shifting perspectives.
Coordination is essential for societal function; pure libertarianism
is unsustainable.
Modern AI, while powerful, currently lacks true creativity and deep
understanding.
Meaning can be more important than moment-to-moment happiness.
In an age of news saturation, it's a battle to maintain focus on
what truly matters and what one can influence. He emphasizes that
attention is the real currency of life and should be spent consciously.
Attention, not time or money, is the most fundamental resource in
life.
Getting past one's past is a skill achieved by processing it to be
rid of it, not to dwell on it.
I think agents are real, but I think that we are far away from
that because we're still at the phase of how do you build reliable
software in production for an enterprise versus the toy apps that you
see on the internet which is like let me vibe code something. I think
these things are worlds apart still. - Chamath Palihapitiya
I think we have not yet figured out how to move the budgets from
experimentation to mainline production. Meaning where large chunks of
the US economy are comfortable enough with the ways in which
hallucinations are managed such that they will replace legacy
deterministic code with this new probabilistic model generated code
meaning model enabled code. - Chamath Palihapitiya
― Trump's First 100 Days, Tariffs Impact Trade, AI Agents,
Amazon Backs Down - All-In Podcast [Link]
Papers and Reports
Orchestrating Agents and Data for Enterprise: A Blueprint
Architecture for Compound AI [Link]
This paper contributes to the enterprise AI landscape by offering a
comprehensive architectural blueprint for deploying agentic, modular,
and data-integrated AI systems that can efficiently leverage LLMs and
enterprise assets.
Github
Google Gemini 2.0 with MCP (Model Context Protocol) Servers -
Gemini Samples [Link]
Maestro - A Framework for Claude Opus, GPT and local LLMs to
Orchestrate Subagents - maestro [Link]
Accelerate Generalist Humanoid Robot Development with NVIDIA
Isaac GR00T N1 - NVIDIA [Link]
Announcing the Agent2Agent Protocol (A2A) - Google for
Developers [Link]
Key Takeaways:
A2A is an open-source protocol backed by 50+ tech giants (e.g.,
Salesforce, SAP, Cohere) and consultancies (e.g., Accenture, Deloitte).
It allows agents from different vendors/frameworks to communicate, share
data, and coordinate tasks without being locked into a single
platform.
Solving Enterprise Pain Points: Breaks down silos by letting agents
interoperate across HR (Workday), CRM (Salesforce), ERP (SAP), and other
systems. Example: A hiring manager’s agent can autonomously source
candidates, schedule interviews, and run background checks by
collaborating with specialized agents.
