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Pursuit of Joy, Fulfillment, and Purpose

  • The Fun Criterion: A Simple Guide to Making Choices

    TLDR:

    The Fun Criterion, from David Deutsch, says: when choosing what to do, pick what feels fun. It’s a sign your whole mind—thoughts, feelings, and instincts—is working together well. Fun guides you when clear answers aren’t enough.

    The Fun Criterion: A Simple Guide to Making Choices

    Have you ever wondered how to decide what to do when you’re stuck? David Deutsch, a thinker and scientist, has an interesting idea called the “Fun Criterion.” It’s not just about having a good time—it’s about using fun as a clue to figure out what’s best for you. Here’s a simple breakdown of what it means and why it matters.

    What’s the Fun Criterion?

    Imagine you’re trying to decide something, like whether to go to the park or stay home and read. Your brain is full of different kinds of thoughts. Some you can explain easily, like “The park is close.” Others are harder to put into words, like a gut feeling that you’d rather stay cozy with a book. And some thoughts you don’t even notice, like a quiet worry about getting tired.

    Deutsch says all these thoughts—whether you can explain them or not—work together to help you decide. But sometimes they clash. You might think the park sounds nice, but you feel like staying home. How do you choose? That’s where the Fun Criterion comes in: pick the option that feels fun. Fun, he says, is a sign that your mind is working well and your ideas are getting along.

    Why Fun?

    Our brains are complicated. We don’t just think with clear ideas like “2 + 2 = 4.” We also use feelings, hunches, and stuff we don’t even realize we know—like how to catch a ball without thinking about it. When you’re faced with a choice, these hidden thoughts can make you feel good or bad about it, even if you don’t know why.

    For example, let’s say you’re picking between two hobbies: painting or running. You might think running is good exercise, but painting keeps pulling you in because it’s exciting. That excitement is your brain’s way of saying, “This works for me!” Deutsch believes that when you follow the fun, you’re letting all parts of your mind—conscious and unconscious—team up to solve the problem.

    Not Just Random Feelings

    This isn’t about chasing every silly whim, like eating candy all day because it feels good. Deutsch warns against that. Some people ignore their feelings and stick to strict rules (“I should run because it’s healthy”), while others only follow emotions without thinking (“Candy makes me happy, so I’ll do that”). Both ways can mess up because they ignore half of what’s going on in your head.

    The Fun Criterion is different. It’s about noticing when something feels fun and makes sense. It’s like a signal that your brain’s many parts—thoughts, feelings, and instincts—are agreeing. When they’re in sync, you feel energized and happy, not stressed or unsure.

    How Does It Work?

    Let’s try a real-life example. Imagine you’re deciding whether to take a new job. Your clear thoughts might say, “It pays more money.” But you feel nervous about it, and the idea of staying at your current job seems more enjoyable. The Fun Criterion says: pay attention to that enjoyment. Maybe your gut knows something your brain hasn’t figured out yet—like the new job might be too stressful. By picking what feels fun, you’re trusting your whole mind to guide you.

    Fun Means Growth

    Deutsch ties this to how we learn and grow. He says our minds are always making guesses and fixing mistakes, kind of like how scientists solve problems. When you choose the fun path, you’re more likely to keep exploring and creating, because it feels good. If something’s boring or painful, you might give up. Fun keeps you going.

    Keep It Simple

    So, next time you’re stuck on a choice—big or small—ask yourself: “What feels fun?” It’s not about being childish or lazy. It’s about listening to your whole self, not just the loudest voice in your head. Fun is like a compass that points you toward what works, even when you can’t explain why.

    That’s the Fun Criterion: a simple, smart way to decide what to do, straight from the mind of David Deutsch. Give it a try—see where fun takes you!

  • How BlackRock Manipulates Companies & Investors: A Tale of Bud Light’s Fall and Corporate America’s Crossroads

     Once the king of the American beer market, Bud Light lost $40 billion in market cap after one polarizing ad campaign—a collapse dissected in Joe Lonsdale’s American Optimist podcast episode, “Former Business Exec: How BlackRock Manipulates Companies & Investors” (uploaded February 20, 2025). Featuring Anson Frericks, a former Anheuser-Busch president, the 42-minute video (2,374 views as of now) unravels how BlackRock manipulation and its peers steer corporate America astray with ESG impact and DEI controversy. How did the Bud Light collapse happen? Why do these frameworks falter? And can businesses rediscover their business mission? Here’s the story—and the solution.

    TL;DR

    Bud Light’s $40 billion loss wasn’t just a marketing flop—it exposed BlackRock, State Street, and Vanguard’s grip on corporate America, pushing stakeholder theory over shareholder value. In Joe Lonsdale’s February 20, 2025, podcast “Former Business Exec: How BlackRock Manipulates Companies & Investors“, ex-Anheuser-Busch exec Anson Frericks reveals how these forces derailed Bud Light, why he co-founded Strive Asset Management with Vivek Ramaswamy to fight back, and how meritocracy could revive American business.

    Executive Summary

    In the latest American Optimist episode, “Former Business Exec: How BlackRock Manipulates Companies & Investors“, tech mogul Joe Lonsdale—co-founder of Palantir and 8VC—interviews Anson Frericks, a Yale and Harvard alum who led Anheuser-Busch’s U.S. operations until its cultural drift. Frericks ties the Anheuser-Busch decline to its 2008 InBev acquisition and a shift from St. Louis to New York, aligning it with ESG and DEI pressures from BlackRock’s $20 trillion empire. Contrasting Milton Friedman’s shareholder primacy with Europe’s World Economic Forum stakeholder theory, he details how these frameworks fueled Bud Light’s 2023 Dylan Mulvaney ad fiasco. Now, through Strive Asset Management and his book Last Call for Bud Light, Frericks charts a path back to customer-focused economic prosperity—watch the full discussion for his insider take.

    Key Takeaways

    • Bud Light’s Collapse: A $40 billion market cap loss followed its 2023 campaign, a misstep Frericks calls “the pin that popped the ESG bubble” (17:07 in the video).
    • BlackRock’s Power: With State Street and Vanguard, BlackRock leverages $20 trillion to enforce ESG via letters, votes, and media (13:50).
    • ESG & DEI Roots: Emerging from Europe’s World Economic Forum and post-2008 PR fixes, these became tools for political control (11:08).
    • Corporate Split: Goldman Sachs retreats from DEI quotas, while Costco doubles down, per Frericks (19:04).
    • Strive’s Solution: Frericks’ firm offers low-fee funds focused on merit and returns, not politics (28:10).

    The Questions This Answers—Explained Metaphorically

    1. How Did Bud Light Fall So Far?

    Metaphor: Picture a hearty oak uprooted from Midwest soil and replanted in a New York penthouse pot. Frericks explains in the video (1:59) that after InBev’s 2008 buyout, Bud Light’s move to NYC exposed it to ESG-DEI gusts. The Dylan Mulvaney ad was the storm that felled it—a king dethroned by losing its roots.

    2. Where Did ESG and DEI Come From?

    Metaphor: Envision a vine slithering from Europe’s World Economic Forum, watered by post-2008 remorse. At 11:08, Frericks traces ESG’s rise to the UN’s 2005 framework and banks’ image repair, with BlackRock pruning firms to fit stakeholder theory—a garden of control, not freedom.

    3. How Does BlackRock Manipulate Companies and Investors?

    Metaphor: BlackRock’s the puppeteer, its $20 trillion strings jerking corporate limbs. Frericks details at 13:50 how annual letters, media pressure, and shareholder votes (30:15) force ESG compliance—turning CEOs into marionettes dancing to a political tune.

    4. Why Did This Hurt Corporate America?

    Metaphor: It’s like chefs abandoning stoves to chase fads, starving their patrons. At 16:17, Frericks notes Bud Light, Disney, and Nike lost focus on customers, burning profits and trust in a futile bid to please stakeholders—a recipe for ruin.

    5. How Can We Fix It?

    Metaphor: Strive Asset Management’s a lighthouse, guiding ships from stormy activism to safe harbors of merit. Frericks shares at 28:10 how his firm with Vivek Ramaswamy rejects ESG mandates, steering firms back to their north star—serving customers and shareholders, not politics.

    The Rise and Fall of Bud Light: A Cautionary Tale

    Bud Light ruled as America’s working-class brew until InBev’s 2008 takeover uprooted it from St. Louis. In the podcast (1:59), Frericks recalls its shift to New York, where 3G Capital’s meritocracy faded under ESG-DEI pressures. By 2023, the Dylan Mulvaney ad—pitched as inclusive—tanked $40 billion and thousands of jobs. “$40 billion’s been erased since this happened,” Frericks laments (00:00 in the video), a wake-up call for brands straying from their base. His book, Last Call for Bud Light (linked in the video description), dives deeper into this ESG backlash.

    BlackRock’s Shadow: The Mechanics of Manipulation

    BlackRock, State Street, and Vanguard wield $20 trillion, owning 20-30% of S&P 500 firms. At 13:50, Frericks outlines their tactics: CEO letters demand “social licenses,” media amplifies ESG goals, and votes ram through proposals—30-40% passed by 2021 (30:15). California’s $280 billion pension fund, only 80% funded, bends to this, shunning oil while padding Texas gains. “They’re forcing behaviors,” Frericks warns (00:00:24), a top-down hijack of free markets and corporate governance.

    ESG and DEI: From Ideals to Ideology

    ESG and DEI sprouted from Europe’s stakeholder theory, gaining ground post-2008 (11:08). Initially a PR fix, they became profit engines—high-fee ESG indexes excluded “non-compliant” firms like Tesla (no unions). Frericks recounts at 21:44 how Bud Light nixed a Black Rifle Coffee deal over “controversy,” showing DEI’s exclusionary twist. “The left used business to get done what they couldn’t through government,” he says (14:47), fueling the DEI controversy.

    Corporate America’s Fork in the Road

    The video (19:04) highlights a divide: Goldman Sachs drops DEI quotas, Costco leans in. Frericks bets on retreaters outperforming, citing his bets against Business Roundtable signers. Yet, Bud Light’s leadership lingers despite losses—European heirs of 3G Capital cling to ESG, missing American pragmatism (24:59). Accountability’s scarce, but Wall Street reform is stirring.

    The Path Forward: Strive and Beyond

    Frericks left Anheuser-Busch in 2021, launching Strive Asset Management with Vivek Ramaswamy to counter the asset managers’ influence (28:10). Offering low-fee funds, Strive pushes firms to “be excellent at their mission”—oil firms drill, tech fosters speech. Its record ETF launch proves demand (33:04). Now with Athletic Capital, Frericks urges courage—challenge pronouns or quotas (37:13). Watch the full episode “Former Business Exec: How BlackRock Manipulates Companies & Investors” for his roadmap to reclaim corporate America and restore economic prosperity.

  • How to Build the Future: Aravind Srinivas on Revolutionizing Search with Perplexity

    TL;DR
    In an insightful interview with Y Combinator’s David Lieb on February 21, 2025, Aravind Srinivas, co-founder and CEO of Perplexity, shares his journey from AI researcher to building a $9 billion-valued company in under three years. He discusses his motivations, the evolution of Perplexity, and his vision to redefine search by prioritizing user experience over traditional ad-driven models, positioning it as a potential challenger to Google.

    Executive Summary
    Aravind Srinivas’s story is one of curiosity, persistence, and bold ambition. From his early days as a PhD student at Berkeley and internships at OpenAI and Google, he identified search as a domain ripe for disruption through AI. Founding Perplexity, Srinivas aimed to create a user-centric, intelligent alternative to conventional search engines. The interview reveals how Perplexity evolved from early Twitter-based demos to a scalable, general-purpose search tool, leveraging advancements in large language models (LLMs). Srinivas emphasizes a relentless focus on user needs, team culture, and a long-term vision to integrate end-to-end solutions—beyond just answers—into everyday life.

    Key Takeaways

    • Origins in AI: Srinivas’s exposure to unsupervised learning and generative AI during his OpenAI internship shaped his vision for a product-driven AI company.
    • Perplexity’s Evolution: Starting with niche demos, Perplexity pivoted to a broader, LLM-powered search engine after realizing the potential of simpler, scalable solutions.
    • User-First Philosophy: Inspired by Google’s Larry Page, Srinivas believes “the user is never wrong,” driving Perplexity’s design to anticipate and clarify user intent.
    • Competing with Giants: Perplexity’s edge lies in its obsession with user experience and product taste, unencumbered by Google’s ad-centric legacy.
    • Future Vision: Srinivas envisions Perplexity as an all-in-one platform, blending fast answers, task fulfillment, and monetization beyond subscriptions.


    In a captivating February 21, 2025, interview hosted by Y Combinator, Aravind Srinivas, co-founder and CEO of Perplexity, unveils the blueprint behind his $9 billion-valued startup. With a background in AI research from Berkeley, OpenAI, and Google, Srinivas is on a mission to transform search into a user-first experience. This SEO-optimized article explores his journey, Perplexity’s rise, and its bold vision to challenge giants like Google, answering key questions about his motivations and strategy through metaphorical lenses.

    From AI Roots to Entrepreneurial Ambition
    Question Answered: What inspired Aravind to start Perplexity?
    Metaphor: A gardener tending to a seedling, Srinivas nurtured his curiosity in AI research until it blossomed into a vision for a company that could grow as tall as the mightiest oaks (Google), fueled by the sunlight of innovation.

    Srinivas’s journey began in India, where his passion for deep learning led him to a PhD at Berkeley. An internship at OpenAI under Ilya Sutskever introduced him to unsupervised learning, planting the seed for a product-driven AI venture. At Google, reading In the Plex sparked his dream of building a company blending research and usability—enter Perplexity. His realization? Search and self-driving cars are rare domains where AI and product development create a flywheel, improving with every user interaction.

    The Birth and Evolution of Perplexity
    Question Answered: How did Perplexity find its potential?
    Metaphor: Like a sailor charting uncharted waters, Srinivas navigated through early demos (Twitter search) with a small crew, only to discover a trade wind—follow-up questions doubling engagement—that propelled Perplexity toward a new horizon.

    Perplexity’s early days were experimental. Srinivas and co-founder Dennis prototyped a Twitter search tool using OpenAI’s Codex, organizing data into tables for SQL queries. User engagement soared when follow-up questions doubled session times, signaling potential beyond niche applications. Pivoting to a general-purpose search engine, Perplexity embraced LLMs for unstructured data, betting on smarter models to outpace Google’s rigid indexing. This shift, sparked by a weekend prototype inspired by OpenAI’s Web GPT, marked its ascent.

    A User-Centric Approach to Search
    Question Answered: How does Perplexity differ from Google?
    Metaphor: Google is a bustling marketplace, hawking wares (ads) amid a sea of stalls (links), while Perplexity is a wise librarian, quietly fetching the exact book you need without pushing a sales pitch.

    Drawing from Larry Page’s mantra, “the user is never wrong,” Srinivas designed Perplexity to anticipate needs, not blame users for vague prompts. Unlike Google’s ad-cluttered results, Perplexity offers a clean, answer-focused experience—think healthy meal versus fast food. This philosophy drives its edge: obsession with user satisfaction and product finesse. Srinivas tracks queries per day, ensuring retention reflects genuine value, not forced interactions.

    Managing a Growing Team
    Question Answered: What’s the secret to managing a growing team?
    Metaphor: Srinivas conducts his orchestra with a steady baton, keeping the rhythm of queries per day in focus, ensuring every musician plays in harmony, not drowned out by the cacophony of bureaucracy.

    As Perplexity grows, Srinivas maintains a flat, data-driven culture. Weekly All Hands meetings spotlight queries per day, fostering transparency without constant scoreboard-watching. He engages directly with engineers on bugs, prioritizing product quality over hierarchy. Hiring focuses on passion for good work, mirroring his detail-obsessed DNA, though he acknowledges the challenge of scaling without slowing down.

    Competing with Google and Beyond
    Google’s ad-driven model and Microsoft’s consumer struggles leave room for Perplexity. Srinivas sees its advantage in agility and taste, unencumbered by legacy systems. While Google’s $200 billion search revenue looms large, Srinivas argues its stock-driven focus hinders bold pivots, giving Perplexity a shot at redefining monetization. He shrugs off early threats like Bing Chat, trusting in Perplexity’s user-first ethos to carve a niche.

    The Future of Search: Perplexity’s Vision
    Question Answered: What’s the future of search according to Srinivas?
    Metaphor: Imagine a trusty guide who not only points you to the mountain peak but hands you the gear to climb it—Perplexity aims to be that companion, merging answers with actions in a seamless journey.

    Srinivas envisions Perplexity as more than a search engine—an end-to-end solution. Whether recommending a sweater or booking a flight, it aims to deliver answers and actions. This requires orchestrating small models, knowledge graphs, and widgets—a daunting task, but one Srinivas believes can rival Google with a decade of perseverance. Unlike subscription-only models, he seeks sustainable monetization, balancing user trust with mass-market utility.

    Why Perplexity Could Win
    Unlike AI-centric firms like OpenAI, Perplexity blends model expertise with user obsession. Its DNA prioritizes product over benchmarks, positioning it to solve real-world problems—shopping, travel, quick facts—without drowning in ad revenue pressures. Srinivas bets on taste and persistence, not just tech, to outmaneuver competitors over the next decade.

    Wrap Up
    Aravind Srinivas’s story is a masterclass in building the future: start with curiosity, iterate with purpose, and obsess over users. Perplexity isn’t just challenging Google—it’s reimagining how we interact with information. As Srinivas steers this ship, the search landscape may never be the same.

  • Keith Rabois on How to Operate: A Deep Dive into Startup Success

    TL;DR: In a recent interview on the Alex LaBossiere podcast, Keith Rabois—a titan of startup investing and operations—shared his hard-earned wisdom on building exceptional companies. Despite the video’s horrendous audio quality, the content shines through as a treasure trove of insights. Rabois, a Managing Partner at Khosla Ventures and CEO of OpenStore, draws from his storied career (PayPal, LinkedIn, Square, and early investments in Airbnb, DoorDash, and Stripe) to discuss founder scarcity, vertical integration, talent acquisition, raising capital, and operational rigor. Key ideas include the rarity of world-class founders, the power of vertically integrated solutions, the critical need to identify “barrels” (force-multiplying individuals), and a shift from measuring outputs to inputs for long-term success.

    Detailed Summary

    The Bottleneck to Innovation: Great Founders Are Scarce (1:56)

    Rabois kicks off with a stark reality: the bottleneck to creating more exceptional startups isn’t capital—it’s founders. He likens world-class founders to Major League Baseball pitchers who can throw a 90-mph fastball: only a tiny fraction of people (5-15 per year) possess the “superpower” to bend an industry to their will. This scarcity drives the frenzy among VCs and angel investors chasing the same few visionaries. For Rabois, you either have this innate potential or you don’t—training can amplify it, but it can’t create it from scratch.

    Vertical Integration: The Path to Trillion-Dollar Businesses (4:35)

    Rabois doubles down on his pinned tweet philosophy: target large, fragmented industries with low Net Promoter Scores (NPS) and deliver a vertically integrated solution. Companies like Apple (smartphones) and Tesla exemplify this—by controlling hardware, software, and chips, they create moats competitors can’t breach for decades. Vertical integration demands more capital and talent, but the payoff is a near-unassailable market position.

    The Hollywood Model: Startups Are Invented, Not Discovered (6:24)

    Rejecting the Silicon Valley trope of “talk to users and iterate,” Rabois advocates a “Hollywood model” where startups are forged through vision and willpower. Like producing a movie, you start with a script (your idea), cast the right co-founders to tackle key risks, and execute relentlessly. This contrasts with throwing ideas at the wall—Rabois believes startups succeed by design, not serendipity.

    “Why Now?”: Timing the Wave (7:41)

    The “Why now?” isn’t about being first, but riding an enabling technological or societal shift. Amazon capitalized on the web’s infancy, while Google thrived as the 11th search engine by leveraging a maturing internet. Rabois cites Nvidia’s pivot to AI chips as a masterstroke of spotting a wave others missed—founders must find cracks in inertia to gain momentum without brute force.

    Multi-Product Companies: Opportunistic Growth (9:50)

    Should you plan to be multi-product from Day 1? Rabois says no—it’s usually opportunistic. Start with one killer product, achieve product-market fit, then expand organically as customers demand adjacent solutions. Forcing multiple products to boost economics (e.g., in SaaS) is less compelling than responding to real synergies.

    Iteration vs. Pivots: Stay Grounded (10:58)

    Rabois estimates 70-90% of successful startups he’s backed stuck to their initial risks and ideas by the seed stage. Pivots work, but only if one foot stays planted—like PayPal shifting from Palm Pilot payments to email-based transactions, leveraging its core email identifier concept.

    Picking Co-Founders: Complementary Superpowers (12:52)

    Co-founders must complement your strengths and align on first principles (e.g., remote vs. in-office). Rabois values partners who sharpen his thinking—someone who, over coffee, asks questions that reframe problems. Misalignment on fundamentals can fracture a startup’s DNA once it solidifies.

    Talent: The Moneyball Strategy (14:51)

    Startups can’t outbid Google for obvious talent, so Rabois hunts for “mispriced” individuals—young prodigies with few data points, disruptive personalities big companies reject, or those with unique histories he’s witnessed firsthand. This arbitrage is a startup’s edge.

    Attracting and Assessing Talent (17:20 – 24:02)

    To attract talent, Rabois suggests a compelling mission (e.g., Palantir’s democracy defense) or differentiated cultural values. Assessing strangers is tough—he relies on sharp questions to gauge potential quickly, but admits prior context (e.g., knowing DoorDash’s Tony Xu) gives him an unfair advantage. References? Crucial but tricky—ask the right questions (e.g., “Can they be a world-class founder?” not “Are they a good employee?”).

    Closing Hires: Matchmaking, Not Selling (25:56)

    Rabois closes hires by aligning roles with candidates’ goals, highlighting challenges they’ll conquer, and addressing blockers (a trick from Jack Dorsey). It’s less about hard-selling and more about ensuring fit—anti-selling, as Mike Maples Jr. does at Floodgate, filters out mismatches.

    Thinking Ahead: The 6-Month Edge (28:28)

    Great leaders think 3-6 months ahead, anticipating problems and prepping solutions. Rabois recalls engineers who scaled systems for traffic spikes—those who react “just in time” miss opportunities requiring lead time.

    Hiring Longevity and Talent Monopolies (31:36 – 33:28)

    Rabois interviewed candidates at Square until 500 employees; DoorDash’s Tony Xu went to 2,000. It’s about setting a high bar early. Creating a talent monopoly (e.g., SpaceX for aerospace, OpenAI for AI) is ideal—if not, vertical execution (like Ramp’s engineering intern pipeline) can draw the best.

    Raising Capital: Aim for Lift, Not Runway (35:44)

    Fundraising isn’t about extending runway—it’s about hitting milestones that prove “lift.” Define inflection points (e.g., growth rate, tech breakthrough), calculate the capital needed, and pitch investors on that trajectory. Too much cash can bloat spending without focus.

    Screening Investors and Building Boards (37:40 – 41:21)

    Rabois urges founders to reference-check investors—70% add little value. Look for those who stay out of the way or offer rare expertise. Boards, per Jack Dorsey’s Square playbook, should be visionaries you’d hire but can’t, spotting blind spots to avoid fatal errors.

    Operating: Triage, Edit, and Empower (44:11 – 59:21)

    • Triaging Problems: Startups are chaotic—Rabois likens it to an ER. Focus on high-leverage issues with 10x upside or downside, letting minor colds resolve themselves.
    • Editing, Not Writing: CEOs edit initiatives for a consistent voice (like The Economist), ensuring alignment across products and teams.
    • Transparency: Share data (dashboards, board decks) so everyone decides with the same context.
    • Barrels: Rare individuals who turn concepts into reality—expand their scope to find them (2-3 per 100 employees is healthy).
    • Task-Relevant Maturity: Sample work based on experience—daily for novices, quarterly for veterans.
    • Delegation: High-conviction, high-consequence decisions stay with the CEO; low-conviction, high-consequence ones need data hunts or 70% certainty for speed.

    Measuring Inputs Over Outputs (59:21)

    Rabois flipped from output-obsessed to input-focused. Outputs discourage risk-taking (e.g., 10% success odds); inputs—like quality of thinking—reward tackling hard problems. Jeff Bezos and coach Bill Walsh echo this: perfect the process, and results follow.

    Underrated Metrics: CAC Payback Rules (1:02:58)

    Rabois obsesses over Customer Acquisition Cost (CAC) to payback ratio—it reveals value proposition strength and capital efficiency. Sub-6 months is thrilling, over 12 months is a red flag. It’s physics applied to business: minimizing friction drives growth.

    Closing Thoughts: Sleep and Challenge (1:05:22)

    What should people ponder? Sleep—for health and success—and challenging yourself. Quoting Ben Franklin, Rabois urges us to “write something worth reading or do something worth writing about.”

    Final Note

    Despite the video’s abysmal audio—think muffled voices and static—this interview is a goldmine for startup enthusiasts. Rabois distills decades of experience into actionable frameworks, blending philosophy with practicality. Plug in some headphones, crank the volume, and absorb the wisdom—it’s worth the effort.

  • The AI Revolution Unveiled: Jonathan Ross on Groq, NVIDIA, and the Future of Inference

    TL;DR

    Jonathan Ross, Groq’s CEO, predicts inference will eclipse training in AI’s future, with Groq’s Language Processing Units (LPUs) outpacing NVIDIA’s GPUs in cost and efficiency. He envisions synthetic data breaking scaling limits, a $1.5 billion Saudi revenue deal fueling Groq’s growth, and AI unlocking human potential through prompt engineering, though he warns of an overabundance trap.

    Detailed Summary

    In a captivating 20VC episode with Harry Stebbings, Jonathan Ross, the mastermind behind Groq and Google’s original Tensor Processing Unit (TPU), outlines a transformative vision for AI. Ross asserts that inference—deploying AI models in real-world scenarios—will soon overshadow training, challenging NVIDIA’s GPU stronghold. Groq’s LPUs, engineered for affordable, high-volume inference, deliver over five times the cost efficiency and three times the energy savings of NVIDIA’s training-focused GPUs by avoiding external memory like HBM. He champions synthetic data from advanced models as a breakthrough, dismantling scaling law barriers and redirecting focus to compute, data, and algorithmic bottlenecks.

    Groq’s explosive growth—from 640 chips in early 2024 to over 40,000 by year-end, aiming for 2 million in 2025—is propelled by a $1.5 billion Saudi revenue deal, not a funding round. Partners like Aramco fund the capital expenditure, sharing profits after a set return, liberating Groq from financial limits. Ross targets NVIDIA’s 40% inference revenue as a weak spot, cautions against a data center investment bubble driven by hyperscaler exaggeration, and foresees AI value concentrating among giants via a power law—yet Groq plans to join them by addressing unmet demands. Reflecting on Groq’s near-failure, salvaged by “Grok Bonds,” he dreams of AI enhancing human agency, potentially empowering 1.4 billion Africans through prompt engineering, while urging vigilance against settling for “good enough” in an abundant future.

    The Big Questions Raised—and Answered

    Ross’s insights provoke profound metaphorical questions about AI’s trajectory and humanity’s role. Here’s what the discussion implicitly asks, paired with his responses:

    • What happens when creation becomes so easy it redefines who gets to create?
      • Answer: Ross champions prompt engineering as a revolutionary force, turning speech into a tool that could unleash 1.4 billion African entrepreneurs. By making creation as simple as talking, AI could shift power from tech gatekeepers to the masses, sparking a global wave of innovation.
    • Can an underdog outrun a titan in a scale-driven game?
      • Answer: Groq can outpace NVIDIA, Ross asserts, by targeting inference—a massive, underserved market—rather than battling over training. With no HBM bottlenecks and a scalable Saudi-backed model, Groq’s agility could topple NVIDIA’s inference share, proving size isn’t everything.
    • What’s the human cost when machines replace our effort?
      • Answer: Ross likens LPUs to tireless employees, predicting a shift from labor to compute-driven economics. Yet, he warns of “financial diabetes”—a loss of drive in an AI-abundant world—urging us to preserve agency lest we become passive consumers of convenience.
    • Is the AI gold rush a promise or a pipe dream?
      • Answer: It’s both. Ross foresees billions wasted on overhyped data centers and “AI t-shirts,” but insists the total value created will outstrip losses. The winners, like Groq, will solve real problems, not chase fleeting trends.
    • How do we keep innovation’s spirit alive amid efficiency’s rise?
      • Answer: By prioritizing human agency and delegation—Ross’s “anti-founder mode”—over micromanagement, he says. Groq’s 25 million token-per-second coin aligns teams to innovate, not just optimize, ensuring efficiency amplifies creativity.
    • What’s the price of chasing a future that might not materialize?
      • Answer: Seven years of struggle taught Ross the emotional and financial toll is steep—Groq nearly died—but strategic bets (like inference) pay off when the wave hits. Resilience turns risk into reward.
    • Will AI’s pursuit drown us in wasted ambition?
      • Answer: Partially, yes—Ross cites VC’s “Keynesian Beauty Contest,” where cash floods copycats. But hyperscalers and problem-solvers like Groq will rise above the noise, turning ambition into tangible progress.
    • Can abundance liberate us without trapping us in ease?
      • Answer: Ross fears AI could erode striving, drawing from his boom-bust childhood. Prompt engineering offers liberation—empowering billions—but only if outliers reject “good enough” and push for excellence.

    Jonathan Ross’s vision is a clarion call: AI’s future isn’t just about faster chips or bigger models—it’s about who wields the tools and how they shape us. Groq’s battle with NVIDIA isn’t merely corporate; it’s a referendum on whether innovation can stay human-centric in an age of machine abundance. As Ross puts it, “Your job is to get positioned for the wave”—and he’s riding it, challenging us to paddle alongside or risk being left ashore.

  • How to Ride the AI Wave: Unlocking Opportunities in Technology Today

    How to Ride the AI Wave: Unlocking Opportunities in Technology Today

    The artificial intelligence (AI) wave is reshaping industries, redefining careers, and revolutionizing daily life. As of February 20, 2025, this transformation offers unprecedented opportunities for individuals and businesses ready to adapt. Understanding AI’s capabilities, integrating it into workflows, navigating its ethical landscape, spotting innovation potential, and preparing for its future evolution are key to thriving in this era. Here’s a practical guide to leveraging AI effectively.

    Grasping AI’s Current Power and Limits

    AI excels at automating repetitive tasks like data entry, analyzing vast datasets to reveal trends, and predicting outcomes such as customer preferences. From powering chatbots to enhancing translations, its real-world applications are vast. In healthcare, AI drives diagnostics; in finance, it catches fraud; in retail, it personalizes shopping experiences. Yet, AI isn’t flawless. Creativity, emotional depth, and adaptability in chaotic scenarios remain human strengths. Recognizing these boundaries ensures AI is applied where it shines—pattern-driven tasks backed by quality data.

    Boosting Efficiency and Value with AI

    Integrating AI into work or business starts with identifying repetitive or data-heavy processes ripe for automation. Tools can streamline email management, generate reports, or predict sales trends, saving time and sharpening decisions. Basic skills like data literacy and interpreting AI outputs empower anyone to harness these tools, while prompt engineering—crafting precise inputs—unlocks even more potential. Businesses can go further by embedding AI into their core offerings, such as delivering personalized services or real-time insights to clients. Weighing costs like software subscriptions or training against benefits like increased revenue or reduced errors ensures a solid return on investment.

    Navigating AI Ethics and Responsibility

    Responsible AI use builds trust and avoids pitfalls. Bias in algorithms, privacy violations, and unclear decision-making pose risks that demand attention. Diverse data reduces unfair outcomes, transparency explains AI choices, and human oversight keeps critical decisions grounded. Regulations like GDPR, CCPA, and emerging frameworks like the EU AI Act set the legal backdrop, varying by region and industry. Staying compliant not only mitigates risks but also strengthens credibility in an AI-driven world.

    Spotting Innovation and Staying Ahead

    AI opens doors to solve overlooked problems and gain a competitive edge. Inefficiencies in logistics, untapped educational personalization, or predictive maintenance in manufacturing are prime targets for AI solutions. Businesses can stand out by offering faster insights, tailored customer experiences, or unique predictive tools—think a consultancy delivering AI-powered market analysis rivals can’t match. Ignoring AI carries risks, too; falling behind competitors or missing efficiency gains could erode market position as adoption becomes standard in many sectors.

    Preparing for AI’s Next Decade

    The future of AI promises deeper automation, seamless integration into everyday tools, and tighter collaboration with humans. Over the next 5-10 years, smarter assistants and advanced task-handling could redefine workflows, though limitations like imperfect creativity will persist. New roles—AI ethicists, data strategists, and system trainers—will emerge, demanding skills in managing AI, ensuring fairness, and decoding its outputs. Staying updated means tracking trusted sources like MIT Technology Review, attending AI conferences like NeurIPS, or joining online communities for real-time insights.

    Why This Matters Now

    The AI wave isn’t just a trend—it’s a shift that rewards those who act. Understanding its strengths unlocks immediate benefits, from efficiency to innovation. Applying it thoughtfully mitigates risks and builds sustainable value. Looking ahead keeps you relevant as AI evolves. Whether you’re an individual enhancing your career or a business reimagining its model, the time to engage is now. Start small—automate a task, explore a tool, or research your industry’s AI landscape—and build momentum to thrive in this transformative era.

  • Nicolai Tangen on Managing the World’s Largest Sovereign Wealth Fund: Insights from The David Rubenstein Show

    Nicolai Tangen isn’t your typical financial titan. On February 20, 2025, he sat down with David Rubenstein on “The David Rubenstein Show: Peer-to-Peer Conversations,” filmed a month earlier at the Bloomberg House in Davos. As CEO of Norges Bank Investment Management, Tangen runs the world’s largest sovereign wealth fund—$1.8 trillion strong, dwarfing all others. The episode, already at 7,983 views on YouTube, pulls back the curtain on a guy who traded hedge fund glory for a shot at serving Norway. Here’s what he revealed.

    The fund, nicknamed the “Oil Fund,” owes its existence to a frigid night in 1969. Phillips Petroleum hit the jackpot on the Norwegian Shelf, striking the biggest offshore oil find ever at the time. Tangen recounted the moment: a 2 a.m. wake-up call to the Ocean Viking platform chief, followed by a Christmas Eve announcement that changed Norway forever. Started in 1996 with 2 billion Norwegian kroner, it’s now a 20-trillion-kroner behemoth, funding 20-25% of the country’s budget thanks to a strict 3% spending cap. Tangen’s job? Steer this giant, owning chunks of over 9,000 companies worldwide, through calm and chaos alike.

    His approach is steady, not sexy. “You want to be widely diversified,” he told Rubenstein. Tactical bets are a nightmare with a fund this size, so he preaches spreading the risk—across assets, across borders. He’s a contrarian at heart, eyeing beaten-down Chinese stocks while others chase U.S. tech. AI’s been a goldmine, with American tech giants padding the fund’s returns and his team boasting a 15% efficiency bump from new tools. But he’s not blind to today’s risks. With Trump in office, Tangen sees U.S. deregulation juicing short-term gains, offset by tariff pain for Europe and inflation threats from tight labor and big debt.

    Pressure’s a constant companion. The fund’s value ticks live on its website—13 updates a second—and Norway’s 5 million citizens watch closely. “There’s always something going wrong somewhere,” Tangen said, shrugging off the endless gripes about too much of this stock or too little of that. He’s applied for another five-year term, banking on his team’s track record and a push for transparency that’s made Norges the most open fund globally. ESG? Still a priority in Norway, despite America’s cooling on it. His worries keep him up at night: inflation spikes or a wild-card disaster—think Covid or a nuclear mess.

    Tangen’s path to this gig is a hell of a tale. Born in Kristiansand, he studied Russian in Norway’s intelligence service before landing at Wharton, where humility took a backseat to world-conquering bravado. He built AKO Capital into a $20 billion hedge fund powerhouse, then walked away, handing his stake to a charitable foundation and joining the Giving Pledge with a billion-plus net worth. “Happiness is about learning,” he said, rejecting the chase for more cash. “The person with the most money when they die has lost.” Now, he skis, picks wild mushrooms for chanterelle spaghetti, and dreams of another degree—maybe not art history, since he bombed that once.

    This isn’t just a finance story—it’s a human one. Tangen’s a rarity: a guy who’s crushed it in the cutthroat private sector, then pivoted to public service without losing his soul. The full interview’s on YouTube (catch it here), and it’s worth every minute. From oil rigs to AI, from Oslo to Davos, he’s proof you can manage a fortune and still keep your feet on the ground.

  • Microsoft’s Majorana 1 Quantum Chip: A Breakthrough in Scalable Computing

    TL;DR:

    Microsoft has unveiled the Majorana 1 quantum chip, leveraging topological qubits for enhanced stability and scalability, aiming for a million-qubit system. This breakthrough, backed by DARPA, accelerates the timeline for practical quantum computing.

    Satya Nadella emphasized AI’s role in economic growth, not just AGI, predicting 10% global GDP expansion through AI-driven enterprise applications. He sees AI transforming SaaS, Office, and industrial automation while rejecting a winner-take-all market.

    Microsoft also introduced Muse, an AI-powered gaming engine capable of real-time world modeling for dynamic, immersive experiences.

    Together, these advances in AI, quantum computing, and gaming position Microsoft at the forefront of the next computing revolution.

    Microsoft has unveiled a game-changing innovation in quantum computing with its new Majorana 1 chip, an advancement poised to accelerate the transition from experimental quantum systems to practical, large-scale computing solutions. This development, coupled with insights from CEO Satya Nadella, signals Microsoft’s ambitious plans for artificial intelligence (AI), economic growth, and the future of computing.

    Microsoft’s Majorana 1 Chip: The Future of Quantum Computing

    Harnessing Majorana Particles for Stable Qubits

    The Majorana 1 chip is built on a new Topological Core architecture that utilizes Majorana particles, first theorized in 1937. Unlike traditional quantum bits (qubits), topological qubits are inherently more stable and less prone to errors—two critical factors for achieving scalable quantum computing.

    Microsoft’s research over the past two decades has led to the development of the world’s first topoconductor, a material designed to enable the observation and control of Majorana particles. This marks a significant step toward creating quantum processors capable of handling real-world computational challenges with greater efficiency and reliability.

    Scalability: From Eight Qubits to One Million

    Currently, the Majorana 1 chip features eight topological qubits but is designed with scalability in mind. Microsoft’s goal is to achieve a million-qubit system, which would enable complex simulations in areas such as medicine, materials science, and artificial intelligence.

    Microsoft Technical Fellow Chetan Nayak described this breakthrough as the equivalent of the “transistor moment” for quantum computing, underscoring its potential to revolutionize industries worldwide.

    Microsoft’s Partnership with DARPA

    This breakthrough has earned Microsoft a place as one of two companies advancing to the final phase of DARPA’s Underexplored Systems for Utility-Scale Quantum Computing (US2QC) program. The goal is to develop a fault-tolerant quantum computing prototype within years, not decades.

    Satya Nadella’s Vision: AI, Quantum, and Economic Growth

    Beyond AGI: AI’s Role in Economic Expansion

    In a recent interview with Dwarkesh Patel, Satya Nadella challenged the hype surrounding Artificial General Intelligence (AGI), arguing that the real benchmark of technological progress should be economic growth. He believes AI should drive a 10% increase in global GDP, rather than simply focusing on intelligence milestones.

    Key takeaways from Nadella’s discussion:

    • AI is not a winner-take-all industry; multiple hyperscalers (like Microsoft Azure) will coexist.
    • AI commoditization is inevitable, but enterprise adoption will define its long-term value.
    • Legal and ethical barriers to AI deployment must be addressed before true mass adoption.

    AI’s Impact on Enterprise and SaaS Markets

    Nadella predicts a fundamental shift in knowledge work as AI tools become deeply embedded in workflows. He envisions AI-powered assistants transforming Office applications, enterprise SaaS platforms, and industrial automation, making AI an indispensable productivity tool rather than a separate industry.

    Microsoft’s AI-Powered Gaming Evolution

    Muse: The World Model for Next-Gen Gaming

    Alongside its quantum breakthrough, Microsoft introduced Muse, an AI-driven gaming engine that leverages real-time world modeling to generate immersive gaming experiences. Muse builds upon advancements in generative AI (such as Sora and DALL-E) but applies them to dynamic environments where player actions shape the game world.

    The Road Ahead: AI, Quantum, and a New Computing Era

    Microsoft’s Majorana 1 chip represents a turning point in quantum computing, positioning the company ahead of competitors like Google and IBM by pursuing topological qubits over traditional quantum designs. When combined with Microsoft’s investments in AI, cloud computing, and gaming, this innovation strengthens its position as a leader in the next era of computational power.

    With quantum computing, AI-driven economic growth, and next-generation gaming, Microsoft is reshaping the future of technology. The next few years will determine whether its bold bets on AI and quantum will yield world-changing results.

  • Microsoft Unveils Majorana 1: A Quantum Leap in Computing

    Introduction Microsoft has introduced Majorana 1, the world’s first quantum chip utilizing a groundbreaking Topological Core architecture. This innovation, built on the newly developed topoconductor material, aims to accelerate the realization of scalable, industrial-grade quantum computing, transforming problem-solving capabilities in fields ranging from materials science to artificial intelligence.

    Topoconductors: The Foundation of Majorana 1 The Majorana 1 chip leverages a revolutionary material class—topoconductors—to enable more reliable and scalable qubits, the fundamental units of quantum computation. This breakthrough positions Microsoft to lead the quantum computing industry towards achieving a million-qubit system within years rather than decades. By integrating error-resistant properties at the hardware level, the Majorana 1 ensures greater qubit stability, a crucial factor for scaling quantum operations.

    Scalability and Real-World Applications Unlike current quantum architectures, which require fine-tuned analog control, Microsoft’s approach employs digital control for qubits, simplifying quantum computations and reducing hardware constraints. This architecture enables the integration of a million qubits on a single chip, unlocking solutions to some of the most complex industrial and environmental challenges, such as:

    • Microplastic Breakdown: Quantum calculations could facilitate the development of catalysts capable of breaking down plastics into harmless byproducts.
    • Self-Healing Materials: Engineering materials that can autonomously repair structural damage in construction and manufacturing.
    • Advanced Enzyme Engineering: Enhancing agricultural productivity and healthcare by designing more efficient biological catalysts.
    • Corrosion Prevention: Analyzing material interactions at the atomic level to create corrosion-resistant structures.

    Microsoft’s Quantum Roadmap and DARPA Collaboration Recognizing the potential of Majorana 1, the Defense Advanced Research Projects Agency (DARPA) has selected Microsoft as one of two companies progressing to the final stage of its US2QC program. This initiative aims to accelerate the development of utility-scale, fault-tolerant quantum computers capable of commercial impact.

    Precision Measurement and Digital Control A key challenge in quantum computing is qubit instability due to environmental perturbations. Microsoft has overcome this hurdle with a pioneering measurement approach that enables digital qubit control, making quantum systems easier to manage and scale. This precise measurement technique distinguishes between one billion and one billion and one electrons, ensuring the accuracy needed for advanced computations.

    Engineering Breakthrough: Atom-By-Atom Material Design Majorana 1 is built on a meticulously engineered materials stack comprising indium arsenide and aluminum. Microsoft designed and fabricated this stack atom by atom to create the necessary topological state for stable qubits. This breakthrough is pivotal in overcoming the scalability limitations of traditional quantum computing approaches.

    Integration with AI and Cloud Computing Quantum computing’s synergy with artificial intelligence will redefine problem-solving across industries. Microsoft’s Azure Quantum platform provides enterprises with early access to quantum capabilities, enabling AI-driven insights and innovation. The combination of quantum computing and AI will revolutionize material science, drug discovery, and sustainable technology development.

    Microsoft’s Majorana 1 chip marks a paradigm shift in quantum computing, paving the way for practical, large-scale quantum applications. With its topologically protected qubits, digital control systems, and scalable architecture, Majorana 1 is set to drive the next frontier of computational advancements. As quantum computing progresses towards commercial viability, industries worldwide stand to benefit from solutions that were previously unattainable with classical computing methods.

  • How Information Overload Drives Extreme Opinions: Insights from Computational Models

    How Information Overload Drives Extreme Opinions: Insights from Computational Models

    TL;DR:
    A recent study shows that excessive exposure to balanced information can drive people toward extreme opinions rather than moderation. This happens due to hardening confirmation bias, where individuals become less receptive to opposing views as their beliefs strengthen. Using two computational models, the research demonstrates that more information availability leads to polarization, even in unbiased environments. The findings challenge traditional views on echo chambers and suggest that reducing information overload may be a more effective way to curb extremism than simply promoting diverse content.

    In an era where digital platforms provide unlimited access to information, one might expect a more informed and balanced society. However, a recent study by Guillaume Deffuant, Marijn A. Keijzer, and Sven Banisch reveals that excessive exposure to unbiased information can drive people toward extreme opinions rather than moderation. Their research, which models opinion dynamics using two different computational approaches, challenges conventional beliefs about information consumption and societal polarization.

    The Paradox of Information Abundance

    The traditional assumption is that exposure to diverse viewpoints should lead to balanced perspectives. However, evidence suggests that political and ideological polarization has intensified in recent years, particularly among engaged groups and elites. This study explores a different explanation: the role of confirmation bias hardening, where individuals become more resistant to opposing information as their views become more extreme.

    Confirmation Bias and Opinion Extremization

    Confirmation bias—the tendency to favor information that aligns with preexisting beliefs—is a well-documented cognitive phenomenon. The authors extend this concept by introducing hardening confirmation bias, meaning that as individuals adopt more extreme views, they become even more selective about the information they accept.

    Using computational simulations, the study demonstrates how abundant exposure to balanced information does not necessarily lead to moderation. Instead, the increasing selectivity in processing information results in a gradual drift toward extremization.

    The Models: Bounded Confidence and Persuasive Arguments

    The researchers employed two different models to simulate the effects of information abundance on opinion formation:

    1. Bounded Confidence Model (BCM)

    • Agents are only influenced by opinions within their confidence interval.
    • As attitudes become extreme, this confidence interval shrinks, making individuals less receptive to moderate perspectives.
    • When information is limited, opinions tend to stay moderate. When information is abundant, gaps in moderate viewpoints disappear, enabling extremization.

    2. Persuasive Argument Model (PAM)

    • Individuals evaluate new arguments based on their current stance.
    • As attitudes strengthen, individuals accept only arguments that reinforce their position.
    • This model shows that even when consuming moderate content, the sheer volume of information can push individuals to extreme viewpoints over time.

    Implications for Society and Online Media

    The study suggests that online platforms may inadvertently fuel polarization, even when presenting diverse and balanced content. Unlike the widely discussed echo chamber effect, this process does not rely on exposure to like-minded communities but instead emerges from cognitive biases interacting with abundant information.

    Key Takeaways:

    • More information does not always lead to moderation—instead, it can push people toward extremes.
    • Hardening confirmation bias makes extreme views more stable, reducing openness to contrary perspectives.
    • Online platforms designed to promote balanced information may still contribute to polarization, as users naturally filter and reinforce their own beliefs.

    Challenges and Future Considerations

    Regulating online media to reduce polarization is not straightforward. Unlike the filter bubble theory, where reducing ideological silos might help, this study suggests that extremization can occur even in a perfectly balanced media environment.

    Potential solutions include:

    • Reducing exposure to excessive amounts of information.
    • Encouraging critical thinking and cognitive flexibility.
    • Designing algorithms that consider not just diversity, but also engagement with alternative perspectives in a meaningful way.

    Conclusion

    The findings challenge common assumptions about the role of digital information in shaping public opinion. Rather than simply blaming filter bubbles, the study highlights how our cognitive tendencies interact with abundant information to drive extremization. Understanding this dynamic is crucial for policymakers, tech companies, and society as we navigate the complexities of information consumption in the digital age.

    Keywords: Opinion dynamics, Confirmation bias, Information overload, Polarization, Digital media, Cognitive bias, Social media influence