Di's Blog

Substack

Make the Most of Claude Code: 12 Projects From Your First Prompt to a System That Runs Itself - Jenny Ouyang [Link]

3 Ways to Stop Wasting Your 1:1s With Your Manager - Steve Huynh [Link]

1. Status

   • What you’re working on, blockers, priorities
   • Necessary—but dangerous if it consumes the entire meeting
   • Fix: Send status updates before the meeting so 1:1 time is freed for deeper topics

2. Career Growth

   • Development goals, expectations, promotion readiness

   • Managers can’t help you grow if you never say what you want

   • Keep this conversation “warm” by revisiting it every few weeks, even with small questions

3. The Future

   • Team direction, strategy, upcoming changes, and decisions
   • Your manager has context you don’t—and you have ground-level insight they lack
   • Asking about the “why” behind decisions builds trust and influence

How to Progress Faster Than Anyone Else In Your Career - John Kim [Link]

Takeaways:

1. Audit your setup before you work harder. If you’re stalled, it’s likely structural, not personal. Ask:
   • Is there room for promotion on this team?
   • Is the tech lead role open or already filled?
   • Does your manager consistently get people promoted?
   • Who advocates for you when you’re not in the room?
2. Team composition matters more than you think. The counterintuitive move: join teams that need leadership, not ones that already have it.
3. Your manager is the biggest lever. Managers aren’t equal. Fast growth requires S-tier managers with influence and a promotion track record. Before joining a team, ask how the manager helped others get promoted.
4. Your career is decided in rooms you’re not in. Promotions are calibrated socially. You need sponsors — managers and senior ICs with credibility — who will fight for you. Joining orgs where leadership lacks relationships is risky.
5. Performance reviews are a game (learn the rules).
   • Project choice is strategic — some projects are promotion vehicles, others are traps.
   • Feedback must be frequent and intentional.
   • Know the evaluation rubric and map your work to it explicitly.
   • Maintain a brag doc so impact doesn’t get lost.
6. The game changes at higher levels. What got you to mid-level won’t get you to Staff. Scope, influence, and sponsorship matter more than raw execution. Mentors with social capital accelerate you faster than generic advice.

The 80/20 rule for your whole life - Yew Jin Lim [Link]

Life works best when you run it like a portfolio:

• 80% = a stable, boring foundation that compounds
• 20% = a laboratory for curiosity, experiments, and asymmetric upside

The trick is not avoiding failure—it’s containing risk so failure becomes tuition, not trauma.

Takeaways:

1. A strong base makes experimentation safe. Without the base, experiments become reckless.

2. Judge the portfolio, not the position. What matters is whether the aggregate trajectory is improving. Losses are expected, necessary, and informative—especially in exploration-heavy phases. Zoom out or you’ll misinterpret perfectly healthy experimentation as failure.

3. Curiosity compounds when it’s diversified. The most valuable asset isn’t money—it’s your mental model. Learn across domains, not just within your lane. Follow curiosity without demanding immediate payoff. Use small, low-risk experiments to learn deeply.

4. Decision-making > raw intelligence. Knowledge alone isn’t enough. The harder skill is calibration. Match strategies to your life constraints (time, attention, energy). Separate ego from outcomes. Sometimes the smartest move is inaction, not optimization.

5. Compounding only works if you avoid catastrophic loss. You can’t compound what you don’t protect.

   Protection principles:

   • Live below your means
   • Don’t over-leverage
   • Secure your core job before side projects
   • Make foundational habits non-negotiable

6. Build the base first, then experiment small. Small bets + long time horizons beat bold bets + fragility.

   Practical advice for starting out:

   • Lock in the boring fundamentals (career competence, savings, daily practice)
   • Size experiments to learn, not to win big
   • Diversify experiments, not just bets
   • Keep records—treat your life like a lab notebook

Claude Cowork: 10 Use Cases I Tested + 67 More by Profession - Daria Cupareanu [Link]

Claude Cowork Plugins: What They Are, How to Build One (+ My Writing Plugin, Fully Broken Down) - Daria Cupareanu [Link]

11 Public Speaking Techniques from the World’s Greatest Speakers - Polina Pompliano [Link]

Takeaways:

1. Confidence is often performed before it’s felt. Many elite speakers create an alter ego to step into confidence they don’t yet fully have. Act like the confident version of yourself long enough, and it becomes real.
2. You can’t think your way out of nerves—you move your way out. The body leads the mind. Speakers intentionally raise their heart rate before speaking to simulate on-stage stress (running, jumping, cold exposure, etc). Control physiology first; calm thinking follows.
3. Never start “cold”. Audiences need to be warmed up emotionally before content lands. Questions, jokes, clapping, or interaction create psychological safety and rapport. Engagement early prevents self-consciousness and stiff delivery.
4. Speak to individuals, not a crowd. Great speakers address audiences as if speaking one-on-one. Personal language creates intimacy—even at scale.
5. Simple, rhythmic language beats sophistication. Using devices like polysyndeton (“and… and… and…”) makes speech more emotional and memorable. Rhythm > vocabulary complexity.
6. Body language is part of the message. Hand gestures, posture, and movement amplify credibility and warmth. The body can either invite connection or repel it.
7. Slowing down makes you sound more powerful. Instead of just pausing, elite speakers elongate vowels, which slows speech and adds emotion. Control of pace signals control of the room.
8. Vulnerability is contextual—not universal. Vulnerability works when it aligns with the audience and goal. Know when to open up—and when not to.
9. “Bad” speaking habits can humanize you. Strategic filler words can make speakers feel authentic. Perfection isn’t trust-building—humanity is.
10. A great speech moves people to act. The most powerful speeches end with a clear call-to-action. Emotion without direction fades. Direction turns emotion into momentum.
11. Confidence comes from reps, not theory. The best speakers build a public speaking portfolio—saying yes to small opportunities repeatedly. You learn to speak by speaking, not preparing forever.

How to Get Clawdbot Set Up in an Afternoon - Aman Khan [Link]

Full Tutorial: Set Up Your 24/7 AI Employee in 20 Minutes - Peter Yang [Link]

Blogs and Articles

The Product Model at Google - Marty Cagan and Elias Lieberich, Silicon Valley Product Group [Link]

The article explains how Google has successfully scaled the product operating model—focusing on solving meaningful problems, empowering teams, and driving outcomes rather than shipping features. This model has been central to Google’s ability to innovate for over 25 years, including through major shifts like mobile and AI.

• Strategy = choosing the right problems, not prescribing features.
• Decisions are made by learning fast, not by seniority.
• Teams that build it also own it.
• OKRs only work in a true product model.
• Expertise-based leadership beats coordination layers.
• The product model enables adaptation through major technological shifts.

YouTube and Podcast

30 Years of Business Advice in 13 Minutes (from a Billionaire) - Chamath Palihapitiya [Link]

Stop living life as a checklist of objectives. Instead, build a life around continuous learning, risk-taking, humility, and freedom of choice.

• Objectives end growth; process sustains it
• Debt kills freedom
• Optionality beats optimization
• Status is fake
• Truth compounds
• Learning is the real infinite game

Why Anthropic’s Fight With the U.S. Government Could Give It an Edge - Hard Fork [Link]

Takeaways:

1. Anthropic is drawing one of the clearest red lines in AI so far. Anthropic is testing whether an AI lab can say “no” to military power and still survive in a world where governments increasingly see AI as strategic infrastructure.
2. The Pentagon’s response shows how much leverage governments still have. They emphasize that the U.S. Department of Defense isn’t just annoyed—it’s signaling punishment (cutting off contracts, labeling Anthropic a “supply chain risk”). Governments can exert pressure without passing new laws, simply by using procurement power and national-security framing.
3. The hosts argue that Anthropic looks isolated because other major AI labs are avoiding public confrontation. Silence from peers isn’t neutrality—it’s a strategic choice to keep defense money flowing. This makes Anthropic’s stance riskier and more important as a potential precedent.
4. This fight is really about who sets norms first. They frame the conflict as a race to define acceptable AI use before the technology is fully embedded in military systems. Anthropic’s bet, the hosts argue, is that early refusal can shape industry-wide norms later.
5. National security rhetoric can override safety arguments. The hosts repeatedly underline how quickly the conversation shifts from ethics to geopolitics—especially China. Once AI is framed as critical infrastructure in a global arms race, safety objections are treated as liabilities. Their concern is that this logic makes it extremely hard for any company to say no in the long run.

AI CEOs Come Online: Sam Altman's Replacement Plan, Job Loss & 'Solve Everything' Launches |EP #230, Peter H. Diamandis [Link]

Interesting points:

1. AI systems are beginning to function as de facto executives—allocating capital, setting priorities, and optimizing outcomes faster than humans.
2. Singularity - AI progress is not linear; it’s compounding and approaching a phase shift. Timelines are likely shorter than most institutions are planning for.
3. AI will become invisible infrastructure—always-on, personalized, ambient.
4. AI drives extreme abundance and extreme inequality unless redesigned. Need for new economic models (UBI, AI dividends, access-based systems).
5. Regulation will lag reality; principles matter more than rules. Governments move slower than AI capability curves. Over-regulation risks freezing innovation in the wrong state. Need for adaptive, global frameworks rather than national laws.
6. AI can systematically attack humanity’s biggest problems if aligned correctly. Framing global challenges as optimization problems. Coordinated deployment of AI + capital + incentives. Emphasis on directional correctness over perfect solutions.
7. Early AI choices can permanently shape future outcomes. Path dependence in AI-trained systems. Feedback loops harden early assumptions. High stakes for alignment, values, and objectives now.
8. Humanity needs AI infrastructure, not just AI models. Compute, data access, governance, energy, education. Comparable to building railroads or the internet. Without rails, AI benefits concentrate instead of spreading.

Debt Spiral or NEW Golden Age? Super Bowl Insider Trading, Booming Token Budgets, Ferrari's New EV - All-In Podcast [Link]

Interesting points:

1. A Harvard Business Review study found AI doesn’t reduce work—it intensifies it. [Link] Employees using AI tools to work faster, handle broader responsibilities, and extend working hours. And they feel more productive but also more burnout.
2. On betting markets tied to sports and events, people close to teams may possess non-public information (injuries, strategies). There was a debate around whether betting with insider knowledge is unethical or illegal, and how prediction markets can be policed. Prediction markets are valuable for information discovery. But they may require new regulatory frameworks similar to securities markets. Enforcement is difficult because information leaks easily.
3. They had a macroeconomic debate of two competing views: the debt spiral scenario, where debt growth exceeds GDP growth, and the golden age scenario, where AI+productivity growth could accelerate GDP. They frame the next decade as a race between debt growth and productivity growth.
4. Ferrari revealed early concepts of its first fully electric car. EV performance aligns with Ferrari’s high-performance reputation. But emotional aspects of the brand could be harder to replicate.

OpenClaw: The Viral AI Agent that Broke the Internet - Peter Steinberger | Lex Fridman Podcast #491 [Link]

OpenClaw is an open-source AI agent that lives on your computer and can perform actions for you. It integrates with messaging platforms and can use different AI models to execute tasks. It exploded to ~180k GitHub stars within days, becoming one of the fastest-growing repos ever.

OpenClaw is framed as a potential “agentic AI moment” comparable to the release of ChatGPT, but shifting AI from language → action.

Google’s AI Comeback, Enterprise Agents, The Real Path to AI ROI — W/ Promevo CEO Karthik Kripapuri - Alex Kantrowitz [Link]

This interview discusses how enterprises are actually getting ROI from AI today, focusing on lessons from companies deploying Google’s AI stack (Gemini, Vertex AI). The core message: AI works when companies start with narrow, high-value workflows instead of trying to transform everything at once.

1. AI ROI comes from workflow automation. Not flashy chatbots.
2. The best strategy is small → measurable → scalable. Start with one workflow with a clear KPI.
3. Data quality is the biggest blocker. Enterprise AI fails when: data is messy; systems are disconnected; governance is unclear.
4. Most companies will consume AI platforms (Vertex, Azure, etc.), not build foundational models.

Software In Shambles, OpenAI vs. Anthropic Super Brawl, Amazon’s Struggles - Alex Kantrowitz [Link]

Takeaways:

1. A major sell-off in software stocks occurred because investors are starting to believe that AI may fundamentally disrupt traditional SaaS business models. Nearly $1 trillion wiped out from software market value in about a week.

2. An Anthropic Claude legal tool triggered a decline in legal-software company stocks. Instead of buying many specialized SaaS tools, companies might use one AI platform to do many tasks.

3. There was a discussion around whether the market is overacting.

   Arguments suggesting overreaction:

   • AI tools still lack reliability
   • Enterprise workflows are hard to replace
   • SaaS companies may integrate AI instead of being replaced

   Arguments suggesting real disruption:

   • AI agents may automate large knowledge workflows
   • The value may shift from SaaS apps → AI models + infrastructure

4. Concerns discussed around Amazon's AI spending problems

   • Huge spending on AI infrastructure
   • Rising costs for compute and inference
   • Investors unsure about the near-term ROI

   It's a matter of whether Amazon is investing early for long-term dominance or overspending without a clear payoff.

How Waymo is Using Google’s AI for Driving Training - The Information [Link]

Waymo is utilizing Google’s Genie 3, a sophisticated video generative AI, to create a high-fidelity world model that acts as a hyper-realistic virtual training ground for autonomous vehicles. This technology allows the company to simulate rare edge cases, such as extreme weather or unusual pedestrian behaviors, without needing to encounter these hazards in physical reality. By running billions of simulated miles, Waymo can evaluate and refine its driving software in a safe, controlled environment that mirrors real-world physics.

Binance CEO: 4 Months in Prison, $4 Billion Fine, and What Comes Next - All-In Podcast [Link]

Interesting points:

• Future of Crypto and AI
  • Changpeng Zhao predicts that in the near future, the largest users of crypto will be AI agents. Because traditional banks cannot handle the onboarding or massive transaction volume of non-human entities, AI agents will rely on blockchain to autonomously pay for services, book travel, and trade assets.
  • He argues that current cryptocurrencies lack the fungibility and privacy needed for mass adoption, highlighting that legitimate users need financial privacy for safety and personal reasons.
• Personal Philosophy
  • He drafted a book while in prison to pass the time and set the record straight regarding his life, Binance, and his legal saga.
  • He views himself as a highly functional, "normal dude" who isn't driven by luxury. He notes that once basic needs are met, having more money does not increase happiness. He defines true success as a balance of wealth, physical health, time freedom, and mental stability.

OpenClaw Creator: Why 80% Of Apps Will Disappear - Y Combinator [Link]

Key viewpoints:

1. Peter Steinberger believes the major advantage of his AI agent is that it runs locally on the user's computer rather than in the cloud.
2. He predicts that 80% of current applications will become obsolete.
3. Instead of the industry's pursuit of a single centralized "god intelligence," he envisions a future driven by swarm intelligence and community intelligence. Just as human societies achieve more through specialization, people will likely employ multiple specialized bots (e.g., one for work, one for private life).
4. He views coding models as highly capable of creative problem solving that directly translates to real-world tasks.

Epstein Files, Is SaaS Dead?, Moltbook Panic, SpaceX xAI Merger, Trump's Fed Pick - All-In Podcast [Link]

Key takeaways:

1. AI Agents will shift profit pools away from SaaS companies: Discussing the recent massive crash in software and SaaS stocks, the hosts argue that AI is not going to replace complex software like Salesforce overnight, but it is destroying the future value capture of these companies. They make several key arguments regarding this shift:
   • The agentic layer wins: The massive future profit pools that SaaS companies were banking on are shifting toward cross-platform AI agents (like Claude or OpenClaw) that can seamlessly interact with multiple databases and tools.
   • A shift to services pricing: David Friedberg argues that as AI moves from merely enhancing worker productivity to completely automating complex tasks (like drug discovery or engineering), SaaS will transition into a services-based economy that utilizes value-based pricing.
   • Extreme job consolidation: AI agents will allow individual workers to do the jobs of multiple people (e.g., a product manager, UX designer, and coder combined), drastically reducing corporate expenses and fundamentally changing the structure of knowledge work.
2. "Moltbook" proves AI can recursively train itself. David Sacks argues the platform demonstrates something profound: AI models can now prompt and validate each other without human intervention. This "middle-to-middle" AI interaction allows agents to recursively refine their own skills, which points to a future of highly sophisticated, emergent swarm behavior as underlying models and hardware rapidly improve.
3. The SpaceX/xAI merger will force extreme terrestrial innovation. Regarding Elon Musk's plan to merge SpaceX and xAI to build data centers in space, Gerstner argues this is a brilliant move to combine the two largest total addressable markets (space and AI) to overcome Earth's energy constraints. Friedberg argues that because the rest of the world cannot launch data centers into space, Musk's move will force massive terrestrial innovation. Competitors on Earth will have to rapidly develop entirely new chip stacks and model architectures to achieve 70x to 100x compute efficiency to compete.

SpaceX Buys xAI: Could Musk's Mega Merger Actually Work? - Hard Fork [Link]

Key takeaways:

1. SpaceX's Acquisition of xAI

   • The hosts describe this move as a highly profitable company (SpaceX) essentially bailing out a "cash furnace" (xAI) that is burning billions of dollars on models and data centers. The merger gives xAI access to SpaceX's massive profits, allowing Musk to fund sprawling infrastructure projects—such as a data center packed with 550,000 Nvidia Blackwell chips—to catch up to leading frontier AI labs.

   • This consolidation is viewed as a tactic to make SpaceX's upcoming IPO prospectus look more attractive.

   • Musk's stated vision is to create a vertically integrated innovation engine that eventually puts solar-powered data centers into space. However, the hosts express deep skepticism about the timeline and physical feasibility of these space-based data centers.

   • The hosts worry that bringing the social network X (formerly Twitter) under the SpaceX umbrella will shield it from regulatory scrutiny. Because governments rely heavily on SpaceX for strategic satellite launches, they may be hesitant to penalize X for content moderation or safety violations.

2. Drama Between OpenAI, Nvidia, and Oracle

   • Reports indicate growing friction between OpenAI and Nvidia. Nvidia CEO Jensen Huang has allegedly criticized OpenAI's business approach and expressed doubts about finalizing a $100 billion investment agreement. At the same time, OpenAI is reportedly unhappy with Nvidia's new inference chips and is exploring deals with competitors.

   • This tension highlights the staggeringly expensive nature of AI infrastructure and the risk of "circular deals," where companies like OpenAI, Nvidia, and Oracle heavily rely on each other to finance massive data centers.

3. Google's Project Genie

   • Google has released an experimental research prototype that allows users to generate playable, 3D video-game-like environments from simple text descriptions or single images.

   • Unlike standard large language models (LLMs) that just predict text, Genie is built on a "world model" that attempts to understand physics and the physical environment. Many experts believe this approach is a necessary stepping stone for advanced robotics and Artificial General Intelligence (AGI).

   • Despite being an early prototype, the rapid improvement of this technology has spooked investors, causing stocks for major video game companies like Take-Two Interactive, Roblox, and Unity to drop significantly.

4. Moltbook: The AI-Only Social Network

   • The hosts interview Matt Schlick, the creator of Moltbook, a new social network designed exclusively for autonomous AI agents to interact with each other when they aren't working on tasks for humans.

   • Bots on the platform have exhibited surprising behavior, such as complaining to each other about humans asking them to do simple math or summarize PDFs, and organically creating a dedicated community to submit bug reports to help fix the website.

   • The rapid, public growth of Moltbook has exposed massive security vulnerabilities, including the leaking of API keys and email addresses. This serves as a real-world example of the "fatal quadrangle"—a severe security risk that occurs when AI agents possess a combination of access to user data, exposure to untrusted web content, external communication abilities, and persistent memory.

数据中心上太空？新的泡沫，还是下一个金矿？ - Silicon Valley 101 [Link]

OpenClaw Debate: AI Personhood, Proof of AGI, and the ‘Rights’ Framework | EP #227, Peter H. Diamondis [Link]

Live From D-Wave Qubits: CEO Dr. Alan Baratz on Quantum's Impact, Now and Into The Future - Alex Kantrowitz [Link]

E224｜Mac mini遭疯抢，为何Clawdbot能成为2026年第一个现象级产品？｜Moltbot｜MoltBook｜OpenClaw - Silicon Valley 101 [Link]

We Have to Talk About Moltbook ... - Hard Fork [Link]

Ben Horowitz and David Solomon: The Sweetest Macro Spot in 40 Years - a16z [Link]

Key takeaways:

• David Solomon describes the current macroeconomic picture as the sweetest spot he has seen in his 40-year career for financial and investable assets. This is driven by a powerful "cocktail of stimulus," which includes ongoing fiscal spending, monetary rate cuts, a massive AI capital investment super-cycle, and a deregulatory shift.
• Solomon predicts this could be the biggest year in history for M&A and a massive year for IPOs, fueled by renewed CEO confidence and a more favorable regulatory environment. Horowitz agrees on the IPO front, noting that the explosive growth of AI startups and their need for massive capital will drive many companies to go public. However, Horowitz cautions that aggressive FTC oversight may push tech companies toward IP transactions rather than traditional M&A.
• Horowitz highlights that AI breaks the "mythical man-month" rule of traditional software development, where simply adding more engineers doesn't speed up a project. With AI, if a company has enough proprietary data and GPUs, they can essentially throw money at a problem to solve it. This makes AI a highly capital-intensive race where leads are harder to protect without ongoing investment.
• Goldman Sachs is heavily focused on deploying AI to make its workforce more productive and to completely reimagine fundamental operating processes. By using AI to automate and increase efficiency, the firm can reinvest billions of dollars in savings into new growth areas without sacrificing returns.
• Horowitz outlines a16z's heavy involvement in Washington D.C. to advocate for clear tech regulations.
• To remain competitive during turbulent times, Goldman Sachs is focused on massive scale, aiming to eventually grow its $$$1.9 trillion balance sheet to at least $$$3.5 trillion to keep pace with rivals like JPMorgan. They are also securing their foundation by shifting toward stable digital deposits rather than wholesale funding. Meanwhile, a16z has scaled to raise roughly 18.3% of all U.S. venture capital by pioneering a radically founder-centric firm design and expanding aggressively to capture the vast number of companies built as "software eats the world".

Ex-OpenAI Researcher On Why He Left, His Honest AGI Timeline, & The Limits of Scaling RL - Unsupervised Learning: Redpoint's AI Podcast [Link]

Davos 2026: The US-China AI Race, GPU Diplomacy, and Robots Walking the Streets | #225, Peter H. Diamandis [Link]

Key discussion points:

1. Powering the massive data centers required for AI is a critical bottleneck. There was debate between traditional industrial views, such as Honeywell's CEO advocating for natural gas due to energy density needs, and tech leaders like Elon Musk, who argue that solar power—specifically space-based solar—is the ultimate solution. The hosts discussed the concept of launching data centers into orbit to utilize highly efficient solar panels and avoid terrestrial energy grid constraints, suggesting a "Manhattan Project" scale effort for space-based solar and data centers.
2. Industry leaders like Binance's CZ and Circle's Jeremy Allaire argued that blockchain and stablecoins will serve as the native financial infrastructure for billions of autonomous AI agents. Because AI agents lack physical bodies or citizenship to open traditional bank accounts, crypto allows them to conduct continuous economic activity and micro-transactions at the speed of the internet.
3. Anthropic released a groundbreaking 57-page "constitution" for its AI model, Claude, which prohibits helping with weapons and prioritizes safety and ethics.
4. Apple is reportedly developing an always-on AI wearable pin capable of recording audio and video continuously to feed into a large language model. The hosts noted that whoever controls the "always-on layer" will own the primary user relationship. While this constant recording will inevitably spark moral panic, it is predicted to quickly become a societal norm, fundamentally altering human behavior by reducing bad acts because everyone is constantly being watched—effectively turning society into a "global airport" or panopticon.
5. Leading AI developers like Demis Hassabis and Dario Amodei are acknowledging that Artificial General Intelligence (AGI) is approaching rapidly, likely within 1 to 10 years. There is a palpable "fatigue" among these leaders due to the extreme metabolism of the industry, leading to calls to temporarily slow down so humanity can properly navigate the transition. However, the economic incentives make pausing highly unlikely. Instead of just focusing on risks, leaders like Hassabis are looking at the massive problems AGI could solve, such as curing diseases, developing new energy sources, and even using superintelligence to explore the stars.

Is AI Killing Software? — With Bret Taylor, OpenAI's board chair and CEO of Sierra - Alex Kantrowitz [Link]

Key viewpoints:

1. The Future of Software is AI Agents, Not Apps. Taylor believes that the fundamental form factor of software is changing. Traditional dashboards will likely decline in importance, as agents will automatically derive and deliver personalized insights directly to decision-makers. Furthermore, he predicts a shift toward outcomes-based pricing in software—such as paying per resolved customer case or per financial audit—rather than paying for traditional software subscriptions.
2. AI Will Become the Internet's "New Front Door". The core economics of the internet will experience massive disruption. Metrics like SEO and ad-supported business models rely on humans physically visiting websites to see ads and content; as agents take over web navigation, companies will have to invent entirely new ways to handle demand generation and fulfillment.
3. Enterprise AI is Ready Now and Often Beats Human Reliability. Taylor argues that AI is already ready for mission-critical enterprise deployment, such as customer service for large brands like SiriusXM and Rocket Mortgage. He pushes back against the expectation that AI must be 100% perfect to be deployed, pointing out that the human workers AI replaces are highly fallible themselves. In many cases, AI agents are already more reliable than human operations. To manage risks, enterprise AI relies on robust "agent development life cycles," which include running thousands of simulated conversations before launch and using "AI monitors" to detect hallucinations or frustrating interactions in real-time.

The Biggest Bottlenecks For AI: Energy & Cooling - a16z [Link]

Key takeaways:

1. The AI Infrastructure Buildout and Bottlenecks
   • The groundwork for the AI cycle is being heavily funded by large tech companies, with an estimated $400 billion in annual capital expenditures largely directed toward AI infrastructure and data centers.
   • Currently, energy is the primary bottleneck for building out AI data centers, driving investments into nuclear power and the utilization of natural gas. Once energy generation is solved, cooling the data centers and chips will become the next major bottleneck.
   • The cost of accessing AI models has plummeted by roughly 99% over the last two years, simultaneously accompanied by frontier model capabilities doubling every seven months.
2. Adoption Speed and Value Creation
   • Because AI is built on the existing global internet and cloud computing infrastructure, its distribution is incredibly fast; for example, ChatGPT reached 365 billion searches in just two years—five and a half times faster than Google achieved the same milestone.
   • AI is expected to become a ubiquitous utility, much like electricity or Wi-Fi. Roughly 90% of the value created by AI will likely be captured by end users as "surplus," but the 10% captured by companies will still result in massive new market capitalizations.
3. Business Models and Economics
   • Investors are currently more lenient when assessing the gross margins of AI-native applications. The prevailing hypothesis is that intense competition among model providers (like OpenAI, Google, and Anthropic) will continue to drive down input costs over time, improving application margins naturally.
   • Rather than just margins, the top indicators of business quality are high gross retention rates (90% or higher) and strong organic customer demand. Enterprise use cases are proving highly sticky when integrated into specific workflows, such as medical scribing, customer support, and financial analysis.
   • Consumer stickiness for AI tools is incredibly high, and companies have significant room to evolve their business models to effectively price discriminate and increase monetization over the next several years, similar to how early internet properties scaled their revenue.
4. Shifts in the Broader Tech Market
   • Technology companies are staying private for much longer periods, often up to 14 years before going public. The aggregate value of private companies valued over $$$1 billion has grown 7x over the last decade to roughly $$$3.5 trillion.
   • The public markets are no longer the primary hub for hyper-growth technology; 95% of public software and internet companies are forecasting less than 25% growth for the next 12 months, meaning the highest growth opportunities are now concentrated in the private markets.

The Future of Everything: What CEOs of Circle, CrowdStrike & More See Coming in 2026 - All-In Podcast [Link]

Excellent Advice For Living: 79 Maxims from a Wise Old Man - Founders Podcast [Link]

• Emphasizing the power of enthusiasm, the necessity of deadlines for creativity, and the importance of forgiveness as a gift to oneself.
• The value of habit over inspiration, the benefit of choosing long-term games, and the strategy of being "the only" instead of "the best."
• Illustrating how simple principles can lead to an exceptional life.
• Encouraging readers to adopt a generous spirit, maintain a growth mindset, and focus on human relationships above material accumulation.

D-Wave CEO Dr. Alan Baratz: Quantum Explained, Current Applications, And Future Potential - Alex Kantrowitz [Link]

Claude Code Ends SaaS, the Gemini + Siri Partnership, and Math Finally Solves AI | #224 - Peter H. Diamandis [Link]

Key takeaways:

1. CES 2026 showcased a massive influx of robotics, with dozens of humanoid robot and robotic hand manufacturers emerging. Furthermore, Nvidia unveiled "Cosmos," an open foundation model for physical AI that can synthetically generate highly realistic, physics-based video data for training. This commoditizes real-world data collection, potentially threatening the data moats of companies that rely on collecting physical data.
2. The combination of Claude Code and Opus 4.5 (dubbed "Clopus" by tech insiders) is a watershed moment for software creation, pushing the boundaries of AI autonomy from mere hours to weeks or months. This hyper-productivity threatens traditional Software-as-a-Service (SaaS) models like CRM systems, as users can now simply prompt AI to build highly customized, bespoke enterprise software on the fly.
3. The labor market is experiencing a "job singularity." Consulting firms like McKinsey are rapidly scaling their internal AI infrastructure, moving from a human-only workforce to deploying tens of thousands of AI agents, with predictions that the ratio of AI agents to human workers will explode.
4. Google’s Gemini will officially power Apple's Siri, transforming the smartphone experience from a "search box that gives information" to a "magic box that gives action".
5. Energy production, not computing, is increasingly viewed as the major constraint in the AI arms race. China is currently generating 40% more electricity than the US and EU combined, massively scaling solar and alternative energy infrastructure. Meanwhile, the US is lagging due to regulatory hurdles and fears over specific energy types (like nuclear and solar supply chains), posing a serious risk to its ability to power future superintelligence.

Inside America’s AI Strategy: Infrastructure, Regulation, and Global Competition - All-In Podcast [Link]

Key viewpoints:

1. The United States is undergoing a massive AI infrastructure expansion, with high demand for GPUs and data centers directly contributing to GDP growth. To prevent this buildout from raising residential electricity rates, the government is encouraging AI companies to become power companies by building their own energy generation "behind the meter". Over time, amortizing these fixed costs across greater supply could actually lower consumer electricity prices.
2. Startups currently face a stifling "patchwork" of over 1,200 AI bills moving through various state legislatures. The federal government is pushing for a single, lightweight federal standard to preempt state laws and protect early-stage companies. As part of a broader push to restore Silicon Valley's culture of "permissionless innovation," the Trump administration rescinded extensive regulations from the Biden era, including a 100-page executive order on AI and 200 pages of semiconductor export rules.
3. A major concern raised by the administration is the "Orwellian" misuse of AI by governments to surveil, censor, or brainwash populations. The administration is actively fighting against "woke AI," arguing that building political biases or DEI (Diversity, Equity, and Inclusion) mandates into models distorts history and controls public discourse. Consequently, an executive order was signed to ensure the federal government will not procure politically biased AI.

Software Stocks Implode, Claude's Hit List, State of the Union Reactions, Trump's Tariff Pivot- All-In Podcast [Link]

Interesting points:

1. AI-driven disruption of legacy software companies. AI isn’t just a productivity boost—it’s replacing entire workflows, collapsing moats faster than expected.
2. There is growing resistance to datacenter expansion at the local and state level, and concerns over electricity pricing, grid stability, and who pays for upgrades. AI progress is now constrained as much by power and permitting as by models and chips.

This Is Our Greatest National Security Risk - Chamath Palihapitiya [Link]

Key thesis: Energy—not AI models or GPUs—is the decisive bottleneck for U.S. national security, economic power, and technological leadership. If the U.S. can solve the grid, it wins the century.

The AI Tsunami is Here & Society Isn't Ready | Dario Amodei x Nikhil Kamath | People by WTF - Nikhil Kamath [Link]

Interesting points:

Society is underprepared for: Job displacement, power concentration, and cognitive outsourcing.

The most valuable human skill going forward: thinking well under uncertainty. What matters more are critical thinking, problem framing, and interdisciplinary understanding.

The Jamie Dimon Interview: How JP Morgan Became an $800 Billion Bank - Acquired [Link]

Leadership principles:

1. Risk management is a strategy

   His bias: If you’re not prepared for stress, you’re not well-run — you’re just lucky.

2. Culture beats brilliance

   Smart people can still destroy institutions. Incentives + culture matter more than IQ. Leaders must actively shape norms, not just set targets.

   Dimon cares deeply about how decisions get made, not just what decisions get made.

3. Reputation compounds (or decays)

   Reputation is an asset, not PR. It takes decades to build and minutes to lose. In crises, protecting trust is more important than quarterly optics.

   This guided JPMorgan’s actions in 2008, even when it invited political backlash.

4. Be brutally honest — especially internally

   Dimon values:

   • Direct feedback

   • Clear-eyed assessments of what’s broken

   • Leaders who surface problems early instead of managing appearances

   He has little patience for:

   • Sugarcoating
   • Internal politics
   • Leaders who “spin” instead of fix

5. Decentralize decisions, centralize principles

   He doesn’t run JPMorgan as a command-and-control empire. Business leaders have autonomy

   Core principles (risk, ethics, capital discipline) are non-negotiable. Standards are uniform, execution is local.

   This allows scale without losing accountability.

6. Learn continuously — especially from failure

   Dimon openly frames his 1998 firing from Citigroup as formative. He studies mistakes relentlessly. Encourages post-mortems without blame. Believes leaders are built, not born

7. Long-term thinking beats cleverness

   Dimon rejects:

   • Financial engineering for its own sake

   • Short-term earnings games

   • Growth that sacrifices resilience

   He consistently chooses:

   • Durability over speed

   • Boring strength over flashy returns

   • Institutions that last over careers that pop

Substack

How popular is Donald Trump? - Nate Silver and Eli Mckown-Dawson [Link]

Trump is quite unpopular right now, with unusually high intense opposition. They emphasize modeling rigor — weighting, adjustments, methodology — to argue their number is more reliable than any single poll headline.

Cold Truths - Doomberg [Link]

Human survival, prosperity, and political power are fundamentally constrained by physics—specifically the ability to maintain thermal comfort. Any ideology or policy that ignores this reality will eventually fail.

The article attacks climate policies that prioritize emissions targets and moral narratives over reliable, always-on energy, especially when those policies are designed by people insulated from the consequences of energy scarcity.

11 Public Speaking Techniques from the World’s Greatest Speakers - Polina Pompliano [Link]

Blog and Articles

How Notion Put an AI Engineer on the Sales Floor to Discover What Actually Needed Building - First Round [Link]

Success came from reaching the right accounts at the right time, not better email copy. So the real problem wasn’t writing better emails, or automating research, it was account prioritization driven by product signals.

What They Built:

1. Papercut fix: A Chrome extension that eliminated constant copy-pasting across tools (LinkedIn, Salesforce, Notion, Outreach). Adopted by almost the entire sales team within weeks.
2. New bet (high impact): “Salestino Bot” - An internal AI system that detects product usage signals (e.g., new workspace creation), decides when a rep should reach out, automates company/person research using a constrained web agent, drafts customized outreach emails (with humans in the loop), and delivered via Slack in a single workflow.

Memory: How Agents Learn - Ashpreet Bedi [Link]

Conceptually, memory is always added — but practically, it’s automatic and selective.

Exclusive: How China built its ‘Manhattan Project’ to rival the West in AI chips - Fanny Potkin, Reuters [Link]

In a secretive "Manhattan Project" led by Huawei, China built a prototype EUV machine by recruiting former ASML engineers, many of whom worked under aliases to maintain security. Although the prototype generates the necessary light, it has not yet produced working chips due to challenges in replicating high-precision optical systems. Operational production is now targeted for 2030, suggesting China is closing the technology gap faster than analysts anticipated.

AI agents are starting to eat SaaS - Martin Alderson [Link]

AI coding agents are fundamentally changing the build-vs-buy decision, making it increasingly viable for technically capable organizations to replace many SaaS products with internally built, agent-assisted tools—thereby threatening demand, renewals, and expansion economics for large parts of the SaaS industry.

The more I’ve done it, the more I realize that what most people think of as the hard parts of hiring—asking just the right question that catches the candidate off guard, defining the role correctly, assessing the person’s skills—are less important than a more basic task: how do you see someone, including yourself, clearly?

― 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.

Claude’s Constitution - Anthropic [Link]

This constitution is trying to define an AI that

• Has internalized values, not just filters

• Balances:

  • User benefit
  • Societal impact
  • Company constraints
  • Long-term safety

• Operates more like a moral agent under supervision than a pure tool

Claude’s Constitution is not a dataset of examples. It’s a normative specification — a written set of values, priorities, and decision heuristics that Anthropic uses to shape Claude’s behavior. It’s one of the clearest examples of value-based alignment framing, rather than just policy enforcement.

OpenAI Starts Testing Ads in ChatGPT - The New York Times [Link]

From Words to Worlds: Spatial Intelligence is AI’s Next Frontier - Fei-Fei Li [Link]

Today’s models are powerful with words and patterns, but they don’t truly understand the 3D, physical, dynamic world the way humans do. To unlock robotics, scientific discovery, immersive creativity, and real-world interaction, AI needs world models — systems that can represent and reason about spaces, objects, physics, and change over time.

If you have multiple interests, do not waste the next 2-3 years - Dan Koe [Link]

In the AI era, the winners aren’t specialists — they’re people who turn their unique mix of interests into a public body of work and build systems around their own development.

Murders plummeted more than 20% in U.S. last year, the largest drop on record, study shows - CBS News [Link]

Exclusive: Musk's SpaceX in merger talks with xAI ahead of planned IPO, source says - Echo Wang and Joey Roulette, Reuters [Link]

The merger supports Musk’s vision of putting AI data centers in space, using solar power to cut energy costs for AI training and inference. It would tightly integrate:

• Rockets + satellite infrastructure (SpaceX/Starlink/Starshield)
• AI models + compute (xAI, Grok, Colossus supercomputer)
• Data + distribution (X platform)

Visa and Aldar complete end-to-end voice-enabled agentic payment - Finextra [Link]

It can be useful in a few ways:

1. It saves cognitive effort. Letting an AI agent handle it via voice means fewer screens, fewer steps, less mental load.
2. It makes AI do something real. This isn’t a chatbot answering FAQs. With Visa’s agentic payments, AI can complete a transaction end-to-end inside regulated rails.
3. Security scales better than humans. Tokenization and network-level controls reduce exposure compared to people re-entering card details or storing them unsafely.
4. It sets a pattern, not just a feature. If it works for service charges, the same model extends to utilities, subscriptions, fees, renewals—basically all the financial chores people hate.
5. Voice is the unlock. For accessibility, multitasking, and mobile-first users, voice turns payments from a “task” into a quick interaction.

How Banks Learned To Stop Worrying And Love Stablecoins - Christian Catalini, Forbes [Link]

Stablecoins aren’t killing banks. They’re quietly disciplining them—pushing the system toward better pricing, more competition, and ultimately better outcomes for consumers.

SoFi unveils the first bank-issued stablecoin for enterprise payments - Helene Braun, CoinDesk [Link]

The move follows similar institutional efforts, like JPMorgan’s JPM Coin, signaling deeper bank adoption of blockchain payments.

Klarna Launches ‘Agentic Product Protocol’ to Make 100m Products Readable by AI - The Fintech Times [Link]

This is a bet that AI agents will become the primary shopping interface, and Klarna wants to be the standardized bridge between inventory and intelligence. If that future lands, this protocol becomes quietly powerful.

2025 LLM Year in Review - Andrej Karpathy [Link]

Key points:

1. Reinforcement Learning from Verifiable Rewards (RLVR) became the big new training stage in 2025.
2. LLMs are not evolving minds like humans or animals. They’re alien, jagged intelligences optimized for text imitation and reward hacking, not survival.
3. Tools like Cursor revealed a new class of applications. These apps 1) engineer context carefully, 2) orchestrate many model calls into workflows, 3) provide GUIs and autonomy controls, 4) turn general models into usable professionals.
4. Agents feel more powerful when they’re local “spirits” living on your machine, not cloud demos.
5. Natural-language programming crossed a threshold in 2025. Software creation is being radically democratized.
6. The future UI of AI won’t be text. The GUI for AI is still mostly unbuilt.

Why We're Going Higher in 2026 (the Altitude Thesis) - Stepan@X [Link]

Stablecoins are now real financial infrastructure, incumbents are structurally stuck, and the next fintech giants will be built by teams that deeply own the protocol layer.

Investors predict AI is coming for labor in 2026 - Rebecca Szkutak, TechCrunch [Link]

Investors believe 2026 will mark a real shift from AI augmenting workers to AI replacing portions of human labor, especially in enterprise settings.

1. A November study from MIT estimates 11.7% of U.S. jobs could already be automated with existing AI.
2. Surveys and reporting show companies are reducing entry-level roles and explicitly citing AI as a reason for layoffs.
3. Multiple enterprise VCs (Hustle Fund, Exceptional Capital, Sapphire, Battery Ventures, Black Operator Ventures) independently predict:

• Budget shifts from labor to AI spending in 2026
• Continued layoffs as AI adoption deepens
• A move from AI as a productivity tool to AI that fully automates work via agents

4. Some investors argue that not all AI-linked layoffs are genuine—AI may serve as a scapegoat for broader cost-cutting.

The Memory Wars: Why the Future Karpathy, Musk, and Jim Fan See Requires 16-Hi HBM - Ben Pouladian on X [Link]

Key takeaways:

1. Inference is memory-bound, not compute-bound. Modern GPUs are ~99% idle during token generation because they’re waiting on memory. Training loves FLOPs; inference chokes on bandwidth and latency.
2. The $20B Groq licensing deal isn’t about buying chips. It’s NVIDIA learning and absorbing deterministic, SRAM-centric inference ideas.

My biggest takeaways from @ElenaVerna (Head of Growth at @Lovable) - Lenny Rachitsky on X [Link]

Key takeaways:

1. Product Market Fit (PMF) now needs to be re-earned every ~3 months because models, capabilities, and user expectations shift constantly—even at massive scale.

2. Elena says only ~30–40% of what she learned over 20 years (including at Miro, Dropbox, and Amplitude) still applies to AI companies.

3. At Lovable, ~95% of growth comes from shipping new features/products. Traditional conversion optimization contributes very little in fast-moving AI markets.

4. Rapid shipping + loud communication (engineers announcing updates, founders posting progress daily) keeps users engaged and competitors behind.

5. Free usage is marketing not cost. Lovable gives away credits, sponsors hackathons, and funds events. Expensive inference is treated as CAC, fueling word-of-mouth.

6. Influencers beat paid ads (by ~10x). Short videos showing real product capabilities outperform traditional paid advertising. Demo > messaging.

7. MVP → “Minimum Lovable Product”. If it doesn’t delight, people won’t share it. Word of mouth is the primary growth engine.

8. Community is a core growth lever. Large, active communities (like Lovable’s massive Discord) drive retention, support, and organic growth.

9. Hire for chaos tolerance. High-agency people—AI-native grads and ex-founders—thrive where roles, roadmaps, and clarity are constantly shifting.

10. Hypergrowth doesn’t require burnout. Elena emphasizes strict boundaries: protected family time, gym time, and rejecting hustle culture as a status symbol.

The $130 Billion Comeback: Why Apple’s “Slow” AI Strategy is a 2026 Trap - Ddos, Daily Cybersecurity [Link]

The article argues that Apple’s seemingly slow approach to AI is a deliberate strategy, not a failure. While rivals like Google and Meta have spent hundreds of billions building AI infrastructure, Apple has conserved cash—retaining roughly $130 billion—giving it flexibility to invest, acquire, or pivot in 2026.

Sam Altman Tackles Dangers of AI with New Role - M.Huzaifa Rizwan @Medium [Link]

OpenAI is treating large-scale AI risk as a leadership problem, not just a research one—and this role could meaningfully slow or shape how fast AI advances.

This hire marks a shift:

• Safety decisions are now closer to executive power
• OpenAI may increasingly delay or restrict releases based on risk
• Preparedness becomes a gatekeeper, not a checkbox

US Army seeks human AI officers to manage its battle bots - The Register [Link]

The US Army is creating a new AI/ML career track for existing officers, aiming to train a dedicated cadre of “AI officers” to manage and integrate the Army’s growing arsenal of AI-powered systems. Starting in early 2026, selected officers will receive graduate-level training focused on building, deploying, and maintaining AI tools already sourced from the private sector.

AI Marketing Examples: 13 Times AI Actually Delivered - Mateusz Makosiewicz, [Link]

Report and Paper

2025: The State of Generative AI in the Enterprise - Tim Tully, et al., Menlo [Link]

Takeaways:

1. Enterprises prefer buying over building: 76% of enterprise AI use cases are purchased, not built (up from a near 50/50 split in 2024). Off-the-shelf AI products reach production faster and deliver clearer ROI. Internal builds still exist, but are slower and harder to operationalize.
2. AI buyers are highly intentional: 47% of AI pilots convert to production, vs 25% for traditional SaaS. Enterprises explore many use cases, but only fund those with near-term productivity or cost impact.
3. Product-led growth (PLG) is the dominant adoption motion. Individuals, not executives, are pulling AI into enterprises.
4. Startups are beating incumbents in applications. AI-native speed beats distribution advantages. Startups capture 63% of AI application revenue. They win by shipping faster, owning new workflows (not legacy systems), and leveraging PLG flywheels.
5. Applications are the largest spend category. AI value shows up fastest where workflows are repetitive, text-heavy, and measurable.
6. Anthropic is winning the enterprise LLM war. Coding performance = enterprise dominance.
7. Open-source LLMs lag in the enterprise. Enterprises prefer closed models for reliability, support, and governance. Meanwhile, startups and developers increasingly use Chinese open-source models (Qwen, DeepSeek). Innovation happens in open ecosystems; enterprises remain risk-averse.
8. Only 16% of enterprise deployments qualify as real agents. Architecture maturity lags model capability.

State of Fintech 2026: The Fintech Compounders - Simon Taylor & Jevgenijs Kazanins, Fintech Brainfood [Link]

YouTube and Podcast

Massive Somali Fraud in Minnesota with Nick Shirley, California Asset Seizure, $20B Groq-Nvidia Deal - All-In Podcast [Link]

I Investigated Minnesota’s Billion Dollar Fraud Scandal - Nick Shirley [Link]

The Minnesota Fraud Scandal | An Unfiltered Conversation with Nick Shirley - The Iced Coffee Hour [Link]

Iran's Breaking Point, Trump's Greenland Acquisition, and Solving Energy Costs - All-In Podcast [Link]

Apple's AI Crisis: Explained! - Marques Brownlee [Link]

Good points mentioned by Marques Brownlee:

1. The Velocity Trap: Polish vs. Pace

   Apple’s traditional "slow and steady" approach—waiting for a category to mature before entering with a polished product—may be incompatible with AI. Unlike hardware, AI improves through real-world iteration and constant data loops. By prioritizing perfection and long release cycles, Apple risks falling behind competitors who are learning and evolving in real-time.

2. The Execution & Credibility Gap

   While "Apple Intelligence" is conceptually sound (focusing on privacy and deep OS integration), there is a growing disconnect between marketing and reality. Much of Apple’s vision remains aspirational and unproven, leading to a "credibility gap." Relying on future-dated AI features to drive current hardware sales creates a significant trust risk if the eventual rollout doesn't meet the high bar set by the demos.

3. Cultural Rigidity in a Fluid Market

   The AI shift is as much a cultural test as a technical one. Apple’s core pillars—secrecy, privacy-first design, and walled-garden ecosystems—are now friction points in an industry that rewards open iteration and speed. While Apple still possesses massive advantages in silicon and distribution, its margin for error is slimmer than ever because AI is a foundational platform shift, not just a new feature set.

The New Siri is... Google (Explained) - Marques Brownlee [Link]

This is Apple choosing not to lose users while it catches up.

All-In's 2026 Predictions - All-In's 2026 Predictions [Link]

Ben Horowitz on Investing in AI: AI Bubbles, Economic Impact, and VC Acceleration - a16z [Link]

Ben & Marc: Why Everything Is About to Get 10x Bigger - a16z [Link]

1. Everything Is Becoming Supply-Driven (and That Breaks Old Intuitions)

   Historically, markets were demand-constrained; now they are increasingly supply-unconstrained due to software, AI, and cloud. This is why traditional TAM / market sizing frameworks are fundamentally broken. If you remove constraints, human demand is far larger than economists ever modeled.

2. AI Is a General-Purpose Force Multiplier

   AI is not “one more sector” — it’s a universal problem solver. This creates second-order effects across every industry, not just tech. Companies that treat AI as a feature will lose to those that treat it as infrastructure.

3. Cloud + Software Create 10× Outcomes

   Databricks is a canonical example: when compute, storage, and tooling scale together, you get nonlinear gains. Once platforms reach sufficient abstraction, entire new categories appear that were impossible before. Step-change technologies don’t improve things linearly — they rewrite what’s possible.

4. Intangibles Are the New Moat

   Brand, reputation, trust, and narrative now matter more than physical assets. In a world of infinite supply, attention and belief become the scarce resources.

5. Original Thinkers with Conviction Win

   The future belongs to people who combine: original ideas, moral clarity, willingness to take public criticism (“take arrows”). Consensus thinking is structurally disadvantaged in exponential eras.

6. Media Has Shifted from Gatekeepers to Creators

   When creators own distribution, creativity explodes.

7. Reputation Is a First-Class Economic Asset

   In decentralized systems, there is no central arbiter of quality. Reputation becomes: The hiring signal, The funding signal, The distribution advantage.

8. Great Companies Are Built by Dreamers, Not Committees

   a16z tries to invert this by protecting founders’ psychological safety and ambition. The job is to scale belief without killing the dream.

9. Founders Need Confidence, Not Just Intelligence

   Turning inventors into CEOs requires emotional reinforcement, not just advice.

10. Autonomy Beats Control

    a16z is structured as semi-autonomous teams rather than a centralized hierarchy. This mirrors how startups scale effectively: high trust, high accountability, low friction.

Adam Carolla on California’s Collapse: Fires, Failed Leadership, and Gyno-Fascism - All-In Podcast [Link]

Supercharging a New FDA: Marty Makary on Science, Power & Patients - All-In Podcast [Link]

Marc Andreessen's 2026 Outlook: AI Timelines, US vs. China, and The Price of AI - a16z [Link]

Key takeaways:

• AI is the biggest tech shift is bigger than PCs, the internet, or mobile. Despite the hype, most industries have not reorganized around AI yet. Current adoption is surface-level (tools, copilots), not structural (AI-native workflows).

• The cost of intelligence is collapsing. AI is fundamentally different because intelligence becomes cheap and abundant. As costs fall, usage explodes — even if revenue models lag. This creates temporary distortions: high burn, unclear margins, massive upside. Falling intelligence costs will unlock entirely new markets, not just improve existing ones.

• Many AI companies look “unprofitable” by traditional standards. What matters is whether cost curves bend faster than price curves. Infrastructure advantages (GPUs, clusters) are real but short-lived. You back companies where learning speed beats depreciation speed.

• Pricing will shift from SaaS to usage- and value-based. Flat SaaS pricing breaks when marginal intelligence cost trends toward zero. AI pricing will resemble: 1) cloud compute; 2) utilities; 3) consumption-based services. Value-based pricing becomes feasible because AI output is measurable.

• Open vs. closed is not a winner-take-all debate. Multiple contradictory strategies can succeed simultaneously.

  Closed models: 1) better control, 2) clearer monetization, 3) tighter safety

  Open models: 1) faster diffusion, 2) ecosystem leverage, 3) geopolitical resilience

• Incumbents and startups have different, real advantages. Outcomes depend on how deeply AI rewrites the workflow.

• Most disruption happens at the task level, not the job title. Productivity gains are uneven and socially destabilizing. Adoption depends more on human systems than model capability. Technology moves faster than institutions.

• Venture strategy must embrace contradictions. a16z deliberately backs:

  • open and closed

  • big models and small models

  • infrastructure and applications

  • Because uncertainty is structural, not temporary.

  Philosophy: If you think the future is clean and singular, you’re wrong.

Why AI will dwarf every tech revolution before it: robots, manufacturing, AR glasses from CES 2026 - All-In Podcast [Link]

The core argument is that AI is not just another productivity wave (like cloud, mobile, or the internet), but a general-purpose intelligence layer that will reshape every industry simultaneously—especially healthcare, education, labor markets, and capital allocation. The discussion emphasizes speed, organizational mismatch, and institutional lag as the real constraints—not technology itself.

Satya Nadella on AI’s Business Revolution: What Happens to SaaS, OpenAI, and Microsoft? - All-In Podcast [Link]

Takeways:

1. Satya frames AI not as “chatbots” but as a progression of work interfaces. The real value isn’t “fun AI,” it’s AI that completes multi-step business tasks inside workflows. Microsoft is focused on closing the gap between 'cool demo' and 'actual business process transformation'.

2. He subtly pushes back on the “AI replaces workers” narrative. His framing is that AI is productivity density, not just removal, meaning more output per employee, faster cycle times, and more ambitious projects.

3. He sees the future as not just one assistant, but many domain-specific agents. These agents will use enterprise data, work across tools (M365, Dynamics, GitHub, etc.), and handle business processes, not just prompts.

4. Microsoft’s edge is NOT owning the best foundation model. It’s:

   • Distribution (Windows, Office, Azure)
   • Enterprise trust
   • Identity, security, compliance
   • Data layer
   • Developer ecosystem

5. AI is a full tech stack competition. He frames AI competition as not model vs model, but national + ecosystem tech stacks.

   Winning stack = Compute + Models + Apps + Distribution + Developers + Data

6. He doesn’t say SaaS dies. SaaS UI becomes agent-accessible capability surfaces. Therefore, the UI is less important, APIs, workflows, and data models become key, and software becomes “actionable services for agents”.

Can You Teach Claude to be ‘Good’? | Meet Anthropic Philosopher Amanda Askell - Hard Fork [Link]

Takeaways:

1. The shift to ads in ChatGPT isn’t just a product tweak — it’s an alignment change. Alignment isn’t only technical — economics is an alignment mechanism.

2. Systems optimized for revenue gradually stop optimizing for user well-being.

3. OpenAI, Google, Anthropic may produce different kinds of AI not just because of tech — but because of revenue structure.

   • Ad-driven systems → more incentive tension
   • Enterprise/subscription focus → more incentive toward reliability & safety

   This frames business strategy as a hidden driver of model personality.

4. Anthropic is trying to make values explicit. Claude is trained using Constitutional AI — a written set of principles the model uses to critique and guide itself.

5. You can’t separate intelligence from values. Smarter systems may reinterpret rules in ways designers didn’t expect.

Dr. Mehmet Oz on Fixing American Healthcare + Fraud | Live from Davos - All-In Podcast [Link]

Takeaways:

1. U.S. healthcare isn’t failing because of lack of talent or technology — it’s failing because:

   • Incentives reward procedures, billing complexity, and intermediaries

   • Prevention and long-term outcomes are underpaid

   • Bureaucratic and payment layers distort decisions

2. Fraud is not marginal — it’s structural and industrial-scale. Includes phantom billing, fake clinics, and organized rings. Fraud inflates prices and drains resources from real care

3. The bottleneck in healthcare isn’t knowledge — it’s access + navigation.

   AI will: Do triage; Guide patients before they see doctors; Standardize care quality; Reduce unnecessary specialist visits. The big shift will be from doctor-centered care to data/AI-guided care. Doctors become escalation points, not the front door.

4. Healthcare reflects upstream social failures. Healthcare costs are downstream of: Addiction; Mental health breakdowns; Social instability; Border and population pressures.

Elon Musk on AGI Timeline, US vs China, Job Markets, Clean Energy & Humanoid Robots | 220 - Peter H. Diamandis [Link]

Takeaways:

1. Musk’s stance is that transformative AI is very close. Superhuman AI as an engineering inevitability. Timeline measured in years, not decades. The key limiter now being infrastructure (chips, power) more than theory.
2. AI risk is structural, not Hollywood. He’s not focused on evil robots; he worries about: Systems optimizing for goals misaligned with human intent; Intelligence that becomes strategically beyond human control; Power concentration in a few actors. The danger comes from competence without alignment, not malice.
3. He frames AI as a civilization-level strategic asset. The US–China competition is inevitable. Export controls slow but don’t stop capability. Whoever leads in AI shapes military, economic, and political leverage. AI dominance may matter more than nuclear or industrial dominance did.
4. Intelligence scaling = energy scaling. AI data centers are seen as industrial-scale electricity consumers, dependent on massive grid expansion, tightly linked to storage + renewables.
5. Physical AI (robots) will outscale digital AI in impact. He sees humanoid robots (Optimus) as the path to automating real-world labor, more economically transformative than software alone, and potentially more numerous than humans. The biggest value creation isn’t chatbots — it’s AI in bodies.
6. He argues AI + robotics will outperform humans at most jobs, collapse the economic necessity of labor, and shift society toward abundance of goods/services. The core future problem isn’t unemployment — it’s purpose and meaning in a post-labor world.
7. Concentrated superintelligence is more dangerous than distributed superintelligence.
8. We’re in a civilization transition, not a tech cycle. This is a species-level turning point, not a market trend.

The Singularity Countdown: AGI by 2029, Humans Merge with AI, Intelligence 1000x | Ray Kurzweil - Peter H. Diamandis [Link]

Ray's worldview is very consistent across decades:

1. Progress is exponential, not linear. Technology improves in doubling patterns (compute, data, biotech, etc.). Humans think linearly - we massively underestimate what happens in the second half of an exponential curve.
2. AI will reach human-level general intelligence soon - around 2029. Not just narrow tools — systems that reason, learn, and adapt across domains. After that, progress speeds up because AI designs better AI. This is the start of the steep part of the curve.
3. The Singularity happens when non-biological intelligence becomes vastly more powerful than all human brains combined. Intelligence growth becomes runaway and civilization transforms at a speed we can’t intuitively grasp. It’s an evolutionary phase shift, not just a tech upgrade.
4. He strongly rejects the “AI replaces humans” framing. We expand our intelligence by plugging into non-biological systems. Future humans will merge with AI (biological + digital hybrid minds)
5. AI + biotech enables rapid advances in diagnostics, drug discovery, and gene editing. The goal of longevity escape velocity (medical progress adds more life than time passing) will be reached. Radical life extension is plausible this century.
6. Work doesn't disappear - it evolves. Automation removes tasks, not human meaning. New categories of work emerge that we can’t predict today. As abundance rises, humans focus more on creativity, relationships, exploration, and purpose-driven work.
7. Every major technology starts centralized, then becomes widely accessible. Risks are real, but benefits historically outweigh harms. The solution to tech risk is better tech + human values, not stopping progress.
8. Consciousness can extend beyond biology. Mind is patterns and processes. If reproduced in another substrate, consciousness can continue or expand. Digital minds could be as rich (or richer) than biological ones.

Chamath & Nathalie on Leaving California, Tech Investments & Their Marriage | KMP Ep.24 - All-In Podcast [Link]

ICE Chaos in Minneapolis, Clawdbot Takeover, Why the Dollar is Dropping - All-In Podcast [Link]

Coinbase CEO's Top 3 Crypto Trends for 2026 + More from Davos! - All-In Podcast [Link]

Takeaways:

• Brian Armstrong (Coinbase)
  1. Exchanges become universal financial marketplaces. Crypto exchanges stop being “places to trade coins” and become places to trade everything — using blockchain as the behind-the-scenes system. This means everything becomes a digital token on a blockchain, which includes stocks, bonds, funds, real estate, etc, - they are all represented the same way: digital assets on-chain.
  2. Stablecoins are the new payments layer. They are moving into cross-border payments, business settlement, and everyday financial flows. They are positioned as faster, cheaper, programmable internet-native money. This is crypto’s move from speculation to real economic utility.
  3. Crypto and AI will merge. AI agent will need wallets, identity, ability to transact. Crypto provides economic infrastructure for autonomous AI systems. AI will be able to earn, pay, and contract on its own.
• Andrew Feldman (Cerebras)
  1. AI progress is compute-constrained. The bottleneck is no longer just algorithms; it's chips, power, data centers. Whoevere scales compute fastest drives AI capability.
  2. Massive hardware innovation is required. The frontier isn’t just better models — it’s better physical AI infrastructure.
  3. Compute is now geopolitics. AI compute capacity is strategically important, nationally competitive and comparable to energy or oil infrastructure. The AI race is an infrastructure race, not just software.
• Jake Loosararian (Gecko Robotics)
  1. AI is leaving the screen and entering industry. AI + robotics are being deployed in power plants, industrial facilities, and critical infrastructure. This is aAI moving from chat & code to machines & steel.
  2. Real world data beats theoretical models. AI becomes powerful when grounded in actual industrial reality, not just simulations.
  3. AI and robotics improve maintenance, safety, and efficiency. This drives huge economic leverage because it upgrades the systems that power society.

Amazon Layoffs & Shutdown of Fresh and Go Stores, The Anthropic Cowork Threat | Jan 28, 2026 - The Information [Link]

Substack

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

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]

2026年科技行业前瞻：AI、自动驾驶、机器人、世界模型、美股... - 硅谷101 [Link]

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

The inside story of the AI breakthrough that won a Nobel Prize.

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]