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Lastly, the crux of the tweet: "Optimistic contrarians are the rarest breed." This speaks to individuals who possess the courage to deviate from the mainstream not for the sake of contrariness, but driven by genuine belief in a different, often better vision. More importantly, they do this with optimism. Unlike cynics, optimistic contrarians see the potential in what others dismiss. They are hopeful about their divergent views, even in the face of criticism or skepticism. They combine the audacity to think differently with the belief that their path, though less traveled, is full of promise.

― Cynicism is easy. Mimicry is easy. Optimistic contrarians are the rarest breed. - Naval's Archive [Link]

“Status games are multiplayer, zero-sum, hierarchical, judged socially. Get grades, applause, titles now – emptiness later. Natural games are single player, positive-sum, internal, judged by nature/markets. Pay in pain now – get wealth, health, knowledge, peace, family later.” — Naval Ravikant

― Play Natural Games, Not Status Games - Naval's Archive [Link]

Your brain builds language pathways through repetition and active use. Every time you practice articulating an idea, explaining a concept, or searching for the precise word, you’re literally rewiring neural connections that make language retrieval easier (Bassett & Mattar, 2017).

― How to Become Well-Spoken - Noteswnat [Link]

One of the good habits I haven't had: "Watch a 10-minute interview clip. Then, try explaining the same topic they discussed in your own words. Notice how much clearer your thinking becomes when you’ve seen it modeled well."

How to articulate yourself intelligently - Dan Koe [Link]

The three frameworks Dan Koe lays out are:

1. Beginner – The Micro Story

   This is meant for fast articulation in short-form writing or speech.

   Best for: quick clarity and attention

   A simple storytelling structure:

   • Problem – state a relatable problem
   • Amplify – show the negative consequences if it’s not solved
   • Solution – present the insight or fix

2. Intermediate – The Pyramid Principle

   This works especially well for podcasts, presentations, and longer explanations.

   Best for: structured thinking and credibility

   A logic-first communication framework:

   • Start with the main idea or conclusion
   • Support it with 3–5 key arguments
   • Back those up with evidence, examples, or data

3. Advanced – Cross-Domain Synthesis

   This is best suited for newsletters, essays, talks, or long-form content.

   Best for: originality and thought leadership

   A deeper, more original structure:

   • Problem + amplify (clear, relatable setup)
   • Cross-domain synthesis (borrow concepts from other fields to explain the idea)
   • Unique process or solution (your own framework or steps)

The difference between average and great is taste.

The future belongs to those who can filter signal from noise. When anyone can produce anything, choosing what deserves to exist becomes the skill.

The ability to learn any skill fast, however, will.

Devon Eriksen talks about the “liberating arts,” the skills that free people have always needed to act on their own behalf:

• Logic: deriving truth from known facts
• Statistics: understanding the implications of data
• Rhetoric: persuading, and spotting persuasion tactics
• Research: gathering information on unknown subjects
• Psychology: discerning the true motives of yourself and others
• Investment: managing and growing assets
• Agency: deciding what to pursue and acting without permission

― Why the next 2 years will matter more than the last 10 - Dan Koe [Link]

If your thoughts are average, they can be compressed.

If your ideas are derivative, they can be predicted.

If your personality is shaped by trends, you can be simulated — and eventually replaced.

The more you become like everyone else, the more you disappear.

To be incompressible is to break the pattern. To do something that hasn’t been done. To think what hasn’t been thought. To speak in a voice that’s unmistakably yours.

It means cultivating taste that isn’t algorithmic.

It means being boring to the AI and fascinating to the human.

Most of all, incompressibility requires solitude. Silence. Reflection.

Space from the noise so you can remember who you are when no one is watching.

― Be Incompressible - Naval's Archive [Link]

Naval’s line, “If you don’t commit to meaningful work, life will fill your time with busywork,” is a reminder of a quiet truth: your time will never stay empty. Something will always come in. The choice is whether it’s chosen by you, or assigned to you by circumstance, employers, algorithms, or the thousand tiny demands that chip away at your attention.

Meaningful work, on the other hand, rarely arrives disguised as something urgent. It doesn’t ping, buzz, or demand anything. It whispers. It waits. It requires intention. It asks you to choose it repeatedly, often against comfort, against convenience, against the easy path. It’s the book unwritten, the skill unmastered, the company unstarted, the craft unrefined. The things that make your life bigger but don’t shout for your attention.

― If You Don’t Commit to Meaningful Work, Life Will Fill Your Time with Busywork - Naval's Archive [Link]

How to fix your entire life in 1 day - Dan Koe [Link]

1. You aren't where you want to be because you aren't the person who would be there.

2. You aren't where you want to be because you don't want to be there.

3. You aren't where you want to be because you are afraid to be there.

4. The life you want lies within a specific level of mind.

5. Intelligence is the ability to get what you want out of life.

   Cybernetics illustrates the properties of intelligent systems.

   • To have a goal.
   • Act toward that goal.
   • Sense where you are.
   • Compare it to the goal.
   • And act again based on that feedback.

   To become more intelligent, you must:

   • Reject the known path
   • Dive into the unknown
   • Set new, higher goals to expand your mind
   • Embrace the chaos and allow for growth
   • Study the generalized principles of nature
   • Become a deep generalist

6. How to launch a completely new life in one day

   Three phases that people go through to successfully flip their identity:

   1. Dissonance – They feel like they don’t belong in their current life, and become sufficiently fed up with their lack of progress.
   2. Uncertainty – They don’t know what comes next, so they either experiment or get lost and feel worse.
   3. Discovery – They discover what they want to pursue and make 6 years of progress in 6 months.

   Questions make you aware of the pain in your current life:

   1. What is the dull and persistent dissatisfaction you've learned to live with? Not the deep suffering but what you've learned to tolerate. (if you don't hate it, you will tolerate it)
   2. What do you complain about repeatedly but never actually change? Write down the three complaints you've voiced most often in the past year.
   3. For each complaint: what would someone who watched your behavior (not your words) conclude that you actually want?
   4. What truth about your current life would be unbearable to admit to someone you deeply respect?

   Questions to come up with your anti vision - a brutal awareness of the life you dod not want to live:

   1. If absolutely nothing changes for the next five years, describe an average Tuesday. Where do you wake up? What does your body feel like? What’s the first thing you think about? Who’s around you? What do you do between 9am and 6pm? How do you feel at 10pm?
   2. Now do it but for ten years. What have you missed? What opportunities closed? Who gave up on you? What do people say about you when you’re not in the room?
   3. You’re at the end of your life. You lived the safe version. You never broke the pattern. What was the cost? What did you never let yourself feel, try, or become?
   4. Who in your life is already living the future you just described? Someone five, ten, twenty years ahead on the same trajectory? What do you feel when you think about becoming them?
   5. What identity would you have to give up to actually change? (”I am the type of person who...”) What would it cost you socially to no longer be that person?
   6. What is the most embarrassing reason you haven’t changed? The one that makes you sound weak, scared, or lazy rather than reasonable?
   7. If your current behavior is a form of self-protection, what exactly are you protecting? And what is that protection costing you?

   Questions to create a minimum viable vision:

   1. Forget practicality for a minute. If you could snap your fingers and be living a different life in three years, not what’s realistic, what you actually want? What does an average Tuesday look like? Same level of detail as question 5.
   2. What would you have to believe about yourself for that life to feel natural rather than forced? Write the identity statement: “I am the type of person who...”
   3. What is one thing you would do this week if you were already that person?

   Random questions throughout the day:

   • 11:00am: What am I avoiding right now by doing what I’m doing?
   • 1:30pm: If someone filmed the last two hours, what would they conclude I want from my life?
   • 3:15pm: Am I moving toward the life I hate or the life I want?
   • 5:00pm: What’s the most important thing I’m pretending isn’t important?
   • 7:30pm: What did I do today out of identity protection rather than genuine desire? (Hint: it’s most things you do)
   • 9:00pm: When did I feel most alive today? When did I feel most dead?
   • What would change if I stopped needing people to see me as [the identity you wrote in question 10]?
   • Where in my life am I trading aliveness for safety?
   • What’s the smallest version of the person I want to become that I could be tomorrow?

   Questions to synthesize insights:

   1. After today, what feels most true about why you’ve been stuck?
   2. What is the actual enemy? Name it clearly. Not circumstances. Not other people. The internal pattern or belief that has been running the show.
   3. Write a single sentence that captures what you refuse to let your life become. This is your anti-vision compressed. It should make you feel something when you read it.
   4. Write a single sentence that captures what you’re building toward, knowing it will evolve. This is your vision MVP.

   To create goals:

   1. One-year lens: What would have to be true in one year for you to know you’ve broken the old pattern? One concrete thing.
   2. One-month lens: What would have to be true in one month for the one-year lens to remain possible?
   3. Daily lens: What are 2-3 actions you can timeblock tomorrow that the person you’re becoming would simply do?

7. Turn your life into a video game

   Six components that lead to a good life:

   • Anti-vision – What is the bane of my existence, or the life I never want to experience again?
   • Vision – What is the ideal life that I think I want and can improve as I work toward it?
   • 1 year goal – What will my life look like in 1 year time, and is that closer to the life I want?
   • 1 month project – What do I need to learn? What skills do I need to acquire? What can I build that will move me closer to the one year goal?
   • Daily levers – What are the priority, needle-moving tasks that bring my project closer to completion?
   • Constraints – What am I not willing to sacrifice to achieve my vision from the ground up?

2025 was the year the comfortable assumptions got stress-tested.

• “Just scale it”? DeepSeek proved architectural efficiency could match brute-force compute – and wiped a trillion dollars off the market.
• “Distribution is all you need”? Despite Microsoft’s insane lead start, OpenAI’s clear consumer and mindshare lead, and Google’s incredible distribution, and Anthropic still beat them all in the enterprise and coding wars.
• “America leads open source”? I count nine competitive Chinese model releases in 2025. Meta shipped two (and Behemoth is still MIA).
• “Bigger models = better models”? Mixture-of-experts, inference-time compute, and distillation ate that thesis alive.

What actually mattered

• Great products beat distribution
• Efficiency mattered more than scale
• Standards emerged
• Multimodal became baseline
• OpenAI Rewrote its own ruless

― 2025 Recap: The Year the Old Rules Broke - AI Supremacy [Link]

What a year.

Blogs and Articles

2025 LLM Year in Review - Andrej Karpathy [Link]

Fara-7B: An Efficient Agentic Model for Computer Use - Microsoft Research Blog [Link]

User gives task → agent acts → browser takes screenshot → model sees screenshot → next action

The Complete Guide to Nano Banana Pro: 10 Tips for Professional Asset Production - Google AI Studio [Link]

Frontier agents, Trainium chips, and Amazon Nova: key announcements from AWS re:Invent 2025 - Amazon News [Link]

Now available: Create AI agents to automate work with Google Workspace Studio - Google Blog [Link]

Introducing Anthropic Interviewer: What 1,250 professionals told us about working with AI - Anthropic [Link]

How People Use AI Agents - Perplexity [Link] [Paper]

Harvard-Perplexity study shows AI agents now shift towards more cognitive work tasks

Introducing Code Wiki: Accelerating your code understanding - Google Blog [Link]

Stanford AI Experts Predict What Will Happen in 2026 - HAI, Stanford University [Link]

Manus Joins Meta: Accelerating AI Innovation for Businesses - Meta [Link]

Payment Fragmentation Is Here to Stay, and Banks Must Adapt - Hussam Kamel, Finextra [Link]

The global payments industry is moving away from harmonisation and toward persistent fragmentation. This shift is structural—not temporary—and banks must fundamentally rethink their payment infrastructures to remain competitive. Fragmentation is structural and enduring. Banks that succeed will be those that embrace divergence, modernise their core payment architectures, and treat flexibility as a strategic advantage rather than a cost burden.

Google, Nvidia, and OpenAI - Ben Thompson, Stratechery [Link]

• Nvidia: structurally strong, but facing long-term margin pressure.
• OpenAI: has the best consumer position, but is undermining itself by avoiding ads.
• Google: the only company that can fight on all dimensions simultaneously — model quality, compute, monetization, and distribution.

The AI war is no longer about who innovates first — it’s about who can sustain dominance at scale.

Apple AI chief steps down following Siri setbacks - Emma Roth, The Verge [Link]

OpenAI CEO declares “code red” as Gemini gains 200 million users in 3 months [Link]

Stop talking about your impact. Start spotlighting theirs. - Jenny Wanger [Link]

The strongest professional reputation isn’t built by showcasing how much you do, but by amplifying how others succeed through what you enable.

The book recommendations mentioned in the article:

1. Wild Courage by Jenny Wood — Cited for advice on sharing wins and gratitude in self-promotion.
2. Give to Grow by Mo Bunell — Recommended as a good resource on the importance of generosity and recognition.
3. Give and Take by Adam Grant — Recommended for insight into how impact and recognition dynamics work in professional settings.

So What's Going to Happen to Product Management Anyway? - Peter Yang, Behind the Craft [Link]

How To Ask for Support from Senior Leaders - Yue Zhao [Link]

1) Mindset Shift: People often avoid asking for help because they see it as a sign of weakness. The author learned that asking for help is an essential leadership skill — especially when challenges exceed your control or expertise.

2) When to Ask for Help

You should consider asking for help when:

• The challenge is outside your area of expertise.
• It requires coordination across teams you don’t control.
• You need exceptions or decisions from leaders outside your chain of command.

The general rule: ask earlier rather than later — problems grow if unaddressed.

3) Types of Help to Ask For

The article breaks down effective asks into levels of involvement a leader might provide:

1. Be a Sounding Board — light request: listen and give perspective.
2. Give Air Cover — leader supports you publicly in meetings or when there’s pushback.
3. Be a Messenger — leader helps deliver messages you can’t reach as easily.
4. Fight With Me — leader actively advocates or argues on your behalf (highest effort).

The author suggests that asking for air cover is often the best default: It’s low-effort for the leader and lets you drive the work while they back you up.

Netflix and the Hollywood End Game - Ben Thompson, Stratechery [Link]

Netflix wins not because it makes the best shows, but because it is the best machine for turning content into sustained value—and now it wants to own the raw materials too.

Execution won’t stop. Strategy will unless you have a system: Jenny Wanger at INDUSTRY 2025 [Link]

• Execution isn’t the bottleneck; unclear strategy is.: Teams over-execute when strategy isn’t explicit, forcing constant validation, rework, and reactive prioritization.
• Lack of strategy clarity creates hidden time leaks.: Without a clear strategic “yes,” leaders and teams struggle to say no—resulting in fragmented effort and slow decision-making.
• An imperfect strategy is better than a silent one.: Strategy doesn’t need to be complete to be effective; even partial clarity reduces noise and accelerates alignment.
• Strategy must function as a system, not a document.: Value comes from embedding strategy into everyday decisions, tradeoffs, and communication—not from standalone artifacts.
• Perceived productivity can mask lack of progress.: Teams can ship continuously while failing to move the business forward if work isn’t anchored to a shared direction.
• Strategy is ultimately a leadership communication discipline.: Clear, consistently reinforced strategy enables faster decisions, greater autonomy, and compounding momentum.

Card fees creep onto restaurant tabs - Justin Bachman, paymentsdive [Link]

Restaurants are increasingly adding credit card surcharges or cash discounts to offset sharply rising card interchange fees. The shift reflects thin margins, declining traffic, and ongoing frustration with card networks, though adoption remains limited due to customer backlash and state regulations.

J.P. Morgan harnesses blockchain for debt issuance amid digital asset adoption boost - Pritam Biswas and Anirban Sen, Reuters [Link]

J.P. Morgan helped issue $50 million of short-term debt using blockchain technology instead of traditional systems. The debt was issued for Galaxy Digital on the Solana blockchain and bought by Coinbase and Franklin Templeton. Payments were handled using USDC, a digital dollar. This deal shows that big financial institutions are starting to seriously use blockchain

What’s going on here, with this human? - Graham Dunca [Link]

The Three-Part Framework for Seeing People Clearly:

1. Seeing Your Reflection in the Window (Self-Awareness)

   • You can’t see others clearly unless you see your own biases, projections, and triggers.

   • Interviews are co-created interactions; your tone, assumptions, and values shape how the other person shows up.

   • Personality frameworks (Myers–Briggs, Big Five, self-monitoring, etc.) are most useful when applied to yourself first.

   • Use multiple frameworks to avoid becoming trapped by a single lens.

   Key idea: Misjudgment often comes from mistaking your own internal reactions for objective insight.

2. Seeing the Elephants in the Room (Unconscious Drivers)

   • Borrowing from Jonathan Haidt: each person has a rider (conscious narrative) and an elephant (unconscious motivations). Interviews mostly capture the rider; references reveal the elephant.

   • Espoused beliefs ≠ actual behavior (“espoused theory” vs. “theory in use”).

   • High-quality reference checks are often 5–10x more valuable than interviews, especially from trusted, calibrated observers.

   • The best signal often comes from:

     • The tone of a reference
     • “Table-pounding” enthusiasm
     • The dog that doesn’t bark (what’s conspicuously missing)

​ Key idea: Humility about your limited perception is a prerequisite for accuracy.

3. Seeing the Water (Context and Ecosystem)

   • There is no such thing as an “A player” in the abstract—performance is context-dependent.

   • People thrive or fail based on subtle environmental factors: culture, incentives, belief loops, and feedback structures.

   • Moving someone to a new ecosystem is risky; strengths in one context can become weaknesses in another.

   • Belief from leaders and teammates can create powerful positive feedback loops.

   • Hiring should focus on fit between person and environment, not just raw talent.

​ Key idea: To understand someone, you must understand the system they came from—and the one you’re putting them into.

YouTube and Podcast

Tucker Carlson: Rise of Nick Fuentes, Paramount vs Netflix, Anti-AI Sentiment, Hottest Takes - All-In Podcast [Link]

Bernie Sanders: Stop All AI, China's EUV Breakthrough, Inflation Down, Golden Age in 2026? - All-In Podcast [Link]

Sacks, Andreessen & Horowitz: How America Wins the AI Race Against China - a16z [Link]

How AI Agents Will Transform in 2026 (a16z Big Ideas) - a16z [Link]

AI stops being something you ask, and becomes something that does.

• Interfaces: chat → action
• Design: human-first → agent-first
• Work: assistance → execution

Big Ideas:

• From prompts to action: AI interfaces are moving beyond chat boxes toward agents that proactively execute tasks and workflows on users’ behalf, acting more like autonomous employees than tools you query.
• From human-readable to agent-readable software: Software, content, and workflows will increasingly be designed to be machine-legible first, enabling agents to reliably navigate, interpret, and operate systems without brittle prompting.
• From demos to deployable voice agents: Voice AI is crossing a threshold from novelty to production-ready systems, becoming practical in domains like healthcare, finance, recruiting, and consumer wellness due to improved reliability and integration.

3 Industries That AI Will Revolutionize In 2026 (a16z Big Ideas) - a16z [Link]

Big Ideas:

• The Electro-Industrial Stack: AI is driving a rebuild of the physical economy—manufacturing, energy, logistics—into an integrated electro-industrial stack where software, hardware, power, and supply chains are tightly coupled, reshaping national competitiveness.
• A Turning Point in Financial Services: Financial services and insurance are reaching an inflection point where replacing legacy systems with AI-native, unified infrastructure unlocks parallel workflows, cleaner data, and structurally higher margins.
• The Dynamic Agent Layer: Static systems of record will be overtaken by a dynamic agent layer where AI agents actively execute work across systems, shifting value from data storage to autonomous coordination and decision-making.

How AI Will Transform Fintech In 2026 - A16z [Link]

Fintech is entering a new upcycle, but it will look very different from the 2020–21 boom. The next phase is driven less by growth-at-all-costs and more by AI-powered fundamentals—especially fraud prevention, underwriting, and infrastructure efficiency—where incumbents and well-positioned platforms (like Plaid) regain an edge.

I shrunk down into an M5 chip - Marques Brownlee [Link]

Incredible video to convey the true sense of scale.

All-In x Kill Tony: A Hilarious Holiday Special - All-In Podcast [Link]

WTF Is Wealth? Ray Dalio Breaks It Down w/ Nikhil Kamath | WTF is Finance Ep 2 - Nikhil Kamath [Link]

Ray Dalio is talking about how money works, why bubbles are inevitable, and how to think across cycles rather than chase narratives.

Takeaways - what Ray Dalio explicitly believes or argues in this episode:

1. Money ≠ Wealth

   Money is not real wealth; it is a claim on wealth. Wealth is real purchasing power backed by: 1) Goods, 2) Services, 3) Productivity. Printing money does not create wealth—it only redistributes claims on existing wealth.

2. Money Has Two Conflicting Functions

   Dalio defines money as: A medium of exchange; A store of wealth

   "When debt is high and governments intervene, money often fails at being a store of wealth." This conflict explains: 1) Inflation, 2) Currency debasement, 3) Asset bubbles

3. Debt Cycles Drive Everything

   Economies move in long-term debt cycles (50–100 years). Excessive debt forces governments into a corner: 1) Raise taxes, 2) Cut spending, 3) Print money. Historically, they always print.

4. The Gold Standard Ending Was a Regime Shift

   Nixon ending the gold standard (1971) permanently changed money. Since then: 1) Money is a policy tool, not a constraint. 2) Governments can promise more than they can actually deliver. This makes currency risk unavoidable.

5. Bubbles Are Created by Easy Money

   Bubbles form when:

   • Money creation outpaces real economic growth
   • People extrapolate recent gains indefinitely
   • Leverage increases faster than income

   Dalio sees bubbles as mechanical, not moral failures.

6. Leverage Is the Silent Killer

   Most people underestimate leverage risk. Leverage: 1) Magnifies gains, 2) Destroys you quickly when wrong

   Liquidity disappears precisely when you need it most.

7. Wealth Preservation > Wealth Maximization

   Dalio emphasizes:

   • Surviving bad regimes matters more than winning good ones.

   • Losing 50% requires a 100% gain to recover.

   • The biggest mistake investors make is over-concentration.

8. Diversification Is Not About Asset Count

   True diversification means:

   • Exposure to uncorrelated return drivers

   • Assets that respond differently to:

     • Growth

     • Inflation

     • Deflation

     • Political stress

   Holding many similar assets ≠ diversification.

9. If You Can’t Beat the Market, Don’t Try

   Most people should not trade or time markets. Dalio believes: 1) Humility beats confidence, 2) Process beats prediction

   Portfolio construction matters more than asset selection.

10. Productive Assets Are the Best Long-Term Store of Wealth

    Dalio prefers assets that:

    • Generate cash flows

    • Adapt to inflation

    • Represent real economic activity

    Examples: 1) Businesses, 2) Innovation, 3) Human capital

11. Gold Has a Role — But It’s Not Everything

    Gold is a hedge against: 1) Currency debasement, 2) Political disorder. But it does not produce income. It should be part of a portfolio, not the portfolio.

12. Crypto Is Unproven as a Long-Term Store of Wealth

    Dalio’s view is cautious, not dismissive: Crypto has some store-of-value characteristics, but: 1) No long multi-cycle history, 2) Regulatory and political uncertainty.

    He treats it as speculative diversification, not core wealth.

13. Real Estate Is Politically Vulnerable

    Real estate is easy to: 1) Tax, 2) Regulate, 3) Seize. Investors underestimate political risk in immovable assets.

14. Prediction Is Less Valuable Than Understanding Systems

    Dalio does not believe in point forecasts. He believes in:

    • Cause–effect relationships

    • Scenario thinking

    • Probabilities, not certainties

15. The Five Forces Drive History

    Dalio believes every country’s trajectory is shaped by:

    1. Debt & money
    2. Internal conflict
    3. External conflict
    4. Technology
    5. Acts of nature

​ Ignore any one of these and your analysis is incomplete.

16. Technology Is the Ultimate Long-Term Wealth Driver

    Innovation increases productivity. Productivity is the foundation of rising living standards. Countries that innovate absorb shocks better than those that don’t.

17. Psychology Determines Success More Than Intelligence

    Successful investors share:

    • Humility

    • Curiosity

    • Willingness to be wrong

    • Ability to learn from pain

​ Ego is the enemy of compounding.

18. Learning Comes From Proximity

    The fastest way to learn is to be near: 1) Great thinkers, 2) Great decision-makers.

19. Wealth Without Purpose Is Empty

    Dalio believes: 1) Legacy matters more than net worth, 2) Passing knowledge forward is the highest form of wealth, 3) Systems and ideas outlast money.

Sundar Pichai: Gemini 3, Vibe Coding and Google's Full Stack Strategy - Google for Developers [Link]

Adam Grant’s "Think Again" offers a refreshing alternative: the power of intellectual humility. While we often define intelligence as the ability to think and learn, Grant argues that in a rapidly changing world, a different cognitive skill matters just as much—the ability to rethink and unlearn.

Grant challenges us to abandon the mindsets of preachers, prosecutors, and politicians in favor of thinking like a "scientist." The core of the first section is the concept of confident humility—having faith in your capability while appreciating how much you have yet to learn. We must detach our opinions from our identity. By basing who we are on our values (e.g., generosity, freedom) rather than our specific beliefs, we can remain open to changing our minds without losing our sense of self.

How do we open other people's minds? Grant suggests that logic and data often fail because they trigger defensiveness. Instead, we should adopt techniques like motivational interviewing, which acts as a mirror to help people find their own motivation to change. Effective disagreement isn't about crushing the opponent; it's about signaling that we are reasonable and open to evolving our own views.

The final section tackles how we can foster a culture of rethinking in our communities. Grant warns against binary bias—the tendency to simplify complex issues into two opposing sides. Complexity and nuance are actually signals of credibility. When we showcase the "shades of gray" and admit uncertainty, we become more persuasive, not less.

This book is a transformative reminder that true wisdom lies not in the certainty of our convictions, but in the confident humility to constantly question what we don't know. And it is worth being picked up occasionally for self-reflection to ensure we remain open to the joy of being wrong.

Here are my favorite quotes from this book:

Individual Rethinking

Recognizing our shortcomings opens the door to doubt. As we question our current understanding, we become curious about what information we are missing. That search leads us to new discoveries, which in turn maintain our humility by reinforcing how much we still have to learn. If knowledge is power, knowing what we don't know is wisdom.

Great thinkers don't harbor doubts because they are impostors. They maintain doubts because they know we're all partially blind and they are committed to improving their sight. They don't boast about how much they know; they marvel at how little they understand. They are aware that each answer raises new questions, and the quest for knowledge is never finished. A mark of lifelong learners is recognizing that they can learn something from everyone they meet.

Arrogance leaves us blind to our weaknesses. Humility is a reflective lens: it helps us see them clearly. Confident humility is a corrective lens: it enables us to overcome those weaknesses.

Attachment. That's what keeps us from recognizing when our opinions are off the mark and rethinking them. To unlock the joy of being wrong, we need to detach. I've learned that two kinds of detachment are especially useful: detaching your present from your past and detaching your opinions from your identity.

Who you are should be a question of what you value, not what you believe. Values are our core principles in life - they might be excellence and generosity, freedom and fairness, or security and integrity. Basing your identity on these kinds of principles enables you to remain open-minded about the best ways to advance them.

The clearest sign of intellectual chemistry isn't agreeing with someone. It's enjoying your disagreements with them. Harmony is the pleasing arrangement of different tones, voices, or instruments, not the combination of identical sounds. Creative tension makes beautiful music.

Interpersonal Rethinking

Convincing other people to think again isn't just about making a good argument - it's about establishing that we have the right motives in doing so. When we concede that someone else has made a good point, we signal that we are scientists trying to get to the truth. "Arguments are often far more combative and adversarial than they need to be", Harish told me, "You should be willing to listen to what someone else is saying and give them a lot of credit for it. It makes you sound like a reasonable person who is taking everything into account."

Being reasonable literally means that we can be reasoned with, that we're open to evolving our views in light of logic and data.

When we gave them different kinds of reasons to donate, we triggered their awareness that someone was trying to persuade them - and they shielded themselves against it. A single line of argument feels like a conversation; multiple lines of argument can become an onslaught.

Taken together, these techniques increase the odds that during a disagreement, other people will abandon an overconfidence cycle and engage in a rethinking cycle. When we point out that there are areas where we agree and acknowledge that they have some valid points, we model confident humility and encourage them to follow suit. When we support our argument with a small number of cohesive, compelling reasons, we encourage them to start doubting their own opinion. And when we ask genuine questions, we leave them intrigued to learn more. We don't have to convince them that we're right - we just need to open their minds to the possibility that they might be wrong. Their natural curiosity might do the rest.

Outside the lab, dismantling stereotypes and decreasing prejudice rarely happen overnight; a key step is getting them to do some counterfactual thinking: helping them consider what they would believe if they were living in an alternative reality.

In psychology, counterfactual thinking involves imagining how the circumstances of our lives could have unfolded differently. When we realize how easily we could have held different stereotypes, we might be more willing to update our views.

Psychologists find that many of our beliefs are cultural truisms: widely shared, but rarely questioned. If we take a closer look at them, we often discover that they rest on shaky foundations. Stereotypes don't have the structural integrity of a carefully built ship. They're more like a tower in the game of Jenga - teetering on a small number of blocks, with some key supports missing. To knock it over, sometimes all we need to do is give it a poke. The hope is that people will rise to the occasion and build new beliefs on a stronger foundation.

They developed the core principles of a practice called motivational interviewing. The central premise is that we can rarely motivate someone else to change. We're better off helping them find their own motivation to change.

Motivational interviewing starts with an attitude of humility and curiosity. We don't know what might motivate someone else to change, but we're genuinely eager to find out. The goal isn't to tell people what to do; it's to help them break out of overconfidence cycles and see new possibilities. Our role is to hold up a mirror so they can see themselves more clearly, and then empower them to examine their beliefs and behaviors. That can activate a rethinking cycle, in which people approach their own views more scientifically. They develop more humility about their knowledge, doubt in their convictions, and curiosity about alternative points of view.

Listening well is more than a matter of talking less. It's a set of skills in asking and responding. It starts with showing more interest in other people's interests rather than trying to judge their status or prove our own. We can all get better at asking "truly curious questions that don't have the hidden agenda of fixing, saving, advising, convincing or correcting", and helping to "facilitate the clear expression of another person's thoughts."

Collective Rethinking

Binary bias is a basic human tendency to seek clarity and closure by simplifying a complex continuum into two categories. To paraphrase the humorist Robert Benchley, there are two kinds of people: those who divide the world into two kinds of people, and those who don't.

An antidote to this proclivity is complexifying: showcasing the range of perspectives on a given topic. We might believe we're making progress by discussing hot-button issues as two sides of a coin, but people are actually more inclined to think again if we present these topics through the many lenses of a prism. To borrow a phrase from Walt Whitman, it takes a multitude of views to help people realize that they too contain multitudes.

A dose of complexity can disrupt overconfidence cycles and spur rethinking cycles. It gives us more humility about our knowledge and more doubts about our opinions, and it can make us curious enough to discover information we were lacking.

This thorny issue is a natural place to explore how we can bring more complexity into our conversations. Fundamentally, that involves drawing attention to the nuances that often get overlooked. It starts with seeking and spotlighting shades of gray.

When we are reading, listening, or watching, we can learn to recognize complexity as a signal of credibility.

Multiple experiments have shown that when experts express doubt, they become more persuasive. When someone knowledgeable admits uncertainty, it surprises people, and they end up paying more attention to the substance of the argument.

In a series of experiments, psychologists demonstrated that when news reports about science included caveats, they succeeded in capturing readers' interest and keeping their minds open.

New research reveals that people are more likely to promote diversity and inclusion when the message is more nuanced (and more accurate). Acknowledging complexity doesn't make speakers and writers less convincing; it makes them more credible. It doesn't lose viewers and readers; it maintains their engagement while stoking their curiosity.

What stands in the way of rethinking isn't the expression of emotion; it's a restricted range of emotion.

Substack

Harvard’s 30-Year Research Reveals: Why You Feel Overwhelmed, Exhausted, and Anxious — and How 25 Tiny Daily Habits Can Restore Inner Calm, Thomas Blake, Everyday Health [Link]

When you don’t feel like doing something, do it for ten minutes anyway.

― The 10-Minute Rule: How Small Windows Create Big Wins - Balanced Discipline [Link]

Humans exist to understand the universe. But we still don’t know what question we’re supposed to be asking.

The biggest opportunities of the 2030s will sit at intersections:

• AI + energy
• robotics + logistics
• satellites + internet
• AI + biology
• space + manufacturing

This is the philosophical layer behind his companies:

• xAI expands intelligence

• Neuralink expands consciousness

• SpaceX expands reach

• Tesla expands autonomy

― Elon Musk’s Most Important Interview in Years - Ruben Dominguez, The VC Corner [Link]

How To Remember Everything You Read - Polymath Investor [Link]

An active reading and retention framework

Your brain loves patterns. Comfort means staying in old circuits. But discomfort shocks the brain. It forces new neural pathways - that's neuroplasticity. Every uncomfortable action - cold showers, public speaking discipline- is literally rewiring your brain into a stronger version of you.

― how to be extremely disciplined - Bella Dane [Link]

How to Trick Your Brain into Doing Difficult Things - Dr. Dominic Ng [Link]

I like this one: Make it fun

• Listen to your favourite podcast during cardio
• Drink your nicest coffee while doing deep work
• Use your comfiest chair only for studying
• Light your favourite candle when writing
• Play specific music while cleaning

The 20-Minute Writing Exercise That Neuroscientists Say Can Solve Your Hardest Problems - Magdalena Ponurska [Link]

The science behind this writing exercise involves cognitive neuroscience and functions as attentional training. It works by simultaneously leveraging three mechanisms:

1. Activating the Prefrontal Cortex: Writing about solving a problem in vivid, present-tense detail activates the prefrontal cortex, which is the brain's planning and problem-solving center. Studies show that the brain treats this detailed written simulation of future scenarios as a form of experience, unlike abstract goal-setting.
2. Priming the Reticular Activating System (RAS): The writing exercise primes the RAS, which serves as the brain's filter for determining what you notice in your environment. By writing about being a person who "found money under rocks," the RAS starts flagging relevant opportunities or "solution-shaped things" that were previously overlooked.
3. Creating Implementation Intentions: When you create a detailed mental scenario of completing a task, you are creating specific "if-then" plans known as implementation intentions. Research indicates that this technique makes people two to three times more likely to follow through than those who simply set abstract goals.

Articulation has nothing to do with sounding smart, but with sounding authentic.

What makes someone dangerously articulate, is the willingness to think out loud without fear of making mistakes. To make your intellectual curiosity visible, and to embrace the possibility of not knowing everything while speaking aloud.

In an uncertain world, embracing uncertainty becomes the foundation of dangerously articulate thinking.

If you fear uncertainty in your life, you need to leverage uncertainty to overcome it.

You need to become obsessed in your own curiosity to become genuinely useful to others.

Reading feeds curiosity. Reading improves how well you ask questions. Reading fuels better synthesis through asking better questions. Reading makes the perspective you have to offer to the world more valuable because you can synthesize everything you have read into solutions that can help people.

― How to become dangerously articulate - Craig Perry [Link]

Four daily habits you must practice: 1) reading, 2) thinking out loud, 3) teaching yourself, 4) writing.

• Self-Promotion is selling your image. It demands praise. It is rooted in ego. (x)

• Authentic Visibility is sharing your expertise. It offers repeatable value. It is rooted in service.

• The Arrogance Trap (The Trophy): This focuses on the outcome. It states the win without showing the struggle. The reader sees a trophy and feels judged. (x)

• The Service Solution (The Map): This focuses on the journey. It shares the failures, the painful moments, and the simple frameworks that finally led to success. The reader sees a map and feels helped.

• The Audience of One Exercise: Picture the single, most valuable person you want to help (e.g., Sarah, Director of Product). Define them by: The Pain they struggle with, The Goal they aim for, and The Fear they are terrified of.

• The Translation Test: Always translate the what (the specific task you did) into the how (the repeatable rule anyone can use).

― How to Build a Personal Brand When You’re a Senior Professional Who Hates Self Promotion - William Meller, You Visible [Link]

"The Strategy of Service

The core idea of Part 1 is professional relief: you don’t have to promote yourself. The anxiety you feel is valid because self-promotion is rooted in ego, but true visibility is rooted in service. This is about adopting the Map perspective-sharing the process and the failures-to eliminate the fear of arrogance. To keep your content focused, define your Audience of One. Finally, remember the Translation Test: your expertise is locked inside your company’s context; always translate internal success into a Portable Principle the market can immediately use."

"The Architecture of Proof

Part 2 is the strategic realization that your LinkedIn profile is a passive, magnetic sales tool. Your Headline must be a 10-second service promise. The About section earns trust by showing a past failure (the scar) that led directly to your unique framework (the solution). The most important shift is in the Experience section-stop listing activity and start listing your professional legacy by detailing the mechanism you engineered and the value it created. Finally, the Featured section provides tangible proof of your competence, fulfilling the promise made in your headline."

“The Protocol of Consistency

The core challenge is that visibility requires consistency, but self-promotion is exhausting. Part 3 turns content creation into a quiet routine. Start with the Daily Capture Ritual to source your ideas from Friction Points and Instruction Moments-the problems you already solved. Batch your writing into a 60-minute Weekly Creation Block, always following the structure of Conflict, Lesson, Illustration, Conversation. Finally, implement the Generosity Loop-committing 5 minutes a day to provide high-value contributions in the comments of others-which is a low-effort way to maximize visibility through service.”

“The Language of Quiet Confidence

Part 4 focuses on refining your voice to ensure your words are precise and evidence-based. You must eliminate the language of demand, which erodes trust, and embrace the Language Test by reframing your message to that of a generous teacher. The greatest tool is the mechanism-naming the specific process or framework you built to prove that your knowledge is systematic and repeatable. Finally, use the word “We” to project confident leadership and credit the process.”

"The Quiet Metric System

Part 5 provides the ultimate relief: you can officially ignore the noisy scoreboards. True authority is not measured by vanity metrics (Likes, views) but by Authority Metrics-specifically, the quality of inbound opportunities and Direct Messages that reference one of your named mechanisms. To sustain this, enforce the Time-Box Rule for writing and embrace the 1/3 Rule to keep the focus on low-effort engagement over high-effort creation."

Foundations: My 1999 (and part of 2000), Michael Burry, Cassandra Unchained [Link]

Articles and Blogs

We Asked Roblox’s C.E.O. About Child Safety. It Got Tense. - The New York Times [Link]

How we built OWL, the new architecture behind our ChatGPT-based browser, Atlas - OpenAI [Link]

Exploring a space-based, scalable AI infrastructure system design - Google Research [Link]

Google is seriously exploring whether AI data centers in space, powered by near-limitless solar energy and connected via optical links, could one day scale machine learning beyond Earth’s physical and environmental constraints.

Thoughts by a non-economist on AI and economics - Boaz Barak, Windows on Theory [Link]

The real economic question is not how good AI is today, but whether its exponential improvement translates into an exponential reduction in human-only tasks—something history has never seen before.

AI progress and recommendations - OpenAI [Link]

Software 1.0 easily automates what you can specify.

Software 2.0 easily automates what you can verify.

― "Sharing an interesting recent conversation on AI's impact on the economy. " - Andrej Karpathy [Link]

Estimating AI productivity gains from Claude conversations - Anthropic [Link]

Current AI already delivers large task-level time savings. Even without future model improvements, widespread adoption could meaningfully boost productivity. However, real gains depend on adoption, integration, and reorganization. The largest historical productivity revolutions came from changing how work is organized, not just doing the same tasks faster.

This study provides a lower-bound, usage-grounded lens on AI’s economic impact—useful for tracking trends, not forecasting destiny.

JPMorgan Rolls Out Deposit Token JPM Coin in Digital Asset Push - Bloomberg [Link]

JPMorgan’s rollout of JPM Coin shows how big banks are using blockchain to modernize real-world payments—faster, always-on, and regulated—without embracing speculative crypto.

The move reflects a wider trend among large financial institutions to modernize payment infrastructure using blockchain while staying within regulated banking frameworks.

Ramp/Brex beat the Amex/Concur experience by bundling the corporate card with AI-powered software. Instead of pulling manual expense reports from one system and importing them to another. The expense report, rules, and controls they’re all embedded together, beautifully.

I think there’s three big lessons if you’re a bank

1. Software is the Product: The integrated software experience is the new competitive moat, not a "portal" bolted onto a legacy product.
2. Automation is the Standard: AI-driven, "zero-touch" workflows are the new customer expectation. The manual expense report is dead.
3. The All-in-One Platform Wins: Customers will always abandon a stack of siloed tools for a single, bundled platform that solves the entire workflow.

― The CFO Dashboard; Ramp, Brex or Mercury - 18 Months Later - Simon Taylor, Fintech Brainfood [Link]

This is a story about recognizing that financial services for growth companies are being re-architected into three different endgames, each optimized for a different definition of scale, control, and user value.

Across all three companies (Ramp, Brex, Mercury), the real trend is re-bundling:

• Software is the product, not a portal layered on top
• Automation and “zero-touch” workflows are now table stakes
• Bundled, end-to-end platforms beat stitched-together TradFi stacks
• UX and workflow ownership are the new moats

Traditional banks aren’t dead—but they are structurally behind.

Two signals stand out:

• JPM Coin launching on Base suggests banks are moving “open loop” on-chain
• Stablecoins are becoming real infrastructure, not just crypto-native tools

This could reshape cross-border payments, treasury management, and bank interoperability—potentially challenging SWIFT.

Google Maps releases new AI tools that let you create interactive projects - TechCrunch [Link]

Google Has Your Data. Gemini Barely Uses It. - Shlok Khemani [Link]

Google’s Gemini has access to unparalleled personal data, but it intentionally underuses it. Gemini’s memory system is carefully designed, transparent, and conservative—prioritizing safety, trust, and control over magical personalization. This restraint is elegant, but it may cost Google its biggest competitive advantage in personal AI.

Github

Tech Interview Handbook - yangshun [Link]

n8n workflows, zie619 [Link]

YouTube and Podcast

Alex Karp, CEO of Palantir: Exclusive Interview Inside PLTR Office - Sourcery with Molly O'shea [Link]

Most corporate and government leaders now believe AI software should work, and they seek out solutions when their own projects fail. The launch of AIP was an "artistic" decision made quickly (he launched it in the "darkness of night" pre-Easter to avoid resistance) based on the insight that LLMs would become commodity products and orchestration would be much more valuable. This change in customer belief has increased Palantir's authority, compressing sales cycles from five years versus nine months to five years versus two or three months.

The focus is on growing the U.S., enhancing the quality of the user experience (UX), and ensuring Palantir remains closest to the things that give America a strategic advantage for decades.

YouTube CEO Neal Mohan on AI, Censorship & the Future of Creators - All-In Podcast [Link]

YouTube CEO Neal Mohan is discussing the massive scale of the platform, the state of the creator economy, and emerging technological challenges. Mohan defended the long-standing 55/45 revenue split within the YouTube Partner Program, citing the billions paid out to creators and the strong return on investment (ROI) that high user engagement delivers to advertisers. He also highlighted the success of subscription products like YouTube Premium and YouTube TV, positioning the platform as the top streaming service in the U.S. The conversation addressed content regulation, confirming a pullback from controversial COVID-era censorship and emphasizing YouTube’s commitment to free expression despite the difficulty of managing diverse global laws and cultural nuances. Crucially, Mohan revealed that YouTube is adapting to the rise of synthetic media by developing new likeness detection tools—modeled after Content ID—and implementing transparency labels for AI-generated content to protect creator identities and address "AI slop."

Elon Musk: OpenAI Betrayal, His Future at Tesla, and the Next Big Thing — Grokipedia - All-In Podcast [Link]

Does OpenAI Need a Bailout? Mamdani Wins, Socialism Rising, Filibuster Nuclear Option - All-In Podcast [Link]

OpenAI CFO Would Support Federal Backstop for Chip Investments - WSJ Video [Link]

When you only know one field deeply, you see problems through that one lens. When you know many fields shallowly, you can't solve complex problems. But when you know one field deeply and have worked across many others, you can take a pattern from field A and apply it to solve a problem in field B.

Everytime you learn something new, immediately find 2-3 examples from completely different areas that use the same idea.

When you struggle and fail, your brain becomes super aware of what you don't know, it creates gaps in your knowledge that your brain wants to fill. When the teaching finally comes, your brain is actively looking for the missing pieces.

― how to actually become a polymath. - riskambition [Link]

How to articulate your thoughts more clearly than 99% of people - Matt Huang [Link]

What does it mean to be articulate?

To express (an idea or feeling) fluently and coherently

• Fluently: ease and grace

  • => delivery:
    • Decrease mental load (word choice, top-down communication)
    • Storytelling
    • Energy

• Coherently: clear and logical

  • => message content/structure:

    • Understanding the topic/issue

    • Knowing the objective

    • Fastest path to explain (less mental load)

      The best speakers are the ones who are able to express the idea or the thing they need from someone in 5-10 seconds or less, Any longer than that and you honestly don't understand the thing that you're trying to explain.

    • Anticipating key questions

They deliberately make other people win bigger than them, not equal, not balanced, bigger. And they do it first before asking for anything.

It triggers psychological debt.

In any interaction, ask what costs me almost nothing, but would be huge for them. Maybe it's connection, maybe it's knowledge you already have, maybe it's taking an annoying task they hate. Give that first, not after, not during, first.

The most successful people aren't doing more, they are doing less, but at level that nobody else can touch, because they are not distracted by good opportunities.

Here's what separates effective people from everyone else, they treat this time (prime time) like a medical emergency, no meetings during prime time, no email, no quick questions, no administrative garbage, this is when you do the one thing that actually moves the needle.

The most effective people are actively bad at most things on purpose. They are not well-rounded, they are sharp in one place and dull in everywhere else.

― how to easily become a highly effective person. - riskambition [Link]

Epstein Files Fallout, Nvidia Risks, Burry's Bad Bet, Google's Breakthrough, Tether's Boom - All-In Podcast [Link]

FULL: Elon Musk Makes Shocking Future Predictions At U.S.-Saudi Arabia Forum Alongside Jensen Huang - Forbes Breaking News [Link]

We Asked Roblox's C.E.O. About Child Safety. It Got Tense. | EP 163 - Hard Fork [Link]

Roblox CEO is Delusional, penguinz0 [Link]

The Roblox CEO Dave Baszucki has been widely criticized after the safety interview on Hard Fork for

• his controversial push to introduce dating services and adult content onto a platform predominantly used by children, initially refusing to set the minimum age at 18.
• being repeatedly combative, defensive, and used aggressive interruptions to avoid legitimate questions.
• when asked about the platform's long-standing issue with predators on Roblox, Baszucki stated that he viewed the problem not merely as a serious issue but as an "opportunity" for safety innovation.
• unironically entertaining a question about implementing educational kid gambling on the platform, suggesting it would be a "brilliant idea" if structured legally.

OpenAI's Code Red, Sacks vs New York Times, New Poverty Line? - All-In Podcast [Link]

AI should either be a guardian angel or a cognitive amplifier.

― Satya Nadella – How Microsoft thinks about AGI, Dwarkesh Patel [Link]

The Thinking Game | Full documentary | Tribeca Film Festival official selection, Google DeepMind [Link]

Anthropic C.E.O.: Massive A.I. Spending Could Haunt Some Companies - The New York Times [Link]

Are Banks Secretly Winning the AI Race? (OpenAI Insider Explains) - Rex Salisbury [Link]

Articles and Blogs

Everything About Transformers - Krupa Dave [Link]

Lean Strategy Making, Standardizing your company’s approach can pay off. Here’s how. - Michael Mankins, Harvard Business Review [Link]

The essential approach to effectively implementing and sustaining a lean strategy involves three stages. By adopting this rigorous, standardized approach to strategy, leading companies are able to reduce waste, move faster, make wiser choices, and gain a competitive edge.

1. Setting Strategic Priorities: This initial stage focuses on defining the company’s direction and identifying the most critical issues to address.

   • Articulate Performance Ambition: Lean strategy begins with articulating or revising a multiyear performance ambition, which encompasses both financial goals (e.g., revenue, profit) and strategic goals (e.g., market-share growth, customer satisfaction). This ambition is aspirational—realistic yet beyond the reach of the current strategy—designed to motivate leaders to surface breakthrough ideas.

   • Compare to Multiyear Outlook (MYO): The ambition is compared against the multiyear outlook (MYO), which projects future performance based only on decisions and resource commitments already made. The MYO is not a plan or forecast; it captures the likely trajectory if current strategies remain unchanged and often depicts a deteriorating competitive position.

   • Identify the Gap and Strategic Backlog: There should be a sizable gap between the ambition and the MYO; if not, the ambition should be revised upward. This gap is closed by addressing issues on the strategic backlog, a document capturing the company’s highest-priority strategic, operational, organizational, and financial challenges.

   • Prioritize and Frame Issues: Issues on the backlog are prioritized based on value at stake (economic impact) and urgency/critical path. They must be described in careful detail and tied to one or more specific decisions that must be made to resolve the challenge.

   • Establish a Decision Calendar: The final step is to create a decision calendar, which outlines when each item on the backlog will be addressed, establishing a steady cadence or “drumbeat” of decision-making.

2. Tackling Priorities in an Ongoing Fashion: Once priorities are set, the organization engages in a continuous process of decision-making, which follows a standard, two-session process for each item on the backlog.

   • Facts and Alternatives Session: In this session, leadership works to fully understand the issue, identify its underlying causes, and develop a comprehensive set of viable options. It is critical to gather facts that reveal the true reasons for underperformance, avoiding the trap of only treating symptoms. Companies must consistently explore multiple strong alternatives and should avoid presenting false choices (like options too extreme or too weak). Successful companies standardize the criteria for assessing these alternatives.

   • Choices and Commitments Session: Here, leadership reviews the alternatives, uses agreed-upon criteria to select the best one, defines performance milestones, and identifies required resources. The outcome is a final decision that includes committing resources in exchange for expected performance improvements.

   • Document and Formalize: The best companies create an explicit decision log to capture the choices made, documenting the alternatives considered, the rejected options, and the rationale for the chosen path, thereby eliminating ambiguity. Strategic choices must drive resource allocation, often formalized through a written, two-way performance contract between the corporate center and business units/functions.

3. Monitoring Business Performance / Monitoring the Results: The final stage involves continuously assessing the organization's success and making necessary adjustments.

   • Regular Assessment: The success of meeting performance commitments, along with the center’s allocation of resources, is regularly assessed at business performance reviews.

   • Determine Need for Strategic Change: These reviews should not merely compare actual performance against the budget (like "weather reports"); instead, the true purpose is to determine whether the company needs to alter its strategic direction. Leaders must probe deeper into the reasons behind performance misses.

   • Revisit or Intensify Efforts: If market or competitive conditions have changed significantly, leadership may return an issue to the strategic backlog to gather new facts, explore new alternatives, and potentially make a different choice. If the facts have not changed, leadership may choose to intensify efforts in certain areas or scale back others to realign performance with goals.

   • Performance Dialogues: Companies like Amgen use performance dialogues at executive meetings to examine execution against commitments made on the strategic backlog, often utilizing metric-monitoring platforms to track both leading and lagging indicators.

   • Highlight Successes: Monitoring also involves exploring the causes of overperformance to identify successful practices that can be replicated.

What People Get Wrong About Psychological Safety, Six misconceptions that have led organizations astray. - Amy C. Edmondson and Michaela J.Kerrissey [Link]

The authors identify six common misconceptions that often hinder the effective implementation of psychological safety in organizations, leading organizations astray:

1. Psychological Safety Means Being Nice

   • The Problem: Safety and comfort are not synonyms; comfort is ease, while safety is being protected from danger. When people prioritize being "nice," they avoid honesty, which leads to ignorance and mediocrity, causing coordination, quality, and learning to suffer.

   • The Reality: Psychological safety is defined as a shared sense of permission for candor and the belief that it is acceptable to take interpersonal risks, such as asking questions or admitting mistakes. It is consistent with kindness, but kindness involves being respectful, caring, and honest, unlike "nice," which is often the easy way out of a difficult conversation.

2. Psychological Safety Means Getting Your Way

   • The Problem: An employee might complain that not having their idea supported made them feel psychologically unsafe. This misinterpretation implies that input must be agreed upon.

   • The Reality: Psychological safety is about ensuring that leaders and teams hear what people think; it does not force agreement. The ultimate goal is to reach a good decision or prevent a defect. Leaders should not tolerate problematic behaviors like bullying or disrespect, and they don't need to agree with every input they receive.

3. Psychological Safety Means Job Security

   • The Problem: This misconception equates psychological safety with freedom from layoffs.

   • The Reality: Psychological safety is defined as the freedom to be constructively candid. An employee who stood up to senior leaders and criticized the company regarding layoffs actually demonstrated that psychological safety existed, as they believed they could speak up without risking their career or generating negative reactions.

4. Psychological Safety Requires a Trade-Off with Performance

   • The Problem: This view is wrong, as psychological safety and accountability are distinct dimensions. Low levels of both dimensions clearly harm performance and morale.

   • The Reality: Superb performance in any uncertain environment requires a commitment to both high standards and psychological safety. Psychological safety is crucial because it enables learning by surfacing information and knowledge vital for competing in a changing world, which counters the tendency for people to hide information, save face, or fall into groupthink.

5. Psychological Safety Is a Policy

   • The Problem: Psychological safety cannot be mandated, similar to trust or motivation. Mandating it is unlikely to produce it; in fact, telling people they must have it "or else" may cause leaders to be kept in the dark.

   • The Reality: Psychological safety is not a quick fix or a policy; rather, it is built interaction by interaction within a group. Creating a climate of candor requires intention, effort, and developing skills through tools such as messaging, modeling, and mentoring by leaders.

6. Psychological Safety Requires a Top-Down Approach

   • The Problem: While leaders certainly matter, the misconception is that they are the sole drivers.

   • The Reality: Psychological safety is ultimately built by everyone at all levels of the company. It is "local," varying substantially across different groups within the same organization. Everyone influences the environment by showing interest in others' ideas, asking questions to draw others out, and responding productively rather than punitively. Focusing on your own team is an effective way to start building a motivated, high-performing environment.

The Power of Mattering at Work, Improving everyday interactions can promote employee retention, engagement, growth, and well-being - Zach Mercurio, Harvard Business Review [Link]

Here are the practical ways organizations and leaders can integrate mattering daily:

I. Seeing and Hearing Others: seeing people (acknowledging them and paying attention to their life details and work ebbs and flows) and hearing people (demonstrating real interest in their feelings and inviting their perspectives within a climate of psychological safety).

• Make Time and Space: Leaders should prioritize and plan relationship building. This involves scheduling regular meetings, avoiding cancellation of one-on-ones, and maximizing casual interactions, such as "watercooler conversations" or moments before meetings start. Employees who spend more time (over six hours a week) interacting with their leaders are 30% more engaged.
• Pay Deep Attention: Renew the intention to pay close attention to transform interactions from transactional to relational.
• Ask More Meaningful Questions: Instead of standard greetings like, "How are you?", ask questions that provide genuine insight into the people being led. Use questions that are:
  • Clear: Have an object and a time frame (e.g., “What has your attention today?”).
  • Open: Give people the opportunity to share experiences (e.g., “What was the most important insight you heard in the meeting?”).
  • Exploratory: Seek to understand rather than evaluate (e.g., “Which parts of today’s projects were most challenging for you and why?”).
• Listen for Total Meaning: Leaders should be alert to the "total meaning" of what people share, including their words, demeanor, facial expressions, and nonverbal cues, to understand their underlying feelings and attitudes.
  • Seek Clarification: Ask questions like, “Can you tell me more?” or “What do you mean when you say ‘fine’?”.
  • Paraphrase or “Loop”: Check understanding by repeating their message (e.g., “What I hear you saying is….Is that accurate?”).
  • Articulate Feelings: Ask questions like, “How did that make you feel?” and validate their emotions (e.g., “I can see that you’re feeling X”).
• Respond Compassionately: When learning about struggles, respond foremost with compassion, avoiding the tendency to normalize despair at work. Even small acts, like allowing an overloaded employee to skip a non-essential meeting, can significantly reduce stress and increase trust.
• Follow Up: Note what is learned and check back in on those details later, or make concrete changes to the business based on the feedback. One manager successfully increased engagement by writing down one detail about each team member weekly and scheduling a brief follow-up micro-check-in.

II. Affirming People and Showing Them They Are Needed

Once a leader truly sees and hears a person, they can affirm them meaningfully by showing how they make a singular impact. This involves giving evidence that they are valued, relied upon, and indispensable.

• Show People Their Unique Gifts: Affirm significance by considering their strengths (what they love and are good at), purpose, perspective, and wisdom. By naming and nurturing these unique gifts, leaders help employees see how they matter.
• Provide "Wise Feedback": When offering criticism or noting areas needing improvement, affirmation must precede it. People are more likely to improve when the leader believes in them, reminds them of their strengths, offers support, and establishes trust beforehand.
• Tell Stories of Significance: Share real, specific personal stories to remind employees of the downstream impact of their work, especially if they are far removed from the end user. Hearing even one such story can increase motivation by up to 400%. Organizations should establish a process for collecting and sharing these stories.
• Show Indispensability through Laddering: To help people feel needed, show how even their small tasks are crucial to a bigger goal or purpose. The "laddering" technique links the individual's input at the bottom to a meaningful ultimate outcome (like the organization's purpose or vision) at the top. Each rung shows how the individual's contribution is needed for the next tangible step, reaffirming their indispensability.
• Verbally Express Reliance: Tell team members how you rely on them, remind them how the organization needs them and their work, and even ask them for help. When people return from an absence, tell them they were missed.

III. Scaling the Mattering Skills Organizationally

To ensure that mattering becomes a cultural norm, senior leaders must scale these skills across the organization using a focused four-step approach.

1. Set the Right Intention and Increase Motivation: Leaders must implement mattering not as a tactic to achieve profits, engagement, or lower turnover, but because it fulfills the basic human need for dignity and the primal need to be seen, heard, and valued. To incite motivation, create an emotional anchor by having leaders answer and share when they most felt they mattered and what skills the person who fostered that feeling used.
2. Develop and Practice the Right Skills: Name the required skills (focused on noticing, affirming, and needing) and tailor them to the organization. This can be documented in a "leadership checklist" defining daily behaviors or a comprehensive guidebook, such as the "How People Matter Here" blueprint created by American Express Global Business Travel. Specific behaviors brainstormed at that company included proactive support when an employee discloses a struggle and describing the “why” before “what” and “how” when assigning tasks.
3. Measure Mattering: Implement measurement and accountability, as people often overestimate their efforts in this area.
   • Self-Assessment: Leaders should use a self-assessment, ideally quarterly in a group setting for peer coaching, to rate how often they display behaviors like remembering details of others' lives, naming unique gifts, and telling others they are relied upon.
   • Team Assessment: To get a more accurate picture, teams should rate their leaders on the same mattering behaviors.
4. Optimize the Environment: Organizations must stop making it difficult for leaders to cultivate mattering. To create a mattering culture, reward and promote leaders for how they make people feel, rather than solely for how much they get people to do. Incentivize and promote leaders whose assessments demonstrate that they dignify, include, respect, and affirm people while still performing well.

Animals vs Ghosts - Andrej Karpathy [Link]

Interesting analogy: current LLMs are "ghosts"—statistical, engineered intelligences deeply tied to human data—rather than "animals"—a hypothetical, purely emergent form of AGI. The key debate is whether this engineered "ghost" intelligence will eventually converge toward a more "animal-like" emergent intelligence, or if it will diverge, remaining a fundamentally different, yet powerful, kind of cognition.

Move Fast and Break Nothing - The Atlantic [Link]

Waymo spent 16 years collecting data before going mainstream. Its robotaxis have logged 96 million miles, achieving 91% fewer serious injury accidents than human drivers. It stands as a rare example of safety-focused AI and restraint in Silicon Valley. By contrast, Tesla’s Austin robotaxis crashed three times in just 7,000 miles, and Cruise infamously dragged a pedestrian 20 feet before GM shut the division down. Despite years of successful highway testing, Waymo still restricts its service to designated city zones.

YouTube Thinks AI Is Its Next Big Bang - Wired [Link]

YouTube plans to use AI to change how videos are made, giving creators tools like DeepMind’s Veo 3 to improve their videos. CEO Neal Mohan said AI could make it easier for more people to create content, even though it raises concerns about what’s real or not. Despite these worries, YouTube is moving forward with AI to stay a leader in video innovation.

Are You Really a Good Listener? - Jeffrey Yip, Colin M. Fisher, Harvard Business Review [Link]

Dos and Don'ts for Effective Workplace Listening

Category	Do	Don't
Pace & Focus (Avoiding Haste)	Set aside adequate, distraction-free time for conversations.	Respond too quickly or rush the conversation, as this makes people feel frustrated or unimportant.
	Focus your attention, demonstrate interest, and ensure you have understood the speaker.	Interrupt the speaker; your first job is to understand the message and intent.
	Ask clarifying questions to explore ambiguity and seek additional details.
Emotional Control (Avoiding Defensiveness)	Calm your own emotions and seek to understand the speaker’s intentions before responding.	React defensively or lash out when concerns or critical feedback are raised.
	Express empathy and avoid being judgmental.	Tell people not to ask questions or validate their worries.
	Buy yourself time before speaking by restating what you heard or thanking the speaker for sharing.
Engagement (Avoiding Invisibility)	Use body language (back channeling) to signal that you are listening, such as maintaining eye contact and adopting an open posture.	Fail to show that you are listening, which can make you appear indifferent or disconnected.
	Use verbal acknowledgments like “I see” or “That makes sense”.
	Reflect the speaker's ideas back by summarizing what you've heard to confirm understanding.
Sustainability (Avoiding Exhaustion)	Establish clear boundaries (e.g., blocking calendar hours or setting time limits on discussions).	Attempt to listen when you are physically or emotionally drained, as you lose the capacity to focus and engage productively.
	Acknowledge your personal limits; it is acceptable and beneficial to reschedule if you are feeling weary.	Become the sole "office therapist" whom everyone turns to for venting and advice.
	Share the listening load by asking colleagues or team members to check in with their peers.
Follow-Up (Avoiding Inaction)	Always close the loop by affirming what you heard, identifying next steps for action, and agreeing on a timeline for checking back in.	Receive the speaker’s message but then fail to follow up on it, which erodes trust.
	Be transparent about what you can or cannot act on, and provide explanations for any limitations (e.g., budget constraints or policy).

Unlocking Pay by Bank’s Potential - Alex Johnson [Link]

Pay by bank is any payment method that transfers funds directly between bank accounts, but the modern definition is a combination of electronic payment rails (ACH, RTP, FedNow) and convenient user experiences enabled by open banking.

Currently, few consumers use it (only 6.4% surveyed), but the biggest barrier is lack of awareness (56% of non-users hadn't heard of it). Once informed, 40% of consumers are interested or intrigued.

The underlying bank-to-bank payment rails are rapidly maturing, with significant growth in Same Day ACH, RTP, and FedNow, along with improved open banking infrastructure.

While lower payment processing costs (compared to credit cards) are a strong motivator, the bigger, more strategic reason for merchants is the ability to gather richer customer data.

Open banking-enabled pay by bank provides customer insights (e.g., historical bank transaction data) that enable:

• Cash Flow Smoothing (e.g., microloans or flexible payment dates).
• Personalized Offers (e.g., targeting a customer who used a competitor).
• Dynamic Risk Management (e.g., better authorization decisions based on future cash flow).

Challenges and Solutions:

• Consumer Adoption: Must be fixed through education, prominent UI placement, and compelling incentives/rewards that are tied to customer-valued behaviors (e.g., double fuel points, loyalty months).
• Merchant Integration: For complex merchants, a hybrid approach could be key—authorizing the bank payment using a virtual card through the existing card processor infrastructure, which makes the integration simpler.

Agentic payments memo - Kahlil Lalji [Link]

A detailed memo written to formalize thoughts on the emerging problem space of agentic payments—payments executed autonomously by AI agents rather than humans.

Today's payment infrastructure (cards, ACH, etc.) is built for slow, human-centric interactions. This breaks agent workflows, which need real-time, low-latency, and low-cost transactions. The goal is to build a new financial infrastructure—a natural language layer and protocol—that allows agents to transact directly and autonomously across any financial rail, starting with ACH.

The memo outlines several opportunities for companies building agentic payment infrastructure:

• Controllable Wallets: Providing agents with easy-to-manage wallets that can be funded Just-in-Time (JIT) to control risk and liability.
• Authorization Tools: Creating real-time (sub-3000ms latency) tools to approve transactions, check funds, and verify counterparty risk synchronously.
• Wedge Strategies: Using existing, manual workflows like Accounts Payable/Accounts Receivable (AP/AR) as a disruption point to introduce agent-driven automation and build the core infrastructure.

Prediction Markets: Understanding Their Impact and Future - OneSafe [Link]

Prediction markets are platforms (like Kalshi and Polymarket) where users trade contracts based on future event outcomes, utilizing collective foresight to predict events better than traditional methods. These markets generate real-time, crowd-sourced data that can be used by fintech companies for crucial functions like risk management and fraud detection in crypto payment systems. This data can also inform crypto treasury APIs for better asset management.

A major hurdle is the legal uncertainty, as prediction markets are often categorized between gambling and financial derivatives, complicating compliance with state and federal laws. Compliance with AML (Anti-Money Laundering) and KYC (Know Your Customer) regulations also poses a burden.

As prediction markets mature, integrating their real-time data into smart contracts could automate payment processes, enhance transparency, and reduce dependence on centralized authorities, ultimately reshaping value transfer in the digital economy.

Building the agentic future of recruiting: how we engineered LinkedIn’s Hiring Assistant - Xiaoyang Gu [Link]

Here is what the Hiring Assistant can do in the hiring process:

• Gathers and refines hiring requirements, including role details and specific qualifications for the job, inferring missing information when needed.
• Generates and runs multiple search queries against the talent network at scale, stores potential candidate profiles, and iteratively refines the search based on performance. It uses LinkedIn's Economic Graph to identify top locations, skills, and talent flows.
  
• Assesses candidates by synthesizing data from their profiles, resumes, and historical engagement. It applies the hiring requirements to produce structured recommendations, surfacing evidence to support its reasoning.
  
• Handles candidate communication, including generating and sending initial outreach and follow-up messages. It can also reply to candidate questions and schedule phone screens.
  
• Prepares tailored screening questions and can observe, transcribe, and summarize conversations, capturing insights and notes.
  
• Continuously refines the hiring requirements and candidate recommendations by analyzing recruiter actions (like adding candidates to pipelines or sending messages). It uses a cognitive memory to adapt to a recruiter's specific preferences and style over time.

Beyond release management: Feature flags for product discovery - Jenny Wanger [Link]

Three Techniques for Product Discovery - It outlines how product managers can use feature flags earlier in the product lifecycle to speed up learning

1. Painted Door: To validate market demand early by showing a button/link for a feature that doesn't fully exist yet, often leading to a survey.
2. Dogfooding: To get proof of value by rolling out a frugally built prototype to employees only for high-quality feedback.
3. Beta Testing with the Right Slice: To confirm functionality and usability by curating a small, targeted group of users (e.g., "Complainers" or low-bandwidth users) most likely to expose edge cases and friction.

OpenAI Looks to Replace the Drudgery of Junior Bankers' Workload - Omar El Chmouri, Bloomberg [Link]

We define a journey as the intersection of a user’s interests, intent, and context at a specific point in time. A user journey is a sequence of user-item interactions, often spanning multiple sessions, that centers on a particular interest and reveals a clear intent — such as exploring trends or making a purchase.

At a high level, we extract keywords from multiple sources and employ hierarchical clustering to generate keyword clusters; each cluster is a journey candidate. We then build specialized models for journey ranking, stage prediction, naming, and expansion. This inference pipeline runs on a streaming system, allowing us to run full inference if there’s algorithm change, or daily incremental inference for recent active users so the journeys respond quickly to a user’s most recent activities.

― Identify User Journeys at Pinterest - Pinterest Engineering [Link]

This is Pinterest's foundation for journey-aware recommendations under the mission of being an inspiration-to-realization platform. The solution is based on the constraint that training data is limited.

The Not-so Bitter Lesson - Marius Vach [Link]

"The Bitter Lesson" is an argument originally proposed by computer scientist Richard Sutton, but frames it as "The Not-so Bitter Lesson."

• Sutton's Core Argument (The Bitter Lesson) states that general methods that leverage search and compute will consistently outperform domain-specific solutions based on human knowledge or clever insights. The "bitter" part suggests that human-crafted domain expertise eventually gets crushed by "dumb brute-force search and compute."
• The Article's Key Reframing (The Not-so Bitter Lesson): The author argues that this lesson is not bitter for engineers; instead, it's a blueprint for better engineering. The human's job shifts from manually crafting solutions to building the infrastructure that exposes the search problem effectively.

This Is How Much Anthropic and Cursor Spend On Amazon Web Services - Edward Zitron [Link]

The article provides exclusive data on the Amazon Web Services (AWS) spending of Anthropic (the AI model provider) and Cursor (an AI coding company and Anthropic's largest customer). It shows that Anthropic's AWS spend alone for the entirety of 2024 was \(\$1.359\) billion against an estimated revenue of up to \(\$600\) million, meaning they spent at least 200% of their revenue on AWS.

It concludes that Anthropic's costs are "out of control" and its current cost of doing business is unsustainable, meaning prices for its services must increase dramatically for the company to ever become profitable.

We are in the "gentleman scientist" era of AI research - Sean Goedecke [Link]

Main points: Many impactful AI research ideas are not complex math breakthroughs, but older, simple concepts or tricks applied to LLMs for the first time (e.g., using Group-Relative Policy Optimization (GRPO)). The surprising success of LLMs is like a "rubber-band engine," creating a wealth of "easy scientific questions" that are accessible to hobbyists and non-experts. Simple, non-academic ideas like Anthropic's "skills" (scripts for the agent) are showing the value of amateur experimentation in rapidly discovering the unknown capabilities of new LLMs.

State of LLMs in Late 2025 - arcbjorn [Link]

AI landscape has shifted from a focus on a single dominant model to a hyper-specialized ecosystem. The key question is no longer "Which AI is smartest?" but "Which AI is the right tool for this job?"

OKRs for Measuring AI Adoption & Effectiveness - Tim Herbig [Link]

The Stablecoin Opportunity That Banks Are Missing - Simon Taylor [Link]

This opportunity is not about the stablecoin itself, but about leading the shift to tokenized, programmable finance.

Stablecoins are a low-cost, international payments rail that opens up opportunities for banks to:

• Be a partner bank for stablecoin issuers.
• Help customers with cross-border payments and treasury management.
• Become the primary "wallet" for corporates, collapsing multiple banking views into a single management center.
• Lead Onchain lending, which could become the next massive opportunity.

Customer Interview Analysis: Where AI Helps and Hurts - Teresa Torres, Product Talk [Link]

Salesforce announces Agentforce 360 as enterprise AI competition heats up - TechCrunch [Link]

The Robot in Your Kitchen - Billy Perrigo, Time [Link]

Figure AI is launching its Figure 03 model, which they hope will be the first mass-producible humanoid suitable for both industrial labor and domestic chores (e.g., emptying the dishwasher, making the bed).

The article highlights the huge risks, including safety (a falling or malfunctioning robot) and privacy (the collection of home data). Adcock is pushing for a rapid first-mover advantage to create a "natural monopoly" where more robots lead to more data, making the robot cheaper and smarter over time.

The arrival of mass-produced robots is predicted to cause a societal shock, potentially leading to widespread wealth creation through collapsing costs, but also creating the risk of mass unemployment and greater social inequality if not managed correctly (e.g., with a Universal Basic Income).

How fast can an LLM go? - Fergus Finn [Link]

The Smol Training Playbook: The Secrets to Building World-Class LLMs - HuggingFace [Link]

Papers and Reports

Reasoning with Sampling: Your Base Model is Smarter Than You Think - Harvard University [Link]

Harvard researchers introduce an iterative sampling method, enabling base LLMs to match RL reasoning benchmarks without retraining.

Global Banking Annual Review 2025: Why precision, not heft, defines the future of banking - McKinsey & Company [Link]

Stress-testing model specs reveals character differences among language models - Alignment Science Blog [Link]

The AI Application Spending Report: Where Startup Dollars Really Go - a16z [Link]

State of AI Report - Nathan Benaich [Link]

YouTube and Podcast

Bryan Johnson’s Best Health Hack Will Help You Sleep Better and Live Longer - All-In Podcast [Link]

1. eat final meal 4 hours before bed
2. turn off the screen 1 hour before bed
3. have amber and red lights in the house, no blue light
4. no caffeine within 6 hours before bed
5. wind down routine to calm down before bed: read a book, go for a walk, do breath work, meditate.

Trump Brokers Gaza Peace Deal, National Guard in Chicago, OpenAI/AMD, AI Roundtripping, Gold Rally - All-In Podcast [Link]

Biggest LBO Ever, SPAC 2.0, Open Source AI Models, State AI Regulation Frenzy - All-In Podcast [Link]

Multicoin Capital’s Kyle Samani on Internet Capital Markets - All-In Podcast [Link]

1929 vs 2025: Andrew Ross Sorkin on Crashes, Bubbles & Lessons Learned - All-In Podcast [Link]

1929: Inside the Greatest Crash in Wall Street History--and How It Shattered a Nation [Amazon]

Trump: Send National Guard to SF, China Rare Earths Trade War, AI's PR Crisis - All-In Podcast [Link]

Andrej Karpathy — “We’re summoning ghosts, not building animals” - Dwarkesh Patel [Link]

Karpathy’s perspective on the limits of reinforcement learning, why AGI progress will feel incremental, lessons from self-driving, LLM cognitive deficits, the evolution of intelligence, and the future of education.

Richard Sutton – Father of RL thinks LLMs are a dead end - Dwarkesh Patel [Link]

Elon Musk: 3 Years of X, OpenAI Lawsuit, Bill Gates, Grokipedia & The Future of Everything - All-In Podcast [Link]

Substack

TBM 384: Prioritization Starts With Strategic Prioritization - John Cutler [Link]

Only 100 Metrics Matter - Ghandra Narayanan [Link]

When your metrics start managing you. - Mike Watson [Link]

Thoughts on the AI buildout - Thoughts on the AI buildout [Link]

Is AI adoption slowing down? - Kyle Poyar, Growth Unhinged [Link]

From Data Points to Storylines - Amy Mitchell and Hodman Murad, Product Management IRL [Link]

Is AI a bubble? - Azeem Azhar and Nathan Warren, Exponential View [Link]

Why America Builds AI Girlfriends and China Makes AI Boyfriends - Zilan Qian [Link]

Import AI 431: Technological Optimism and Appropriate Fear - Jack Clark, Import AI [Link]

Being a leader requires 'followers' only, those who volunteer to go where you are going rather than being incentivized to, threatened to, or having to. And leadership requires a vision of the world that does not yet exist and the ability to communicate it. The former is the tangible result of what the world would like if we spent every day pursuing WHY, due to the power of WHY in inspiring action. The inspirational book 'Start with Why: How Great Leaders Inspire Everyone to Take Action' written by Simon Sinek explores this concept deeply, arguing that the most successful and inspiring leaders communicate from the inside out—starting with their 'Why' (purpose or belief), then 'How' (process), and finally 'What' (product or service). This is a very inspiring book to read, for any type of leaders who is pursuing profound fulfillment.

Below are the quotations I've selected from the book.

Manipulations are the norm, but the better alternative is inspiration.

Beyond the business world, manipulations are the norm in politics today as well. Just as manipulations can drive a sale but not create loyalty, so too can they help a candidate get elected, but they don't create a foundation for leadership. Leadership requires people to stick with you through thick and thin. Leadership is the ability to rally people not for a single event, but for years. In business, leadership means that customers will continue to support your company even when you slip up.

Manipulative techniques have become such a mainstay in American business today that it has become virtually impossible for some to kick the habit. Like any addiction, the drive is not to get sober, but to find the next fix faster and more frequently. And as good as the short-term highs may feel, they have a deleterious impact on the long-term health of an organization. Addicted to the short-term results, business today has largely become a series of quick fixes added on one after another after another.

Leaders who choose to inspire people rather than manipulate people follow the concept of 'The Golden Circle'.

The Golden Circle is an alternative perspective to existing assumptions about why some leaders and organizations have achieved such a disproportionate degree of influence.

This alternative perspective is not just useful for changing the world; there are practical applications for the ability to inspire, too. It can be used as a guide to vastly improve leadership, corporate culture, hiring, product development, sales, and marketing. It even explains loyalty and how to create enough momentum to turn an idea into a social movement.

Companies try to sell us WHAT they do, but we buy WHY they do it. This is what I mean when I say they communicate from the outside in; they lead with WHAT and HOW. When communicating from inside out, however, the WHY is offered as the reason to buy and the WHATs serve as the tangible proof of that belief. The things we can point to rationalize or explain the reasons we're drawn to one product, company or idea over another.

When the WHY is absent, imbalance is produced and manipulations thrive. And when manupulations thrive, uncertainty increases for buyers, instability increases for sellers and stress increases for all.

Biologically, the limbic brain drives behaviors (decisions). Great leaders win hearts before minds.

We are drawn to leaders and organizations that are good at communicating what they believe. Their ability to make us feel like we belong, to make us feel special, safe, and not alone is part of what gives them the ability to inspire us. Those whom we consider great leaders all have an ability to draw us close and to command our loyalty. And we feel a strong bond with those who are also drawn to the same leaders and organizations.

The newest area of the brain, our Homo Sapien brain, is the neocortex, which corresponds with the WHAT level. The neocortex is responsible for rational and analytical thought and language. The middle two sections comprise the limbic brain. The limbic brain is responsible for all of our feelings, such as trust and loyalty. It's also responsible for all human behavior and all our decision making, but it has no capacity for language.

When we communicate from the outside in, when we communicate WHAT we do first, yes, people can understand vast amounts of complicated information, like facts and features, but it does not drive behavior. But when we communicate from the inside out, we're talking directly to the part of the brain allows us to rationalize those decisions.

Our limbic brain is powerful, powerful enough to drive behavior that sometimes contradicts our rational and analytical understanding of a situation. We often trust our gut, even if the decision flies in the face of all the facts and figures. Richard Restak, a well-known neuroscientist, talks about this in his book, The Naked Brain. When you force people to make decisions with only the rational part of their brain, they almost invariably end up 'overthinking.' These rational decisions tend to take longer to make, says Restak, and can often be of lower quality. In contrast, decisions made with the limbic brain, gut decisions, tend to be faster, higher-quality decisions.

Our limbic brains are smart and often know the right thing to do. It is our inability to verbalize the reasons that may cause us to doubt ourselves or trust the empirical evidence when our gut tells us not to.

People don't buy WHAT you do, they buy WHY you do it. A failure to communicate WHY creates nothing but stress or doubt.

Those decisions started with WHY - the emotional component of the decision - and then the rational components allowed the buyer to verbalize or rationalize the reasons for their decision.

Great leaders are those who trust their gut. They are those who understand the art before the science. They win hearts before minds. They are the ones who start with WHY. "I can make a decision with 30 percent of the information, " said former Secretary of State Colin Powell. "Anything more than 80 percent is too much." There is always a level at which we trust ourselves or those around us to guide us, and don't always feel we need all the facts and figures.

Our hope, dreams, hearts, and guts drive us to try new things, not logic or facts.

If we were all rational, there would be no small businesses, there would be no exploration, there would be very little innovation and there would be no great leaders to inspire all those things. It is the undying belief in something bigger and better that drives that kind of behavior.

In reality, their purchase decision and their loyalty are deeply personal. They don't really care about Apple; it's all about them.

Products are not just symbols of what the company believes, they also serve as symbols of what the loyal buyers believe.

Products with a clear sense of WHY give people a way to tell the outside world who they are and what they believe.

Clarity of WHY, discipline of HOW, and Consistency of WHAT are all needed.

Ask the best salesmen what it takes to be a great salesman. They will always tell you that it helps when you really believe in the product you're selling... When salesmen actually believe in the thing they are selling, then the words that come out of their mouths are authentic. When belief enters the equation, passion exudes from the salesman. It is this authenticity that produces the relationships upon which all the best sales organizations are based. Relationships also build trust. And with trust comes loyalty. Absent a balanced Golden Circle means no authenticity, which means no strong relationships, which means no trust. And you're back at square one selling on price, service, quality or features. You are back to being like everyone else. Worse, without that authenticity, companies resort to manipulation: pricing, promotions, peer pressure, fear, take your pick. Effective? Of course, but only for the short term.

If they buy something that doesn't clearly embody their own sense of WHY, then those around them have little evidence to paint a clear and accurate picture of who they are. The human animal is a social animal. We're very good at sensing subtleties in behavior and judging people accordingly. We get good feelings and bad feelings about companies, just as we get good feelings and bad feelings about people. There are some people we just feel we can trust and others we just feel we can't.

Trust begins to emerge when we have a sense that the driver of behaviors is anything but self-gain.

Trust is not a checklist. Fulfilling all your responsibilities does not create trust. Trust is a feeling, not a rational experience. We trust some people and companies even when things go wrong, and we don't trust others even though everything might have gone exactly as it should have. A completed checklist does not guarantee trust. Trust begins to emerge when we have a sense that another person or organization is driven by things other than their own self-gain.

Those who lead are able to do so because those who follow trust that the decisions made at the top have the best interests of the group at heart. In turn, those who trust work hard because they feel like they are working for something bigger than themselves.

When people come to work with a higher sense of purpose, they find it easier to weather hard times or even to find opportunity in those hard times. People who come to work with a clear sense of WHY are less prone to giving up after a few failures because they understand the higher cause.

Finding the people who believe what you believe

We do better in cultures in which we are good fits. We do better in places that reflect our own values and beliefs. Just as the goal is not to do business with anyone who simply wants what you have, but to do business with people who believe what you believe, so too is it beneficial to live and work in a place where you will naturally thrive because your values and beliefs align with the values and beliefs of that culture.

When employees belong, they will guarantee your success. And they won't be working hard and looking for innovative solutions for you, they will be doing it for themselves.

As Herb Kelleher famously said, "you don't hire for skills, you hire for attitude. You can always teach skills."

The truth is, almost every person on the planet is passionate; we are not all passionate for the same things.

The goal is to hire those who are passionate for your WHY, your purpose, cause or belief, and who have the attitude that fits your culture.

Great companies don't hire skilled people and motivate them; they hire already motivated people and inspire them.

If those inside the organization are a good fit, the opportunity to "go the extra mile", to explore, to invent, to innovate, to advance, and more importantly, to do so again and again and again, increases dramatically. Only with mutual trust can an organization become great.

The Law of Diffussion

Our population is broken into five segments that fall across a bell curve: innovators, early adopters, early majority, late majority and laggards.

Early adopters are willing to pay a premium or suffer some level of inconvenience to own a product or espouse an idea that feels right. Their willingness to suffer an inconvenience or pay a premium had less to do with how great the product was and more to do with their own sense of who they are. They wanted to be the first.

The farther right you go on the curve, the more you will encounter the clients and customers who may need what you have, but don't necessarily believe what you believe. As clients, they are the ones for whom, no matter how hard you work, it's never enough. Everything usually boils down to price with them. They are rarely loyal. They rarely give referrals and sometimes you may even wonder out loud why you still do business with them, "They just don't get it," our gut tells us. The importance of identifying this group is so that you can avoid doing business with them.

There is an irony to mass-market success, as it turns out. It's near impossible to achieve if you point your marketing and resources to the middle of the bell, if you attempt to woo those who represent the middle of the curve without first appealing to the early adopters. It can be done, but at a massive expense. This is because the early majority, according to Rogers, will not try something until someone else has tried it first. The early majority, indeed the entire majority, needs the recommendation of someone else who has already sampled the product or service.

That's what a manipulation is. They may buy, but they won't be loyal. Don't forget, loyalty is when people are willing to suffer some inconvenience or pay a premium to do business with you. They may even turn down a better offer from someone else - something the late majority rarely does.

Get enough people on the left side of the curve on your side and they encourage the rest to follow.

Energy excites. Charisma inspires.

Charisma has nothing to do with energy; it comes from a clarity of WHY. It comes from absolute conviction in an ideal bigger than oneself. Energy, in contrast, comes from a good night's sleep or lots of caffeine. Energy can excite. But only charisma can inspire. Charisma commands loyalty. Energy does not.

Golden Circle matches an organization

Sitting at the top of the system, representing the WHY, is a leader; in the case of a company, that's usually the CEO. The next level down, the HOW level, typically includes the senior executives who are inspired by the leader's vision and know HOW to bring it to life. Don't forget that a WHY is just a belief, HOWs are the actions we take to realize that belief and WHATs are the results of those actions. No matter how charismatic or inspiring the leader is, if there are not people in the organization inspired to bring that vision to reality, to build an infrastructure with systems and processes, then at best, inefficiency reigns, and at worst, failure results.

WHY-types are focused on the things most people can't see, like the future. HOW-types are focused on things most people can see and tend to be better at building structures and processes and getting things done.

Most people in the world are HOW-types. Most people are quite functional in the real world and can do their jobs and do very well. Some may be very successful and even make millions of dollars, but they will never build billion-dollar businesses or change the world. HOW-types don't need WHY-types to do well. Buy WHY-guys, for all their vision and imagination, often get the short end of the stick. Without someone inspired by their vision and the knowledge to make it a reality, most WHY-types end up as starving visionaries, people with all the answers but never accomplishing much themselves.

When a company is small, it revolves around the personality of the founder. There is no debate that the founder's personality is the personality of the company. As a company grows, the CEO's job is to personify the WHY. To ooze of it. To talk about it. To preach it. To be a symbol of what the company believes.

We all know when a company's WHY goes fuzzy. Split can happen.

For Wal-Mart, WHAT they do and HOW they are doing it hasn't changed. And it has nothing to do with Wal-Mart being a 'corporation'; they were one of those before the love started to decline. What has changed is that their WHY went fuzzy. And we all know it. A company once so loved is simply not as loved anymore. The negative feelings we have for the company are real, but the part of the brain that is able to explain why we feel so negatively toward them has trouble explaining what changed. So we rationalize and point to the most tangible things we can see - size and money. If we, as outsiders, have lost clarity of Wal-Mart's WHY, it's a good sign that the WHY has gone fuzzy inside the company also. If it's not clear on the inside, it will never be clear on the outside. What is clear is that the Wal-Mart of today is not the Wal-Mart that Sam Walton built.

It's too easy to say that all they care about is their bottom line. All companies are in business to make money, but being successful at it is not the reason why things change so drastically. That only points to a symptom. Without understanding the reason it happened in the first place, the pattern will repeat for every other company that makes it big. It is not destiny or some mystical business cycle that transforms successful companies into impersonal Goliaths. It's people.

For most of us, somewhere in the journey, we forget WHY we set out on the journey in the first place. Somewhere in the course of all those achievements, an inevitable split happens.

Those with an ability to never lose sight of WHY, no matter how little or how much they achieve, can inspire us. Those with the ability to never lose sight of WHY and also achieve the milestones that keep everyone focused in the right direction are the great leaders.

As this metric grows, any company can become a 'leading' company. But it is the ability to inspire, to maintain clarity of WHY, that gives only a few people and organizations the ability to lead. The moment at which the clarity of WHY starts to go fuzzy is the split. At this point, organizations may be loud, but they are no longer clear.

The challenge isn't to cling to the leader, it's to find effective ways to keep the founding vision alive forever.

For an organization to continue to inspire and lead beyond the lifetime of its founder, the founder's WHY. must be extracted and integrated into the culture of the company. What's more, a strong succession plan should aim to find next generation. Future leaders and employees alike must be inspired by something bigger than the force of personality of the founder and must see beyond profit and shareholder value alone.

The WHY originates from looking back

Before it can gain any power or achieve any impact, an arrow must be pulled backward, 180 degrees away from the target. And that's also where a WHY derives its power. The WHY does not come from looking ahead at what you want to achieve and figuring out an appropriate strategy to get there. It is not born out of any market research. It does not come from extensive interviews with customers or even employees. It comes from looking in the completely opposite direction from where you are now. Finding WHY is a process of discovery, not invention.

Substack

How to Handle Visionary Leaders Without Losing the Team - Amy Mitchell, Product Management IRL [Link]

Microsoft announced AI credits for Copilot in Microsoft 365 in January. Salesforce added a new flexible, credit-based model for their AI agent in May. Cursor shifted to credit-based pricing in June (and faced some real pushback from users). Not to be outdone, OpenAI recently replaced seat licenses with a pooled credit model for its Enterprise plans.

― Why everyone’s switching to AI credits - Kyle Poyar, Growth Unhinged [Link]

Companies are transitioning to credit-based pricing models, particularly for AI services, for several key reasons related to managing costs, maximizing profitability, accommodating evolving AI technology, and establishing market standards.

• The shift to credit-based models is largely driven by challenges related to AI operational expenses and usage patterns.
• Companies are using credits as a mechanism to transition from flat-rate pricing toward models based on the value delivered.
• The move by major technology companies validates and standardizes the credit model for AI consumption.
• Credit models offer flexibility for both vendors and users.

1. Focus on what you can do. End on an affirmative.
2. Cite trade-offs.
3. Get more info to make an informed decision.
4. Add “because” to share your rationale.
5. Give the benefit of the doubt.

― Why "'no' is a complete sentence" is dangerous advice - Wes Kao's Newsletter [Link]

How to make your writing C.R.I.S.P. - Dan Hock's Essays [Link]

How To Expand Your Influence Skills - Yue Zhao, The Uncommon Executive [Link]

Shaping the opinions of others, or building influence, is about more than just data and logic. It's about understanding and managing emotions.

Handling your own emotions: Notice and reflect on what is driving your actions, such as fear, and then name it. This helps you move forward with clarity and confidence.

Leading others through their emotions: When you want to get buy-in for your ideas, help people process their emotions. You can do this by creating a space that welcomes emotions, validating their concerns, and then shifting their focus to what they can do to move forward.

The Hidden Rulebook of Corporate Politics (and How to Use It to Your Advantage) - Gaurav Jain, The Good Boss [Link]

I have to review this article regularly.

The moment you stop believing in the corporate fiction is the moment you can start using it. Once you see it as infrastructure rather than identity, as a resource rather than a calling, everything shifts.

Your corporate role doesn't need to be meaningful. It needs to be useful. Useful for building skills, for funding your real projects, for buying time while you figure out what matters to you.

The death of the corporate role isn't a crisis. It's freedom from having to pretend your spreadsheet about spreadsheets is your life's work.

― The death of the corporate job - Alex Mccann, Still Wandering [Link]

Good piece.

Articles and Blogs

President Trump, Tech Leaders Unite to Power American AI Dominance - The White House [Link]

The August jobs report has economists alarmed. Here are their 3 top takeaways. - CBS News [Link]

The August jobs report is raising concerns among economists due to several alarming trends. Employers added only 22,000 nonfarm jobs, which is significantly lower than the 80,000 jobs that analysts had forecast. The unemployment rate also rose to 4.3%, the highest level since October 2021.

The three top takeaways are

1. The job market is stalling
2. Job growth is at its lowest level in 15 years
3. The federal reserve will likely cut interest rates

The Recession is Already Happening for Many Americans - Bloomberg [Link]

Read the text messages between Charlie Kirk accused and roommate - BBC [Link]

U.S. Investors, Trump Close In on TikTok Deal With China - Raffaele Huang, Lingling Wei, Alex Leary, The Wall Street Journal [Link]

The near-finalized framework of a deal between the U.S. and China concerning the popular social media application TikTok, involves creating a new U.S. entity to manage the app’s American operations, with an investor consortium, including Oracle, taking a roughly 80% controlling stake, which satisfies a recent U.S. law regarding foreign ownership. A key component of the agreement is the establishment of American control over user data and the crucial content-recommendation algorithms, although they will be based on technology licensed from TikTok's Chinese parent company, ByteDance. Furthermore, the article notes that President Trump has delayed the TikTok ban until December as negotiations conclude, signaling the resolution of a multi-year national security dispute over the app's influence in the U.S. Both Chinese and American officials have reached a basic consensus on the terms, which also include Oracle managing U.S. user data at its facilities in Texas.

Google brings Gemini in Chrome to US users, unveils agentic browsing capabilities, and more - TechCrunch [Link]

NotebookLM - Jason Spielman [Link]

Tesla Dojo: The rise and fall of Elon Musk’s AI supercomputer - TechCrunch [Link]

Dojo was a custom-built supercomputer intended to be the cornerstone of Tesla's AI ambitions, specifically for training the neural networks of its Full Self-Driving (FSD) technology and humanoid robots.

The primary strategic reasons cited for the project's termination include:

1. the strategic pivot to AI6 chips. The AI6 chip is Tesla’s new strategic bet on a chip design intended to scale across FSD, Tesla’s Optimus humanoid robots, and high-performance AI training in data centers
2. moving away from hardware self-reliance. Dojo was intended to reduce reliance on expensive eand difficulty-to-secure Nvidia GPUs, but Tesla is now "going all-in on partnerships" with major chip providers, including Nvidia, AMD, and Samsung (which will build the AI6 chip)
3. technological and compatibility hurdles. Dojo’s design, based on proprietary D1 chips, faced inherent technological challenges related to integration with the broader AI ecosystem
4. internal competition and redundency. In August 2024, Tesla began promoting Cortex, described as the company’s "giant new AI training supercluster" being built at Tesla HQ in Austin. Cortex was later deployed at Gigafactory Texas.

You are by default a product leader, navigating product directions with data.

Data scientists at Meta don’t just analyze data — they transform business questions into data-driven product visions that help building better human connections.

The most successful data scientists that I’ve worked with not only excel at adapting their approach to the specific data-problem quadrant they’re operating in, but also are effective in working with Cross-Functional partners to drive collaboration pushing product strategy development forward.

With Product Managers:

• Speak in terms of business problems, not data techniques
• Help PMs translate intuition into testable hypotheses
• Co-create metrics frameworks that balance short and long-term objectives

With Engineering:

• Bridge implementation and insight by understanding technical constraints
• Design analytics requirements that respect engineering resources
• Create feedback loops that allow for continuous improvement

With Design/User Researchers:

• Humanize data insights through collaborative storytelling
• Provide quantitative context for qualitative user research
• Partner on creating experiences that naturally generate valuable data

Deb Liu, former VP of Meta, highlighted in herproduct strategy framework: “a great product strategy is opinionated, objective, operable, and obvious.” Data scientists are uniquely positioned to help product teams achieve these qualities through:

• Opinionated: Grounding strategic choices in data-backed insights
• Objective: Bringing analytical rigor to opportunity sizing and risk assessment
• Operable: Creating measurement frameworks that make execution tractable
• Obvious: Revealing patterns that make the path forward clear to all stakeholders

― Meta’s Data Scientist’s Framework for Navigating Product Strategy as Data Leaders - Medium [Link]

Quadrant 1: The Pioneer (Low Data, Broad Problem)

• Strategic Approach:

  1. Identify North Star metrics
  2. Design metric framework
  3. Conduct minimum viable analytics, quasi-experiment
  4. Generate data insights to identify problems and guide early decisions
  5. Create product strategy to drive measurable improvements

  Note: a good strategy decides which problems to prioritize in solving as well as those we choose not to solve.

  Best Practice: narrowing the problem space through structured discovery.

• Collaboration among design, PM, and XFN

  1. Define (north star) metrics
  2. Translate business questions into testable hypotheses
  3. Use analytics to yield insights

Quadrant 2: The Craftsperson (Low Data, Concrete Problem)

• Strategic Approach:

  1. Design targeted data collection aligned to the specific problem
  2. Develop creative measurement frameworks that work with sparse data
  3. Leverage analogous data from similar contexts

  Note: focus on setting clear learning milestones rather than promising specific outcomes. The goal is to systematically reduce uncertainty around a concrete problem with iterative data learnings to update our beliefs.

Quadrant 3: The Explorer (High Data, Broad Problem)

• Strategic Approach:

  1. Pattern recognition at scale to identify unrecognized opportunities (e.g., opportunity sizing model, gap analysis framework)
  2. Segmentation and clustering to create structure in an ambiguous space (e.g., segmentation model)
  3. Insight translation that transforms data patterns into business narratives

  Note: structure the problem space through data, allowing the product team to move from broad exploration to targeted opportunities. The role is to transform overwhelming data into clear strategic choices for your product partners.

Quadrant 4: The Optimizer (High Data, Concrete Problem)

• Strategic Approach:
  1. Metric deep-dive and monitoring
  2. Analytics modeling that uncovers non-obvious optimization opportunities (e.g., multi-armed bandit system, analytics framework, feedback loop)
  3. Continuous learning systems that adapt as conditions change

Best Practices for Developing a Product Strategy - Deb Liu [Link]

A New Ranking Framework for Better Notification Quality on Instagram - Engineering at Meta [Link]

While existing machine learning (ML) models optimize for high engagement, they can result in repetitive and potentially "spammy" notifications, leading users to disable them. To combat this, the new framework applies a multiplicative penalty to notification scores based on their similarity to recently sent ones, using criteria such as author and product type. This strategy has successfully reduced notification volume while increasing engagement rates by ensuring a more varied and personalized mix of content.

The methodology begins with the existing machine learning (ML) models, which calculate a base score for notification candidates based on factors like the probability of a user clicking (Click-Through-Rate or CTR) and time spent. The new framework introduces a diversity layer on top of these existing engagement ML models.

The methodology involves the following steps:

1. Evaluation of Similarity: The diversity layer evaluates each notification candidate's similarity to recently sent notifications across multiple dimensions, such as content, author, notification type, and product surface.
2. Application of Penalties: The system applies carefully calibrated penalties, expressed as multiplicative demotion factors, to downrank candidates that are too similar or repetitive to recent notifications.
3. Re-ranking: The adjusted scores (base relevance score multiplied by the demotion factor) are used to re-rank the candidates.
4. Selection: The final selection process uses a quality bar to choose the top-ranked candidate that successfully passes both the ranking and diversity criteria.

Within the diversity layer, the methodology is mathematically implemented using a multiplicative demotion factor applied to the base relevance score:

\[\text{Final Score } (S(c)) = \text{Base Ranking Score } (R(c)) \cdot \text{Diversity Demotion Multiplier } (D(c))\]

Key aspects of this calculation include:

• Demotion Multiplier (\(D(c)\)): This is a penalty factor where the value falls within the range of 0 to 1 (\(D(c) \in\)), reducing the score based on similarity to recently sent notifications.
• Similarity Signal: To calculate \(D(c)\), a similarity signal (\(p_i(c)\)) is computed for a set of semantic dimensions (e.g., author, product type) using a maximal marginal relevance (MMR) approach.
• Binary Baseline: In the baseline implementation, the similarity signal \(p_i(c)\) is binary: it equals 1 if the similarity exceeds a predefined threshold (\(\tau_i\)), and 0 otherwise.
• Flexible Control: The methodology defines the final demotion multiplier using adjustable weights (\(w_i\)), which control the strength of demotion for each respective dimension.

The State of AI in Financial Services in 2025 — views from our front row seats - Peter Hung, Illuminate Financial [Link]

Agentic Design Pattern [Link] [code]

Product Roadmap Examples - Janna Bastow, ProdPad [Link]

The best roadmaps aren't checklists; they tell a story about why something is being built. They show how short-term initiatives connect to long-term strategic goals.

The Now, Next, Later Framework is a core pattern, reflecting the reality of uncertainty. Now initiatives are tight, concrete, and focused on current goals (e.g., MVP launch). Next initiatives are more exploratory bets. Later initiatives are deliberately fuzzy, long-term aspirations that signal intent without making firm promises.

Effective roadmaps frame initiatives as problems to solve and tie them to clear outcomes and business objectives (e.g., "reduce onboarding friction" instead of "ship a new login flow"). This keeps the team flexible and focused on results.

There is no one-size-fits-all roadmap. A startup's roadmap is about survival and proving a hypothesis. A scale-up's roadmap is about smoothing friction and deepening engagement. A hardware roadmap must account for manufacturing cycles, while a mission-critical one must prioritize compliance and security.

Expanding economic opportunity with AI - OpenAI [Link]

GenAI Doesn’t Just Increase Productivity. It Expands Capabilities - BCG [Link]

A point made around 'reskilling': While GenAI can immediately boost a worker's aptitude for new tasks, it does not necessarily "reskill" them in a traditional sense. The study found that participants were able to perform complex data-science tasks with the help of GenAI, but they did not retain the knowledge or skills gained after the tools were taken away. The article refers to GenAI as an "exoskeleton" that enables workers to do more, but does not intrinsically change what they have learned.

Building Etsy Buyer Profiles with LLMs - Isobel Scot, Etsy Code as Craft [Link]

Non-Obvious Tips for Landing the Job You Want - Deb Liu [Link]

Seven Non-Obvious Strategies

1. Never rely only on online submission: Avoid the "digital dustbin" by finding an alternative path in, such as a referral, connection, or direct reach-out.
2. Ask for advice, not a job: Sincerely seek guidance on entering a field or company, as people are often generous and may uncover new opportunities for you.
3. Give them a reason to say yes: Counter the process of finding reasons to say no (misspellings, poor grammar) by providing human connection points like shared alma maters, hobbies, or passions to hack affinity bias.
4. Find the “you-shaped hole”: Seek roles where your unique skills, experience, or passion make you the best bet, demonstrating you can "hit the ground running on day one".
5. See the world through the hiring manager’s eyes: Hiring managers prioritize managing risk because a bad hire is costly. Your job is to close the asymmetry of information, prove you are a "sure bet," and show you are a great return on investment.
6. Do the job before you get the job: Demonstrate initiative by acting like an employee; use the product, talk to customers, and bring specific ideas or prototypes to show you want this job.
7. Tailor your resume (and your story) for the role: Treat your resume as a "living document" to tell a specific story, reframing factual experiences to align with the target role and "speak the language of the hiring company".

American Express is Accepted at 160 Million Merchants Around the World; Since 2017, Amex-Accepting Locations Have Increased by Nearly 5x - Business Wire [Link]

Hallucinations persist partly because current evaluation methods set the wrong incentives. While evaluations themselves do not directly cause hallucinations, most evaluations measure model performance in a way that encourages guessing rather than honesty about uncertainty.

― Why language models hallucinate - OpenAI [Link]

Several clarifications mentioned in this blog:

1. Accuracy will never reach 100%
2. Hallucinations are not inevitable. Language models can choose to abstain when uncertain. Abstaining (indicating uncertainty) is better than providing confident, incorrect information, aligning with the core value of humility
3. Avoiding hallucination can be easier for a small model to know its limits. Being "calibrated" (knowing its confidence) requires much less computation than being accurate
4. To measure hallucinations, all of the primary evaluation metrics need to be reworked to reward expressions of uncertainty. Hallucination evals have little effect against hundreds of traditional accuracy-based evals that punish humility

How to Think About GPUs - How to Scale Your Model [Link]

'A Systems View of LLMs on TPUs'

Anthropic Economic Index report: Uneven geographic and enterprise AI adoption - Anthropic [Link] [PDF]

Key findings:

I. Adoption Speed and Shift to Delegation

• AI adoption is occurring at an unprecedented speed, reaching in two years the adoption rates that took the internet around five years. In the US, 40% of employees report using AI at work, doubling the rate from two years prior in 2023.
• Usage patterns on Claude.ai show a net shift toward delegation (automation). The share of "Directive" conversations, where users delegate complete tasks, jumped from 27% to 39%, meaning automation usage now exceeds augmentation usage for the first time.
• There is sustained growth in knowledge-intensive tasks like education and science. In coding, there is a net shift of 7.4 percentage points toward program creation and away from debugging, suggesting models have become more reliable.

2. Geographic Concentration and Inequality Risk

• AI usage is highly geographically concentrated and correlates strongly with income across countries. A 1% increase in GDP per working-age capita is associated with a 0.7% increase in Claude usage per capita.
• Small, technologically advanced economies lead in per-capita adoption, with Israel (7x expected usage) and Singapore (4.57x expected usage) being top examples.
• Low-adoption countries are more likely to delegate complete tasks (automation), while high-adoption countries tend toward greater learning and collaborative iteration (augmentation), even when controlling for task mix.
• Current usage patterns suggest that AI benefits may concentrate in already-rich regions, potentially increasing global economic inequality.

3. Enterprise Automation and Deployment Bottlenecks

• Enterprise usage via the 1P API is predominantly automation-dominant, with 77% of business uses involving automation patterns (delegating tasks programmatically), compared to about 50% for Claude.ai users.
• Business deployment is largely price-insensitive. Model capabilities and the economic value of automation appear to matter more than cost, as higher-cost tasks tend to have higher usage rates.
• For complex tasks, deployment is constrained by the access to information rather than just model capabilities. Companies face a bottleneck requiring costly data modernization and organizational investments to centralize the contextual information needed for sophisticated AI use.

Papers and Reports

NCRI Assassination Culture Brief - NCRI and Rutgers University [Link]

Political violence targeting figures like Donald Trump and Elon Musk is becoming normalized. The report's key findings are based on a survey and social media analysis. Main points:

1. Growing justification for violence
2. The rise of "Assassination Culture"
3. Social Media as an Amplifier

YouTube and Podcast

Trump Takes On the Fed, US-Intel Deal, Why Bankruptcies Are Up, OpenAI's Longevity Breakthrough - All-In Podcast [Link]

Elon Musk on DOGE, Optimus, Starlink Smartphones, Evolving with AI, Why the West is Imploding (All-In Summit) - All-In Potcast [Link]

Inside the White House Tech Dinner, Weak Jobs Report, Tariffs Court Challenge, Google Wins Antitrust - All-In Podcast [Link]

预测未来，还是操纵未来？Polymarket的崛起之路与争议【深度】- 硅谷101 [Link]

E206｜临近机器人GPT-3时刻，具身智能开源模型的加速演进 - 硅谷101播客 [Link]

Charlie Kirk Murder, Assassination Culture in America, Jimmy Kimmel Suspended, Ellison Media Empire - All-In Podcast [Link]

How AI Will End 50 Years of Economic Stagnation, with Tyler Cowen - Working Intelligence [Link]

H-1B Shakeup, Kimmel Apology, Autism Causes, California Hate Speech Law - All-In Podcast [Link]

Social Media

If you believe free speech is for you but not your political opponents, you're illiberal.

If no contrary evidence could change your beliefs, you're a fundamentalist.

If you believe the state should punish those with contrary views, you're a totalitarian.

If you believe political opponents should be punished with violence or death, you're a terrorist.

― J.K. Rowling [Link]

To build an AI native product, a PM needs mastery of the following - vision, opinionated UX design - model intuition to extract max value - ability to go from pixels -> evals -> hill climb - understanding of agentic flows - tools, context, safety guardrails - deep user understanding - lot more than previously because of the nature of agents

― AI PM mastery is a rare skill - Madhu Guru [Link]

The Systems Thinker's Blindspot - Shreyas Doshi [link]

I read the book "Never Split the Difference : Negotiating As If Your Life Depended On It" by Chris Voss a month ago and finally got time to write some notes down. I love this type of book that provides structured, practical suggestions for achieving a goal, backed by academic research and theories.

This book is building its argument on some well-established, peer-reviewed psychological theories that show human decision-making is often more emotional and irrational than we'd like to believe. Voss grounds his approach in Daniel Kahneman and Amos Tversky's foundational research on behavioral economics and cognitive psychology. The specific concepts highlighted in the book are: cognitive biases, the framing effect, loss aversion, system 1 and system 2 thinking:

• Cognitive biases: People are not purely rational actors. Instead, our decisions are influenced by systematic, unconscious, and irrational mental shortcuts.
• The framing effect: People respond differently to the same choice depending on how it's presented or "framed." For example, framing a negotiation in terms of what the other party stands to lose is often more powerful than framing it in terms of what they stand to gain.
• Loss aversion: A core tenet of Prospect Theory, this principle states that the psychological pain of a loss is roughly twice as powerful as the pleasure of an equivalent gain.
• System 1 and system 2 thinking: Introduced in Kahneman's book, Thinking, Fast and Slow, this model describes two distinct modes of thought. System 1 is our fast, instinctive, and emotional mind. System 2 is our slow, deliberate, and logical mind. Voss's techniques are designed to bypass the logical System 2 and appeal directly to the emotional and intuitive System 1.

The central tenets of Chris Voss's effective negotiation strategy are rooted in emotional intelligence and a shift from a competitive to a collaborative mindset. Rather than seeking a compromise, his methods focus on understanding the other party to influence their decision-making. The key elements of his approach include:

• Tactical empathy: intentionally using empathy to understand the other person's perspective, emotions, and motivations. The goal is to build a trust-based relationship, not necessarily to agree with them.
• Active learning: it's important to truly listen to what the other person is saying, rather than just waiting for your turn to speak. This includes paying attention to their words, tone, and body language, to uncover their real needs and fears.
• Calibrated questions: open-ended questions that start with 'how' or 'what', and are designed to give the other person the illusion of control while you guide them toward a solution that benefits both sides.
• Key techniques:
  • Mirroring: repeating the last one to three key words of what the other person has said. This encourages them to elaborate and creates a sense of rapport.
  • Labeling: verbally identifying the acknowledging the other person's emotions. This helps to diffuse negative emotions and makes them feel heard.
  • The power of 'no': 'no' is not a failure but a critical starting point. It makes the other party feel safe and in control, and it allows you to get past insincere agreements to uncover the true issues.
  • "That's right" as the goal: Instead of aiming for "yes," the ultimate goal is to get the other person to say, "That's right." This phrase signifies that they feel you have accurately understood their position and worldview, creating a turning point in the negotiation.

Other impressive key lessons to remember:

1. Be ready for possible surprises, and use skills to reveal the surprises
2. View assumptions as hypotheses and use the negotiation to test them rigorously
3. Negotiation is not a battle but a process of discovery with the goal of uncovering as much information as possible
4. Let the person be in a positive frame of mind. Positivity creates mental agility in both you and your counterpart
5. Keep voice calm and slow. Create an aura of authority and trustworthiness without triggering defensiveness
6. Use positive / playful voice as default. Use direct or assertive voice rarely
7. Acknowledging the other person's situation to convey that you are listening
8. Focus first on clearing the barriers to agreement
9. Pause and let the other party to fill in the silence
10. Label your counterpart's fears to diffuse their power and generate safety, well-being, and trust
11. Accusation audit: List the worst things that the other party could say about you and say them before the other person can
12. All negotiations are defined by a network of subterranean desires and needs
13. Don't compromise. Meeting halfway often leads to bad deals for both sides
14. Approaching deadlines entice people to rush the negotiating process and do impulsive things that againt their best interests
15. Before you make an offer, emotionally anchor them by saying how bad it will be. When you get to numbers, set an extreme anchor to make your 'real' offer seem reasonable, or usse a range to seem less aggressive.
16. People will take more risks to avoid a loss than to realize a gain.
17. Avoid asking questions that can be answered by 'yes'. Ask calibrated questions that start with the words 'how' or 'what'. Avoid asking questions starting with 'why' which is always an accusation in any language.
18. Calibrate questions to point your counterpart toward solving your problems.
19. There is always a team on the other side. You are vulnerable if you don't influence those behind the table.
20. Asking 'how' question gives counterpart an illusion of control and leads them to contemplate yoru problems when making their demand.
21. Identify the motivations of players 'behind the table'. You can do so by asking how a deal will affect everybody else and how on board they are.
22. Pay 38% attention to tone of voice and 55% to body language. The rest 7% is on words.
23. Test whether 'yes' is real or counterfeit by using calibrated questions, summaries, and labels to get your counterpart to reaffirm their agreement at least three times.
24. Pay attention to a person's use of pronouns which offers deep insights into his or her relative authority. If you are hearing a lot of 'I', 'me', and 'my', the real power to decide probably lies elsewhere. Picking up a lot of 'we', 'they', and 'them', it's more likely you are dealing directly with a savvy decision maker keeping his options open.
25. Humor and humanity are the best ways to break the ice and remove roadblocks.
26. Identify your counterpart's negotiation style: Accomodator, Assertive, or Analyst.
27. Prepare dodging tactics to avoid getting sucked into the compromise trap.
28. Learn to take a punch or punch back without anger. The guy across the table is not the problem, the situation is.
29. Prepare an Ackerman plan:
    1. Set you target price (goal)
    2. Set your first offer at 65% of your target price
    3. Calculate three raises of decreasing increments (to 85%, 95%, and 100%)
    4. Use lots of empath and different ways of saying 'No' to getthe other side to counter before you increase your offer
    5. when calculating the final amount, use precise, non round numbers like, $37,893 rather than $38,000. It gives the number credibility and weight.
    6. On your final number, throw in a non monetary item (that they probably don't want) to show you are at your limit.
30. Black swans are leverage multipliers. Remember the three types of leverages: positive (the ability to give someone what they want); negative (the ability to hurt someone); and normative (using your counterpart's norms to bring them around).
31. Understand the other side's 'religion / worldview' (reason for being) so that we are able to speak persuasively, develop options that resonate for them, and build influence. Black swan usually dwells in the hidden negotiation space.
32. People are more apt to concede to someone they share a cultural similarity with.
33. Get face time with the counterpart.

Selected Quotes:

What good negotiators do when labeling is to address those underlying emotions. Labeling negatives diffuses them (or defuses them, in extreme cases); labeling positives reinforces them.

Great negotiators seek 'No' because they know that's often when the real negotiation begins.

Whether you call it "buy-in" or 'engagement' or something else, good negotiators know that their job isn't to put on a great performance but to gently guide their counterpart to discover their goal as his own.

Never split the difference. Creative solutions are almost always preceded by some degree of risk, annoyance, confusion, and conflict. Accommodation and compromise produce none of that. You've got to embrace the hard stuff. That's where the great deals are. And that's what great negotiators do.

If you can get the other party to reveal their problems, pain, and unmet objectives - if you can get at what people are really buying - then you can sell them a vision of their problem that leaves your proposal as the perfect solution.

When you are selling yourself to a manager, sell yourself as more than a body for a job; sell yourself, and your success, as a way they can validate their own intelligence and broadcast it to the rest of the company. Make sure they know you'll act as a flesh-and-blood argument for their importance.

The key issue here is if someone gives you guidance, they will watch you to see if you follow their advice. They will have a personal stake in seeing you succeed. You've just recruited your first unofficial mentor.

Negotiation was coaxing, not overcoming; co-opting, not defeating. Most importantly, successful negotiation involved getting your counterpart to do the work for you and suggest your solution himself. It involved giving him the illusion of control while you, in fact, were the one defining the conversation.

Asking for help in this manner (give illusion of control by asking calibrated questions), after you've already been engaged ina dialogue, is an incredibly powerful negotiating technique for transforming encounters from confrontational showdowns into joint problem-solving sessions. And calibrated questions are the best tool.

Expression of anger increase a negotiator's advantage and final take. Anger shows passion and conviction that can help sway the other side to accept less. However, by heightening your counterpart's sensitivity to danger and fear, your anger reduces the resources they have for other cognitive activity, setting them up to make bad concessions that will likely lead to implementation problems, thus reducing your gains.

Also beware: researchers have also found that disingenuous expressions of unfelt anger - faking it - backfire, leading to inractable demadns and destroying trust. For anger to be effective, it has to be real, the key for it is to be under control because anger also reduces our cognitive ability.

No deal is better than a bad deal. Once you're clear on what you bottom line s, you have to be willing to walk away. Never be needy for a deal.

Think of punching back and boundary-setting tactics as a flattened S-curve: you've accelerated up the slope of a negotiation and hit a plateau that requires you to temporarily stop any progress, escalate or de-escalate the issue acting as the obstable, and eventually bring the relationship backto a state of rapport and get back on the slope. Taking a positive, constructive approach to conflict involves understanding that the bond is fundamental to any resolution. Never create an enemy.

By positioning your demands within the worldview your conuterpart uses to make decisions, you show them respect and that gets your attention and results. Knowing your counterpart's religion is more than just gaining normative leverage per se. Rather, it's gaining a holistic understanding of your counterpart's worldview and using that knowledge to inform your negotiating moves.

Two tips for reading religion correctly:

1. Review everything you hear
2. Use backup listeners whose only job is to listen between the lines. They will hear things you miss.

When you recognize that your counterpart is not irrational, but simply ill-informed, constrained, or obeying interests that you do not yet know, your field of movement greatly expands. And that allows you to negotiate much more effectively.

The Art of 'No':

Saying "No" gives the speaker the feeling of safety, security, and control. You use a question that prompts a "No" answer, and your counterpart feels that by turningyou down hehas proved that he's in the driver's seat. Good negotiators welcome - even invite - a solid "No" to start, as a sign that the other party is engaged and thinking.

Gun for a "Yes" straight off the bat, though, and your counterpart gets defensive, wary,and skittish. That's why I tell my students that, if you are trying to sell something, don't start with "Doyou have a few minutes to talk?" Instead ask, "Is now a bad time to talk?" Either you get "Yes, it's a bad time" followed by a good time or a request to go away, or you get "No, it's not" and total focus.

It's a reaffirmation of autonomy. It is not a use or abuse of power; it is not an act of rejection; it is not a manifestation of stubbornness; it is not the end of the negotiation.

"No" has a lot of skills:

• "No" allows the real issues to be brought forth
• "No" protects people from making - and lets them correct - ineffective decisions
• "No" slows things down so that people can freely embrace their decisions and the agreements they enter into
• "No" helps people feel safe, secure, emotionally comfortable, and in control of their decisions
• "No" moves everyone's efforts forward

There is a big difference between making your counterpart feel that they can say "No" and actually getting them to say it. Sometimes, if you are talking to somebody who is just not listening, the only way you can crack their cranium is to antagonize them into "No".

One great way to do this is to mislabel one of the other party's emotions or desires. You say something that you know is totally wrong. That forces them to listen and makes them comfortable correcting you.

Another way to force "No" in a negotiation is to ask the other party what they don't want. People are comfortable saying "No" here because it feels like self-protection. And once you've gotten them to say "No", people are much more open to moving forward toward new options and ideas.

Articles and Blogs

Developers, Reinvented - Thomas Dohmke [Link]

Actionable Insights for Individual Developers

To successfully transition into the role of an AI Collaborator or Strategist, developers must focus on strategic adoption and skill augmentation:

1. Embrace Experimentation and Iterate Aggressively
2. Achieve AI Fluency: Commit to continuous learning and adaptability to understand the capabilities and constraints of different AI tools, platforms, and models given the "breakneck" speed of innovation.
3. Shift Focus to Delegation and Orchestration: Move from writing code to architecting and verifying.
4. Prioritize Verification and Quality Control: developers must rigorously review, test, and verify AI-generated code.
5. Maintain Deep Foundational Knowledge: Continue to deepen understanding of programming basics, algorithms, data structures, and overall software systems.
6. Elevate Systems and Product Thinking: Adopt a hybrid mindset that incorporates engineering, design, and product management.
7. Increase Ambition View AI tools as a way to raise the ceiling of achievable outcomes and expand scope, rather than merely focusing on "time saved" or reducing effort.

Actionable Insights for Strategy and Tool Development

For companies and those building future tools, the focus should be on redefining success and ensuring the developer experience is fulfilling:

1. Update Success Metrics: Measure the ability to raise the ceiling of the work and outcomes accomplished (increasing ambition).
2. Invest in Advanced Capabilities: Recognize that achieving ambitious, expanded scopes requires investing in the most advanced agentic capabilities.
3. Ensure Fulfillment During Transition: Tool builders should design future tools to be intuitive, delightful, and cater to developers’ curiosity to keep them fulfilled and happy during the transition period.

Guided Learning in Gemini: From answers to understanding - Maureen, Heymans, Google Blog [Link]

Why developer expertise matters more than ever in the age of AI - Laura Lindeman, Github Blog [Link]

While AI tools like GitHub Copilot significantly boost coding speed, human critical thinking and fundamental developer skills remain essential for building resilient, scalable, and secure software. There are three core areas developers must master to thrive with AI: excellence in pull requests, thorough code reviews, and investment in clear documentation.

We must build AI for people; not to be a person - Mustafa Suleyman [Link]

The author argues that Seemingly Conscious AI (SCAI) is an inevitable and unwelcome outcome given current technological capabilities, warning that the illusion of consciousness could lead people to dangerously advocate for AI rights, welfare, and even citizenship, leading to societal polarization and psychological risks. The essay emphasizes the urgent need for clear guardrails and design principles in the AI industry to ensure that AI companions remain tools maximizing human utility while actively minimizing markers of consciousness.

Chatbots Can Trigger a Mental Health Crisis. What to Know About ‘AI Psychosis’ - Robert Hart, Time [Link]

AI psychosis - users develop delusions or distorted beliefs after extensive use of chatbots like ChatGPT. Those with a personal or family history of psychosis, or those with personality traits susceptible to fringe beliefs, may be more vulnerable. Extended use, often hours every day, is a significant risk factor. Experts advise users to view AI chatbots as tools, not friends, and to avoid relying on them for emotional support. They recommend that companies collect more data, work with mental health professionals, and build safeguards directly into their models, such as prompting users to take breaks or issuing "warning labels."

How companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.

This finding is particularly relevant in financial services and other highly regulated sectors, where many firms are building their own proprietary generative AI systems in 2025. Yet, MIT’s research suggests companies see far more failures when going solo.

― MIT report: 95% of generative AI pilots at companies are failing - Sherly Estrada, Fortune [Link]

I talked to Sam Altman about the GPT-5 launch fiasco - Alex Heath, The Verge [Link]

• Chaotic rollout of GPT-5 - Altman admitted the company "totally screwed up" some aspects, though API traffic and user numbers continued to climb.
• Altman's extensive ambitions
  • Planning to spend trillions of dollars on data center construction to address GPU capacity constraints;
  • Aggressively scaling ChatGPT, which is already one of the most widely used products on earth, with the goal of reaching billions of people a day and becoming the third biggest website in the world (surpassing Instagram and Facebook);
  • Interested in buying Google Chrome if it becomes available;
  • Confirming OpenAI's interest in developing new consumer hardware and a brain-computer interface to rival Neuralink.
• AI Bubble - Investors, as a whole, are currently overexcited about AI. He explained that when bubbles occur, "smart people get overexcited about a kernel of truth".

Mark Zuckerberg Shakes Up Meta’s A.I. Efforts, Again - Mike Isaac and Eli Tan, The New York Times [Link]

Mark Zuckerberg initiated a significant restructuring of Meta’s artificial intelligence division in a push for "superintelligence." This reorganization involves splitting the current AI division into four distinct groups focused on research, superintelligence, product development, and infrastructure, which is intended to help Meta compete more effectively in the AI arms race. Furthermore, the company is considering a major strategic shift from exclusively using its own open-source models to exploring the use of third-party or closed-source AI technology to power its products.

Meta Freezes AI Hiring After Blockbuster Spending Spree - The Wall Street Journal [Link]

Meta Platforms has frozen hiring in its artificial-intelligence division following months of aggressive recruitment, which saw the company hire over fifty new researchers and engineers. This hiring freeze is happening alongside a significant reorganization of its AI operations, now consolidated under the umbrella of Meta Superintelligence Labs.

Papers and Reports

Build AI in America - Anthropic [Link]

Accelerating life sciences research - OpenAI [Link]

OpenAI and Retro Biosciences have collaborated to create a miniature, specialized version of GPT-4o called GPT-4b micro for protein engineering.

YouTube and Podcasts

Generative AI Foundations on AWS Technical Deep Dive Series - AWS [Link]

Trump AI Speech & Action Plan, DC Summit Recap, Hot GDP Print, Trade Deals, Altman Warns No Privacy - All-In Podcast [Link]

Sam Altman | This Past Weekend w/ Theo Von [Link]

OpenAI's GPT-5 Flop, AI's Unlimited Market, China's Big Advantage, Rise in Socialism, Housing Crisis - All-In Podcast [Link]

AI Psychosis, America's Broken Social Fabric, Trump Takes Over DC Police, Is VC Broken? - All-In Podcast [Link]

AI Bubble Pops, Zuck Freezes Hiring, Newsom’s 2028 Surge, Russia/Ukraine Endgame - All-In Podcast [Link]

Trump Takes On the Fed, US-Intel Deal, Why Bankruptcies Are Up, OpenAI's Longevity Breakthrough - All-In Podcast [Link]

失控的芬太尼：药物滥用背后的权力、金钱与死亡【深度】- 硅谷101 [Link]

E204｜运动品牌的成长烦恼：lulu低谷与Alo Yoga崛起 - 硅谷101播客 [Link]

Substack

Generative AI is revolutionizing how code is written. In just the past 6 months, coding assistant tools like Cursor, Windsurf, Lovable, Bolt, and Replit have evolved from being cute ways to help with 10-20% of code to now generating the majority of code for many startups. 1 in 4 companies in the latest YC batch have 95% of their code written by AI.

This new way to build products is much faster and simpler than before, it involves just 4 steps.

1. Prioritize features by impact
2. Ship simple version or clickable prototype
3. Test at scale with users, measure impact
4. Iterate or kill

― The Lean Startup is Dead - Fletcher Richman [Link]

A key part of being a lifelong learner is retaining what you are learning and comparing ideas and putting learning into our lives.

I choose a certain number of topics/books that I want to read/learn each year and focus on reading those books deliberately.

I find reading to be a more positive habit than scrolling mindlessly on my phone or watching YouTube videos. I do those things as well but I try to change my habits by choosing books instead. I also read multiple books at a time. This helps me avoid feeling the dread of picking up a challenging or long book when I am tired after a long day.

I have tried different retention techniques over the years, and have found these to work best for me. At first, these were slower and felt less efficient, but I have gotten faster and better at utilizing these tips with practice.

― How To Remember What You Read - Ryan Hall, Read and Think Deeply [Link]

Ryan Hall's top five tips for retaining more of what you are reading:

• Underline or highlight key ideas or phrases.

  • When reading deeply, always have a pen or highlighter in hand.
  • On the first read-through, underline or highlight any key concepts, ideas, characters, or quotes.

  This practice makes the reader interactive with the text and enables quick review of key concepts after reading. Reviewing these key ideas after finishing a chapter is helpful and increases focus as you actively look for points to underline.

• Write in books.

  • As you read and underline, write notes in the margins. These notes can include key ideas, questions, or indications if you don't understand a section or disagree with something.
  • Notes are often single words or short phrases, like "Habit Stacking" when reading Atomic Habits. These words stand out when you revisit a section or chapter, keeping your mind engaged.
  • For digital readers (like on a Kindle), keep a notes app open on your phone to jot down words or phrases related to the chapter. (A separate source comment also notes that Kindles allow unlimited marginal notes without needing a separate app).

• Briefly summarize each section or chapter immediately after you have read it.

  • Keep a notebook for reading notes, where you can write the date, book title, and chapter. Highlighting different books with different colors can help distinguish ideas from various books.
  • Immediately after finishing a chapter or section, briefly summarize it in your own words, keeping it short (1-3 sentences). Putting ideas into your own words helps formulate thoughts and allows you to test your understanding of the concepts.

• Talk to others or teach someone else.

  • Tell someone else about what you are reading and learning. This verbal processing forces your mind to recall what you have read and put the pieces together, leading to greater retention.

• Write reviews or summaries.

  • After finishing a book, write a review or a summary. It doesn't need to be elaborate; the goal is to start the process of putting thoughts on paper or keyboard to let your mind work through what you've learned. Try to recall key plot points, ideas, and quotes, referencing your notebook notes and margin annotations.
  • Summarize what you've read and ideas you'd like to incorporate into your life. For nonfiction, try to apply one idea into your life. Another comment also suggests writing a summary paragraph of each chapter and then summarizing those in a review.

Additionally, bonus tips:

• Re-read classic or deeper non-fiction books, as they are often meant to be revisited and "wrestled with".
• Listen to podcasts or interviews with the author (especially for nonfiction) after reading the book, as authors may provide more context or better explanations in an interview format.

Write Everything Down (and not in your notes app) - Megan, Typewriter Time [Link]

The author found that digital notes were easily forgotten and lacked the tangible connection and memory associated with handwriting. By shifting to a dedicated creative writing notebook, the author experienced improved recall, a more thoughtful writing process, and a stronger connection to their ideas and progress. The piece advocates for the benefits of physical writing for creative endeavors and personal reflection, highlighting how it fosters a deeper engagement with one's own thoughts and creations, a sentiment echoed by the included comments.

Suggestions:

• Switch to handwriting everything in notebooks instead of using your phone.
• Use a dedicated notebook for creative writing only.
• Write down ideas and pieces by hand.
• Constantly flip back through the pages of your physical notebook.
• Write out observations about your growth and areas for improvement directly within the same notebook.
• Create an index in your notebook so you can find things easily.
• Tab pages of importance.
• Scratch out things when you're stuck or frustrated. This allows for a "messy and alive" notebook that reflects the organic nature of the creative process, unlike the clean digital interface.

Brookfield: Undervalued Giant In An Overvalued Market! - Capitalist Letters [Link]

Context Engineering: Bringing Engineering Discipline to Prompts - Addy Osmani, Elevate [Link]

Context engineering components:

• Systemic Approach: It's a system, not a one-off prompt, where the final prompt is woven together programmatically from multiple components (e.g., role instruction, user query, fetched data, examples).
  
• Dynamic and Situation-Specific: Context assembly happens per request, adapting to the query or conversation state. This involves including different information depending on the situation, such as a summary of a multi-turn conversation or a relevant excerpt from a document.
  
• Blending Multiple Content Types: It covers instructional context (prompts, guidance, examples), knowledge context (domain information, facts via retrieval), and tools context (information from tool outputs like web searches or database queries).
  
• Format and Clarity: It's about how information is presented, not just what is included. This means compressing and structuring information for the model's comprehension, using formatting like bullet points, headings, JSON, or pseudo-code, and labeling sections (e.g., "Relevant documentation:").

You can learn anything in 2 weeks - Dan Koe, Puture / Proof [Link]

• "skill acquisition = technique stacking." Instead of trying to learn an entire skill (like playing the guitar or Photoshop), you should focus on specific techniques needed for a direct purpose.
• "pure focus" as the missing ingredient for rapid learning. To achieve this, he suggests "tactical stress" – putting yourself in a high-pressure situation with a strong deadline that forces you to learn quickly to avoid negative consequences. This pain of the current situation outweighs the pain of learning, propelling you forward.

How to instantly be better at things - Cate Hall, Useful Fictions [Link]

Suggestions:

• Mimic others, especially those better than you.
• Simulate the thinking of experts: Even without direct observation of someone's thoughts, you can improve by asking yourself "what would a better [chess player/person/etc.] do?"
• Mimic generally competent individuals for new tasks
• Ignore existing standards and aspire to a higher level: Recognize that many skills are "pre-competitive," meaning current standards don't reflect the full potential. Aim to be better than anyone you've ever seen, rather than just slightly better than those around you. This involves a commitment to rigorous effort and exploration beyond perceived limits.

Cultivating a state of mind where new ideas are born - Henrik Karlsson, Escaping Flatland [Link]

Techniques to maintain the creative state

• Ritualistic work habits: Establishing consistent routines for creative work (e.g., daily writing sessions at a specific time and place) can induce a state akin to self-hypnosis, fostering a non-judgmental zone.
• Delaying exposure: Introducing a long delay between creation and public presentation can reduce self-censorship, as the creator feels detached from immediate judgment.
• Viewing work in religious terms: Framing the creative process as a service to a higher power can provide the necessary awe and daring to push into the unknown.
• Strategic collaboration: Working with supportive, open-minded collaborators who challenge rather than conform can be beneficial.
• Subverting expectations: Actively seeking out ideas or approaches that feel slightly uncomfortable or that one might be "ashamed of liking" can lead to truly original work.
• Working at speed: Forcing oneself to produce work rapidly can bypass self-censorship and allow raw, unfiltered ideas to emerge.

Where do Tech Returns Come From? - Eric Flaningam, Generative Value [Link]

The article suggests that successful technology investing requires embracing uncertainty, understanding the "base rates" of different company categories, recognizing where true differentiation lies (often beyond just technology), viewing market size from a first-principles perspective, and being aware of the unique opportunities unlocked by new technology waves.

1. The next \(\$100B\) company will not look like the last: Value in technology is driven by "anomalous" companies founded by "anomalous" people, making pattern matching ineffective. The most successful companies create new categories.
2. Know the game you’re playing: Different categories have different "Slugging Ratios" (Value/Company). Consumer companies, often network-driven marketplaces with winner-take-all dynamics, have the highest upside, while Hardtech companies also have high slugging ratios but are riskier. Enterprise software, while less "Power Lawed," offers more predictable returns and is suitable for an expanding venture capital landscape due to its scalability, moats, and lower operating costs.
3. Software is like chicken, 80% of it tastes the same: Technical differentiation in enterprise software is often nuanced. Sales, marketing, and building "mindshare" are as, if not more, important than technical moats, especially as software becomes easier to build and features are quickly replicated. The "GPT Wrapper" argument for AI applications is analogous to how many successful enterprise software companies were essentially "database wrappers."
4. “Market size” may be the single greatest reason for investors missing great companies: Humans struggle with uncertainty, and new markets introduce exactly that. Many successful companies like Palantir, Shopify, and Uber created new markets that didn't exist before, leading to investors underestimating their potential market size. Companies with "multiple-expansion tailwinds" and strong platforms also tend to be underestimated.
5. Companies resemble the technology waves they ride in on: New technology waves (internet, mobile, cloud, AI) unlock the ability for new businesses to exist. AI, for example, is enabling anyone to create software and automate voice/text-based workflows, expanding the market significantly and allowing for the creation of entirely new categories (e.g., legal software companies like Harvey reaching \(\$5B\)+ valuations quickly).
6. Don't underestimate the Power Law, ever: While mentioned throughout, this point emphasizes the extreme concentration of value in a very small number of companies. The article states that the top seven companies in its dataset accounted for nearly 50% of the \(\$13\) trillion in value creation.

The Great Mental Models: Visual Book Summary - DoubleThink [Link]

The Map is Not The Territory: This model highlights that maps (including mental models) simplify reality and are imperfect reductions of what they represent. While useful, they lack perfect fidelity and should be used carefully, as the real world is complex.

Circle of Competence: Emphasizes the importance of knowing what you know and, critically, what you don't know. It's dangerous to incorrectly assume knowledge. The advice is to operate within your area of expertise and outsource the rest.

Falsifiability: States that for a theory to be confirmed, it must be challengeable. Instead of trying to prove a theory correct, one should try to prove it incorrect. A theory becomes stronger when rigorous experimentation fails to disprove it.

First Principles Thinking: Involves breaking down a problem into its fundamental, non-reducible parts to challenge pre-existing assumptions. It's an effective way to clarify and approach complex problems by building solutions from the bottom up, often using techniques like Socratic Questioning and the Five Whys.

Thought Experiment: Refers to mentally simulating situations to test theories or reach conclusions, rather than conducting physical experiments. It allows for gaining confidence in answers, as illustrated by comparing hypothetical basketball games.

Necessity and Sufficiency: Explains that having all necessary conditions does not guarantee all sufficient conditions are met. Meeting necessary conditions might make success possible, but it doesn't assure it (e.g., knowing how to write vs. being a New York Times Bestseller).

Second-Order Thinking: Encourages looking beyond immediate consequences to consider the "consequence of the consequence" or further. It involves thinking several steps ahead to avoid short-term positive decisions that lead to long-term negative effects.

Probabilistic Thinking: Acknowledges that the future cannot be predicted perfectly, but this model helps improve the accuracy of guesses using three main concepts:

• Bayesian Thinking: Using all relevant prior information for informed decisions in unfamiliar scenarios.
• Fat-Tailed Curves: Understanding that the more extreme scenarios are possible, the higher the likelihood of any one of them occurring.
• Asymmetries: Assessing the probability that your estimates accurately reflect the real world.

Correlation vs. Causation: Highlights that a correlation between two things does not necessarily mean one causes the other. Large datasets can yield strong correlations purely by chance, as demonstrated by the unrelated alignment of Walgreens customer satisfaction and Russell Crowe's movie appearances.

Inversion Principle: A thinking tool that involves approaching a situation from the opposite end of the usual starting point to reframe a problem into a solution (e.g., "make money" becomes "avoid going into debt").

Hanlon’s Razor: Suggests that one should "never attribute to malice that which can be adequately explained by stupidity." It implies that actions that seem ill-intended are often accidents or misunderstandings, and the explanation assuming the least intent is most likely correct.

Occam’s Razor: States that when multiple explanations are possible, the one that makes the fewest assumptions is generally the most probable and closest to the truth. In essence, the simplest explanation is usually the correct one.

Amazon: Betting The Farm - App Economy Insights [Link]

Tesla: From Bad to Worse - App Economy Insights [Link]

On the EV Landscape (specifically Tesla's automotive business):

• The author highlights that Tesla's year is going "from bad to worse". Global deliveries have fallen by 13%, marking their steepest quarterly drop ever.
• Tesla's revenue is declining, margins are compressing, and cash flow has dried out. Automotive revenue specifically fell by 16% year-over-year. The author notes that "Q2 results remain very poor" if Tesla is viewed purely as an auto business.
• Historically strong margins, supported by gigafactory scale, direct-to-consumer sales, and minimal marketing costs, are now being eroded by price cuts and rising competition.
• A return to growth, which was predicted earlier in the year, now "looks unlikely". The company has also withheld full-year guidance due to factors like trade policy and political backlash, adding to the uncertainty.
• The author expresses concern about "mounting evidence of brand erosion".

On the AV Landscape (specifically Tesla's Robotaxi program):

• The author acknowledges that Tesla's robotaxi program "could unlock tremendous value". Elon Musk himself emphasizes that "autonomy is the story" for Tesla.
• Despite its significant potential, the author cautions that even if these "moonshots in robotaxi and robotics succeed," they are "years away from offsetting collapsing vehicle demand". This indicates that the robotaxi program is not seen as an immediate solution to Tesla's current financial woes in its automotive segment.
• The robotaxi program faces considerable regulatory hurdles.
• The author raises a critical question about whether the ongoing "brand erosion" could "undermine even the most ambitious upside" of the robotaxi program.
• The author foresees an "upcoming robotaxi war" among Big Tech companies, suggesting a highly competitive environment for autonomous vehicles.

Microsoft: AI Crossroads - App Economy Insights [Link]

Figma Files for IPO - App Economy Insights [Link]

Figma's Current State and Potential:

• The author asserts that Figma is "not just a great product—it’s a great business" and describes its growth since monetizing in 2017 as "one of the most explosive runs in SaaS history".
• Figma achieved $ $749$ million in FY24 revenue, up 48% year-over-year, with over 1,000 customers paying \(\$100\)K+ annually, and 95% of Fortune 500 companies using Figma. The author considers its product-led, freemium "Land and Expand" growth model to be "hard to manufacture—and even harder to replicate".
• The author highlights Figma's transformation "from a design tool into a full-stack product platform". It is "evolving into a full product development suite", with new tools like FigJam, Dev Mode, and Figma Make (AI-driven prototyping) expanding its reach across the entire product lifecycle.
• Figma is positioned as a "productivity platform disguised as a design tool", which, in the author's view, separates it "from legacy tools and what opens the door to much broader enterprise budgets" by serving designers, engineers, product managers, marketers, and executives.
• Figma's "web-first" and "multiplayer by default" approach gave it a "distinct edge over incumbents like Adobe".

Figma's Uncertainty and Challenges:

• The author notes the collapse of the \(\$20\) billion Adobe acquisition due to "antitrust concerns in the US, UK, and Europe". While Figma received a "\(\$1\) billion breakup fee" and regained independence, this past scrutiny highlights a challenging regulatory environment that companies of Figma's scale can face.
• The author points out a significant "catch" in Figma's reported net dollar retention of 132% in Q1 FY25. They state that the metric "only includes customers still spending over \(\$10,000\) today, then looks back at what those same customers were spending a year ago." This indicates a potential lack of clarity or transparency in how a key growth metric is presented, which could be an uncertainty for investors trying to assess actual customer retention.

Articles and Blogs

How we built our multi-agent research system - Anthropic [Link]

Building the Hugging Face MCP Server - Hugging Face [Link]

Claude for Financial Services - Anthropic [Link]

Cursor on Web and Mobile - Cursor [Link]

How to Build an MCP Server in 5 Lines of Python - Hugging Face [Link]

Claude Code in Action - Anthropic [Link]

This is a 10-lesson guide covering GitHub automation, custom workflows, and MCP integration. Teaches you how to use Claude Code to automate dev tasks in 36 minutes.

Papers and Reports

A Survey of Context Engineering for Large Language Models [Link]

YouTube and Podcasts

Grok 4 Wows, The Bitter Lesson, Elon's Third Party, AI Browsers, SCOTUS backs POTUS on RIFs - All-In Podcast [Link]

Trump vs Powell, Solving the Debt Crisis, The $10T AGI Prize, GENIUS Act Becomes Law - All-In Podcast [Link]

Silicon Valley Insider EXPOSES Cult-Like AI Companies | Aaron Bastani Meets Karen Hao [Link]

Karen Hao, an expert in mechanical engineering and journalism, provides a comprehensive critique of the A industry, detailing her opinions, arguments, and proposals across various topics during the interview.

• Understanding AI and its Definition

  • Hao argues that the term "artificial intelligence" is poorly defined and was originally coined in 1956 by John McCarthy to attract more attention and funding for his research, essentially as a marketing term. She notes that while AI generally refers to recreating human intelligence in computers, there is no scientific consensus on what human intelligence is, contributing to the term's ambiguity.
  • AI serves as an "umbrella" term encompassing various technologies that simulate human behaviors or tasks, ranging from Siri to ChatGPT, which operate on vastly different scales and have different use cases. Hao uses the analogy that AI is like the word "transportation" to illustrate its vagueness: just as "transportation" can refer to bicycles or rockets, AI can refer to vastly different technologies with different purposes and costs. She finds it frustrating and unproductive when politicians use the term vaguely, suggesting it means "progress" without specifying the type of AI or its potential costs, which she compares to promoting rockets for commuting when more efficient alternatives exist.

• Environmental and Public Health Costs of AI Development

  • Hao emphasizes that the resource consumption required to develop and use generative AI models is quite extraordinary. She cites a McKinsey report projecting that within the next five years, current data center and supercomputer expansion for AI will require adding around half to 1.2 times the amount of energy consumed in the UK annually to the global grid. A significant portion of this energy will be serviced by fossil fuels, including natural gas and the extended lives of coal plants.
  • Hao highlights that this acceleration not only impacts the climate crisis but also exacerbates public health crises, citing Elon Musk's xAI's Colossus in Memphis, Tennessee, which is powered by 35 unlicensed methane gas turbines pumping toxic air pollutants into the community. She argues that "unlicensed" means the company completely ignored existing environmental regulations.
  • She stresses the undertalked about issue of water consumption: AI data centers require fresh, potable water for cooling to prevent corrosion and bacterial growth, often using public drinking water infrastructure. She notes that two-thirds of new AI data centers are being built in water-scarce areas, providing the example of Montevideo, Uruguay, where Google proposed a data center during a historic drought.

• The Business Case and Ideology Driving AI

  • Hao contends that the business case for AI is currently unclear, noting that even Microsoft has started pulling back investments in data centers and its CEO, Satya Nadella, has expressed skepticism about the "race to AGI". She argues that what drives the fervor in the absence of a clear business case is an ideology or a "quasi-religious fervor". People genuinely believe in the ability to fundamentally recreate human intelligence, seeing it as the most important civilizational goal.
  • She explains that this ideological drive from startups like OpenAI and Anthropic pressures larger, more traditional tech giants to invest heavily, as shareholders demand an AI strategy, often due to consumer shifts (like using ChatGPT as search). Hao explains that OpenAI's pitch to investors is that funding could lead to being the first to AGI for "the biggest returns you've ever seen" or, failing that, could automate human tasks to replace labor, generating significant returns.
  • She warns of a "bandwagon mentality" among investors. Crucially, she highlights that if the AI bubble pops, the risk is not just for Silicon Valley but will have ripple effects across the global economy, as investments often come from public endowments.

• OpenAI's Origins and Sam Altman's Leadership

  • Hao reveals that OpenAI started as a nonprofit in late 2015, co-founded by Elon Musk and Sam Altman, as an "anti-Google" initiative to conduct fundamental AI research without commercial pressures. Musk specifically feared Google's DeepMind could lead to AI going "very badly wrong" (sentience, harming humans). The original "open" in OpenAI stood for open source, and for its first year, the company genuinely open-sourced its code and research. Hao speculates that the nonprofit status was a recruitment tool to attract talent, as they couldn't compete with Google's salaries but could offer a compelling sense of mission. However, within less than a year, the bottleneck shifted from talent to capital, leading to the decision to convert to a for-profit entity. This shift also led to a falling out between Musk and Altman over who would be CEO.
  • Regarding Sam Altman, Hao portrays him as a "master manipulator" and "understander of human psychology". She notes that Altman was not publicly well-known but was a critical "lynchpin" within the tech industry, having cultivated relationships with powerful networks and policymakers early in his career as president of Y Combinator. Hao states that people who worked with Altman consistently told her they didn't know what he truly believed because he would often say he believed what the person he was talking to believed, even if those beliefs were diametrically opposed. She concludes that Altman's comparative advantages as a leader include his ability to persuade people to join his "quest," acquire necessary resources (capital, land, energy, water, laws), and instill a powerful sense of belief in his vision among his team. She describes his work as being most effective in one-on-one meetings where he can tailor his message to achieve his goals.

• Critique of Big Tech as a Corporate Empire

  • Hao argues that if allowed to expand unfettered, these corporate empires will ultimately erode democracy. Hao states that tech leaders view the rest of the world, including other Western countries, as "resources"—territories from which to acquire land, labor, minerals, energy, and water for their data centers. She highlights that data center expansion often targets economically vulnerable communities in rural areas of the US and UK, which are often uninformed about the true costs, such as bans on new housing construction due to massive electricity consumption, or the depletion of fresh water supplies. Hao laments that politicians are often unaware of these negative consequences.
  • She argues that the idea that "you need colossal data centers to build AI systems" is a "false trade-off". Before OpenAI, AI research was trending towards "tiny AI systems" requiring little computational resources, showing that AI innovation can occur without these massive, resource-intensive approaches. Hao points out that most AI experts today are employed by these companies, which she likens to climate scientists being bankrolled by oil and gas companies, leading to biased information that serves the company's interests rather than scientific grounding.

• Exploitative Labor Practices

  • Hao exposes grueling exploitative practices in the global AI supply chain, particularly regarding content moderation for OpenAI. Kenyan workers were contracted to sift through "reams of the worst text on the internet," including child sexual abuse, hate speech, and violent content, to build content moderation filters for ChatGPT. She details how this work traumatized workers, causing PTSD, personality changes, and family breakdowns, like the story of Moffat, whose family left him due to his changed demeanor. These workers were paid only a few dollars an hour.
  • She also discusses data annotation, a long-standing part of the AI industry. Venezuelan refugees in Colombia, highly educated but desperate due to their country's economic crisis, became cheap labor for labeling data for self-driving cars and retail platforms. Hao describes the structural exploitation where workers compete for tasks on platforms, leading to immense anxiety and control over their lives, exemplified by a woman who wouldn't walk outside during weekdays for fear of missing tasks and would wake up at 3 AM if an alarm signaled a new task. She asserts that there is no moral justification for why these workers, whose contributions are critical, are paid pennies while company insiders receive multi-million dollar compensation packages; the only "justification" is an ideological one that some people are superior.

• Proposals for Public Action and Shaping the Future of AI

  • Hao believes that anyone in the world can take action to shape the AI development trajectory. She proposes thinking of AI development as a "full supply chain of AI development", where various resources (data, land, energy, water) and deployment spaces (schools, hospitals, offices) are points of democratic contestation.

• She suggests the public can reclaim ownership over resources. She encourages people to contest the spaces where AI is deployed. Hao also advises people to research AI technologies and vendors to make informed choices about which AI systems to use.

• She expresses optimism that widespread, democratic contestation at every stage of the AI development and deployment pipeline can "reverse the imperial conquest of these companies" and lead to a more broadly beneficial trajectory for AI.

How to Gamify Your Life (And Reinvent Yourself ... Fast) - Dan Koe [Link]

The Future of Work (How to Become AI-First) - Dan Koe [Link]

Winning the AI Race Part 1: Michael Kratsios, Kelly Loeffler, Shyam Sankar, Chris Power [Link]

Winning the AI Race Part 2: Vice President JD Vance [Link]

Winning the AI Race Part 3: Jensen Huang, Lisa Su, James Litinsky, Chase Lochmiller [Link]

Turbo-Scaling GenAI at DoorDash: From Product Knowledge Graph to Real-Time Personalization - Predibase [Link]

I finished reading Peter Thiel's 'Zero to One: Notes on Startups, or How to Build the Future' today. With the rapid advancements and widespread discussion around AI, the core arguments about technology, human-machine collaboration, and the nature of progress hold up remarkably well. And in some ways, as a manifesto for building a better future, what's written in this book is even more relevant now.

Chapter 2 Party Like It's 1999 outlines four lessons learned from the dot-com crash that became 'dogma' in the startup world, however, Thiel argues that these dogmas are largely incorrect and that the opposite principles are probably more correct:

1. Make incremental advances: Grand visions were seen as bubble-inflating, so small, incremental steps became the preferred path.

   Thiel: It is better to risk boldness than triviality.

2. Stay lean and flexible: Planning was deemed arrogant, and "agnostic experimentation" became the norm.

   Thiel: A bad plan is better than no plan.

3. Improve on the competition: Focus on existing customers and recognizable products, improving on what competitors already offer.

   Thiel: Competitive markets destroy profits.

4. Focus on product, not sales: If a product requires advertising or salespeople, it's not good enough; viral growth is the only sustainable growth.

   Thiel: Sales matters just as much as product.

"The most contrarian thing of all is not to oppose the crowd, but to think for yourself."

In this end this chapter, Thiel is challenging the reader to not simply adopt the prevailing "lessons learned" from the past, but to critically evaluate them. He suggests that true contrarianism isn't just about disagreeing with the majority for the sake of it, but about independent thought and forming your own conclusions, even if those conclusions align with or contradict the crowd. It's about genuine intellectual autonomy.

In Chapter 3 All Happy Companies Are Different, he argues that successful companies are unique and that true value comes from creating a monopoly rather than competing in existing markets. Thiel uses the economic models of "perfect competition" and "monopoly" to explain this difference. In perfect competition, firms sell identical products, have no market power, and thus, in the long run, make no economic profit as new entrants drive prices down. A monopoly, conversely, owns its market, allowing it to set prices and maximize profits due to a lack of close substitutes. He asserts that competition is destructive, leading to a ruthless struggle for survival and zero profits. Monopolies, on the other hand, can afford to focus on long-term innovation, employee well-being, and broader societal impact because they are not constantly battling for survival. Creative monopolies are powerful engines for progress as they introduce entirely new categories of abundance to the world.

He then discusses how both monopolists and non-monopolists tend to misrepresent their market conditions. Monopolists (like Google) downplay their dominance by broadly defining their market to avoid scrutiny, while non-monopolists (like a new restaurant owner) narrowly define their market to appear unique and avoid acknowledging intense competition. Thiel emphasizes that losing sight of competitive reality by focusing on trivial differentiators is a fatal mistake for startups.

"All happy companies are different: each one earns a monopoly by solving a unique problem. All failed companies are the same: they failed to escape competition."

This is the core message from the book. Entrepreneurs should strive to build unique, monopolistic businesses by creating something entirely new.

Chapter 3 primarily focuses on the economic and strategic advantages of monopoly and the destructive nature of perfect competition. Chapter 4: "The Ideology of Competition" shifts the focus to the societal and psychological impact of competition. Competition is not merely an economic concept but a deeply ingrained "ideology" that pervades our society, from education to personal aspirations. He reminds readers that this competition can blind people to real opportunities and lead to irrational behavior and missed chances, and suggests us to recognize and resist the pervasive ideology of competition.

Chapter 5 Last Mover Advantage discusses how a great business is defined by its ability to generate future cash flows and argues that being a last mover (i.e., to make the last great development in a market and enjoy long-term monopoly profits) is more advantageous than being a first mover. It outlines four characteristics of monopoly that contribute to a company's durability:

1. Proprietary Technology: This makes a product difficult to replicate, ideally being at least 10 times better than its closest substitute (e.g., Google's search algorithms, PayPal's payment system for eBay, Amazon's book selection, Apple's integrated design).

2. Network Effects: The product becomes more valuable as more people use it (e.g., Facebook). Thiel emphasizes that such businesses must start with a very small, focused market to get initial users.

3. Economies of Scale: Fixed costs can be spread over increasing sales, making the business stronger as it grows. Software companies are particularly suited for this due to near-zero marginal costs.

4. Branding: A strong brand creates a monopoly (e.g., Apple). However, branding needs to be built on substantive advantages, not just surface-level polish.

"You've probably heard about 'first mover advantage': if you're the first entrant into a market, you can capture significant market share while competitors scramble to get started. But moving first is a tactic, not a goal. What really matters is generating cash flows in the future, so being the first mover doesn't do you any good if someone else comes along and unseats you. It's much better to be the last mover— that is, to make the last great development in a specific market and enjoy years or even decades of monopoly profits. The way to do that is to dominate a small niche and scale up from there, toward your ambitious long-term vision. In this one particular at least, business is like chess. Grandmaster José Raúl Capablanca put it well: to succeed, 'you must study the endgame before everything else.'"

Thiel's advice for startups (1) start small and monopolize, 2) scale up gradually, and 3) don't discrupt) reminds me of Google's AI strategy in recent two years, which seems to align with the 'last mover advantage' mentality. Instead of trying to release one massive, all-encompassing AI that competes directly with established players across every front, Google has released or integrated AI into many "smaller" applications or features (workspace, photos, maps, gemini, etc). Each of these can be seen as a "small market" or specific use case where AI offers a distinct advantage, allowing Google to "monopolize" that particular user experience. After establishing AI capabilities in focused areas, they are integrating these more broadly. Successful AI features in Workspace might then be leveraged for enterprise solutions. Advancements in image recognition from Photos could be applied to broader visual search or other AI models. The iterative development of Bard/Gemini, starting as a conversational AI and gradually expanding its capabilities (multimodality, coding, planning), is a clear example of scaling up. They build upon established user bases and technological strengths. While Google is certainly competing, their strategy doesn't always seem to be about a direct, disruptive frontal assault that immediately aims to destroy an incumbent. Instead, it's often about: 1) leveraging their exsiting ecosystem, 2) focusing on unique capabilities, 3) creating new user behaviors.

In Chapter 6 You Are Not a Lottery Ticket, Thiel described the concept of definite vs. indefinite futures and asserts that the prevailing indefinite optimism, particularly in the US, is unsustainable. He argues that real progress and success require definite plans and individual effort.

“When Baby Boomers grow up and write books to explain why one or another individual is successful, they point to the power of a particular individual's context as determined by chance. But they miss the even bigger social context for their own preferred explanations: a whole generation learned from childhood to overrate the power of chance and underrate the importance of planning."

The core of Chapter 7 Follow the Money applies the power law to venture capital (VC). Venture returns are not normally distributed (where most companies perform average). Instead, they follow a power law: a small handful of companies radically outperform all others, often returning more than the entire rest of the fund combined. People often fail to see the power law, which is a fundamental law of the universe, because it only becomes clear over time; early-stage companies in a portfolio might look similar before exponential growth kicks in. Despite being a niche (less than 1% of new businesses receive VC funding), venture-backed companies disproportionately drive the economy, creating 11% of private sector jobs and generating 21% of GDP. The largest tech companies, all venture-backed, are worth more than all other tech companies combined.

Understanding the power law means focusing on the singular, most important things (e.g., one best market, one dominant distribution strategy). To achieve disproportionate success, one must identify and focus relentlessly on those few critical elements.

In Chapter 8 Secrets, Thiel begins by posing his contrarian question ("What important truth do very few people agree with you on?") in the context of secrets. He states that a good answer to this question implies the existence of secrets – something important, unknown, difficult, but achievable. He argues that secrets still exist and are crucial for progress.Secrets can lead to monumental advancements in science, medicine, and technology (e.g., curing diseases, new energy sources). In business, secrets can lead to valuable companies built on overlooked opportunities, like Airbnb (untapped supply and unaddressed demand in lodging) and Uber/Lyft (connecting drivers and riders). In terms of how to find secrets, Thiel has discussed about 1) secrets of nature from studying physical world, vs. secrets about people from understanding human nature, 2) looking at the fields that matter but haven't been standardized.

Chapter 9 Foundations is around 'Thiel's law': a startup messed up at its foundation cannot be fixed, providing guidance on fundamental level: co-founder relationships, ownership, possession, and control, small boards, full time commitment, equity is the king, founding moment, etc. Chapter 10 The Mechanics of Mafia highlights the importance of company culture. Chapter 11 If You Build It Will They Come stresses that distribution (sales, marketing, advertising) is often underestimated and is just as crucial as product development.

"The founding moment of a company, however, really does happen just once: only at the very start do you have the opportunity to set the rules that will align people toward the creation of value in the future.

The most valuable kind of company maintains an openness to invention that is most characteristic of beginnings. This leads to a second, less obvious understanding of the founding: it lasts as long as a company is creating new things, and it ends when creation stops. If you get the founding moment right, you can do more than create a valuable company: you can steer its distant future toward the creation of new things instead of the stewardship of inherited success. You might even extend its founding indefinitely."

"'Company culture' doesn't exist apart from the company itself: no company has a culture; every company is a culture. A startup is a team of people on a mission, and a good culture is just what that looks like on the inside."

There is a core debate right now around whether AI is going to replace human‘s jobs, and this book offers powerful arguments for the "AI as complement, not replacement" side. Thiel explicitly argued against the "substitution fallacy" in Chapter 12 (Man and Machine), stating that computers and humans have different strengths and will thrive through collaboration. Although Generative AI is unprecedented with its impact on human society nuanced to discuss, I agree there are fundamental differences in intelligence between humans and AI. Human possess intentionality, true innovation, empathy, and emotional intelligence, and human judgment is needed when there are ethical concerns or complex problems. AI as a tool can do augmentation to increase productivity, but not automation. Historically speaking, tech development always creates more jobs than destroyed. While some roles are eliminated, new roles emerge: AI engineers, Prompt Engineers, AI Product Managers, etc. In essence, it's about a redefinition of work, rather than elimination.

"People compete for jobs and for resources; computers compete for neither."

Globalization is about substitution. Technology is about complementarity.

Chapter 13 Seeing Green analyzes the failure of the cleantech bubble, attributing it to a widespread failure to answer the seven critical questions every successful business must address.

1. The Engineering Question: Most offered only incremental, not breakthrough (10x better), technology (e.g., Solyndra's inefficient cylindrical solar cells).
2. The Timing Question: They misjudged market readiness and the slow, linear progress of solar technology compared to exponential tech.
3. The Monopoly Question: They pursued "trillion-dollar markets" that were fiercely competitive, rather than small, defensible niches.
4. The People Question: Teams were often led by "salesman-executives" lacking technical expertise, focusing on fundraising over product. (Thiel suggests a "never invest in a tech CEO that wears a suit" rule.)
5. The Distribution Question: Companies often overlooked effective distribution, leading to complex and inconvenient sales models (e.g., Better Place's battery swapping).
6. The Durability Question: They failed to anticipate competition (e.g., from China) or market shifts (e.g., the rise of fracking).
7. The Secret Question: They based their ventures on "conventional truths" (the need for a cleaner world), which everyone agreed on, rather than unique, hidden insights.

"The 1990s had one big idea: the internet is going to be big. But too many internet companies had exactly that same idea and no others. An entrepreneur can't benefit from macroscale insight unless his own plans begin at the micro-scale. Cleantech companies faced the same problem: no matter how much the world needs energy, only a firm that offers a superior solution for a specific energy problem can make money. No sector will ever be so important that merely participating in it will be enough to build a great company."

Chapter 14 The Founder's Paradox explores the often extreme, contradictory, and seemingly peculiar traits of successful founders, arguing that these unique characteristics are both powerful for a company and carry inherent dangers for the founder. Society needs founders – unusual individuals who can make authoritative decisions, inspire loyalty, and plan long-term, moving companies beyond incrementalism. However, founders must be wary of overestimating their own power and succumbing to their own myth, mistaking public adulation or criticism for truth. The greatest danger for a founder is losing their mind; for a business, it's losing its myth and vision.

The current AI boom feels very much like an "accelerating takeoff " in terms of technological advancement, which is mentioned in the final chapter "Conclusion: Stagnation or Singularity", as one of the Nick Bostrom's four possible patterns for humanity's future. Accelerating Takeoff (Singularity) is the most difficult scenario to imagine: new technologies so powerful that they transcend current understanding, leading to a much better future. Ray Kurzweil's "Singularity is near" concept, based on exponential growth trends, is mentioned as a prominent view of this outcome. However, as Thiel's book is a manifesto for building a better future and criticizes 'indefinite optimism', in the context of AI boom, 'Singularity' is not a predetermined destination, but the choices we make today:

• Are we using AI for "0 to 1" innovation to solve truly hard problems and create new value, or are we just using it for "1 to n" incremental improvements and fierce competition?
• Are we making definite plans for how AI will integrate with and enhance human capabilities, or are we succumbing to "indefinite fears" or blind optimism?
• Are we building companies around unique AI-driven insights that can create sustainable monopolies, or are we simply entering crowded AI markets hoping for a piece of an existing pie?