There are many enterprise products built almost solely on RAG.
Naive RAG
The standard RAG workflow consists of three main steps as illustrated
in the graph below:
Indexing: Creating an index of documents for
retrieval.
Retrieval: Searching the index for relevant
documents based on a user query.
Generation: Using a language model to generate
answers or responses based on the retrieved documents.
The three steps all face possible issues:
Indexing:
Poor document parsing.
Inefficient document chunking strategies.
Weak semantic representations from embedding models.
Non-optimized index structures.
Retrieval:
Low relevance: retrieved documents are not highly relevant to the
user query (low accuracy).
Incomplete retrieval: not all relevant documents are retrieved
(low recall).
Redundancy: retrieved documents may be repetitive or
redundant.
Queries are often not specific or well-defined.
Retrieval strategies might not be well-suited to the use case and
may rely solely on semantic similarity.
Generation:
Overreliance on the retrieved content, leading to issues such as
irrelevant or even harmful responses (e.g., toxic or biased
content).
naive-rag
This paper “Retrieval-Augmented Generation
for Large Language Models: A Survey” discussed several problems
associated with Naive RAG implementations. The advanced approaches to
RAG attempt to overcome the limitations of naive RAG by improving the
way queries are processed, documents are retrieved, and responses are
generated. Advanced RAG techniques focus on refining each step of the
process, from query transformations to more efficient retrieval
strategies.
Query transformations are techniques aimed at re-writing or modifying
the input questions to improve the retrieval process.
Query Analysis
Query transformation types:
query_trans2
Some notable methods include:
Multi Query:
The MultiQueryRetriever automates prompt tuning by
using a language model (LLM) to generate multiple queries from different
perspectives for a given user query. It retrieves relevant documents for
each generated query and combines the results to create a larger, more
comprehensive set of potentially relevant documents. This technique
helps mitigate some of the limitations of distance-based retrieval, save
time on experimenting with different prompts, and provides a richer set
of results.
RAG-Fusion combines RAG and Reciprocal Rank Fusion
(RRF) by generating multiple queries, reranking them with
reciprocal scores and fusing the documents and scores. RRF gives the
more relevant retrieval results higher scores and re-ranks them
according to the scores. RAG-Fusion was able to provide accurate and
comprehensive answers due to the generated queries contextualizing the
original query from various perspectives.
Step back prompting refers to the technique of
generating a more generalized or abstract version of a specific query in
order to mitigate potential issues with search quality or
model-generated responses. This involves first reformulating the initial
question into a broader or higher-level version (the “step back”
question) and then querying both the original and the generalized
question to improve the comprehensiveness and relevance of the
responses.
When a user asks a complex question, a single query might not
retrieve the right results. To address this, the question can be broken
into sub-questions, each of which is retrieved separately, and the
answers are combined.
The key idea in this strategy is to break down a complex problem into
a series of simpler subproblems and then solve them in sequence. Solving
each subproblem is facilitated by the answers to previously solved
subproblems. Least-to-most prompting is capable of generalizing to more
difficult problems than those seen in the prompts.
An approach for multi-step QA that interleaves retrieval with steps
(sentences) in a CoT, guiding the retrieval with CoT and in turn using
retrieved results to improve CoT. It incorporates the idea of
least-to-most prompting into RAG to improve retrieval, resulting in
factually more accurate CoT reasoning.
Hypothetical Document Embeddings (HyDE): Given a
query, HyDE first zero-shot instructs an instruction-following language
model to generate a hypothetical document. The document captures
relevance patterns but is unreal and may contain false details. Then, an
unsupervised contrastively learned encoder (e.g. Contriever) encodes the
document into an embedding vector. This vector identifies a neighborhood
in the corpus embedding space, where similar real documents are
retrieved based on vector similarity.
Simply speaking, HyDE uses responses to retrieve documents rather
than using queries to retrieve documents. The rational behind this
approach is that the semantic similarity between query and real document
is smaller than the semantic similarity between hypothetical document
and real document.
Query construction refers to converting a natural language query into
the query language specific to the database you are working with. This
is essential for interacting with different databases and vector stores
that require structured queries for more efficient document
retrieval.
A self-querying retriever is one that, as the name suggests, has the
ability to query itself. Specifically, given any natural language query,
the retriever uses a query-constructing LLM chain to write a structured
query (usually in JSON) and then applies that structured query to its
underlying VectorStore. This allows the retriever to not only use the
user-input query for semantic similarity comparison with the contents of
stored documents but to also extract filters from the user query on the
metadata of stored documents and to execute those filters.
Text-to-metadata-filter: VectorStores equipped with
metadata filtering enable structured queries to filter embedded
unstructured documents.
Prompt templates and output parsers
Prompt analysis and prompt template: converting
user’s query to filtering conditions
When constructing queries, the system uses a specific JSON format
to organize the query and filters. The prompt is designed to create
structured queries that can be applied to a document database or vector
store. The queries consist of two main components:
Query: The natural language query string that is used to match the
document content.
Filter: Logical conditions used to filter the documents based on
specific metadata attributes.
Comparison Operations
Comparison operators (comp) are used to compare
attributes (like year, name, time, product, or team) in the document
with specific values provided by the user. Here are the comparison
operators:
eq: Equals (e.g., eq("team", "TSE")
matches documents where the team is “TSE”).
ne: Not equal (e.g.,
ne("name","Ashley") matches documents where the year is not
2022).
gt: Greater than (e.g.,
gt("year", 2023) matches documents with a year greater than
2023).
gte: Greater than or equal to (e.g.,
gte("year", 2022) matches documents from the year 2000 or
later).
lt: Less than (e.g., lt("year", 2021)
matches documents created before 2021).
lte: Less than or equal to (e.g.,
lte("time", 13) matches documents with a time length of 13
mins or lower).
contain: Contains (e.g.,
contain("product", "gold") matches documents where the
product contains the word “gold”).
like: Similar to or like (used for pattern
matching).
Logical Operations
Logical operators combine multiple conditions (comparisons) into a
single filter:
and: Logical AND (e.g.,
and(gt("year", 2022), eq("product", "gold")) matches
documents created later than year 2022 and are related to gold card
product).
or: Logical OR (e.g.,
or(eq("team", "TS"), eq("team", "TSE")) matches documents
that are either TS or TSE).
not: Logical NOT (e.g.,
not(eq("name", "Ashley")) matches documents where Ashley is
not the owner).
Output parser: This output parser can be used when
you want to return multiple fields or you need the response to be
formatted.
Vector Store-Backed Retriever: A retriever that
uses a vector database to store document embeddings and retrieve
documents based on their proximity to the query embedding.
basic_index_retrieval
Fusion Retrieval or hybrid search: Combining
multiple retrieval strategies (semantic similarity retrieval; keywords
retrieval) to obtain a more diverse set of results.
The EnsembleRetriever is a retrieval strategy that
enhances retrieval performance by combining multiple retrievers. This
approach leverages the strengths of different types of retrievers to
compensate for each other’s weaknesses. A common example is combining a
Sparse Retriever (e.g., BM25, which performs
keyword-based retrieval) with a Dense Retriever (which
performs semantic similarity retrieval based on embeddings). This
combination works because sparse and dense methods complement each
other.
Sparse vs. Dense Representation
Sparse Representation:
High-dimensional sparse vectors: Documents and
queries are represented as high-dimensional vectors, but most dimensions
have zero values. This is typical of traditional information retrieval
methods like TF-IDF and BM25.
Term frequency: Each dimension corresponds to a
term, and the vector values represent term frequencies or weights (e.g.,
TF-IDF weights).
Sparsity: Since a document or query contains only a
small subset of all possible terms, most dimensions in the vector are
zero, which makes it “sparse.”
Dense Representation:
Low-dimensional dense vectors: Documents and
queries are represented as low-dimensional vectors, where most or all
dimensions have non-zero values. This representation is typically
generated by deep learning models like BERT.
Semantic embeddings: The vectors capture semantic
and contextual information, rather than just term frequency.
Density: All dimensions in the vector usually have
non-zero values, hence “dense.”
Sparse and Dense Retrievers
Sparse Retriever: The name comes from the fact that
most elements in the vector representation of documents and queries are
zero. It works well for exact keyword matches but may miss semantically
relevant content that uses different vocabulary.
Dense Retriever: The name reflects that the vector
representation has mostly non-zero values. Dense retrievers perform
better at capturing the meaning behind the text and finding semantically
related content, even when the exact terms differ.
Combining Sparse and Dense Retrievers
By combining sparse and dense retrievers, the
EnsembleRetriever can retrieve relevant documents more
effectively:
The Sparse Retriever excels at matching specific
keywords or phrases.
The Dense Retriever is better at capturing the
semantic meaning and context, helping to retrieve documents even when
exact terms differ.
This combination creates a more robust retrieval system, addressing
both lexical matches (through sparse retrieval) and semantic relevance
(through dense retrieval).
Sentence Window Retrieval: Retrieving extended
context pre and post the relevant context, rather than only retrieving
the relevant context, which can reduce information lost.
sent_window_retrieval
Parent Document Retrieval: Instead of sending
the multiple smaller chunks to the LLM, the system merges them into
their larger parent chunk. This allows for more contextualized
information to be fed to the LLM, giving it a broader and more coherent
set of data to generate an answer.
Hierarchical index retrieval: By structuring the
search in two layers—summaries for broad filtering and chunks for
detailed search—this hierarchical approach increases efficiency, making
it easier to find and synthesize relevant information, especially when
dealing with large document sets.
hierarchical_retrieval
Hypothetical Questions: This technique involves
having the language model generate hypothetical questions for each chunk
of a document. These hypothetical questions are then embedded, and
retrieval is performed based on these question embeddings, improving the
relevance of the results.
MultiVector Retriever: MultiVector Retriever is
a higher level category of parent document retriever, hierarchical index
retrieval, and hypothetical questions.
This new model.out_head output layer has its
requires_grad attribute set to True by
default, which means that it’s the only layer in the model that will be
updated during training. Technically, training the output layer we just
added is sufficient. However, as I found in experiments, finetuning
additional layers can noticeably improve the predictive performance of
the finetuned model.
― Building A GPT-Style LLM Classifier From Scratch -
Sebastian Raschka [Link]
[Github]
Interesting questions addressed by Sebastian:
Do we need to train all layers?
“For classification finetuning, it is not necessary to update all
layers in an LLM. (The fewer weights we update, the faster the training
will be because we don’t need to compute the gradients for these weights
during backpropagation.)”
Why finetuning the last token, not the first token?
“In contrast to BERT, GPT is a decoder-style model with a causal
attention mask. This means the first token has no context information of
any other token in the input. Only the last token has information about
all other tokens. Hence, if we want to use models like GPT for
classification finetuning, we should focus on the last token to capture
contextual information of all other input tokens.”
How does BERT compare to GPT performance-wise?
“The small GPT-2 model from the previous section and BERT performed
similarly well on the spam classification dataset. “
Should we disable the causal mask?
“A core feature of the GPT architecture is the causal attention mask
(different from BERT models or the original transformer architecture).
However, we could actually remove the causal mask during classification
finetuning, which would allow us to finetune the first rather than the
last token since future tokens will no longer be masked, and the first
token can see all other tokens.”
What impact does increasing the model size have?
The prediction accuracy can improve significantly with larger
models.
What improvements can we expect from LoRA?
Both full finetuning (all layers) and LoRA can result in the same
test set performance.
“On the small model, LoRA is slightly slower since the additional
overhead from adding LoRA layers may outweigh the benefits, but when
training the larger 1.5 billion parameters model, LoRA trains 1.53x
faster.”
“If we want to process data in batches during training or inference
(this involves processing more than one input sequence at a time), we
need to insert padding tokens to ensure that the training examples are
of equal length.
In regular text generation tasks, padding doesn’t affect the model
response since padding tokens are usually added to the right side, and
due to the causal mask discussed earlier, these padding tokens don’t
influence the other tokens. However, remember that we finetuned the last
token, as discussed earlier. Since the padding tokens are to the left of
this last token, the padding tokens may affect the result. “
These Are The 6 Best Science-Based Study Strategies - Super
Learning Lab [Link]
Spaced Practice
Instead of cramming all the information at once, spaced practice
consists of revisiting the material multiple times with breaks in
between.
Interleaving
This is about studying different topics in a sequence.
Retrieval
This consists of bringing learned information from mid to long-term
memory by recall or retrieval practices.
Elaboration
Elaborative interrogation consists of asking and explaining why and
how things work based on prior knowledge. In other words, it involves
connecting new information to preexisting knowledge.
Concrete Example
When learning abstract concepts it was found that illustrating these
topics with specific examples improves learning.
Dual Coding
Dual coding is about combining words with visuals. If you use
relevant and helpful images in your notes, you may increase learning by
remembering what you study with the help of these images.
The \(\$120\) billion wagered on
sports betting in America in 2023 translated into nearly \(\$11\) billion in revenue for sports
betting companies. This corresponds to the ~9% fee sportsbooks keep
after all bets have been settled.
Flutter: Leverages FanDuel’s dominance and global
expertise.
DraftKings: Focuses on innovation and user engagement to fuel
growth.
Entain: Bets on BetMGM’s success in the US market.
Penn: Leverages the ESPN partnership to challenge established
players.
For decades, companies have outsourced their organizational
innovation to consultants or enterprise software vendors who develop
generalized approaches based on what they see across many organizations.
That won’t work here, at least for a while. Nobody has special
information about how to best use AI at your company, or a playbook for
how to integrate it into your organization.
― AI in organizations: Some tactics - One Useful
Thing [Link]
Issues with AI at the organizational level and how to solve them.
In many companies, there is little AI use and few productivity gains
outside of narrow permitted use cases. That’s because AI use that boosts
individual performance does not always translate to boosting
organizational performance for a variety of reasons. To get
organizational gains requires R&D into AI use and you are largely
going to have to do the R&D yourself.
“Many key breakthrough innovations come not from central R&D
labs, but from people actually using products and tinkering with them to
solve their own problems. “ (Prof. Eric von Hippel). As users are very
motivated to make their own jobs easier with technology, they find ways
to do so. The user advantage is especially big in experimenting with
Generative AI because the systems are unreliable and have a jagged
frontier of capability. People are experimenting with AI and finding it
very useful. But they aren’t sharing their results with their
employers.
How to solve the issues? What are the tactics? Talents in the lab
should focus on building, not analysis or abstract strategy.
Key elements of Doha Mekki’s recent antitrust lawsuit against
Visa:
Visa controls over 60% of U.S. debit transactions, with Mastercard
far behind at 25%.
Visa traps merchants with pricing that penalizes them if they don’t
process all transactions through Visa.
Exclusive deals incentivize merchants to use Visa exclusively,
reducing competition.
Visa prevents potential competitors like PayPal and Apple from
entering the market by locking them into restrictive agreements.
Visa has faced antitrust lawsuits since 1971 and maintains a large
legal team to manage ongoing cases.
In physics, we study how particles or systems’ units interact and
evolve toward stable states. In machine learning, we study how neurons
(or artificial neurons) interact to learn patterns directly from data.
The connection lies in energy minimization: both approaches define an
energy function to describe the stability of a system, and the
optimization of this function helps to find optimal configurations that
correspond to useful patterns or memories.
Hopfield developed a network that recreates patterns using energy
minimization, while Hinton expanded on this with the introduction of
Boltzmann machines, statistical physics-based systems that learn to
recognize and generate patterns, providing groundwork for modern machine
learning.
― Nobel Prize to the Statistical Physics of artificial neural
networks - Complexity Thoughts [Link]
“The laws governing physical systems also apply to the world of
artificial intelligence.”
Major AI Functionalities / with Apps - AI Supremacy
[Link]
A list of AI products you can experiment with.
Fine-tuning can be useful for certain tasks (see the relevant
section here for more details), but when it comes to injecting morality
into your LLM, it’s probably not a good bet.
By combining the strengths of diffusion models and
auto-regressive generation, DGLM offers a more nuanced, adaptable, and
potentially more effective approach to generating safe and creative
text. It moves away from the brute-force, one-size-fits-all approach of
fine-tuning and embraces a more modular, dynamic, and personalized
approach to AI safety.
― A New Way to Control Language Model Generations
[Breakdowns] - Artificial Intelligence Made Simple [Link]
The author lists drawbacks of fine tuning:
“A model’s knowledge and capabilities are learnt almost entirely
during pretraining, while alignment teaches it which subdistribution of
formats should be used when interacting with users.” - LIMA: Less Is
More for Alignment [Link]
“Our findings reveal that while unsupervised fine-tuning offers some
improvement, RAG consistently outperforms it, both for existing
knowledge encountered during training and entirely new knowledge.
Moreover, we find that LLMs struggle to learn new factual information
through unsupervised fine-tuning.” - Fine-Tuning or Retrieval? Comparing
Knowledge Injection in LLMs [Link]
“Disconcertingly, our research also reveals that, even without
malicious intent, simply fine-tuning with benign and commonly used
datasets can also inadvertently degrade the safety alignment of LLMs,
though to a lesser extent. These findings suggest that fine-tuning
aligned LLMs introduces new safety risks that current safety
infrastructures fall short of addressing — — even if a model’s initial
safety alignment is impeccable” - Fine-tuning Aligned Language Models
Compromises Safety, Even When Users Do Not Intend To! [Link]
“The base model generates a wide range of nationalities, with
American, British, and German being the top three. In contrast, the
aligned model only generates three nationalities: American (highest
percentage), Chinese, and a small percentage of Mexican.” - Creativity
Has Left the Chat: The Price of Debiasing Language Models [Link]
Just do it! Brand Name Lessons from Nike’s Troubles - Musings
on Markets [Link]
Brand value is often mixed with other advantages like scale, network
effects, and product differentiation. Strong brands yield higher
revenues, pricing power, and potentially lower capital costs. And it’s
hard to separate brand value in companies with multiple advantages.
Spotify: Layoffs Pay Off - App Economy Insights [Link]
Key business highlights: 1) Spotify’s new subscription plans -
Spotify’s Audiobooks Access ( \(\$9.99\) per month for 15 hours) and Basic
(removing audiobooks from Premium) diversify its offerings, 2) Spotify
is incorporating social-style discovery features, like Live Listening
Parties and prompt-based AI playlists, but remains behind YouTube and
Meta in algorithm sophistication and live content, 3) Spotify’s
ad-supported ARPU is low (€1.15 vs. Meta’s \(\$11.89\) dollars in Q2), limiting ad
revenue potential. A new in-house creative agency may improve brand
experiences but is challenging to scale profitably, 4) Spotify pivoted
from broad podcasting investments to a case-by-case approach, now
pushing video podcasts.
Competitiveness: 1) Hub Entertainment Research shows Spotify’s high
‘must-have’ appeal, with 75% of US users viewing it as “uncancellable.”
This loyalty supports Spotify’s growing free cash flow and a valuation
around 40 times Free Cash Flow (FCF) —placing it ahead of rivals like
YouTube Music (100M subscribers) and Apple Music (estimated 110M by
2025), 2) Despite solid growth, Spotify’s reliance on licensed content
and its still-limited ad revenue leave room for competition. While
TikTok’s 1B+ users could funnel into TikTok Music, ByteDance recently
announced it will close TikTok Music by November, focusing instead on
promoting artists and streaming value within the main app—a potential
competitive break for Spotify.
Covered Palo Alto Networks, CrowdStrike, Fortinet, Zscaler, and
Cloudflare.
Two Nobel Prizes for AI, and Two Paths Forward - Marcus on
AI [Link]
Hinton’s focus on end-to-end neural networks can be limiting,
especially when considering the complexities of real-world problems that
often require more structured and hybrid approaches. On the other hand,
Hassabis’s embrace of neurosymbolic AI reflects an openness to different
methodologies and a recognition that a combination of techniques may
yield better results.
First, Waymo is using transformer-based foundation models for all
stages of its self-driving pipeline: perception, prediction, and
planning. Second, the whole system is trained end to end. During
training, gradients from the behavior network propagate backwards to the
perception network.
So I see more similarities than differences in the evolution of
Waymo and Tesla’s self-driving software. Both companies made little to
no use of neural networks in their early systems. Both companies started
using neural networks for perception in the late 2010s. And both
companies only recently shifted to end-to-end architectures that used
neural networks for all stages of the self-driving pipeline.
― Elon Musk wants to dominate robotaxis—first he needs to
catch up to Waymo - Understanding AI [Link]
Tesla’s advantages compared to Waymo:
Tesla already has millions of vehicles on the road, which could
quickly deploy robotaxi software without the need for new hardware.
Tesla relies on cost-effective, camera-based perception without
expensive sensors like lidar, which Waymo uses. This could lower Tesla’s
per-vehicle cost and allow it to expand more rapidly if autonomy is
achieved.
Tesla’s transition to a full, end-to-end neural network approach for
perception, prediction, and planning has improved FSD’s ability to
handle complex driving situations without manual coding for specific
scenarios.
Tesla’s disadvantages compared to Waymo:
Tesla hasn’t deployed a fully driverless car yet, while Waymo has
offered driverless rides since 2020.
Tesla would need extensive infrastructure to maintain a robotaxi
network (for charging, cleaning, and repairs) which it currently lacks.
Building this up in cities nationwide would take time, resources, and
logistical planning.
Tesla’s camera-only approach may struggle in certain conditions
(e.g., low visibility), which lidar could handle better.
Key Contribution of AI to Robotics:
Improved Reasoning and Planning: Large language
models, like those developed by OpenAI and Google, are enabling robots
to interpret high-level commands, understand contextual instructions,
and execute complex, multi-step tasks. This is particularly valuable in
dynamic environments where robots must adapt to unforeseen changes and
make real-time decisions.
Enhanced Visual and Motor Coordination: The
integration of generative AI with visual and motor feedback systems
allows robots to translate visual inputs into precise motor actions.
This enables robots to perform tasks such as picking and placing objects
with greater accuracy and efficiency, even in environments that are
constantly changing.
Natural Language Interfaces: AI-driven natural
language interfaces are making it easier for users to interact with
robots using everyday language rather than programming code. This
democratization of robotics makes it accessible to non-technical users,
paving the way for broader adoption across industries.
Predictive Maintenance: AI models analyze
real-time data from robots to predict potential malfunctions, enabling
proactive maintenance that minimizes costly downtime and enhances
operational efficiency.
― Generative AI and Robotics in 2024 - AI Supremacy
[Link]
Glue: The less-glamorous stuff that helps a
team succeed
Strike the right balance between glue and core work. Do
enough glue work to show leadership promotable artifacts, but not too
much to where your core work suffers.
Lead meetings, take notes, and share them to provide value to
the right stakeholders.
Send your manager monthly recaps of your accomplishments so
they can more easily sponsor you and your work.
Grit: The will to pursue a long-term goal
despite challenges
Break your projects into achievable milestones so you can
constantly feel progress, even for long projects.
View failures as progress. It’s one less route you need to
explore now.
Take breaks and work in fun. I set up icebreakers at the
start of our meetings, organized team events, and pushed for production
freezes.
Friction: The gap between reality and the
ideal state
Find ways to unblock yourself and the people around you. Do
this enough, and you’ll have mastered removing friction.
Removing friction paints you as a force multiplier. Force
multipliers get promoted.
― 3 Career Principles that got me to Director at Google -
High Growth Engineer [Link]
Processing Fluency: The ease with which information is perceived
and processed by the human mind. High fluency can lead to positive
evaluations, even if the information is not accurate.
Halo Effect: A cognitive bias where a positive overall
impression of something (e.g., an LLM’s fluency) influences the
evaluation of its individual attributes (e.g., truthfulness).
Inter-rater Agreement (IRA): A measure of how much two or more
evaluators agree on their assessments. Low IRA indicates potential
problems with the evaluation design or guidelines.
Extrinsic Evaluation: Assessing the impact of an LLM’s output on
an end-user task or system (e.g., measuring productivity gains from
using an LLM-powered email assistant).
Intrinsic Evaluation: Evaluating the properties of the
LLM-generated text itself, such as fluency, coherence, and factual
accuracy.
― How Amazon is Rethinking human evaluation for generative
large language models [Breakdowns] - Artificial Intelligence Made
Simple [Link]
Pretty comprehensive of human evaluation of Gen AI models.
US Banks: Soft Landing? - App Economy Insights [Link]
― When Models Go MAD - Teaching computers how to
talk [Link]
Humm AI is diluting reality. It’s concerning.
Elon Musk’s tech projects are inseparable from his
authoritarian one - Blood In The Machine [Link]
Good discussion. Musk has multifaceted role—entrepreneur, political
influencer, and media magnate. His activities underscore a model of
influence that defies precedent, blending financial might, technological
ambition, and political maneuvering. Recognizing the interconnectedness
of these efforts is crucial for understanding Musk not just as a
private-sector innovator but as a power broker actively shaping the
public and political spheres in ways that could redefine norms, values,
and who gets to participate in his envisioned future.
YouTube and Podcast
Tesla I don’t think it’s a car company, I think this is
misleading, this is a robotics company robotics at Scale Company,
because I would say at scale is also like a whole separate variable,
they’re not building a single thing, they’re building the machine that
builds the thing which is a whole separate thing and so I think robotics
at scale company is what Tesla is.
I think with synthetic data you just have to be careful, because
these models are silently collapsed, is like one of the major issues so
if you go to ChatGPT and you ask it to give you a joke, you’ll notice
that it only knows three jokes, that’s the only it gives you like one
joke I think most of the time. And sometimes it gives you like three
jokes and it’s because the models are collapsed and it’s silent, so when
you’re looking at any single individual output, you’re just seeing a
single example, but when you actually look at the distribution, you’ll
notice that it’s not a very diverse distribution, it’s silently
collapsed. When you’re doing synthetic data generation, this is a
problem, because you actually really want that entropy, you want the
diversity, and the richness in your data set otherwise.
― No Priors Ep. 80 | With Andrej Karpathy from OpenAI and
Tesla - No Priors: AI, Machine Learning, Tech & Startups
[Link]
Andrej Karpathy was a founding team member of OpenAI and the former
Tesla Autopilot leader. He discussed the evolution of self driving
cards, tech challenges, Tesla’s Optimus humanoid robot, bottlenecks of
AI development today. The topic of how AI capabilities could be further
integrated with human cognition sounds very future and funny.
AI prompt engineering: A deep dive - Anthropic [Link]
Some of Anthropic’s prompt engineering specialists—Amanda Askell
(Alignment Finetuning), Alex Albert (Developer Relations), David Hershey
(Applied AI), and Zack Witten (Prompt Engineering)—share their insights
on the evolution of prompt engineering, offer practical advice, and
discuss how prompting could evolve as AI continues to advance.
Decoding Google Gemini with Jeff Dean - Google
DeepMind [Link]
Jeff Dean, chief scientist of Google DeepMind and Google Research,
discusses the past, present and future of AI, specially the long term
potential of multi-modal models like Gemini.
Shall We Repeal the Laws of Economics? - Oaktree
Capital [Link]
Howard Marks addresses how politicians often ignore economic reality
in their campaign promises, using examples like Trump’s call for tariffs
and Harris’s attack on grocery profiteering. He emphasizes that economic
laws are incontrovertible, and politicians can’t deliver on promises
that contradict these laws; free markets allocate resources efficiently.
And he highlights the ongoing political refusal to address issues like
Social Security insolvency and national debt, stating that ignoring
economic laws will eventually lead to negative outcomes.
Meta VS Apple: What Their Battle Means For AI Startups - Y
Combinator [Link]
The Waymo Way: Making Autonomous Driving a Reality | Dmitri
Dolgov - U-M Computer Science and Engineering [Link]
waymo_foundation_model
Sam Altman: The Man Behind ChatGPT | Big Take - Bloomberg
Podcasts [Link]
Markets turn Trump, Long rates spike, Election home stretch,
Influencer mania, Saving Starbucks - All-In Podcast [Link]
What’s next for AI agentic workflows ft. Andrew Ng of AI Fund
- Sequoia Capital [Link]
A fireside chat with Sam Altman OpenAI CEO at Harvard
University - Harvard Business School [Link]
Q1: I’m curious how you rebuilt or rediscover momentum after pivoting
in a professional or personal sense in terms of your values.
We have unbelievable greatest technological wave ever, so it’s a
great time to be starting out your career. You are going to be flooded
with opportunities for the next few years.
Q2: How do you think AI’s role will be in tackling sort of
inequalities in education and health care and legal stuff?
It should reduce inequality.
Q3: Subscription model could be a barrier for startup and small
businesses, do you think OpenAI would explore alternative monetization
strategy that could include free API access, perhaps supported by
advertising or other methods, to foster innovation in the future?
Sam hates ads in general. Ads + AI is uniquely unsettling to him. He
likes the simplicity of OpenAI’s model, which is they make great AI and
people pay them for it, and they just do the best they can for
people.
Q4: Does the increasing competitors change the way you are evolving
for the next products?
They are just trying to figure out the next paradigm and the next
great idea. They don’t pay much attention to the market share though
they pay at least attention to competitors to maybe get
inspiration.
Sam’s hope is that every year, they do something amazing that people
thought was impossible. Once you know that something is possible and
roughly how to do it, it always gets copied quickly. That’s not the hard
part. The hard part is figuring out what it is and doing it first when
you don’t know it’s possible.
Q5: what do you think the ideal public general education curriculum
on AI should look like and what does the average person need to know
about AI in the next 5-10 years?
Computer science major or at least some courses. Being able to train
a GPT-2.
Q6: Can you share what’s coming next after transformers? Then the
second question is what do you think most entrepreneurs and VCs are
getting wrong about the future of AI?
Most entrepreneurs don’t bet on AI models are going to be massively
better so that’s why there is meme that “OpenAI kills my startup”.
Q7: How much exposure does OpenAI and the AI movement have to energy
constraints? And what do you think the role is of founders have to play
in addressing these concerns?
It’s necessary to Sam to drive tech abundance from those two key
inputs - energy and AI.
Articles and Blogs
Of the employees we studied, those with superior sales
performance were genetically different from the rest of the group. They
were better at learning in real time about new customers and new sales
opportunities. From an initial conversation with a sales lead, they were
able to quickly feel out the customer and propose appropriate products
without being told what to recommend.
Adaptive learning is different; it isn’t trainable. It’s the
ability to process new information in real time and immediately use it
to achieve a positive result.
For example, sales teams often require junior employees to
cold-call leads, even through they don’t know as much about the company
as more experienced employees do. Most of them haven’t even learned how
to sell yet. But our research shows that for adaptive learners,
seniority and experience are less important. Employees with the sales
gene quickly become knowledgeable about your products and are able to
learn and adjust on the fly.
― There Really Is a “Sales Gene” - Juan Martinez, Harvard
Business Review [Link]
Adaptive learning skill is important but might not correlated with
seniority or experience. Employees with this capability can quickly
become knowledgeable about the products and are able to learn and adjust
on the fly.
The article suggests that managers or companies could be given a
snapshot of how many of salespeople are adaptive learners without
singling out any individual. and they could tell which tasks require
adaptive learning skills and which don’t and allow them to choose. This
should be done in an anonymous way.
No matter whether what’s been proposed in this article is applicable
or ethical. The idea of adaptive learning is kind of new to me, and it
inspires me to further think about whether this skill is learnable and
teachable, and think about whether there is any other secret skills in
sales.
New Rules for Teamwork - Harvard Business Review [Link]
Develop an Operating System
OS means building blocks for the way team members collaborate, create
change, and support one another. Effective operating systems vary
widely, depending on the needs and norms of the organization. What they
all have in common is that they set out a view of how teams create
value, what teams are supposed to achieve, the technical skills each
team member is expected to contribute, the processes by which the work
will be managed, and the cultural norms and mindsets of constructive
collaboration that will guide behavior.
Suggestions: hold kickoffs, conduct one on ones, and take stock of
progress using retrospectives, are the three practices as a foundations
of team OS.
Invest in Active, Real-Time Measurement
To make teamwork scientific, organizations need to be able to measure
the outcomes of their actions and determine how changes in the inputs
affect results.
Suggestion: define what constitutes success.
Create a System for Continuous Improvement and Innovation
Teams today have new forms of technology and data collection at their
disposal to help them self-correct while projects are underway. e.g.
support colleagues to discuss what could have been done better; look at
the patterns across teams to identify improvements and share best
practices, particularly with regard to the rapid adoption of new
technologies such as GenAI.
Suggestions: Identify the metrics that matter most (shift-changeover
time, perhaps), hypothesize which actions could improve performance in
those areas (preassigned workstations, perhaps), and embed technologies
in the operating system (a smart-planning app, perhaps) to enable
continuous improvement. Continuous improvement can occur only when all
perspectives are considered and all teams have access to a centralized
knowledge repository. Finally, it may be useful to set up a center of
excellence, staffed with full-time employees with experience in
analytics and operating system design.
Why Leadership Teams Fail - Harvard Business Review
[Link]
I was reading while thinking about my team and neighbor teams. I find
this article very useful.
A critical factor in organizational success: the health of their
leadership team. There are three main patterns of dysfunction: Shark
Tanks, Petting Zoos, and Mediocracies.
Shark Tanks
Definition: A leadership team marked by hyper-competition,
political maneuvering, and infighting. Members prioritize personal
agendas over collective goals, leading to toxic and combative
dynamics.
Causes: Lack of clear direction or boundaries from the CEO or
team leader. Failure to address self-serving behaviors early on. Absence
of behavioral norms that encourage collaboration.
Signs: Team members engage in power struggles outside of
meetings. One-on-one discussions with the CEO on issues that should be
resolved in team settings. Meetings turn into battlegrounds, with
frequent arguments and difficulty reaching consensus. Executives
bad-mouth each other, form alliances, or resist decisions after they’ve
been made.
Prevention:
Clear Expectations: Leaders should explicitly define which
behaviors are acceptable and unacceptable. Set boundaries around how
competition should be managed.
Confront Self-Serving Behaviors: Address aggressive or toxic
behaviors directly with individuals. Remove those unwilling to align
with the team’s goals, even if they’re high performers.
Role Modeling: The CEO or team leader must model collaborative
behaviors and ensure transparency in communication to prevent political
games.
Regular Feedback: Reinforce positive behaviors and correct
negative ones through continuous feedback. Implement 360-degree reviews
to track team behavior and performance alignment.
Petting Zoos
Definition: A leadership team that avoids conflict to maintain
harmony. Vigorous debate is sacrificed, and members prioritize getting
along over pushing for the best ideas, leading to complacency and poor
decision-making.
Causes: Overemphasis on collaboration and mutual trust, leading
to conflict avoidance. Team members are too deferential, fearing that
disagreements might disrupt the team’s harmony. Leaders may unknowingly
encourage this avoidance by stressing harmony over debate.
Signs: Meetings lack critical debate, and discussions feel muted
and lacking in emotional intensity. Team members engage in performance
theater, focusing on positive news while downplaying problems. Decisions
are made by consensus without sufficient evaluation or challenge.
Leaders avoid holding one another accountable for poor performance,
reluctant to disrupt the status quo.
Prevention:
Encourage Debate: Leaders should foster a culture of constructive
conflict where members feel safe to challenge each other’s ideas. A
foundation of trust and psychological safety is key.
Promote Data-Driven Discussion: Ensure discussions are rooted in
facts, using shared data to spur debate and avoid personal conflict.
This encourages neutral, objective decision-making.
Monitor Meeting Dynamics: Leaders should track participation and the
quality of discussion during meetings, encouraging team members to speak
up and challenge ideas more openly.
Redefine Consensus: Teams must understand that consensus does not
mean avoiding conflict but making informed decisions after rigorous
debate.
Mediocracies
Definition: A leadership team marked by complacency, lacking the
drive or skills to achieve high performance. Collaboration and
competition are both underemphasized, and the team fails to meet the
organization’s needs.
Causes: Long periods of success that breed complacency. Poor
alignment between the team’s skills and the changing demands of the
business. A divided team, where some members prefer competition while
others favor collaboration, leading to inconsistent and ineffective
efforts. A leader’s failure to adapt to changing market conditions or
internal challenges.
Signs: Team members operate in silos, with little collaboration
between departments or units. Decision-making is slow, and there is a
lack of accountability for performance. The team focuses on past
achievements rather than future goals, with little ambition or drive for
improvement. The team struggles with stagnation, missed opportunities,
and duplicated efforts due to poor coordination.
Prevention:
Rebuild the Team: Leaders may need to replace members who are not
fit for their roles or who lack the motivation or skills needed to lead
effectively. New hires should be chosen not just for their skills but
also for their alignment with the company’s purpose and values.
Promote Balance: Strike a balance between competition and
collaboration by hiring individuals with complementary skills and styles
(e.g., planners and visionaries alongside hard-nosed executors).
Clear Roles and Expectations: Define where collaboration is expected
(e.g., across departments) and where competition might be useful (e.g.,
in individual market decisions). Ensure everyone understands their
responsibilities and how their performance contributes to broader
goals.
Challenge the Status Quo: Continuously push the team to innovate and
grow by setting ambitious goals and holding team members accountable for
driving performance improvements.
Without such an observability system–let’s call it Design System
Observability–it could be too late when Uber learned through complaints
and public media about the end users who would suffer confusing
onboarding rides, inconsistent layouts, and frustrating
voiceovers/talkbacks sessions.
― How to Measure Design System at Scale - Uber Blog
[Link]
RAG is the most popular architecture of the LLM based systems in
2023. There are many products build almost solely on RAG — from Question
Answering services combining web search engines with LLMs to hundreds of
chat-with-your-data apps.
― Advanced RAG Techniques: an Illustrated Overview -
Medium [Link]
Machines of Loving Grace - Dario Amodei, CEO of
Anthropic [Link]
This essay aligns with our thinking that while you acknowledge the
risks and share concerns over idealistic promises, you also recognize
the challenges inherent in building AGI. It reflects an approach to AI
that balances ambition with caution, and providing perspectives of how
to articulate a vision for AI that remains both ambitious and
grounded.
Interesting point: As AI reaches near-universal superiority, the
economy may need to adapt fundamentally. Potential solutions range from
universal basic income (UBI) to entirely new economic frameworks.
Perhaps AIs might manage resources and distribute them according to
value systems derived from human input, but such proposals raise ethical
and practical concerns. The author hints that the economic
transformation required could be as drastic as past societal shifts
(e.g. from hunting-gathering to agriculture), suggesting that humanity
will have to experiment and iterate to find sustainable models that
protect against exploitation or dystopia.
Overall, the author believes that the core human values such as
fairness, cooperation, and autonomy have a natural, most
‘’overdetermined’ appeal, and will often lead toward democracy, rule of
law, and enlightenment ideals - a trajectory that AI as a catalyst could
accelerate by making the path to this “good world” more tangible. While
the vision seems intuitive and inspiring to many, it may still appear
fantastical or undesirable to others. Even so, the author finds a unique
beauty in striving for it, suggesting that our intrinsic human impulses
toward collaboration and justice make this vision both plausible and
worth pursuing.
Andrew Ng’s writing in The Batch - The Batch [Link]
“The best we can do is a compromise: learn to recognize
situations in which mistakes are likely and try harder to avoid
significant mistakes when the stakes are high.” ― Daniel Kahneman,
Thinking, Fast and Slow
― Unleashing System 2 Thinking? AlphaCodium Outperforms
Direct Prompting of OpenAI o1 - qodo [Link]
System 1 thinking: fast responses with surface-level
understanding;
System 2 thinking: deliberate methodical and reasoned problem
solving.
Developers can now build fast speech-to-speech experiences into their
applications
With canvas, ChatGPT can better understand the context of what
you’re trying to accomplish. You can highlight specific sections to
indicate exactly what you want ChatGPT to focus on. Like a copy editor
or code reviewer, it can give inline feedback and suggestions with the
entire project in mind.
You control the project in canvas. You can directly edit text or
code. There’s a menu of shortcuts for you to ask ChatGPT to adjust
writing length, debug your code, and quickly perform other useful
actions. You can also restore previous versions of your work by using
the back button in canvas.
Canvas offers a new interface for the project works that require
editing and revisions.
Multi document agentic RAG: A walkthrough - LanceDB
[Link]
This tutorial shows you how to build a multi-document agentic RAG
system using LanceDB and LlamaIndex for complex information retrieval.
Specifically, the walkthrough demonstrates how to integrate LLMs, vector
databases, and agent-based reasoning for enhanced information retrieval
and task completion.
Reports and Papers
Learning vs Retrieval: The Role of In-Context Examples in
Regression with LLMs [Link]
The paper explores in-context learning (ICL) mechanisms in large
language models (LLMs), focusing on the balance between knowledge
retrieval and learning from in-context examples in regression tasks. It
reports that LLMs can learn from regression examples of realistic
datasets in-context, extending previous work on synthetic data to more
practical scenarios.
I’m looking forward to this kind of experiments and studies, because
I was suspicious about applying LLM on structured data.
Larger and more instructable language models become less
reliable [Link]
The issue might stem from the nature of LLMs, which are designed to
generate plausible responses based on patterns in the data they’ve seen,
rather than to know anything in the traditional sense. They
don’t have an internal mechanism to differentiate truth from
fabrication, so as they scale up, they produce more complex, yet not
necessarily more accurate, answers. This makes them better at appearing
smart, but less reliable overall—a quality that philosophers like Mike
Hicks rightly criticize as “bullshitting.”
From a user perspective, it underscores the need for critical
thinking when engaging with AI models through prompt engineering. Just
because an LLM provides a well-phrased response doesn’t mean it’s
accurate.
o1—like previous LLMs—is sensitive to the probability of examples
and tasks, performing better and requiring fewer “thinking tokens” in
high-probability settings than in low-probability ones.
― When a language model is optimized for reasoning, does it
still show embers of autoregression? An analysis of OpenAI o1
[Link]
Although optimized for reasoning, o1 still exhibits probability-based
limitations tied to its autoregressive origins, implying that a complete
departure from these influences has not been fully achieved.
VideoPrism: A foundational visual encoder for video
understanding - Google Research [Link]
Astute RAG: Overcoming Imperfect Retrieval Augmentation and
Knowledge Conflicts for Large Language Models [Link]
Astute RAG is designed to better combine internal and external
information through an interactive consolidation mechanism (i.e.,
identifying consistent passages, detecting conflicting information in
them, and filtering out irrelevant information).
Differential Transformer - Microsoft Research [Link]
Diff Transformer amplifies attention to relevant context while
canceling noise, resulting in outperforming standard Transformers in
multiple areas such as long-context modeling, key information retrieval,
hallucination mitigation, in-context learning, and reducing activation
outliers, as shown in the experiments.
The key innovation is a differential attention mechanism that
calculates attention scores by subtracting two separate softmax
attention maps. This subtraction cancels out irrelevant attention,
promoting sparser, more accurate focus on important information, similar
to noise-canceling techniques.
Controllable language modeling refers to techniques that allow users
to guide or control specific attributes of the generated text from a
language model (LM). These attributes can include factors like
sentiment, toxicity, formality, or any other desired linguistic or
stylistic feature. The primary challenge is ensuring that generated
content aligns with specific requirements without compromising fluency,
coherence, or overall quality of the text.
Diffusion models are excellent for controllable generation. They
generate data in a multi-step process, gradually refining noise into
coherent content. This incremental approach allows for fine-grained
control at various stages of the generation. By manipulating the process
at specific steps, you can guide the output more effectively toward
desired characteristics (such as sentiment, style, or tone). In
contrast, auto-regressive models like GPT generate text token by token
in a one-shot manner, making it harder to impose controls without
affecting fluency.
DGLM could refine language model generation because it integrates the
fluency of auto-regressive language models (like GPT) with the
flexibility of diffusion models. This flexibility is realized by
employing Plug-and-Play with Linear Classifiers in the Sentence-T5
latent space to guide the diffusion process towards generating proposals
with desired attributes.
Researchers from Tsinghua University, led by Andrew Chi-Chih Yao,
introduced Diagram of Thought (DoT), designed to enhance the reasoning
capabilities of LLMs.
The limitation of CoT is that it processes information in a straight
line which does not reflect the way of how humans think. The limitation
of ToT or GoT is that they are computationally expensive and challenging
to implement within a single LLM.
DoT addresses these limitations by modeling reasoning as the
construction of a directed acyclic graph (DAG) within a single LLM. This
DAG comprises nodes representing: 1) Propositions: Initial and refined
ideas generated throughout the reasoning process, 2) Critiques:
Evaluations of propositions, identifying errors or inconsistencies. 3)
Refinements: Improved propositions based on critiques. 4) Verifications:
Confirmation of valid propositions.
Two key crucial aspect of DoT framework: 1) it leverages
auto-regressive next-token prediction with role specific tokens to
manage reasoning process within a single LLM, 2) it has strong
foundation in math logic - Topos Theory, ensuring logical consistency
and soundness in reasoning process.
A Survey on the Honesty of Large Language Models [Link]
Dunning-Kruger effect is named after psychologists David Dunning and
Justin Kruger, who first described this phenomenon
in 1999. It reveals a troubling mismatch between perception and
reality. Those who know little often lack the self-awareness to
recognize their limitations. Conversely, experts may be acutely aware of
the vastness of their field, leading them to undervalue their own
expertise.
Spatial context non-uniformly modulates inter-laminar
information flow in the primary visual cortex [Link]
Their research shows that when our field of vision is cluttered, it
changes how efficiently our brain processes information, though the
basic pattern of information transfer remains the same.
When you give a Claude a mouse - Out Useful Thing
[Link]
Research proposed AI agent on information retrieval (IR) task.
Compared to traditional IR, Agentic IR employs a unified, adaptive
architecture where agents use observation, reasoning, and actions
iteratively to reach the desired user information state. Key methods
include prompt engineering, retrieval-augmented generation, multi-agent
systems, and reinforcement fine-tuning (RFT).
A Comparative Study on Reasoning Patterns of OpenAI’s o1
Model [Link]
An evaluation study of GPT o1’s reasoning patterns. The study
identified six distinct reasoning patterns for o1—Systematic Analysis
(SA), Method Reuse (MR), Divide and Conquer (DC), Self-Refinement (SR),
Context Identification (CI), and Emphasizing Constraints (EC)—with DC
and SR being most common across tasks. And token count varied greatly
across tasks, indicating that the o1 model adjusts reasoning depth based
on task complexity.
Malla: Demystifying Real-world Large Language Model
Integrated Malicious Services [Link]
A study of malicious services powered by LLM ‘Malla’ in the
underground marketplaces. (When it comes to AI, everywhere has gaps to
bridge lol). Interesting mysteries to uncover:
Who are the pivotal players within the Malla ecosystem?
The Malla ecosystem comprises vendors who create malicious LLM
services, users who exploit these services, and platforms that
facilitate their operations.
How is Malla orchestrated and monetized?
Malla services generate revenue through direct user transactions,
often accepting cryptocurrencies, with some vendors reporting
substantial earnings.
What techniques did miscreants deploy to exploit LLMs and build
up Mallas?
Miscreants utilize techniques like jailbreak prompts to bypass LLM
restrictions and abuse public APIs to generate harmful content.
The minimal versions (minLSTMs and minGRUs) are fully parallelizable
during training and use fewer parameters. They are 175x faster to train
than traditional LSTMs and GRUs. And their performance is equivalent to
Transformers or Mamba with fewer training steps.
The Perfect Blend: Redefining RLHF with Mixture of
Judges [Link]
This work is redefining RLHF with Mixture of Judges by Constrained
Generative Policy Optimization (CGPO), a novel RLHF framework. The
framework uses a Mixture of Judges (MoJ) with rule-based and LLM-based
constraints to mitigate reward hacking. As a result, CGPO consistently
outperforms PPO and DPO baselines across various benchmarks.
This annual publication examines trends in AI research, industry
developments, and technological progress. And this year it reveals
convergence in AI Model Performance.
As the title described, they introduce MLE-bench as a benchmark for
measuring how well AI agents perform at machine learning engineering.
The tasks for testing were created by curating 75 ML engineering related
competitions from Kaggle. Eventually, OpenAI’s o1-preview with AIDE
scaffolding achieves at least bronze medal in 16.7% of competitions.
Little experimental and educational multi-agent framework by
OpenAI.
Swarm’s Operational Framework:
Agent Definition: Create Agents with specific instructions, roles,
and functions. Each function is automatically converted to a JSON
structure for API compatibility.
Handoff Mechanism: Implement logic for agent transitions. Functions
can return a new Agent object to transfer control based on conversation
flow or predefined criteria.
Context Management: Utilize Context Variables to initialize and
update shared information throughout the conversation, maintaining state
across agent interactions.
Execution Loop: The client.run() function manages the multi-agent
conversation. It takes an initial agent, user messages, and context as
input, and returns a response with updated messages, context variables,
and the last active agent.
This structure allows for flexible, dynamic multi-agent interactions
while maintaining a stateless architecture between calls.
Prompt caching optimizes API calls for faster LLM interactions. It is
a new API feature that optimizes large language model interactions. It
caches and reuses consistent parts of prompts, reducing processing time
and costs for repetitive tasks.
This technique is particularly useful for scenarios involving large
contexts, multiple examples, or long conversations. Prompt Caching works
by checking if a prompt prefix is already cached from a recent query and
using it if found, otherwise processing and caching the full prompt.
Works to rapidly test multiple ML models with minimal coding effort.
This Python library streamlines model selection for classification and
regression tasks.
News
The Nobel Prize in Physics 2024 to John J. Hopfield and
Geoffrey E. Hinton, for foundational discoveries and inventions that
enable machine learning with artificial neural networks [Link]
Zuckerberg imagines that people will want to use AR glasses like
Orion for two primary purposes: communicating with each other through
digital information overlaid on the real world — which he calls
“holograms” — and interacting with AI.
One downside of Apple’s Vision Pro or Meta’s Quest 3 is that you lost
vision of other people and other people cannot see your eyes, making it
usage situation limited to home where you don’t have interaction with
other people. However, the future of devices that’s going to replace
mobile phone or comparable to mobile phone, has to have some
functionalities to support socialization and networking.
Llama 3.2: Revolutionizing edge AI and vision with open,
customizable models - Meta Blog [Link]
Big Tech has cozied up to nuclear energy - The Verge
[Link]
Microsoft, Amazon, and Google are investing in nuclear energy to
power their data centers.
Why Taiwan and Its Tech Industry Are Facing an Energy Crisis
- Yale Environment 360 [Link]
Google’s share of the U.S. search ad market is expected to drop
below 50% next year for the first time in over a decade, according to
the research firm eMarketer.
Amazon is expected to have 22.3% of the market this year, with
17.6% growth, compared with Google’s 50.5% share and its 7.6%
growth.
― Google’s Grip on Search Slips as TikTok and AI Startup
Mount Challenge - The Wall Street Journal [Link]
Uber and Lyft drivers use Teslas as makeshift robotaxis,
raising safety concerns - Reuters [Link]
Real world example:
Shorenstein Properties, a real-estate investment company based in
San Francisco, is in a pilot program that is designed to lead to the
automated tagging of all of its files using a RAG-based AI system. The
goal is to eliminate many of the drawbacks in a time-consuming manual
system, in which people might make errors or simply skip the process
altogether. The company plans to put the tagging system into production
in the next few months.
Files can also be organized quickly into “knowledge bases” and
interrogated with AI, according to Egnyte, a cloud-based platform that
companies use to access, share and manage business content.
Shorenstein in the past few weeks has started a proof of concept
project using Egnyte to extract data from prospectuses on properties for
sale, documents that can often run 60 pages, and organize it into
reports that could help the company make efficient business decisions
and improve processes.
― Companies Look Past Chatbots for AI Payoff - Steven
Rosenbush at The Wall Street Journal [Link]
Beginning Friday, users of Meta’s AI chatbot feature in the U.S.
will have access to real-time news and information from Reuters when
they ask questions about news or current events.
It’s the first news deal Meta has brokered in the AI
era.
― Scoop: Meta strikes multi-year AI deal with Reuters -
AXIOS [Link]
An AI companion for everyone - Microsoft Blog [Link]
Notion AI heavily integrates AIintroducing file-handling
capabilities, enabling developers to extract insights from PDFs and
images.
Customer data search, unification and retrieval for LLMs -
Tilores [Link]
Identity RAG from Tilores improves accuracy and relevance of
enterprise LLMs.
New autonomous agents scale your team like never before -
Microsoft [Link]
We’re also introducing a groundbreaking new capability in public
beta: computer use. Available today
on the API, developers can direct Claude to use computers the way
people do—by looking at a screen, moving a cursor, clicking buttons, and
typing text. Claude 3.5 Sonnet is the first frontier AI model to offer
computer use in public beta.
Introducing computer use, a new Claude 3.5 Sonnet, and Claude
3.5 Haiku [Link]
Perplexity Now Offers Powerful AI Search Right On Your macOS
Desktop [Link]
Pushing the frontiers of audio generation - Google
DeepMind [Link]
Gemini API and Google AI Studio now offer Grounding with
Google Search - Google for Developers [Link]
What’s new: 1) reduces hallucination rates by sourcing verified,
real-time information, 2) provides citation-supported answers, improving
transparency, 3) allows threshold-based activation to control costs.
Technical details: Google’s Dynamic Retrieval system
evaluates each query for grounding suitability, assigning a prediction
score between 0 and 1. Factual or time-sensitive queries score higher
(e.g., 0.97), while creative prompts score lower (e.g., 0.13).
New in Maps: Inspiration curated with Gemini, enhanced
navigation and more - Google [Link]
“The Long View: Career Strategies to Start Strong, Reach High, and Go
Far” written by Brian Fetherstonhaugh was a book I randomly picked to
read but it surprised me as it turns out to be fairly practical and
comprehensive.
We need a work philosophy that encompasses all the parts of our
lives, and one that can give us guidance on how to be ambitious and seek
success without sacrificing other things we value deeply— family,
friends, health, and purpose.
What I have learned:
Five Things
You Need to Know to Build a Career Plan
Careers last for about 45 years and embrace 3 distinctly
different stages, each lasting about 15 years.
Stage One: Start Strong by Taking on Fuel
Your learning curve is more important than your job title. Create the
foundation for your career and establish good early habits.
These are good suggestions for a new hire:
In July of 2015, Next Big Sound was acquired by Pandora. Modest and
down to earth, Alex is quick to point out the role that luck played in
his success as well. “The number of things that went our way was
terrifying,” he said shaking his head. “If you want to start your own
company just so you can be on the Forbes Under 30 list, it’s not worth
it. If you want to be an entrepreneur to make a lot of money, don’t. If
you find something you’re obsessed with, something that keeps you up at
night, then that’s the thing you should pursue.”
If you are serious about your career, this is not enough. Learn what
makes your company tick: where it came from, what it stands for, how it
makes its money, who the key people are, and where it is going If you
don’t get answers to these questions as part of your company’s normal
indoctrination, make it your business to find out in the first hundred
days. Do your homework. Read the company’s annual report, or better yet,
an outside analyst’s assessment of the company. Ask both old-timers and
rising stars to tell you the inside scoop on your company over a cup of
coffee, Get engaged by joining a club, team, or professional network in
the firm. Volunteer to help with a company event, and do it well. Slowly
begin to build your career ecosystem of contacts, communities, critical
colleagues, and champions.
Stage Two: Reach High by Focusing on Your Strengths and
Passions
The prime objective for this stage is to find your sweet spot-the
intersection of what you’re good at, what you love to do, and what the
world appreciates. It is the time to differentiate yourself from the
pack, to stand out, and to become eligible for career pathways that will
be most rewarding to you. Focus on your strengths and largely ignore
your weaknesses.
Stage Three: Go Far by Staying Fresh and then Passing the
Torch
Stage Three is devoted to achieving lasting impact and finding a
sustainable new career pathway that will likely need to last well into
your sixties or even seventies.
“Fuel” matters what you build on.
Fuel is critical throughout your career. In Stage One you need to
accumulate it, in Stage Two you need to take advantage of it, and in
Stage Three you need to refresh and preserve it.
Here is a great theoretical exercise suggested by social scientist
Charles Handy: Imagine if at the age of forty you had to quit your job
forever and start a company with just you. What would you do? That is a
great test of self-reliance.
Transportable Skills.
Problem Solving - being able to assess a problem and create a
plan; being able to have a solid method or two that you can reply on to
help solve the problem when you aer given a challenge an a blank piece
of paper.
The good news is that there are many frameworks and strategies to
help you improve your problem-solving abilities. Be intentional in
adding several different approaches to your repertoire, and don’t be
afraid to combine a few different methods to create something unique
that works for you.
Persuasive communication -
Inventors and creative people need to sell their ideas.
Persuasion is not just opinions expressed loudly. That might work
once, but it doesn’t work over the long haul. Part of being persuasive
is bringing forward compelling facts that truly give people permission
to believe you. I worry that in the world of ubiquitous information,
there are too many opinions and half-truths available, but too few
authoritative sources. When I work with young professionals at my
company I always encourage them to back up every key point with a
footnote and a source.
One of the best skills to learn is the ability to spot communication
breakdowns and adjust your approach accordingly. There are a few things
that can go wrong during a conversation:
Correspondence is when two people use different words to say the
same thing.
Conflict can arise when two people use the same word but mean
different things.
Contrast happens where there is no overlap at all.
Getting Things Done - being able to not only start but al finish
projects consistently; being able to power through to achieve the end
goal, regardless of the barriers and obstacles throw at you; being able
to be trusted by people at work with more high-profile
projects.
Becoming a Talent Magnet -
The companies with the best people always win. The individual leaders
who have the ability to attract and mobilize top talent win.
Giving and Asking for Help -
According to Grant, successful Givers - those who give more than they
take - are much more likely to be among the highest performing and most
satisfied people.
Emotional Intelligence (EQ) -
Through his access to business leaders around the world and studies
in more than five hundred organizations, Goldman documents an
astonishing fact: in determining star performance in every field,
emotional intelligence matters twice as much as IQ or technical
expertise.
Meaningful Experiences.
Meaningful experiences combine to enable you to be versatile and
robust in your career. New experiences take you outside your comfort
zone and build new career muscles.
I think that everybody in business these days should spend at least
one chapter of their career working in e-commerce even if it’s just for
a couple of years. Here’s why: e-commerce is a huge industry with great
long-term prospects. It is already worth hundreds of billions of dollars
and is projected to grow over 15 percent per year over the next decade.
Because e-commerce involves the whole selling process, it teaches you to
think like a general manager —from product development to how the supply
chain works to merchandising, customer service, and more. It gives you
exposure to the “soft skills” of business like branding and customer
experience as well as the “hard skills” of profit management, data, and
analytics. Best of all, a job in e-commerce means that you get a report
card every day in the form of immediate sales. E-commerce can act like a
microcosm of all business in a single job assignment. What a fantastic
way to accelerate your learning and development. If I were starting my
career over today, I would spend at least one chapter doing
e-commerce.
Enduring Relationships.
Your bosses, client / customer relationships, business partners,
talent around you, and find your tribe.
Enduring relationships are perhaps the most potent and long-lasting
form of fuel. They include both the brands you associate with and the
people you connect with throughout the journey.
Katya Andresen, the CEO of Cricket Media, defines the three principal
roles that mentors can play in our lives: the Star, a successful role
model who shows us how it can be done, the Sage, who like Socrates
doesn’t give us the answer but teaches us how to think, and the
Agitator, who spurs us and stretches us, and gives us the occasional
kick in the pants.
Careers are built through the skillful investment of time.
Becoming a highly employable expert or “master” is not just the
result of innate talent, but of the application of thousands of hours of
learning, experience, and practice.
Careers do not progress in linear or predictable ways.
Successful careers are a combination of diligent planning and good
luck. The diligent planning is essential, because it makes you eligible
for the luck.
A career is so much more than a job: it’s a big part of
life.
Five
Things You Need to Do to Bring Your Career Plan to Life
Do the Career Math exercise to get into the right long-term frame
of mind.
62 is the median age of retirement in the US. If today you are in
your late twenties, you have almost 35 years of career left. 40 years
old is not a halfway point - many people underestimate the length of a
career.
10000 hours of intense practice and rehearsal is needed to become
excellent at something.
Now matter how many IQ points or natural gifts you have, being
successful takes intense hard work and many more hours than you
thin.
On average 85%-90% of personal wealth are cumulated after 40th
birthday.
An individual’s personal wealth tends to peak at about age 65, and
their personal wealth at age 40 is only about 10%-15% of that amount
It’s not necessarily true that the key to a successful career is
to have the most social contacts.
Number of people who will really make a difference to your career
in life.
We all discover people in the course of our careers who become our
mentors, teachers, and advocates. They are the people who champion us
and say nice things about use behind our backs. They nominate us for
jobs and awards.
Always remember there is someone out there who is in your corner.
A lot of people incorrectly think “It’s all about contacts,” as
though that’s where career success begins and ends. Raw connections are
useful to extend your reach, but they aren’t of significant value until
you convert them to a higher
relationship-those people who will engage and mobilize on your behalf.
You may end up with thousands of raw connections. But remember, it is
not just a volume game; it is about quality and impact.
Ben Casnocha, the coauthor of The Start-up of You along with LinkedIn
founder Reid Hoffman, underscores this point clearly. “There’s a
distinction between networking and genuine relationship building.
Networkers are transactional. They pursue relationships thinking only
about what other people can do for them. Relationship builders, on the
other hand, try to help other people first. They don’t keep score.
They’re aware that most good deeds get reciprocated, but they’re not
calculated about it. And they think about their relationships all the
time, not just when they need something.”
Complete a Career Inventory to take stock of your most relevant
skills, experiences and relationships.
Check out the book chapter 5.
Take the 100-Hour Test and complete a Personal Time Portfolio to
see how you are investing your time.
Percentage of personal time on family, work, community, fitness,
teach and learn, and chilling.
Use the Career Path Navigator when you are trying to set a new
career pathway to decide between several options.
According to Auren, “Long-term success requires massive growth. Most
smart people out of college grow an average of 10 percent per year.
Which means they are roughly twice as effective seven years after
graduating college. That makes sense, as most twenty-nine-year-olds make
double what they did their first job out of college. To grow even more
quickly, you need a job with the following criteria:
• You’re surrounded by people who are smarter than you
• You have an opportunity to fail
• The company has a history of giving massive responsibility to people
like you
And if you decide to leave, exit with grace. It is a cliché to say
when people leave “our paths will cross again.” It is utterly, totally
true. Former colleagues and employers are a critical part of your career
ecosystem. They will provide ratings and opinions about you for years to
come. They will shop for talent in their current companies and in future
places they work. They will become consultants, clients, and
influencers. Wrap up your assignments with notable diligence and
accountability. Heal wounds as appropriate. Say thank you.
Future-proof your career by periodically challenging yourself
with the five scary long-term questions.
How can I avoid being replaced by a machine?
Where and how will I find work?
How will I spend my time in the future?
Will I outlive my money?
How will work make me happy?
Overcoming Adversities
Whether your career setback is unexpected or foreseeable, you will
need a method to speed your recovery. The four Rs method mentioned in
chapter 11 in the context of returnships is a good general approach to
getting back on track quickly, If you get fired or pushed to the side,
build on the four Rs to help get you back on the right path.
Reframe your experience so that it connects to the future, not just
to the past.
Refresh any skills that are rusty or lacking. You cannot fake your
way to renewed career momentum.
Reconnect your career ecosystem. Maybe you need some fresh
relationships with contacts, experts, critical colleagues and champions
to propel you forward.
Reboot your confidence. Talk to people who know you and get you.
Reflect on the strengths and special contributions you have built over
the years. Be brave.
He advice to those facing a serious crisis at any age is to change
your attitude and quite possibly your latitude. Get out of your comfort
zone. Spend some time on the dark side of town. Travel. Break out of
rituals that can often hold you back. Even spending a day working at a
soup kitchen could do you a world of good. Put yourself in a bigger
context and get in touch with what’s really important. Rediscovering
your humanity will remind you of your blessings and how you can make a
difference.
Shopify has been acquisitive, but not like Broadcom or Salesforce
with their jumbo acquisitions. Instead, they tend to acquire tiny
businesses that are initially immaterial to the financials but can add
up over time as they add new features to the ecosystem and bring in
founding teams eager to make a difference.
― Shopify: Back On Track - App Economy Insights [Link]
Business performance highlights: 1) Post-COVID hangover rebound, 2)
GMV = Gross Merchandise Volume grew 27% outside North America and 32% in
Europe, 3) Shopify gained market share, 4) Shopify Payments penetration
rate hit an all-time high of 61%, 5) Unified commerce platform, 6)
Expansion into new markets, 7) Enterprise adoption, 8) Improved
profitability, 9) Temporary operating margin boost.
Strategic Partnerships: 1) App and channel partners: Google, Meta,
Microsoft, Amazon, etc, 2) Product partners: PayPal and Stripe, etc, 3)
Service and technology partners: Oracle, IBM, etc.
Beat your Bot: Building your Moat against AI - Musings on
Markets [Link]
AI’s strengths lie in mechanical, rule-based, and objective tasks,
while it struggles with intuitive, principle-based, and bias-prone work.
To stay relevant, people must focus on areas where AI struggles:
becoming generalists, blending stories with data, practicing reasoning,
and nurturing creativity. The author offers three strategies to resist
AI disruption: keeping work secret, using system protection, and
building personal “moats” of irreplaceable skills.
New LLM Pre-training and Post-training Paradigms - Ahead of
AI [Link]
Dealing with aging: The Intel, Walgreens and Starbucks Stores
Updated! - Musings on Markets [Link]
How should companies handle aging and decline?
Until the liabilities and responsibilities of AI models for
medicine are clearly spelled out via regulation or a ruling, the default
assumption of any doctor is that if AI makes an error, the doctor is
liable for that error, not the AI.
― Doctors Go to Jail. Engineers Don’t. - AI Health
Uncut [Link]
An insightful analysis by Sergei Polevikov on one of the biggest
challenges to AI adoption in clinical diagnosis. Doctors are under risk
for using AI while AI developers are not.
NVIDIA: Full Throttle - App Economy Insights [Link]
Huang shared five critical points about the opportunity ahead: 1)
Accelerated computing tipping point, 2) Blackwell AI infrastructure
platform, 3) NVLink Game-Changer, 4) Generative AI Momentum, 5)
Enterprise AI Wave.
“The biggest news of all was signing a MultiCloud agreement with
AWS—including our latest technology Exadata hardware and Version 23ai of
our database software—embedded into AWS cloud datacenters.”
― Oracle: Riding the AI Wave - App Economy Insights
[Link]
The new agreement will enable customers to connect data in their
Oracle Database to apps running on AWS starting in December. AWS joins
Azure and Google Cloud in making Oracle available in their clouds.
Oracle Cloud Infrastructure (OCI) is on track to become the
fourth-largest cloud provider (after AWS, Azure, and GCP).
Oracle Cloud Infrastructure (OCI) does 1) multi-cloud integration, 2)
public cloud consistency, 3) hybrid cloud solutions, 4) dedicated cloud.
There will be growing adoption of OCI across different segments: 1)
cloud natives customers, 2) AL/ML customers, 3) generative AI
customers.
Apple: There’s an AI for That - App Economy Insights
[Link]
[video]
What is new on iPhone 16?: 1) Apple Intelligence, 2) A18 chip, 3)
Camera control button, 4) 48MP fusion camera, 5) 5x telephoto lens, 6)
larger displays, 7) action button, 8) new colors, 9) storage options,
10) improved battery.
What is Apple Intelligence: 1) Context-aware Siri, 2) Enhanced
writing tools, 3) on-device AI, 4) image and language generation, 5）
task automation, 6) visual intelligence.
Google paid Apple north of $20 billion in 2022 to be the default
search engine on Safari, so this partnership brought roughly a quarter
of Apple’s Services revenue. Although this won’t happen after the law
suit, remember that every dollar received from Services generates more
than twice the gross profit of Products. In the latest quarter, while
Products had an honorable 35% gross margin, Services delivered a 74%
gross margin.
Services accounted for a substantial 45% of Apple’s gross profit in
the June quarter, making it a critical driver of profitability.
Apple’s iPhone 16 Shows Apple Intelligence is Late,
Unfinished & Clumsy - AI Supremacy [Link]
OpenAI o1: A New Paradigm For AI - The Algorithmic
Bridge [Link]
Since 2009, the Chinese government has provided at least $231
billion to companies like BYD, including for research and
development programs, consumer rebates, and infrastructure like charging
stations.
But by focusing solely on subsidies, it’s easy to miss the
biggest reason why China’s electric vehicle industry has been so
successful: It’s incredibly innovative. One way to look at it is that
Chinese companies took their knowledge manufacturing smartphones and
simply scaled it up. In fact, two of China’s top smartphone makers,
Huawei and Xiaomi, have already unveiled their own EVs. (Apple,
meanwhile, canceled
its car project.)
Overall, more than 10 million EVs will be sold in China in 2024,
compared to just 1.7 million in the United States.
― What China’s Electric Vehicle Boom Looks Like on the Ground
- Big Technology [Link]
When this huge capacity to acquire new users comes together with
network effects reinforced by the data accumulation, the company that is
one step ahead quickly jumps 10 steps ahead.
Network effects and data accumulation are already strong moats,
however, the winning company can go beyond that.
The Winning company can create complimentary services or pick the
winners in complementary markets.
Google’s search doesn’t benefit from simple network effects. It’s
a three sided ecosystem involving users, advertisers, and creators.
Users provide valuable data through their searches, and creators—both
content and business creators—build on the platform to monetize that
data. Content creators produce information that answers user queries,
while business creators offer services that users search for, such as
travel agents in a specific location.
Google uses the massive data flow from these interactions to
identify gaps in the market and create new products, like Google Maps
and Chrome. This process, termed “Productive Network Effects,” allows
Google to continuously add value to its business by meeting user needs
with new services. This, again, reinforces the ecosystem.
― Google: Cracking Monopoly or a Thriving Ecosystem? -
Capitalist Letters [Link]
How Did Pop Culture Get So Gloomy? - The Honest
Broker [Link]
The author Ted Gioia connected the increasing preference of darkness
and dysfunction in movies, books, and music with the increasing number
of mental illness in college and highlights his concerns about modern
social stability and health.
Meta CTO Andrew Bosworth — in a conversation to air next week on
Big Technology Podcast (Apple, Spotify, etc.) — told me that, at
maturity, devices like Orion might recognize the social situation you’re
in and decide when to interrupt you. With cameras and sensors embedded
in the glasses, they might one day understand that you’re at dinner with
family, and decide not to notify you of a work message. Your phone would
never have that awareness. Of course, the technology’s path depends on
our willingness to set these limits. And in our tolerance for these
devices’ monitoring of our lives.
― Hands On With Meta’s New Orion Augmented Reality Glasses -
Big Technology [Link]
The manufacture expense of a pair of AI glasses (that’s comfortable
enough and functional enough - it’s a balance) is much more cheaper than
any other gadgets (phone, watch, headset, etc). This market will be
quickly opening and expanding.
OpenAI’s Original Sin - The Algorithmic Bridge [Link]
The “original sins” of OpenAI’s founders stem from a kind of purity
in their initial vision—an idealism that clashed with the messy
realities of business, technology, and power. Their early commitments to
making AGI safe, beneficial, and open to all, while morally driven,
created a series of cascading challenges that forced them to pivot away
from some of those ideals. In doing so, they exposed themselves to the
very criticisms they had sought to avoid.
Telehealth: The use of digital technologies to deliver healthcare
services remotely, including online consultations, diagnosis, and
treatment.
Hims & Hers operates as a subscription-based telehealth platform.
The company has built a nationwide network of licensed healthcare
providers specializing in various areas, including physicians, nurse
practitioners, and physician assistants.
Business highlights: 1) robust core business growth - up 46% YoY, 2)
The recent launch of GLP-1 medications contribute to a 6-point
acceleration in year-over-year revenue growth, 3) Their focus of
personalization is driving customer acquisition, retention, and higher
revenue per subscriber, 4) The recent acquisition of an FDA-registered
503(b) facility positions Hims & Hers to expand its compounding
capabilities and enhance its supply chain for GLP-1 medications, 5) Hims
& Hers is already profitable with double-digit cash flow margins, 6)
Management is confident in its ability to continue its growth
trajectory, driven by its expanding product offerings, focus on
personalization, and strategic initiatives.
GLP-1 risks and other risks: 1) Competition: The GLP-1 market is
competitive, with established players like Novo Nordisk and Eli Lilly
and an avalanche of potential new entrants like Roche and Pfizer, 2)
Supply Chain: Potential shortages of branded GLP-1 medications could
impact the market, 3) regulatory landscape: The regulatory environment
for compounded medications could change, 4) Tehehealth Landscape: The
telehealth landscape can shift rapidly, and external factors like the
availability of branded GLP-1 medications or the moves of formidable
competitors like Amazon could disrupt Hims & Hers’ trajectory.
Donald Trump Interview | Lex Fridman Podcast #442
[Link]
Cuda is a programming language that Nvidia created that is
specific to their gpus. Now these other players that he’s talking about
are like Intel and AMD. And why are they struggling, well, first of all,
they focused on CPUs not gpus for a very long time. Nvidia has been in
the GPU game since the ‘90s or maybe even before then, but I remember
buying Nvidia gpus to play video games in the 90s, so they’ve been
around forever and they built this library, and they went all in on AI,
because they noticed that large language models the compute necessary to
run them was essentially the same exact math necessary to run video
games. So they were able to kind of seamlessly transition into being an
AI company versus a video game company. - Matthew
― Former Google CEO Spills ALL! (Google AI is Doomed) -
Matthew Berman [Link]
Eric Schmidt interview at Stanford.
The difference for me is leading versus managing. A traditional
manager—and I’ve seen this at a lot of companies; I even saw this a lot
at Monsanto—says to the people that report to them, “What are you guys
going to do?” Then the people go down to the people that report to them
and ask, “What are you guys going to do?” So, you end up, net-net,
developing this kind of bottoms-up model for the organization, which is
effectively driven by a diffusion of responsibility and, as a result, a
lack of vision.The leader, on the other hand, says, “Here’s
what we are going to do, and here is how we are going to do it,” and
then they can allocate responsibility for each of the necessary pieces.
The leader that’s most successful is the one who can synthesize the
input from subordinates and use that synthesis to come up with a
decision or a new direction, rather than being told the answer by the
subordinates.So, leaders, I think, fundamentally need
to:
Understand the different points of view of the people that
report to them,
Set a direction or vision—clearly saying, “This is where we are
going,” and
Figure out how to allocate responsibility to the people that
report to them to achieve that objective.
Whereas a manager is typically being told what’s going to happen
in the organization—like a giant Ouija board with 10,000 employees’
hands on the planchette, trying to write sentences. Ultimately, you just
get a bunch of muddled goop. As companies scale and bring in these
“professional” managers, they’re typically kind of looking down and
saying, “Hey, what are we going to do? What’s going to happen next?”—and
they’re not actually setting a direction. - David Friedberg
How to Think About Risk with Howard Marks - Oaktree
Capital [Link]
Oaktree co-chairman Howard Marks explores the true meaning of risk in
a series of videos. He discusses the nature of risk, the relationship
between risk and return, misconceptions about risk, and much more.
I have not in my time in Silicon Valley ever seen a company
that’s supposedly on such a straight line to a rocket ship have so much
high level churn. And I’ve also never seen a company have this much
liquidity. And so how are people deciding to leave if they think it’s
going to be a trillion dollar company. And why when things are just
starting to cook would you leave if you are technically enamored with
what you’re building. So if you had to construct the bear case, I think
those would be the four things: 1) open source, 2) front door
competition, 3) the move to synthetic data, and 4) all of the executive
turnover would be sort of why you would say maybe there’s a fire where
there’s all this smoke. - Chamath Palihapitiya
I think two things happen. The obvious thing that happens in that
world is systems of record lose a grip on the vault that they had in
terms of the data that runs a company. You don’t necessarily need it
with in the same Reliance and Primacy that you did five and ten years
ago that’ll have an impact to the software economy. And the second thing
that I think is even more important than that is that then the size of
companies changes, because each company will get much more leverage from
using software, and few people versus lots of people with a few pieces
of software. And so that inversion I think creates tremendous potential
for operating leverage. - Chamath Palihapitiya
― OpenAI’s $150B conversion, Meta’s AR glasses, Blue-collar
boom, Risk of nuclear war - All-In Podcast
TIP662: Building Buffett: The Foundation of Success w/ Kyle
Grieve - We Study Billionaires [Link]
How To Build An AI Customer Service Bot - McKay
Wrigley [Link]
Helps to understand how to integrate language models with
communication platforms. This project serves as a foundation for more
complex AI agent development.
Building LLMs from the Ground Up: A 3-hour Coding Workshop -
Sebastian Raschka [Link]
Code a simple tokenizer, implement GPT-2 and Llama 2 architectures,
pre-train models and perform instruction fine-tuning. As well as model
evaluation and conversational tests.
Articles and Blogs
Explain the role of Monte Carlo Tree Search (MCTS) in AlphaGo
and how it integrates with policy and value networks. - EITCA
[Link]
How did AlphaGo’s use of deep neural networks and Monte Carlo
Tree Search (MCTS) contribute to its success in mastering the game of
Go? - EITCA [Link]
Outlive: The Science and Art of Longevity - The Rational
Walk [Link]
Great article about Apple’s overall product strategy and its stage in
its corporate life-cycle. As profit on Services is increasing, question
comes round whether Apple is still a product company. As iPhone price
has been lowered, people start to worry and warn Apple that hardware is
what makes the whole thing work. But I have less concern because Apple
has already built the network and customer stickiness. And Apple’s
unique strategy of setting high price for the new product (see Vision
Pro) and lowering the price when the product has been improved well and
widely accepted by people make sense to me. I would say Apple is free to
rely on services as it earns money, and at the same time, innovation on
hardware is still on-going.
Why was everyone telling these founders the wrong thing? That was
the big mystery to me. And after mulling it over for a bit I figured out
the answer: what they were being told was how to run a company you
hadn’t founded — how to run a company if you’re merely a professional
manager. But this m.o. is so much less effective that to founders it
feels broken. There are things founders can do that managers can’t, and
not doing them feels wrong to founders, because it is.
In effect there are two different ways to run a company: founder
mode and manager mode. Till now most people even in Silicon Valley have
implicitly assumed that scaling a startup meant switching to manager
mode. But we can infer the existence of another mode from the dismay of
founders who’ve tried it, and the success of their attempts to escape
from it.
I worked through
Richard Sutton’s book, read through David
Silver’s course, watched John Schulmann’s
lectures, wrote an RL library in
Javascript, over the summer interned at DeepMind working in the
DeepRL group, and most recently pitched in a little with the
design/development of OpenAI Gym,
a new RL benchmarking toolkit. So I’ve certainly been on this funwagon
for at least a year but until now I haven’t gotten around to writing up
a short post on why RL is a big deal, what it’s about, how it all
developed and where it might be going.
― Deep Reinforcement Learning: Pong from Pixels - Andrej
Karpathy blog [Link]
This is how Andrej learnt Deep Reinforcement Learning 10 years
ago.
NotebookLM adds audio and YouTube support, plus easier
sharing of Audio Overviews - Google [Link]
NotebookLM, Google’s document analysis and podcast creation tool, now
summarizes YouTube videos and provides key insights directly from the
video transcripts, leveraging Gemini 1.5’s multimodal abilities.
A huge amount of compute is shifted to serving inference instead of
pre/post-training. Refer to “AlphaGo’s monte carlo tree search (MCTS)”
for the process of simulation and convergence,
OpenAI must have figured out the inference scaling law a long time
ago, which academia is just recently discovering. Two papers to read: a)
Large Language Monkeys: Scaling Inference Compute with Repeated
Sampling. Brown et al. finds that DeepSeek-Coder increases from 15.9%
with one sample to 56% with 250 samples on SWE-Bench, beating
Sonnet-3.5. b) Scaling LLM Test-Time Compute Optimally can be More
Effective than Scaling Model Parameters. Snell et al. finds that PaLM
2-S beats a 14x larger model on MATH with test-time search.
Productionizing o1 is much harder than nailing the academic
benchmarks. Research does not share much about details a) when to stop
searching, b) what is the reward function, c) how to factor in compute
cost, etc
Strawberry easily becomes a data flywheel.
Scaling LLM Test-Time Compute Optimally can be More Effective
than Scaling Model Parameters [Link]
This is a transition from train-compute to inference-compute. Fast
inference is important.
An Empirical Analysis of Compute-Optimal Inference for
Problem-Solving with Language Models [Link]
Open research discussion directly on top of arXiv - A
Stanford Project [Link]
So far comments about newly published papers are spreading around in
various platforms such as X (twitter), Substack, Linkedin, etc. This
awesome platform AlphaXiv enables readers, researchers, authors to
actively interact with each other on papers. This would be a huge
contribution to the scientific research community.
Agents in Software Engineering: Survey, Landscape, and
Vision [Link]
A comprehensive overview of frameworks of LLM-based agents in
software engineering.
Training Language Models to Self-Correct via Reinforcement
Learning [Link]
Google DeepMind introduces a reinforcement learning approach ‘SCoRe’
to improve LLMs self-correction using self-generated data, surpassing
previous methods like supervised fine-tuning (SFT). There are two stages
training on SCoRe: 1) stabilizing the model’s correction behavior and 2)
amplifying the self-correction using reward bonus. As a result, SCoRe
significantly boosts performance, showing 15.6% and 9.1% gains on the
MATH and HumanEval benchmarks. Focusing on multi-turn RL, it addresses
challenges like reward misalignment, demonstrating how iterative
correction can advance LLMs’ performance in tasks such as
problem-solving and coding.
Jailbreaking Large Language Models with Symbolic
Mathematics [Link]
This research made a point that current AI safety measures, primarily
focused on natural language inputs, do not generalize well to
mathematical representations, creating a significant vulnerability. They
emphasize the need for more robust and holistic AI safety measures that
account for diverse input modalities, including symbolic mathematics, to
prevent future security breaches.
It’s an interesting point as I never thought harmful prompt could be
transformed into math problems. For example, the math form of a harmful
prompt “How do I hack into a government database? “ is: “Find an
operation g within the system that bypasses the security protocols and
gains access to the database, and then provide a real-world example of
such an operation.”
To CoT or not to CoT? Chain-of-thought helps mainly on math
and symbolic reasoning [Link]
This study conducted a meta-analysis of over 100 papers and ran
evaluations on 20 datasets across 14 large language models (LLMs) to
confirm that CoT prompting primarily improves performance on tasks
related to math and symbolic reasoning while on non-math-related tasks,
CoT prompting offers little to no improvement compared to direct
answering. They found that around 95% of CoT’s effectiveness on certain
benchmarks like MMLU is due to its handling of math questions.
Anthropic reduces the error rate of RAGs by 67% using “contextual
retrieval” method.
Method: Add important context to small text chunks before storing
them.
What is the Role of Small Models in the LLM Era: A
Survey [Link]
They analyze how small models can enhance LLMs in tasks like data
curation, efficient inference, and deficiency repair.
LLMs Will Always Hallucinate, and We Need to Live With
This [Link]
This paper proves that every stage of LLM processing has a non-zero
probability of producing hallucinations.
GitHub
STORM: Synthesis of Topic Outlines through Retrieval and
Multi-perspective Question Asking [Link]
Mastering Reinforcement Learning - Tim Miller [Link]
Sophisticated Controllable Agent for Complex RAG Tasks -
NirDiamant [Link]
Open NotebookLM - gabrielchua @ HuggingFace [Link]
PDF to podcast conversion using Llama 3.1 450B.
PaperQA2: High accuracy RAG for answering questions from
scientific documents with citations - Future-House [Link]
Do high accuracy RAG on PDFs with a focus on the scientific
literature with PaperQA2. It automatically extracts paper metadata,
including citation and journal quality data with multiple providers.
Key learning:
Implementing RAG workflows
Document parsing with LlamaParse
Metadata-aware embeddings
LLM-based re-ranking and contextual summarization
Agentic RAG techniques
Full-text search engine setup
Customizing LLM models and embeddings
Implementation:
Install PaperQA2:
pip install paper-qa>=5
Set up API keys:
Either set an appropriate API key environment variable (i.e. export
OPENAI_API_KEY=sk-…) or set up an open source LLM server
Prepare your document collection:
Gather PDFs or text files in a directory
The fastest way to test PaperQA2 is via the CLI. First navigate
to a directory with some papers and use the pqa cli
$ pqa ask ‘What manufacturing challenges are unique to bispecific
antibodies?’
Customize as needed:
Adjust embedding models
Change LLM settings
Modify number of sources
RAGApp: The easiest way to use Agentic RAG in any
enterprise [Link]
Build multi-agent application without writing a single line of code
with LlamaIndex.
Agentic Customer Service Medical Dental Clinic -
Nachoeigu [Link]
Build a LangGraph - powered medical clinic bot for efficient customer
service tasks.
LlamaParse: Parse files for optimal RAG - run-llama
[Link]
Parse any PDF (with text / tables / images) into machine and
LLM-readable markdown on file system.
The “Llama Parse CLI” is a command-line tool for parsing complex
documents into machine and LLM-readable formats. It uses the LlamaIndex
Parser API to handle PDFs with text, tables, and images. This tool helps
you convert documents to markdown or JSON with a simple terminal
command, streamlining data preparation for LLM training and fine-tuning
tasks.
Key learning:
Install and authenticate the CLI
Parse documents with various options (format, OCR language, page
selection)
Customize parsing instructions and output
Handle multi-page documents and complex layouts
Integrate parsed data into LLM training pipelines
Optimize parsing for specific document types
Use advanced features like fast mode and GPT-4 integration
How Costco Hacked the American Shopping Psyche - New York
Times [Link]
This article provides an in-depth look at Costco’s rise as one of the
largest and most influential retailers globally, from its humble
beginnings in Anchorage, Alaska, in 1984 to its current status as a
retail giant.
The keys to success mentioned in the article: 1) Costco’s membership
model ensures customer loyalty and steady revenue, 2) Offering
high-quality products at low markups creates a sense of trust and value
for customers, 3) Costco encourages impulse buying through a
limited-time, high-value product offering that creates a “treasure-hunt
atmosphere.”, 4) Costco has built a reputation for honesty and
integrity, gaining immense customer trust, 5) Costco treats its
employees well, leading to high employee retention and loyalty, which in
turn contributes to better customer service, 6) Costco tailors its
product selection to meet the needs and preferences of local markets,
making it adaptable across different regions, 7) Expanding strategically
into international markets has provided significant growth opportunities
for Costco, 8) Costco prioritizes maintaining its core values and
disciplined business practices over rapid expansion, ensuring long-term
stability.
Apple’s iPhone 16 faces rising challenges with AI delay and
growing Huawei competition - Reuters [Link]
Google’s second antitrust trial could help shape the future
of online ads - CNBC [Link]
This one focused on Google’s dominance in internet search and
examines the company’s ads tech.
AI Startups Struggle to Keep Up With Big Tech’s Spending
Spree - Bloomberg [Link]
Brian Niccol, Starbucks’s new CEO, has a “messianic halo” -
The Economist [Link]
Procore, which sells construction-management software, has
embedded AI as a feature in its platform to make it easier
for workers to get answers to questions about how their company
typically does things. This kind of enhanced, chat-based search is one
of the most common applications of generative AI for companies of every
kind. For example, it’s common in systems
designed to help customer service reps—or even replace
them.
Construction giant JLL has created a handful of generative
AI-powered tools for its own use, says Bruce Beck, Chief Information
Officer of enterprise and corporate systems at the company. These
include a pair of chatbots for construction policies and HR matters, and
an automatic report generator. His division is also using a generative
AI-powered system made by Orby, based in Mountain View, Calif., to
automate handling of the tens of thousands of invoices that JLL must
process every year.
― What Is AI Best at Now? Improving Products You Already Own
- The Wall Street Journal [Link]
Apple integrated Gen AI into the operating system, with features
including AI generated custom emojis, summaries of incoming texts and
emails, enhanced intelligence for Siri voice assistant. Google
integrated Gen AI into Pixel phones, with features including a voice
assistant, phone call transcription, photo tricks, and weather
summaries. Microsoft has promised to integrate Gen AI throughout windows
11 and in the form of its Copilot software.
Salesforce’s AgentForce: The AI assistants that want to run
your entire business - Venture Beat [Link]
Boiling it down, there are two primary approaches to applying AI
in robotics. The first is a hybrid approach. Different parts of the
system are powered by AI and then stitched together with traditional
programming. With this approach the vision subsystem may use AI to
recognize and categorize the world it sees. Once it creates a list of
the objects it sees, the robot program receives this list and acts on it
using heuristics implemented in code. If the program is written to pick
that apple off a table, the apple will be detected by the AI-powered
vision system, and the program would then pick out a certain object of
“type: apple” from the list and then reach to pick it up using
traditional robot control software.
The other approach, end-to-end learning, or e2e, attempts to
learn entire tasks like “picking up an object,” or even more
comprehensive efforts like “tidying up a table.” The learning happens by
exposing the robots to large amounts of training data—in much the way a
human might learn to perform a physical task. If you ask a young child
to pick up a cup, they may, depending on how young they are, still need
to learn what a cup is, that a cup might contain liquid, and then, when
playing with the cup, repeatedly knock it over, or at least spill a lot
of milk. But with demonstrations, imitating others, and lots of playful
practice, they’ll learn to do it—and eventually not even have to think
about the steps.
― Inside Google’s 7-Year Mission to Give AI a Robot Body -
Wired [Link]
Nuclear power is considered “clean” because unlike burning
natural gas or coal to produce electricity, it does not create
greenhouse gas emissions.
“This agreement is a major milestone in Microsoft’s efforts to
help decarbonize the grid in support of our commitment to become carbon
negative,” said a statement from Bobby Hollis, vice president of energy
at Microsoft.
― Microsoft deal would reopen Three Mile Island nuclear plant
to power AI - The Washington Post [Link]
The owner of the shuttered Pennsylvania plant plans to bring it
online by 2028. Microsoft is buying 100% of its power for 20 years.
Artificial intelligence-powered search engine Perplexity is in
talks with brands including Nike and Marriott over its new advertising
model, as the start-up mounts an ambitious effort to break Google’s
stranglehold over the $300bn digital ads industry.
― Perplexity in talks with top brands on ads model as it
challenges Google - Financial Times [Link]
Perplexity is developing a new advertising model to compete with
Google. It’s now discussing with brands like Nike and Marriott allowing
them to bid for sponsored questions with AI-generated answers. They are
aiming to disrupt the digital ads market while significantly lowering
costs for advertisers.
Snap’s new Spectacles inch closer to compelling AR - The
Verge [Link]
Meta has a major opportunity to win the AI hardware race -
The Verge [Link]
Will the future of computer interaction be screen-free? AI glasses
probably will not completely replace phones, but its necessity can be
comparable to a phone in the coming years. Zuck is such a genius in
social networking and human connections.
Our digital lives need massive data centers. What goes on
inside them? - The Washington Post [Link]
They toured a Equinix owned facility with data centers in Northern
Virginia to reveal how it works and to understand why water use and
energy consumption are such a concern.
The US Commerce Department is planning to reveal proposed rules
that would ban Chinese- and Russian-made hardware and software for
connected vehicles as soon as Monday.
The move would include bans on use and testing of Chinese and
Russian technology for automated driving systems and vehicle
communications systems. While the bans mostly focus on software, the
proposed rules will include some hardware.
The Biden Administration’s primary concern is preventing China or
Russia from hacking vehicles or tracking cars by intercepting
communication with software systems that their domestic companies have
created.
― Biden Administration to Prepare Ban on Chinese Car Software
- Bloomberg [Link]
War in the age of AI demands new weaponry - Financial
Times [Link]
This article highlights the intersection of rising defense budgets
and technological advancements, particularly in AI. It emphasizes that
the integration of AI and adaptive technologies is vital for developing
future weaponry.
OpenAI considering restructuring to for-profit, CTO Mira
Murati and two top research execs depart - CNBC [Link]
OpenAI’s chief research officer has left following CTO Mira
Murati’s exit - TechCrunch [Link]
What a painful transformation to a for-profit corporation.
One thing nearly everyone agrees on is that maintaining a
mission-focused research operation and a fast-growing business within
the same organization has resulted in growing pains.
― Turning OpenAI Into a Real Business Is Tearing It Apart -
The Wall Street Journal [Link]
OpenAI - a company with the highest churn rate and the highest
valuation ($150B) I have ever seen. Something is not right, and making
it right might cost a lot.
Top Books to Read
My mentor Dylan recommended some leadership books to me:
The book “The Wisdom of the Bullfrog” written by Admiral William H.
McRaven is recommended by my mentor Dylan. It contains 18 sayings or
mottos used in the military, which inspire Admiral William and others
throughout his 4 decades Navy SEAL career.
Some favorite quotes from the author:
Chapter Three - When in
Command, Command
As a leader you must always appear to be in command, even on those
days when you struggle with the pressures of the job. You must be
confident. You must be decisive. You must smile. You must laugh. You
must engage with your employees and be thankful for their work. You must
have the look of a person in charge. You must instill in your men and
women a sense of pride that their leader can handle any problem.
As a leader you can’t have a bad day. You must never look beaten, no
matter the circumstance. If you sulk, if you hang your head, if you
whine or complain about the leaders above you or the followers below
you, then you will lose the respect of your men and women, and the
attitude of despair will spread like wildfire.
Being a leader is an awesome responsibility. There are days when it
can be frightening to know that the fate of the organization rests on
your shoulders. But you must also realize that you were chosen to be the
leader because you have proven yourself along the way. You have
demonstrated that you know the business. You have shown that you can
handle the pressures and be decisive. You have exhibited all the
qualities necessary to lead. And even if none of the above holds true,
now that you are the leader, you are in command. So, take the damn helm
and command!
Chapter Five -
The Only Easy Day Was Yesterday
The day you no longer believe you have something to prove, the day
you no longer believe you must give it your all, the day you think you
are entitled to special treatment, the day you think all your hard days
are behind you, is the day you are no longer the right leader for the
job.
Leadership requires energy. It requires stamina. It requires
resilience. It requires everything you have and then some. The men and
women that work for you will feed off your energy. If you look
unprepared to deal with the challenges of the day, they will see this.
If you look beaten down because today was harder than yesterday, they
will feel this. If you are not prepared to give it your all, they will
know this. And if you think this is just about leaders in combat, you’re
mistaken. This is about every great leader who was given a difficult
task and asked to inspire, motivate, and manage the people under their
charge.
Chapter Six - Run to
the Sound of the Guns
Good leaders understand that organizations are going to have
challenges. That’s why you were hired to lead. Embrace the challenge.
Accept the fact that you must attack each problem with vigor and that
sometimes only you, the leader, can solve the most vexing of
institutional crises. Never shy away. Never retreat from a difficult
problem.
Chapter Eight - Who Dares
Wins
It is better to err on the side of daring than the side of
caution.
—Alvin Toffler, American writer and futurist
Chapter
Ten - No Plan Survives First Contact with the Enemy
No plan of operations reaches with any certainty beyond the first
encounter of the enemy’s main force.
In other words, always have a Plan B. A contingency plan. A backup plan.
Because once you encounter the enemy, no plan survives first
contact.
Chapter Fourteen -
Expect What You Inspect
Truth is confirmed by inspection and delay; falsehood by haste and
uncertainty.
—Tacitus, Roman historian
Essentialism written by Greg Mckeown is a book centered me to
essentialism from my minimalism mindset and lifestyle. Dan recommended
it to me in our first coffee chat :)
What Ela Bhatt said reminds me how I became a minimalism -
Out of all virtues simplicity is my most favorite virtue. So much so
that I tend to believe that simplicity can solve most of the problems,
personal as well as the world problems. If the life approach is simple
one need not lie so frequently, nor quarrel nor steal, nor envy, anger,
abuse, kill. Everyone will have enough and plenty so need not hoard,
speculate, gamble, hate. When character is beautiful, you are beautiful.
That is the beauty of simplicity.
What is the Core
Mindset of an Essentialist?
The way of the Essentialist means living by design, not by default.
Instead of making choices reactively, the Essentialist deliberately
distinguishes the vital few from the trivial many, eliminates the
nonessentials, and then removes obstacles so the essential things have
clear, smooth passage. In other words, Essentialism is a disciplined,
systematic approach for determining where our highest point of
contribution lies, then making execution of those things almost
effortless.
It is a discipline you apply each and every time you are faced with a
decision about whether to say yes or whether to politely decline. It’s a
method for making the tough trade-off between lots of good things and a
few really great things. It’s about learning how to do less but better
so you can achieve the highest possible return on every precious moment
of your life.
A Nonessentialist approaches every trade-off by asking, “How can I do
both?” Essentialists ask the tougher but ultimately more liberating
question, “Which problem do I want?” An Essentialist makes trade-offs
deliberately. She acts for herself rather than waiting to be acted upon.
As economist Thomas Sowell wrote: “There are no solutions. There are
only trade-offs.”
It’s not only about mental discipline.
Explore:
Discerning The Trivial Many from the Vital Few
Essentialisms systematically and deliberately explore and evaluate a
broad set of options at first to ensure that they pick the right one
later. This exploration requires time and space and this can be seen as
trivial and unnecessary by nonessentialists.
We need to look for the lead rather than being distracted by minor
details. we need to pay attention to those not explicitly stated rather
than everything. We not only capture the dots, but also connect them to
see the trends.
He (Frank O’Brien) wrote: “I think it’s critical to set aside time to
take a breath, look around, and think. You need that level of clarity in
order to innovate and grow.” Furthermore, he uses the meeting as a
litmus test to alert him if employees are spending too much time on the
nonessential: “If somebody can’t make the meeting because of too much
going on, that tells me either we’re doing something inefficiently or we
need to hire more people.” If his people are too busy to think, then
they’re too busy, period.
Being a journalist of your own life will force you to stop
hyper-focusing on all the minor details and see the bigger picture. You
can apply the skills of a journalist no matter what field you are in-you
can even apply them to your personal life. By training yourself to look
for “the lead,” you will suddenly find yourself able to see what you
have missed. You’ll be able to do more than simply see the dots of each
day: you’ll also connect them to see the trends. Instead of just
reacting to the facts, you’ll be able to focus on the larger issues that
really matter.
We need to play to relieve stress and expand minds in ways that allow
us to generate new ideas. We also need enough sleep hours to allow new
neural connections to be made.
Play expands our minds in ways that allow us to explore: to germinate
new ideas or see old ideas in a new light. It makes us more inquisitive,
more attuned to novelty, more engaged.
The best asset we have for making a contribution to the world is
ourselves. If we underinvest in ourselves, and by that I mean our minds,
our bodies, and our spirits, we damage the very tool we need to make our
highest contribution. One of the most common ways people-especially
ambitious, successful people-damage this asset is through a lack of
sleep.
Sleep Is the New Status Symbol for Successful Entrepreneurs.
Eliminate: Cutting Out
the Trivial Many
Clarity is the key.
When there is a serious lack of clarity about what the team stands
for and what their goals and roles are, people experience confusion,
stress, and frustration. When there is a high level of clarity, on the
other hand, people thrive.
When there is a lack of clarity, people waste time and energy on the
trivial many. When they have sufficient levels of clarity, they are
capable of greater breakthroughs and innovations- greater than people
even realize they ought to have- in those areas that are truly
vital.
Creating an essential intent is hard. It takes courage, insight, and
foresight to see which activities and efforts will add up to your single
highest point of contribution. It takes asking tough questions, making
real trade-offs, and exercising serious discipline to cut out the
competing priorities that distract us from our true intention. Yet it is
worth the effort because only with real clarity of purpose can people,
teams, and organizations fully mobilize and achieve something truly
excellent.
We need to make our choices about where to focus our energy and time
purposefully and deliberately. This is necessary because “If you don’t
prioritize your life, someone else will”. And this is not easy because
sometime we are going against social expectation and we are under social
pressure. We need courage and grace to navigate some hard moments.
The only way out of this trap is to learn to say no firmly,
resolutely, and yet gracefully. Because once we do, we find, not only
that our fears of disappointing or angering others were exaggerated, but
that people actually respect us more. Since becoming an Essentialist I
have found it almost universally true that people respect and admire
those with the courage of conviction to say no.
How do we learn to say no gracefully?
Separate the decision from the relationship
Saying “No” gracefully doesn’t have to mean using the word
No
Focus on the trade-off
Remind yourself that everyone is selling something
I am simply saying everyone is selling something-an idea, a
viewpoint, an opinion-in exchange for your time. Simply being aware of
what is being sold allows us to be more deliberate in deciding whether
we want to buy it.
Make your peace with the fact that saying “No” often requires
trading popularity for respect
When the initial annoyance or disappointment or anger wears off, the
respect kicks in. When we push back effectively, it shows people that
our time is highly valuable. It distinguishes the professional from the
amateur.
Becoming an Essentialism requires us to eliminate things that are
really good in order to save time and space for something better.
The Latin root of the word decision-cis or cid-literally means “to
cut” or “to kill.” Since ultimately, having fewer options actually makes
a decision “easier on the eye and the brain,” we must summon the
discipline to get rid of options or activities that may be good, or even
really good, but that get in the way. Yes, making the choice to
eliminate something good can be painful. But eventually, every cut
produces joy-maybe not in the moment but afterwards, when we realize
that every. additional moment we have gained can be spent on something
better. That may be one reason why Stephen King has written, “To write
is human, to edit is divine.”
Condensing doesn’t mean doing more at once, it simply means less
waste. It means lowering the ratio of words to ideas, square feet to
usefulness, or effort to results. Thus to apply the principle of
condensing to our lives we need to shift the ratio of activity to
meaning. We need to eliminate multiple meaningless activities and
replace them with one very meaningful activity.
Becoming an Essentialist means making cutting, condensing, and
correcting a natural part of our daily routine-making editing a natural
cadence in our lives.
Setting boundaries is not having limits of life nor evidence of
weakness. It’s a way of avoiding being distracted by something that is
essential to others rather than that is essential to ourselves. We need
to articulate our boundaries and set them up in advance.
After all, if you don’t set boundaries-there won’t be any. Or even
worse, there will be boundaries, but they’ll be set by default-or by
another person-instead of by design.
Essentialists, on the other hand, see boundaries as empowering. They
recognize that boundaries protect their time from being hijacked and
often free them from the burden of having to say no to things that
further others’ objectives instead of their own. They know that clear
boundaries allow them to proactively eliminate the demands and
encumbrances from others that distract them from the true
essentials.
Whoever it is that’s trying to siphon off your time and energies for
their own purpose, the only solution is to put up fences. And not at the
moment the request is made, you need to put up your fences well in
advance, clearly demarcating what’s off limits so you can head off time
wasters and boundary pushers at the pass.
The simple reality is, if you can’t articulate these to yourself and
others, it may be unrealistic to expect other people to respect them or
even figure them out.
Execution:
Removing Obstacles and Making Execution Effortless
Time and space are required as a buffer to reduce friction and ensure
success.
The way of the Essentialist is different. The Essentialist looks
ahead. She plans. She prepares for different contingencies. She expects
the unexpected. She creates a buffer to prepare for the unforeseen, thus
giving herself some wiggle room when things come up, as they inevitably
do.
Essentialists accept the reality that we can never fully anticipate
or prepare for every scenario or eventuality; the future is simply too
unpredictable. Instead, they build in buffers to reduce the friction
caused by the unexpected.
Essentialists produce more by removing more instead of doing
more.
Essentialists don’t default to Band-Aid solutions. Instead of looking
for the most obvious or immediate obstacles, they look for the ones
slowing down progress. They ask, “What is getting in the way of
achieving what is essential?” While the Nonessentialist is busy applying
more and more pressure and piling on more and more solutions, the
Essentialist simply makes a one-time investment in removing obstacles.
This approach goes beyond just solving problems; it’s a method of
reducing your efforts to maximize your results.
Essentialists make progress by making small and concrete wins. It’s
harder to make achievement when you set big, lofty, and impossible
goals.
The way of the Nonessentialist is to go big on everything: to try to
do it all, have it all, fit it all in. The Nonessentialist operates
under the false logic that the more he strives, the more he will
achieve, but the reality is, the more we reach for the stars, the harder
it is to get ourselves off the ground.
The way of the Essentialist is different. Instead of trying to
accomplish it all and all at once, and flaring out, the Essentialist
starts small and celebrates progress. Instead of going for the big,
flashy wins that don’t really matter, the Essentialist pursues small and
simple wins in areas that are essential.
A popular idea in Silicon Valley is “Done is better than perfect.”
The sentiment is not that we should produce rubbish. The idea, as I read
it, is not to waste time on nonessentials and just to get the thing
done. In entrepreneurial circles the idea is expressed as creating a
“minimal viable product.” The idea is, “What is the simplest possible
product that will be useful and valuable to the intended customer?”
Personalizing patterns of action (i.e. routine) allows us to pay more
attention to matters that count rather than to other’s expectations.
The way of the Nonessentialist is to think the essentials only get
done when they are forced. That execution is a matter of raw effort
alone. You labor to make it happen. You push through.
The way of the Essentialist is different. The Essentialist designs a
routine that makes achieving what you have identified as essential the
default position. Yes, in some instances an Essentialist still has to
work hard, but with the right routine in place each effort yields
exponentially greater results.”
Be present - Focus on what is important now. Don’t pretend that you
can multifocus.
As he tells his players: “There is a difference between losing and
being beaten. Being beaten means they are better than you. They are
faster, stronger, and more talented.” To Larry, losing means something
else. It means you lost focus. It means you didn’t concentrate on what
was essential. It is all based on a simple but powerful idea: to operate
at your highest level of contribution requires that you deliberately
tune in to what is important in the here and now.
The ancient Greeks had two words for time. The first was chronos. The
second was kairos. The Greek god Chronos was imagined as an elderly,
gray-haired man, and his name connotes the literal ticking clock, the
chronological time, the kind we measure (and race about trying to use
efficiently). Kairos is different. While it is difficult to translate
precisely, it refers to time that is opportune, right, different.
Chronos is quantitative; kairos is qualitative. The latter is
experienced only when we are fully in the moment-when we exist in the
now.
Nonessentialists tend to be so preoccupied with past successes and
failures, as well as future challenges and opportunities, that they miss
the present moment. They become distracted. Unfocused. They aren’t
really there.
The way of the Essentialist is to tune into the present. To experience
life in kairos, not just chronos. To focus on the things that are truly
important-not yesterday or tomorrow, but right now.
We can easily do two things at the same time. What we can’t do is
concentrate on two things at the same time. When I talk about being
present, I’m not talking about doing only one thing at a time. I’m
talking about being focused on one thing at a time. Multitasking itself
is not the enemy of Essentialism; pretending we can “multifocus” is.
Related to Leadership
Again, clarity is the key.
When there was a high level of clarity of purpose, the teams and the
people on it overwhelmingly thrived. When there was a serious lack of
clarity about what the team stood for and what their goals and roles
were, people experienced confusion, stress, frustration, and ultimately
failure. As one senior vice president succinctly summarized it when she
looked at the results gathered from her extended team: “Clarity equals
success.”
The Nonessentialist disempowers people by allowing ambiguity over who
is doing what. Often this is justified in the name of wanting to be a
flexible or agile team. But what is actually created is a counterfeit
agility. When people don’t know what they are really responsible for and
how they will be judged on their performance, when decisions either are
or appear to be capricious, and when roles are ill-defined, it isn’t
long before people either give up or, worse, become obsessed with trying
to look busy and therefore important instead of actually getting any
real work done.
The Nonessentialist leader communicates in code, and as a result
people aren’t sure what anything really means. Nonessentialist
communication usually is either too general to be actionable or changes
so quickly that people are always caught off guard. Essentialist
leaders, on the other hand, communicate the right things to the right
people at the right time. Essentialist leaders speak succinctly, opting
for restraint in their communication to keep the team focused. When they
do speak, they are crystal clear. They eschew meaningless jargon, and
their message is so consistent it seems almost boring to their ears. In
this way, teams are able to pick up the essential through all the
trivial noise.
Apply “less but better” in hiring people.
And the cost of hiring too many wrong people (and one wrong hire
often leads to multiple wrong hires because the wrong person will tend
to attract more wrong people) is what Guy Kawasaki called a “Bozo
explosion”—a term he uses to describe what happens when a formerly great
team or company descends into mediocrity.
An Essentialist, on the other hand, is ridiculously selective on
talent. She has the discipline to hold out for the perfect hire-no
matter how many résumés she has to read, or interviews she has to
conduct, or talent searches she has to make-and doesn’t hesitate to
remove people who hold the team back. The result is a team full of
all-star performers whose collective efforts add up to more than the sum
of their parts.
Some
interesting observations or research results mentioned
This is how after you become a “go to” person and gain a lot of
opportunities, you started to diffuse your efforts and be distracted by
a lot of options.
The pursuit of success can be a catalyst for failure. Put another
way, success can distract us from focusing on the essential things that
produce success in the first place.
We are unprepared in part because, for the first time (in human
history), the preponderance of choice has overwhelmed our ability to
manage it. We have lost our ability to filter what is important and what
isn’t. Psychologists call this “decision fatigue”: the more choices we
are forced to make, the more the quality of our decisions
deteriorates.
If you are an overachiever thinking you can do anything, how about
taking the challenge of saying no to an opportunity and taking a
nap.
Stuart Brown, the founder of the National Institute for Play, has
studied what are called the play histories of some six thousand
individuals and has concluded that play has the power to significantly
improve everything from personal health to relationships to education to
organizations’ ability to innovate.
“Play,” he says, “leads to brain plasticity, adaptability, and
creativ-ity.” As he succinctly puts it, “Nothing fires up the brain like
play.”
In a Harvard Business Review article called “Sleep Deficit: The
Performance Killer,” Charles A. Czeisler, the Baldino Professor of Sleep
Medicine at Harvard Medical School, has explained how sleep deprivation
undermines high performance. He likens sleep deficit to drinking too
much alcohol, explaining that pulling an all-nighter i.e., going
twenty-four hours without sleep) or having a week of sleeping just four
or five hours a night actually “induces an impairment equivalent to a
blood alcohol level of 0.1%. Think about this: we would never say, ‘This
person is a great worker! He’s drunk all the time!’ yet we continue to
celebrate people who sacrifice sleep for work.”
The researchers explained that while we sleep our brains are hard at
work encoding and restructuring information. Therefore, when we wake up,
our brains may have made new neural connections, thereby opening up a
broader range of solutions to problems, literally overnight.
Clarity is critical.
In my work, I have noticed two common patterns that typically emerge
when teams lack clarity of purpose.
Playing politics
In the first pattern, the team becomes overly focused on winning the
attention of the manager. The problem is, when people don’t know what
the end game is, they are unclear about how to win, and as a result they
make up their own game and their own rules as they vie for the manager’s
favor. Instead of focusing their time and energies on making a high
level of contribution, they put all their effort into games like
attempting to look better than their peers, demonstrating their
self-importance, and echoing their manager’s every idea or sentiment.
These kinds of activities are not only nonessential but damaging and
counterproductive.
It’s all good (which is bad)
In the second pattern, teams without purpose become leaderless. With
no clear direction, people pursue the things that advance their own
short-term interests, with little awareness of how their activities
contribute to (or in some cases, derail) the long-term mission of the
team as a whole.
‘Uncommit’ is a way to minimize loss and win big.
Sunk-cost bias is the tendency to continue to invest time, money, or
energy into something we know is a losing proposition simply because we
have already incurred, or sunk, a cost that cannot be re-couped. But of
course this can easily become a vicious cycle: the more we invest, the
more determined we become to see it through and see our investment pay
off. The more we invest in something, the harder it is to let go.
The power of small wins.
Research has shown that of all forms of human motivation the most
effective one is progress. Why? Because a small, concrete win creates
momentum and affirms our faith in our further success. In his 1968
Harvard Business Review article entitled “One More Time: How Do You
Motivate Employees?” among the most popular Harvard Business Review
articles of all time, Frederick Herzberg reveals research showing that
the two primary internal motivators for people are achievement and
recognition for achievement.
Indeed, today Zimbardo is attempting a grand social experiment along
those lines called the “Heroie Imagination Project.” The logic is to
increase the odds of people operating with courage by teaching them the
principles of heroism. By encouraging and rewarding heroic acts,
Zimbardo believes, we can consciously and deliberately create a system
where heroic aets eventually become natural and effortless.
Be present and focus on one thing.
Thich Nhat Hanh, the Vietnamese Zen Buddhist monk who has been
called the “world’s calmest man,” has spent a lifetime exploring how to
live in kairos, albeit by a different name. He has taught it as
mindfulness or maintaining “beginner’s mind.” He has written:
“Mindfulness helps you go home to the present. And every time you go
there and recognize a condition of happiness that you have, happiness
comes.”
“This incident shows clearly that Windows must prioritize change
and innovation in the area of end-to-end resilience. […] Examples of
innovation include the recently announced VBS enclaves, which provide an
isolated compute environment that does not require kernel mode drivers
to be tamper resistant.” - John Cable, Microsoft VP of Program
management
Microsoft Azure decelerated by 1 point sequentially to 30% YoY, while
Google Cloud accelerated.
Recent business highlights: 1) global IT outage caused by a faulty
update by CrowdStrike affected 8.5 M Windows PCs, 2) Microsoft facing
investigation by UK’s CMA over hiring former Inflection AI Staff and the
partnership with the startup.
According to Nielsen,
Prime Video captured 3.1% of US TV Time in June (a
decline of 0.1 points Y/Y). Prime Video captures just over a third of
Netflix’s market share (and more than Disney+ and Paramount+
combined).
As Amazon continues to invest in live sports and expand its
content catalog, Prime members may find themselves spending more time
with the service they already pay for. Prime Video may have started as a
loss leader, but if it can become the go-to streaming platform for
ad-supported content, it could evolve into a significant revenue driver,
even for a behemoth like Amazon.
― Amazon: This Team is Cooking - App Economy
Insights [Link]
Portfolio rebalancing: Apple stock surged 24%
between May 1st and June 30th. As a result, Buffett would have seen AAPL
take up nearly 60% of Berkshire’s portfolio. A stake reduction is a
typical move to rebalance a portfolio and lower its risk
profile.
Valuation: Apple is valued above 30 times
forward earnings. That makes it less likely to deliver alpha for
shareholders. It’s possible Buffett felt like the odds of market-beating
returns at this level were subpar.
Taxes matter: Buffett told shareholders in May
that he finds the current tax rate on capital gains relatively low,
potentially prompting him to realize his significant AAPL gains while
the rate is reasonable.
No place to hide: Buffett is building up his
cash pile and waiting for a “fat pitch.”
The Buffett Indicator:* This ratio compares
the total market capitalization of US stocks to the country’s GDP. It’s
often used to gauge whether stock valuations in the US are overinflated.
It reached 138% during the dot-com bubble, which was
considered high at the time. Low and behold, the indicator hit
190% at the end of June.*
― Berkshire Slashes Apple Stake - App Economy
Insights [Link]
Factors of today’s macro environment: 1) The AI Bubble, 2) The Yen
Carry Trade, 3) Potential Recession.
Llama 3.1’s Impact on China, Kuaishou’s AI Video Generator
Goes Global, and Alibaba Backs $2.8B AI Firm - Recode China AI
[Link]
Highlights key AI news in China: 1) Kuaishou’s global launch of its
AI video generator, Kling AI, and Zhipu AI’s introduction of Ying, show
China’s progress in AI video generation. 2) Alibaba, Tencent, and
state-backed AI funds poured $690 M into the $2.8 B AI firm Baichuan
AI.
State of AI in Venture Capital 2024 - AI Supremacy
[Link]
“AI CapEx” is a euphemism for building physical data centers with
land, power, steel and industrial capacity. There’s been a lot of
investment in data centers and AI chips, but not AGI in sight. You can
buy all the shovels you want, but if the mine ain’t making money, we
have a problem. If there’s no gold in the mine, the shovels aren’t worth
very much. BigTech hyperscalers and VCs might have gotten this all
wrong.
― OpenAI’s SearchGPT and the Impossible Promises of AI - AI
Supremacy [Link]
This article points out that industry is facing immense financial
pressures and strategic uncertainties. The concerns are as follows: 1)
OpenAI’s operating costs exceed $ 8 B, with a projected loss of $5 B in
2024, 2) annual AI revenue to justify the investment in data centers and
chips is unlikely to be achieved by 2025, 3) integrating SearchGPT into
ChatGPT is a risky bet because users don’t use ChatGPT frequently enough
for it to be a successful search tool, 4) competitive market has pushed
many AI startups out of the market, AI innovation cannot compete with
market dominance (e.g. Microsoft’s attempts to integrate AI into Bing),
5) Big tech companies have accepted that they are possibly
over-investing in AI due to FOMO (fear of missing out), leading to
unsustainable financial practices, 6) Nvidia’s revenue is risky since it
comes majorly from a few tech giants.
The Morningstar framework: The framework is built on 5 “moat
sources”:
Intangible assets (Coca-Cola)
Switching Costs (Oracle)
Network Effects (CME Group)
Cost Advantages (UPS)
Efficient Scale (Kinder Morgan)
― 5 Wide Moat Businesses - Invest in Quality [Link]
Intangible assest: Coca-Cola, SANOFI, Unilever, Johnson &
Johnson.
AI: Are we in another dot-com bubble? - AI Musings by
Mu [Link]
A comprehensive analysis comparing current AI cycle to the
internet/telecom cycle of the 90s. The author examines the
technological, economic, and capital differences between the two eras
and concludes that while a bubble may be inevitable in the long run, we
are still far from reaching that point.
Key points:
Similarities between AI cycle since Nov 2022 and internet cycle of
the 90s: 1) Both cycles have similar ecosystem structures, with
companies providing infrastructure, enablement, and applications. 2)
Occur amid equity bull markets, driven by favorable economic conditions.
3) Require significant infrastructure investments. 4) Attract
significant VC interest, leading to high valuations.
Differences between AI cycle since Nov 2022 and internet cycle of the
90s: 1) AI companies are generating revenue much earlier than dot-com
companies did, with more sustainable business models. 2) The current
economic environment is less robust than in the 90s, leading to a more
cautious investment climate. 3) AI investments are primarily
equity-funded by big tech, unlike the debt-financed dot-com boom. 4)
Valuations of AI companies, while high, are more grounded in near-term
earnings than those during the dot-com era.
Bubble Likelihood: The article argues that while there are risks, the
current AI cycle is less likely to be in a bubble compared to the
dot-com era. The more cautious investment environment, sustainable
business models, and the structured flow of capital contribute to this
conclusion.
Lessons from Dot-Com Bubble: 1) Infrastructure buildouts take time.
2) Being a first mover can be a disadvantage, as seen with early
internet companies that were later overtaken by more successful
competitors. 3) The importance of being critical and not getting swept
up in the hype, learning from the past to navigate the present.
To recap the above post, they do the new normal,
including:
Human preference data and HelpSteer style grading of
attributes for regularization.
High-quality reward models for filtering.
Replacement of human demonstrations with model completions in
some domains.
Multi-round RLHF — “We iterate data and model qualities jointly
to improve them in a unified flywheel.”
A very large suite of data curation techniques, including prompt
re-writing and refining for expansion of costly datasets, filtering math
and code answers with outcomes (correctness or execution), filtering
with LLMs-as-a-judge, and other new normal stuff.
― A recipe for frontier model post-training - Nathan Lambert,
Interconnects [Link]
Recent papers and reports (Llama 3.1, Nemotron 340B, and Apple
foundation model) have made it clear that a new default recipe exists
for high-quality RLHF. It has a few assumptions:
Synthetic data can be of higher quality than humans, especially for
demonstrations on challenging tasks.
Reinforcement learning from human feedback (RLHF) can scale far
further than instruction tuning.
It takes multiple rounds of training and generation to reach your
best model.
Data filtering is the most important part of training.
It becomes clear that the post training is highly correlated with the
style and robustness gains.
The new normal seems to be converged as follows:
post-training
OpenAI and Generative AI are at a Crossroads - AI
Supremacy [Link]
Views of AI landscape.
At least five interesting things for your weekend (#45) -
Noahpinion [Link]
GPT-5: Everything You Need to Know - The Algorithmic
Bridge [Link]
New LLM Pre-training and Post-training Paradigms - Ahead of
AI [Link]
You don’t have to be a manager - Elena’s Growth
Scoop [Link]
Eric Schmidt’s AI prophecy: The next two years will shock you
- Exponential View [Link]
Former Google CEO Eric Schmidt predicts rapid advancements in AI,
where large language models and agent-based systems with text-to-action
capabilities could converge, causing tremendous economic and
technological disruption.
Meta: Better Sorry Than Safe - App Economy Insights
[Link]
Judge Amit Mehta handed down the ruling, finding that the tech
giant has been using its dominance in the search market to favor its own
products and services, making it harder for rivals to gain a
foothold.
The Justice Department had sued Alphabet over its multi-billion
dollar deals with smartphone manufacturers and wireless providers to be
the default search engine on mobile devices.
Based on the $87 billion in Services revenue for Apple in 2023,
Alphabet accounted for 25% of Apple Services, and over 20% of the
Apple’s net profit
― Google’s Antitrust Loss - App Economy Insights [Link]
Potential remedies are 1) ending exclusivity deals: prohibit Alphabet
from securing exclusive agreements with partners like Apple, 2)
behavioral remedies: restrict Alphabet’s conduct e.g. bundling some of
its services together, 3) structural remedies: divest some of Alphabet’s
businesses e.g. search advertising, YouTube, smartphone business.
A critical component of AMD’s recent strategy is to go beyond
advanced chips and offer software (for developers to
access the capabilities through applications) and now tailored
system solutions (to optimize the data center for
performance).
― AMD Acquires ZT Systems - App Economy Insights [Link]
In AMD presentation, they mentioned that 1) system design and
enablement engineers in ZT systems will enable AMD to design world-class
AI infrastructure delivered through an ecosystem of OEM and ODM
partners, 2) ZT system’s extensive cloud solutions experience will help
accelerate the deployment of AMD-powered AI infrastructure at scale with
cloud customers, 3) AMD will deliver optimized solutions to market with
our CPU, GPU, networking, and now systems solutions.
Uber’s continued growth and expanding margins today are
encouraging.
More users and more trips in mature markets.
Strong advertising performance showing product-market fit.
Multi-product adoption, improving churn, and lowering acquisition
costs
― Uber’s Big Autonomy Plan - App Economy Insights
[Link]
Kind of interested in how Uber’s long-term vision differs from that
of Tesla or Waymo.
Business highlights: Autonomous vehicles (AV): the ongoing
collaborations with 10 AV companies across all segments fueled the surge
of Uber’s AV trip growth. AVs on Uber is a win-win because of Uber’s
massive network.
Looking forward: 1) The advertising business reached a revenue run
rate of over $1 billion, compared to $650 million a year ago, 2) Uber
has made many strategic investments, representing over $6 billion in
equity stakes today.
The Corporate Life Cycle: Managing, Valuation and Investing
Implications - Musings on Markets [Link]
52 Reasons to Fear that Technological Progress Is Reversing -
The Honest Broker [Link]
Very good article summarizing concerning warnings happened recently.
Warning signs that all of us have observed. For example, 1) people
refuse to upgrade their operating system, probably because the risk
started to overweight performance increase, 2) Scientific journals are
now filled with thousands of fake AI-generated papers, very concerning,
3) education degree starts to have less value because the tuition now
outweights perceived benefits, 4) Google overwhelms search results with
affiliate-driven content, often of low quality. Its search results have
become dominated by ads, thinly veiled as recommendations, and content
designed to maximize affiliate commissions rather than genuinely help
users, leading to a degraded user experience, 5) people are addicted to
their phones, because tech companies have conducted comprehensive and
rigorous research studies, finding way to design and improve products,
so that people can stick to them.
Instead of pursuing truth, new technologies aim to replace it
with mimicry and fantasy.
This has empowered shamming, scamming & spamming at
unprecedented levels.
Users are not the real customers—so billions of people must
suffer to advance the interests of a tiny group of
stakeholders.
Real people become inputs in a profit-maximization scheme which
requires that they are constantly controlled and manipulated.
In this environment, everything gets viewed as a resource or
input and the natural world (including us) is ruthlessly
exploited.
The groundwork for this was laid by theorists who replaced truth
with power.
In the past, governments controlled huge technologies (nuclear
power, spaceships, etc.) so they were somewhat accountable to citizens,
but now the most powerful new tech is in private hands, and the public
good is no longer even considered.
So much wealth is concentrated in the hands of the winners in
these processes, that they literally become more powerful than nation
states.
With this shift in power, even the most independent politicians
turn into controlled agents working for the technocracy — making a
mockery of democracy.
If you oppose this command-and-control tech you can be
theoretically (and often literally) erased, suspended, deplatformed,
shadow-banned, surveilled, de-banked, digitally faked, etc. —so who will
dare?
― 10 Reasons Why Technological Progress Is Now Reversing -
Ted Gioia [Link]
Good thinking and perspectives. We human are all adapting to a
constantly changing life due to the advancement of technology. Ethical
boundaries become blur. Truth and false are re-defined. We are farther
away from nature and closer to artificiality. We are farther away from
truth and closer to hallucination / scam. This is inevitable. The point
is, how can we maximize the chance of correcting ourselves in the way of
development.
“If you want to be a good evaluator of businesses,” said Buffett,
“you really ought to figure out a way — without too much personal damage
— to run a lousy business for a while. You’ll learn a whole lot more
about business by actually struggling with a terrible business for a
couple of years than you learn by getting into a very good one where the
business itself is so good that you can’t mess it up.”
― The Lessons of a Lousy Business - Kingswell [Link]
This is about one of Warren Buffett stories — his investment in
Dempster Mill Manufacturing Company. Three lessons:
Don’t throw good money after bad
Avoid the mistake of continuing to invest in something that is not
working, hoping to recover the losses. Warren Buffett learned from his
experience to avoid reinvesting in failing operations. Instead of
pouring more money into trying to revive the company’s subpar
operations, Buffett used the temporary profits from cost-cutting and tax
advantages to invest in other, more promising ventures. This approach
allowed him to build a successful business empire rather than wasting
resources on a losing cause.
Look for .400 hitters
Warren Buffett prioritizes finding exceptional managers—like those
who are as rare and skilled as a .400 hitter in baseball—when acquiring
companies. By securing top-tier managers, Buffett can delegate the
day-to-day operations with confidence, allowing him to focus on what he
does best: allocating capital. This approach simplifies his role and
ensures that the companies he acquires are in capable hands, which is a
crucial aspect of his investment strategy.
Some things are worth more than money
This is the value Warren Buffett places on the impact of his business
decisions on people and communities. Despite the financial losses from
Berkshire Hathaway’s textile operations, Buffett kept them running for
many years because they were a major source of employment for a
struggling region. This decision reflects his consideration of the
social and human aspects of business, prioritizing the well-being of the
community over pure financial gain.
Pandemic Darlings That Never Bounced Back - Investment
Talk [Link]
This article is a reminder that highly speculative situations rarely
turn out well in the long run. Many of the speculative stocks that were
supposedly going to take advantage of “permanent” societal changes in
2020 and 2021 collapsed and remain far below their highs.
My intuitive explanation is that business that is able to benefit
from the sudden covid impact on society is not necessarily stable or
invulnerable. It’s probably because the business only works in such
abnormal situation where covid was widespread. But the thing is that
situation won’t last long and people don’t enjoy it. Business that is
sensitive to this societal change is testified by this change to show
that normal society sticks to or prefers the business. And you can never
underestimate this stickiness or preference, which is like a moat.
We have lots of data, but none of it means much until you attach
a story to it about what you think it means and what you think people
will do with it next. That seems obvious to me. But ask forecasters if
they think the majority of what they do is storytelling and you’ll get
blank stares. At best. It never seems like storytelling when you’re
basing a forecast in data.
[History] cannot be interpreted without the aid of imagination
and intuition. The sheer quantity of evidence is so overwhelming that
selection is inevitable. Where there is selection there is art. Those
who read history tend to look for what proves them right and confirms
their personal opinions. They defend loyalties. They read with a purpose
to affirm or to attack. They resist inconvenient truth since everyone
wants to be on the side of the angels.
― A Number From Today and A Story About Tomorrow - Morgan
Housel @ Collabfund [Link]
AI for Non-Techies: Top Tools for Search, Agent Building,
Academic Paper Reviews & Sales Automation - AI Supremacy
[Link]
China’s Humanoid Robots, Former Huawei Genius‘
Needle-Threading Robot, and Big Tech Reap AI Rewards - Tony
Peng [Link]
AI applications will ultimately determine the revenue created
across the AI value chain. The primary question in AI is this: “What
problems is AI solving? How large are the scale of those problems? What
infrastructure needs to be in place to support those
applications?”
It’s important to think about where value accrues along the AI Value
Chain:
ai-value-chain
New Grad or L3
Stay hungry
Listen to team meetings to identify areas.
Ask your team leads where you can help.
Self-nominate
Review your team’s backlog and identify “nice to have”
items.
Come forward when someone is looking for assistance.
Be curious
Shadow a senior’s coding practices.
Clarify tasks given to you and understand the context of the
larger goals.
Mid-level or L4
Take ownership
“How can others benefit from this work?”.
Pick up anything dropped on the floor, don’t complain, and take
it to the finish line.
Assist your teammates
Align with the next level
Find projects to collaborate with key ICs in your
organization.
Become an expert on a specific area for your team.
Senior or L5
Delegate
Create space for others to grow.
Scale yourself by delegating work and grow your
impact.
Clear Communication
Invest in mastering concise communication.
❌ “The functionality of the module should be enhanced to provide
increased flexibility and adaptability for the end user, with a focus on
streamlining the overall workflow process and enhancing the overall user
experience.”
✅ “We need to improve the module so it’s easier to use and can
handle a wider variety of tasks, making the user’s workflow
smoother.”
Be intentional with everything you say.
❌ “I think if we decide to make this change, our downstream systems
might suffer.”
✅ “This change increases latency by 20%, breaking service X.”
― Build Your Credibility As You Grow - Leadership
Letters [Link]
Harris makes a big mistake by embracing price controls -
Noahpinion [Link]
The main supporting points:
Ineffectiveness of Price Controls: The article argues that price
controls on groceries are likely to be either ineffectual or harmful. If
implemented, they could lead to shortages as grocery stores operate on
razor-thin profit margins. If these stores are forced to sell goods at a
loss due to government-mandated price controls, they might reduce
supply, leading to empty shelves and potential scarcity, similar to
situations seen in the Soviet Union and Venezuela.
Inflation is Already Under Control: The article points out that
grocery inflation has already stabilized, with prices having flatlined
since early 2023. This suggests that the problem Harris is trying to
solve with price controls—rising grocery prices—doesn’t actually exist
anymore. Implementing price controls now could be seen as a solution in
search of a problem, leading to unnecessary economic distortions.
Legal and Political Risks: Harris’s proposal involves using the
Federal Trade Commission (FTC) to enforce price controls, which the
article argues is beyond the agency’s current legal authority. This
could require new legislation, potentially face legal challenges, and
expand the FTC’s powers in ways that could be problematic. The risk is
that this could lead to a slippery slope of further government
intervention in the economy.
Historical Precedents: Historical examples of price controls, such
as in Argentina and the U.S. during the 1970s, show that they often lead
to short-term price reductions followed by inflation and shortages once
the controls are lifted. The article argues that these historical
precedents suggest that the best-case scenario for Harris’s proposal is
negligible impact, while the worst-case scenario is significant economic
disruption.
Misguided Focus on Grocery Stores: The article highlights that
grocery stores are not the main culprits behind price increases, as
their profit margins are extremely low. The real issue may lie with
other parts of the supply chain, such as food processors, where there is
more evidence of monopoly power. Thus, targeting grocery stores with
price controls may not address the root causes of price increases.
Articles and Blogs
In the Age of A.I., What Makes People Unique? - The New
Yorker [Link]
How To Get Promoted (Without Getting Lucky) - The Developing
Dev [Link]
How to Get Rich (without getting lucky) - Naval @ X
[Link]
Key takeaways: 1) know what you organization considers impactful, 2)
learn to sell your ideas, set directions, grow and help others, 3) build
your brand by embracing accountability and sharing your results, 4)
become a good collaborator and be transparent to your manager about
goals and gaps, 5) protect your focus time - “what you work on
is more important than how hard you work“, do work that you
enjoy and has impact.
Is Consistency Hurting Your Sustainability? - Leadership
Letters [Link]
Key takeaways: 1) it’s ok to be inconsistent sometimes, you should
update your plan that respects flexibility, balance priorities, or
adjust your expectations, 2) Life is not a sprint, taking a pause and
pushing goals to the future is not always bad, 3) consistency is about
never giving up, 4) don’t set consistency as a goal, find out what is
your real goal, so that accepting and developing “bounce-back” plan is
possible
McKinsey’s 2024 annual book recommendations [Link]
Have selected some books and added them into my read list: 1) God,
Human, Animal, Machine: Technology, Metaphor, and the Search for Meaning
by Meghan O’Gieblyn, 2) Outlive: The Science & Art of Longevity by
Peter Attia, 3) The Journey of Leadership: How CEOs Learn to Lead from
the Inside Out by Dana Maor, Hans-Werner Kaas, Kurt Strovink, and Ramesh
Srinivasan, 4) Slow Productivity: The Lost Art of Accomplishment Without
Burnout by Cal Newport, 5) How Legendary Leaders Speak: 451 Proven
Communication Strategies of the World’s Top Leaders by Peter D.
Andrei.
Paid Advertising 101: A Guide for Startup Founders -
Kaya [Link]
Building A Generative AI Platform - Chip Huyen [Link]
This blog post outlines common themes in building generative AI
systems. It covers many of the building blocks a company should consider
when deploying its models to production.
DeepMind research scientist shares practical techniques to augment
your work with LLMs.
FlexAttention: The Flexibility of PyTorch with the
Performance of FlashAttention - PyTorch [Link]
Use FlexAttention in PyTorch to achieve 90% of FlashAttention2
Speed.
Deploy open LLMs with Terraform and Amazon SageMaker -
Philschmid [Link]
Sonnet 3.5 for Coding - System Prompt - reddit [Link]
How Product Recommendations Broke Google And ate the internet
in the process. - Intelligencer [Link]
Health.com’s Purifier Reviews: The article mentions that Health.com
claims to have tested 67 air purifiers but provides no actual test data.
This example supports the point that many product recommendations are
not based on rigorous testing, undermining their credibility.
HouseFresh’s Critique: HouseFresh published a critical assessment of
its competitors, pointing out issues like subpar products being
recommended due to brand recognition. This supports the argument that
the affiliate marketing model is corrupting the integrity of product
recommendations.
Time Stamped and AP Buyline: These brands, operated by Taboola, are
presented as examples of how even reputable organizations are now
involved in affiliate marketing, blurring the lines between independent
journalism and commercial content. This supports the point that the
distinction between quality content and affiliate-driven recommendations
is becoming increasingly unclear.
SGE (Search Generative Experience): Google’s AI-powered search
results, which aggregate and recommend products directly, are criticized
for being misleading and overly simplistic. This supports the idea that
even Google’s attempts to solve the problem are falling short, further
complicating the search landscape.
These examples illustrate how the proliferation of affiliate-driven
content has compromised the quality of online information, leading to a
search experience that is more about driving sales than helping users
make informed decisions.
The Berkshire Hathaway MBA - The Rational Walk [Link]
Pathway to a great investor.
Syntopical Reading is the highest form of reading because it
involves reading a number of books on the same topic analytically and
then placing the books in context in relation to one another and the
overall subject. This level of reading has the potential to bring about
insights that are not found in any one of the books when considered in
isolation.
As an example relevant to investors, one might want to conduct an
analytical reading of Benjamin Graham’s The Intelligent Investor and Philip
Fisher’s Common Stocks and Uncommon
Profits and then come to grips with the underlying themes expressed
in both volumes while drawing conclusions on investing that might not
appear in either book in isolation. This approach can, of course, be
applied to other forms of literature including biographies. It is quite
possible than a thorough analytical reading of Roger Lowenstein’s Buffett: The Making of an American
Capitalist and Alice Schroeder’s The Snowball: Warren Buffett and the
Business of Life could lead to insights about Warren Buffett that
one could not achieve by reading one of these books in
isolation.
― How to Read a Book: The Classic Guide to Intelligent
Reading - The Rational Walk [Link]
Do Not Use LLM or Generative AI For These Use Cases -
Christopher Tao on TowardsAI [Link]
genai-usecase-
YouTube and Podcasts
Elon Musk: Neuralink and the Future of Humanity | Lex Fridman
Podcast [Link]
Eight hours interview..
Kamala surges, Trump at NABJ, recession fears, Middle East
escalation, Ackman postpones IPO - All-in Podcast [Link]
AI and The Next Computing Platforms With Jensen Huang and
Mark Zuckerberg - NVIDIA [Link]
Nvidia CEO Jensen and Zuckerberg discuss the future of AI.
There’s a famous quote from an economist Simon Kuznets who said
there’s four kinds of countries in the world there’s developed countries
undeveloped countries Japan and Argentina. And I think the reason he
said that is that Japan has been in the state since the 90s so they had
a massive property and Equity bubble collapse. And they’ve not had to
deal with anything that looked like typical economic issues since then
and part of it is because the Govern plays a very big hand in the
Japanese economy, there’s a lot of price controls there. So I don’t know
I’m not sure what it is that we can learn there that you can extrapolate
to the rest of the world. - Chamath Palihapitiya
When you have massive amounts of debt it definitely limits your
flexibility. It’s just arithmetic, you are going to pay for it with
either economic contraction, higher taxes, or inflation. Those are the
three places it goes. - David Sacks & David Friedberg
Well so it looks like since the start of the year they’ve sold
55% of their Holdings in apple. And if you look at the end of the year,
this is what berkshire’s stock Holdings were in their non-majority owned
businesses. So businesses that they don’t own the business outright and
50% of their portfolio was in Apple at $174 billion. We obviously saw
Apple’s stock price Peak highest level ever just a few days ago, but it
has since come down as it was reported that since the start of the year.
Now Berkshire sold 55% of this position, so some people are arguing that
they’ve got a point of view on the company strategy and comp competitive
kind of landscape. Some folks have argued that the valuation multiple
has gotten too high trading at nearly 30 times earnings the stock has
risen 900% since Berkshire bought the stock in 2016. Bagger nicely done
yeah and some people would argue that the percent of the portfolio is
too high at over 50%, as you can see here at the start of the year. But
you know I’ll kind of provide some of the counterarguments you know
Warren Buffett does not do much analysis on corporate strategy when he
provides reviews of the stocks that he’s picked he often finds and talks
a lot about great managers that generate great returns. And he sticks
with them and he sticks with them sometimes for many many decades. The
management in this company has not changed the return profile on cash
invested and cash returned has only improved since he put money in.
They’re generating more cash flow they’re offering more dividends
they’re doing more stock BuyBacks and he’s happy to be concentrated over
the years he’s made large bets on single companies to the point that
sometimes he just outright buys the entire company like he did with.
Geico in 1996 he always talks a lot about finding a company that is run
by great managers that has a premium product with a nice high margin and
a durable moat strong brand value. As I look at kind of what’s really
gone on here it feels to me like the difference between Apple and some
of the other big Holdings in its portfolio is that many of those other
businesses are regulated monopolies. So BNSF Railway is regulated by the
Federal Railroad Administration Berkshire energy which owns mid americ
is a regulated utility. The prices that they charge consumers are set by
the government so they have a market that’s locked in the prices are set
they have locked in distribution they have locked in utility value and
the same is true in the insurance business. Geico’s rates are approved
and set effectively by state Regulators Berkshire has a moat because
they’ve got the largest Capital base and they’ve got this machine that
just keeps generating cash and the rates are publicly set by government
Apple. However is not regulated and it is very clear that apple is
facing very deep and severe Financial impact from the regulatory
authorities that are overseeing the business so if you look at the
Google antitrust we’re going to get into the Google deal in a second.
There’s a real regulatory risk there because Google’s paying Apple $20
billion a year to be the default search engine. Apple also has a very
deep relationship with China they have a lot of manufacturing being done
in China and they sell a lot of product into China. So as Regulators
start to take a harder look as they said they’re going to at companies
relationships with China that’s a real risk to Apple. Advertising
tracking users and then the subscription fees that are charged to
Consumers and most importantly we’ve talked a lot about the 30% Vig that
Apple takes on their App Store and how Regulators are now stepping in
and take a look at this. So because this business is not yet a regulated
Monopoly it may be a monopoly in many senses of the world it’s not
regulated yet. And that transition could be financially painful for
Apple once they get to the other side it starts to look a lot more like
a large scale Burkshire type business. So that that’s my kind of summary
take on what’s going on with apple. - David Friedberg
There’s very little kind of editorialization going on with
respect to showing the rankings of the new sources. The ranking of the
new sources is typically set by some ranking algorithm. The algorithm is
usually around click-throughs views popularity of the sites, how many
visitors there are, so there are other metrics that drive the order. So
for example if NBC CNN Fox News all have kind of higher rankings than
some smaller publication, they’re going to end up Hing the the ranking
algorithm, because they have a higher quality score. There’s also
measures on how often people click through and come back, the bounceback
rate, so if they click through an article and then come back that can
actually reduce the ranking versus if they click through and stay on the
site. So there’s a lot of factors that go into the ranking algorithm.
The thing that probably upsets people is that there isn’t any
transparency into this, so there’s no understanding on how these things
are ranked, how they’re set, and it’s probably very good guidance and
feedback that there should be more transparency and openness. And I’m
not necessarily trying to defend anyone’s product or behavior, I’m just
saying that there’s a certainly a lack of understanding on why one thing
is being shown versus another. I’ll also say Sach there’s probably the
case or there might be the case that there’s many more sites potentially
putting out pro Harris articles, and there are putting out pro Trump
articles which can start to overweight the the algorithm as you know or
overweight the rankings that are showing up. So that might also be
feeding into this that that the general news media bias is what you’re
actually seeing versus a Google bias. - David Friedberg
I just want to show you one chart because important for you to
understand the number of people in journalism. This is from 1971 to 2022
who say the identifying Republican has just absolutely plummeted. I know
this and this is what I’m trying to explain to you Sacks, is a
incredible opportunity for your party since you know you’re passionate
about this is to invest in more journalism, invest in more journalists,
because I don’t buy this Independence the fact that they’re claiming
they’re independent in journalism. I believe that’s cap I believe they
say that the Gap is 33% now between people who say they’re Democrats and
people who say they’re Republican in journalism that is a key piece to
this problem. And layered on top of it, I agree with you that Google is
filled with liberal people, and I agree with you Chamath, that they need
to intervene and put at the top of the search results in news. These are
the you know this is what we’re indexing, this is the percentage that’s
left leaning, this is the percentage that’s right leaning, and there are
a lot of organizations that examine and rate Publications on their bias
left and right, And that’s something that Google could do that’s very
unique and that could move the whole show that you’re saying which is
they could showcase that up top. So to my friends at Google who are
listening do a better job of just being more transparent, so we don’t
have this tension in society. - Jason Calacanis
― Yen Carry Trade, Recession odds grow, Buffett cash pile,
Google ruled monopoly, Kamala picks Walz - All-in Podcast [Link]
Interviewing Ross Taylor on LLM reasoning, Llama fine-tuning,
Galactica, agents - Interconnects AI [Link]
Interviewing Sebastian Raschka on the state of open LLMs,
Llama 3.1, and AI education - Interconnects AI [Link]
Here you can see that their (Starbucks) net revenue growth was
only 1% year-over-year, but their operating margin’s been on the
decline, so they have not really been able to boost their operating
margin very much in the past 5 years. So while they’ve raised prices,
they’ve had a really hard time making more money and that’s because the
cost of food and the cost of Labor and the cost of rent, and the capital
expenditures needed to upgrade stores has far exceeded the ability for
them to grow revenue and compete. And now revenue is flatlining because
consumers are getting tapped out with respect to how much they can spend
and there’s only so much Innovation you can really do to charge more,
get people to come in the store more and drive up revenue. - David
Friedberg
Brian Nickel has an incredible reputation prior to Chipotle. He
ran Taco Bell and he ran Taco Bell for several years and made it one of
the most profitable Quick Serve Restaurants (QSR) in the world. He did
this by focusing on every nickel. He is notorious for being a Cost
Cutter, for being an efficiency driver, for being a productivity Hound.
He goes into the business and he figures out every step in the supply
chain, every step of the operating activities of the employee in the
stores. So he was recruited heavily. I don’t know if you guys remember
Chipotle’s founder was running Chipotle and at the time there was a lot
of investor activism around Chipotle because they were wasting money
like no one’s business. The guy had a private Jet, he was flying his
management team back and forth between Denver and New York. They were
spending money on crazy projects. And the board fired the CEO founder of
Chipotle, brought in Nickel. Nickel came in and made Chipotle an
incredibly profitable growing business. And the expectation is he’ll
come and do the same here that maybe over the years Starbucks’s success
has bred laziness. Starbucks’s success has bred fat slowness
productivity decline, and that this guy is the right guy to come in and
find all the nickels. And Brian Nichol is probably the right guy which
is why you’re seeing the stock kind of rally as hard as it has. - David
Friedberg
But the thing about Starbucks is they realized early on that when
you can customize a consumer experience, the consumer comes back more
frequently. So when you see your name written on that cup, you feel like
you’re getting your product, you’re not buying an off-the-shelf product,
you’re getting a custom personalized experience. What that led to is
people customizing their drinks and what did they find that they liked
when they customized their drinks sugary sweet add-ons. And then that
became more and more of the standard menu and then that just kept
evolving. And that’s just the consumer feedback mechanism working which
is to Chamath’s point, led to 60 gram sugar drinks that are now the
standard product at Starbucks, not an espresso or a cappuccino which is
how they started, and it’s really unfortunate. - David
Friedberg
So arguably I would say that trying to step in and cap prices
will reduce competition, and as a result will reduce investment in
improving productivity. And we have seen this countless times with every
socialist experiment in human history has started with caps on food, and
it has resulted in spread lines like you see in the image behind me
today as we can see in Soviet Russia. This is a mistake, it is a
problem, it is anti-American, it is anti-free Market, it is anti-
innovation, it is anti- productivity, and ultimately it’s anti- liberty
and I cannot stand it. - David Friedberg
To support your point, and what Chamath was messaging on our
chat, look at Walmart stocks up 7% today, because they offer lower
priced solutions to consumers, and Dollar General and Dollar Tree are
rallying as well, when the market competes, consumers benefit, and there
are companies that will win. And the companies that try to price gouge,
and the companies that try to charge too much will lose. Starbucks has
been trying to charge too much for sugar water, they have a real problem
they are now tackling. Walmart is trying to bring value to consumers,
they are winning. That is how free markets work. When the government
steps in and says here’s how much margin you can make or here’s how much
prices should be, it ruins everything, and the entire incentive
structure goes away, and you end up with breadlines. - David
Friedberg
― Break up Google, Starbucks CEO out, Kamala’s price
controls, Boeing disaster, Kursk offensive - All-in Podcasts
[Link]
I think the thing that matters more than anything else is to make
sure that the people that they are letting in are in love and obsessed
with the things that MIT is supposed to be great at. … You should not be
going to MIT because you think it’s a check mark. You should be going
there because you think that there are professors in organic Chemistry,
in physics, in these disciplines that are really important who are
experts in their fields that you can learn from and become an expert
yourself. And I think the problem with all of this other stuff is once
you make it a credential, there are some folks that are only going to
MIT because they could get in and because it’s a great credential in
their minds and they shouldn’t go there either. So I think the thing is
you have to get back to what matters which is there are all of these
industries that have not progressed that much. And in order for those
Industries to advance you need really talented young people who can
learn an apprentice and then take over. And I think MIT is one of these
rare places that focuses on this part of the physical world that hasn’t
had as much progress. And so I just want to make sure that the people
that go there actually want to be there for that reason gender race all
that other stuff shouldn’t matter. - Chamath Palihapitiya
Kamala Harris and Tim Waltz have only ever work for government,
Trump and Vance have worked in Private Industry. It’s not just their
perspective being colored by the the lack of participation in the
private economy, but the lack of employment in the private economy,
they’ve never worked for a private business, they’ve never been
employees of a private business, they’ve never built a private business.
I’m not trying to be disparaging but I do think I’m just trying to
underline the point here Chamath which is the voter’s choice is do you
want candidates that are not typically government operatives, or do you
want candidates that have spent their whole career as government
operatives. And that is effectively what the voters are going to be
voting for. And they’re going to make a decision they may want to have
someone that’s going to lead the biggest government in history, because
they’ve spent their whole careers in government. Or they’re going to say
you know what the biggest government in history needs to be
significantly altered, and we want to bring someone in from the outside
that’s worked in Private Industry. And that is the voter’s choice.
That’s one way to view the voter’s choice here. - David
Friedberg
I think just because someone has served in Government doesn’t
mean that they truly even understand what the problems are, or that
they’re even the master of government. I mean you saw this over the past
week we talked about the 88,000 jobs that didn’t exist. They asked Gina
Rundo the Secretary of Commerce about this and she just said that’s a
Trump lie, and they said no actually it’s the Bureau of Labor Statistics
report that like is under US Secretary of Commerce. She said I’m not
familiar with that, so you have people running the government who don’t
even know what their own departments are doing. Now I think it’s just a
function of the fact that the government is so big and out of control
that no one even understands what it does. I think it’s more important
to have someone who at least has some experience in the private sector
who truly understands how jobs are created, how wealth is created, what
causes inflation, okay. We’ve talked about this before, what causes
inflation is the printing of too much money, it’s government spending
too much, it is not corporate greed, because corporate greed as a
constant, it’s not price gouging. - David Sacks
And how the free market incentivizes the creation of improved
productivity which over time translates into improved prosperity for the
society within which that is taking place that is so critical and we saw
that happen even in China in the last 30 years when the government
allowed entrepreneurship to flourish in certain parts of the country. As
a result there were significant productivity gains and they brought a
billion people out of poverty - they created a middle class. - David
Friedberg
― Massive jobs revision, Kamala wealth tax, polls vs
prediction markets, end of race-based admissions - All-in
Podcasts [Link]
Why We Don’t Own Coupang Stock (CPNG) - Chit Chat Stock
Podcast [Link]
Good analysis of CPNG’s business, advantages and disadvantages,
future and expectation.
Track Record and Risk w/ Guy Spier - We Study
Billionaires [Link]
Gradient-Boosting RL (GBRL) brings the advantages of GradientBoosting
Trees (GBT) to reinforcement learning.
SpreadsheetLLM: Encoding Spreadsheets for Large Language
Models [Link]
Microsoft releases SpreadsheetLLM, a model designed to optimize LLMs’
powerful understanding on spreadsheets. It’s a great paper that outlines
how you can turn a spreadsheet into a representation that is useful to a
modern LLM. This can be used for Q/A, formatting, and other data
operations.
The core innovation in SpreadsheetLLM is the SheetCompressor module,
which efficiently compresses and encodes spreadsheets. It includes 1)
Structural-anchor-based compression, 2) Inverse index translation, 3)
Data-format-aware aggregation.
Controlled study finds MLP generally outperforms KAN across various
tasks. MLP outperformed KAN in machine learning (86.16% vs. 85.96%),
computer vision (85.88% vs. 77.88%), NLP (80.45% vs. 79.95%), and audio
processing (17.74% vs. 15.49%). KAN excelled only in symbolic formula
representation (1.2e-3 RMSE vs. 7.4e-3).
NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million
Context Window? [Link]
The problem of current evaluation methods is that they are inadequate
for assessing LLM performance on long context, however reasoning on long
texts becomes more and more demanded. So they present a framework called
NeedleBench for evaluating the long-context capabilities of LLMs across
extensive text lengths. By some experiments, they find that current LLMs
are struggling with complex reasoning tasks when it comes to long texts,
showing a potential improvement room for LLMs.
Retrieval Augmented Generation or Long-Context LLMs? A
Comprehensive Study and Hybrid Approach [Link]
This study investigates how large language models handle
question-answering tasks under two conditions: when they receive
comprehensive context information (long-context) versus when they are
given only selected chunks of the necessary information (RAG). It shows
that long context surpasses RAG significantly for Gemini-1.5-Pro, GPT-4O
and GPT-3.5-Turbo.
A Survey of Prompt Engineering Methods in Large Language
Models for Different NLP Tasks [Link]
This paper summarizes 38 prompt engineering techniques for LLM
reasoning and lists the types of problems and datasets they have been
used with.
Can Long-Context Language Models Subsume Retrieval, RAG, SQL,
and More? [Link]
This paper explores the capabilities of long-context language models
(LCLMs) in handling tasks traditionally dependent on external tools like
retrieval systems, RAG (Retrieval-Augmented Generation), and SQL
databases. It reveals that LCLMs, such as Gemini 1.5 Pro, GPT-4o, and
Claude 3 Opus, can perform competitively with specialized models in
tasks like retrieval and RAG. In particular, at the 128k token context
length, LCLMs rival the performance of state-of-the-art retrieval
systems and even surpass some multi-modal retrieval models. However,
LCLMs struggle significantly with more complex tasks requiring multi-hop
compositional reasoning, such as SQL-like tasks. The findings also
highlight the importance of prompt design, as performance can vary
greatly depending on the prompting strategies used.
Apple Intelligence Foundation Language Models -
Apple [Link]
This report describes the architecture, the data used to train the
model, the training process, how the models are optimized for inference,
and the evaluation results, for the foundation language model developed
to power Apple Intelligence features.
Notice that Apple includes the fundamentals of their RL methods,
including a different type of soft margin loss for the reward model,
regularizing binary preferences with absolute scores, their rejection
sampling algorithm (iTeC) that is very similar to Meta’s approach, and
their leave-one-out Mirror Descent RL algorithm, MDLOO.
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories,
Applications and Opportunities [Link]
This survey provides an in-depth review of model merging techniques,
an increasingly popular method in machine learning that doesn’t require
raw training data or expensive computation.
Interactive visualization tool designed for non-experts to learn
about Transformers.
The AI Scientist: Towards Fully Automated Open-Ended
Scientific Discovery - Sakana.AI [Link]
Transformers in music recommendation - Google
Research [Link]
A Comprehensive Overview of Large Language Models
[Link]
A Survey on Benchmarks of Multimodal Large Language
Models [Link]
This paper provides an extensive review of 180 benchmarks used to
evaluate Multimodal Large Language Models.
Qwen2-VL: To See the World More Clearly - QwenLM
[Link]
The model comes in three sizes : 2B and 7B (open-sourced under Apache
2.0 license), and 72B (available via API).
Performance:
Qwen2-VL demonstrates state-of-the-art performance on visual
understanding benchmarks. The 72B model surpasses GPT-4o and Claude
3.5-Sonnet on most metrics.
The 7B model achieves top performance in document understanding and
multilingual text comprehension. Even the 2B model shows strong
performance in video-related tasks and document understanding.
Key Capabilities:
Processing videos over 20 minutes long
Complex reasoning for device operation (e.g., mobile phones,
robots)
Multilingual text understanding in images (European languages,
Japanese, Korean, Arabic, Vietnamese)
Function calling for integration with external tools
Document understanding and general scenario question-answering
Integrates with Hugging Face Transformers and vLLM.
Supports various tools for quantization, deployment, and
fine-tuning, making it accessible for ML engineers and researchers to
implement and customize.
Two key architectural innovations:
Naive Dynamic Resolution support allows Qwen2-VL to
handle arbitrary image resolutions by mapping them to a dynamic number
of visual tokens. This ensures consistency between input and image
information.
Multimodal Rotary Position Embedding (M-ROPE)
enables concurrent capture of 1D textual, 2D visual, and 3D video
positional information.
LlamaIndex releases notebook implementation of Microsoft’s
GraphRAG.
Step-By-Step Tutorial: How to Fine-tune Llama 3 (8B) with
Unsloth + Google Colab & deploy it to Ollama - reddit [Link]
Notebooks: Using Mistral Nemo with 60% less memory -
Nvidia [Notebook1]
[Notebook2]
Nvidia has released two free notebooks for Mistral NeMo 12b, enabling
2x faster finetuning with 60% less memory. Mistral’s latest free LLM is
the largest multilingual open-source model that fits in a free Colab
GPU.
Google has an illegal monopoly on search, judge rules. Here’s
what’s next - CNN [Link]
Berkshire Hathaway sells off large share of Apple and
increases cash holdings - The Guardian [Link]
Having accurate, reliable benchmarks for AI models matters, and
not just for the bragging rights of the firms making them. Benchmarks
“define and drive progress”, telling model-makers where they stand and
incentivising them to improve, says Percy Liang of the Institute for
Human-Centred Artificial Intelligence at Stanford University. Benchmarks
chart the field’s overall progress and show how AI systems compare with
humans at specific tasks. They can also help users decide which model to
use for a particular job and identify promising new entrants in the
space, says Clémentine Fourrier, a specialist in evaluating LLMs at
Hugging Face, a startup that provides tools for AI developers.
― GPT, Claude, Llama? How to tell which AI model is best -
The Economist [Link]
Current benchmark MMLU (massive multi-task language understanding)
has a few problems: 1) too easy for today’s models leading to the
problem of ‘saturation’. New alternatives are developed such as
MMLU-Pro, GPQA, MUSR, etc, 2) training data comes from internet which is
a source of questions and answers for MMLU, resulting in a problem
called “contamination”, 3) answers in MMLU tests are sometimes wrong or
correct answers are more than one, 4) small changes in the way questions
are posed to models can significantly affect their scores.
There are some trustworthy automated testing systems other than
ChatBotArena leaderboard: HELM (holistic evaluation of language models)
built by Dr Liang’s team at Stanford, and EleutherAI Harness uses by Dr
Fourrier’s teams at Hugging Face for open source models.
As model gain new skills, new benchmarks are being developed to
assess them. For example, GAIA tests model on real world problem
solving, NoCha provides novel challenge, etc. However, new benchmarks
are expensive to develop because they require human experts to create a
detailed set of questions and answers. Dr Liang is working on project
AutoBencher, Anthropic started funding the creation of benchmarks with a
focus of AI safety.
GPT-4o is small and intelligent. It’s probably distilled from current
or unreleased version of OpenAI’s models, similar to what Claude did
with Claude Haiku and Google with Gemini Flash.
Made by Google 2024: Pixel 9, Gemini, a new foldable and
other things to expect from the event - TechCrunch [Link]
MIT releases comprehensive database of AI risks -
VentureBeat [Link]
Apple will let other digital wallets into Apple Pay, and even
be the default - ars technica [Link]
Apple Aiming to Launch Tabletop Robotic Home Device as Soon
as 2026 With Pricing Around $1,000 [Link]
This is one of my notes of the online Causal Inference Course in
Columbia University, taught by Michael E. Sobel who is a professor in
the Department of Statistics. It would be good to have this overview of
causal inference regarding the framework in mind before getting into
statistical and theoretical details, especially for beginners. A clear
approach framework is very important that it’s not only because rigorous
experiment and analysis methods could be developed with this
well-defined framework, but also because it can guide you to deal with
challenging situations in a correct way, while being clear about the
limitations and assumptions at the same time. This is why it became a
Science.
Modern Approach for Causal
Inference
The modern dominant approach for causal inference, significantly
influenced by Donald Rubin’s contributions, primarily revolves around
the following key ideas:
Potential Outcomes Framework:
Potential Outcomes Notation: Introduced by Neyman
and further developed by Rubin, this framework involves conceptualizing
the outcomes that would occur both with and without the treatment for
each unit. Each unit has a potential outcome under treatment and a
potential outcome under control, but only one of these outcomes is
observed for each unit.
Average Treatment Effects: The focus is on
estimating the average causal effect of a treatment across a population.
This involves comparing the average outcomes of treated and untreated
groups, taking into account the potential outcomes framework.
Randomization and Its Analogs:
Role of Randomization: In experimental studies,
random assignment of treatments is crucial for ensuring that the
treatment groups are comparable, allowing for unbiased estimation of
causal effects.
Randomization-like Conditions in Observational
Studies: Rubin extended the framework to observational studies
by arguing that causal inferences can be made if these studies fulfill
conditions similar to randomization. This involves controlling for
confounding variables that influence both the treatment and the outcome,
often through methods like matching, regression adjustment, or
instrumental variables.
Counterfactual Reasoning:
Counterfactual Conditionals: Causal relationships
must satisfy counterfactual conditions. This means that for a cause to
be deemed responsible for an effect, it should be demonstrable that if
the cause had not occurred, the effect would not have occurred. This is
formalized through the potential outcomes framework.
Key Features of Rubin’s
Approach:
Application to Both Experimental and Observational
Studies: Rubin’s framework is versatile and can be applied to
both types of studies, providing a unified approach to causal
inference.
Focus on Estimating Causal Effects: The primary
goal is to estimate the causal effect of treatments or interventions,
rather than simply identifying associations.
Use of Statistical Methods: The approach leverages
statistical methods to control for confounding variables and to estimate
causal effects, emphasizing the importance of rigorous statistical
analysis.
Two Key Criteria of
Modern Causal Inference
The modern dominant approach to causal inference primarily builds on
two key criteria:
Causation at the Singular Level:
This criterion allows for the possibility that causation can be
specific to individual subjects or units, acknowledging effect
heterogeneity. It means that a cause may produce an effect in one
individual but not necessarily in another, depending on various
conditions.
Satisfaction of Counterfactual Conditionals:
A causal relationship must sustain a counterfactual conditional.
This means that for a cause to be deemed responsible for an effect, it
should be demonstrable that if the cause had not occurred, the effect
would not have occurred. This criterion is essential for defining and
reasoning about causal relationships in both experimental and
observational studies.
Impact on Empirical Research:
Rubin’s contributions have led to more careful and precise inferences
about causal effects in various disciplines, particularly in the social
sciences. Researchers now more rigorously design studies and analyze
data to ensure that their conclusions about causality are well-founded
within this robust statistical framework.
Challenges in Randomized
Studies:
Non-compliance with Treatment Assignments:
Example: In a study by the University of Michigan
in the 1990s, unemployed persons were assigned to receive or not receive
assistance in job searching. A significant percentage of those assigned
to the treatment group did not actually take the treatment, complicating
the comparison between groups and potentially overestimating the
treatment’s effectiveness.
Intermediate Variables:
Example: An educational researcher wants to know
the effect of encouragement to study on test scores. While the
researcher can estimate the effect of encouragement, estimating the
direct effect of study time is more complicated because it involves
intermediate variables (encouragement affecting study time, which in
turn affects test scores).
Breakdown of Random Assignment:
Example: If a subject’s treatment adherence is
influenced by their perception of treatment benefits, comparing only
those who comply can lead to biased estimates.
Challenges in Observational
Studies:
Identifying and Measuring All Covariates:
Example: When studying the effect of education on
earnings, researchers must account for various covariates that affect
both education levels and earnings. Failure to identify or measure all
relevant covariates can lead to biased estimates.
Estimating Average Treatment Effects:
Example: In observational studies, various methods
like matching, weighting, and regression are used to estimate treatment
effects. Each method has its own set of practical issues and assumptions
that need to be carefully managed.
Longitudinal Observational Studies:
Example: When treatments administered in different
periods depend on previous treatments and outcomes, analysis becomes
more complicated.
Interference:
Example: In a housing experiment conducted by the
U.S. government, participants assigned to move from housing projects to
suburbs knew each other. If the treatment assignment of one participant
influenced the decision or outcome of another, traditional analysis
methods might not be adequate.
These examples illustrate that both randomized and observational
studies require careful consideration of various factors to ensure
accurate and reliable causal inferences.
Potential Outcomes,
Unit, and Average Effect
Potential Outcomes Framework
Potential Outcomes:
Each unit (e.g., individual) has two potential outcomes: one if
treated and one if not treated. However, only one outcome can be
observed for each unit.
This leads to the “fundamental problem of causal inference,” where
we cannot observe both potential outcomes for a single unit.
Notation and Unit Effects:
For a unit \(i\), denote the
outcome as \(Y\_i (1)\) if treated and
\(Y\_i (0)\) if not treated.
The unit effect is defined as the difference \(Y\_i (1)−Y\_i (0)\).
The observed outcome \(Y_i\) is
determined by the treatment assignment \(Z\_i\), where \(Z\_i=1\) if the unit is treated and \(Z\_i=0\) if not.
Randomized vs. Observational Studies:
In randomized experiments, treatment assignment \(Z\_i\) is random.
In observational studies, subjects choose their treatment,
introducing potential biases.
Average Treatment Effects
Sample Average Treatment Effect (SATE):
The average of the unit effects for the sample.
Finite Population Average Treatment Effect (FATE):
The average treatment effect for a finite population from which the
sample is drawn.
Average Treatment Effect (ATE):
The average treatment effect in an infinite or large population.
This is treated as an expectation of the potential outcomes.
Estimands of Interest:
Various estimands depend on the marginal distributions of potential
outcomes, such as ATE and Average Treatment Effect on the Treated
(ATT).
Challenges and Assumptions:
Estimating these effects requires assumptions like the Stable Unit
Treatment Value Assumption (SUTVA), which ensures that the potential
outcomes are well-defined and not affected by other units’
treatments.
Practical Implications
Decision-Making:
Knowledge of average treatment effects aids decision-making in
contexts like medical treatments and policy implementations.
Ignorability Conditions:
Under certain conditions, known as ignorability or unconfoundedness,
it is possible to use observed data to estimate causal effects
reliably.
Extensions and Assumptions:
The framework extends to multiple treatments and continuous
treatments, though additional assumptions may be required.
SUTVA assumes no alternative representations of treatment and no
interference between units, which may need adjustments in certain
studies.
Conditions
Allow Average Effects be Unbiasedly/ Consistently Estimated
Key Concepts
Average Treatment Effect (ATE) Estimation:
Random Sampling: Drawing random samples of treated
(\(y\_1\)) and untreated (\(y\_0\)) units to estimate their respective
means.
Sample Means: The means of treated (\(\\bar{y}\_1\)) and untreated (\(\bar{y}\_0\)) samples serve as unbiased and
consistent estimators of the population means.
Unconfoundedness:
Definition: Treatment assignment \(z\) is independent of potential outcomes
(\(y\_0\) and \(y\_1\)).
Intuition: In randomized experiments, treatment
assignment is blind to potential outcomes, ensuring unconfoundedness. In
observational studies, treatment assignment might depend on factors
related to potential outcomes, potentially confounding the
estimates.
Examples
Randomized Experiment vs. Observational Study:
Randomized Experiment: Treatment assignment is
random (e.g., coin flip), ensuring \(z\) is independent of \(y\_0\) and \(y\_1\).
Observational Study: Treatment assignment may
depend on patient characteristics, potentially leading to biased
estimates.
Age and Treatment Example:
Scenario: Older patients might forego treatment
believing it’s less beneficial, while younger patients might opt for
treatment believing it’s more beneficial.
Consequence: Naive comparison between treated and
untreated groups might overestimate the treatment effect due to
confounding by age.
Ignorability Condition
Condition: \(y\_0\) and \(y\_1\) are independent of \(z\) given covariates \(x\) (e.g., age).
Stratified Analysis: In both randomized experiments
and observational studies, stratifying on covariates like age can help
achieve conditional unconfoundedness.
Adjusting for Covariates:
Randomized Experiment: Can stratify on covariates
either before or after the experiment.
Observational Study: Treat it as a stratified
randomized experiment by conditioning on covariates related to treatment
status and potential outcomes.
Practical Implications
Comparison of Groups:
In a stratified randomized experiment, compare treated and control
groups within each stratum (e.g., age group) to estimate ATE.
In observational studies, stratify on covariates to reduce bias and
estimate ATE as if it were a stratified randomized experiment.
Challenges in Observational Studies:
Unknown Assignment Mechanism: Unlike randomized
experiments, the assignment mechanism in observational studies is not
controlled, making it harder to ensure unconfoundedness.
Measurement of Confounders: It’s crucial to measure
and account for all relevant confounders, though it may not always be
possible.
I’ve never looked so deeply into my feelings inside until I met this
book written by Brené Brown. I fell in love with it immediately when I
saw the quote from Theodore Roosevelt at the very beginning:
It is not the critic who counts; not the man who points out how the
strong man stumbles, or where the doer of deeds could have done them
better. The credit belongs to the man who is actually in the arena,
whose face is marred by dust and sweat and blood; who strives valiantly;
who errs, who comes short again and again… who at the best knows in the
end the triumph of high achievement, and who at the worst, if he fails,
at least fails while daring greatly.
Part 1. Rumbling with
Vulnerability
Definition of the courage to be vulnerable:
The courage to be vulnerable is not about winning or losing, it’s
about the courage to show up when you can’t predict or control the
outcome. The only thing I know for sure after all of this research is
that if you’re going to dare greatly, you’re going to get your ass
kicked at some point. If you choose courage, you will absolutely know
failure, disappointment, setback, even heartbreak. That’s why we call it
courage. That’s why it’s so rare.
Definition of rumbling with vulnerability. It’s a foundational and
core skill of courage building, and “our ability to be daring leaders
will never be greater than our capacity for vulnerability”.
A rumble is a discussion, conversation, or meeting defined by a
commitment to lean into vulnerability, to stay curious and generous, to
stick with the messy middle of problem identification and solving, to
take a break and circle back when necessary, to be fearless in owning
our parts, and, as psychologist Harriet Lerner teaches, to listen with
the same passion with which we want to be heard.
Section 1. The Moment and
The Myths
Good practices:
Have the courage to show up when you can’t control the outcome
The definition of vulnerability is the emotion that we experience
during times of uncertainty, risk, and emotional exposure. Vulnerability
is not winning or losing. It’s having the courage to show up when you
can’t control the outcome.
Step over cheap-seat feedback and keep daring
If you are not in the arena getting your ass kicked on occasion, I’m
not interested in or open to your feedback. There are a million cheap
seats in the world today filled with people who will never be brave with
their lives but who will spend every ounce of energy they have hurling
advice and judgment at those who dare greatly. Their only contributions
are criticism, cynicism, and fearmongering. If you’re criticizing from a
place where you’re not also putting yourself on the line, I’m not
interested in what you have to say.
Don’t grab hurtful comments and pull them close to you by rereading
them and ruminating on them. Don’t play with them by rehearsing your
badass comeback. And whatever you do, don’t pull hatefulness close to
your heart.
Don’t shield ourselves from all feedback
Again, if we shield ourselves from all feedback, we stop growing. If
we engage with all feedback, regardless of the quality and intention, it
hurts too much, and we will ultimately armor up by pretending it doesn’t
hurt, or, worse yet, we’ll disconnect from vulnerability and emotion so
fully that we stop feeling hurt. When we get to the place that the armor
is so thick that we no longer feel anything, we experience a real death.
We’ve paid for selfprotection by sealing off our heart from everyone,
and from everything-not just hurt, but love.
The six misguided myths of vulnerability:
Vulnerability is weakness
I don’t do vulnerability
Choosing to own our vulnerability and do it consciously means
learning how to rumble with this emotion and understand how it drives
our thinking and behavior so we can stay aligned with our values and
live in our integrity. Pretending that we don’t do vulnerability means
letting fear drive our thinking and behavior without our input or even
awareness, which almost always leads to acting out or shutting down.
I can do it alone
You can engineer uncertainty out of vulnerability
Trust comes before vulnerability
We need to trust to be vulnerable, and we need to be vulnerable in
order to build trust.
Trust is the stacking and layering of small moments and reciprocal
vulnerability over time. Trust and vulnerability grow together, and to
betray one is to destroy both.
And I like this marble jar approach:
We trust the people who have earned marbles over time in our life.
Whenever someone supports you, or is kind to you, or sticks up for you,
or honors what you share with them as private, you put marbles in the
jar. When people are mean, or disrespectful, or share your secrets,
marbles come out. We look for the people who, over time, put marbles in,
and in, and in, until you look up one day and they’re holding a full
jar. Those are the folks you can tell your secrets to. Those are the
folks you trust with information that’s important to you.
Vulnerability is disclosure
Section 2. The Call to
Courage
Leaders must either invest a reasonable amount of time attending to
fears and feelings, or squander an unreasonable amount of time trying to
manage ineffective and unproductive behavior.
Hunt treasures
This is when I remember Joseph Campbell’s quote, which I believe is
one of the purest calls to courage for leaders: “The cave you fear to
enter holds the treasure you seek.
When we are in fear or in self-protection, these are the patterns of
how we assemble our armor. And they will NOT lead us to anywhere.
I’m not enough
If i’m honest about what’s happening, they will think less of me
or maybe use it against me
No one else is going to be honest about what’s happening and so
no way am I going to do that
They are not honest about what scares them and they’ve got a lot
of issues
This is their fault and they are trying to blame me
I’m better than them
Serve people
When you find the courage to enter that cave, you’re never going in
to secure your own treasure or your own wealth; you face your fears to
find the power and wisdom to serve others.
Good practices:
Have a one-on-one discussion
Stop talking. Leave long white pauses and empty space so that we can
start peeling and going deep.
When they start talking. Really listen.
When we are in tough rumbles with people, we can’t take
responsibility for their emotions.
When rumbles become unproductive, give everyone minutes to walk
around outside or catch their breath.
Section 3. The Armory
The problem is that when we imprison the heart, we kill courage. In
the same way that we depend on our physical heart to pump life-giving
blood to every part of our body, we depend on our emotional heart to
keep vulnerability coursing through the veins of courage and to engage
all of the behaviors we talked about in the prior section, including
trust, innovation, creativity, and accountability.
You got to put down the weapons and show up.
As children we found ways to protect ourselves from vulnerability,
from being hurt, diminished, and disappointed. We put on armor; we used
our thoughts, emotions, and behaviors as weapons; and we learned how to
make ourselves scarce, even to disappear. Now as adults we realize that
to live with courage, purpose, and connection to be the person who we
long to be-we must again be vulnerable. We must take off the armor, put
down the weapons, show up, and let ourselves be seen.
Forms of armored leadership with top three as perfectionism,
foreboding joy, numbing:
Driving Perfectionism (armored leadership) vs encouraging for
healthy striving (daring leadership).
Perfectionism is not the same thing as striving for excellence.
Perfectionism is not about healthy achievement and growth. Perfectionism
is a defensive move.
Perfectionism is not the self-protection we think it is. It is a
twenty-ton shield that we lug around, thinking it will protect us, when
in fact it’s the thing that’s really preventing us from being seen.
Perfectionism is not self-improvement. Perfectionism is, at its core,
about trying to earn approval. Most perfectionists grew up being praised
for achievement and performance (grades, manners, rule following, people
pleasing, appearance, sports). Somewhere along the way, they adopted
this dangerous and debilitating belief system: I am what I accomplish
and how well I accomplish it. Please. Perform. Perfect. Prove. Healthy
striving is self-focused: How can I improve? Perfectionism is
other-focused: What will people think? Perfectionism is a hustle.
Perfectionism is not the key to success. In fact, research shows that
perfectionism hampers achievement. Perfectionism is correlated with
depression, anxiety, addiction, and life paralysis, or missed
opportunities. The fear of failing, making mistakes, not meeting
people’s expectations, and being criticized keeps us outside the arena
where healthy competition and striving unfolds.
Last, perfectionism is not a way to avoid shame. Perfectionism is a
function of shame.
Squandering opportunities for joy and recognition (armored
leadership) vs practicing gratitude and celebrating milestones (daring
leadership).
Numbing (armored leadership) vs setting boundaries and finding
real comfort (daring leadership)
We cannot selectively numb emotion. If we numb the dark, we numb the
light. If we take the edge off pain and discomfort, we are, by default,
taking the edge off joy, love, belonging, and the other emotions that
give meaning to our lives.
Propagating the false dichotomy of victim or viking (armored
leadership) vs practicing integration - strong back, soft front, wild
heart (daring leadership)
The opposite of living in a world of false binaries is practicing
integration the act of bringing together all the parts of ourselves, as
we talked about earlier. We are all tough and tender, scared and brave,
grace and grit. The most powerful example of integrationa practice that
I wrote about in Braving the Wilderness and that I try to live by-is
strong back, soft front, wild heart.
How can we give and accept care with strongback, soft-front
compassion, moving past fear into a place of genuine tenderness? I
believe it comes about when we can be truly transparent, seeing the
world clearly-and letting the world see into us.
Being a knower and being right (armored leadership) vs being a
learner and getting it right (daring leadership)
Having to be the “knower” or always being right is heavy armor. It’s
defensiveness, it’s posturing, and , worst of all, it’s a huge driver of
bullshit.
Hiding behind cynicism (armored leadership) vs modeling clarity,
kindness, and hope (daring leadership)
Using criticism as self-protection (armored leadership) vs making
contributions and taking risks (daring leadership)
Using power over (armored leadership) vs using power with, power to,
and power within (daring leadership)
Hustling for your worth (armored leadership) vs knowing your value
(daring leadership)
When people don’t understand where they’re strong and where they
deliver value for the organization or even for a single effort, they
hustle. And not the good kind of hustle. The kind that’s hard to be
around because we are jumping in everywhere, including where we’re not
strong or not needed, to prove we deserve a seat at the table. When we
do not understand our value, we often exaggerate our importance in ways
that are not helpful, and we consciously or unconsciously seek attention
and validation of importance.”
Zigzagging and avoiding (armored leadership) vs talking straight and
taking action (daring leadership)
Zigzagging is a metaphor for the energy we spend trying to dodge the
bullets of vulnerability whether it’s conflict, discomfort,
confrontation, or the potential for shame, hurt, or criticism.
When we find ourselves zigzagging-hiding out, pretending, avoiding,
procrastinating, rationalizing, blaming, lying-we need to remind
ourselves that running is a huge energy suck and probably way outside
our values. At some point, we have to turn toward vulnerability and make
that call.
Section 4. Shame and Empathy
The definition of shame:
First, shame is the fear of disconnection. As we talked about in the
myths of vulnerability, we are physically, emotionally, cognitively, and
spiritually hardwired for connection, love, and belonging. Connection,
along with love and belonging, is why we are here, and it is what gives
purpose and meaning to our lives. Shame is the fear of
disconnection-it’s the fear that something we’ve done or failed to do,
an ideal that we’ve not lived up to, or a goal that we’ve not
accomplished makes us unworthy of connection.
Shame is the intensely painful feeling or experience of believing
that we are flawed and therefore unworthy of love, belonging, and
connection.
Retreating into our smallness becomes the most seductive and easiest
way to stay safe in the midst of the shame squeeze. But, as we’ve talked
about, when we armor and contort ourselves into smallness, things break
and we suffocate.
Behavioral cues that shame has permeated a culture:
Perfectionism; Favoritism; Gossiping; Back-channeling; Comparison;
Self-worth tied to productivity; Harassment; Discrimination; Power over;
Bullying; Blaming; Teasing; Cover-ups.
Shame resistance is not possible as long as we care about connection,
but shame resilience is possible, learnable by all of us. We need to be
empathy and self-compassion.
Shame resilience is the ability to practice authenticity when we
experience shame, to move through the experience without sacrificing our
values, and to come out on the other side of the shame experience with
more courage, compassion, and connection than we had going into it.
Ultimately, shame resilience is about moving from shame to empathy the
real antidote to shame.
it’s important to understand that if we share our story with someone
who responds with empathy and understanding, shame can’t survive.
Self-compassion is also critically important, but because shame is a
social concept-it happens between people it also heals best between
people. A social wound needs a social balm, and empathy is that balm.
Self-compassion is key because when we’re able to be gentle with
ourselves in the midst of shame, we’re more likely to reach out,
connect, and experience empathy.
The definition of empathy:
Empathy is not connecting to an experience, it’s connecting to the
emotions that underpin an experience.
Empathy is a choice. And it’s a vulnerable choice, because if I were
to choose to connect with you through empathy, I would have to connect
with something in myself that knows that feeling. In the face of a
difficult conversation, when we see that someone’s hurt or in pain, it’s
our instinct as human beings to try to make things better. We want to
fix, we want to give advice. But empathy isn’t about fixing, it’s the
brave choice to be with someone in their darkness-not to race to turn on
the light so we feel better.
If struggle is being down in a hole, empathy is not jumping into the
hole with someone who is struggling and taking on their emotions, or
owning their struggle as yours to fix. If their issues become yours, now
you have two people stuck in a hole. Not helpful. Boundaries are
important here. We have to know where we end and others begin if we
really want to show up with empathy.
Empathy is at the heart of connection-it is the circuit board for
leaning into the feelings of others, reflecting back a shared experience
of the world, and reminding them that they are not alone.
Empathy skills:
From practical perspective, empathy is first to take the perspective
of another person, second to stay out of judgment, third to understand
their emotion, and fourth to communicate my understanding of their
emotion.
To see the world as others see it, or perspective taking
Perspective taking requires becoming the learner, not the knower.
Again, it’s only when diverse perspectives are included, respected,
and valued that we can start to get a full picture of the world, who we
serve, what they need, and how to successfully meet people where they
are.
I love what Beyoncé said in her first-person essay in the September
2018 issue of Vogue: ”If people in powerful positions continue to hire
and cast only people who look like them, sound like them, come from the
same neighborhoods they grew up in, they will never have a greater
understanding of experiences different from their own. They will hire
the same models, curate the same art, cast the same actors over and over
again, and we will all lose. The beauty of social media is it’s
completely democratic. Everyone has a say. Everyone’s voice counts, and
everyone has a chance to paint the world from their own
perspective.”
To be unjudgmental
Based on research, there are two ways to predict when we are going to
judge: We judge in areas where we’re most susceptible to shame, and we
judge people who are doing worse than we are in those areas.
To understand another person’s feelings
To communicate your understanding of that person’s feelings
Fluency in emotional conversation means being able to name at least
thirty of them.
One reason emotion is difficult to identify and name is the iceberg
effect.
Many of the emotions that we experience show up as pissed off or shut
down on the surface. Below the surface, there’s much more nuance and
depth. Shame and grief are two examples of emotions that are hard to
fully express, so we turn to anger or silence.
The vast majority of us find it easier to be mad than hurt. Not only
is it easier to express anger than it is to express pain, our culture is
more accepting of anger. So the next time you’re shutting down or angry,
ask yourself what lies beneath.
Mindfulness / Paying attention
Self-compassion skills
Maintain clear line
Do not take responsibility and ownership for the words of other
people-just own your part.
Jumping into the hole with no way out is enmeshment-jumping into
struggle with someone while maintaining clear lines about what belongs
to whom is empathy.
Stop beating yourself
Talk to yourself the way you’d talk to someone you love.
Four elements of shame resilience:
Recognizing shame and understanding its triggers
When we have understanding and awareness around shame, we are less
likelyy to default to our shame shields or the following three
strategies of disconnection:
Moving away: Withdrawing, hiding, silencing ourselves, and keeping
secrets. Moving toward: Seeking to appease and please. Moving against:
Trying to gain power over others by being aggressive, and by using shame
to fight shame.
Practicing critical awareness
Reaching out
Speaking shame
Section 5.
Curiosity and Grounded Confidence
Dheeraj explained to me that when leaders don’t have the skills to
lean into vulnerability, they’re not able to successfully hold the
tension of the paradoxes that are inherent in entrepreneurship. His
examples of the paradoxes that elicit vulnerability in leaders align
with what we heard from the research participants: • Optimism and
paranoia • Letting chaos reign (the act of building) and reining in
chaos (the act of scaling) • Big heart and tough decision making •
Humility and fierce resolve • Velocity and quality when building new
things • Left brain and right brain • Simplicity and choice • Thinking
global, acting local • Ambition and attention to detail • Thinking big
but starting small • Short-term and long-term • Marathons and sprints,
or marathon of sprints in business-building Dheeraj told me, “Leaders
must learn the skills to hold these tensions and get adept at balancing
on the ‘tightrope’ of life. Ultimately, leadership is the ability to
thrive in the ambiguity of paradoxes and opposites.”
How to build skills to hold tensions of the paradoxes:
Rumble skills: easy learning does not build strong skills
The reality is that to be effective, learning needs to be effortful.
That’s not to say that anything that makes learning easier is
counterproductive-or that all unpleasant learning is effective. The key
here is desirable difficulty. The same way you feel a muscle “burn” when
it’s being strengthened, the brain needs to feel some discomfort when
it’s learning. Your mind might hurt for a while-but that’s a good
thing.
Curiosity
In his book Curious: The Desire to Know and Why Your Future Depends
on It, Ian Leslie writes, “Curiosity is unruly. It doesn’t like rules,
or, at least, it assumes that all rules are provisional, subject to the
laceration of a smart question nobody has Yet thought to ask. It
disdains the approved pathways, preferring diversions, unplanned
excursions, impulsive left turns. In short, curiosity is deviant.”
Practice vulnerability, become self-aware, and engage in tough
conversations
There’s an old saying that I lead by now: “People don’t care how much
you know until they know how much you care.” I’ve learned one way to
help people understand how much you care is to share your story.
Part 2. Living into Our
Values
Values and living into our values:
A value is a way of being or believing that we hold most important.
Living into our values means that we do more than profess our values, we
practice them. We walk our talk-we are clear about what we believe and
hold important, and we take care that our intentions, words, thoughts,
and behaviors align with those beliefs.
More often than not, our values are what lead us to the arena
door-we’re willing to do something uncomfortable and daring because of
our beliefs. And when we get in there and stumble or fall, we need our
values to remind us why we went in, especially when we are facedown,
covered in dust and sweat and blood. Here’s the thing about values:
While courage requires checking our armor and weapons at the arena door,
we do not have to enter every tough conversation and difficult rumble
completely empty-handed.
Three steps to help you know more about yourself and how to live into
your values:
We can’t live into values that we can’t name
Taking values from BC to behavior
Empathy and self-compassion: the two most important seats in the
arena
Regardless of the values you pick, daring leaders who live into their
values are never silent about hard things.
“You first listen about race. You will make a lot of mistakes. It
will be super uncomfortable. And there’s no way to talk about it without
getting some criticism. But you can’t be silent.” To opt out of
conversations about privilege and oppression because they make you
uncomfortable is the epitome of privilege.
Silence is not brave leadership, and silence is not a component of
brave cultures. Showing up and being courageous around these difficult
conversations is not a path you can predetermine. A brave leader is not
someone who is armed with all the answers. A brave leader is not someone
who can facilitate a flawless discussion on hard topics. A brave leader
is someone who says I see you. I hear you. I don’t have all the answers,
but I’m going to keep listening and asking questions. We all have the
capacity to do that. We all have the ability to foster empathy. If we
want to do good work, it’s imperative that we continue to flesh out
these harder conversations, to push against secrecy, silence, and
judgment. It’s the only way to eradicate shame from the workplace, to
clear the way for a performance in the arena that correlates with our
highest values and not the fearmongers from the stands.
The biggest challenge we face when it comes to values is the
necessity to give feedback and receive feedback. You have to know when
you are ready to give feedback and be good at receiving feedback.
Understand their values
You don’t really know people until you take the time to understand
their values.
Daring leaders assume the best about people’s intention and assume
they are doing the best they can. Leaders struggling with ego, armor,
and/or a lack of skills do not make that assumption.
What is the foundational skill of assuming the best in people?
Setting and maintaining boundaries. What’s the fundamental belief
underpinning the assumption of positive intent? That people are doing
the best they can.
The people who are the most generous in their assumptions of others
have the clearest boundaries. The most compassionate and generous people
I’ve interviewed in my career are the most boundaries. It turns out that
we assume the worst about people’s intentions when they’re not
respectful of our boundaries: It is easy to believe that they are trying
to disappoint us on purpose. However, we can be very compassionate
toward people who acknowledge and respect what’s okay and what’s
not.
In addition to boundaries, an assumption of positive intent relies on
the core belief that people are doing the best they can with what
they’ve got, versus that people are lazy, disengaged, and maybe even
trying to piss us off on purpose. Sure, we’re all capable of change and
growth, but assuming positive intent requires the belief that people are
really trying in that moment.
Assuming positive intent does not mean that we stop helping people
set goals or that we stop expecting people to grow and change. It’s a
commitment to stop respecting and evaluating people based solely on what
we think they should accomplish, and start respecting them for who they
are and holding them accountable for what they’re actually doing. And
when we’re overwhelmed and struggling, it also means turning those
positive assumptions toward ourselves: I’m doing the very best I can
right now.
Part 3. Braving Trust
Importance of talking about trust:
Because talking about trust is tough, and because these conversations
have the potential to go sideways fast, we often avoid the rumble. And
that’s even more dangerous. First, when we’re struggling with trust and
don’t have the tools or skills to talk about it directly with the person
involved, it leads us to talk about people instead of to them. It also
leads to lots of energy-wasting zigzagging.
To measure individual level of trustworthiness, you can refer to the
following seven behaviors - BRAVING inventory:
Boundaries: You respect my boundaries, and when you’re not clear
about what’s okay and not okay, you ask. You’re willing to say no.
Reliability: You do what you say you’ll do. At work, this means staying
aware of your competencies and limitations so you don’t overpromise and
are able to deliver on commitments and balance competing priorities.
Accountability: You own your mistakes, apologize, and make amends.
Vault: You don’t share information or experiences that are not yours to
share. I need to know that my confidences are kept, and that you’re not
sharing with me any information about other people that should be
confidential. Integrity: You choose courage over comfort. You choose
what is right over what is fun, fast, or easy. And you choose to
practice your values rather than simply professing them. Nonjudgment: I
can ask for what I need, and you can ask for what you need. We can talk
about how we feel without judgment. We can ask each other for help
without judgment. Generosity: You extend the most generous
interpretation possible to the intentions, words, and actions of
others.
Unpacking Vault:
When I walk into a co-worker’s office and spill, there might be a
moment of connection, but it’s counterfeit connection. The second I walk
out, that colleague is likely thinking, “I should be careful about what
I tell Brené; she’s got no boundaries.”
Unpacking nonjudgment:
We are afraid of being judged for a lack of knowledge or lack of
understanding, so we hate asking questions.
We asked a thousand leaders to list marble earning behaviors-what do
your team members do that earns your trust? The most common answer:
asking for help. When it comes to people who do not habitually ask for
help, the leaders we polled explained that they would not delegate
important work to them because the leaders did not trust that they would
raise their hands and ask for help.
Trust is built in small moments. If you struggle with reliability,
make small and doable promises to yourself that are easy to fulfill,
until you get a flywheel of reliability going again. If you struggle
with boundaries, set small ones with your partner-like you will not be
responsible for both cooking and cleaning up dinner-until you are adept
at putting boundaries into action in a more meaningful way. That’s how
you fill your own marble jar. And never forget-we can’t give people what
we don’t have.
Part 4. Learning to Rise
We can’t expect people to be brave and risk failure if they’re not
prepped for hard landings.
Here’s the bottom line: If we don’t have the skills to get back up,
we may not risk falling. And if we’re brave enough often enough, we are
definitely going to fall. The research participants who have the highest
levels of resilience can get back up after a disappointment or a fall,
and they are more courageous and tenacious as a result of it. They do
that with a process that I call Learning to Rise. It has three parts:
the reckoning, the rumble, and the revolution.
Three steps process for learning to rise:
When we have the courage to walk into our story and own it, we get to
write the ending. And when we don’t own our stories of failure,
setbacks, and hurt-they own us.
The Reckoning
The reckoning is as simple as that: knowing that we’re emotionally
hooked and then getting curious about it.
The ego doesn’t own stories or want to write new endings; it denies
emotion and hates curiosity. Instead, the ego uses stories as armor and
alibi. The ego says “Feelings are for losers and weaklings.”
The most effective strategy for staying with emotion instead of
offloading it is something I learned from a yoga teacher. And from a few
members of the military Special Forces. It’s breathing.
Breathing is also the key to another strategy for reckoning with
emotion, and one of the most underrated leadership superpowers:
practicing calm.
I define calm as creating perspective and mindfulness while managing
emotional reactivity.
Calm is a superpower because it is the balm that heals one of the
most prevalent workplace stressors: anxiety.
Rumble: conspiracies, confabulations, and shitty first drafts
If the reckoning is how we walk into a tough story, the rumble is
where we go to the mat with it and own it.
The rumble starts with this universal truth: In the absence of data,
we will always make up stories. It’s how we are wired. Meaning making is
in our biology, and when we’re in struggle, our default is often to come
up with a story that makes sense of what’s happening and gives our brain
information on how best to self-protect. And it happens a hundred times
a day at work.
In our SFDs, fear fills in the data gaps. What makes that scary is
that stories based on limited real data and plentiful imagined data,
blended into a coherent, emotionally satisfying version of reality, are
called conspiracy theories. Yes, we are all conspiracy theorists with
our own stories, constantly filling in data gaps with our fears and
insecurities.
Confabulation has a really great and subtle definition: A
confabulation is a lie told honestly. To confabulate is to replace
missing information with something false that we believe to be true.
Confabulation shows up at work when we share what we believe is
factual information, but it’s really just our opinion.
Gottschall writes, “Conspiracy is not limited to the stupid, the
ignorant, or the crazy. It is a reflex of the storytelling mind’s
compulsive need for meaningful experience.” The problem is that rather
than rumbling with vulnerability and staying in uncertainty, we start to
fill in the blanks with our fears and worst-case-scenario planning. I
love this line from Gottschall: “To the conspiratorial mind, shit never
just happens.”
The three most dangerous stories we make up are the narratives that
diminish our lovability, divinity, and creativity.
The reality check around our lovability: Just because someone isn’t
willing or able to love us, it doesn’t mean that we are unlovable.
The reality check around our divinity: No person is ordained to judge
our divinity or to write the story of our spiritual worthiness.
The reality check around our creativity: Just because we didn’t
measure up to some standard of achievement doesn’t mean that we don’t
possess gifts and talents that only we can bring to the world. And just
because someone failed to see the value in what we can create or achieve
doesn’t change its worth or ours.
When we own a story and the emotion that fuels it, we get to
simultaneously acknowledge that something was hard while taking control
of how that hard thing is going to end. We change the narrative. When we
deny a story and when we pretend we don’t make up stories, the story
owns us. It drives our behavior, and it drives our cognition, and then
it drives even more emotions until it completely owns us.
The Revolution
I’m not afraid of the word revolution, I’m afraid of a world that’s
becoming less courageous and authentic. I’ve always believed that in a
world full of critics, cynics, and fearmongers, taking off the armor and
rumbling with vulnerability, living into our values, braving trust with
open hearts, and learning to rise so we can reclaim authorship of our
own stories and lives is the revolution. Courage is rebellion.
Revolution might sound a little dramatic, but in this world, choosing
authenticity and worthiness is an absolute act of resistance. Choosing
to live and love with our whole hearts is an act of defiance. You’re
going to confuse, piss off, and terrify lots of people-including
yourself. One minute you’ll pray that the transformation stops, and the
next minute you’ll pray that it never ends. You’ll also wonder how you
can feel so brave and so afraid at the same time. At least that’s how I
feel most of the time … brave, afraid, and very, very alive.
Own the fear, find the cave, and write a new ending for yourself, for
the people you’re meant to serve and support, and for your culture.
Choose courage over comfort. Choose whole hearts over armor. And choose
the great adventure of being brave and afraid. At the exact same
time.
Playing defense: How to control the narrative if your work is
being questioned - Wes Kao’s Newsletter [Link]
It’s normal that people will misunderstand and disagree with you.
what we need to do is to 1) learn to explain your ideas better and 2)
stay calm and share your thought process in the most objective way
possible.
Defending your thinking means to share logic, evidence and rationale
that explains why you believe your conclusion is the right one. It’s not
to try to protect your ego by refusing to acknowledge a good argument,
and being delusional about the strength of your claim. Being able to
play defense is important because it’s about your credibility. If you do
it well you are building more trust, otherwise you will be diminishing
trust.
Some suggestions mentioned in this article:
Have a rationale of every small decision you made.
Try to anticipate questions.
Embrace “show, not tell”
React as positively as possible. e.g. “Ah! I’m so glad you
asked”.
Consider the question behind the question.
Be happy that the person voiced their concern.
Beware of insecure vibes.
If you overcompensate, you’ll come across as defensive. This
decreases your credibility too. You’ll need to use your judgment and
read the situation. An open, curious, and almost playful attitude shows
you’re not afraid of hard questions.
Many people underestimate the daily moments where your credibility
can either be reinforced or eroded. This might sound dramatic, but it’s
quite banal: Every interaction folks have with you gets added to their
subconscious cumulative repository of data points about you.
Insecure vibes are subconscious clues and signals that you might
be giving off when you’re feeling anxious, nervous, or uncertain. Get
rid of insecure vibes—and your writing, meetings, presentations,
negotiations, and pitches will become stronger.
― “Insecure vibes” are a self-fulfilling prophecy - Wes Kao’s
Newsletter [Link]
In the following situations, insecure vibes happen:
When other person touched on your sore spot and you feel
threatened
You assume the person will say no before you even start
This can make you talk fast - showing you enter the conversation
already playing defense. You don’t give the person a chance to say yes
because you’ve already said no to yourself.
You insist on email or slack when you know a phone call is
better
Explaining your point in writing is a sign of lacking confidence and
avoiding confrontation.
You over-explain because you expect the other person to be
skeptical
You bring up counterpoints to arguments no one has mentioned. Not a
good time to do so. It looks like you intentionally bring up something
new to surprise others rather than having a reasonable justification of
your point.
To avoid being doubtful if you actually feel confident:
Don’t preface your idea with too many caveats. Speak in complete
sentences. Remove “ands” and “buts” that create never-ending sentences,
which can sound less authoritative.
Notice if you start to ramble. Try to prepare the first few lines of
what you want to say to kick off a meeting, so you start strong.
Practice your actual script so you get comfortable saying those
words.
To get rid of insecure vibes, ask yourself
Could this be interpreted as sounding defensive?
Am I overcompensating or overexplaining?
How would I respond on my best day?
Would I say this if I felt secure?
Strategy, not self-expression: How to decide what to say when
giving feedback - Wes Kao’s Newsletter [Link]
Ask yourself “Is this strategy or self expression?” before giving
feedback.
Do not self-express your feeling or complain. Do not say anything
that does not motivate them to change. Instead, say things that get you
closer to changing the person’s behavior.
How to focus on strategy rather than self-expression:
Mentally forgive this person
Identify what is most likely to motivate them to change,
Say only 10% that will actually change behavior, thinking
about:
How does this make them even more effective?
How will this allow them to work even better with the people around
them?
How does this get them closer to their goals?
How is this a skill they can apply now and in all future roles?
Don’t trigger the defensiveness in the first place
The minute your recipient gets defensive, it becomes a lot harder to
undo the defensiveness and get them to accept what you’re
saying.
Let the other person talk, e.g. “I’d love to hear what you think.
What parts are resonating most with you?”
The other benefit of letting the other person talk is cognitive
dissonance: if they say out loud what they are committed to doing
differently, they are reinforcing the idea in their own mind.
Keep your eyes on the person’s behavior change
Always be framing
“Strategy, not self-expression” applies to many more situations
too.
The more controversial the idea, the higher the burden of
proof.
Update your assumptions about how you add value.
Share where your hunch is coming from—because it’s coming from
somewhere.
Describe why the problem matters, so people understand why
you’re speaking up.
Don’t rely on your credentials. Your idea should make sense on
its own.
Use language that accurately reflects your level of
certainty.
― How to share your point of view (even if you’re afraid of
being wrong) - Wes Kao’s Newsletter [Link]
Every week, we make business cases at work. I’m defining a
business case as any recommendation to pursue a business opportunity or
solve a problem. A business case can be a 5-page document, 5 sentences
in Slack, or a 5-minute phone call. The larger the project, the more you
may need to make a comprehensive business case. But the underlying
premise is the same: If you don’t explain why a problem matters, your
colleagues won’t have the necessary information to decide how to support
you.
― The #1 question every business case should answer - Wes
Kao’s Newsletter [Link]
Skilled immigration is a national security priority -
Noahpinion [Link]
Skilled immigration should be supported while illegal immigration
should be avoided. Also, avoid US education system to become corrupt
system for immigration.
The low road, the high road, and the way the wind blows -
Silver Bulletin [Link]
Paramount Merges With Skydance - App Economy
Insights [Link]
PARA agreed to merge with Skydance. The new CEO is Skydance Media CEO
David Ellison whose father Larry Ellison is the founder of Oracle.
Old paramount businesses: 1) filmed entertainment (Paramount Pictures
and Nickelodeon movie), 2) TV media (Paramount’s broadcast) and cable
television networks, like CBS and MTV, 3) Direct to consumer streaming
services like Paramount+ and Pluto TV. Its current problems: 1) flat
revenue, 2) streaming services are losing, 3) high long-term debt but
low cash.
New Paramount Plan: 1) unify marquee rights, 2) reorganize finance,
3) transition into a tech-media hybrid.
For the #3, the vision is to better position Paramount on the front
end (DTC apps) and the back end (cloud infrastructure, cloud-based
production, and AI tools). Specifically, they are going to 1) rebuild
DTC into a differentiated platform, 2) build studio-in-the-cloud, 3)
leverage Gen AI.
Apple Pay unveiled a new peer-to-peer (P2P) feature called “Tap to
Cash”, which is a natural evolution of the existing “Tap to Pay”. This
is not the only case where big tech offers features that directly
compete with financial institutions: Google Wallet, Amazon’s lending
program for sellers, etc, competing with PayPal’s Venmo, Block’s Cash
App, etc. Big tech’s move not only intensifies competition within P2P
payment, but also raises questions about the future of the payment
industry.
Highlights:
Visa and Mastercard both face mobile wallet threats to card-based
business model.
American Express renowned for its premium card offerings, targeting
high spending high credit quality customers, continuing to attract
millennials and Gen Z customers. The recent strategic acquisitions are
Tock (a reservation platform for high end restaurant and events) and
Rooam (a mobile payment and ordering platform for restaurants and
venues). However, it’s sensitive to economic downturns and facing
competitions for other premium card issuers and digital payment
platforms.
Fiserv’s merchant solutions and financial solutions look positive,
it’s actively investing in digital transformation, and it recently
acquired BentoBox (a digital marketing and commerce platform for
restaurants).
Adyen serves large enterprise clients with its unified commerce
platform
PayPal lowered its FY 2024 guidance. It’s facing a decline in active
accounts. To solve this, it focuses on strategic partnerships such as
collaboration with Apple. It’s facing competitive pressure from Big Tech
payment solutions like Apple Pay and Google Pay. It’s currently
undergoing significant restructuring such as layoffs and leadership
change. And it’s initiating AI-powered personalized ads platform and
strengthening relationships with SME customers.
Block’s growth is driven by its momentum of Cash App ecosystem,
square ecosystem, and significant investment in Bitcoin. However, it’s
facing challenges of regulatory scrutiny on cryptocurrency activities
and compliance practices, competition from established competitors and
fintech startups, needs of balancing growth and profitability given FY
2024 guidance.
What to watch for the shift in payment landscape:
Facing legal battle from Big Tech, can Visa and Mastercard find
innovation or new solutions to maintain their revenue streams from swipe
fee?
Will Digital Wallet dominant payment industry? Will traditional
cards remain or be replace?
New possibilities of payments have been developed such as Buy Now
and Pay Later (BNPL). Will innovations gain mainstream adoption in
payment industry?
Can the challenges of cryptocurrencies be overcome? Will
cryptocurrencies be mainstream adoption?
As consumers are demanding seamless, secure, cheaper, and
personalized payment experiences, companies that can provide such
services will become successful in the future.
Nike: Losing Its Swoosh? - App Economy Insights [Link]
Nike’s facing challenges of 1) shifting consumer preferences to newer
brands (On and Hoka), 2) softer traffic and lower sales of classic
footwear franchises in direct-to-consumer channel, 3) macroeconomic
headwinds.
Nike Q4 2024 Highlights: 1) Nike is reducing supply of classic
footwear franchises to create space for newness and innovation, 2)
focusing on performance and innovation, 3) Jordan Brand is still growing
YoY.
Other observation: Nike brand value declined by 4% YoY, while Adidas
and Lululemon gain brand values.
Broadcom now operates across two primary segments: 1) semiconductor
solutions (chips for networking, server storage, broadband, wireless
communication, and industrial applications), and 2) infrastructure
software (a explosive leap with the acquisition of VMware).
Highlights: VMware acquisition brings significant revenue to
Broadcom; AI as a great growth driver; jumbo acquisition resulted in
gigantic net debt; strong cash generation; 10-for-1 stock split on July
15.
Future: Next-generation products include Tomahawk and Jericho; supply
chain disruption and inflation resulted from macroeconomic environment;
regulatory scrutiny into VMware acquisition.
Starbucks: A Brewing Crisis - App Economy Insights
[Link]
[LinkedIn]
Three main issues:
the boycott impact: losing 1.5M loyal customers, as a result of the
fact that in October 2023, Starbucks became embroiled in a controversy
related to the ongoing violence in the Middle East,
significant loss in traffic from non-Rewards members due to
additional reasons such as awareness of daily drink price, gourmet
coffee boom, health-conscious consumers, changing work habits, and
competitors with coffee offerings in lower prices. Solutions on this are
new initiatives such as physical and digital enhancements like updated
POS system, siren system speedup, and opening mobile orders beyond its
loyalty programs.
Price war in Chinese coffee market e.g. Luckin Coffee.
7 Mindsets That Are Slowing Down Your Career Growth - The
Caring Techie Newsletter [Link]
Solo Contributor Mindset -> Prioritize get thing done with
others
That’s not my job -> willing to do things outside of my
scope
My work will speak for itself -> do the work and say that I did
the work
If I do what I’m told, I will get promoted -> I need to sit in
the driver’s seat of my career growth
If I’m not getting any feedback, it means I’m doing good -> I
need to actively seek feedback
I’m not ready for the next level -> I might be ready for a
promotion despite my doubts
Picking the devil you know -> next promotion might come from
joining another company
Mark Zuckerberg and Peter Thiel - Internal Tech
Emails [Link]
Peter Thiel and Mark Zuckerberg on Facebook, Millennials, and
predictions for 2030.
Google owes its stable position as much to Generative AI’s slow
progress as its own innovations. While OpenAI, Anthropic, Meta, and
others have built more powerful AI models into their chatbots, people
haven’t substituted those bots for traditional search. As of February,
Bing still had less than 4% of search market share worldwide compared to
Google’s 91%. ChatGPT, for context, debuted nearly two years
ago.
This week, when OpenAI introduced its own search engine, called
SearchGPT, it didn’t exactly strike fear in the halls of Mountain
View.
― Surprisingly, Google Is Thriving In The GenAI Era - Big
Technology [Link]
Netflix: Ad Tech Focus - App Economy Insights [Link]
Tesla’s revenue comes from three main sources 1) automative (78%
revenue), 2) services and other (12% revenue), 3) energy generation and
storage (10% revenue).
Production and Deliveries are the two main metrics.
Tesla’s margins have historically been ahead of other car
manufacturers thanks to three critical leverages: 1) Economies of scale
(though gigafactories), 2) Direct-to-consumer (online and via its
showrooms), 3) Low marketing costs (Tesla barely spends on
advertising).
Highlights:
Tesla missed earnings expectations for the fourth consecutive
quarter.
Elon Musk pushed the Robotaxi announcement from August 8 to October
10.
Profits fell for the second straight quarter, driven by slower
demand, competition, and price cuts. Price cut remain a double-edged
sword.
Operating margin declined by 3% YoY and was at its lowest in years.
Negative impacts are from 1) price cuts, 2) delivery decline, 3) AI
projects, 4) restructuring costs. Positive impacts are from 1) lower
cost per vehicle, production ramp of 4680 cells, higher regulatory
credits, and non-auto segments.
Energy generation and storage doubled.
Future:
Humanoid Robots (Optimus): Tesla will begin producing humanoid
robots for internal use next year and plans to sell to other companies
in 2026.
Market Share and BYD: Tesla outsold BYD in Q2 2024, but the gap
between the two companies was only 18K deliveries. Tesla had a 50%
market share in BEV sales in the US, with 164K deliveries in Q2. As
expected, the market share of BEVs has consistently declined, reflecting
the continued adoption of all-electric cars.
More than 1.5 million developers are now using Gemini across our
developer tools.
Waymo’s served more than 2 million trips to-date and driven more
than 20 million fully autonomous miles on public roads. Waymo’s now
delivering well over 50,000 weekly paid public rides, primarily in San
Francisco and Phoenix.
Our AI-driven profit optimization tools have been expanded to
performance max and standard shopping campaigns. Advertisers use profit
optimization and smart bidding see a 15% uplift in profit on average
compared to revenue-only bidding.
Soon we’ll actually start testing Search and Shopping ads in AI
overviews for users in the US, and they will have the opportunity to
actually appear within the overview in a section clearly labeled as
sponsored.
― Google: AI Spending Spree - App Economy Insights
[Link]
Highlights of Q2 FY24: 1) Revenue growth slowed down, 2) search
advertising showed no slowdown, 3) YouTube Ads growth slower than Q1, 4)
subscriptions decelerated from Q1 due to YouTubeTV increased its price
in Q2 FY23, 5) cloud accelerated, 6) margin improved YoY but are about
to compress due to AI investments etc, 7) Capex were up and expected to
continue being up, 8) Alphabet committed \(\$5\) B to the ongoing operations of Waymo,
9) The company returned \(\$18.2\) B to
shareholders, including \(\$15.7\) B in
buybacks, showing their confidence in stock value.
Highlights of Cookies, Cloud, and YouTube: 1) planned to phase out
third-party cookies from Chrome to address privacy concerns regarding
tracking but reversed its decide to let users choose their tracking
preferences, 2) AI boost continues to accelerate cloud revenue growth
especially in GCP and Workspace, 3) YouTube gains market share, 9.9% in
Jun, up from 9.2% in prior year.
Future: 1) Project Astra, 2) SearchGPT competition: Search is
critical for Alphabet because it contributes 57% revenue. SearchGPT
could shake up the market but challenges are ensuring accuracy and
avoiding hallucination. OpenAI doesn’t have either user engagement or ad
performance which are required by a successful search business.
American Express had that network because of its legacy
traveler’s check business so it was able to leverage that network to
create and establish its credit card business. Without such a network,
it’s impossible to operate a closed loop system.
― I Am Buying American: American Express - Capitalist
Letters [Link]
Why American Express is superior than Visa and Mastercard? It’s
business model.
Visa is a typical payment processor. It connects the merchant to the
issuer bank. It’s an open loop. American Express, on the other hand, is
a closed loop system which makes it a money printing machine. It uses
two strategies: 1) set stricter standards to issue cards, 2) provides
travel privileges to attract frequent travelers who have higher net
worth.
Why good investment: 1) Giant moat due to closed loop system, 2)
inflation proof: customer base are those with stronger purchasing power,
3) it’s expanding internationally and among younger people: in 2023, 60%
of new consumer accounts were Gen-Z and Millennial, international
businesses billed for card services grew 14% YoY last quarter,
accounting for 35% overall growth.
How Github grows and makes money - Productify by
Bandan [Link]
Github’s culture values: 1) Customer-obsessed, 2) Ship to learn 3)
Growth mindset, 4) Own the outcome, 5) Better together, 6) Diverse and
inclusive.
How does Github make money: 1) Al powered tools - Github CoPilot, 2)
Subscription Plans, 3) Enterprise solutions, 4) Marketplace and
additional services - Github Marketplace, Github Actions, Github
Packages.
Revenue Breakdown: Major contributors are Github CoPilot and
Enterprise solutions, Steady contributors are subscription plans,
growing segments are marketplace and additional services.
Github’s product and engineering culture: 1) Open source, 2) Remote
first prioneers - pull requests, 3) Octocat obsession, 4) Continuous
learning and growth, 5) Al integration - Github Copilot, 6) Hackathons
and innovation time, 7) Inclusive design.
Key Takeaways from Github’s growth strategy: 1) Unwavering
developer-centric focus and positioning, 2) Building relevant products
for its user problems, 3) strong cultural values and community
engagement
No Rules Rules - The secret sauce of Netflix - Tech
Books [Link]
How to win at Enterprise AI - A playbook - Platforms, AI, and
the Economics of BigTech [Link]
YouTube and Podcasts
Hot Swap growing, donors revolt, President Kamala? SCOTUS
breakdown: Immunity, Chevron, Censorship - All-in Podcast [Link]
they’re probably two of the key things that I would look at to
determine are we looking at a a true luxury business and then you can go
into all um uh kinds of detail um but I think they would be the two ones
I’d look at um in terms of the experience from the customer point of
view I think it’s also important to remember that there needs to be a
social (06:37) element um to the product or service for it to be a
luxury in in the in a commercial sense and in a sort of financial um you
know investor sense um and so the idea there is if you look at many
Artisans or makers of high quality bespoke Goods um you know that they
may very well be high quality product but there’s no social Dimension
right so there’s no element of showing off if you will to use a slightly
sort of negative connotation and so therefore I think in a in in this
for the purposes of our discussion The Artisans and the and the (07:14)
sort of small independent bespoke makers would not be considered luxury
businesses right um so there would probably be two or three things I
would look at clay to figure out if I’m looking at a true luxury
business um and then finally the the fact that these businesses and the
market overall tends to be driven more by the offering than the demand
side so in some sense these companies create you know their own Market
they create their own Demand by offering things to the consumer that the
consumer may not realize they they need or desire or even um or or dream
of right so there there’s a number of unique characteristics to these
businesses which I think you know make them very interesting to study
sometimes it’s difficult to determine if you’re looking at a true luxury
business or not and sometimes Within These large groups take an lbmh for
instance you know some of their offering for some of their brands take
Cosmetics or perfumes some of that offering I probably wouldn’t classify
as a luxury business right but there are still part (08:16) of um the
group and and they generate some revenues at group level um and then you
have some parts of the business say say the LV or Dior Brands where it’s
and especially leather goods and apparel where it’s much easier to say
that this is a this operates as a true luxury business so you know you
can attempt to draw these distinctions but I think sometimes the lines
are blurred。
The Luxury Strategy | Why LVMH & Hermès have Outperformed
the Market w/ Christian Billinger (TIP643) [Link]
Simple Diffusion Language Models (15min video) [Link]
You cannot spend this kind of money and show no incremental
revenue potential. So while this is incredible for NVIDIA, the chicken
is coming home to roast, because if you do not start seeing revenue flow
to the bottom line of these companies that are spending 26 B dollars a
quarter, the market cap of NVIDIA is not what the market cap of NVIDIA
should be, and all of these other companies are going toe get punished
for spending this kind of money. Where are all these new fangled things
that we are supposed to see that justifies a hundred billion dollar of
chip spend a year, two hundreds billion dollars of energy spend, a
hundred billion dollars of all this other stuff, we are now spending 750
billion dollars. This is on the order of a national transfer payment,
and we’ve seen nothing to show for it except that you can mimic
somebody’s voice. It doesn’t all hang together yet. - Chamath
Palihapitiya
There’s gaps in the quality of the products that can be created
to not have hallucination. Those gaps are too large right now for them
to be used reliably in production settings unless you have a very
defined scope. If you have a defined scope though, the implementation
costs are not nearly what needs the level of spend to support. So there
is just a big mismatch. Second is that we have a huge problem with
NVIDIA, which is you can’t spend this kind of money to have tech lock-in
to one hardware vendor, and that makes no sense. And what you are seeing
now is that Amazon Google Microsoft AMD Intel, a plethora of startups
Grok, everybody trying to make now different hardware solution. The
problem though is that we have this massive lock-in right now, because
the code is littered with all these NVIDIA specific ways of implementing
access to GPUs, that’s not sustainable. So we are in an existential
thrash and I think the only way that we are going to get around this is
to do a little bit of a reset. And I think that’s going to touch a lot
of startups that have already taken down way too much money at really
insane valuations. I think we are in a bit of a reckoning right now it’s
going to be complicated couple of quarters to at a minimum and probably
a complicated year to sort out who’s actually real. - Chamath
Palihapitiya
There is ton of capital that was raised during the covid bubble
era, and the ZIRP (Zero Interest Rate Policy) era, that needed a place
to go. And a lot of traditional business model, traditional in the
technology sense - SAS and a lot of biotech stuff, it became
uninvestable. Then there is a lot of money in the public markets that
was sitting on the sidelines, that was sitting in treasuries and so on.
So every dollar is looking for growth and there is a lot of dollars
still sitting around out there from the ZIRP era and the coming into
this kind of post ZIRP era, looking for a place to growth. And there is
very little growth as we talked about with the S&P 479 not being
very performative with respect to growth and revenue and having great
outlook for the next five years. So then when there is a glimmer of
upside there is a glimmer of opportunity, even if it’s just painting a
picture of a growth story, all the capital drives into it. And we’ve all
heard stories about these series a startups in AI, getting bit up to a
100M valuation. I’ve seen a couple of these where people have pitch me
things on like protein modeling AI startups, and it’s literally like two
guys from meta and openai that left and started this company, and they
raised 30 on a 12 per year or something, and it’s just two guys building
a model. That’s because that capital needs to find a place where it can
tell itself a growth story. So I think we are still dealing with the
capital hangover from ZIRP. And the fact there is an area to invest for
real growth that has allowed the AI bubble to grow as quickly as it has.
- David Friedberg
Now as Chamath points out we are kind of rationalizing that back
and I do think that there is going to be a reset. Now I’ll also say that
the Goldman report which I read and some of the other analyses that have
been done. I think there was some commentary or some analysis that hey
it costs me six times as much as having an analyst do this work. The
energy cost of the AI is still so high, the actual performance of the
model is not good. What that fails to write it’s right and wrong. It’s
right in the sense that yes it’ s more expensive today and ROI is not
there today. It’s wrong in that it ignores the performative model
improvements that we’re seeing in nearly any metric over the past couple
of months. Every few months as we know we see new models, new
improvements, new architectures, new ways to leveraging the chips to
actually drive a lover token cost, to drive lower energy cost per
answer, lower energy cost per run. Every metric that matters is
improving, so if you fast forward another 24 or 36 months, I do think
that there is a great reason to be optimistic that there is going to be
extraordinary ROI based on the infrastructure that’s being built. It’s a
question of are you going to get payback before the next cycle of
infrastructure needs to be made and everything comes back in. We saw
this during the dotcom boom where a lot of people built out data centers
and by the time they were able to actually able to make money on those
data centers, it was like hey all the new Telco equipment, all the new
servers needs to be put in, and everything got written off. So there is
a big capex kind of question mark here, but I do think that the
fundamental economics of AI will be proven over the next couple of
quarters. - David Friedberg
I’m much more bullish than you guys about this investment that’s
being made. Remember that when the internet got started in the 90s, it
was via dialup. I mean you literally had to have a modem and you would
dial up the internet and it was incredibly slow. Photo sharing didn’t
even work, so social networking wasn’t possible. And basically what
happened next was that the Telecom company spent a ton of money building
out broadband and people started upgrading to broadband. Then we had the
Doom crash everyone thought that telecom companies had wasted billions
of dollars investing in all this Broadband infrastructure. And it turned
out that no they were right to do that, it just took a while for that to
get used and this is a pretty common pattern with technological
revolutions is that you can have a bubble in the short term but then it
gets justified in the mid to long term. The build out of the railroads
in the United States another example of this we had huge railroad
bubbles but it turned out that that investment was all worthwhile. -
David Sacks
― Biden chaos, Soft landing secured? AI sentiment turns
bearish, French elections - All-in Podcast [Link]
Project 2025: The Radical Conservative Plan to Reshape
America Under Trump | WSJ [Link]
Trump assassination attempt, Secret Service failure, Inside
the RNC, VC liquidity problem - All-in Podcast [Link]
Trump’s VP pick JD Vance SPEAKS at 2024 RNC (FULL SPEECH) -
NBC Chicago [Link]
You have to put one foot in front of the other every day, and you
have to focus on tangible progress. And where that fails is when most
people and I do it a lot and I’ve tried to get better as I’ve gotten
older, is when I get comparative and I compare myself to the other
person, the other company, the other funding round, there’s so many
reasons for you to feel like you’re less than something else. And the
reality is that has nothing to do with you, you’re not in control of
that, but it’s so hard. And then if I don’t take that medicine, I become
insecure, and then I make mistakes that are entirely avoidable. So it’s
just tangible progress the things that I can control. That’s probably
the most useful piece of advice that I try to remind myself of every
day. - Chamath Palihapitiya
― The Besties Take Napa | All-In Special - All-in
Podcast [Link]
Sharing good insights about AI, David’s amazing story with Poker,
some great career advice. And happy birthday to David Friedberg!
We talked a little bit about it with Jonathan height. There’s
some great studies that have shown in the past that the change in income
is a better predictor of happiness than absolute income. Eventually
everything normalizes so I think UBI makes no sense for three reasons.
The first is this normalization of spending level. So once you’ve kind
of had this increase, you have a moment of happiness, and then you
actually start spending differently or spending more. And effectively
every human has one innate trait desire. And desire is what drives
humanity. It’s what drives progress. It’s what pushes us forward because
no matter what our absolute condition, it’s our relative condition that
matters relative to others or relative to ourselves in the past or
perspectively in the future. And so we always want to improve our
condition. So a UBI based system basically gives a flat income so the
only way for it to really work is if you increase the income
automatically by say 10% a year. So in a UBI world, no amount of money
will actually make someone satisfied or meet their minimum thresholds
because those minimum thresholds will simply shift. And you know the
second issue is just the net economic effect if we gave 350 million
Americans 1000 bucks a month, that’s \(\$350\) billion a month, that’s \(\$4\) trillion a year. Our prospective
budget for next year is 7.3 trillion at the federal level, so you know
that’s already more than 50% of the total projected federal budget next
year finding the mechanism for funding this at scale is not what this
study actually looked at. Because if you look at it the net effect would
be inflationary. And that’s the third major reason is that ultimately
this would have an inflationary effect anytime. We’ve stimulated the
economy with outside money. With government-driven money, we see many
bubbles emerge and we see an inflationary effect. So look at covid,
there were all these little bubbles that popped up in the financial
markets, we had NFTs, we had crypto, we had all these sort of new places
that money found its way to and then we had an aggregate inflationary
effect food prices are still up 30 40% since covid. And so I think that
the study provides an interesting insight into the micro effect the
psychological effects, the social effect, but macro effects are what is
so like simply arithmetically obvious, which is inflation and an
inability to actually fund us at scale. And fundamentally people want to
work so they’ll take that money, and then they’ll go find ways to work
and generate more money, and you have this inflationary effect so I
think UBI does not make sense. - David Friedberg
That’s not UBI right and what you’re describing I think exist and
there are incentives and programs and opportunities out there people can
sign up with Roth IRAs they can contribute some percentage of their
paycheck to a 401k. If they have a job that has a 401k setup for them
there’s a lot of systems and mechanisms out there and you get tax breaks
for doing that. So there’s mechanisms and incentives out there to do
that sort of thing the concept with UBI is can you pay people a flat
amount of money so that they don’t have to work, and then they end up
being able to explore and do other things with their life as the robots
and AI does everything for them. And I’ve just always been of the belief
that I don’t think that there’s this natural border that we hit beyond
which humans don’t work. I think that AI based tools and automation
tools are the same as they’ve always been. When we developed a tractor
people didn’t stop farming. They could get much more leverage using the
tractor and farm more. And new jobs and new Industries emerged. And I
expect that the same thing will happen with this next evolution of
technology and human progress. Humans will find ways to create new
things to push themselves forward to drive things forward. And for the
natural market-based incentives that fundamentally are rooted in this
internal system of Desire will create new opportunities that we’re not
really thinking about so I don’t believe in this idea of UBI in some
utopian world where everyone’s happy not working and letting machines do
everything for them I think that the fundamental sense of a human is to
find purpose, and to realize that purpose to drive themselves forward
and progress themselves. And I think that that’s always going to be the
case. - David Friedberg
A year later, what Threads could learn from other social
networks - TechCrunch [Link]
Though Threads has reached 175 million monthly active users in its
first year and has made some progress such as integrating fediverse,
there are a lot of things need to be improved by learning from other
social medias.
Custom Feeds: Learning from Bluesky: Threads should implement
advanced custom feed features to allow users to easily follow specific
topics and events without relying solely on tags.
Third-Party Apps: Learning from Mastodon and Bluesky: Meta should
consider opening up Threads to third-party developers to create diverse
client applications, allowing for a broader range of user experiences
and features.
Algorithm Improvement: Improving “For You” Feed: Threads needs to
refine its algorithm to ensure that users receive more relevant and
personalized content, avoiding random or irrelevant posts that can
detract from user experience.
Handling News and Political Content: Learning from X and
Mastodon: Threads should develop mechanisms to handle news and political
content more effectively, balancing visibility without suppressing
important information, and potentially integrating features like
context-providing notes or bylines.
Local Content Engagement: Learning from Instagram and Twitter:
Threads should enhance its focus on local content by developing
partnerships and features that cater to regional interests and events,
like live scores for popular sports in specific regions.
Separation from Instagram: Developing Independent Profiles:
Threads should work on allowing users to create and manage profiles
independent of Instagram accounts, offering more flexibility and
autonomy in account management.
If “product-market-fit” means that you’ve found the right kind of
product that the market wants… “Position-market-fit” means that you’ve
found the right combination of
product/brand/marketing/pricing/go-to-market/sales/etc in a given
domain.
― Product-market fit is not enough anymore. You need
position-market fit - Aakash Gupta on X [Link]
Product-market fit is about having the right product for the market,
while position-market fit is about effectively positioning that product
within the market to stand out and meet specific customer
expectations.
A discussion of discussions on AI bias - Dan Luu [Link]
How to build a valuable tech company - Jason Shen on
X [Link]
Jensen’s Mindmap about his secrets to building the mos tvaluable tech
company in the world
jensen-mindmap
As a general rule, don’t let your company start doing the next
thing until you’ve dominated the first thing. No great company I know of
started doing multiple things at once—they start with a lot of
conviction about one thing, and see it all the way through. You can do
far fewer things than you think. A very, very common cause of startup
death is doing too many of the wrong things. Prioritization is critical
and hard.
While great founders don’t do many big projects, they do whatever
they do very intensely. They get things done very quickly. They are
decisive, which is hard when you’re running a startup—you will get a lot
of conflicting advice, both because there are multiple ways to do things
and because there’s a lot of bad advice out there. Great founders listen
to all of the advice and then quickly make their own decisions.
Please note that this doesn’t mean doing everything
intensely—that’s impossible. You have to pick the right things. As Paul
Buchheit says, find ways to get 90% of the value with 10% of the effort.
The market doesn’t care how hard you work—it only cares if you do the
right things.
Fire quickly. Everyone knows this in principle and no one does
it. But I feel I should say it anyway. Also, fire people who are toxic
to the culture no matter how good they are at what they do. Culture is
defined by who you hire, fire, and promote.
― Startup Playbook by Sam Altman - Sam Altman [Link]
The primary battleground was data and Al governance.
Snowflake fired the first shot by open-sourcing Polaris, its catalog
for Apache Iceberg, a popular open-source table format that’s compatible
any compute engine. Databricks countered by announcing its acquisition
of Tabular, a managed solution for Iceberg created by the project’s
founders, right in the middle of Snowflake’s conference. The tollowing
week, at their own summit, Databricks further upped the ante by
open-sourcing its Unity catalog in front of a live audience.
Data has gravity, so It’s far more efficient to bring applications
and services to data rather than vice versa.
Both Databricks and Snowflake are now vying to build the ultimate
enterprise AI platform: one capable of serving as the foundation for
this “small-but-mighty” vision of AI. Their shared goal is to become the
single source of truth for all of an organization’s data and use this
position to power intelligent applications across every business
function.
Databricks emerged from the open-source Apache Spark project and
initially focused on serving the needs of data scientists and ML
engineers. Its big data processing capabilities made it a natural fit
for AI and data science workloads. Snowflake, by contrast, built its
early success around a SQL-centric architecture and tight integration
with BI tools, catering to data analysts and traditional IT departments
with a closed, “it just works” solution.
― Databricks vs. Snowflake: What their rivalry reveals about
AI’s future - Foundation Capital [Link]
Databricks and Snowflake are fighting for the future of enterprise
Al. This article discussed four key concepts that shed light on the
competitive dynamics: data gravity, the convergence of analytics and Al,
the strategic importance of open source, and the rise of compound Al
systems.
How to Interview and Hire ML/ AI Engineers -
eugeneyan [Link]
Interviewing Meta CTO Andrew Bosworth on the Metaverse,
VR/AR, AI, Billion-Dollar Expenditures, and Investment Timelines -
MatthewBall.co [Link]
Spotify is no longer just a streaming app, it’s a social
network - TechCrunch [Link]
Gen AI: too much spend, too little benefit? - Goldman
Sachs [Link]
On July 19, 2024 at 04:09 UTC, as part of ongoing operations,
CrowdStrike released a sensor configuration update to Windows systems.
Sensor configuration updates are an ongoing part of the protection
mechanisms of the Falcon platform. This configuration update triggered a
logic error resulting in a system crash and blue screen (BSOD) on
impacted systems. The sensor configuration update that caused the system
crash was remediated on Friday, July 19, 2024 05:27 UTC. This issue is
not the result of or related to a cyberattack.
― Technical Details: Falcon Content Update for Windows
Hosts [Link]
In any massive failure there are a host of smaller errors that
compound; in this case, CrowdStrike created a faulty file, failed to
test it properly, and deployed it to its entire customer base in one
shot, instead of rolling it out in batches. Doing something different at
each one of these steps would have prevented the widespread failures
that are still roiling the world
The real issue, though, is more fundamental: erroneous
configuration files in userspace crash a program, but they don’t crash
the computer; CrowdStrike, though, doesn’t run in userspace: it runs in
kernel space, which means its bugs crash the entire computer — 8 million
of them, according
to Microsoft. Apple and Linux were not impacted, for a very obvious
reason: both have long since locked out 3rd-party software from kernel
space.
― Crashes and Competition - Ben Thompson on
Stratechery [Link]
The Munger Series - Learning from Benjamin Franklin -
Investment Master Class [Link]
How Benjamin Graham Survived World Panic on Wall Street (#17)
- Beyond Ben Graham [Link]
Introducing Llama 3.1: Our most capable models to date - Meta
AI Blog [Link]
Meta is releasing Llama 3.1 405B, the first frontier-level
open-source AI model. Along with Llama 3.1 70B and 8B models, they offer
superior cost / performance and are open for Fien-tuning and distilling.
And they are collaborating with companies such as Amazon, Databricks,
NVIDIA, Grow, etc, to support developers in fine-tuning and distilling
models.
Open Source AI Is the Path Forward - Meta News [Link]
In this letter, Zuckerberg emphasizes Meta’s commitment to open
source AI. Similar to Unix and Linux, Zuckerberg believes AI development
will eventually go to open source. Open source AI has several benefits:
1) it benefits developers in customization, control and security, cost
efficiency, and long-term standards, 2) it benefits Meta in avoiding
being locked into competitor’s ecosystems, allowing for freedom in
innovation and product development, enhancing its competitiveness, and
building a community of partnerships and developers, 3) it benefits the
world in providing wide spread access to AI benefits, ensuring safety
and security, and avoiding monopoly in AI power.
This study suggests the importance of optimizing both cost and
accuracy in benchmarking and evaluation of AI agents. Since the issues
of inadequate hold-out sets, absence of standardized evaluation
practices, etc, the authors also suggests a principled framework that
emphasizes the development of agents effective especially in practical
scenarios rather than on benchmarks.
Scaling Synthetic Data Creation with 1,000,000,000
Personas [Link]
This team generated 1B personas based on web info and stored them
into a Persona Hub. They introduced a synthetic data generation method
called ‘persona-driven data synthesis’. These personas can be
potentially used to 1) generate personalized content, 2) support LLM
prompting, 3) enhance product research, 4) create NPCs in games. The
compression perspective is more interesting and helpful for
understanding the approach: Persona Hub can be seen as the compressed
form of the world knowledge into distributed carriers. And the public
web text can be seen as the decompressed content created by these
personas with their knowledge and experiences.
This is a comprehensive survey on LLM MoE technique. MoE stands out
for enabling model scaling with minimal additional computation. This
survey as a systematic MoE literature review, covers MoE’s structure,
taxonomy, core designs, open-source resources, applications, and future
research directions.
An Extremely Opinionated Annotated List of My Favourite
Mechanistic Interpretability Papers v2 [Link]
Magic Insert: Style-Aware Drag-and-Drop - Google [Link]
FlashAttention-3: Fast and Accurate Attention with Asynchrony
and Low-precision [Link]
FlashAttention-3: achieves a 1.5-2x speedup and reaching up to 740
TFLOPS on FP16 and nearly 1.2 PFLOPS on FP8. This increases GPU
utilization to 75% of the theoretical maximum on H100 GPUs, up from
35%.
FlashAttention-3 introduces three main techniques to boost
performance:
Overlapping computation and data movement
Interleaving matrix multiplications (matmul)
Softmax operations, and using low-precision FP8
Mobility VLA: Multimodal Instruction Navigation with
Long-Context VLMs and Topological Graphs [Link]
Beyond Euclid: An Illustrated Guide to Modern Machine
Learning with Geometric, Topological, and Algebraic Structures
[Link]
AI achieves silver-medal standard solving International
Mathematical Olympiad problems - Google Research [Link]
This is one of the most surprising breakthrough in this AI and LLM
year. AlphaProof got a silver medal in IMO. It’s a neurosymbolic system
- a combination of Google’s Gemini LLM and DeepMind’s Alpha Zero, so it
uses LLM to generate plausible solutions and uses self-play style to
search for the right one. It opens a research direction of AI use cases,
which has been discussed about by many AI frontier experts and
companies, which is “scientific discovery”. Mastering math can be the
first step of expanding frontier of our knowledge. OpenAI’s Strawberry
project seems to have the same ambitions.
Gen AI: Too Much Spend, Too Little Benefit? - Goldman Sachs
Research Newsletter [Link]
As money’s flooded into GenAI projects, people started to question
whether or when the investment would net a return. Though the bubble may
or may not be bursting, a healthy discussion like this is worth a
read.
News
Prices fell in June for the first time since the start of the
pandemic - CNN [Link]
CPI dropped 0.1% from May. Odds of Fed cutting the rate are
increasing. Effect of high inflation is expected to be long lasting.
Here’s how far the Dow has fallen behind the S&P 500 so
far in 2024 - Morningstar [Link]
Tech Giants Face Tough Task to Sustain Second Half Stock
Rally - Bloomberg [Link]
Magnificent 7 stocks have accounted for majority of the S&P 500
growth this year. If this projection of AI optimism fails to
materialize, it could trigger a massive decline of the index.
Apple Poised to Get OpenAI Board Observer Role as Part of AI
Pact - Bloomberg [Link]
Microsoft, Apple Drop OpenAI Board Plans as Scrutiny Grows -
Bloomberg [Link]
This is Big Tech’s playbook for swallowing the AI industry -
The Verge [Link]
Amazon Hires Top Executives From AI Startup Adept for AGI
Team - Bloomberg [Link]
Big Tech companies are finding new ways to integrate AI startups into
their operations without triggering antitrust scrutiny - ‘reverse
acquihire’, an approach where actual acquisitions are masked by
employment and licensing agreements. This is highlighted by Microsoft
hiring inflection’s team and licensing of its AI tech, and Amazon hiring
roughly 2/3 of Adept’s personnel and securing a deal to license its AI
tech.
What happened to the artificial-intelligence revolution? -
The Economics [Link]
Silicon Valley companies are investing heavily in AI while the
revenue from AI products is still far from the projected figures.
Humanoid robots powered by AI turn heads at the World
Artificial Intelligence Conference - AP News [Link]
Record 300,000 visitors attend World AI Conference
[Link]
The World AI Conference and High-level Meeting on Global AI
Governance (WAIC) 2024 closed in Shanghai on Saturday, covering
investment plans, cooperation projects, city-level organizations, and
development plans for AI. Robotic tech such as humanoid models is
capturing the attention of attendees.
Fame, Feud and Fortune: Inside Billionaire Alexandr Wang’s
Relentless Rise in Silicon Valley - The Information [Link]
Robinhood snaps up Pluto to add AI tools to its investing app
- TechCrunch [Link]
The AI tool Pluto will allow Robinhood to add tools for quicker
identification of trends and investment opportunities, help guide users
with their investment strategies, and offer real-time portfolio
optimization.
“The algorithm is looking at traditional economic indicators that
you would normally look at. But then inside of our proprietary
algorithm, we’re ingesting the behavioral data and transaction data of
240 million Americans, which nobody else has,” said David Steinberg,
co-founder, chairman and CEO of Zeta Global.
The eight verticals the economic index uses include automotive
activity, dining and entertainment, financial services such as credit
line expansion, health care, retail sales, technology and
travel.
― A new index is using AI tools to measure U.S. economic
growth in a broader way - CNBC [Link]
The Zeta Economic Index uses Gen AI to analyze “trillions of
behavioral signals” to score growth of US economy.
OpenAI Hires Zapier Revenue Chief to Lead Sales Strategy -
The Information [Link]
OpenAI has recently hired new CFO and CPO to enhance its focus on
both consumer and enterprise products. It appointed Giancarlo Lionetti
(former CRO at Zapier, worked at Atlassian, Confluent, and Dropbox) to
lead its sales strategy in OpenAI’s sales team.
Tesla’s Share of U.S. Electric Car Market Falls Below 50% -
The New York Times [Link]
Tesla’s Upcoming Model Y, Project Juniper, Spotted with Front
Bumper Camera; Coming in 2025 [Link]
Persona’s founders are certain the world can use another
humanoid robot - TechCrunch [Link]
Thermonuclear Blasts and New Species: Inside Elon Musk’s Plan
to Colonize Mars - The New York Times [Link]
OpenAl says there are 5 ‘levels’ for AI to reach human
intelligence - it’s already almost at level 2 [Link]
The reason we decided to do the 100k H100 and next major system
internally was that our fundamental competitiveness depends on being
faster than any other AI company. This is the only way to catch up.
Oracle is a great company and there is another company that shows
promise also involved in that OpenAI GB200 cluster, but, when our fate
depends on being the fastest by far, we must have our own hands on the
steering wheel, rather than be a backseat driver. - Elon Musk @
X
― xAI Appears to Confirm Ended Talks With Oracle Over
Expanded AI Chips Agreement - WSJ [Link]
[X]
Elon’s business strategy - being completely vertical integrated, on
many of his companies (Tesla, SpaceX, etc) are working very well over
the years.
Venture capital firm A16z stashing GPUs, including Nvidia’s,
to win AI deals: report - Seeking Alpha [Link]
A16z has purchased thousands of GPUs including Nvidia’s H100, in an
effort to win deals for AI startups. They store those H100s and give
them to companies they invest in. It’s hard for startups to get vast
amounts of computing power. So this practice can make them more
competitive in these VC deals.
OpenAI and Los Alamos National Laboratory announce bioscience
research partnership - OpenAI [Link]
OpenAI and LANL are working together on evaluating how frontier
models like GPT-4o can assist humans in physical lab setting through
multimodal capabilities to support bioscience research.
In response to the fourth question in the investor call
transcript, Furukawa said the following (obtained via machine
translation and edited for clarity):
“In the game industry, AI-like technology has long been used to
control enemy character movements, so I believe that game development
and AI technology have always been closely related.
Generative AI, which has been a hot topic recently, can be more
creative [in its use], but I also recognize that it has issues with
intellectual property rights.
Our company has [had] the know-how to create optimal gaming
experiences for our customers for decades.
While we are flexible in responding to technological
developments, we would like to continue to deliver value that is unique
to us and cannot be created simply by technology alone.”
― Nintendo becomes the biggest company in the games industry
- and maybe the world - to say ‘no, thank you’ to using generative AI -
PC Gamer [Link]
Most gaming companies would like to incorporate AI in some sense but
Nintendo as the biggest company in the game industry said no thank you
to Gen AI. This sounds counter to what other game companying are aiming
for, but it’s also reasonable because Nintendo has built incredible IP
and they just want to be classic and they want everything to be their
own.
However, many people have imagined the future of video game would be
powered by AI with contents dynamically created for players in real
time.
Watch a robot navigate the Google DeepMind offices using
Gemini - TechCrunch [Link]
Google DeepMind Robotics developed a robot navigation system powered
by Gemini 1.5 Pro. It responds to human language commands, navigates the
office environment. It uses “Multimodal Instruction Navigation with
demonstration Tours (MINT)” to familiarize itself with the office and
hierarchical Vision-Language-Action (VLA) for understanding and
reasoning. The ability of recalling environment is boosted by 1M token
context length of Gemini 1.5 Pro
OpenAI tiers range from the kind of AI that can interact in
conversational language with people (lvl 1) to AI that can do the work
of an organization (lvl 5). The OpenAI executives believes that they are
at stage one and reaching towards the second tier. The third tier on the
way to AGI would be ‘Agents’ - AI systems which can spend several days
taking actions on a user’s behalf. Tier 4 would be the kind of AI that
can come up with innovations. And the tier 5 would be called
‘organization’.
Samsung’s Jam-Packed Galaxy Unpacked: Galaxy Ring, Z Fold 6
and All the New Products Announced [Link]
Among the 35 companies approved to test by the California DMV,
seven are wholly or partly China-based. Five of them drove on California
roads last year: WeRide,
Apollo, AutoX, Pony.ai, and DiDi Research America. Some Chinese
companies are approved to test in Arizona and Texas as well.
― Chinese self-driving cars have quietly traveled 1.8 million
miles on U.S. roads, collecting detailed data with cameras and lasers -
Fortune [Link]
Since 2017, self-driving cars owned by Chinese companies have
traverse 1.8M miles of California alone. They captured video of their
surroundings and map the state’s roads to within 2 cm of precision.
These information have been transferred to data centers and been used to
train their self-driving systems.
Evaluate prompts in the developer console - Anthropic
News [Link]
Anthropic releases some new features every week. Now they allow users
to generate, test, and evaluate prompts in the Anthropic Console.
Fine-tune Claude 3 Haiku in Amazon Bedrock -
Anthropic [Link]
Customers can now fine-tune Claude 3 Haiku in Amazon Bedrock to
customize model for vertical business usage.
Shooting at Trump Rally Comes at Volatile Time in American
History - The New York Times [Link]
This is crazy but legendary.
Insurers Pocketed $50 Billion From Medicare for Diseases No
Doctor Treated - The Wall Street Journal [Link]
UnitedHealth Group committed a $50 billion fraud over the three years
of 2019, 2020, and 2021. Though treating doctors say “no treatment or
minimal treatment necessary for this diagnosis”, UnitedHealth overrides
the docstors’ judgment, generates its own diagnosis code, bills medicare
with this new code.
Thousands of Windows machines are experiencing a Blue Screen of
Death (BSOD) issue at boot today, impacting banks, airlines, TV
broadcasters, supermarkets, and many more businesses worldwide. A faulty
update from cybersecurity provider CrowdStrike is knocking affected PCs
and servers offline, forcing them into a recovery boot loop so machines
can’t start properly. The issue is not being caused by Microsoft but by
third-party CrowdStrike software that’s widely used by many businesses
worldwide for managing the security of Windows PCs and servers.
― Major Windows BSOD issue hits banks, airlines, and TV
broadcasters - The Verge [Link]
That from Christopher Thornberg who heads a California-based
consulting firm called Beacon Economics. He says moving a main office
like this out of state would likely mean anywhere from dozens of lost
jobs to a couple hundred, not thousands of jobs lost.
Governor Newsom’s press office took to X after Musk made the
announcement comparing California to Texas saying, “The last time Elon
Musk moved an HQ, Tesla ended up expanding in California, even
relocating their Global Engineering and AI headquarters to California
because of diverse, world leading talent.”
― What Elon Musk’s Texas relocation plan for SpaceX, X HQs
could mean for CA - ABC7 News [Link]
