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  • 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 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

  • Amazon’s War on Kindle Book Ownership: The February 2025 Crackdown That Should Outrage Readers

    In a shocking move that cements Amazon’s control over digital books, the tech giant is gutting Kindle book ownership even further in February 2025. If you thought you owned the Kindle books you paid for, think again. Amazon is set to eliminate the “Download & Transfer via USB” feature, a critical function for users who want to maintain local copies of their books. Instead, Amazon is forcing readers into its ecosystem, where their access to books is entirely at the company’s mercy.

    What’s Changing in February 2025?

    Amazon is making two significant anti-consumer changes:

    1. Eliminating Local Backups: Starting February 26, 2025, Kindle users will no longer be able to download books to their computer and transfer them via USB to their e-readers. This means no more offline backups, no more DRM stripping for true ownership, and no more control over the books you bought. Instead, Amazon will dictate how and when you can access your own library, requiring Wi-Fi for all transfers.
    2. Killing Kindle Vella: The company is also shutting down its Kindle Vella platform, its failed attempt at serialized storytelling. While this affects a smaller group of readers and writers, it highlights Amazon’s lack of commitment to platforms that don’t immediately serve its bottom line.

    How to Download and Backup Kindle Books Before the Change

    If you want to preserve your access to your Kindle books before Amazon removes the download feature, follow these steps:

    1. Go to Your Amazon Content Library: Log in to your Amazon account and navigate to “Manage Your Content and Devices.”
    2. Select the Books You Want to Keep: Find the Kindle books you have purchased and select them.
    3. Download to Your Computer: Click the “Download & Transfer via USB” option. Choose your registered Kindle device to initiate the download.
    4. Save Files Securely: Store the downloaded files in a secure location on your computer or external storage.
    5. Strip DRM (If Needed): Use DRM removal tools like Calibre with the appropriate plugins to ensure you have full access to your books even if Amazon revokes them.
    6. Transfer to Alternative Devices: Convert and move the files to open-source e-readers like Kobo or Onyx Boox to maintain long-term control.

    Why This Matters: You Don’t Own Your Kindle Books

    This move reinforces the ugly truth about Kindle purchases: you’re not buying books—you’re renting them under Amazon’s terms. If Amazon ever decides to revoke access, change its DRM policies, or shut down a service, your entire digital library is at risk. And now, by removing USB transfers, Amazon is ensuring that no reader can create an independent archive of their books.

    The Dangers of Amazon’s Control Over Digital Books

    • No Offline Backups: Without USB transfer, if Amazon removes a book from your library (which it has done before), there’s no way to keep a local copy.
    • DRM Lock-in: Digital Rights Management (DRM) already prevents users from freely transferring books between devices. Now, without USB transfers, removing DRM for fair-use purposes will become even harder.
    • Amazon’s Kill Switch: Amazon has remotely deleted purchased books from customers’ devices in the past. With all books now dependent on Amazon’s cloud, your library could disappear overnight.
    • Monopoly Power: This move makes it even harder for readers to break free from Amazon’s walled garden. It’s a clear step toward total corporate control over digital literature.

    How Readers Can Fight Back

    1. Stop Buying Kindle Books: Support independent ebook retailers like Kobo, Smashwords, or Bookshop.org, which allow for real ownership of your purchases.
    2. Use Open Formats: Purchase books in DRM-free formats like EPUB or PDF instead of Amazon’s locked-down AZW format.
    3. Consider Alternative E-Readers: Devices like the Kobo Clara or Onyx Boox offer more flexibility and don’t tie you to Amazon’s restrictive policies.
    4. Speak Out: Public backlash has forced tech companies to reverse anti-consumer decisions before. Demand that Amazon reinstate local download options.

    Amazon’s Endgame: Total Control Over Books

    Amazon has built its Kindle empire on the illusion of ownership. With this latest move, the company is showing its hand—forcing users into a closed system where they have zero control over their books. If readers don’t push back now, the future of digital reading will be nothing more than a glorified rental service dictated by corporate greed.

    It’s time to reclaim digital book ownership before Amazon erases it entirely.

  • Seth Godin on Playing the Right Game and Strategy as a Superpower: Key Questions and Answers

    Seth Godin discusses the importance of strategy over tactics, emphasizing that real strategy is about long-term vision, systems thinking, and understanding the game being played. He highlights four key components of strategy: systems, time, games, and empathy. Godin explains that successful businesses understand their market’s underlying systems, play long-term games, and create conditions that foster growth through network effects. He contrasts companies that innovated strategically (Google, Microsoft, Starbucks) with those that failed by focusing on short-term tactics. He also emphasizes that status and affiliation drive human behavior and business success. Lastly, he warns about the risks of AI-driven business “enshittification”, where companies degrade user experience for profit.


    Core Ideas:

    • Strategy is about long-term vision, not short-term tactics.
    • Understand systems, time, games, and empathy.
    • Good strategy stays constant; tactics evolve.
    • The best strategies align with market psychology and systemic incentives.

    Examples:

    • Microsoft followed IBM’s strategy: “No one gets fired for buying our product.”
    • Google prioritized user experience over short-term revenue.
    • Starbucks built an identity around social experience, not coffee.

    Key Lessons:

    1. Systems: Recognize the hidden forces shaping decisions.
    2. Time: Play the long game; shortcuts rarely work.
    3. Games: Understand incentives, competition, and market dynamics.
    4. Empathy: Identify your ideal audience and serve them uniquely.

    Execution Strategies:

    • Define the smallest viable audience and serve them exceptionally.
    • Create conditions where your product spreads naturally (e.g., network effects).
    • Build credibility through consistency and long-term commitment.
    • Price signals value—charging more can increase perceived worth.

    Wrap:

    • Ask: “If I had to charge 10x more, what would I do differently?”
    • Decision quality matters more than outcome—good strategy withstands failure.
    • AI will replace repetitive work—use it as leverage.
    • The best way to win is choosing the right game to play.

    Seth Godin recently joined Tim Ferriss on The Tim Ferriss Show to discuss strategy, decision-making, and playing the right game in business and life. The conversation touched on the core principles of strategy, why tactics alone aren’t enough, and how successful companies and individuals shape the conditions for their own success. Below are the key questions Godin raises and the insights he provides.

    1. What is strategy, and how is it different from tactics?

    Answer:

    Strategy is a long-term philosophy of becoming, whereas tactics are the specific steps taken along the way. Many people mistake strategy for a series of short-term actions when, in reality, strategy is about being clear on the change you seek to make, who you seek to change, and the system in which you operate.

    Example:

    • Microsoft and IBM’s strategy: “No one ever got fired for buying Microsoft,” mirroring IBM’s earlier strategy. Their consistent strategy ensured market dominance despite changing tactics.
    • Google vs. Yahoo: Google’s strategy was to send people away quickly with relevant search results, while Yahoo aimed to keep users on its platform. This strategic difference ultimately helped Google succeed.

    2. What are the four core ingredients of a successful strategy?

    Answer:

    1. Systems – Understanding the invisible forces at play.
    2. Time – Having a long-term perspective rather than seeking instant results.
    3. Games – Knowing the rules of the game you are playing and leveraging them.
    4. Empathy – Seeing the world through the eyes of your audience and crafting a product or service that meets their needs.

    Example:

    • Starbucks’ strategy: It wasn’t about coffee; it was about creating a third place where people felt a sense of belonging.
    • Google’s long-term perspective: Sergey Brin emphasized that Google would get better over time, so they deliberately delayed aggressive promotion in the early days.

    3. How do systems shape decisions and success?

    Answer:

    Systems are often invisible but dictate behavior. Successful individuals and companies recognize the systems they are working within and either leverage or reshape them.

    Example:

    • The wedding industry is shaped by unspoken norms—people spend slightly more than their peers to signal status.
    • The college admissions system pressures students into chasing grades and degrees because of an entrenched societal structure.

    4. How does time influence strategic thinking?

    Answer:

    Short-term decision-making leads to reactive choices, while long-term strategic thinking allows for compounding success.

    Example:

    • Jeff Bezos and Amazon: Bezos trained Wall Street to accept long-term growth over short-term profits, ensuring Amazon could reinvest aggressively.
    • Google’s launch strategy: Instead of rushing to get early users, they waited until the product was mature enough to impress users, leading to lasting adoption.

    5. What role do games play in strategy?

    Answer:

    Every decision operates within a game—whether it’s merging lanes in traffic or competing in a marketplace. Understanding the rules and incentives within the game allows for better strategic positioning.

    Example:

    • Google Ads: Instead of competing directly with traditional advertising agencies, Google created an auction-based ad system that gradually pulled in marketers.
    • Netflix’s strategic misstep: Binge-watching helped them gain market share, but it also reduced the social conversation around their shows, missing out on word-of-mouth marketing.

    6. What is empathy’s role in strategy?

    Answer:

    Empathy is about deeply understanding what your audience values. Businesses often push their products without considering what customers actually want.

    Example:

    • Ferrari vs. Volvo: A Ferrari dealer won’t try to sell a six-passenger car. Understanding the right audience is crucial.
    • Magic: The Gathering’s success: It provided both affiliation (a community of players) and status (owning valuable, rare cards), driving its network effect.

    7. How can businesses create network effects?

    Answer:

    Network effects occur when a product becomes more valuable as more people use it.

    Example:

    • Fax machines and email: The more people who had them, the more essential they became.
    • Krispy Kreme’s pricing model: Buying a dozen was cheaper than buying four, encouraging customers to share and spread brand awareness.

    8. How do companies avoid false proxies when making decisions?

    Answer:

    Many companies measure the wrong things, leading to poor decisions.

    Example:

    • Hiring mistakes: Companies often hire based on interview performance rather than real-world performance. A better approach is to give potential hires a small project to see how they work.
    • Stock market misalignment: Businesses obsessed with short-term stock prices often make poor long-term strategic choices.

    9. How should entrepreneurs think about pricing and market positioning?

    Answer:

    Instead of competing on price, consider how to provide 10x the value.

    Example:

    • Concierge medicine: Doctors offering premium services can charge much higher prices by providing an exceptional experience rather than relying on insurance reimbursements.
    • Bottled water industry: Charging infinitely more than tap water, yet people still buy it due to perceived value.

    10. What is the difference between a good decision and a good outcome?

    Answer:

    A good decision is based on sound reasoning and strategy, even if the outcome isn’t favorable.

    Example:

    • Pete Carroll’s Super Bowl decision: The infamous pass play that lost the game was statistically a sound decision, but the outcome was unfavorable.
    • Stock investing: Making a well-researched investment that loses money doesn’t mean the decision was wrong—it means variance played a role.

    11. What is the risk of AI and automation?

    Answer:

    AI is poised to replace average work. People who do routine, repetitive tasks are at risk of being replaced, while those who leverage AI to enhance their skills will thrive.

    Example:

    • Radiologists and AI: AI is already outperforming average radiologists in reading X-rays. The best radiologists, however, use AI as a tool to improve their accuracy.
    • Writers using AI: Instead of fearing AI, writers can use it for idea generation, editing, and enhancing their creative process.

    Wrap

    Seth Godin’s insights in this interview reinforce the importance of playing the right game, understanding systems, and thinking long-term. Success isn’t about following a checklist of tactics but about designing the right conditions for success. Whether you’re an entrepreneur, investor, or creative professional, these lessons provide a foundation for making strategic, lasting decisions.

    Key Takeaways:

    • Strategy is a long-term game, while tactics are short-term moves.
    • Understanding systems allows you to work within or reshape them.
    • Network effects and empathy are powerful tools for growth.
    • Decision-making should be based on good reasoning, not just outcomes.
    • AI and automation will reward those who use them effectively and replace those who don’t.

    By asking the right questions, you can shift your approach from chasing short-term wins to building something meaningful and sustainable.