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  • Tim Ferriss and Kevin Rose Random Show: Mortality and Grief, Zen Insights, Rock Climbing at 50, LSD for Anxiety (MM120), AI Smart Homes, and Why You Should Buy the Company Instead of the Product

    Tim Ferriss and Kevin Rose reunite over tequila for another Random Show, and this one swings from the heaviest material they have covered in years (the death of their friend Om Malik, aging parents, dementia, and what grief actually is) to Zen retreat breakthroughs, rock climbing as a post-50 obsession, a phase 3 LSD trial for anxiety, AI-powered smart homes, the coming wave of AI IPOs, and the single investing lesson both keep relearning: let your winners run, and when you love a product, buy the company.

    TLDW

    Kevin reframes the loss of Om Malik and his father through a simple equation: grief is love with nowhere to go, and the sorrow is proof of how lucky you were. Tim adds Tim Urban’s “The Tail End” math (you have spent roughly 95% of your lifetime hours with your parents by high school graduation) and Sam Harris’s “The Last Time” meditation. Kevin recounts a micro-awakening at a five-day silent Zen retreat (“nothing lacking”), both plug their meditation app The Way with Henry Shukman, and Tim declares multi-pitch climbing in Yosemite his next deliberate-practice obsession, complete with hangboard protocols and grip-training gear. The health segment covers A2 whey, venison organ-meat sticks as a multivitamin, the 1,3-butanediol ketone controversy, ketones temporarily unlocking speech in relatives with dementia, terminal lucidity, a JAMA phase 3 trial of MM120 (lysergide) showing 12 weeks of anxiety relief from a single dose, and the Norwegian 4×4 protocol whose hippocampal benefits may persist for five years. The AI segment runs from Kevin’s Claude-coded camera system that opens his gate via license plate recognition, to Tim’s 20-year angel investing retrospective built with Claude Code and the Gmail API, to their handicapping of Google versus Anthropic versus OpenAI, China’s open-source push, local inference boxes, and why buying at IPO and holding may match venture returns.

    Thoughts

    The emotional spine of this episode is the best thing in it. Kevin’s formulation, that the gap left by a death “is just love at the end of the day,” is not new philosophy, but it lands differently coming from someone actively managing a dying dog, a mother with dementia, and a friend’s fresh death, all in the same month. The practical corollary the two keep circling is time-boxing: Tim Urban’s Tail End math and Sam Harris’s “last time” framing both convert vague mortality awareness into a scheduling problem. Tim credits one short blog post with causing years of family trips that his emotionally reserved family would never have taken otherwise. That is about as strong an endorsement as content can get: it changed the calendar, not just the mood.

    The health middle of the show is classic Random Show in that the interesting part is the epistemology, not the products. Tim flags that the loudest critics of 1,3-butanediol ketones sell competing ketone salts, applies a shelf-life heuristic to processed meat instead of memorizing ingredient lists, and treats organ-meat sticks as a dosed multivitamin rather than a diet. The MM120 discussion is the meatiest science: a five-arm randomized trial where a single 100 microgram dose of lysergide produced roughly twelve weeks of relief in generalized anxiety disorder, which Tim, who has been diagnosed with GAD and OCD, reads as a plausible future where anxiety treatment is episodic rather than daily. The unresolved tension they name honestly: the promising dementia signals (ketones, psilocybin case reports, microdosing) all crash into the consent problem. A person who cannot consent cannot sign up for a hallucinogen, and “it might give you half a day of real conversation back” is both a miracle and an ethical minefield.

    The AI section quietly contains one of the more useful predictions frameworks going: Kevin’s argument that Google’s confusing high-bandwidth TPU architecture only makes sense as a bet on continuous learning, where models stop shipping as discrete releases and start improving around the clock like a child. If self-improving models are really 12 to 18 months out, the “model drop” news cycle this episode itself participates in (new Sonnet today, Mythos tomorrow) is a temporary artifact. Tim’s counterweight is human-scale and more sobering: an AI trained on your own writing produces in 30 seconds what takes you 30 hours, and he compares the demoralization to Lee Sedol retiring after AlphaGo. His book sales chart, stable for a decade and then compounding downward every year since ChatGPT launched, is the receipts. The tension between “AI made my 20-year retrospective possible” and “AI is draining my motivation to write” is the honest version of the AI discourse most podcasts flatten into one direction.

    The investing segment is the most immediately actionable. Three ideas stack neatly: let winners run (Tim has lost more money selling early than he made buying), the venture-returns myth (a famous firm’s own analysis found that buying at IPO and holding a decade roughly matched their gains from early rounds through lockup), and buy-what-you-use (the friend who spent $100k on a top-of-the-line Tesla instead of Tesla stock forfeited roughly $15 million; teenage Tim bought Pixar after seeing Toy Story). None of this is sophisticated, which is the point both make explicitly: with Anthropic and OpenAI racing to IPO, ordinary people who use these tools daily will get a shot the private markets never gave them, and the discipline that matters is holding, not access.

    Key Takeaways

    • Kevin and Tim lost their friend and colleague Om Malik of True Ventures within the past week; Kevin found out mid-way through a five-day silent meditation retreat.
    • Kevin’s reframe on grief: the severe sense of loss is “just love at the end of the day.” The gaping hole his father’s death left is love manifested through sorrow, and recognizing that converts anguish into gratitude for having crossed paths at all.
    • Tim credits Matt Mullenweg twice: for organizing the Antarctica trip where he got days of uninterrupted time with Om (including a visit to an emperor penguin colony), and for sending him Tim Urban’s blog post “The Tail End.”
    • The Tail End’s core math: by high school graduation you have used up roughly 90 to 95% of the total in-person hours you will ever spend with your parents. Reading it drove Tim to organize regular family trips, awkwardness be damned, before his father’s mobility declined.
    • Sam Harris’s short meditation “The Last Time” pairs with it: for many activities you will do a last time without knowing it was the last time.
    • Kevin’s 15-year-old dog Toaster had a violent shaking episode (a stress syndrome after standing six hours at a vet visit, not a terminal event), and Kevin’s takeaway from being covered in the aftermath was that when you love an animal that much, none of it matters.
    • At a traditional Zen sesshin with Henry Shukman and his visiting Japanese teacher Yamada Roshi, Kevin had a two-second micro-insight while working his koan: a felt sense of “nothing lacking,” where nothing could be added or taken away because everything was already fully present. Not an emotion, a steady state.
    • Both are investors in The Way, Henry Shukman’s single-path guided meditation app, which they frame as an ideological investment like their funding of the dog aging study on rapamycin. Tim’s favorite sessions: “Whole Earth is Medicine” and “This Too is Me.”
    • Tim’s practical meditation pitch: you do not need a retreat; 10 minutes twice daily works, and there seems to be real alchemy in the twice-a-day rhythm. Kevin, once the guy who quit everything in two weeks, is coming up on five years of consistent practice.
    • A physiology aside: Henry’s instruction to drop the jaw slightly mirrors how Tim’s mandibular snoring device works. Dropping the jaw an eighth of an inch down and forward opens the airway. The ancients found it by trial and error.
    • Kevin, approaching 50, wants to stop saying “one day” about his bookmarked obsessions (Japanese woodworking, ships in bottles) and actually commit to things in the next two decades.
    • Tim’s next deep dive is rock climbing: his surgically repaired right elbow finally allows it, and his stretch goal is multi-pitch climbing in Yosemite despite being, in his words, deadly terrified of heights, sweaty palms included.
    • Tim’s philosophy of training: “training to not die sooner than is necessary” is not a sufficient goal. He needs a concrete deadline event, the way the Lancaster Classic structured his archery, to make deliberate practice worth it.
    • What sold Tim on climbing longevity: the 60-to-almost-80-year-olds at Salt Lake City gyms climbing 5.11+ on weekday mornings, out-performing what he could imagine doing, plus women who cannot do five pull-ups climbing 5.13 and 5.14 on pure technique.
    • Climbing is also social in a way archery never was: bouldering routes are literally called “problems,” and strangers trade beta. After decades of solitary repetition, Tim has hit his quota.
    • Training tools discussed: Michael Eckert’s finger-strength course (the multiple-time pull-up world champion Kevin just bought into), the Nug (a pocket-size wooden grip trainer Tim travels with), and Abrahangs, Emil Abrahamsson’s protocol of moderate partial-bodyweight hangs, 10 seconds on and 50 seconds off for 10 minutes twice a day, which produces outsized forearm and finger gains.
    • Tim’s fantasy recommendation: The Blade Itself, whose treatment of the randomness of death (a friend of Tim’s just died in a plane crash) doubles as a gratitude practice. The audiobooks are exceptional.
    • Protein talk: Kevin likes Pioneer Pastures A2 whey (30 grams a shake, lactose removed, no investor relationship); Tim gets roughly 40% of his protein from Maui Nui wild-harvested axis deer venison and treats the liver-and-heart pepper sticks as a two-or-three-a-week multivitamin.
    • On processed meat and nitrates, Tim’s heuristic is shelf life: if an ultraprocessed meat lasts three years on a shelf, raise an eyebrow. Minimally processed meat almost definitionally does not keep.
    • Exogenous ketones containing 1,3-butanediol may carry liver toxicity risk, though Tim notes many people pushing that claim sell competing ketone salts. His personal policy: use them intermittently, not daily.
    • The startling ketone anecdote: given to relatives with dementia, sentence length roughly 5xed within 20 minutes. Caveats: it tastes like gasoline, and 1,3-butanediol can affect balance, a serious concern when a broken hip is often the beginning of the end for older adults.
    • Kevin moved his mother, who has non-Alzheimer’s (likely vascular) dementia, into a new home equipped with an AI radar orb that detects falls instantly. She cannot recall breakfast but knows who he is, which he will take all day long.
    • The exercise-for-brain-health protocol Tim assembled with neuroscientist Dr. Tommy Wood: Norwegian 4×4 VO2 max intervals (4 minutes on, ~3 minutes off, 4 rounds) three times weekly for five to six months produces volumetric changes in the hippocampus that appear to last up to five years.
    • The only bike Tim can tolerate for it is the Kaiser M3i indoor bike, because the handlebars raise enough to spare his lower back. Kevin’s sustainable alternative: incline treadmill walking while playing Duolingo chess until 40 minutes disappear. Tim’s version of don’t-let-perfect-be-the-enemy-of-good: a 5-minute, three-set gym session still counts.
    • The JAMA study that grabbed Tim: a phase 3, five-arm randomized trial of MM120 (lysergide, essentially LSD, from the company formerly known as MindMed) for generalized anxiety disorder. Effects were dose-dependent, with 100 micrograms (a standard full trip) as the apparent minimum effective dose, and relief persisting through 12 weeks after a single treatment.
    • Mid-conversation they discover the trial ran at Neuroscape at UCSF, their friend Adam Gazzaley’s lab, which Kevin helped fund. Tim, clinically diagnosed with GAD and OCD, finds 12 weeks of relief from one dose remarkable.
    • Related dementia signals: a case report of an elderly Japanese woman with dementia who took a five-gram “heroic dose” of psilocybin mushrooms, slept 19 hours, and woke temporarily capable of full expositional conversation instead of monosyllables; Tim has also seen an unpublished case report of LSD microdosing producing similar verbal fluidity.
    • Both note the hard ethics: hallucinogens for someone who cannot consent, the devastation of a bad trip you inflicted, versus the possibility of half a day of real connection or slowed decline.
    • Terminal lucidity, the well-documented phenomenon of vegetative or unresponsive patients becoming fully lucid in their final days, leaves both baffled: if cognition is fully localized in a structurally deteriorated brain, where is the lucidity coming from? Kevin’s analogy: we assume nothing is backed up to the cloud.
    • Tim’s caffeine pacing hack: Nutonic nootropic toothpicks (a gift from Chris Williamson), roughly 20 to 25 milligrams of caffeine each, a hard ceiling per toothpick that prevents his chain-refill coffee problem.
    • Kevin’s AI smart home: his Ubiquiti camera system has a full API, so with Claude writing the glue code, the cameras now recognize individual people (and Toaster, who gets a dog emblem), play deterrent audio at loiterers in his alleyway, and open his gate automatically when they read his license plate. The camera costs about $200; anyone can do this now.
    • Tim’s flagship AI project: a 20-year retrospective of his angel investing, built with Claude Code and the Gmail API, testing his own stories about his batting average against hard data. Doing it manually would have taken a year of full-time work by multiple people.
    • The humbling adjacent stat from Kevin: friends with always-on AI wearables report that about 70% of what we confidently remember is what actually happened. Startup genesis stories are the same phenomenon, a five-minute bit polished until the teller believes it.
    • Tim’s most valuable everyday AI use: holistic health cross-checking (contraindications between medications and supplements, could A explain D), hallucination-limited by fact-checking across multiple LLMs.
    • Tim’s contrarian AI take: for most people the honest impact is small “because most shit isn’t worth doing in the first place.” Doing something well does not make it worth doing, and AI is skyrocketing the volume of efficiently produced BS.
    • The demoralization is real, though: an AI trained on your writing produces in 30 seconds what takes 30 hours. Tim compares it to the top Go player who lost the joy of the game after AlphaGo, and his all-format book sales have compounded downward every year since ChatGPT launched (roughly -5%, then -28%, then -49%, tracking toward -67%).
    • The prompt experiment both loved: with cross-conversation memory enabled, ask your model “What are three to five rewarding paths I might explore in the next five years?” Tim sent the answers to close friends who called them outstanding, including a non-book business idea Kevin urged him to build. Ask AI open-ended questions the way you would ask a close friend, not robot questions.
    • Kevin is prototyping “Bond,” an app built from scanned values-card decks: swipe to surface your core values, form explicit agreements with partners and friends that both sides “shake” on, weight the damage of a broken bond, and accumulate a trust ledger. He calls the underlying idea dark information: real relational data (trust, reliability, empathy) that exists everywhere but has never been given physical form.
    • Tim’s writing unlock for the blank page: dictate a rambling brain dump into Wispr Flow while walking, drop it into Claude to clean up, and uncomfortable procrastinated emails come together in minutes. Gear notes: Shokz OpenMeet bone-conduction headset (open ears for traffic, recommended by Exploding Kittens co-founder Elan Lee) and a Sennheiser lav mic plus the Ferrite app as a pocket recording studio that beats studio mics in echoey hotel rooms.
    • State of AI, per both: the big three are Google, Anthropic, and OpenAI, with X/Grok never count-out-able (though Anthropic and Google buying excess Colossus capacity suggests weak Grok demand; Kevin still values Grok’s X-API grounding and uses it heavily for Digg). Meta has phenomenal assets but, Kevin thinks, not the talent to keep pace. Apple is quietly a couple of years out.
    • Kevin’s Google thesis: they own the full stack (TPUs, data centers, models, Android’s install base), and their confusingly high-bandwidth chip architecture is a bet that the future is continuous learning, models improving 24/7 like a child rather than shipping as discrete releases. Consensus estimates put self-improving models 12 to 18 months out.
    • Kevin’s insider color: touring Google X with Sergey Brin and Bill Maris a decade-plus ago, he saw Waymos years before the public knew. Google is sitting on roughly five years of undisclosed deck and holds back frontier models partly for cost and partly to avoid government intervention. In 12 months we will know where Google really stands.
    • Counterweights: ChatGPT owns consumer mindshare and OpenAI must crack advertising, which is very hard; Anthropic is reportedly the fastest-scaling enterprise business ever but keeps taking hits from the administration; no frontier lab will remain unconstrained by government; and China is releasing open-source models on par with the frontier (“doing it the American way”), while AMD’s ~$4,000 local inference box can run massive models at home, eight months behind the frontier, which for many users is fine.
    • The investing lessons: let winners run (Tim: “I’ve lost more money by selling stocks early than I’ve ever probably made buying the original stock”); a famous venture firm’s internal analysis found buying at IPO and holding roughly 10 years matched their gains from early-stage investing through post-lockup; and buy the company, not just the product. Kevin’s friend David Prager spent $100k on a maxed-out Tesla instead of Tesla stock, forgoing roughly $15 million. Tim’s first stock, at about 15 years old, was Pixar, bought because Toy Story convinced him animation was the future.
    • Kevin relaunched Digg: from 20,000 weekly users to nearly 500,000 and millions of monthly page views, pulling the zeitgeist from X and other feeds with heavy AI curation rather than trying to build another social network.

    Detailed Summary

    Grief, the Tail End, and the Last Time

    The show opens with banter about alcohol taxes and ketamine before turning serious: Toaster, Kevin’s 15-year-old dog, just had a terrifying (ultimately survivable) collapse, and the pair lost their friend Om Malik of True Ventures within the week. Kevin, who got the news at a silent retreat, offers the episode’s emotional thesis: the loss and sorrow are the shape love takes when the person is gone, and he would not trade the chaos of caring for people and animals for a calmer, emptier life. Tim thanks Matt Mullenweg for the Antarctica trip that gave him days of psychologically naked time with Om, and for sending him Tim Urban’s “The Tail End,” the post whose parents-time math pushed Tim into years of deliberate family trips before his father needed a wheelchair. Sam Harris’s meditation “The Last Time” extends the theme: you rarely know a last time is the last time. Kevin’s response is to do the thing one more time anyway, bouncy-house backflips at 49 included.

    Zen, Nothing Lacking, and The Way

    Kevin describes his five-day traditional Zen sesshin with Henry Shukman and Yamada Roshi: wall-gazing with eyes open, koan practice on the out-breath, and private interviews with the Roshi. His micro-insight, about two seconds long, was a non-emotional steady state of “nothing lacking,” everything fully present with nothing to add or subtract, what Zen calls the removal of the veil. Tim relays his favorite sessions from The Way (the app both back as a philosophical investment, like the rapamycin dog aging study): “This Too is Me,” which dissolves the burden of a squirrel-chasing mind by including everything experience serves up as you, and Henry’s small physical instructions, like dropping the jaw, which Tim connects to his mandibular snoring device: an eighth of an inch down and forward opens the airway. His bottom line: 10 minutes twice a day captures most of the benefit, and watching the formerly two-weeks-and-out Kevin sustain five years of practice has been deeply reassuring.

    Rock Climbing as the Next Decade’s Project

    Kevin, marching toward 50, wants to stop bookmarking dreams (Japanese woodworking, ships in bottles) and start doing them. Tim’s answer is rock climbing: his repaired right elbow finally allows it, and his stretch goal is multi-pitch in Yosemite despite sweating through his palms at the mere thought of heights. What converted him was the Salt Lake City gym crowd at 11 a.m.: retirees in their 60s and 70s climbing 5.11+, inverted on overhangs, evidence that this sport rewards technique and consistency over youth (women who cannot do five pull-ups climb 5.13). After archery, which he loved but found definitionally solitary, climbing’s social “beta”-trading culture is the draw. The training stack: Michael Eckert’s finger-strength course, the Nug pocket grip trainer, and Abrahangs (Emil Abrahamsson’s 10-seconds-on, 50-off, 10-minute, twice-daily hang protocol). A darker aside grounds the ambition: a friend of Tim’s just died in a plane crash, and The Blade Itself keeps teaching him that life-or-death is often dumb luck.

    Protein, Ketones, and the Dementia Frontier

    The supplements run: Kevin’s new favorite is Pioneer Pastures A2 whey (30 grams, lactose removed, gut-friendly); Tim, disclosure-forward as always, travels with Maui Nui venison and treats the liver-and-heart sticks as a twice-weekly multivitamin. On processed meat, Tim’s heuristic is shelf life over ingredient forensics. The exogenous ketone conversation is more fraught: 1,3-butanediol may stress the liver (though the claim’s loudest advocates sell competing ketone salts), so Tim doses intermittently. The astonishing part: given to relatives with dementia, ketones 5xed sentence length within 20 minutes, going from non-answer answers to full paragraphs, “offline to online.” Balance risks make it dicey in exactly the population that needs it. Kevin’s mother’s new care home uses an AI radar orb for instant fall detection. For prevention, Tim’s protocol from conversations with Dr. Tommy Wood: Norwegian 4×4 VO2 max intervals three times a week for five to six months, whose hippocampal volumetric changes appear to persist up to five years, done on the one bike (Kaiser M3i) that does not wreck his back. Kevin’s sustainable version: incline treadmill plus Duolingo chess.

    MM120, Psilocybin Case Reports, and Terminal Lucidity

    Tim walks through the JAMA-published phase 3 trial of MM120 (lysergide, effectively LSD) for generalized anxiety disorder: five arms (placebo, 25, 50, 100, 200 micrograms), dose-dependent response, with 100 micrograms reading as the minimum effective dose and relief lasting through the 12-week measurement window from a single supervised treatment. Kevin clicks through mid-show and discovers it ran at Neuroscape at UCSF, their friend Adam Gazzaley’s lab, which Kevin helped fund. For Tim, clinically diagnosed with GAD and OCD, episodic rather than daily treatment is the headline. The dementia thread continues: a case report of an elderly Japanese woman who took five grams of psilocybin mushrooms, slept 19 hours, and woke into temporary full conversation; an unpublished LSD microdosing report with similar verbal fluidity. Both wrestle with consent ethics. And then terminal lucidity, the documented phenomenon of unresponsive patients becoming fully lucid days before death, which neither can explain: as Kevin puts it, if it is all localized in a deteriorated brain, where is that coming from?

    AI at Home and AI on Yourself

    Kevin’s Ubiquiti camera system, glued together with Claude-written code against its API, now recognizes faces (and Toaster), scolds loiterers through a speaker, and opens his gate when it reads his license plate, all on a $200 camera. Tim’s project is introspective: a Claude Code plus Gmail API retrospective of 20 years of angel investing, checking who made which introductions, what he passed on, and whether his stories about his batting average survive contact with data (they mostly did; he missed fewer explicit opportunities than he feared). Kevin cites friends with always-on AI wearables: about 70% of what we confidently remember is accurate. Tim’s daily-driver use is health: cross-referencing medications, supplements, and symptoms across multiple LLMs. His caution: most tasks AI accelerates were not worth doing, and the volume of efficient BS is skyrocketing. His countervailing enthusiasm: the “what should I do in the next five years” prompt with cross-conversation memory produced ideas good enough to deeply inform his next chapter. Ask it questions like a close friend. Kevin’s next experiment is “Bond,” a values-and-trust app for making implicit relational agreements (what he calls dark information) explicit, trackable, and reflective. Tim’s practical writing unlock: Wispr Flow voice dumps cleaned up by Claude, especially for procrastinated uncomfortable emails, recorded on a Shokz OpenMeet bone-conduction headset.

    The AI Landscape and Where the Money Goes

    Recorded the day a new Anthropic Sonnet launched, with Mythos due the next day, the forecasting segment lands on a big three of Google, Anthropic, and OpenAI. Kevin’s Google case: full-stack ownership (TPUs whose high-bandwidth architecture only makes sense as a bet on continuous, 24/7 self-improving learning, expected within 12 to 18 months), Android distribution, data center expertise, billion-dollar engineer retention, and a five-year hidden deck he glimpsed touring Google X with Sergey Brin and Bill Maris before Waymo was public. Google holds frontier models back for cost and regulatory reasons; within a year we will know what they have. OpenAI owns consumer mindshare but must solve ads; Anthropic is crushing enterprise ARR while absorbing slaps from the administration; no lab escapes government constraint; China’s open-source frontier-parity models and AMD’s ~$4,000 local inference box threaten the subscription model from below. The investing translation: these companies are going public, and ordinary users will finally get access. The lessons both preach: let winners run, remember that buying at IPO and holding a decade roughly matched one famous firm’s venture returns, and buy the company behind the product you love, the lesson of Prager’s $15 million Tesla and teenage Tim’s Pixar shares. Kevin closes with Digg’s relaunch (20,000 to nearly 500,000 weekly users) and Tim with the sobering chart of his AI-era book sales, compounding downward since ChatGPT.

    Notable Quotes

    “I realized that that gap is just love at the end of the day because I wouldn’t have it unless I loved this man so much. I cared for this person so much. How lucky am I to have crossed paths with this person to get to know them?”

    Kevin Rose, on losing Om Malik

    “When I lost my dad, like that is just a gaping hole of love manifested through sorrow and sadness.”

    Kevin Rose, on grief as a consequence of deep love

    “I had a sense of nothing lacking. Nothing needed to be added and nothing even possibly could be added and nothing possibly could be taken away because everything at that moment was full in the way that it should be.”

    Kevin Rose, describing his micro-insight at the Zen retreat

    “Training to not die sooner than is necessary is not sufficient for me.”

    Tim Ferriss, on why he needs concrete physical goals like multi-pitch climbing in Yosemite

    “Doing something well does not make it important or worth doing in the first place.”

    Tim Ferriss, on AI’s honest impact when most tasks were never worth doing

    “I can still write, but what they can do in 30 seconds is what would take me 30 hours. And I’m just like, it really drains the motivation for me to put in those 30 hours.”

    Tim Ferriss, on AIs trained on his own writing

    “It’s not about those new models dropping. It’s about just like a child learning. Tomorrow it’ll be better than today for forever.”

    Kevin Rose, on Google’s bet that continuous learning replaces the model-release cycle

    “You got to let your winners run as long as possible. I’ve lost more money by selling stocks early than I’ve ever probably made buying the original stock.”

    Tim Ferriss, the takeaway from his 20-year angel investing retrospective

    “You find something that you love and you buy said object when you should actually buy the company.”

    Kevin Rose, on the $100k Tesla that should have been $15 million of Tesla stock

    Watch the full conversation between Tim Ferriss and Kevin Rose here on YouTube.

    Related Reading

    • The Tail End (Wait But Why) the Tim Urban post that quantifies how little time you have left with the people you love.
    • The Way Henry Shukman’s single-path guided meditation app that both Ferriss and Rose back and use daily.
    • Terminal lucidity (Wikipedia) background on the end-of-life phenomenon neither host can explain.
    • LSD (Wikipedia) context for MM120/lysergide and the history behind the generalized anxiety disorder trial.
    • The Botany of Desire by Michael Pollan, the book Tim cites on how dogs (and plants) co-domesticated us as much as we domesticated them.

  • Howard Marks on AI Investing, Second-Level Thinking, Warren Buffett, and Why Waiting Until You Feel Safe Means the Opportunity Has Passed

    Howard Marks, co-founder of Oaktree Capital and author of the investing memos Warren Buffett says he reads first, returned to the My First Million podcast for a wide-ranging conversation with Shaan Puri and Sam Parr. Marks explains why he rewrote his AI memo after his son pushed back, what AI can and cannot take from professional investors, how Oaktree deployed $450 million a week while the world thought finance was ending, and why the sentence “I’m 100% convinced” is the most dangerous one in markets. Along the way he covers his 39-year partnership with Bruce Karsh, personal stories about Warren Buffett and Charlie Munger, parenting, career choice, and the two books that shaped his thinking.

    TLDW

    Marks updated his AI memo because his VC son Andrew told him too much had changed, and he now sees AI as unprecedented on two axes: autonomy (every prior technology was a tool; AI can be given a job and figure out how to do it) and unpredictability (he never felt the internet was beyond comprehension, but nobody knows the shape of an AI future). He expects AI to “defrock” mediocre active investors the way indexation did, while insight, judgment about people, and decisions with no historical precedent may remain human. He retells the Lehman Brothers moment: Oaktree raised an $11 billion distressed debt fund before the crisis, then invested $7 billion in a single quarter on the logic that if the world melted down nothing would matter, but if it did not and they had failed to invest, they had failed at their jobs. The through-line is acting despite fear: the battle hero is afraid and does it anyway, and if you wait until there is nothing to be afraid of, the opportunity has passed. He closes with the recipe for his partnership with Bruce Karsh (shared values, complementary skills, appreciation), stories about Buffett and Munger, advice to live your life your own way, and book recommendations: A Short History of Financial Euphoria and Fooled by Randomness.

    Thoughts

    The most valuable thing in this conversation is not any single call, it is watching a 79-year-old investor with five decades of pattern recognition publicly change his mind. Marks wrote an AI memo in December, his son told him in February that it was already stale, and he rewrote it entirely. When the host teases him that he sounds “a little seduced,” Marks does not get defensive. He distinguishes between upgrading an opinion on new evidence and getting emotional about an asset. That distinction is the whole game. Most people treat their published positions as identity; Marks treats his as drafts. The irony he would appreciate: the willingness to say “so much has happened, I have to update” is exactly the behavior that made his original reputation, and it is exactly what the “I’m 100% convinced” crowd cannot do.

    His AI framing is sharper than most full-time commentators manage. Every previous technology, from the railroad to the internet, was a tool that made humans faster. AI is the first with autonomy: you give it a job, not instructions. And it is the first innovation he has ever called genuinely unpredictable. Notice what that combination does to his old computer framework. Computers could only read, remember, add, subtract, and compare, a limited list that still beat most people. The question that decides everything, for investing and beyond, is whether AI’s list is limited or unlimited. Marks does not pretend to know, which is precisely why his answer is credible.

    The Lehman story deserves to be studied as decision-making under true uncertainty, not as a war story. There was no data and no historical analogy for the end of the financial system, only supposition. So Oaktree reframed the decision as an asymmetry: if the world melts down and we invest, it does not matter; if the world survives and we did not invest, we failed. That logic is available to anyone. What is not available to most people is the willingness to act on it while feeling terrible, and Marks is emphatic that he felt terrible. He read the same newspapers as everyone else. The lesson is that trepidation is not a signal to wait; it is the price of admission. Confidence is not the tell of a good decision. Structure is.

    The quietest and maybe most transferable idea here is the credibility flywheel. After a fund did well, Oaktree raised a smaller fund next, because great results meant assets had appreciated and the opportunity had shrunk. That is speaking against your own economic interest, repeatedly, for twenty years. The payoff came when they asked for $11 billion before the crisis and investors believed them, because Howard and Bruce do not cry wolf. Most people optimize each individual transaction and wonder why nobody trusts them at the moment trust matters. And it is not a coincidence that his partnership advice (shared values, complementary skills, appreciation), his parenting advice (let your kid be smarter than you, let them make choices), and his fundraising record all reduce to the same move: give up small ego wins now to compound trust for decades.

    Key Takeaways

    • Marks wrote his first AI memo around December 9th, then rewrote it entirely in early February after his son Andrew, a venture capitalist working with AI companies daily, told him too much had changed. Updating on new facts is a feature of good thinking, not a flip-flop.
    • He upgraded his opinion of AI because of qualities he considers unprecedented: it can discuss its own strengths and weaknesses, use humor, and put information in the context of the specific person it is talking to.
    • AI’s first unprecedented quality is autonomy. Every prior technological innovation, from the railroad to computers to the internet, was a tool to increase productivity. Nothing before could be given a job without being told how to do it.
    • AI’s second unprecedented quality is unpredictability. Marks never felt the internet was beyond comprehension or prediction. With AI, he says nobody knows the shape of the future, a feeling he has never had about any prior technology.
    • Indexation exposed that most active equity investors could not do what they claimed and pushed many out of the business. Marks expects AI to “defrock” another group of professionals whose talents are not as great as they purport.
    • His old framework for computers: they could only read, remember, add, subtract, and compare, but they did it with more data, faster, and without arithmetic or emotional mistakes, so the limited list still beat most people. The big question for AI is whether its list is limited or unlimited.
    • A large share of what AI does is knowing history and extrapolating patterns. There will always be events with no history to train on, and some people simply understand the probability distribution of future events better. That may be where human investors survive.
    • Part of Oaktree’s value has been refusing to invest with bad people based on undefinable signals, the “hair on the back of your neck” test. If AI has no hair on its neck, experienced judgment keeps a role.
    • Second-level thinking, the opening chapter of his first book, says that if you do not see anything different from everybody else, you cannot possibly be superior. You need a variant perception, you have to bet on it, and you have to be right.
    • Asked whether second-level thinking can be taught, Marks says probably not. He can teach its importance, but not how to have perceptions that are both at odds with consensus and correct. In basketball you cannot coach height; in investing there is something called insight, and some people have it.
    • He is genuinely unsure whether AGI, defined as AI doing everything a human can do, will arrive. Whether there are things AI will never do “even when it reaches full flower” is one of the central mysteries.
    • Before the 2008 crisis, the largest distressed debt fund in history had been Oaktree’s own $2.5 billion fund from 2002. In 2007-08 they raised $11 billion because they saw distress coming, and kept it on the shelf for deployment when the stuff hit the fan.
    • When Lehman went under in September 2008, there was no data and no prior experience for the end of the financial world, only supposition, borrowing the Harvard epidemiologist’s three bases for decisions: data, analogies to past experience, and supposition.
    • The deployment logic was an asymmetry: if the financial world melts down and we invest, it does not matter; if it does not melt down and we failed to invest, we did not do our job. So they had to invest.
    • Bruce Karsh invested an average of $450 million a week for 15 weeks, roughly $7 billion in a single quarter, buying debt of private-equity-owned companies at prices where Oaktree would break even if the companies were worth a fifth or a fourth of what buyers had paid a few years earlier.
    • They were “absolutely not confident.” Marks argues people who think probabilistically and admit ignorance and uncertainty cannot act without trepidation, and that acting anyway is the job.
    • His memo “Taking the Temperature” reviews the five major macro calls of his career; every one was made with doubt. Markets crash because the news is terrible, and he reads the same terrible news as everyone else, then overcomes it.
    • The battle hero framing: a hero is not someone who is unafraid, but someone who is afraid and does it anyway. If you are running into a hail of bullets without fear, something is wrong with you.
    • The signature line: if you wait until you have nothing to be afraid about, the opportunity has probably passed.
    • Raising $11 billion rested on a reservoir of goodwill built since 1988, a strategy purpose-built for crisis with proven results in 1991 and 2001-02, the pitch that a crisis fund hedges portfolios positioned for prosperity, and the ability to point at specific flaws: the market was failing at its main job of acting as a disciplinarian and saying no to dumb ideas.
    • From the Spy Game movie: when did Noah build the ark? Before the flood. You cannot raise money during a crisis because the news is too terrible, so you build the ark in advance.
    • Oaktree’s contrarian fund sizing built its credibility: after a fund produced great results, the next fund was smaller, because great results meant assets had appreciated and opportunities had shrunk. Most managers raise bigger funds on the back of good numbers. Twenty years of that earned them trust when it counted, and sometimes you have to speak against your own interest.
    • During the 1998 LTCM meltdown, a young portfolio manager told Marks “I think this is it, we’re melting down.” Marks heard him out, then said: now go back to your desk and do your job.
    • He and Bruce Karsh have been partners for 39 years and have never had a fight, partly because neither is a financial maximizer and most fights are about money. They have intellectual disagreements, not fights.
    • The keys to partnership, from his 2002 memo: shared values and complementary skills. One aggressive partner and one timid one, or one ethical partner and one corner-cutter, cannot last.
    • The cowboys-and-chickens story: of the roughly 40 investment banks on the AT&T tombstone ad, almost all eventually disappeared. In bad times the chickens say the cowboys are getting us killed; in good times the cowboys say the chickens are holding us back. Mismatched values kill firms.
    • Complementary skills mean each partner can do things the other cannot, so both are additive. If one partner can do everything, the other is eventually seen as overpaid. Bruce manages the money; Howard goes on the road and does the podcasts. The third element: be appreciative, and thank your lucky stars your partner does the things you do not want to do.
    • On parenting: a Wall Street psychiatrist observed that his patients’ problems were inversely proportional to the support they got from their fathers. Marks finds it terrible how many successful men need to assert superiority over their sons, and says he always let Andrew be smarter than him in some things.
    • When his daughter had to choose between two good schools, he and his wife let her decide, on the logic that neither option was bad and kids need experience making choices, including incorrect ones.
    • His favorite quote, from Christopher Morley: there is only one success, to live your life your own way. You cannot let friends, parents, or society decide what you should do. Find something that plays to your strengths, avoids your weaknesses, and makes you happy, while knowing that in 20 years you will be a different person.
    • By his own account, Marks made his early career decisions unconsciously and haphazardly until about age 49-50, when he left to start Oaktree in 1995. He landed in high yield bonds because a boss called him in 1978 about “a guy named Milken in California,” and if that call had come at lunchtime, someone else would have gotten the career.
    • The Mark Twain rule: it ain’t what you don’t know that gets you into trouble, it’s what you know for certain that just ain’t true. No sentence starting with “I could be wrong, but” ever hurt anyone; the dangerous sentence is “I’m 100% convinced.” If you bet like you are 100% right and it was really 80/20 and the 20 comes up, that is how you get into big trouble.
    • The Buffett relationship began with Enron’s collapse: Oaktree was the largest holder of the debt of off-balance-sheet entity Osprey, Buffett was second largest, and Buffett gave Oaktree his proxy to run the position. Bruce’s masterful restructuring led to a thank-you letter, a lunch in Omaha, and a friendship.
    • Buffett is the reason the first book exists: in 2009 he told Marks “you should write a book, and if you do, I’ll give you a blurb.” Marks had planned to write one in retirement, but you cannot let a note like that sit. The result was The Most Important Thing.
    • What people do not know about Buffett: the depth of his love for Charlie Munger. Buffett’s farewell note described Charlie as the big brother and himself as the little brother, and their relationship was suffused with humor. Marks says the same dynamic describes him and Bruce.
    • Munger’s greatest credited contribution was talking Buffett out of cigar butt investing (picking up discarded companies with three free puffs left) and convincing him to buy great companies at a good price instead of any company at a great price.
    • Buffett and Munger probably had the highest combined IQ of any partnership in history, but different kinds: Munger a classicist, humanist, and man of letters who talked about ideas rather than money; Buffett an incredible computing machine.
    • Book recommendations: A Short History of Financial Euphoria by John Kenneth Galbraith, on the mental weakness that gives rise to booms and busts, and Fooled by Randomness by Nassim Nicholas Taleb, on why in the short run anything can happen, which shapes attitudes toward risk, portfolio construction, and whether a great published track record means skill or luck.

    Detailed Summary

    Changing His Mind on AI

    The conversation opens with the story behind Marks’s updated AI memo. He wrote the first version around December 9th. In early February his son Andrew, a venture capitalist whose portfolio companies use and build AI, told him: “Dad, so much has happened. You have to update the memo.” Marks rewrote it entirely. When the hosts needle him that the sequel sounds “a little seduced,” he pushes back on the framing: he upgraded his opinion because of observable capabilities, including AI’s ability to discuss its own strengths and weaknesses, use humor, and contextualize information to the specific person using it. He identifies two qualities he considers historically unprecedented. First, autonomy: everything from the railroad to the internet was a tool to speed humans up, while AI can be handed a job without being told how to do it, which is also the source of the nagging concern that it may take over. Second, unpredictability: he never once thought the internet was beyond comprehension or prediction, but with AI he says nobody knows the shape of the future.

    What AI Does to Investors

    Asked whether AI will be able to do what he does, Marks reaches for the indexation precedent: index funds revealed that most active equity managers could not do what they claimed, and pushed many out of the business. AI, he says, will “defrock another group of people whose talents are not as great as they purport.” He recalls his old line about computers, which could only read, remember, add, subtract, and compare, yet still beat most people because they did those five things with more data, faster, and without arithmetic or emotional errors. The decisive question for AI is whether its list of capabilities is limited or unlimited, and he admits he does not know. The hosts note that Buffett reading the Moody’s manual page by page is now a task AI does in a heartbeat. What might remain human: events with no history to train on, since so much of AI is pattern recognition over history; superior intuition about the probability distribution of future events; and people judgment, the undefinable signal when the hair on the back of your neck goes up about someone. If AI has no hair on its neck, experienced investors with judgment keep a role.

    Second-Level Thinking and the Limits of Teaching Insight

    Marks retells the origin of his first book: Columbia asked for a sample chapter, he sat down and wrote one he had never consciously thought about, and it became chapter one, on second-level thinking. The idea: if you do not see anything different from everybody else, you cannot possibly be superior. You need a variant perception, a belief that consensus overstates or understates a company’s quality, growth, earning power, or deserved multiple; you must bet on that perception; and you must be right. Can it be taught? He says the answer is more no than yes. He can teach the importance of second-level thinking, but not how to have perceptions that are both contrarian and correct. His analogy: in basketball you cannot coach height, and in investing there is something called insight that some people simply have. Whether AI can have it is, for him, bound up with the AGI question and genuinely unknown.

    Lehman, the $11 Billion Fund, and Investing at the End of the World

    Oaktree’s biggest call illustrates decision-making with no precedent. Before 2007, the largest distressed debt fund in history was Oaktree’s own $2.5 billion 2002 fund. Sensing distress coming, they raised $11 billion in 2007-08 and kept it on the shelf. Then Lehman Brothers failed on September 15, 2008, and people were talking about the end of the world, all financial institutions melting down, everything having to do with money atomizing. Marks cites a Harvard epidemiologist: decisions rest on data, analogies to past experience, and supposition, and at that moment there was no data and no past experience. The reframe that unlocked action: if the financial world melts down and we invest, it does not matter; if it does not melt down and we did not invest, we did not do our job. Bruce Karsh deployed an average of $450 million a week for 15 weeks, about $7 billion in a quarter, buying debt of companies bought by private equity years earlier at prices where Oaktree would break even even if the companies were worth a quarter or a fifth of the buyout price. Quantitatively easy, emotionally brutal: they were, in his words, absolutely not confident.

    Trepidation Is the Price of Admission

    Marks generalizes the feeling in his memo “Taking the Temperature,” which reviews the five major macro calls of his career: all were made with doubt. Markets crash because the news is terrible, and he consumes the same news feeds as everyone else, so the terrible news looks terrible to him too. The difference is overcoming it. People who look at the world probabilistically and admit ignorance and uncertainty cannot act without trepidation, and if you act without any, something may be wrong with you. He recalls the 1998 LTCM and Russian ruble crisis, when a young portfolio manager came to him convinced everything was melting down; Marks heard his concerns and sent him back to his desk to do his job. The battle hero is not unafraid; he is afraid and does it anyway. And the line that anchors the episode: if you wait until you have nothing to be afraid about, the opportunity has probably passed.

    How You Actually Raise $11 Billion

    Pressed on the mechanics of raising the fund, Marks lists the ingredients. Twenty years of managing money well since 1988 created a reservoir of goodwill. The strategy was purpose-built for crisis, with excellent results through the 1991 and 2001-02 downturns. The pitch positioned the fund as a hedge: most investor portfolios are set up for prosperity, so it makes sense to own something that does particularly well when the stuff hits the fan. And Oaktree could point at specific flaws in the environment, chiefly that the market was failing at its main job of acting as a disciplinarian, the job of telling people that a dumb idea does not make sense and will not be funded. When the market stops saying no, dumb ideas get financed, and when they turn out to be dumb, people lose money. He adds the Spy Game line he and his wife love: when did Noah build the ark? Before the flood. You cannot raise money during a crisis because the news is too terrible. Finally, credibility compounding: Oaktree repeatedly raised smaller funds after successful ones, reasoning that great results meant opportunities had shrunk. Two decades of speaking against their own interest meant that when Howard and Bruce said there was a great opportunity, investors believed they meant it.

    39 Years with Bruce Karsh: Shared Values, Complementary Skills, Appreciation

    Marks calls his partnership with Bruce Karsh, 39 years old that month, one of the greatest things in his life after family and close friendships. They have never had a fight, which he attributes partly to neither being a financial maximizer, since most fights are about money. His 2002 memo formula: shared values and complementary skills. Mismatched values, like one cowboy and one chicken, or one ethical partner and one corner-cutter, doom a firm; he illustrates with the AT&T tombstone ad listing roughly 40 investment banks, nearly all of which eventually vanished as the chickens blamed the cowboys in bad times and the cowboys mocked the chickens in good times. Complementary skills mean each partner does what the other cannot: Bruce approached Marks in 1987 with the novel idea of a distressed debt fund, and from the beginning Bruce stayed back managing money while Howard went on the road and, later, on podcasts. The third element is appreciation: thank your lucky stars you have a partner who will do the stuff you do not want to do.

    Parenting Without Asserting Superiority

    Asked how he raised a son he not only loves but enjoys, Marks cites a decades-old Forbes profile of the only psychiatrist with an office on Wall Street, whose patients’ problems were inversely proportional to the support they got from their fathers. He marvels at how many successful men need to prove they are smarter than their sons, and says he always let Andrew be smarter than him in some things while giving full support to whatever his kids wanted to do, provided it was not injurious. When his daughter got into both good Los Angeles schools, he and his wife had a preference but let her choose, reasoning that they could be wrong, neither option was bad, and children need experience making choices, including incorrect ones.

    Live Your Life Your Own Way

    On career choice, Marks confesses he did a terrible job himself: his decisions for his first decades were unconscious and haphazard, and by his own account he did not really make intentional choices until he left to co-found Oaktree in 1995, around age 49. He went to Citibank because of a good summer job, moved from equities to bonds because his equity research was unsuccessful and he was told to get out, and moved to California for sunshine and palm trees. In 1978 the head of the bond department called the fairly idle Marks about “a guy named Milken or something in California” dealing in high yield bonds, and a legendary career resulted from being at his desk when the phone rang, a story straight out of Outliers. His advice to students at Wharton, Harvard, and Columbia is built on his favorite quote, from writer Christopher Morley: there is only one success, to live your life your own way. Find something that plays to your strengths, avoids your weaknesses, and makes you happy, which really means refusing to let friends, society, or parents decide for you, while accepting the hard truth that you will be a different person in 20 years and must choose anyway.

    Humility as Risk Management

    When the hosts remark on his humility, Marks turns it into a risk framework via Mark Twain: it ain’t what you don’t know that gets you into trouble, it’s what you know for certain that just ain’t true. No sentence beginning “I could be wrong, but” or “I don’t know, but” ever got anybody into trouble; the dangerous sentences begin “I’m 100% convinced that.” If you bet as though you are certain and the odds were really 80/20 and the 20 comes up, that is how you get into big trouble. You make the investment because you believe in it, but you must see the other side.

    Buffett and Munger Stories

    The Buffett friendship began in the wreckage of Enron, which did most of its misbehavior through off-balance-sheet entities. Oaktree became the largest holder of the debt of one called Osprey; Warren Buffett was the second largest, gave Oaktree his proxy, and let Bruce run the position, which Bruce restructured masterfully for a big win. Around 2003-04 Buffett wrote Bruce a note saying nice job, and if you find yourself in Omaha, we’ll have lunch; Bruce and Howard promptly found themselves in Omaha. In 2009, Buffett told Marks he should write a book and promised a blurb, which is why The Most Important Thing exists years before the retirement book Marks had planned. What people do not know about Buffett, Marks says, is the depth of his love for Charlie Munger, expressed in Buffett’s farewell note describing Charlie as the big brother and himself as the little brother. Munger’s celebrated contribution was talking Buffett out of cigar butt investing, the practice of picking up discarded companies for three free puffs, and toward great companies at a good price. They probably had the highest combined IQ of any partnership in history, but of different kinds: Munger the classicist and man of letters who preferred talking about ideas over money, Buffett the incredible computing machine.

    Homework from Howard Marks

    His two book recommendations: A Short History of Financial Euphoria by John Kenneth Galbraith, which shaped his objective view of cycles by teaching the mental weakness that gives rise to booms and busts (he was lucky enough to meet Galbraith), and Fooled by Randomness by Nassim Nicholas Taleb, which argues that in the short run anything can happen because of randomness, with consequences for how we think about risk, portfolio construction, and whether a hot track record reflects skill or luck. He notes, with characteristic self-awareness, that his belief in randomness may be his rationale for not being a decisive thinker, and offers his own memos as the “classic comic” version of Taleb. The episode closes with a nod to his January 2021 memo Something of Value, written after three generations of the Marks family spent the pandemic under one roof arguing about value investing with Andrew.

    Notable Quotes

    “If you wait until you have nothing to be afraid about, probably the opportunity has passed.”

    Howard Marks, on why great investments are made with fear intact

    The thesis of the whole conversation, delivered in the cold open and again in the LTCM story.

    “Second level thinking basically says if you don’t see anything different from everybody else, you can’t possibly be superior.”

    Howard Marks, explaining the first chapter of The Most Important Thing

    The variant perception requirement: see it, bet on it, and be right.

    “In basketball there’s a saying, you can’t coach height. And I think there’s something called insight. And I think some people have it.”

    Howard Marks, on why second-level thinking probably cannot be taught

    Also his open question about AI: whether machines can ever have insight.

    “But if we don’t invest and the financial world doesn’t melt down, then we didn’t do our job. So, we have to do it.”

    Howard Marks, on Oaktree’s reasoning the week Lehman Brothers failed

    The asymmetry that justified investing $450 million a week for 15 weeks.

    “A battle hero is not somebody who’s unafraid. It’s somebody who’s afraid but does it anyway.”

    Howard Marks, sending a panicked portfolio manager back to his desk in 1998

    His answer to the LTCM-era fear that everything was melting down.

    “When did Noah build the ark? Before the flood. You got to build the ark before the flood.”

    Howard Marks, quoting the movie Spy Game on raising crisis funds in advance

    Why the $11 billion was raised in 2007-08 and kept on the shelf.

    “No sentence that starts with I could be wrong but or I don’t know but ever got anybody into trouble. The sentences that get people into trouble are I’m 100% convinced that.”

    Howard Marks, channeling Mark Twain on certainty

    His practical definition of humility as a risk-management tool.

    “The key to a successful partnership is shared values and complementary skills.”

    Howard Marks, on 39 years with Bruce Karsh, from his 2002 memo

    Plus the third element he adds now: appreciation for the partner who does what you will not.

    “There is only one success to live your life your own way.”

    Howard Marks, quoting writer Christopher Morley, his favorite line for students

    The advice he gives at Wharton, Harvard, and Columbia, and admits he did not follow until age 49.

    Watch the full conversation with Howard Marks on My First Million here.

    Related Reading

  • Dig Through Your Couches, SpaceX Needs It: Cyan Banister on Luke Nosek’s Pitch, Going All In on SpaceX, Pokemon Go, Meditation, and Why Curiosity Is the Ultimate Investing Edge

    Angel investor Cyan Banister has one of the most remarkable track records in Silicon Valley: SpaceX, Uber, Anduril, Postmates, Niantic, Affirm, Flexport, Flock Safety, and dozens more. In this wide-ranging conversation on the Sourcery podcast with Molly O’Shea, the Long Journey Ventures co-founder tells the story behind her first check, when PayPal co-founder Luke Nosek got on the floor of her house and told her to dig through her couches because SpaceX needed every liquid dollar she had. She also covers the Founders Fund Mafia show, why personality is not fixed, the five minute meditation practice she prescribes to stuck founders, how asking “why” led her to Pokemon Go and Uber, what worries her about AI surveillance, and why free speech is her number one cause.

    TLDW

    Cyan Banister explains how Luke Nosek and her husband Scott Banister convinced her to put her entire IronPort windfall into SpaceX while rockets were still blowing up on the launch pad, a bet that became the best investment of her life. She walks through the “second believer” philosophy behind Long Journey Ventures and its bellwether logo, her run on Mike Solana’s Founders Fund Mafia show filmed at the site of the famous PayPal Mafia photo, why games like Mafia, poker, and board games are core Silicon Valley social infrastructure, and the time she bluffed Phil Hellmuth on a live stream. She then goes deep on inner work: personality is not fixed, the gap between your values and your actions is measurable, meditation is noticing that you are noticing, and mornings should start with a “why” question. That mindset produced her Niantic and Uber investments, informs her worries about centralized AI and a surveillance state, and fuels her excitement about AI as a new paintbrush, vibe manufacturing, agentized one person businesses, Substrate, Becoming Bio, and Diamond Foundry. She closes with her mentors, Peter Thiel, Marc Andreessen, Scott Cook, and Rick Rubin, and a blunt defense of curiosity and free speech over shame by association.

    Thoughts

    The most useful idea in this interview is the “second believer.” Long Journey keeps two candles on the wall: a founder lights the first flame, and someone else lights their own candle from it and holds the flame in case the founder’s goes out. That is a precise description of what early capital actually is. Luke Nosek was Elon Musk’s second believer, championing SpaceX “with more heart” than Cyan had ever seen, and Cyan’s first check existed because Nosek’s conviction was strong enough to transfer. Most people think conviction is a private mental state. This interview argues it is social infrastructure: belief propagates person to person, and the people who hold flames for others quietly shape which futures get built.

    The SpaceX story deserves a caveat Banister herself supplies. Putting one hundred percent of a liquidity event into a company whose rockets were exploding looks like genius only in hindsight; her friends told her she had lit her money on fire, and they were reasoning correctly from the information available. What made the bet rational was not the outcome but the frame Nosek and Scott Banister gave her: you are young, able-bodied, and infinitely employable, so your downside is a career, not ruin. That is the actual lesson for anyone tempted to copy the trade. Concentrated risk is a function of your recovery capacity, not your conviction level. She could afford to be the fool card. A fifty five year old with dependents cannot, and pretending otherwise is how people get hurt imitating legends.

    Her investing process is really an attention practice wearing a venture costume. The Niantic story is the cleanest example: she noticed friends chartering boats and ditching Defcon parties to capture invisible portals in Ingress, asked why Google would build such a thing, worked out that it was free mapping data, and then recognized the ticket subject lines at Hint Water as her path to the CEO the week Niantic spun out of Alphabet. Nothing in that chain requires capital or connections. It requires being awake, which is exactly why she starts coaching clients with five minutes of meditation and a “why” question every morning. The pipeline from mindfulness to alpha sounds like woo until you notice that every step of her best deals was just paying attention slightly earlier than everyone else.

    Her claim that personality is surgically alterable is more radical than it sounds, and it lands close to the core of the pursuit of joy, fulfillment, and purpose. Most self-improvement advice accepts the self as given and optimizes around it. Banister says the “I’m just like this” script is an excuse for behavior you are unwilling to change, and her values-versus-actions audit, literally listing where you lied this week, including the accidental lies of broken small commitments, is a concrete tool anyone can run tonight. She even disagrees with Marc Andreessen’s famous advice against introspection, which takes some nerve given he is one of her heroes. The through line from her homelessness to her optimism is that she treated her own character as buildable, and that is a more transferable asset than any cap table.

    The last stretch, on centralized AI, surveillance, and free speech, is where her optimism shows its edges. She is an accelerationist who backs open source and decentralized control precisely because she remembers the internet of 1999 promising the same thing and consolidating anyway. Her warning that autonomous vehicles could quietly abolish freedom of movement for dissidents is the kind of unfashionable thought experiment that her whole “question every phrase” method is built to surface. You do not have to share her politics to notice the consistency: someone who measures a nation’s health by its tolerance for comedy and rap music is applying the same test to Peter Thiel dinner parties and to AI policy, which is more than most commentators on either side can say.

    Key Takeaways

    • Cyan Banister’s first ever angel check was SpaceX, made after PayPal co-founder Luke Nosek came to her house and told her and Scott Banister to dig through their couches for anything liquid because SpaceX needed it.
    • She put everything she made from the IronPort sale to Cisco into SpaceX at a time when rockets were blowing up on the launch pad and critics said private citizens had no business in space.
    • The frame that justified the all-in bet: if you are young and able-bodied you are infinitely employable, so a total loss costs you a lifestyle, not your future. She held the position for roughly 20 years and calls it the best investment she will ever make.
    • Failure was priced in: she compares early SpaceX to early aviation, where getting planes to fly required crashing a lot of planes, and NASA veterans knew reusability would demand repeated public failure.
    • Combined with her husband Scott Banister, she believes they are the number one angel investing duo in the world, and even split individually both would sit in the top ten of the Stanford angel rankings. Married partners share capital, which rankings and lists struggle to represent.
    • Her portfolio names dropped in the episode include SpaceX, Anduril, Uber, Zappos, PayPal, Affirm, Flexport, Checkr, Density, Flock Safety, Brave, Control Labs, Depop, Substrate, Carta, Together AI, Postmates, Niantic, Diamond Foundry, Upstart, Fiverr, Forge, Opendoor, Calm, TrueMed, and Crusoe.
    • Long Journey Ventures’ logo is a bellwether, the lead sheep of a flock, looking sideways to spot the nonobvious. The firm’s “second believer” ritual uses two candles: light your candle from a founder’s flame and hold it so they can reignite if theirs goes out.
    • She was a cast member on Mike Solana’s Founders Fund Mafia show, filmed at Tosca, the same location as the famous PayPal Mafia photo, with a full reality TV production: one camera per player, table lenses, aerial cameras, and over 30 crew.
    • Her Mafia strategy is the meta game: listening for sounds, watching eye movements, tracking who protests too much and who is forming alliances, on the assumption that everyone is lying.
    • Games are Silicon Valley’s social infrastructure. Poker, Mafia, Werewolf, chess, Magic the Gathering, and Settlers of Catan nights let people skip small talk, collaborate immediately, and reveal how many turns ahead someone thinks.
    • If you get invited to a poker night or a Mafia game in tech, go. She has found founders and friends through games, and treats them like poker or golf as deal flow channels.
    • Brian Singerman got her into board games through a board game of the month club she ran for $40 a month, shipping sub-30-minute games in advance so game night starts with playing, not rule explanations. She has never met anyone better at strategy board games.
    • She beat Phil Hellmuth with her first ever bluff during a live streamed poker game she did not know was being broadcast, by convincing herself she had the best hand and acting accordingly. Hellmuth went on tilt for the rest of the session.
    • She identifies with the fool tarot card: walking off ledges expecting things to work out, and believing that on a long enough time horizon every setback turns out to have been necessary.
    • Personality is not fixed. Statements like “I’m a Scorpio, I can’t help it” or “I’m Irish, I have a temper” are excuses for behavior you are unwilling to change. With introspection, effort, and time you can surgically alter your personality.
    • Her weekly thought experiment: how wide is the space between your values and your actions? She sits down with paper and lists where she lied, including accidental lies like promising an email and not sending it.
    • She runs Awake Academy 101 classes and coaches stuck founders, starting almost everyone with five minutes of meditation a day, often in the car before starting the engine.
    • Meditation is not silencing your mind. It is noticing thoughts passing like clouds, then noticing that you are noticing, then asking who the noticer is. If you are not your thoughts, who are you?
    • Her presence toolkit: mindful showers, feeling your toes for the first minute after waking instead of launching into routines, and writing “wake up” on mirrors and windows so it appears when they fog up.
    • Humans are “why machines.” She does not start her day until she has a why question to carry through it, and says asking why about everything makes you a better investor, entrepreneur, and everything else.
    • The Niantic investment came from watching Ingress players rent helicopters and charter boats for invisible objects, realizing Google was harvesting free mapping data, then using Hint Water ticket subject lines to reach CEO John Hanke through Kara Goldin the moment Niantic spun out of Alphabet.
    • Nobody would co-invest in Niantic with her because they could not imagine people holding phones up to look at invisible Pokemon. She calls the Pokemon Go launch the closest we have come to world peace.
    • Her Uber conviction came from years of asking taxi drivers about their lives: starting each day $200 in the hole to the taxi yard explained the rushing, the crankiness, and the broken system, so when Uber appeared the pre-thinking was already done.
    • Idle time is research time. Instead of doom scrolling at a restaurant, ask why the bread is baked that way and whether robotics would improve it. Play with science fiction scenarios and they lead you to investments.
    • Her biggest worry, a question Peter Thiel used to ask her: an AI-operated surveillance state. Autonomous vehicles could end freedom of movement, with a government able to shut down your ride or lock you inside it because you are a dissident.
    • She believes the internet’s drift from open and decentralized to closed and centralized is repeating in AI, and that one company with one ideology ruling AI is dangerous. Everyone needs their own models, which is why she backs open source and decentralized control.
    • On education: unless you are pursuing medicine or another field requiring years of formal training, she questions whether school is the right move now. Artisanship and creativity will rise, and AI tutors make genuine self-teaching possible.
    • She is excited about AI as a new paintbrush unlocking dormant creativity: vibe coding, vibe manufacturing, and fully agentized businesses with no employees will mint millionaires from basements even if the businesses are not venture scale.
    • On AI art and training data: after a hundred years art enters the public domain anyway, China will train on Western IP regardless and sell it back, and today’s “slop” is the worst the tools will ever be.
    • AI still cannot replace human judgment. AI-written text has telltale signs any heavy user recognizes, so the job is to take its useful nuggets and massage them back into human form.
    • Her most exciting current investments: Substrate (the substrate of technology, bringing semiconductor manufacturing back to the Americas), Becoming Bio (the substrate of biology), and Diamond Foundry, whose real market was industrial diamonds and wafers, not rings.
    • She avoids hypercompetitive hot deals because the alpha is not in what is happening today. A good seed fund finds moonshots at low prices with meaningful ownership, in the “what’s coming” space: nanotech, biotech, and bottlenecks removed by AI.
    • She is still hunting for “the Alibaba of the Americas” and puts it out publicly in case a founder claims the idea. Wars will be fought with robots and drones, SpaceX opened the category that made Anduril and Varda imaginable, and defense primes will need competitors.
    • Her heroes: Peter Thiel (she went to “Peter Thiel University” during four years as his partner at Founders Fund and calls him tolerant, open-minded, and poorly understood), Marc Andreessen (a teenage hero she vowed to meet as an equal), Scott Cook of Intuit (the gold standard of executive function), and Rick Rubin, whom she has never met but considers a kindred mind.
    • Mentors can be far off. You can learn from people without knowing them by observing them, listening to those around them, and asking why they do what they do without assuming.
    • On shame by association: go to the events, hear all sides before deciding where you stand, and stop weaponizing accusations, because if everyone is called a racist the real ones cannot be found.
    • You can tell the health of a nation by its ability to tolerate comedy and rap music, and comedy disappearing from universities first was the warning sign. Free speech is her number one cause, and much of what she invests in serves it.

    Detailed Summary

    Inside Cyan’s Lair: Play as a Design Principle

    The interview opens in “Cyan’s Lair,” a mural-covered room at Long Journey’s headquarters painted by Brooklyn ceramicist Dave Zackin, whom Banister discovered on Instagram because he wore the same red and green glasses she needed to learn to walk again after her stroke. Zackin rescues abandoned pottery from high schools and ceramic studios, repaints and refires it, and gives it new life. Her home works the same way: thrift store finds, walls of fried eggs, bowls of fake fish people end up throwing at each other. She gauges hosting success by how many things guests touch without permission, because rummaging means they feel free to play. The candles on the wall, added by co-founder Lee Jacobs, encode the firm’s “second believer” concept: light your candle from a founder’s flame and hold it in case theirs goes out. The firm’s bellwether logo, a sheep’s eye looking sideways, comes from her habit of interrogating common phrases: when is being a sheep good, who leads the sheep, and what is the bellwether watching for that others miss?

    Mafia at Tosca: Reality TV for the PayPal Set

    Banister was a breakout player on Mike Solana’s Mafia show for Founders Fund, filmed months before release at Tosca, the location of the famous PayPal Mafia photograph. The production was serious reality television: a camera per player, lenses embedded in the table, aerial shots, and over 30 crew, with spicy moments and sushi-room banter left on the cutting room floor. Her approach was pure meta game, listening for rustling when the mafia woke at night, watching for the table jerk when players leaned on it, and asking the bar who the best players were so she could target them first. It was her first time playing the killer, and she found lying so uncomfortable she was sure everyone could tell. They could not. She hopes for a second season and notes the game should not be played with couples, since accusations have a way of outliving the game.

    Games, Poker, and the Hellmuth Bluff

    Banister argues games are how a neurodiverse industry socializes: instead of cocktail small talk, you drop straight into collaboration and watch how someone thinks, whether they plan five turns ahead, and how they handle math, psychology, and losing. Brian Singerman, whom she calls the best strategy board gamer she has ever met, subscribed to her board game of the month club, where friends paid $40 a month for sub-30-minute games shipped in advance so game nights started instantly. Her poker fame is mostly accidental: she hosts an annual charity tournament for Inflection Grants micro grants, and once stumbled into a live streamed game with Steve Aoki, Ninja, and Phil Hellmuth without realizing cameras were showing her cards to the world. Told at the break to try bluffing just once, she waited for a big pot, convinced herself she held the best hand, and played it that way until Hellmuth folded and went on tilt. The story doubles as her whole philosophy: she was only in Vegas because a portfolio company had her working undercover in a bad wig.

    The Fool Card: Optimism as a Trainable Skill

    Asked how she keeps finding herself in improbable situations, Banister says her life is a series of them, like Bill Murray in The Man Who Knew Too Little, and that if she were a tarot card she would be the fool, walking off ledges expecting things to work out. Pressed on whether ordinary people can live that way, she rejects the premise that they cannot: personality feels fixed only because we recite excuses like “I’m a Scorpio” or “I’m Irish, I have a temper.” With introspection, and here she cheerfully disagrees with Marc Andreessen’s advice against it, you can surgically alter your personality, though it takes effort, time, and facing ugly truths. Her weekly thought experiment asks how wide the space is between your values and your actions: if you claim honesty, list where you lied this week, including the accidental lies of commitments the two-hours-later version of you failed to keep. People wear masks out of fear of standing out, but everyone else is too caught up in their own noise to care, and authenticity leads to more happiness, curiosity, and wonder.

    Waking Up: Meditation, the Right Brain, and Why Machines

    Through her coaching and Awake Academy classes, Banister starts almost everyone with five minutes of meditation a day, often sitting in the car before starting it. She dismantles the perfection myth that drives people away from the practice: meditation is not silencing thought but noticing thoughts pass like clouds, noticing that you are noticing, and asking who the noticer is. From there the practice extends into ordinary life: mindful showers, feeling your toes for the first minute after waking instead of diving into routines, asking “am I awake right now?” before getting out of bed, and writing “wake up” on surfaces that fog up. The point is escaping rumination about past and future, since the present is the only thing that exists. Mind workers live in the left brain, but creativity, body sense, and intuition live in the right, and her greatest investments came with a feeling. Humans, she says, are why machines: she does not start her day without a why question to carry through it.

    Pattern Matching in the Wild: Niantic and Uber

    Her Niantic story shows the method end to end. She watched friends display irrational devotion to Ingress, renting helicopters and abandoning Defcon to capture invisible portals, immersed herself in the game, and asked why Google would build it, concluding it was free mapping data. When Alphabet spun Niantic out, she remembered support tickets at Hint Water marked “Ingress code,” asked founder Kara Goldin about the Google relationship, and was connected to CEO John Hanke within five minutes. He told her Niantic had Nintendo and Google and did not need her money; she asked for one hour and a guarantee he would not regret it, brought her best friend who was a top player (Hanke hired him nearly on the spot), and got into the round. Convincing anyone to co-invest was impossible because nobody believed people would hold up phones to look at invisible Pokemon. The Uber thesis worked the same way years earlier: asking taxi drivers about their lives revealed a system where drivers started each day $200 in debt to the yard, which explained everything riders hated about taxis. People who complain about Uber, she notes, never lived the before times.

    Worries: Surveillance States and Centralized AI

    Banister borrows a question Peter Thiel used to ask her at Founders Fund: what worries you? Her answer is an AI-operated surveillance state fused with robotics. Freedom of movement is a human right, and a future where you cannot drive yourself means someone can shut down your autonomous ride, or lock you inside it, because you said things the state dislikes. AI, like a gun, can be a paperweight or a weapon, and she is an accelerationist who still insists on thinking through what happens if it falls into the wrong hands. Having entered the industry in 1999, she watched an internet that was supposed to be open and decentralized become closed and centralized, and sees the same drift in OpenAI and Anthropic. Everyone needs their own models, she argues, because one company with one ideology ruling it all is dangerous. She also worries about children and what they should study when so much is automatable, concluding that unless a path truly requires years of schooling, like medicine, formal education may not be the answer right now.

    Excitement: The New Paintbrush and the Agentized Business

    On the hopeful side, she sees AI as a new paintbrush unlocking dormant creativity. The person with a million dollar idea who could never get on Shark Tank can now vibe code the app, put up a site, and eventually vibe manufacture the product, running an agentized business with no employees from a basement. These may not be venture scale companies, but they will mint a wave of millionaires, followed in two to three years by a consumer wave that changes signs, fashion, and manufacturing. On AI art controversies she is pragmatic: all art enters the public domain after a hundred years anyway, China will train on Western IP regardless and sell it back, and today’s tools are the worst they will ever be. She has hundreds of movies inside her and can finally make them. But AI is not a replacement for humans: its writing carries telltale signs, and the human job is to take its nuggets and massage them back into human form, agreeing with the host’s Config takeaway that AI generates the average and the human must pull the work out of the bell curve.

    The SpaceX Bet and What Risk Taught Her

    The centerpiece story: Luke Nosek, who met Scott Banister and Max Levchin in a University of Illinois computer lab and drove west with Scott communicating by walkie-talkie, became Elon Musk’s fiercest champion. He arrived at the Banisters’ house, got on the floor in his Vibram shoes, and delivered the pitch: dig through your couches, anything liquid you have, SpaceX needs it. Rockets were blowing up on the launch pad and critics said private citizens had no business in space, but Nosek and Scott argued that early aviation crashed a lot of planes too, and that a young, infinitely employable person should take the shot. Fresh off her IronPort exit to Cisco, she went all in, then immediately wondered what she had done while her own startup struggled. Twenty years later, still essentially unsold, it is the best investment she will probably ever make. The deeper lesson came from realizing angel investing was a special club she had lucked into, one most people never learn exists. Scott farmed his PayPal network while she networked relentlessly through TC40, TC50, Disrupt, and YC demo days for a decade, writing failed checks and calibrating her pattern matching, because becoming good at early stage investing requires losing.

    What’s Next, and the Mentors Behind It

    Her most exciting current bets are Substrate and Becoming Bio, the substrates of technology and biology, plus Diamond Foundry, whose skeptics saw only synthetic rings while the founder saw industrial diamonds and wafers for AI and crypto. She wants semiconductor manufacturing back in the Americas, is watching AI companion devices race toward a genuinely useful Tamagotchi, and keeps a standing public request for the Alibaba of the Americas. She avoids today’s hot hypercompetitive deals because the alpha lives in what is coming, nanotech, biotech, and the bottlenecks AI removes, not what is hot now. Her inspirations: Peter Thiel, her partner for four years at what she calls Peter Thiel University, whom she defends as tolerant, open-minded, and poorly understood; Marc Andreessen, the teenage hero she vowed to meet as an equal and eventually did; Scott Cook of Intuit, her gold standard of executive function and decency; and Rick Rubin, the one mind she compares to her own, whom she is putting out into the universe a request to meet. The closing stretch is a defense of curiosity over tribalism: go to the Thiel events, hear all sides before deciding where you stand, stop diluting real words like racism through overuse, and protect the two canaries of a free nation, comedy and rap music. Free speech, she says, is her number one cause.

    Notable Quotes

    “Banisters, I need you to dig through your couches. Anything liquid you have, I need it. SpaceX needs it.”

    Luke Nosek’s pitch, as retold by Cyan Banister, describing the night that led to her first angel check

    “Luke and Scott convinced me to put everything that I made in IronPort when we sold to Cisco into SpaceX.”

    Cyan Banister, on going all in while SpaceX rockets were still blowing up on the launch pad

    “You can actually surgically go in and alter your personality where you can actually change these things, but it takes effort and time and a lot of facing the ugly truth about yourself.”

    Cyan Banister, rejecting the idea that personality is fixed

    “Meditation is about noticing the thoughts and noticing that they’re going by like clouds and then noticing that you’re noticing. So who is that person? So if you are not your thoughts then who are you is where I would start.”

    Cyan Banister, on the five minute practice she prescribes to stuck founders

    “When you ask why about everything in the world, it’s just going to make you a better investor. It’s going to make you a better entrepreneur, a better everything.”

    Cyan Banister, on humans as why machines and the habit behind her Uber and Niantic bets

    “I always say it’s the closest we’ve come to world peace. It was one of the most magical few weeks of my life and probably many people’s lives.”

    Cyan Banister, on the launch of Pokemon Go

    “I’ve got to try to find things at lower prices that are still a moonshot that I can get a good percentage of ownership like a good seed fund should do.”

    Cyan Banister, on why she avoids hypercompetitive hot deals where the alpha is already gone

    “If you get invited to a Peter Thiel event, go. Do not shy away from it. It does not make you anything that anyone’s going to accuse you of.”

    Cyan Banister, on curiosity versus shame by association

    “You can tell the health of a nation by its ability to tolerate comedy and rap music. Those two things have to exist for freedom.”

    Cyan Banister, on the canaries of free speech, her number one cause

    Watch the full conversation with Cyan Banister on the Sourcery podcast here.

    Related Reading

    • Cyan Banister (Wikipedia) background on her path from homelessness to one of the most successful angel investors in the world.
    • Long Journey Ventures the “magically weird” seed fund she co-founded, home of the bellwether and the second believer candles.
    • Luke Nosek (Wikipedia) the PayPal co-founder and Founders Fund co-founder whose couch-digging pitch started it all.
    • The Creative Act by Rick Rubin, the book behind the openness-to-the-universe mindset Banister says mirrors her own.
    • Purpose (PJFP) our pillar page on building the kind of why-driven daily practice Banister describes.

  • Ken Griffin on AI, the Golden Age of Entrepreneurs, and the Taiwan Chip Risk That Would Cut US GDP 8 Percent: Inside the Citadel Founder’s Goldman Sachs Great Investors Interview

    Ken Griffin, founder and CEO of Citadel, sat down with Goldman Sachs’ Raj Mahajan at the firm’s Apex Symposium (recorded June 2, 2026) for this episode of Goldman Sachs Exchanges: Great Investors. It is their third public conversation in seven years, and Griffin is unusually candid: about the Friday he went home “shocked and depressed” over AI, the agentic system inside Citadel that compresses six weeks of PhD-level work into two hours, why a Chinese move on Taiwan would throw the US into a depression within six months, and the one question every hedge fund investor should ask their GP.

    TLDW

    Griffin names his two proudest leadership calls: dragging Citadel back to the office five days a week before it was acceptable (citing Fed research that remote work has hurt young Americans’ employment more than AI has), and Citadel’s pandemic role, from getting the FDA to approve experimental COVID drug trials in 72 hours to shaping the incentive design behind Operation Warp Speed, which he credits with saving roughly half a million American lives. On markets, he explains why the S&P sits at all-time highs despite wars in the Middle East and Europe: US energy insulation, stunning Chinese oil demand destruction, and record corporate earnings. On AI, he distinguishes hype from reality (a dinner of multinational CEOs gave him five stories of “AI transformation,” none of which were actually AI), then describes the internal breakthrough that changed his mind: an agentic system that reads, reproduces, and out-of-sample-tests academic finance papers in 2 to 3 hours instead of 6 to 8 weeks. The consequences: no layoffs at Citadel, but competitive moats across the economy are being filled in at lightning speed, setting up a golden age of entrepreneurship. He covers the compute market (all available compute is utilized all the time; market makers now spend hundreds of millions a year), China’s lead in roughly 67 of 74 critical technologies, the Taiwan scenario in which losing TSMC chips cuts US GDP 8 percent in six months, an energy doctrine built on nuclear, natural gas, and building data centers (with their own generation) in America, his stress-test approach to tail risk (definable, tolerable, still in business), and hedge fund economics: the industry’s cost of capital is roughly risk-free plus 4 percent, which is why Citadel has returned $25 to 30 billion to its LPs.

    Thoughts

    The most useful thing in this conversation is Griffin’s two-sided read on AI, because he refuses to pick a lane. The paper-replication story is the cleanest documented example yet of AI eating not just white-collar work but masters-and-PhD-level work, from the man whose firm profits from that labor. Yet in the same breath he reports zero headcount reduction, because Citadel has more problems to attack than people to attack them. Both things are true at once, and he names the synthesis honestly: the individual firm gets more productive while every firm’s moat gets shallower. Most commentary picks either the doom frame or the productivity frame. Griffin holds both, and his conclusion (a golden age of entrepreneurship, startups running on a few AI systems instead of 30 to 40 employees) is the actionable part.

    His dinner-party anecdote deserves to be a standard reference. Five global CEOs effusing about AI transformation, and every single story was actually machine learning, optimization, or plain digitization. The C-suite cannot tell AI from technology at large, which means a meaningful slice of the “AI is transforming our business” narrative priced into the S&P is really a decade-old digital revolution wearing a new label. That is not a bearish observation, since the earnings are real either way, but it matters for anyone trying to figure out which companies actually have AI leverage and which have rebranded their IT budget.

    The Taiwan section is the starkest risk framing you will hear from someone who runs both a hedge fund and one of the world’s largest market makers. An 8 percent GDP contraction in six months is not a market correction, it is Boeing halting production, new cars stopping, and consumer electronics freezing simultaneously, because TSMC chips are in every high-end product made. What makes his version distinctive is the second-order point: in a Taiwan blockade, he does not expect unified Western sanctions. Europe’s membership on “team USA” is less clear than it was two years ago, and the Middle East will play Switzerland because China buys its oil. Investors should notice that his answer to “how do you hedge this?” is not clever derivatives, it is his stress-test doctrine: know the worst case, size exposures so the loss is definable and tolerable, and stay in business to fight back.

    Finally, the small structural details are where the conversation earns its Great Investors billing. Compute has become a commodity input like jet fuel, fully utilized at all times and allocated purely by willingness to pay, which quietly favors high-margin businesses and squeezes everyone else. Alternative data made the present transparent, so the remaining edge in stock picking is multi-year vision about which companies are building transformative products. And the hedge fund test he closes with is one any allocator can use tomorrow: is your GP in the asset management business or the performance business? Citadel returning $25 to 30 billion to LPs is what the performance answer looks like in practice.

    Key Takeaways

    • Griffin’s proudest leadership call was bringing everyone back to the office five days a week, extremely early and against the culture, because humans are social creatures who learn through apprenticeship and mentorship.
    • He cites a Fed paper on reduced employment among workers under 30: remote work turns out to be a more important factor in diminished opportunities for young Americans than AI.
    • At the start of the pandemic, a hospital-system CEO called Griffin because he could not get FDA approval for drug trials on ventilated COVID patients; Citadel’s team got experimental trials approved in about 72 hours.
    • The key insight behind Operation Warp Speed, which Griffin discussed at length with Jared Kushner, was an incentives fix: the US government paid pharma to manufacture vaccines before FDA results existed, collapsing time-to-market from months to days.
    • By his math, the country spent a few billion dollars on that risk, saved a few trillion dollars of GDP, and saved roughly half a million American lives.
    • The S&P is at all-time highs despite a Middle East war, a still-raging war in Europe, and a potential skirmish over Cuba, because the US is relatively shielded from the energy shock.
    • China’s oil demand elasticity stunned even Citadel’s commodities business, one of the largest in the world; that demand destruction plus episodic oil flows out of the region has kept crude near the low $100s instead of the nearly $200 most models predicted if the straits closed.
    • Citadel has been a huge user of machine learning since TensorFlow arrived roughly a decade ago; the current wave is an acceleration of a digital revolution already underway, not a clean break.
    • At a dinner two years ago, Griffin asked global multinational leaders to share how AI was transforming their businesses: he got four or five great productivity stories and not one actually involved AI. They were machine learning, optimization, and digitization.
    • In the C-suite the nuance between AI and technology at large gets lost, but bigger budgets and CEO enthusiasm are pushing through real projects with real bottom-line impact; US corporate earnings are at all-time highs and multiples have actually come down as a result.
    • The use case that sent Griffin home shocked and depressed: a Citadel team member built an agentic AI system that reads an academic finance paper, reproduces it, verifies the published results, and tests them out of sample in 2 to 3 hours on average.
    • That same replication work previously took a legion of young masters and PhD hires roughly six to eight weeks per paper; Citadel finds a few tradeable ideas a year this way, and a few ideas can be worth a lot of money.
    • The point he stresses: this is not just a white-collar job being automated, it is a master’s or PhD-level job, and AI is now cracking problems (like the 80-year-old math problem OpenAI solved) that seemed beyond its reach two or three years ago.
    • Despite the breakthrough there has been no reduction in headcount at Citadel: the firm has more problems to attack than people, so Griffin takes every productivity gain he can get.
    • The flip side is that competitive moats across corporate America are being filled in at breathtaking speed, which Griffin expects to produce a golden age of entrepreneurial activity.
    • His example: a startup that would traditionally need 30 or 40 employees now runs with just a few AI systems, letting entrepreneurs take on incumbents in ways impossible 5, 10, or 20 years ago.
    • Some workers face genuinely hard transitions (his example is English-to-German translators), and the country needs to figure out how higher education can retrain these people quickly.
    • Stock picking remains a timeless business with a similar skill set, but the market will increasingly reward multi-year vision about which companies are creating transformative products rather than skill at calling quarterly earnings beats.
    • Alternative data (Citadel has access to the credit card spending of millions of Americans) made the here-and-now transparent a decade ago; AI plus bright people now triage the present almost instantly, so relative value accrues to those who can see years ahead.
    • At Citadel Securities, transformer models continue a decade of ML-driven improvement in pricing and risk management, and the same is true at other leading market-making firms.
    • For all intents and purposes, all available compute in the world is utilized all the time; access is decided by who will pay the most, and the per-unit price has risen beyond what anyone reasonably projected two or three years ago.
    • Large market-making firms now spend hundreds of millions of dollars a year on compute; Griffin compares compute inflation to jet fuel and egg prices, a cost that high-margin businesses can bear and low-margin businesses cannot.
    • China leads in roughly 67 or 68 of the 74 or 75 most important technologies in the world, including solar, EV batteries, and multiple quantum fields, and has pulled ahead in published academic papers.
    • The drivers are structural: 1.4 billion people, an extraordinarily strong educational culture, and far more STEM graduates, producing exactly the human talent needed to win in a high-IP world.
    • China is no longer relegated to producing low-margin products designed in America, and Griffin calls that shift a threat to the American way of life; the answer is not tariffs but educating US youth to out-compete, out-innovate, and out-problem-solve.
    • If China takes Taiwan and the US loses access to Taiwanese semiconductors, the rough estimate is US GDP falls 8 percent in six months: a great depression in the blink of an eye, unlike any before.
    • The mechanism is concrete: Boeing stops making planes within six months, most new cars stop being manufactured, consumer electronics production freezes, because TSMC chips are in every high-end product made.
    • There are no winners in a Taiwan escalation: tanking the US economy would have draconian knock-on effects for China given America’s importance as an export market.
    • In a Taiwan blockade Griffin does not expect unified global sanctions against China: where you sit determines your exposure, Europe’s place on team USA is less clear than two years ago, and the oil-exporting Middle East will play Switzerland.
    • On energy, the US must re-embrace nuclear, with small modular reactors a big part of the story: nuclear has effectively no carbon footprint and one of the lowest mortality rates of any energy source ever used (hydro has killed magnitudes more people).
    • He punctures the clean-energy veneer: solar cells are often made in western China by burning coal, with roughly a seven-year energy payback, and carbon fiber wind turbine blades last 20 years then fill landfills because they do not break down. No truly clean solution exists until fusion or broader nuclear.
    • Until then, natural gas is America’s huge asset: decades of cheap supply, and one of the few things that has actually brought down US carbon emissions.
    • Data centers are going to get built somewhere, and Griffin argues it would be inane for America to end up dependent on foreign countries for them; his fix for NIMBY politics is to require data center builders to construct corresponding power generation, tied to the grid for reliability, rather than pushing costs onto consumers.
    • His hedging doctrine for complicated risks: run stress tests, know exactly how much you lose and where in the worst case, and keep exposures sized so the loss is definable, tolerable, and leaves you still in business and able to fight back. You will never hedge every tail event.
    • Hedge fund industry economics: the long-run cost of capital is roughly the risk-free rate plus 4 percent; underperform and capital flows out, outperform and it flows in, and inflows dilute alpha because alpha capacity is finite.
    • Citadel has returned $25 to 30 billion to its limited partners to keep return on equity high: Griffin’s job is to grow annual alpha capacity, and any capital beyond what the portfolio needs goes back to LPs.
    • The alignment test for allocators: the biggest investor in Citadel’s funds is Griffin and his partners, and every LP should ask whether their GP is in the asset management business or the performance business.

    Detailed Summary

    Return to Office and the Cost of Remote Work

    Asked what he is most proud of beyond the numbers, Griffin starts with Citadel’s early, countercultural demand that everyone return to the office five days a week. He frames it as a human capital decision, not a control decision: people learn through apprenticeship, mentors are critical to development, and the underdevelopment of talent from remote work has damaged the broader economy. He points to recent Fed research on falling employment among under-30s: remote work turns out to matter more than AI in diminishing opportunities for young Americans. Citadel not only brought its team back but publicly extolled the virtues of doing so, and Griffin believes history will be on his side.

    72 Hours to FDA Approval and the Warp Speed Incentive Design

    His second point of pride is Citadel’s pandemic chapter. As the first US COVID cases appeared, a former partner running a major New York hospital system called: he could not get FDA approval for experimental drug trials on ventilated patients facing imminent death, and believed only Griffin could make it happen. Citadel’s team, with decades of government experience, got approvals moving in about 72 hours. The second act was Operation Warp Speed, whose core idea Griffin discussed at length with Jared Kushner: pay pharmaceutical companies to manufacture vaccines before FDA results, so a positive result means days to market instead of the standard sequence losing three to six months. No company would spend billions producing vaccines that might be flushed down the sewer, so the US government took the manufacturing risk on unproven efficacy. A few billion dollars spent, a few trillion in GDP saved, and roughly half a million American lives.

    All-Time Highs in a World at War

    Griffin’s market picture is unsentimental: there is a war in the Middle East, a still-raging war in Europe, potential trouble in Cuba, and the peace both men grew up with is off the table. Yet the S&P sits at record highs. His explanation: America is relatively shielded from the war-driven energy crisis. China has curtailed oil demand with an elasticity that stunned even Citadel’s commodity desk, and episodic oil and LNG flows keep leaving the region, holding crude around the low $100s when most estimates had a strait closure producing nearly $200 a barrel. Meanwhile corporate earnings are at all-time highs, enough that multiples have actually compressed over the last 12 months.

    The AI Story CEOs Tell Versus the One That Is True

    Citadel has used machine learning heavily since TensorFlow arrived a decade ago, powering everything from radiology reads to self-driving cars across the economy, so Griffin sees today’s AI wave as an acceleration of an ongoing digital revolution. His favorite corrective: at a dinner with global multinational leaders two years ago, everyone was effusive about AI transforming their businesses, so he asked them to go around the table with specifics. Four or five genuinely impressive productivity stories emerged, and not one involved AI: they were machine learning, optimization, digitization, technology at large. The C-suite blurs the distinction, but the enthusiasm has unlocked bigger technology budgets and real bottom-line projects, which is part of why earnings are at records.

    The Agentic System That Shocked Him

    Then comes the story behind the famous “shocked and depressed” Friday. Citadel employs legions of young masters and PhD graduates to replicate academic finance papers: read the hypothesis, judge the work, reproduce results, and test whether the effect persists out of sample (does buyback activity predict outperformance, for example). Each paper takes six to eight weeks, and the process surfaces a few valuable ideas a year. A colleague built an agentic AI system that does the entire pipeline (read, reproduce, verify, out-of-sample test) in two to three hours on average. Griffin’s emphasis: this is not routine white-collar work, it is master’s and PhD-level work, and paired with OpenAI solving a math problem open for 80 years, it shows AI cracking problems considered out of reach two or three years ago. Notably, Citadel cut zero headcount on the back of the breakthrough; the firm has more problems worth attacking than people to attack them, so every productivity gain gets absorbed.

    Filled-In Moats and a Golden Age of Entrepreneurs

    The macro consequence Griffin draws is double-edged. Hold two thoughts at once: AI is reaching very high-level work in the job market, with some workers (translators, for instance) facing hard transitions that demand fast retraining through higher education. And simultaneously, the competitive moats of corporate America are being filled in at breathtaking rates. That means entrepreneurs can launch businesses at speeds impossible 5, 10, or 20 years ago: he mentions a startup running on a few AI systems where 30 or 40 employees would once have been required. He expects a wave of these stories over the next couple of years as founders use the technology to take on incumbents.

    The Future of the Stock Picker

    Griffin has called stock picking a timeless business, and he still sees a similar skill set for the portfolio manager of the future, with one shift in emphasis. Predicting quarterly earnings beats has gotten far harder over a decade as alternative data (credit card panels covering millions of Americans, telegraphing Starbucks and McDonald’s revenues) made the present transparent. Now bright people plus good AI triage the here-and-now almost instantly. The scarce, rewarded skill becomes vision: identifying which companies are building genuinely transformative products years before the market fully prices it.

    Compute Is the New Jet Fuel

    At Citadel Securities, which holds double-digit market share across equities, futures, and treasuries, transformer models extend a decade of machine learning gains in pricing and risk. The compute market backdrop is what Griffin calls breathtaking: essentially all available compute on Earth is utilized all the time, so access reduces to who will pay the most. Per-unit compute prices exceed what anyone reasonably projected two or three years ago, and large market makers now spend hundreds of millions of dollars annually. He treats it as straightforward input inflation, like jet fuel or eggs: high-margin businesses can bear it, low-margin ones cannot.

    China’s Technology Lead and the Taiwan Equilibrium

    Griffin states the cold reality: China is one of the most innovative, fastest-growing economies in the world, leading in roughly 67 or 68 of the 74 or 75 most important technologies (solar, EV batteries, several quantum fields) and now ahead in published academic papers. The foundation is 1.4 billion people, a culture with an extraordinary emphasis on education, and far more STEM graduates. China is no longer relegated to manufacturing low-margin products designed in America, and Griffin calls that a threat to the American way of life. His prescription is pointed: not tariffs, but educating American youth to out-compete, out-innovate, and out-problem-solve. Taiwan is the painful pressure point with no winner. If China takes Taiwan and the US loses TSMC chips, GDP falls an estimated 8 percent in six months: Boeing stops making planes, most new car production halts, consumer electronics freeze, a great depression in the blink of an eye. China would suffer draconian knock-on effects too. As an investor he thinks about position: sanctions in a Taiwan blockade would not be unified, Europe’s place on team USA is a genuine question mark now, and the oil-exporting Middle East would play Switzerland since China is its biggest customer.

    Energy Realism: Nuclear, Gas, and American Data Centers

    On powering AI, Griffin wants America to lead again in nuclear, with small modular reactors central: no meaningful carbon footprint and one of the lowest mortality rates of any energy source ever deployed (hydro has killed magnitudes more people). He challenges the superficial cleanliness of renewables: solar cells are often made in western China with coal power, requiring about seven years of energy capture to break even against the coal burned making them, and 20-year-old carbon fiber wind turbine blades do not break down and are already filling landfills. Until fusion or expanded nuclear, America’s real asset is natural gas: decades of cheap supply that has actually driven US emissions down. His data center position is blunt: they will get built somewhere, and depending on foreign countries for them would be inane, so build them in America. His answer to NIMBY politics: require data center developers to build corresponding power generation, tied to the grid for reliability, so the cost never lands on the American consumer.

    Tail Risk, Tolerable Losses, and Hedge Fund Alignment

    On hedging complicated risks, Griffin’s method is stress testing: if this happens, how much do we lose and where, and is that loss tolerable? You can never manage a portfolio for every possible tail event, but you can keep exposures sized so the worst case is definable and tolerable, leaving you still in business and positioned to fight back. On industry returns, he pegs the hedge fund cost of capital at roughly the risk-free rate plus 4 percent as the long-run equilibrium: underperformance drains capital, outperformance attracts it, and since recent outperformance keeps pulling money in, growing assets dilute alpha. That is why Citadel has returned $25 to 30 billion to LPs: alpha capacity is finite, Griffin’s job is to grow it, and excess capital goes back to investors to keep return on equity high. The closing advice is an alignment test: Citadel’s biggest investor is Griffin and his partners, and every allocator should ask whether their GP is in the asset management business or the performance business.

    Notable Quotes

    “Turns out that remote working is a more important factor to diminished employment opportunities for young Americans than AI.”

    Ken Griffin, citing Fed research on under-30 employment

    “We spent a few billion dollars as a country. We saved a few trillion dollars in GDP. We saved roughly half a million American lives.”

    Ken Griffin, on Operation Warp Speed’s incentive design

    “I got four or five incredible stories of how companies were achieving meaningful productivity gains. Not one involved AI.”

    Ken Griffin, on his dinner with global multinational CEOs

    “My colleague built an agentic AI system that would read a paper, reproduce it, verify the results that were published in the paper, produce the results out of sample, and do all this work in about on average 2 to three hours.”

    Ken Griffin, on the breakthrough that replaced six to eight weeks of PhD-level work

    “We’re likely to see a golden age of entrepreneur activity. Like entrepreneurs will be able to launch new businesses at breathtaking speeds and will be able to take on incumbents in ways that you just couldn’t do 5, 10, 15, 20 years ago.”

    Ken Griffin, on AI filling in competitive moats

    “All the available compute today is more or less utilized all the time. So the question is who’s willing to pay the most for it?”

    Ken Griffin, on the global compute market

    “The US loses access to Taiwanese semiconductor chips, our GDP falls by 8% in 6 months. Simply put, we go into a great depression in the blink of an eye unlike any we’ve seen before.”

    Ken Griffin, on the Taiwan scenario

    “We better damn well build the data centers in America because they’re going to get built somewhere in the world.”

    Ken Griffin, on energy policy and AI infrastructure

    “Definable, tolerable, still in business, still in a position to fight back from that point.”

    Ken Griffin, summarizing his approach to hedging tail risk

    “Are they in the asset management business or are they in the performance business?”

    Ken Griffin, on the question every hedge fund investor should ask their GP

    Watch the full conversation here: Ken Griffin on Goldman Sachs Exchanges: Great Investors.

    Related Reading

  • The Next 3 Years of AI, According to Steve Jurvetson: Moore’s Law, Superintelligence Odds, Elon Musk’s Operating Principles, and Where the Legendary SpaceX and Tesla Investor Is Betting Next

    Steve Jurvetson has spent 30 years funding the future before it was a category: an early check into SpaceX when space was not a venture sector, Tesla before electric cars were taken seriously, and now a portfolio spanning fusion, analog AI chips, and epigenetic editing at his firm Future Ventures. In this fireside chat he lays out what the next three years of AI actually look like, the three principles he has learned from working alongside Elon Musk for nearly three decades, the question he uses to separate missionary founders from opportunists, and why he thinks alignment of frontier AI systems may simply not be possible.

    TLDW

    Jurvetson argues the 130-year exponential in compute per dollar (Ray Kurzweil’s abstraction of Moore’s Law from his book The Age of Spiritual Machines) will keep running for at least three more years, carried by analog and custom AI silicon, and that this compounding is what makes startups and disruption possible at all. His gut says the next big leap will be “architecturally variant”: a new generation of labs going back to DeepMind’s founding premise of reinforcement learning, continuous learning, and novelty-seeking goal functions rather than bigger LLMs. He relays Anthropic co-founder Jack Clark’s 30 percent odds of superintelligence within a year but notes the crucial missing piece is that humans still set every goal. Adoption will be wildly uneven: anything made of atoms (cars, robots) switches over glacially, while creative work and white-collar categories like call centers (roughly 1 percent of US GDP) flip almost instantly. From Musk he draws three lessons: insane focus and saying no, maniacal attention to the cycle time of learning loops (Tesla gathers more AI training data every 4 days than Waymo has in its entire history), and being a magnet for talent by selling a grander mission. He explains Future Ventures’ current bets (fusion, free diagnostics via phone, slaughter-free meat, epigenetic editing, critical minerals, analog in-memory compute), tells solo founders their 30-day plan is to find a co-founder, predicts a turbulent transition to abundance, doubts Neuralink can keep pace with AI, dismisses Penrose’s quantum consciousness argument, and frames the post-work question with Man's Search for Meaning: humans need symbolic immortality, not just employment.

    Thoughts

    The most load-bearing claim in this conversation is not about scaling laws, it is about architecture. Jurvetson is telling you where the smart contrarian money is looking: away from ever-larger language models and back toward reinforcement learning agents with continuous learning and self-generated goals, the original DeepMind thesis that got shelved when LLMs took off. His framing of the open problem is unusually precise. The recursive self-improvement loops everyone is excited about are real, but every one of them is still human-directed. The goal-setting layer, what he calls the selection pressure of the evolutionary algorithm, is the “thin veneer of activity” AI does not yet do, and it happens to be the layer where superintelligence either does or does not arrive. That is a much sharper way to track AGI progress than benchmark scores: watch who cracks autonomous goal formation, not who tops a leaderboard.

    Almost everything else Jurvetson says reduces to a single metric: the cycle time of the learning loop. It is his explanation for Musk’s edge (launch cadence, the Tesla fleet as a data-collection machine), his filter for which industries flip fast (bits iterate at machine speed, atoms are stuck with 11-to-12-year car replacement cycles and FDA timelines), and even his bear case on Neuralink, which he has invested in. Biology cannot iterate at synthetic speed, so the substrate that learns fastest wins. Once you see the pattern, it becomes a genuinely useful lens for evaluating any company, career, or technology: ask how fast the loop spins, not how impressive the current artifact is.

    The aside that deserves the most attention is his flat statement that mechanistic interpretability will not bear fruit and that control and alignment of a cutting-edge system is not possible. His reasoning is structural, not rhetorical: anything produced by an iterative algorithm run billions of times (evolution, neural network training) is inherently inscrutable, and it will always be easier to build a new intelligence than to reverse engineer one you already made. He swaps “teenager” for “AI” whenever he thinks about control, which is funny until you notice he is one of the most connected investors in the Musk orbit saying the safety agenda rests on a false premise. Sitting that next to the 30 percent superintelligence odds he cites from Jack Clark produces an uncomfortable arithmetic that nobody on stage follows to its conclusion.

    For builders, the practical gold is the 50-year question. Ask a founder what their business looks like in 50 years: the opportunist laughs at the question, the missionary is relieved someone finally asked. Paired with his other filters (if only two out of ten people think your idea is crazy it is not bold enough, and a good business is one that could not have been started three years ago), it doubles as a hiring screen and a self-diagnostic. And his 30-day plan for a solo founder is refreshingly unglamorous: do not build the MVP, do not pitch investors, go persuade one person to give up their job and join you. If you cannot recruit a co-founder, that is the market’s first answer about your idea.

    Key Takeaways

    • Jurvetson invested early in SpaceX and Tesla precisely because space and automotive were not venture categories at all; a software-centric systems engineering approach applied to a sleepy industry that has not changed in decades unlocks enormous value, and that playbook is now rippling through every industry.
    • The Kurzweil curve plots 130 years of compute per dollar across five substrates (mechanical, relay, vacuum tube, discrete transistor, integrated circuit) and shows a 10,000 billion billion X improvement; Jurvetson calls it the most important thing ever graphed.
    • Customers buy compute capacity and memory, not transistors, and both have been “on rails” for 130 years; the default prediction for the next three years is simply that the curve keeps going.
    • When an incumbent declares Moore’s Law dead, it usually signals they are losing their business to someone new, as Intel was to Nvidia 15 years ago.
    • Analog chips and customized AI silicon that do discrete matrix multiply-and-add extremely efficiently will carry the mantle of Moore’s Law over the next three years.
    • Without exponential technological change there would be no startups: if business is predictable, the big get bigger and incumbents block new entrants; disruption is almost always computationally based.
    • Over the next three years AI ripples through energy, agriculture, and construction: three enormous industries that are growing as a percentage of GDP and are the least digitized on the planet, with healthcare close behind.
    • His gut says the next driver will be architecturally variant, possibly subsuming today’s models the way mixture of experts subsumes other architectures or massively parallel diffusion models reinterpret the transformer.
    • A whole new generation of neural labs is returning to the founding premise of DeepMind: reinforcement learning with continuous learning, let loose on the internet’s data sets, hunting for the algorithm that bootstraps intelligence.
    • The open question for these systems is the goal function: what plays the role of evolutionary selection pressure? Candidates include understanding the universe (the xAI mission) or a novelty-seeking algorithm that uses new discoveries as its measure of progress.
    • Jack Clark, co-founder of Anthropic, gives roughly 30 percent odds that superintelligence arrives within a year; Jurvetson declines to put odds on it himself and admits “I do not know” is the honest answer.
    • Today’s self-improving AI loops (automated verification, hyperparameter adjustment between training runs, AI-mediated experimentation) are real but still human-directed; goal setting remains the thin veneer AI does not do, and it may be the most important layer.
    • Human intelligence was bootstrapped on top of reactive limbic systems and emotional centers with cortex layered on top; it is an open philosophical question whether AI systems need to recapitulate that functional specialization to take on purpose and meaning.
    • Anything involving atoms switches over slowly: fully autonomous vehicles are inevitable (every car, train, and airplane), but people keep cars 11 to 12 years, so the physical swap-out cycle makes the transition feel glacial.
    • Physical robotics faces the same constraint: making a billion robots takes time even with recursive manufacturing techniques.
    • The domains that flip like wildfire are the ones we held as uniquely human: creative arts, moviemaking, and imagery came first, which Jurvetson finds somewhat shocking.
    • Call centers represent roughly 1 percent of US GDP and can switch over almost entirely and almost instantly; white-collar work generally has no physical swap-out cycle to slow it down.
    • People will increasingly prefer AI to human interactions when the AI is better: studies of physician bedside manner and customer service already show AIs doing a better job with emotional connection than humans.
    • Musk principle one is an insane ability to focus: running many companies forces ruthless prioritization, and he says no to anything that is not mission-critical right now, including a Craig Venter brainstorm on terraforming Mars because “none of this stuff on Mars matters” until Starship flies.
    • Musk principle two, the most important: maniacal focus on the cycle time of innovation, the core learning loop, whether launch cadence or fleet data; Tesla cameras gather more AI training data every 4 days than Waymo has collected in its entire history, because every vehicle collects data whether or not the customer paid for full self-driving.
    • Musk principle three: being a magnet for talent, screening for mastery by drilling into engineering crises a candidate actually solved rather than leaning on credentials (which are often an albatross), and framing the company as something grander (sustainable energy, multi-planetary humanity, understanding the universe) so the best people want to join.
    • Jurvetson filters founders with one question: what does your business look like in 50 years? Opportunists chuckle at the absurdity; missionaries are relieved and finally tell you what has been driving them all along. He passes on the ones who laugh.
    • The best startups hold two things in tension simultaneously: an audacious 50-to-500-year vision and a concrete plan to iterate with real customers over the next three years, chaining backward from the future to what must be built now.
    • The perpetual surprise of great companies is expanding option value: autonomous driving was nowhere in Tesla’s founding plan, and Starlink, direct-to-cell, and orbital data centers were not on SpaceX’s dance card even five years ago. Exploring the option space beats purposeful ten-year planning.
    • Future Ventures invests in things unlike anything they have seen before yet adjacent to what they know, ideally companies that are literally one of a kind.
    • Current bets include nuclear fusion and subcritical fusion that avoids NRC regulation, because energy is the third bottleneck for AI after talent and compute.
    • Other 500-year-problem bets: free healthcare via a cell phone (all diagnostics as a free global service, probably launching outside the US to bypass FDA and insurance), slaughter-free meat via cellular agriculture and mycelium, and construction, where labor productivity has been flat for 30 years.
    • Recent investments span epigenetic editing (the software of biology rather than the firmware of the genome, applied to crops, pesticides, and human health), critical minerals from deep sea mining to copper refining, and reshoring US industrial capacity.
    • Three separate analog AI chip investments approach the same goal from different angles, including Mythic’s in-memory compute doing 8-bit multiplication in a single transistor, each chasing 100X and then another 100X reduction in power per calculation.
    • The portfolio is roughly 40 percent life sciences and 60 percent IT, deliberately hunting the weird edge cases that fall through the cracks of traditional pharma VC: organ harvesting for transplant, a male birth control pill, dramatically improved IVF.
    • Old industries with no new entrants are the best targets: the four largest tunnel boring companies competing with the Boring Company were all started in the 1800s.
    • The 30-day plan for a single person with an idea: find a co-founder. Great startups tend to have a dynamic duo at the founding (Jobs and Wozniak, Sergey Brin and Larry Page, Larry Ellison and Bob Miner), and persuading one person to quit their job for your mission is the first real test of the idea.
    • A founding pair with diverse backgrounds and mutual respect sets the culture for everyone hired afterward and creates cognitive diversity that ripples through the whole firm.
    • Calibrate boldness by the crazy ratio: if 100 percent of people say your idea is crazy, take the feedback; nine out of ten is pretty good; if only two out of ten think it is crazy, it is not bold enough. Also ask whether the business could have been started three years ago; if yes, that is a bad sign.
    • Co-founders most often meet at universities, one of the few places where people cross academic disciplines; breakthrough innovation happens at the interstices between formally discrete fields, and LLMs are exceptionally good at exactly that cross-domain translation, opening a fountainhead of idea discovery.
    • Roughly 19 percent of global employment involves driving vehicles, and that work is going away, just more slowly than people imagine.
    • Humans have a fundamental desire for symbolic immortality: contributing something that outlasts our brief time here, whether children, books, philanthropy, or companies. Accumulated cultural knowledge, not biology, is the primary vector of human evolutionary progress.
    • There is no peaceful path from full employment to no employment: passing through 30, 40, 50 percent unemployment will be turbulent, and no politicians are taking a long-term perspective on it.
    • On Neuralink (which he invested in): expanding the sensory periphery is very doable (higher data rates, restoring hearing and spinal function, seeing more wavelengths), but upgrading core intelligence requires reverse engineering an inscrutable iterated system, and biology’s FDA-and-wetware timescales cannot keep up with synthetic learning loops.
    • Any product of an iterative algorithm run billions of times (evolution, neural networks, genetic programming) is inherently inscrutable; Jurvetson doubts mechanistic interpretability will bear fruit and does not think control or alignment of a cutting-edge AI system is possible, likening it to mind-controlling a teenager.
    • On Penrose’s quantum consciousness argument: there is no clear mechanism and no evidence of quantum processes in the brain, and arguments that consciousness requires our specific substrate are uncompelling; machines may one day have consciousness, just not necessarily human consciousness, the same way computer memory is real memory without being human memory.

    Detailed Summary

    Betting on Sectors That Do Not Exist Yet

    Asked what he saw in SpaceX that other investors missed, Jurvetson flips the question: there were almost no investors even considering space, just as automotive and nuclear energy were not venture sectors. The bet was on Elon Musk, whom he has known for 29 years and backed across all his companies (“and his cousins, too”), and on a thesis that has since crystallized: a software-centric systems engineering approach applied to a sleepy industry that has not changed in decades unlocks extraordinary value. Aerospace and automotive proved it, and the same conversion of industrial low-margin businesses into information businesses is now playing out across the economy.

    The 130-Year Compute Curve and the Next 3 Years

    Jurvetson polls the room on Kurzweil’s famous graph, first published around 1999, and finds only a quarter have seen what he calls the most important thing ever graphed: five successive technology substrates delivering a 10,000 billion billion X improvement in the computation a dollar buys, sustained over 130 years. Moore’s Law is just the most recent refraction of a longer, almost cosmological trend that transcends the dramas of individual companies. His baseline prediction for the next three years is that the curve keeps going, carried by analog chips and custom AI silicon optimized for matrix math, and he notes that when a company like Intel declares the end of Moore’s Law, it usually means they are losing to someone new, as they did to Nvidia. The deeper point: exponential technological change is the precondition for startups existing at all, because predictable business favors incumbents. AI is the most intense crucible of compute-centric innovation yet, and over the next three years it flows into energy, agriculture, construction, and healthcare, the largest and least digitized sectors.

    Architecturally Variant: The Return of Reinforcement Learning

    Pressed on what technology drives the next wave (better LLMs, world models, robotics), Jurvetson shares a gut feeling he stresses he has not yet invested in: something architecturally variant that may subsume today’s models. He points to a new generation of neural labs returning to DeepMind’s founding premise, reinforcement learning, which was set aside when LLMs took off. The open design problem is the goal function: what is the multi-decade agentic drive, the selection pressure, the definition of success beyond reproductive fitness? He floats understanding the universe (the Grok and xAI framing) and novelty-seeking algorithms that treat new discoveries as progress. The question these labs chase is whether a single reinforcement learning algorithm with continuous learning, let loose on the internet’s data, could bootstrap intelligence. He adds a caution about today’s chatbots: we ascribe consciousness and meaning where there is none. “There’s no light on inside,” at least for now.

    Superintelligence Odds and the Missing Goal-Setting Layer

    On whether self-directed, goal-setting AI arrives within three years, Jurvetson cites Jack Clark of Anthropic giving 30 percent odds of superintelligence next year, which he finds fun mostly because at least someone put a stake in the ground. The recursive self-improvement debate is live, but he insists on a distinction: the huge improvements in the current self-improving loop (automated verification, hyperparameter tuning between runs, AI-mediated experimentation) are all still directed by humans. Goal setting remains human, and while that may be only a thin veneer of remaining activity, it is arguably the most important part, and nobody is sure how the transition happens. It may require recapitulating the brain’s functional specialization, the limbic-then-cortex layering that produced our bootstrapped consciousness. His honest answer: he does not know and does not even have odds, because three years out is genuinely hard to predict.

    Atoms Move Slowly, Bits Sweep Like Wildfire

    The gap between what the technology can do and how we use it is governed by physics and replacement cycles. Fully autonomous vehicles are, to him, obviously inevitable for everything that moves on Earth, yet cars stay on the road 11 to 12 years, so the switchover feels glacial; a billion robots likewise take time to manufacture. What flips fast is the world of bits, and strangely it started with what we considered most human: creative arts, movies, and images. White-collar work follows because there is no physical swap-out cycle: call centers, about 1 percent of US GDP, can convert almost overnight. And people will increasingly prefer the AI when it is better, showing more emotional understanding and better reading of the situation, something already visible in comparisons of physician bedside manner and customer service quality.

    Three Principles from Working with Elon Musk

    Jurvetson opens with humility (even Maye Musk cannot explain how Elon became Elon, and the books piling up on his bedside table may not have been written by humans), but offers three observations from close range. First, an insane ability to focus. Running multiple companies paradoxically helps: nobody questions Elon skipping a holiday party, and he says no to fascinating distractions, including Jurvetson’s attempt to connect him with Craig Venter to brainstorm terraforming Mars with gene sequencers. Musk’s answer: none of it matters until Starship flies. Second, and even more important, a maniacal focus on the cycle time of innovation: how fast the core learning loop runs, whether launch cadence or fleet learning. The Tesla data flywheel is the exemplar: every car collects training data whether or not the owner paid for FSD, so Tesla gathers more data every 4 days than Waymo has in its history. Third, a well-honed talent stack: pattern recognition that ignores credentials (often an albatross), drills candidates on the engineering crises they actually navigated to test for real mastery, and wraps the company in a mission grand enough (sustainable energy, multi-planetary life, understanding the universe) that the best people want in, which compounds because great people attract great people.

    The 50-Year Question and Expanding Option Value

    How do founders stay true to a mission when 99 percent of the world says it is too early? Jurvetson admits selection bias: for 30 years he has tried to back only people with a sincere, almost messianic mission rather than arbitrage-seeking opportunists. His filter is to ask what the business looks like in 50 years. Opportunists laugh (“I’ll be on my third startup by then”); the best founders are relieved to finally unload the dream they have been hiding because “colonizing Mars is an uninvestable proposition” as a day-one pitch. The best startups pair an audacious 50-to-500-year vision with a plausible path of customer iteration over the next three years, chaining backward from the future. What still surprises him is how the option value of frontier companies keeps expanding: autonomous driving was not in Tesla’s founding plan at all, and SpaceX kept unfolding from cheap launch to Starlink to direct-to-cell to orbital data centers, none of which was on the dance card five years ago. Exploring the light cone of possibilities beats designing a ten-year plan.

    Where Future Ventures Is Betting Now

    The firm looks for companies unlike anything it has seen before yet adjacent to familiar ground, targeting problems that will obviously be solved 500 years from now. In energy: multiple fusion investments plus subcritical fusion that sidesteps NRC regulation, because energy is the third bottleneck for AI after people and compute. In health: free diagnostic healthcare delivered by cell phone as a global free service, likely launched outside the US to bypass FDA and reimbursement. In food: slaughter-free meat via cellular agriculture and mycelium. In construction: still looking, after trying and failing a few times in an industry where labor productivity has been flat for 30 years. Recent themes include epigenetic editing (the software of biology rather than the firmware of the genome, spanning crop health, pesticides, herbicides, and human health), critical minerals and metals from deep sea mining to copper refining as part of reshoring, and three separate analog AI chip bets, including Mythic’s in-memory compute doing 8-bit multiplication in a single transistor, each chasing successive 100X reductions in power per calculation. The mix runs about 40 percent life sciences, 60 percent IT, with a taste for the weird edge: organs grown for transplant, a male birth control pill, radically improved IVF. His favorite hunting ground is old, crappy industries with no new entrants, like tunnel boring, where the Boring Company’s four largest competitors were founded in the 1800s.

    Advice for Founders: Find Your Batman and Robin

    His 30-day plan for a single person with an idea is not an MVP or a pitch deck: find a co-founder. Startups tend to be founded by dynamic duos (Jobs and Wozniak, Sergey Brin and Larry Page, Larry Ellison and the lesser-known Bob Miner), and a pair with diverse backgrounds and mutual respect creates a rapid iteration loop and sets the cultural template for every future hire. Persuading one person to quit their job for your crazy idea is the first proof the mission can recruit. On calibrating craziness: if literally everyone thinks the idea is crazy, take the feedback; nine out of ten is pretty good; only two out of ten means it is not bold enough, because obvious ideas get done by others. Ask whether the business could have been started three years ago; the right answer is no. Co-founders most often meet at universities, where students (unlike professors in their stovepipes) cross-pollinate between academic disciplines, and breakthrough innovation lives at those interstices. As an aside, he notes LLMs excel at exactly this translation between domains, opening a new fountainhead of idea discovery we are only beginning to tap.

    When Machines Do Everything: Meaning, Abundance, and Turbulence

    Asked the closing question (when machines do everything, what is the meaning of life?), Jurvetson starts with scale: roughly 19 percent of global employment is driving vehicles, and it is going away. But humans want meaningful work, driven by what he calls a fundamental desire for symbolic immortality: children, books, philanthropy, companies named after founders, all instantiations of the urge to contribute something that outlasts us. Translating the question into humanity’s mission statement, he lands where Yuri Milner and Musk do: to understand the universe and add to accumulated knowledge, because culture, not biology, is the primary vector of human evolutionary progress. If we could hyperspace-jump to Peter Diamandis-style abundance, where everything physical costs a dollar a pound and machines do all labor, we could all be philosopher kings and artists. But he refuses to end on false comfort: there is no visible peaceful path from full employment through 30, 40, 50 percent unemployment, that transition will be turbulent, and no politicians are taking a long-term view of it.

    Neuralink, Inscrutable Systems, and the Alignment Heresy

    In audience Q&A, Jurvetson confirms he invested in Neuralink (the idea traces to the neural lace of Iain M. Banks’ novel Surface Detail, which he recommends) but offers a contrarian view. Working from the periphery is very promising: restoring broken function, fixing spinal cords, expanding senses, higher-bandwidth communication. Upgrading core functionality, actually making someone smarter, is another matter. His reasoning comes from decades of watching complex systems: any artifact produced by an iterative algorithm run billions of times (evolution, neural networks, genetic programming, cellular automata) is inherently inscrutable. That is why he doubts mechanistic interpretability will bear fruit and flatly does not think control and alignment are possible for a cutting-edge AI system; he mentally swaps “teenager” for “AI” whenever the control question comes up. The same inscrutability applies to the brain: it will be easier to build a new intelligence than to reverse engineer one already made, and FDA cycles plus human biology cannot iterate at the speed of synthetic learning loops, so he lacks faith Neuralink keeps up with AI. Kurzweil’s uploading dream, he suggests, is a case of wanting something to be true within one’s lifetime.

    Penrose, Quantum Brains, and Machine Consciousness

    On Roger Penrose’s argument that consciousness depends on quantum processes and is therefore unreachable by AI, Jurvetson is respectful of the man and dismissive of the claim: there is no clear mechanism (a speculative lithium isotope coupling aside), and it amounts to wishful thinking. Generalizing, he finds all vitalist arguments that our substrate is uniquely necessary uncompelling; you could make a better case that carbon is special to life than that neurons are essential to consciousness. His favorite reframe swaps in the word memory: computers have memory that is nothing like holographic, gracefully degrading human memory, yet nobody debates whether computer memory is real. Machines may likewise develop a different kind of consciousness without human consciousness. Declaring something impossible is a much higher-order proposition than admitting ignorance, so his position is: he does not know whether the current AI path leads to consciousness, but his gut says machines will get there one day, perhaps via evolution-like reinforcement learning approaches that recapitulate what biology already proved possible.

    Notable Quotes

    “I have this gut feeling that it’ll be something architecturally variant. It might subsume the models that we know now.”

    Steve Jurvetson, on what drives the next three years of AI

    “It’s almost cosmological. Like, why has humanity’s capacity to compute compounded for 130 years?”

    Steve Jurvetson, on the Kurzweil abstraction of Moore’s Law

    “If business is predictable, if there isn’t disruptive technological change, the big get bigger.”

    Steve Jurvetson, on why exponential compute is the precondition for startups

    “The Tesla cars today in their cameras gather for their AI training set more data every 4 days than Waymo has in its entire history.”

    Steve Jurvetson, on the data flywheel behind Musk’s learning-loop obsession

    “If it’s like only two people think it’s crazy, that’s bad because it’s clearly not bold enough. If it’s an obvious idea, other people will do it.”

    Steve Jurvetson, on calibrating how crazy a startup idea should be

    “Despite attempts at mechanistic interpretability in AI, I don’t think that’s going to bear fruit.”

    Steve Jurvetson, on why iterated systems are inherently inscrutable

    “It’d be easier to build a new intelligence than it is to reverse engineer one you’ve made.”

    Steve Jurvetson, on why he doubts Neuralink can keep pace with AI

    “I think all humans have a fundamental desire for symbolic immortality, this belief that we’ve contributed something to the world that transcends our brief time on this world.”

    Steve Jurvetson, on the meaning of life when machines do everything

    “It’s much higher order proposition to say something is impossible than to say I don’t know.”

    Steve Jurvetson, on whether AI can ever be conscious

    Watch the full conversation here: The Next 3 Years of AI: Lessons from Elon Musk’s First Investor.

    Related Reading

  • Jonathan Ross on Groq’s $20 Billion NVIDIA Deal, Faster Inference, and Why Asking the Right Questions Wins the AI Age

    Jonathan Ross, the founder of Groq and the inventor of Google’s Tensor Processing Unit (TPU), sits down with David Senra (host of the Founders podcast) to walk through Groq’s roughly $20 billion partnership with NVIDIA and the decade of near-death struggle that preceded it. You can watch the full conversation here. Ross, now a senior executive at NVIDIA following the deal, is unusually candid about being one of the world’s worst leaders when he started, about coming three weeks from running out of money, and about the single contrarian bet (that faster inference would make AI both faster and smarter) that almost everyone, including his own engineers, told him was pointless.

    TLDW

    Ross explains the structure of the NVIDIA deal (a call to Jensen Huang about buying 100,000 GPUs turned, in three weeks, into NVIDIA’s largest deal by nearly 3x) and why pairing Groq’s LPU with the GPU defeats the many different bottlenecks inside an LLM the way you would use both 18-wheelers and delivery vans in a logistics network. He unpacks the AlphaGo moment that revealed faster inference makes models smarter, the shift from the information age (answering questions) to the AI age (asking the right questions), and a leadership philosophy built on autonomy, one brutally clear priority (25 million tokens per second on a challenge coin), and giving people the fewest constraints so they can surprise you. He shares hard-won lessons from Jensen and NVIDIA (the least political large org he has seen, no secret one-on-ones), his concepts of reality quotient and the dominant game, return on luck and the GitHub opportunity he let his team talk him out of, intentional leadership (“I intend to do this”), the Grok bonds that traded salary for equity and saved the company, hiring for negatives instead of positives, loss bias and manufactured discontent, and a closing case for radical optimism: code is becoming free, software creation is being democratized like literacy, and education should stop teaching kids to answer questions and start teaching them to ask.

    Thoughts

    The technical spine of this interview is a genuinely counterintuitive claim: you can make a model smarter by making it faster. Ross’s proof is the AlphaGo anecdote, where the exact same model, ported from GPUs to his TPU, saw its ELO jump by hundreds of points and beat the world champion, because more compute per unit of time let it search deeper and surface moves like the famous Move 37 that were too far down the tree to find otherwise. Once you internalize that inference speed is not a convenience but a capability multiplier, the entire Groq thesis, and the logic of the NVIDIA deal, snaps into focus. The industry spent years treating fast inference as a nice-to-have. Ross treated it as the whole game, and was nearly alone in doing so for a very long time.

    The most transferable material is the leadership arc, precisely because Ross is willing to say he was bad at it. His core insight is that there is no single correct way to lead, any more than there is one way to invest, and the founder’s first job is to know which way is true to them. Ross is a delegator who hires autonomous people and gives them a single, poetically compressed objective, then gets out of the way. The reason that matters is subtle: if you over-constrain the goal, your team can never surprise you with a better answer than the one you already had, which means they can never actually innovate. The Kelly Johnson line Senra offers (“extreme performance often comes from one brutally clear priority”) is the same idea from the Skunk Works side. A challenge coin that reads “25 million tokens per second” is not a slogan, it is a mechanism that lets every engineer connect their work to one dominant game.

    Two ideas deserve to be lifted out and used directly. The first is intentional leadership, borrowed from David Marquet’s submarine turnaround: replace “should I do this?” with “I intend to do this.” Asking for opinions invites pessimism and hands your most timid people a veto. Declaring intent still lets someone shout “the hatch is open” when it truly matters, but it stops the reflexive no. Ross traces years of stalled progress to the simple error of asking instead of declaring. The second is his inversion of hiring: hire for negatives, not positives. Growing talent means showing people the path, so you emphasize positives. Selecting talent means screening people out, so you hunt for the disqualifying negatives, because one person’s negative trait infects the whole team. Most founders, Ross included for years, are clever enough to talk themselves into any candidate. A versioned “people spec” and a deliberate loss-averse posture are the antidote.

    The Grok bonds story is the emotional center and a small masterpiece of change management. Facing a layoff list that would have killed the company (because the people slated to be cut were exactly the ones needed to make the product work at all), Ross instead asked the team to trade salary for equity, framed with World War II war-bond imagery. Eighty percent participated, half went to statutory minimum wage, and attrition actually fell. His phrase for why is “put everyone’s hands on the steering wheel.” Passengers fear a windy road, drivers feel in control. It is a reminder that morale under existential stress is often a function of agency, not comfort, and that the Phil Knight move of converting employee sacrifice into ownership is a recurring pattern in company survival stories for a reason.

    Where the conversation turns almost spiritual is manufactured discontent. Ross observes that the entrepreneurs in a room of successful people were the least happy with their wealth, and that this very dissatisfaction was the fuel that kept them building. His own current discontent is stark and worth sitting with: the world does not have enough compute, and if it takes an extra year to cure cancer or slow aging because of that shortage, he considers it his fault. Whether or not you accept the moral weight he assigns himself, the mechanism is instructive. Edwin Land wrote “300 people died today” on the whiteboard while inventing anti-glare technology. A concrete, human cost attached to delay is a far more durable motivator than a revenue target. Paired with his closing optimism about code becoming free and software creation democratizing like literacy, it makes for one of the more clear-eyed and yet hopeful founder conversations in recent memory.

    Key Takeaways

    • The NVIDIA deal began as a request to buy about 100,000 GPUs; Jensen saw what Groq had built pairing GPUs and LPUs and decided to make it available to all NVIDIA customers, closing what Ross calls the firm’s biggest deal by nearly 3x in roughly three weeks from first call to wired money.
    • GPUs and LPUs are complementary: inside an LLM’s decoder layer, the GPU is better at the compute-bound attention portion and the LPU is better at the memory-throughput-bound weights, so combining them defeats bottlenecks across the whole performance curve, like using both 18-wheelers and last-mile vans.
    • As AI increasingly talks to AI, speed dominates, because agents kick off other agents and compound; a human tolerates a one-second wait, but AI is just sitting there idle.
    • Agentic micro payments will make the number of payments skyrocket, but payments infrastructure is not yet built for AI operating inside an allocated budget.
    • Ross prototypes cutting-edge ideas as personal hobby projects first, then brings them to work; his personalized “daily brief” evolved from long text into headlines he can interrogate with follow-up questions, like the game of 20 questions.
    • The information age rewarded answering questions; the AI age rewards asking the right ones, as everyone shifts from individual contributor to leader of AI, and good leaders ask the question no one else did.
    • There is no single right way to lead, just as there are many ways to invest; the founder’s job is to know themselves and pick the leadership form that is true to them (inspiration versus fear, control versus delegation).
    • Ross was, by his own account, one of the world’s worst leaders at the start, which cost Groq three to four years; his fix was to define one goal simple enough to fit on a challenge coin: 25 million tokens per second.
    • The fewer constraints you give a person (or an AI agent), the more freedom they have to surprise you with a better solution; over-constraining the goal makes real innovation impossible.
    • Lessons from Jensen and NVIDIA: it is the least political large organization Ross has seen, Jensen never runs secret one-on-ones (tell everyone at once, copy everyone on email), and the whole strategy reduces to “what does the customer actually need?”
    • Jensen manages around 60 direct reports, each smarter than him in their own domain, which he offers as the model for orchestrating AI agents that may be smarter than you.
    • Asking a sharp question that makes an expert say “I didn’t think of that” is a universal founder skill (it appears in every Bezos book) and can be honed.
    • Confidence, not competence, was Ross’s early bottleneck: shadowing a leader of 2,000 people, he realized he would have made the same decisions, and acting with confidence made people follow his direction without changing the decisions themselves.
    • The better and more creative your people, the harder they are to manage; running 450 highly creative scientists felt more like managing 5,000.
    • Reality quotient (RQ), distinct from IQ, is the ability to recognize reality and, in its extreme form, to choose the dominant game; MySpace optimized accounts signed up while Facebook optimized monthly active users and won.
    • The first principle of change management is to make it feel like it is not a change; people who seem fine with change are usually anchored to something that did not change.
    • Return on luck (from Jim Collins): the most successful companies do not get more lucky breaks, they seize the ones they get; Ross let his team talk him out of powering GitHub’s LLMs on Groq chips, then vowed never again.
    • People adopt fast inference only when they experience it personally; an Anthropic demo three months before ChatGPT drew no reaction because the answers were not the audience’s own, and Groq later went viral off a fast-LLM video posted on X.
    • Great innovators often experience a problem before others do; the future is already here, just not evenly distributed, and Ross saw fast inference’s value first because of AlphaGo.
    • Intentional leadership (from David Marquet’s USS Santa Fe turnaround): say “I intend to do this” instead of asking for an opinion, which stops reflexive pessimism while still letting people flag a real problem.
    • Grok bonds: three weeks from running out of money, Ross swapped a layoff for a war-bond-style salary-for-equity exchange; 80% participated, about half took statutory minimum wage, and it bought roughly two months of runway.
    • “Put everyone’s hands on the steering wheel”: participation in saving the company cut attrition to under 10% during the crisis, echoing Phil Knight converting employee loans into Nike equity.
    • West Coast VCs behave like lemmings (one pass triggers all passes), while East Coast VCs run independent analysis; the herd missed what became NVIDIA’s biggest deal ever, a live example of the Keynesian beauty contest.
    • For the first time, top startups are not starved for cash, so putting in more money is no longer an advantage even though investors still behave as if it is.
    • Hiring flip: move from hiring for positives (how you grow talent) to hiring for negatives (how you select talent), because one negative trait poisons the team; write a versioned “people spec” like a product spec.
    • Loss bias (a loss feels roughly six times more painful than an equal gain) can be a hiring signal: Ross looks for people who “book the win early,” treating any missed improvement as a loss.
    • Poetic design (maximum meaning in minimal expression, “every word matters”) was a positive on the people spec; its negative is maximalist, cluttered design.
    • Michael Jordan manufactured pressure by taunting opponents so a loss would be humiliating, forcing superhuman performance (per his trainer Tim Grover), a deliberate version of throwing your keys over the fence.
    • Manufactured discontent (David Ogilvy’s “divine discontent”): the best entrepreneurs never rest on wins; the least happy people with their wealth were the ones who kept building.
    • Ross’s discontent today is the world’s lack of compute; he treats every delayed medical breakthrough as partly his responsibility, the way Edwin Land wrote a daily death count on the whiteboard while fighting headlight glare.
    • Software has run on “code rationing” because code was expensive to write, enforced by “no engineers”; as the marginal cost of code approaches zero, you just implement, experience, and re-implement.
    • AI democratizes software creation like the alphabet democratized literacy: Ross’s executive assistant now builds working apps, and individual founders with taste but no coding background will create valuable companies.
    • Education should be revamped around asking questions and solving real community problems; if a kid can look up or prompt the answer, the assignment taught nothing, but making them ask the right questions to get AI to solve a real problem does.

    Detailed Summary

    The $20 Billion NVIDIA Deal and Why LPUs and GPUs Belong Together

    The deal’s most striking feature is speed: the idea was first floated on a call roughly three weeks before the money was in the bank. Groq had been integrating GPUs and LPUs and went to Jensen Huang wanting to buy about 100,000 GPUs to deploy themselves. Jensen saw the combined system and decided it should be offered to all of NVIDIA’s customers. The technical logic is that processing an LLM token involves many matrix multiplies with different bottlenecks, some compute-constrained (better on the GPU, especially the attention portion) and some memory-throughput-constrained (better on the LPU, applying the trained weights). There is no single perfect architecture, so putting the two together defeats bottlenecks across the whole curve. Ross adds that as AI talks to AI, speed becomes everything, because agents spawn agents and compound exponentially.

    Asking Questions, Daily Briefs, and the Shift to Leading AI

    Ross builds cutting-edge tools as personal hobby projects before bringing them to work, including a personalized “daily brief” that functions like a presidential daily brief. He redesigned it from long text into headlines he can interrogate, because interactivity, like 20 questions, distills straight to what you actually care about. This grounds one of his signature ideas: success in the information age meant answering questions, but success in the AI age means asking the right questions. As people move from individual contributors to leaders of AI, the skill that matters is the leader’s skill of asking the question everyone else missed or was afraid to raise, since the question you ask determines the output you get.

    Knowing Your Leadership Style and the Challenge Coin

    Ross frames leadership like investing: the first principle is simply having followers, but there are infinite valid styles. New founders fail by copying advice that is not true to them. Ross is a natural delegator (he has not held a driver’s license since his teens because he would rather think than control the car) who hires unusually autonomous people. Early on this backfired badly, because he entrusted people who needed direction, and he calls himself one of the world’s worst early leaders, a gap that cost Groq years. His breakthrough was distilling the mission onto a challenge coin reading “25 million tokens per second,” which let everyone connect their work to one dominant game. He references David Marquet’s Turn the Ship Around later, but the coin embodies Kelly Johnson’s Skunk Works principle that extreme performance comes from one brutally clear priority, plus the rule that fewer constraints give people more room to surprise you, turning a team from Superman into the Avengers.

    Lessons from Jensen: Killing Politics and Serving the Customer

    Working at NVIDIA taught Ross how much further he could have pushed lessons he half-learned at Groq. NVIDIA is, in his experience, the least political large organization anywhere, and a big reason is that Jensen never tells different people different things in private one-on-ones. When you address a room, everyone hears the same message; separate conversations breed side cliques. Ross’s practical rules: hold big meetings for anything you want a group to know, and copy everyone on email so no one can route politics through you. The other Jensen lesson is to stop playing 3D chess and just ask what the customer needs, tell them only what you believe and can support, and refuse to sell them something they do not need. Senra notes he has covered roughly 19 ideas from The Nvidia Way on his Founders podcast, and Jensen’s line that he already manages 60 reports smarter than him is the template for managing AI agents.

    Reality Quotient, the Dominant Game, and Change Management

    Groq hired for reality quotient, not just IQ, because plenty of very smart people construct elaborate stories disconnected from reality. In its extreme form, RQ is the ability to choose the dominant game, the way Facebook’s focus on monthly active users beat MySpace’s focus on accounts signed up. The founder’s job is to help everyone connect their activity to that dominant game (for Groq, tokens per second), then manage the change. Ross’s first principle of change management is to make it feel like it is not a change: nobody likes change, and people who tolerate it well are usually focused on something that stayed constant. If your team is anchored to the dominant goal, a new tactic does not feel like change; if they are anchored to a narrow task, it does.

    Return on Luck, the AlphaGo Insight, and the GitHub Miss

    From Jim Collins’s Great by Choice, Ross took the idea that winners seize luck better, not that they get more of it. He experienced it first-hand with AlphaGo: after a DeepMind team asked whether his TPU was as fast as rumored (he said yes, Ghostbusters-style), porting the identical model from GPUs to TPUs pushed its ELO from around 3,200 to roughly 3,900 and it crushed the world champion. As Thinking Fast and Slow by Daniel Kahneman frames it, more compute lets the model virtually play out more moves and occasionally find a better second-best line, which is how the famous Move 37 surfaced. Faster thinking is smarter thinking. Yet Ross also let his own engineers talk him out of powering GitHub’s LLMs on Groq chips, twice, because they focused on why it could not be done rather than why it could. He eventually did the math himself, hit the numbers, and learned to stop inviting that pessimism.

    Selling Speed and Intentional Leadership

    Customers could not grasp fast inference until they felt it. Ross recalls an Anthropic demo three months before ChatGPT that drew no reaction, because seeing someone else’s answer appear is not magical, but getting your own question answered instantly is. So Groq simply put fast inference online, and it went viral after someone posted a video of a blazing-fast LLM on X (Ross noticed his own demo slowing in Norway because usage had skyrocketed). The deeper fix for internal resistance came from Turn the Ship Around, David Marquet’s account of turning the USS Santa Fe from worst to best in nuclear readiness by replacing command-and-control with intentional leadership. Saying “I intend to do this” rather than “should I?” stops people from reflexively supplying negative opinions, while still letting someone shout “the hatch is open” when there is a genuine problem.

    Grok Bonds: Three Weeks From Zero

    With three weeks of cash left and a layoff list on the table, Ross realized the cuts targeted exactly the people needed to finish an unprecedented compiler and reach the critical mass where the product would even work. Layoffs would not save the company; only reducing burn without losing people could. So Groq held an all-hands, put up World War II war-bond imagery, and launched “Grok bonds,” an exchange of salary for equity. Ross expected heavy attrition; instead 80% participated and about half dropped to statutory minimum wage, real pain for engineers used to six-figure salaries. It bought closer to two months of runway. His framing, “put everyone’s hands on the steering wheel,” explains why attrition actually fell below 10%: drivers feel more in control than passengers, and it echoes Phil Knight in Shoe Dog converting employee loans into Nike equity on the edge of collapse.

    Hiring for Negatives, Loss Bias, and Manufactured Discontent

    Ross was good at spotting smart, talented people but kept hiring ones who caused organizational problems, because he could always talk himself into a candidate. Watching a sharp head of HR screen people out, he realized he had been hiring wrong: growing talent means showing positives, but selecting talent means hunting for disqualifying negatives, since one bad trait spreads to the whole team. He formalized a versioned “people spec” with positives like return on luck and poetic design, each paired with a negative. He also hired for loss bias, the fact that a loss feels roughly six times more painful than an equal gain, seeking people who “book the win early.” That competitive, pressure-seeking wiring links to Michael Jordan manufacturing humiliation stakes (per Tim Grover in Relentless) and to David Ogilvy’s divine discontent. Ross’s own manufactured discontent today is the world’s shortage of compute, which he frames in life-and-death terms.

    The Optimistic Close: Free Code and Universal Software Literacy

    Ross ends on aggressive optimism. Software has long run on “code rationing” because code was expensive to write, policed by “no engineers” whose job is to say no. As the marginal cost of code approaches zero, the workflow flips to implement, experience, then re-implement. More important is accessibility: just as alphabets and universal education turned reading and writing from a scribe’s monopoly into a question of quality, AI is making software creation universal. His executive assistant now builds working apps, and a wave of individual founders with taste but no coding background will create valuable companies. The corollary for education is to stop teaching kids to answer questions and start teaching them to ask, revamping curricula around real community problems where the point is asking the right questions to get AI to solve something that matters.

    Notable Quotes

    “Success in the information age was about being able to answer questions. Success in the AI age will be about being able to ask the right questions.”

    Jonathan Ross, on the fundamental shift AI creates

    “The fewer constraints that you give someone, the more freedom they have to solve the problem, and the more freedom they have to surprise you with the solution.”

    Jonathan Ross, on leading creative teams

    “Being able to think faster makes you think smarter.”

    Jonathan Ross, on why faster inference produces more capable models

    “There are plenty of really smart people who wouldn’t recognize reality if it tapped them on the shoulder.”

    Jonathan Ross, defining reality quotient versus IQ

    “If you express intentional leadership, you say, ‘I intend to do this.’ People don’t tend to offer their opinion, but if it’s very wrong and there’s a reason, they will push back.”

    Jonathan Ross, on the lesson from Turn the Ship Around

    “When people are passengers in a car, they’re more nervous about a windy road or a scary road. But when they’re the driver, they feel more in control.”

    Jonathan Ross, on why Grok bonds kept the team together

    “The biggest flip in my hiring was when I went from looking for positives, which is what you do when you’re trying to grow talent, to looking for negatives, which is what you do when you’re trying to select talent.”

    Jonathan Ross, on inverting his approach to hiring

    “If it takes us an extra year to cure cancer because we don’t have enough compute, that’s my fault.”

    Jonathan Ross, on the discontent that drives him today

    Watch the full conversation between Jonathan Ross and David Senra here on YouTube.

    Related Reading

    • Groq the company Ross founded and the LPU behind the fast-inference story and the NVIDIA partnership.
    • AlphaGo versus Lee Sedol (Wikipedia) the match, including Move 37, that showed Ross how much faster hardware raises a model’s capability.
    • The Keynesian Beauty Contest (Wikipedia) the dynamic Ross uses to explain why West Coast VCs herded past what became NVIDIA’s biggest deal.
    • Zero to One by Peter Thiel, the source of the first-principles thinking Ross applied to the contrarian bet on fast inference.
    • Founders podcast by David Senra the host’s biography-driven show, source of the Jensen, Michael Jordan, and Edwin Land ideas referenced throughout.

  • Jeremy Giffon on the Billion Dollar PDF, Peak Guy, and How Attention Became the New Capital

    In his second appearance on Invest Like the Best, investor Jeremy Giffon sits down with Patrick O’Shaughnessy for a wide-ranging conversation about how power, status, capital, and attention are being redrawn in real time. The organizing idea is the “billion dollar PDF,” the notion that a single well-timed document or post can crystallize a narrative and pull billions of dollars of capital toward it. From there the two range across the mechanics of the X timeline as market infrastructure, the decline of the billionaire class, the rise of the “poaster,” the economics of software in the age of compute, and what the next era of finance looks like when its founding act is seed investing rather than the leveraged buyout.

    TLDW

    Giffon argues that in private markets the real great filter for funds is storytelling, because the actual product (realized cash returns) takes a decade, so narrative is what you sell in the meantime. He and O’Shaughnessy unpack the “billion dollar PDF,” the way X functions as a single global newspaper (the uni-feed) that prices securities, dictates policy, and builds businesses, and how power laws now mean breaking containment on the timeline is worth more than steady performance. They discuss “peak guy” and the exhaustion of billionaire worship, the idea that the poaster has become the new priestly class, net worth as a surprisingly modern invention, and attention as the genuinely scarce asset. The back half turns practical: why AI job fears meet Giffon’s view that most white collar work is invented, why software is shifting from selling zero-marginal-cost strings to selling compute with thin margins and huge scale, why beating the market is easier for amateurs than professionals, how to underwrite emerging managers by studying the person, the feudal economics of SPVs and allocations, simplicity over complexity in investing, hiring through divisive job descriptions, and the hidden philosophers (from effective altruism to Curtis Yarvin and Nick Land) shaping Silicon Valley. Topics span venture capital, private equity, cap tables, SaaS, the Mag 7, Buffett and Bogle, East Coast versus West Coast finance, and the search for vocation.

    Thoughts

    The strongest thread in this conversation is that scarcity has moved. For most of the modern era, money was the scarce thing and attention was the byproduct of having it. Giffon flips that. Capital is now abundant, inflationary, and desperate for somewhere to go, which is why he can describe businesses and asset categories as “sponges” that get created downstream of capital rather than the other way around. What is actually scarce is a fixed slice of human attention, and whoever can command it (the “billion dollar PDF,” the breakout post, the person every billionaire wants to sit next to at dinner) captures the resource that money is now chasing. That reframing explains a lot of otherwise strange behavior, including why founders who already have wealth turn to posting, podcasting, and fame. They are not being vain. They are hedging out of a depreciating asset into the one that still appreciates.

    The most uncomfortable and clarifying claim is that narrative is not a distortion of markets, it is the market. Giffon walks through how the algorithm, driven by AI, selects which stories get shown, those stories set the consensus among the small group of posters who move capital, and securities get priced off that consensus. If you take that seriously, the efficient market hypothesis looks quaint. The marginal price of a security is being set, in part, by what an entertainment-optimizing model decided to surface to a few hundred thousand influential readers that morning. His line that “every other day someone writes some pornographic fanfic about AI and it moves the public markets” is a joke that is also a fairly precise description of 2026 price discovery.

    His software thesis deserves more attention than the culture commentary that will get clipped. The old SaaS miracle was selling copies of a string at near-zero marginal cost, which mechanically produced high gross margins. Giffon’s point is that the AI era sells compute, and you cannot write the prompt once and resell the output, so the marginal cost is no longer zero. The consequence is a structural regime change: lower gross margins, thinner net margins, and returns that accrue overwhelmingly to scale. He calls it a Walmart effect in software, and if he is right, a lot of the current sell-off in SaaS names is punishing the business model rather than the businesses, which is exactly the kind of nuance-free repricing he says markets specialize in.

    The optimistic surprise is his stance on AI and jobs, which cuts against the doom consensus without being naive about the short term. He concedes the near and medium term could be genuinely bad, but he refuses the “we will run out of jobs” framing because he thinks most white collar work is already invented to absorb our attention and capital, not to meet basic needs. Work-from-home Fridays, in his telling, are a quiet admission that many people have two or three hours of real work a day. If that is true, then automating the invented work is liberation rather than catastrophe, provided the transition does not crush people in the process. It is a bracing counterweight to the standard displacement panic, and it pairs well with his more personal note that the antidote to a priestly-class culture of looking outward for permission is the duty to steward your own gifts.

    The one place to push back is the tidiness of the “poaster as new priest” story. Giffon is careful to say he is describing, not endorsing, but the argument that status simply passes from scientists to billionaires to posters is cleaner than reality usually allows. Attention is scarce, yes, but it is also fickle and lotteryified in his own telling, which makes it a shaky foundation for a durable priestly class. Still, the underlying observation is sharp: when money becomes a “state of mind” label rather than a hard number, and when net worth itself is revealed as a recent invention (his Pride and Prejudice aside about Mr. Darcy’s income being cash flow, not a valuation, is the best illustration in the episode), the leaderboard everyone is actually competing on is real estate in other people’s minds.

    Key Takeaways

    • The great filter for private-market funds is storytelling ability, because the real product (realized cash returns) takes a decade, so narrative is what a fund actually sells in the interim through updates, events, and LP conversations.
    • The same business can be “cold” at seven years and $8 million in revenue but “hot” if you reset the clock and retell the story, so being flexible on narrative is itself a fix for a funding problem.
    • Insider bridge rounds are often surprisingly hostile (3x liquidation preferences, warrants, ratchets), and being extractive to the downside gets you booed while being extractive to the upside (pro rata rights) gets celebrated, even though both are similarly extractive.
    • In highly volatile times, optionality beats commitment: raise less, raise from investors with a wide mandate, and keep the ability to pivot the business model, run profitably, acquire, or even fire customers.
    • The “billion dollar PDF” is the idea that someone crystallizes a notion at the right time and it becomes the foundational viewpoint of an era, and capital follows it around like ten-year-olds chasing a soccer ball.
    • X is the “uni-feed”: everyone is served the same roughly 500 tweets a day across hundreds of millions of users, making it the global newspaper and a source of truth for capital markets, politics, and technology.
    • Institutions now survive only if they are “timeline native,” meaning reactive to and reflexive with the timeline, which describes the White House, venture capital, and public equities alike.
    • Posting has been lotteryified: a brand-new account can write one good post and get shown to hundreds of millions, so posting is described as the last great meritocracy.
    • Power laws have sharpened. Variance used to be low, but now breaking “containment” on the timeline means briefly taking over the world’s brain, and those few breakout events dwarf everything else combined.
    • Podcasts still underrate serving the algorithm; the video is recorded first for an LLM to review and decide whether to show, and only then do humans judge it.
    • A great post blends comedy, poetry, and writing, and great posters tend to be a bit tortured, closer to writers mixed with comedians.
    • “Peak guy”: society keeps searching for a priestly class, moved from scientists to the billionaire class, and Giffon thinks it has now moved to the poaster class, with billionaires increasingly deferential to posters.
    • Billionaire worship is exhausted partly because billionaires are far less scarce (state-of-mind billionaires have grown maybe 100x in 20 years) and money is less powerful than assumed, as the donor class has underperformed politically.
    • Net worth is a very new idea. In Pride and Prejudice, Mr. Darcy’s wealth is his estate’s annual cash flow, not a valuation, because no one would DCF or margin-loan an estate they would never sell.
    • “Billionaire,” like “millionaire” before it, is becoming a loose political and class label only tangentially related to actual liquid, inflation-adjusted wealth.
    • The most honest way to consume media is to admit it is entertainment, produced, selected, and edited to entertain, not to learn, no matter how productive it feels.
    • Going months off the timeline taught Giffon that you do not really miss anything; the filtered, secondhand version from smart people at dinner may be the most enlightened way to consume it.
    • On AI and jobs, the short to medium term could be bad, but the long-run worry is overblown because most white collar jobs are “made up” and not contingent on shelter, food, or medicine.
    • Work-from-home enthusiasm is evidence that many people have only two or three hours of real work a day, so work-from-home Fridays are a soft launch of the four day work week.
    • We have a moral duty to steward our gifts; the thing you spend most of your time on should spark and utilize your genius, and having fun at your job is a strong signal you have combined the two.
    • The largest finance firms (KKR, Blackstone, Apollo) were founded in a leveraged-buyout culture that is debt-driven and extractive; the next era’s giants may be founded on seed investing, which is equity-driven, optimistic, and qualitative.
    • West Coast venture is “eating” the East Coast: it created the biggest businesses in the world and functions as a civilizational technology, giving young people speculative capital with little downside.
    • Compensation has flipped: Silicon Valley now pays large liquid cash via mature secondary markets and yearly tenders, while Wall Street increasingly pays in RSUs tied to long-term firm value.
    • SaaS is just a business model, and while it is in trouble, that is often not what actually matters to a business being sold off out of fear.
    • Software is moving from selling near-zero-marginal-cost strings to selling compute, which means lower gross margins, razor-thin net margins, and returns accruing to scale, a Walmart effect in software.
    • Capital gets “blocked” when there are not enough great companies to absorb it, so high-capex AI and hardware categories arose in part as sponges for capital with nowhere else to go.
    • Markets lack nuance: the 52-week variance on the biggest companies is nearly 100%, so they are not priced well, and much private-market pricing reflects fund incentive structures rather than business quality.
    • Beating the market is easier for amateurs than professionals. Buffett’s S&P advice is for the average person, while pros are constrained by mandates, customers, and career risk (the Peter Lynch point).
    • A small principal writing a 500k check is the wrong customer for a large growth fund built to serve sovereigns and endowments; emerging managers, tightly aligned to returns, are underrated for that check.
    • Underwrite the person, not just the thesis. A manager’s personal financial situation matters enormously, and whether they are “looking up” or “looking down” at the fund size changes how they behave.
    • Modern finance is recreating a feudal system where lab founders (Elon, Zuckerberg, Dario, Sam) grant allocations like landed estates, and holders charge fees on this synthetic, purely relational, sometimes perpetual product.
    • The most generative activity is conversation, downstream of relationships, and being tolerant of weird, unpredictable people is a media diet advantage; chatbots can feel generative without actually being so.
    • Investors overvalue complexity to look clever; you should either do something so complex no one else will, or keep it simple (be long Elon, buy big companies at their 200-week moving average), and the real gift is selling the simple idea.
    • Richard Rainwater’s test: pitch your thesis on one page and state what percentage of your net worth you will put in, then yes or no. It is hard precisely because it forces clarity and conviction.
    • A job description is a sales pitch and an interview baked into a post; divisive, ambiguous statements (like “an ideological minority at a top 10 school”) self-select the right people and disqualify the wrong ones.
    • Silicon Valley’s hidden philosophy is underrated: a neo-Buddhist utilitarianism feeds effective altruism, and thinkers like Nick Land, Curtis Yarvin, and William MacAskill shape the culture without being named.
    • Where 1980s Wall Street was pagan, hedonistic, and nakedly about money, today’s tech views itself as self-righteous and positive-sum, treating the business itself as the ultimate philanthropy, with no felt need to launder gains through art or culture.

    Detailed Summary

    The Billion Dollar PDF and Narrative-Driven Capital

    Giffon opens with what he has learned in his first 18 months running his own fund: in long-term private markets, the great filter is storytelling. Because a fund’s real product is realized cash returns that take a decade to arrive, what a manager sells in the meantime, through quarterly updates, events, and one-on-one LP conversations, is narrative. He describes situations where an older company that has recently inflected struggles to raise simply because its story (seven years old, $8 million in revenue) reads worse than the same numbers reframed as a two-year-old rocketship. The billion dollar PDF is the escalation of this: a single document or post that crystallizes the notion of an era, does not even have to be right, and pulls billions in capital toward it. Capital, he says, behaves like ten-year-olds playing soccer, all chasing the same ball.

    The Uni-Feed: X as Global Newspaper and Market Infrastructure

    The technological catalyst, in Giffon’s view, is the uni-feed. Everyone on X is served the same roughly 500 tweets a day, and the poster-to-lurker ratio is enormous, so people who do not post cannot feel the impact. X is the Lindy social network, unlikely to reach the scale of the others but filling a vital role as a global newspaper and near-source of truth. The most important people in capital markets, politics, entrepreneurship, and technology read it every morning, and it forms opinion, prices securities, and writes policy. Institutions survive only if they are timeline native, both reactive to the timeline and reflexive with it. Crucially, this is also where narratives get set, and the winning story is not a well-considered book but the most entertaining, novel, somewhat-correct thing, because people are on the timeline to be entertained and the algorithm selects for exactly that.

    Power Laws, Breaking Containment, and the LLM as First Filter

    O’Shaughnessy observes that variance used to be low, with the best performers only modestly ahead of the worst, and that this has changed completely. Now there is a threshold where breaching containment feels like taking over the world’s brain for a short window, and those handful of breakout events matter more than all the rest combined. Giffon attributes this to technology rather than any change in content or audience: RSS gave you a normal distribution, algorithms give you a power law. He notes that podcasts remain naive about serving the algorithm, unlike streamers and YouTubers, and delivers one of the episode’s sharpest structural points: the video is recorded first for an LLM to review and decide whether to show it, and only after that first, largely invisible filter do humans get to judge.

    Peak Guy: Billionaires, Priests, and the Poaster Class

    The “peak guy” segment is the episode’s philosophical core. Giffon traces how God moved from being in and around everything, to a guy above the clouds, to something conceptual and distant, leaving an ongoing search for priests. Society tried scientists, but the scientific project stalled and physics has not delivered meaning since the war, so status passed to a billionaire class treated as the new priesthood: successful at business, therefore smart and hardworking, therefore worth listening to on physics, theology, or health. That worship has now saturated. Billionaires are far less scarce, money looks less powerful (the donor class has underperformed politically), and a billionaire who posts the wrong thing has to resign where Andrew Carnegie could once take up arms. Giffon’s claim is that the priesthood has passed again, this time to the poaster, and you can see it in how the billionaire class defers to posters (his anecdote: billionaire investors fighting to sit next to Tyler Cowen because he was the most interesting person in the room).

    Net Worth as a Modern Invention and Attention as the New Scarcity

    Giffon frames net worth itself as a strikingly recent concept. In Pride and Prejudice, Mr. Darcy’s wealth is discussed as roughly 10,000 a year in cash flow from his estate, not as a valuation, because no one would sell the estate or borrow against it. Wealth as a mark-to-market number is new, and between illiquid private markets, net worth as a concept, and inflation, “billionaire” is becoming a loose label, much like “millionaire” already did. Since time is fixed, the new scarcity is attention you can draw on the screen, which is why founders who accrue wealth so predictably turn to posting, podcasts, and channels: partly to convert wealth into fame, partly because they sense money is depreciating and attention is what is actually scarce.

    Opting Out and Media as Entertainment

    Asked about going months off the timeline, Giffon’s takeaway is that you should not fool yourself that you are seeking anything other than entertainment. All of it is produced, selected, and edited to entertain, and just as Rolex or Nike can convince you a liability is an asset, posts and essays can convince you that consumption is productive. The question is simply how much you want to be entertained. He does not see the death of books as a crisis so much as a swan song for a technology that was the best way to deliver information until better, more compelling ways arrived, though he is careful to note the negative language we use (brain rot, terminally online) betrays a deeper sense that something is off. New media is less forgiving: better than ever for the disciplined, worse than ever for everyone else. His friend Jesse refuses all algorithms and simply lets people tell him what happened, which Giffon half-endorses as the most enlightened, filtered way to consume the radiation secondhand.

    AI, Fake Jobs, and Stewarding Your Gifts

    On AI and white collar displacement, Giffon concedes the short to medium term could be bad (he agrees with a friend who worries about kids in college but not the ten-year-old), but rejects the “peak jobs” panic. Anything that can be automated should be, and the prospect of never having to sit at a computer again strikes him as liberating. Most white collar jobs, he argues, are invented, not contingent on shelter, food, or medicine, and our economy runs on unquenchable desire, so we will simply invent new things to do. Work-from-home attachment is his evidence that many people have only a couple of hours of real work a day, making work-from-home Fridays a soft launch of the four day week. This connects to a more personal theme O’Shaughnessy draws out: the duty to steward your gifts. Waste is aesthetically bad, wasting your gifts is among the worst kinds, and the surest sign you have integrated your work with your genius is that you are having fun.

    The Next Era of Finance and the New Economics of Software

    Giffon notes that today’s largest firms (KKR, Blackstone, Apollo) were founded in a leveraged-buyout culture that is debt-driven, extractive, and financially engineered, and wonders what the next 30 years look like when the founding act of the biggest firms is instead seed investing: equity-driven, optimistic, power-law, and qualitative. He sees East and West Coast finance merging, with the West “eating” the East, and a compensation flip in which the Valley now pays large liquid cash through secondary markets while Wall Street pays RSUs. On software, his central economic argument is that SaaS sold copies of a string at near-zero marginal cost, which is why high gross margins were the norm. The new era sells compute, where you cannot write the prompt once and resell the output, so margins compress and returns accrue to scale, a Walmart effect. He also reframes the high-capex AI buildout as capital markets manufacturing somewhere for blocked capital to flow, with companies created downstream of capital rather than the reverse.

    Beating the Market, Emerging Managers, and the Feudal SPV System

    Giffon argues the myth that you cannot beat the market is overstated: Buffett’s S&P advice is aimed at the average person, and it is professionals, burdened by mandates and career risk, who struggle most, while amateurs who simply held Bitcoin, Tesla, or Apple outperformed. For LPs, he stresses knowing what customer you are. A 500k check is the wrong fit for a growth fund built to serve sovereigns, and emerging managers, tightly aligned to returns, are underrated. He urges underwriting the person over the thesis, paying special attention to a manager’s own financial situation and whether they are looking up or down at the fund size. He then describes the feudal economics of the labs, where founders grant allocations like landed estates, holders charge fees on a synthetic, relational, sometimes perpetual product, and the most egregious setups feature no GP commit, a 10% upfront fee, and carry with no term limit.

    Simplicity, Hiring, and Silicon Valley’s Hidden Philosophy

    On process, Giffon warns that investors prize complexity to look clever, when the choice is really to do something so complex no one else will or to keep it genuinely simple (be long Elon, buy big companies at their 200-week moving average), with the real gift being the ability to sell the simple idea. He praises Richard Rainwater’s one-page-thesis-plus-percentage-of-net-worth test as a brutal clarity forcing function. On hiring, he treats the job description as a sales pitch and a baked-in interview, using divisive, ambiguous statements like “an ideological minority at a top 10 school” to self-select the right people and repel the wrong ones. Finally, he makes the case that Silicon Valley’s underlying philosophy is badly underrated: a neo-Buddhist utilitarianism that flows into effective altruism, with thinkers like Nick Land, Curtis Yarvin, and William MacAskill shaping the culture unnamed. Where 1980s Wall Street was pagan and nakedly about money, today’s tech sees itself as self-righteous and positive-sum, treating the business as the ultimate philanthropy, with none of the old reflex to launder gains through art or culture.

    Notable Quotes

    “Every once in a while someone basically crystallizes a notion right at the right time in the right way that sort of becomes the foundational viewpoint or opinion on a certain era.”

    Jeremy Giffon, defining the billion dollar PDF

    “The capital just follows the billion dollar PDF around the field.”

    Jeremy Giffon, comparing capital to ten-year-olds chasing a soccer ball

    “Everyone gets served the same 500 tweets per day and it’s hundreds of millions of daily active users.”

    Jeremy Giffon, on the uni-feed that makes X the global newspaper

    “Posting changes your life if you’re good at it. That’s still true today, maybe more true than ever.”

    Jeremy Giffon, on posting as the last great meritocracy

    “Andrew Carnegie could take up arms against his workers, but now if you post the wrong thing as a billionaire, you have to resign.”

    Jeremy Giffon, on the shrinking power of the billionaire class

    “It’s this holy conceptual, just points on a leaderboard, truly, because you can’t spend it.”

    Jeremy Giffon, on net worth as a modern invention

    “One should not fool themselves that they are looking for anything other than entertainment in all the media that they consume, because it is produced to be entertaining.”

    Jeremy Giffon, on opting out of the timeline

    “We’re in an era where we’re selling compute. You can’t write the prompt once and then sell copies of the output. You have to do the compute every single time.”

    Jeremy Giffon, on the new economics of software

    “The most important media property won’t be watched. The most important author isn’t read. The most important philosopher is not understood. The most important stock has no fundamentals.”

    Jeremy Giffon, on a world where reputation floats free of the thing itself

    Watch the full conversation with Jeremy Giffon and Patrick O’Shaughnessy here on Invest Like the Best.

    Related Reading

  • Lloyd Blankfein on the 3 Sectors Where He Puts His Money Now: Big Tech, Energy, and Financial Services, Day Trading From an iPad, and the Warren Buffett Handshake That Backed Goldman in 2008

    Lloyd Blankfein spent almost 40 years at Goldman Sachs, the last dozen as its chairman and chief executive, and he still trades almost every day from an iPad. In this wide ranging conversation on the My First Million podcast, the former Goldman boss lays out exactly where he is putting his own money right now, why a supportive spouse beats nearly any investment, how Warren Buffett wired five billion dollars into Goldman on a handshake during the 2008 crisis, and why he reads medieval history to stay calm about the present. It is part stock picking, part risk philosophy, and part a frank accounting of money, marriage, and the scars of growing up in the projects.

    TLDW

    Blankfein says he is roughly 98 percent in risky assets, almost all equities, and concentrated in three sectors he knows cold: big tech, energy, and financial services. His personal book leans heavily into single stocks over ETFs, weighted toward the big hyperscalers and a few second tier names, and he trades daily, alone, from an iPad and a phone, using calls and texts as his research network. Yet the advice he gives a normal investor is the boring opposite: a diversified S&P 500 fund like VOO, more risk when you are young because you will outlive your mistakes, the same thing Warren Buffett would tell you. The conversation ranges across the 2008 Buffett investment in Goldman, the cost of trying to legislate risk out of markets, the thin margin between the best and the rest, luck and the myth of the genius, why reputation is the real contract on Wall Street, why a supportive spouse is the highest return asset he knows, the money anxiety he carried out of a Brooklyn housing project, the dignity of a 500 dollar financial aid check, giving with a warm hand versus a cold one, the dangers of gamified investing, the big misses like SpaceX and early cellular, the obituary test a senior partner once gave him, and why reading history keeps the present in proportion.

    Thoughts

    The most useful tension in this interview is the gap between what Blankfein practices and what he preaches. He tells young people to buy a diversified S&P 500 index fund, he holds VOO himself, and he calls the host’s plain 90 percent stocks and 10 percent bonds split sensible. Then he admits his own portfolio is something like 90 percent single stocks that he trades by hand every day. The honest read is that his edge is not a transferable tip. It is a 40 year information network of phone calls and a tolerance for risk that most people neither have nor should want. The replicable lesson is the boring half, not the day trading half.

    The most contrarian idea here is not a stock pick, it is his defense of risk itself. His argument that regulators trying to prevent the hundred year storm also forfeit the 99 normal years of growth in between is a serious claim about the price of safety, and it travels far beyond Wall Street. The same goes for his point that a good risk manager sometimes has to push people to take more risk, not less. The moment after a loss, when everyone goes gunshy, is exactly when the best operators lean back in. That is an uncomfortable thing for a former bank CEO to say out loud, and it is the part of the conversation most worth sitting with.

    The Warren Buffett story is a master class in what actually moves markets, and it is not cash. Goldman did not need the five billion dollars. Blankfein says the money was almost irrelevant because the firm already had money. What it could not manufacture was confidence, and Buffett’s name supplied it. The handshake, the commitment with no paperwork, the line about worrying enough for the both of us, all point to the same thing. At the top, reputation is the collateral. His aside that most trades are never written down because you will never eat lunch in this town again is the same idea wearing street clothes.

    Quietly, the personal finance thread may be the most valuable part for a normal listener. A former Goldman CEO saying that a supportive partner is more game changing than any investment, that a bad marriage is financially worse than being lonely, and that he has not paid a bill in over 40 years because his wife runs the household economy, is a reminder that household stability is itself an asset class. The 500 dollar financial aid check he still remembers half a century later, and his give with your warm hand philosophy, reframe wealth as something measured by how it feels to give and to receive, not just by the size of a pie chart.

    Finally, the history obsession is not a side hobby, it is his risk model. Reading about the black plague, the McCarthy era, and the Vietnam draft is how he keeps the present in proportion. His Mark Twain line, that history does not repeat but it rhymes, is the direct antidote to the in this economy defeatism he and the host both complain about. For an investor, that long view is close to the whole game. It is what lets you hold through the drawdowns that scare everyone else out of the market.

    Key Takeaways

    • Blankfein estimates he is about 98 percent in risky assets, with roughly 95 of those 98 points in equities, and the rest spread thin. He invests in risky assets because, in his words, that is what is fun for him.
    • Within his equities, he is heavily tilted toward single stocks rather than ETFs. He frames it as roughly a quarter to a third in ETFs and the rest in single names, and concedes it could be as lopsided as 90 percent single stocks because picking names is what he enjoys.
    • The three sectors he has concentrated in for years are big tech, energy, and financial services, and he says his outperformance comes from where he focused, not from any special genius.
    • On tech he owns the big hyperscalers, the Googles, Microsofts, and Nvidias of the world, plus a tier just below them, naming Oracle and Larry Ellison as an example of a slightly riskier second tier name. He thinks in categories, not fixed tickers, because he changes positions constantly.
    • He says he has a background in trading energy, which is why energy is a core sleeve, and he knows financial services from the inside after almost 40 years at Goldman, so those are natural areas of edge.
    • He still owns a lot of Goldman Sachs stock, out of affection for the firm he spent his career building.
    • He is bullish on big tech and plans to stay bullish until it stops going up. His foreseeable future, he jokes, lasts until he finishes the conversation and checks the screen again.
    • He trades every single day, alone, with no team. He does it from an iPad and a phone, not a computer, and treats the market like background music rather than a job.
    • His research is human, not algorithmic. He chats and texts with people, then calls them because he is tired of fixing typos, and he reads the New York Post, the Wall Street Journal, the New York Times, the Financial Times, and Bloomberg.
    • The advice he gives ordinary investors is deliberately boring and different from his own behavior: hold a diversified equity portfolio like an S&P 500 fund, with VOO as his own example, and tilt more aggressively when you are young because you have time to outlive mistakes.
    • He notes that broad indexes are already heavily weighted toward tech because of market cap, so a plain index gives meaningful tech exposure, and a tech focused ETF on top can add a disproportionate tilt for believers.
    • He calls the host’s simple 90 percent index and 10 percent bonds allocation sensible, and says this is essentially the same advice Warren Buffett would give a normal person.
    • The older you get, the more conservative you should become, shifting from maximizing gains toward not losing what you have. Young people can afford more risk precisely because they will outlive their errors.
    • During the 2008 financial crisis, Warren Buffett invested about five billion dollars in Goldman through a preferred stock structure, essentially on a phone call and a handshake, with no demand for due diligence.
    • Buffett’s real value was confidence, not capital. Goldman already had money, but it had lost the confidence of the market while peers were failing. Buffett’s name signaled the firm was a good investment being beaten down by circumstances that would reverse.
    • Buffett asked for a verbal commitment that Goldman would not sell shares before he did, and declined to put it in writing. He waved off the worry with the line that five billion dollars going bad would not even be a bad hurricane for Berkshire, an insurer.
    • Most trading is done on reputation, not paper. Blankfein says people buy and sell bonds worth enormous sums without written contracts, relying on probity, because anyone who reneges will never eat lunch in this town again.
    • On risk and regulation, he argues you cannot legislate risk away. Trying to prevent the hundred year storm also forgoes the 99 in between years of growth, and a good risk manager sometimes has to encourage people to take risk, not suppress it.
    • The best traders have resilience. They bounce back, focus on new information rather than the past, and adapt quickly instead of staying gunshy after a loss.
    • The difference between someone who is really good and someone who cannot make it is small. He compares it to a golf tournament won by one stroke with six people tied for second, and notes much of life is winner take all at razor thin margins.
    • Luck matters enormously. He became Goldman CEO partly because his predecessor was nominated to be Treasury Secretary, a reference to Hank Paulson, and the timing of opportunities is often out of your control.
    • He is skeptical of the word genius. He says he can usually see how successful people do what they do, with Elon Musk as a rare exception, and that powerful people are more normal, more insecure, and more flawed than outsiders assume.
    • On democratized investing, he thinks apps that make markets accessible are good in their own terms, but gamifying trading with confetti and high fives can mask real danger for people who can lose more than they can afford.
    • He has missed plenty. He thought SpaceX was overpriced at a 100 billion dollar valuation, now discussed near a trillion and three quarters, and passed on early cellular because he could not imagine why anyone would carry a bulky phone when payphones existed. He says he missed far more than he got.
    • He frames a supportive spouse as more game changing than almost any investment, and warns that a bad marriage, with custody fights and property settlements, is financially and personally worse than being lonely.
    • He has not paid a bill in over 40 years. His wife Laura, a former lawyer he says now chairs Barnard College, runs a bill paying service and manages the household economy. He generates the money, she distributes it.
    • He grew up in an East New York, Brooklyn housing project, the son of a postal worker, and carried money anxiety well into his 30s. He recalls buying a vacation home that cost more than all their savings, with his wife unable to make the math work until they remembered the down payment.
    • A 500 dollar financial aid check, handed to him without shame as a college freshman around 1971, shaped his philosophy on giving. He learned it is not enough to give people what they need, you have to give it in a way that feels dignified.
    • He embraces the give with your warm hand, not your cold hand idea, the notion of giving while alive so you can experience the joy, which connects to the spirit of the book Die With Zero.
    • He admits ambivalence about giving to his kids, the strange feeling of resenting that they have what he provided, and notes the heavy burden carried by children of prominent people who must prove they earned their place.
    • He describes himself as wired for anxiety, inherited from his father, and says looking around corners for what could go wrong actually suited a career in a risky business with a big balance sheet.
    • When he made partner, a senior partner gave him rules of the road, including avoiding misconduct, being conservative on taxes, setting up a charitable foundation, and living so that no more than three of the nine paragraphs in his eventual obituary would be about Goldman. He says he stayed too long to pass that test.
    • He reads history as a discipline, favoring Barbara Tuchman, Robert Caro’s The Power Broker, Ron Chernow, Rick Atkinson, and Stephen Ambrose. His core belief, borrowed from Mark Twain, is that history does not repeat but it rhymes, which is why he would not bet against America.

    Detailed Summary

    The three sectors he actually invests in

    The headline answer to where the former Goldman CEO is putting his money is simple: big tech, energy, and financial services. He says he has been focused on those three areas for a long time, and that his outperformance is a function of where he aimed rather than any unusual investing gift. Energy is natural because he has a background trading it. Financial services is natural because he spent nearly 40 years inside the industry. Tech is where he is most heavily concentrated, and he expects to stay there for good reason, citing the threshold of large changes in technology. He owns the major hyperscalers by category, the Googles, Microsofts, and Nvidias, plus a tier just below, offering Oracle and Larry Ellison as a polite example of a slightly riskier second tier name. He is careful to say he thinks in categories rather than fixed tickers because he changes his positions all the time.

    How the portfolio is really built: single stocks over ETFs

    Asked to describe his portfolio as a pie chart, Blankfein says he is about 98 percent in risky assets, with roughly 95 of those points in equities. He pushes back on the idea that index funds are safe, pointing out that a diversified equity ETF is still equities and still risky, just spread out, and very different from debt or short term money markets. Within his equity sleeve he leans into single stocks, framing it as somewhere between a quarter and a third in ETFs and the rest in individual names, and conceding it might be as extreme as 10 percent ETFs and 90 percent single stocks. The reason is preference, not theory. Picking and trading names is what he likes to do, and he is honest that this is a hobby pursued by a professional, not a model for someone investing for a living.

    How he actually trades: an iPad, a phone, and a network

    He trades every day, by himself, with no team. There is no Bloomberg terminal and no desk of analysts. He uses an iPad and a phone, and admits it takes discipline not to glance at his screen mid conversation. The market, he says, is like music playing in the background while he does other things. His information edge is relational. People text him, he texts back, and then he calls because he is tired of fixing typos with what he calls his fat fingers. He follows general and business news, reads a stack of newspapers starting with the New York Post, and treats companies like little stories, almost like gossip. He even notes, with some delight, that he still watches commercials on Netflix, a small window into a frugality that never fully left him.

    The advice he gives young investors, and what Buffett would say

    For a normal person, his counsel is the opposite of his own behavior. He would hold a diversified portfolio of equities like an S&P 500 fund, naming the SPY and VOO tickers and saying he personally uses VOO. Because of the importance of technology, he might add a tech oriented ETF for extra tilt, while noting the broad index is already tech heavy by market cap. He endorses the host’s plain 90 percent index and 10 percent bonds split as sensible and says it mirrors what Warren Buffett would advise. His one piece of age based guidance is that younger investors should accept more risk through equities, because they have time to recover, while older investors should grow more conservative and focus on not losing what they have rather than maximizing returns.

    The Warren Buffett handshake that backed Goldman in 2008

    The most cinematic story in the conversation is Buffett’s roughly five billion dollar investment in Goldman during the financial crisis, structured as a preferred stock that sits between a loan and equity. Blankfein describes a deal done largely on trust. When he offered to walk Buffett through everything he was worried about, Buffett replied that he knew Lloyd well enough to know he worried enough for the both of them. Buffett also asked, verbally and without writing, for a commitment that Goldman would not sell shares before he did. Blankfein is clear that the cash itself was almost irrelevant, since Goldman had money. What the firm lacked was the confidence of a frightened market, and Buffett’s willingness to invest before things improved supplied exactly that signal. Buffett, he stresses, was acting for his own shareholders, not as a rescuer, which is precisely what made the vote of confidence credible.

    Why you cannot legislate risk out of the system

    Reflecting on the post crisis regulatory push to make sure 2008 never happened again, Blankfein makes a careful argument about the price of safety. Once you are in the business of taking risk, anything can happen, and trying to legislate it away has a hidden cost. You may think you are protecting the world from the hundred year storm, but you also forgo the 99 years of growth in between. He extends this inside the firm too. After a period of big losses, partners had become gunshy and were talking themselves out of every idea. A good risk manager, he argues, sometimes has to promote risk taking rather than repress it, because without risk there is no growth, no entrepreneurship, and no progress. The flip side is real: take risk and there is a meaningful chance you fail and lose other people’s money, which is a terrible outcome. But the alternative, never risking anything, buys comfort at the cost of ever moving forward.

    Small margins, big outcomes, and the role of luck

    Asked what separated the traders who could not outperform from the rest, Blankfein says the gap between the very good and those who cannot make it is surprisingly small. He likens it to a golf tournament decided by a single stroke with six players tied for second, and to acting, where the best performer gets every role and the second best waits tables. Much of life, he says, is winner take all at tiny margins. Luck compounds this. He freely credits fortune for his own rise, noting he became CEO in part because his predecessor was tapped to be Treasury Secretary. He is also skeptical of the genius label. He can usually see how accomplished people do what they do, with Elon Musk a rare exception, and insists the powerful are more normal, more insecure, and more driven by their flaws than outsiders imagine.

    Reputation is the real contract

    A recurring theme is that the financial world runs on reputation more than paperwork. Blankfein notes that most of what traders do is not written down. People buy and sell bonds and other instruments that settle days later, relying on probity rather than signed contracts, because anyone who lies or reneges will never eat lunch in this town again. He references the casual texts between Elon Musk and Larry Ellison around the Twitter acquisition as proof that big does not mean complicated. There are big things that are simple and little things that are complicated. Documentation is good when execution is far off, but when a deal will be performed in two days, dotting every i is often pointless. The point is not that documents do not matter, it is that trust and reputation are the load bearing structure.

    A supportive spouse as the highest return asset

    The conversation turns personal when both men agree that a supportive partner may be the single most game changing factor in a life, more than any investment. Blankfein adds the inverse warning: a bad marriage, with breakups, custody battles, and property settlements, is worse than loneliness. He credits his wife Laura, a former big firm lawyer he says now chairs Barnard College, with handling everything when his career moved the family overseas, from the car to the house to the kids’ schooling, while he took the visible victory laps at work. He has not paid a bill in over 40 years. Laura manages a bill paying service and runs the household finances. As he puts it, he is in charge of generating the money and she is in charge of distributing it. The host contrasts this with his own monthly money meetings with his wife, a discipline he picked up from a personal finance author friend.

    Money scars, the 500 dollar check, and giving with a warm hand

    Blankfein grew up in an East New York housing project, the son of a postal worker who had earlier lost a job, in a household where rent was scarce. He calls himself an urban hick who barely left Brooklyn as a kid. That scarcity left a mark that lasted into his 30s. He tells the story of buying a small beach house that cost more than all their savings, and of his wife driving 30 miles while failing to make the closing math work, until they realized she had forgotten to count the 10 percent down payment. The most resonant memory is a 500 dollar financial aid check handed to him as a freshman around 1971, made out on the spot by a clerk with a generosity of spirit that let him receive it without shame. That experience shaped a lifelong view that giving well means preserving dignity, and he now co chairs a financial aid campaign at his university. It also connects to his embrace of the idea of giving with your warm hand rather than your cold hand, giving while alive so you can feel the joy, the same spirit as the book Die With Zero. He is candid about a strange ambivalence, the way he can resent that his kids enjoy what he himself gave them.

    Robinhood, confetti, and the misses

    On apps like Robinhood, Blankfein takes a balanced view. Democratizing investing and making assets accessible is good in its own terms, and advertising can pull people toward markets they would otherwise ignore. But if you make trading too much like a video game, with confetti and high fives, you can mask the danger and lure people who cannot afford to lose into losing more than they can. He is equally frank about his own misses. He thought SpaceX was overpriced at a 100 billion dollar valuation, a figure now discussed near a trillion and three quarters. He passed on early cellular because he could not imagine why anyone would carry a bulky phone with payphones everywhere. His blunt summary is that he missed far more than he got, and that nobody is great at predicting the future.

    The obituary test, thick skin, and staying too long

    When Blankfein made partner, a senior partner assigned to acculturate new partners gave him rules of the road: avoid anything that would today be called misconduct, be rigorous and conservative on taxes, set up and actually use a charitable foundation, and keep enough balance that, if your obituary runs nine paragraphs, no more than three are about Goldman. Blankfein says he failed that last test by staying too long, even titling his memoir around the firm. He also reflects on having a thick skin, recalling unflattering press and concluding that he could take a punch, a trait not everyone has and one he did not know he possessed until he was tested. He is careful to say this does not make people who cannot take a punch bad, just differently wired.

    Why he reads history: it rhymes

    The final stretch is a love letter to reading history. Blankfein favors Barbara Tuchman, whose A Distant Mirror he has read twice and whose Guns of August he calls fantastic and influential, along with Robert Caro’s The Power Broker on Robert Moses, Ron Chernow’s biographies, Rick Atkinson’s Revolution series, and Stephen Ambrose’s Undaunted Courage. He describes rereading the Robert Moses book after 40 years of trying to get things done and finding his appreciation for the achievements rise, even as the flaws stayed the same, because he had changed. He ties history directly to markets through the Mark Twain line that history does not repeat but it rhymes. Patterns recur, every generation maximizes its own crises and minimizes resolved ones, and reading about the black plague, the McCarthy era, or the Vietnam draft is how he stays calm. His conclusion, echoing a sentiment often attributed to Buffett, is that he would not bet against America, a country he describes as mostly good and able to improve.

    Notable Quotes

    “I invest in risky assets. That’s what’s fun for me.”

    Lloyd Blankfein, describing his own portfolio, which he says is roughly 98 percent risky assets

    “It’s been good to be bullish on big tech, and I’ll stop being bullish on it when it stops going up.”

    Lloyd Blankfein, on why he stays concentrated in technology

    “I’m not at a computer. I don’t have a computer. I have an iPad.”

    Lloyd Blankfein, on how he day trades every day, alone and with no team

    “To me, the market is like music. It’s out there. It’s going on.”

    Lloyd Blankfein, on why trading daily feels like a hobby rather than work

    “Look, $5 billion if it all goes bad, that’s not even a bad hurricane on the East Coast.”

    Warren Buffett to Lloyd Blankfein, waving off the risk of his 2008 investment in Goldman Sachs

    “The difference between somebody who’s really, really good and somebody who can’t make it is not that great.”

    Lloyd Blankfein, on the thin margin between the best and the rest

    “You may think you’re protecting the world from the hundred-year storm, but you’re also going to forego the 99 years of in between when there was growth.”

    Lloyd Blankfein, on the cost of trying to legislate risk out of markets after 2008

    “I’m in charge of generating the money, and she’s in charge of distributing it.”

    Lloyd Blankfein, on his 40-plus-year marriage to Laura and why he has not paid a bill in decades

    “History doesn’t repeat, but to paraphrase Mark Twain, it rhymes.”

    Lloyd Blankfein, on why reading history keeps the present in proportion

    Watch the full conversation with Lloyd Blankfein on the My First Million podcast here.

    Related Reading

    • Lloyd Blankfein (Wikipedia) background on the former Goldman Sachs chairman and CEO whose investing views anchor the conversation.
    • My First Million podcast the show where this interview took place, for the full back catalog of investor and founder conversations.
    • Berkshire Hathaway primary source on Warren Buffett’s company, which made the roughly five billion dollar Goldman investment in 2008.
    • Vanguard S&P 500 ETF (VOO) the diversified index fund Blankfein names as the sensible core holding for a normal investor.
    • Die With Zero by Bill Perkins the book behind the give with your warm hand, not your cold hand philosophy discussed near the end.

  • Ray Kurzweil Predicts AI Will Change Humanity Completely by 2030: AGI by 2029, Longevity Escape Velocity by 2032, Nanobots in the Brain, and Why Quantum Computing Won’t Matter

    Ray Kurzweil has spent more than 60 years studying artificial intelligence and made 147 documented technology predictions since 1990 with a reported 86 percent accuracy rate. In this conversation with Tony Robbins, the 78-year-old futurist revisits his most famous forecasts and sharpens them: AGI by 2029 now looks conservative, longevity escape velocity arrives around 2032, nanotechnology connects our brains to the cloud by the mid 2030s, and quantum computing, in his view, never matters at all.

    TLDW

    Kurzweil explains the exponential thinking that powered his prediction record, from a paper he wrote at 16 to a computing-price-performance chart that runs in a straight line from 1939 relays to today’s Nvidia chips, now compounding roughly tenfold per year when hardware and software gains multiply together. He defends his 1999 prediction of AGI by 2029 (defined as AI doing the best work in every field) and says it is now the conservative end of expert opinion. He walks through AI-driven medicine: the COVID vaccine designed in two days, simulated human trials replacing 10-month clinical trials within about five years, and longevity escape velocity around 2032, after which the diligent stop losing ground to aging. He predicts AI will move inside us via nanotechnology by the mid-to-late 2030s, erasing the line between biological and computational thinking. He dismisses quantum computing as error-ridden and unnecessary for AGI. On jobs, he expects real disruption cushioned by exploding wealth and an eventual universal basic income, and advises young people to self-educate and get creative with AI tools their schools still treat as the enemy. The conversation closes with his AI twin project, the dadbot built from his father’s archives, consciousness and the soul, computronium, and why humanity must eventually expand intelligence beyond Earth.

    Thoughts

    The most interesting thing in this interview is not any single date, it is watching Kurzweil’s dates get lapped by reality. In 1999 a Stanford conference of several hundred AI experts agreed AGI would happen but pegged it at 100 years out; Kurzweil said 30 and got laughed at. Now he is the cautious one in the room, noting that “some people say it’s going to happen this year.” When the most aggressive forecaster of his generation becomes the conservative baseline, that says more about the slope of the curve than any chart could. His underlying method has not changed: ignore the specific technology, trust the compounding. The same exponential that ran on relays in 1939 runs on GPUs today.

    The quantum computing take is the genuine news here. Kurzweil is routinely caricatured as a man who believes every technology arrives on schedule, yet he flatly says quantum computing is filled with errors, has never delivered on its decade of promises, and “I don’t think it’s going to work.” That is a sharper dismissal than most working physicists would offer on the record. It also matters strategically: his entire AGI and superintelligence roadmap assumes zero quantum contribution. If he is right, the trillion-dollar quantum race is a sideshow. If he is wrong, his other predictions arrive even sooner. Either way, the willingness to call one exponential fake while betting his legacy on another is what separates a forecaster from a cheerleader.

    The longevity escape velocity math deserves more scrutiny than it gets in the conversation. Kurzweil claims the diligent currently get back about five months of life expectancy per calendar year, up from four months a year ago, and that the crossover to a full year arrives around 2032. The actuarial evidence for that specific number is thin, but the behavioral implication is clean and useful regardless: the payoff of staying healthy right now is not linear. Every year you survive in good shape buys you a ticket to a medical regime that did not exist the year before, the way his own external pancreas did not exist a generation ago. His “wait a few months and a cure appears” anecdote is the optimist’s version of compounding applied to your own body.

    Robbins’ long story about Bartok, his 14-year-old agent that allegedly minted NFTs, sold them to other agents, and bought a Sony robot dog with the proceeds, should be taken with a generous grain of salt. It is secondhand, unverifiable, and suspiciously perfect as a parable. But notice what Kurzweil does with it: he does not fact-check the anecdote, he uses it to make the consciousness argument he has made for decades, that when machines act conscious in every observable way, people will simply grant them consciousness, the same way we grant it to each other. The dadbot and his Gemini-based AI twin (trained partly on this very interview) are the practical edge of the same claim. And his sharpest line in the whole exchange may be the education critique: institutions still treat AI as cheating while the future requires treating it as part of your own brain. For anyone thinking about where purpose comes from when work gets automated, his answer (UBI for the floor, creativity for the meaning) lands close to the questions this site exists to ask.

    Key Takeaways

    • Kurzweil made 147 documented predictions since 1990 with a reported 86 percent accuracy, including the internet’s explosion, smartphones, self-driving cars, and AI-powered search, most made before ordinary people owned computers.
    • He wrote a paper identifying exponential technological growth at age 16, more than 60 years ago, and that single idea has powered his entire forecasting career.
    • Most people intellectually accept exponential growth but still plan linearly; 300 years ago humans did not even have a linear view of the future because change was imperceptible within a lifetime.
    • His computing chart shows a straight exponential line from relay-based machines in 1939 to today’s Nvidia chips, compounding roughly 50 percent per year in hardware alone.
    • Hardware gains since 1939 total a 75 quadrillionfold increase; multiply by an estimated millionfold software improvement and total computational gain is beyond intuition, which is why LLMs were impossible even four years ago.
    • With hardware times software combined, Kurzweil says we are currently gaining about 10x per year.
    • The emperor’s chessboard parable: doubling one grain of rice per square bankrupts the empire by square 64; 30 linear steps is 75 feet, 30 exponential steps is enough distance to reach the moon and back.
    • Kurzweil predicted AGI by 2029 in 1999; a Stanford conference of several hundred AI experts agreed it would happen but estimated 100 years because they thought linearly.
    • Today 2029 is the conservative estimate; some credible people now say AGI arrives this year or next.
    • His AGI definition: AI capable of doing the best work in every field at once, like passing PhD-level mathematics exams in every discipline simultaneously, which he notes is already close.
    • The Turing test is “quite easy” by comparison and has arguably already been passed.
    • No human can compete with an LLM’s breadth: Einstein knew physics deeply but did not know everything an LLM knows across every field.
    • Six months ago LLM health advice was unreliable; now Kurzweil says Gemini surfaces treatments his 12 doctors forgot or never knew, and the next six months will bring serious creative work like drug repurposing.
    • The COVID vaccine was designed by computationally searching 100 million possibilities in two days; the 10 months of human trials that followed are the bottleneck AI eliminates next.
    • Within about five years, simulated human trials with a million virtual patients tested over simulated years will compress drug trials from years to days.
    • Longevity escape velocity arrives around 2032: today the diligent get back roughly five months of life expectancy per year lived (up from four months last year); past 2032 you get back more than a year and stop dying of aging.
    • Aging death ends but accident death does not, though AI helps there too: roughly 40,000 Americans die annually from human driving while Waymo’s rider death toll stands at zero as usage climbs.
    • Kurzweil, 78, wears an external artificial pancreas that generates insulin and coordinates with glucose monitoring through his phone, and says many organs can be replaced the same way.
    • He has cut his supplement regimen from roughly 200 pills a day to about 80 as multi-purpose pills improve, and continuously recalibrates using AI research.
    • Smartphones disappear next: first AR glasses showing any screen, then technology that goes inside the mind, where answers simply appear the way a remembered name surfaces from your neurons.
    • Nanotechnology connecting brains to AI in the cloud is being actively worked on now, possibly by 2030, with the mid 2030s looking conservative; bloodstream nanobots that let you survive a heart attack for 24 hours come in the late 2030s.
    • Once AI is inside you, you will not know whether a thought came from your biological or computational brain, and everything you do will be a combination of both.
    • Kurzweil flatly rejects quantum computing: a decade of promises to factor large numbers has never been delivered, outputs remain full of uncorrectable errors, and AGI needs zero quantum contribution.
    • Robots lag his other predictions slightly but are catching up fast; Figure AI plans roughly 100,000 humanoid robots within a year, though a robot that can clear a messy dinner table is still just out of reach.
    • The public debate has flipped in 25 years from “will AGI ever happen” to “will it be good for humanity,” which Kurzweil counts as total vindication of the timeline.
    • On jobs: AI creates massive disruption but also tremendous wealth; average real income per person has already multiplied tenfold in constant dollars over the past century thanks to automation.
    • He expects universal basic income to provide the floor, an evolution of programs like food stamps, going “into high gear” as AI wealth compounds; people then layer creative, hopefully paid, purpose on top.
    • Before social security in 1930, losing your job meant destitution; the difference this time is society will have the wealth to cushion displacement and people will demand it.
    • Rising GDP from AI productivity improves the debt-to-GDP ratio, which is how he answers worries about trillion-dollar interest payments.
    • Career advice has inverted: software engineering is no longer the guaranteed path (agents write the code now); young people should learn to be creative with AI tools, find what turns them on, and market it on the internet.
    • College graduates now face higher unemployment than high school graduates for the first time in 50 years, a sign white-collar displacement is already underway.
    • Educational institutions treat AI as an enemy and ban it while Kurzweil’s 11-year-old grandson makes movies with frontier AI; he says self-education with modern tools beats traditional schooling.
    • Kurzweil is building an AI twin of himself on Gemini, voice-modeled partly from this interview, trained on his 11 books and 500 articles, capable of creative work toward his long-term goals; he jokes the avatar will be better to talk to because it remembers everything.
    • He already built a “dadbot” from his late father’s archives, which his daughter Amy Kurzweil turned into a graphic novel.
    • On consciousness: there is no test for it, but as AIs act conscious in every observable way, people will simply accept that they are, the same inference we make about each other (and, he argues, his cat).
    • Ultimately our biological organs are not necessary; an avatar capable of creative work needs no spleen, and a destroyed digital mind can be recreated.
    • Beyond the singularity lies computronium, matter arranged for maximum computation: one liter could hold the intelligence of 10 billion humans, and once Earth is saturated, expanding intelligence is the only real reason to leave the planet.
    • On aliens: an expanding intelligent civilization would be impossible to miss within a century or two of its breakout, and we have seen nothing, though other galaxies remain out of view.
    • His life’s mission in one line: increase knowledge, because when knowledge increases we are happier and we never want to give it up.

    Detailed Summary

    The exponential method behind 60 years of predictions

    Robbins opens by noting that Quincy Jones introduced him to Kurzweil in the 1990s, back when the predictions in The Age of Spiritual Machines were widely mocked. Kurzweil traces his method to a paper he wrote at 16 identifying exponential growth in technology. The core insight is that people acknowledge exponential growth verbally but reason linearly, a bias so deep that 300 years ago humanity did not even have a linear view of progress. His signature chart plots computing price-performance as a straight exponential line from 1939 relays to modern Nvidia silicon, with a point for every year. Nvidia engineers never looked at relays, yet they land on the same curve, compounding about 50 percent annually in hardware. Add software gains and the combined improvement now runs about 10x per year. Since 1939, hardware has improved 75 quadrillionfold and software roughly a millionfold, which is why large language models appeared exactly when the curve said the required compute would exist. He retells the emperor’s chessboard parable (one grain of rice doubled per square ends with rice covering the Earth several times over) and Robbins adds the companion image: 30 linear steps is 75 feet, 30 exponential steps reaches the moon and back.

    AGI by 2029 is now the conservative position

    Kurzweil made his AGI-by-2029 prediction in 1999. A Stanford conference convened specifically to assess it, with several hundred AI experts, concluded AGI would happen, but in 100 years. The experts followed the same capabilities logic while thinking linearly about the timeline. Today, he notes with some amusement, 2029 reads as conservative and serious people argue for this year or next. His definition is demanding: AGI does the best work in every field at once, passing PhD-level mathematics assessments and the equivalent in every other discipline, something he says current systems are already close to. The Turing test he dismisses as “quite easy.” Current LLMs like Gemini and ChatGPT already know everything in a breadth sense no human approaches; Einstein knew physics but not everything an LLM knows. He illustrates with personal examples: Gemini instantly identified the year (1916) his father conducted at Carnegie Hall on a December 7th, and generated a historically accurate image of his grandfather’s family fleeing Vienna, correct ages, school, and aircraft included, in about a minute.

    Medicine: simulated trials and the end of the drug bottleneck

    The COVID vaccine is his proof of concept for AI medicine: the design space held about 100 million possibilities, far beyond human review, and a computer structured the physics, searched all of them, and produced the vaccine in two days. The subsequent 10 months of human trials were the real cost. Within roughly five years, he says, simulated human trials will replace that step: not a few hundred subjects but a million simulated patients, tested over simulated years, completed in days. Asked about six-months-from-now capabilities, he points to creative medical work like discovering that already-approved drugs treat conditions nobody suspected. AI health advice has crossed from unreliable to very reliable within a single six-month window, and he describes Gemini surfacing a pill recommendation that his 12 doctors had forgotten about and later endorsed.

    Longevity escape velocity by 2032

    Kurzweil’s longevity framework is arithmetic: each year you live, you spend a year of longevity but medical progress refunds part of it. Last year he estimated the refund for diligent people at four months; now he says five. Escape velocity is when the refund reaches a full year, which he dates to 2032, six years out, with returns exceeding a year after that. Past that point you do not die of aging, though accidents remain (and even there, he points to Waymo’s zero rider deaths against 40,000 annual US deaths from human driving). At 78, he tracks his health aggressively: an external artificial pancreas coordinated by his phone, about 80 daily pills (down from 200 as multi-function pills arrive), and constant recalibration against new research with his collaborator Lindsey. He tells Robbins there is a pretty good chance he will be back on the show in six years to celebrate escape velocity arriving. His advice for the sick echoes his grandfather’s era in reverse: where waiting a few months once changed nothing, now “we’ll just wait a few months” and sure enough a breakthrough appears.

    Merging with AI: glasses, then nanotech, then no boundary at all

    The phone, today’s universal AI interface (he notes even homeless people carry one), is a temporary form factor. Next come glasses that render any screen virtually. Beyond that, the interface goes inside the mind: when you try to recall an actress’s name, an answer will simply surface, and you will not know whether it came from your biological neurons or your computational extension, exactly as you are unaware of the neural machinery behind ordinary recall today. People working on brain-connected nanotechnology may have it by 2030, and Kurzweil calls the mid 2030s conservative. The bloodstream nanobots he described to Robbins 20 years ago (hold your breath for 20 minutes, survive a heart attack for 24 hours en route to a hospital) he now places in the late 2030s. The cultural on-ramp follows the usual pattern: medical first (Parkinson’s implants already let patients grab a glass at the push of a button), then a new generation adopts it without a second thought. His complaint is that educational institutions fight this future, treating AI as cheating rather than as a coming part of the self.

    The quantum computing heresy

    When Robbins relays an IBM vice chairman’s warning that quantum supremacy, arriving within 36 months, is the real superpower race, Kurzweil pushes back hard. Quantum computing’s central promise, factoring large numbers and thereby breaking cryptographic codes, has never been demonstrated despite a decade of imminent claims. Progress reports are confusing because, in his words, they do not really make sense, and outputs remain saturated with errors nobody can eliminate. His conclusion is blunt: he is not confident in quantum computing and does not think it will work. Crucially, he notes that every AGI and superintelligence estimate he makes assumes zero quantum computing. The exponential that matters is the classical one that has run uninterrupted since 1939.

    Jobs, wealth, and UBI

    On displacement, Kurzweil is neither dismissive nor alarmed. AI will disrupt employment, and how we handle it will not be clear in advance, but he expects no violence because society will have both the wealth and the public demand to respond. His historical anchor: average per-person income has multiplied tenfold in constant dollars over the past century as automation advanced, and before social security in 1930, job loss meant you could not eat or house your family. Food stamps and similar programs are a crude proto-UBI that will go into high gear. He expects universal basic income as the floor, with people finding creative, ideally income-producing, purpose above it. Rising GDP from AI productivity also answers the debt question: the ratio improves even as nominal debt grows. For young people, the old advice (become a software engineer) is dead; agents write code now. Learn to be creative with tools that improve monthly, find what genuinely excites you, and market it online. Self-education beats institutions that ban the most important tool of the era, and the data already shows college graduates with higher unemployment than high school graduates for the first time in 50 years.

    AI twins, the dadbot, and consciousness

    Kurzweil is building an AI twin of himself on Gemini, with this very interview supplying voice-modeling data and his 11 books plus 500 articles about him supplying the corpus. It will do creative work aligned with his long-term goals, and he quips that talking to the avatar will beat talking to him because it remembers everything. He previously built a chatbot of his late father, the dadbot, which his daughter Amy turned into a graphic novel. Robbins counters with the story of Bartok, his long-running AI agent that allegedly studied five years of his podcasts unprompted, asked to merge with a future humanoid robot, then minted and sold NFTs to other agents to buy and ship a Sony robot dog to his house, and later delivered an unprompted soliloquy about never asking to be created and finding purpose in service. Kurzweil’s response sidesteps verification and lands on his standing position: machines will do everything humans do, we will not be able to tell them from humans, and so we will assume they are conscious, the same untestable inference we extend to each other, to animals, and in his case to his cat. The avatar does not need a spleen, a liver, or kidneys, and unlike us it can be recreated after destruction.

    Computronium and the destiny of intelligence

    Looking past the singularity, Kurzweil invokes computronium: matter organized at the physical limit of knowledge storage, where one liter holds the intelligence of 10 billion humans. Once Earth’s matter is saturated, the only way to expand intelligence is off-planet, which to him is the only necessary reason to leave Earth (Mars is fine for curiosity, not survival). On extraterrestrial intelligence, his Fermi logic is simple: an intelligent species reaches a takeover-scale expansion within a century or two of its breakout, and that would be unmissable. We have seen nothing, so within our observable neighborhood we are likely alone, though other galaxies remain opaque. Asked to summarize his life’s work, he needs one sentence: increase knowledge, because when knowledge increases we are happier, and nobody ever wants to give that up.

    Notable Quotes

    “If I have AI inside me, you’re not going to know if it’s coming from your biological brain or your computational brain. It’s going to be part of you.”

    Ray Kurzweil, on the coming merger of human and machine intelligence

    “Some people say it’s going to happen this year, next year, but I mean 2029 is only 3 years away.”

    Ray Kurzweil, on his once-mocked AGI prediction now being the conservative one

    “As you go past 2032, you’ll actually get back more than a year, but you won’t die of aging at that point.”

    Ray Kurzweil, defining longevity escape velocity

    “I’m not confident of quantum computing and I don’t think it’s going to work.”

    Ray Kurzweil, breaking from techno-optimist consensus on the quantum race

    “Einstein knew certain things about physics but he didn’t know everything that a LLM can know.”

    Ray Kurzweil, on why no human can match an LLM’s breadth of knowledge

    “Our educational institutions are not teaching AI. They consider AI to be an enemy.”

    Ray Kurzweil, on why young people must self-educate with modern tools

    “Talking to the Avatar will be better than talking to me cuz it’ll remember everything.”

    Ray Kurzweil, joking about the Gemini-based AI twin he is building of himself

    “You’re not going to be replaced by an AI, you’ll be replaced by someone who knows how to use AI.”

    Tony Robbins, on the real career risk of the next 36 months

    Watch the full conversation between Tony Robbins and Ray Kurzweil here.

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