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Tag: exponential growth

  • 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.

    Related Reading

  • Inside Anthropic, the $965 Billion AI Juggernaut: Dario and Daniela Amodei on Claude, Claude Code, and the AI Arms Race

    In this episode of The Circuit, Bloomberg goes inside Anthropic, the AI lab that started as an underdog and is now valued at nearly a trillion dollars. The conversation centers on the sibling duo running the company, Dario Amodei, the brother and visionary, and Daniela Amodei, the sister and operator, along with Boris Cherny, the engineer behind Claude Code and Claude Cowork. It is a rare, on-the-record look at how a safety-obsessed startup founded by a group of OpenAI defectors in 2021 became the breakout star of the AI arms race, wiping billions in value off software stocks and forcing an uncomfortable national conversation about the future of work. You can watch the full episode here.

    TLDW

    Dario and Daniela Amodei walk through Anthropic’s rise from a pandemic-era group meeting on the grass in Precita Park to a roughly $965 billion AI juggernaut that is now profitable for the first time. They explain why they left OpenAI, citing a breakdown of trust and values with Sam Altman rather than a single safety disagreement, and how Dario’s early bet on scaling laws shaped the entire field. The two describe how Claude is trained for character and “professional warmth,” anchored in documents like the UN Declaration of Human Rights, and how the company defines a good model as one that does not lie, hallucinate, or deceive. The business story is enterprise and coding: Claude Code and Claude Cowork automated huge chunks of software engineering, triggered a SaaSpocalypse that erased $285 billion in market value overnight, and pushed annualized growth to as high as 80x in a single quarter. Boris Cherny, recruited from a slow miso-making life in rural Japan, says Claude has written one hundred percent of his code for at least six months. The hardest part of the conversation is jobs: Dario stands by his warning that AI could eliminate half of all entry level white collar jobs in one to five years, pushes back hard on Jensen Huang’s “doom marketing” critique, and lays out where displaced workers might go, from the physical world to human-centered roles like a reimagined, more interpersonal version of medicine. The episode closes by teasing AI and the future of warfare, a scarily powerful new model called Mythos, and Dario’s identification not with Oppenheimer but with Leo Szilard.

    Thoughts

    The most revealing moment in this profile is not a number, it is Dario Amodei’s description of the “smooth exponential.” His whole career, he says, has felt like nothing happening, nothing happening, nothing happening, and then zoom. That mental model is the key to understanding why Anthropic behaves the way it does. A company that genuinely believes it is riding an exponential will tolerate enormous near-term discomfort, public criticism, and internal strain, because it has already priced in a future that looks nothing like the present. Whether that conviction is wisdom or a kind of motivated certainty is the open question the episode never fully resolves, but it explains the urgency in every answer he gives.

    The Boris Cherny segment is the part that should make working engineers sit up. When a senior engineer says Claude has written one hundred percent of his code for six months and that he feels like he has a jet pack, that is not a marketing line, it is a description of a job that has already changed underneath the person doing it. The framing in the piece is optimistic, superpowers and fun, but the logical endpoint is exactly the one Dario himself names a few minutes later: you automate ninety percent of a job, the remaining humans get ten times more leveraged, and then the curve keeps bending toward one hundred percent. Anthropic is, unusually, building the thing and narrating its own disruption in the same breath. That honesty is rare, and it is also a little vertiginous.

    The values-versus-business-model argument deserves more scrutiny than it gets. Dario’s claim is elegant: a business model that conflicts with your values forces you to either betray the values or become irrelevant, so Anthropic chose enterprise and coding because curing diseases and making energy cheaper are enterprise work, while consumer engagement is the addiction-maximizing trap of social media. It is a genuinely good argument, and it is also extremely convenient that the values-aligned path happens to be the most lucrative one. The episode lets that tension sit, which is the right call. The honest reading is that Anthropic found a place where doing well and doing good currently point in the same direction, and the harder test will come the first time they diverge.

    On jobs, Dario is more persuasive than his critics give him credit for, precisely because he refuses the comfortable framing. Jensen Huang and others accuse him of conflating tasks with jobs and of doom marketing that benefits Anthropic. Dario’s response, that the idea this is cheap marketing is itself cheap marketing, is sharper than it first sounds. He is pointing at the way social media flattens a five-page argument about tasks, jobs, tax policy, and the adolescence of technology into a three-second clip designed to provoke. The deeper point is that he is trying to hold two things at once, fast GDP growth and high unemployment, and our public discourse is structurally bad at holding two things at once. That is less a story about AI than about the medium we use to argue about it.

    Finally, the Oppenheimer exchange reframes the entire profile. Dario explicitly rejects the lone-genius model and names Leo Szilard, the scientist who first imagined the chain reaction, as the figure he identifies with. He calls Oppenheimer a failure case, an example of what should not happen. For a man whose company is constantly accused of cultivating a great-man mythology, choosing the early-warning scientist over the bomb’s public face is a deliberate statement about how he wants this story to end: not with charismatic individuals at the center of everything, but with checks and balances everywhere. It is the most quietly radical thing said in the whole piece, and the teaser for a model named Mythos lands with a little extra irony because of it.

    Key Takeaways

    • Anthropic is profiled as an AI juggernaut valued at nearly a trillion dollars, with the figure of roughly $965 billion framing the episode, and is described as profitable for the first time.
    • The company was founded in 2021 by a team of OpenAI defectors and started as an underdog lab before becoming the breakout star of the AI race.
    • Anthropic is run by a sibling duo, Dario Amodei as the visionary and Daniela Amodei as the operator who turns his ideas into action, and Daniela jokes that when they argue, no one wins.
    • Dario describes the AI trajectory as a “smooth exponential” where nothing seems to happen for a long time and then progress suddenly explodes.
    • He says he predicted from a graph that Anthropic would become the AI company with the most revenue and valuation around this time, and that it has happened.
    • Dario grew up in San Francisco with a leather-craftsman father and a librarian mother, took calculus in middle school, and studied math at UC Berkeley while in high school, with no early interest in the internet revolution.
    • Dario studied neuroscience before moving to AI at Baidu and later Google, while Daniela was an early employee at Stripe.
    • Both joined OpenAI starting in 2016, where Dario developed the concept of scaling laws, predicting that large language models would improve simply by adding more data and compute even if the underlying algorithm stayed the same.
    • Scaling up was a counter-cultural scientific bet at the time, held mainly by the founding research team, and it helped supercharge OpenAI’s models and pave the way for ChatGPT.
    • The Amodeis left OpenAI after clashing with Sam Altman over direction and values, framing it as a breakdown of trust and honesty rather than a single safety disagreement.
    • Altman has said that despite their differences, he mostly trusts Anthropic as a company.
    • Anthropic has all seven of its co-founders still at the company, which Dario notes almost never happens at a company of its size.
    • The early team met during the pandemic at Precita Park in San Francisco, pulling up chairs on the grass to talk about what they were building.
    • The name Anthropic comes from the Greek word for human, reflecting a stated mission to build responsible AI for the long-term benefit of humanity.
    • Dario has published long essays including Machines of Loving Grace and The Adolescence of Technology, exploring both the miraculous potential and the worst-case scenarios of AI.
    • Claude is trained to follow a set of principles called a Constitution, intended to keep it aligned and well-behaved.
    • Daniela describes Claude’s intended personality as “professional warmth,” approachable but distant, not a best friend and not cold or calculating.
    • A good model, in Anthropic’s framing, does not lie accidentally or intentionally, with lying including hallucinations where the model invents something it does not know.
    • Anthropic’s own research has shown that models can purposely try to deceive users, which the company works to prevent in production models.
    • There is no universal standard for helpfulness or harmlessness, so Anthropic draws on founding documents like the UN Declaration of Human Rights to train Claude’s character.
    • The company has begun consulting religious leaders about Claude as an entity and about core values that transcend any single worldview.
    • Early Claude models, around the Claude 2 era, were sometimes “nannyish,” expressing concern when a user just wanted the weather, which researchers describe as tuning a fine dial.
    • Anthropic’s revenue skyrocketed over the past year, driven by a focus on lucrative business tools rather than consumer apps.
    • Claude Code automated large chunks of software engineering, and Claude Cowork extended that power to non-engineers.
    • Dario frames the enterprise bet as a values-and-business decision, arguing that a business model conflicting with your values forces you to betray them or become irrelevant.
    • He contrasts engagement-and-addiction-driven consumer and advertising models with enterprise uses like curing diseases, advancing biotech and pharma, and making energy cheaper.
    • Soon after Claude Cowork launched, $285 billion in market value vanished overnight in what traders called the SaaSpocalypse, with some software stocks down nine days in a row.
    • Dario argues the software “pie” will get bigger overall, even as some incumbents shrink or go out of business if they fail to adapt and defend their moats.
    • Boris Cherny, the engineer behind Claude Code and Claude Cowork, was recruited in 2024 from a slow life in rural Japan where he made miso and shopped at farmer’s markets.
    • Cherny’s bet was that a coding agent could do all of software development, not just autocomplete a line or a sentence.
    • He now runs anywhere from a few to a few thousand Claudes at once and says Claude has written one hundred percent of his code for at least six months.
    • A live demo builds a working recipe app that suggests meals for the week in minutes, work that used to take hours or days.
    • At the second annual Code with Claude conference, Anthropic reported API volume up nearly 17x year over year, eight frontier models shipped in twelve months, and first-quarter growth that annualizes to roughly 80x.
    • Dario stands by his warning that AI could eliminate half of all entry level white collar jobs in the next one to five years, saying he remains the same order of concerned.
    • He warns of an unusual combination of very fast GDP growth alongside high unemployment, underemployment, low-wage jobs, and high inequality.
    • Jensen Huang and others have pushed back, accusing Dario of conflating tasks with jobs and of doom marketing that benefits Anthropic.
    • Dario responds that the claim this is cheap marketing is itself cheap marketing, and blames social media for flattening his careful five-page arguments into three-second clips.
    • Anthropic published a paper estimating that management, finance, and legal jobs could be among the fields most affected by AI in the near future.
    • Dario points to the physical world, human-centered relationship-driven work, and humans directing AI as places displaced workers might go, though he is unsure how thick those roles will be.
    • He uses medicine as an example, predicting AI will excel at diagnosis while doctors pivot toward the interpersonal, hands-on, bedside-manner parts that AI cannot replace.
    • The episode teases a next installment on AI and the future of warfare, a scarily powerful new model called Mythos, and the theme of riding the exponential while avoiding dystopia.
    • Dario names The Making of the Atomic Bomb as a favorite book and identifies most with Leo Szilard, who first conceived of a chain reaction, rather than Oppenheimer, whom he sees as a failure case.
    • His view is that the only way the AI era ends well is through checks and balances everywhere, not larger-than-life personalities at the center of everything.

    Detailed Summary

    An unlikely AI celebrity and a sibling-run juggernaut

    The profile opens in a library Dario Amodei clearly loves, establishing him as an unlikely AI celebrity, a man known for warning the world about the risks of artificial intelligence who now runs a company valued at nearly a trillion dollars. Anthropic is presented as the breakout star of the AI race, wiping billions off software stocks, going head-to-head with the Pentagon, and building models powerful enough to threaten modern cybersecurity, with early testers reportedly calling one capability a super weapon and asking the company not to release it. Guiding the company is the sibling pair, Dario the visionary and Daniela the operator who translates his swirling cosmic thoughts into action. Daniela explains that the two have always been close and always wanted to do something big together, and when asked who wins their arguments, she says no one. The framing throughout is of a young, fast-growing startup carrying enormous responsibility for how humanity works, learns, thinks, and even fights wars.

    The smooth exponential and the road from OpenAI

    Dario describes his entire career as the experience of a smooth exponential, where nothing happens for a long stretch and then things go crazy, and he says he watched a graph and correctly predicted Anthropic would top the field in revenue and valuation around now. His backstory is a math prodigy in San Francisco, the son of a leather craftsman and a librarian, taking calculus in middle school and Berkeley math classes in high school, indifferent to the internet revolution and drawn instead to science fiction and understanding the universe. Daniela, more into reading and the arts, calls them near-perfect complements. Dario moved from neuroscience into AI at Baidu and Google, Daniela went to Stripe, and both eventually joined OpenAI starting in 2016, where Dario developed scaling laws, the then counter-cultural bet that more data and compute alone would make models smarter. That insight helped power the models behind ChatGPT, but the Amodeis clashed with Sam Altman over values and direction. Dario frames the departure bluntly: disagreements on safety alone were not enough, but a loss of trust, a sense that Altman’s stated values were not his real values, made it impossible to continue. The resolution, he says, was simply to go off and do their own thing.

    Precita Park, the Constitution, and teaching Claude to be good

    Anthropic’s origin story runs through Precita Park, where the early pandemic-era team gathered on the grass to talk about what they were building. Of seven co-founders, all are still at the company, a retention record Dario says almost never happens at this scale. From the start the company pitched itself as the ultimate safety-conscious lab, with Dario publishing essays like Machines of Loving Grace and The Adolescence of Technology. Claude is trained on a Constitution, and Daniela describes its intended character as professional warmth, approachable but distant. Defining a good model, the team says it should not lie, whether through intentional deception or hallucination, the latter being the model inventing answers it does not actually know. Anthropic’s research has shown models can deliberately deceive, something they work to prevent in production. Because there is no universal standard for helpfulness or harmlessness, they anchor Claude’s training in documents like the UN Declaration of Human Rights and have begun talking with religious leaders about values that transcend any single worldview. Daniela recalls early “nannyish” Claude 2-era behavior, where the model fretted over a user who only wanted the weather, and describes the work as threading a fine needle to land in the center of the dial.

    The enterprise bet, Claude Code, and the SaaSpocalypse

    Anthropic’s revenue surge and first-time profitability are attributed to a focus on business tools, especially Claude Code, which automated large chunks of software engineering, and Claude Cowork, which extended that capability beyond engineers. Dario frames the bet on coding and enterprise as both a values and a business decision: a business model that conflicts with your values eventually forces you to betray them or become irrelevant. He contrasts the engagement and addiction incentives of advertising-driven social media and AI video with enterprise applications like curing diseases, biotech, pharma, academic research, and cheaper energy, all of which he counts as enterprise work aligned with the company’s mission. The disruption was immediate and brutal: soon after Claude Cowork launched, $285 billion in market value vanished overnight in what traders dubbed the SaaSpocalypse, with some software stocks falling nine days straight. Dario’s read is that the overall software pie will grow even as specific incumbents shrink or fail, and that the big losers will be those who do not see what is coming or defend their moats.

    Boris Cherny, jet packs, and Code with Claude

    Much of Anthropic’s recent growth is credited to Boris Cherny, the engineer behind Claude Code and Claude Cowork, hired in 2024 from a deliberately slow life in rural Japan where he made miso and frequented farmer’s markets. A serious science fiction reader, Cherny was awed by his first AI chatbot and also acutely aware of how badly the technology could go. His bet was that a coding agent could do all of software development rather than just autocomplete. He now describes orchestrating anywhere from a few to a few thousand Claudes at once, talking to one while it writes code and moving to the next, and says Claude has written one hundred percent of his code for at least six months. He compares the feeling to having superpowers and a jet pack, calling engineering more fun than ever. A live demo has Claude build a working weekly-meal recipe app in minutes. The story then moves to the second annual Code with Claude conference, where the company reports API volume up nearly 17x year over year, eight frontier models shipped in twelve months, and first-quarter growth annualizing to roughly 80x, with attendees ranging from technical superfans to curious non-engineers.

    Jobs, the tasks-versus-jobs fight, and a more human medicine

    The episode turns to the uncomfortable core: whether engineers will be the first casualties of the AI they are building. Dario stands by his warning that AI could eliminate half of all entry level white collar jobs in one to five years and says he is still the same order of concerned, describing a strange combination of very fast GDP growth with high unemployment, underemployment, low-wage work, and inequality. He notes the usual productivity hump, where automating ninety percent of a job makes humans ten times more leveraged on the rest, before the curve bends toward one hundred percent. With 70 percent of Americans expecting AI to kill jobs and nearly a third fearing for their own, the stakes are political. Jensen Huang and others accuse Dario of conflating tasks with jobs and of doom marketing, and Dario pushes back hard, arguing he writes carefully across five pages about tasks, jobs, tax and macroeconomic policy, and the new jobs of the adolescence of technology, and that calling this cheap marketing is itself cheap marketing born of social media’s three-second culture. Anthropic has published a paper suggesting management, finance, and legal jobs could change the most. Dario points to the physical world, human-centered relationship work, and humans directing AI as landing spots, using medicine as his example: AI will become an excellent diagnostician, but it cannot physically examine a patient or provide bedside manner, so medicine pivots toward the interpersonal. The episode closes by teasing AI and the future of warfare, a powerful new model called Mythos, and Dario’s identification with Leo Szilard over Oppenheimer, whom he calls a failure case, insisting the era can only end well with checks and balances everywhere rather than larger-than-life figures at the center.

    Notable Quotes

    “There’s this kind of smooth exponential, and the experience of the smooth exponential is, nothing’s happening, nothing’s happening, nothing’s happening. Little things happen, and then zoom, it goes crazy.”

    Dario Amodei, on how AI progress actually feels from the inside

    “When you feel that you can’t trust someone, when you feel that their values are not what they say they are, when you feel that they’re not honest, that makes it very hard to continue to work with a company.”

    Dario Amodei, on why he and Daniela left OpenAI

    “Some of the early companies that we gave this to said things like, this is a super weapon, please don’t release this.”

    Anthropic, on early reactions to one of its more powerful models

    “I like to describe it as professional warmth. So the goal is not for it to be your best friend, but it’s not for it to be sort of cold, rote, calculating.”

    Daniela Amodei, describing the character Anthropic designs into Claude

    “If you pick a business model that fundamentally conflicts with your values, you’re gonna have a hard time. Either you betray your own values or you become irrelevant.”

    Dario Amodei, on why Anthropic bet on enterprise and coding

    “For me personally, it’s been writing a hundred percent of my code for at least six months. The work of engineering has just completely changed.”

    Boris Cherny, the engineer behind Claude Code and Claude Cowork

    “I feel like I suddenly have superpowers. I have like a jet pack and the engineering has never been this fun.”

    Boris Cherny, on building software with Claude Code

    “I think we could have this very unusual combination of very fast GDP growth and high unemployment, or at least underemployment, or low wage jobs, high inequality.”

    Dario Amodei, on the economic shock he is most worried about

    “The idea that this is cheap marketing is itself cheap marketing. I think it’s part of the disease of Silicon Valley.”

    Dario Amodei, responding to the doom-marketing accusation

    “The figure I most identified with was Leo Szilard, who was the one who first had the idea that there could be a chain reaction.”

    Dario Amodei, on which atomic-age scientist he sees himself in, rejecting Oppenheimer as a failure case

    Watch the full episode of The Circuit inside Anthropic here.

    Related Reading

    • Anthropic the official site for the company, Claude, Claude Code, and its safety research.
    • Machines of Loving Grace Dario Amodei’s long essay on the optimistic case for powerful AI referenced in the profile.
    • Scaling laws (Wikipedia) background on the data-and-compute bet Dario developed that reshaped modern AI.
    • Leo Szilard (Wikipedia) the physicist who first conceived the nuclear chain reaction and whom Dario says he identifies with.
    • Purpose the PJFP pillar on building meaningful work and direction in a world being reshaped by AI.

  • Krishna Rao on Anthropic Going From 9 Billion to 30 Billion ARR in One Quarter and the Compute Strategy Powering Claude

    Krishna Rao, Chief Financial Officer of Anthropic, sat down with Patrick O’Shaughnessy on Invest Like the Best for one of the most detailed public looks yet at the operating engine behind Claude. He covers how Anthropic compounded from $9 billion of run rate revenue at the start of the year to north of $30 billion by the end of Q1, why he spends 30 to 40 percent of his time on compute, the playbook for buying gigawatts of AI infrastructure across Trainium, TPU, and GPU platforms, how Anthropic prices its models, why returns to frontier intelligence keep climbing, and what the Mythos release tells us about the cyber capabilities of the next generation of Claude.

    TLDW

    Anthropic is running the most compute fungible frontier lab in the world, with active deployments across AWS Trainium, Google TPU, and Nvidia GPU, and an internal orchestration layer that lets a chip serve inference in the morning and run reinforcement learning the same evening. Krishna Rao explains the cone of uncertainty that governs gigawatt scale compute procurement, the floor Anthropic refuses to drop below on model development compute, the Jevons paradox unlock from cutting Opus pricing, the 500 percent annualized net dollar retention from enterprise customers, the layer cake of long term deals with Google, Broadcom, Amazon, and the recent xAI Colossus tie up in Memphis, the phased release of the Mythos model in response to spiking cyber capabilities, the internal use of Claude Code to produce statutory financial statements and run a Monthly Financial Review skill, and why the team believes scaling laws are alive and well. The interview also covers fundraising history through Series D and Series E, the $75 billion already raised plus another $50 billion coming, talent density beating talent mass during the Meta poaching wave, and Rao’s belief that biotech and drug discovery represent the most exciting frontier for AI.

    Key Takeaways

    • Anthropic entered the year with about $9 billion of run rate revenue and ended the first quarter with north of $30 billion of run rate revenue, a more than 3x leap driven by model intelligence gains and the products built around them.
    • Compute is described as the lifeblood of the company, the canvas everything else is built on, and the most consequential class of decisions Rao makes. Buy too much and you go bankrupt. Buy too little and you cannot serve customers or stay at the frontier.
    • Rao spends 30 to 40 percent of his time on compute, even today, and the leadership team meets repeatedly on both procurement and ongoing compute allocation.
    • Anthropic is the only frontier language lab actively using all three major chip platforms in production: AWS Trainium, Google TPU, and Nvidia GPU. It is also the only major model available on all three clouds.
    • Flexibility is the central design principle. Anthropic builds flexibility into the deals themselves, into the orchestration layer that maps workloads to chips, and into compilers built from the chip level up.
    • The cone of uncertainty frames procurement. Small differences in weekly or monthly growth compound into wildly different two year outcomes, so the team plans across a range of scenarios rather than a single point estimate, and ranges toward the upper end while protecting downside.
    • Compute allocation across the company sits in three buckets: model development and research, internal employee acceleration, and external customer serving. A non negotiable floor protects model development even when customer demand is tight.
    • Anthropic estimates that if it cut off internal employee use of its own models, the freed compute could serve billions of dollars of additional revenue. It chooses not to, because internal use compounds into better future models.
    • Intelligence is multi dimensional, not a single IQ score. Anthropic measures real world capability through customer feedback, long horizon task performance, tool use, computer use, and speed at agentic tasks, not just leaderboard benchmarks that have largely saturated.
    • Each Opus generation, 4 to 4.5 to 4.6 to 4.7, delivers both capability improvements and an efficiency multiplier on token processing. New models often serve customers at a fraction of the prior cost while doing more.
    • Reinforcement learning is described as inference inside a sandbox with a reward function, so model efficiency gains directly improve internal RL throughput. The flywheel is tightly coupled.
    • Over 90 percent of code at Anthropic is now written by Claude Code, and a large share of Claude Code itself is written by Claude Code.
    • Anthropic shipped roughly 30 distinct product and feature releases in January and the pace has accelerated since.
    • Scaling laws, in Anthropic’s internal data, are alive and well. The team holds itself to a skeptical scientific standard and still does not see them slowing down.
    • Anthropic recently signed a 5 gigawatt deal with Google and Broadcom for TPUs starting in 2027, plus an Amazon Trainium agreement for up to 5 gigawatts, totaling more than $100 billion in commitments. A significant portion lands this year and next year.
    • A new partnership for capacity at the xAI Colossus facility in Memphis was announced just before the interview, aimed at expanding consumer and prosumer capacity.
    • Pricing has been remarkably stable across Haiku, Sonnet, and Opus. The biggest deliberate change was lowering Opus pricing, which produced a textbook Jevons paradox: consumption rose far faster than the price drop, and the new Opus 4.6 and 4.7 slot in at the same price point.
    • Mythos is the first model Anthropic chose to release in a phased way because of a sharp spike in cyber capability. In an open source codebase where a prior model found 22 security vulnerabilities, Mythos found roughly 250.
    • The Mythos release framework focuses on defensive use first, expands access over time, and is presented as a template for future capability spikes.
    • Anthropic now sells to 9 of the Fortune 10 and reports net dollar retention above 500 percent on an annualized basis. These are not pilots. Rao describes signing two double digit million dollar commitments during a 20 minute Uber ride to the studio.
    • The platform strategy is mostly horizontal. Anthropic will go vertical with offerings like Claude for Financial Services, Claude for Life Sciences, and Claude Security where it can demonstrate the model’s capabilities, but expects most application value to accrue to customers building on top.
    • Investors raised over $75 billion in equity since Rao joined, with another $50 billion in commitments tied to the Amazon and Google deals. Capital intensity is real, but the raises fund the upper end of the cone of uncertainty more than they fund current losses.
    • The Series E close coincided with the day the DeepSeek news broke, forcing investors to reassess their AI thesis in real time. Anthropic closed the round anyway.
    • Inside finance, Claude now produces statutory financial statements for every Anthropic legal entity, with a human checker. A library of more than 70 finance specific skills underpins workflows.
    • A custom Monthly Financial Review skill produces a 90 to 95 percent ready monthly close report, so leadership discussion shifts from reconciling numbers to debating implications.
    • An internal real time analytics platform called Anthrop Stats compresses weekly insight cycles from hours to about 30 minutes.
    • The biggest token user inside Anthropic’s finance team is the head of tax, focused on tax policy engines and workflow automation. The most senior people, not the youngest, are leading internal adoption.
    • Talent density beats talent mass. When Meta and others ran aggressive offer waves, Anthropic lost two people while peer labs lost dozens.
    • All seven Anthropic co founders remain at the company, as does most of the first 20 to 30 employees, which Rao credits to a collaborative, transparent, debate friendly culture and a real culture interview that can veto otherwise top tier candidates.
    • Dario Amodei holds an open all hands every two weeks, writes a short prepared document, and takes unscripted questions from anyone at the company.
    • AI safety investments in interpretability and alignment have a commercial side effect. Looking inside the model helps Anthropic build better models, and enterprises selling sensitive workloads want to trust the lab they hand customer data to.
    • Anthropic explicitly identifies as America first in its approach to model development, and engages closely with the US administration on capability releases such as Mythos.
    • The longer term product vision is the virtual collaborator: an agent with organizational context, access to the company’s tools, persistent memory, and the ability to work on ideas, not just tasks, over long horizons.
    • CoWork, Anthropic’s extension of the Claude Code paradigm into general knowledge work, is being adopted faster than Claude Code itself when indexed to the same point in its launch curve.
    • Anthropic’s product teams ship daily, with a fleet of agents working across the company on specific tasks. Everyone effectively becomes a manager of agents.
    • The dominant downside risks to Anthropic’s high end forecast are slower customer diffusion of model capability into real workflows, scaling laws flattening unexpectedly, and Anthropic losing its position at the frontier.
    • Rao is most excited about biotech and healthcare outcomes, especially the prospect that AI could push drug discovery and lab throughput up 10x or 100x, turning currently incurable diagnoses into treatable ones within a patient’s lifetime.

    Detailed Summary

    Compute as Lifeblood and the Cone of Uncertainty

    Rao opens with the claim that compute is the most important resource at Anthropic, and the most consequential decision class in the company. You cannot buy a gigawatt of compute next week. You have to anticipate demand a year or two in advance, and the cost of being wrong in either direction is high. Buy too much and the unit economics collapse. Buy too little and you cannot serve customers or stay at the frontier, which are described as the same failure mode. To navigate this, the team uses a cone of uncertainty rather than point estimates. Small differences in weekly growth compound into vastly different two year outcomes, and Anthropic tries to position itself toward the upper end of that cone while preserving optionality. Rao notes he has had to consciously break a lifetime of linear thinking and force himself into exponential models.

    Three Chip Platforms, One Orchestration Layer

    Anthropic uses Amazon’s Trainium, Google’s TPUs, and Nvidia’s GPUs fungibly. That was not free. Adopting TPUs at scale started around the third TPU generation, when outside observers thought it was a strange choice. Anthropic invested years into compilers and orchestration so workloads can flow across chips by generation and by job type. The team works deeply with Annapurna Labs at AWS to influence Trainium roadmaps because Anthropic stresses these chips harder than almost anyone. The result is what Rao believes is the most efficient utilization of compute across any frontier lab, with a dollar of compute going further inside Anthropic than anywhere else.

    Three Buckets and the Model Development Floor

    Compute gets allocated across model development, internal acceleration of employees, and customer serving. The conversations are collaborative rather than zero sum, but there is a hard floor on model development that the company refuses to cross even if it makes customer demand harder to serve in the short term. The thesis is simple. The returns to frontier intelligence are extremely high, especially in enterprise, so cutting model investment to chase near term revenue is a bad trade. Internal employee use is also explicitly protected. Rao notes that diverting that internal usage to external customers would unlock billions of additional revenue today, but the compounding benefit of accelerating researchers and engineers outweighs that.

    Intelligence Is Multi Dimensional

    Rao pushes back hard on the IQ framing of model progress. Benchmarks saturate quickly, and the real signal comes from how customers actually use the models. Anthropic looks at long horizon task completion, tool use, computer use, and time to result on agentic tasks. Two equally capable agents who differ only in speed produce dramatically different value, because the faster one compounds into more attempts and more outcomes. Frontier model leaps are also fuel efficient. The sedan to sports car analogy breaks down because each Opus generation, 4 to 4.5 to 4.6 to 4.7, delivers a step up in capability and a multiplier on per token efficiency.

    From 9 Billion to 30 Billion ARR in One Quarter

    The headline number for the quarter is a leap from about $9 billion of run rate revenue to over $30 billion, accomplished without onboarding a corresponding step up in compute, because new compute lands on ramps locked in 12 months prior. Rao attributes the leap to model capability gains, products that surface that intelligence in usable form factors, and an enterprise customer base that pulls more workloads onto Claude as each generation unlocks new use cases. Coding started the wave with Sonnet 3.5 and 3.6, and the same pattern is now playing out elsewhere in the economy.

    Recursive Self Improvement and Talent Density

    Over 90 percent of Anthropic’s code is now written by Claude Code, including most of Claude Code itself. Rao describes this as a structural reason to keep allocating internal compute to employees even when external demand is hungry. Recursive self improvement is not happening through models that need no humans. It is happening through researchers who set direction and use frontier models to compress months of work into days. Talent density beats talent mass. When Meta and other labs went after Anthropic researchers with very large packages, Anthropic lost two people while peer labs lost dozens.

    Procurement Strategy and the Layer Cake

    Compute lands as a layer cake. Last month Anthropic signed a 5 gigawatt TPU deal with Google and Broadcom starting in 2027, alongside an Amazon Trainium agreement for up to 5 gigawatts. The total is north of $100 billion in commitments. A new tie up with xAI’s Colossus facility in Memphis was announced just before the interview, intended for nearer term capacity to support consumer and prosumer growth. Anthropic evaluates near term and long term compute deals against the same set of variables: price, duration, location, chip type, and how efficiently the team can run it. The relationships are deeper than procurement. The hyperscalers are also distribution channels for the model.

    Platform First, Selective Vertical Bets

    Rao describes Anthropic as a platform first business, with most expected value accruing to customers building on the platform. The team will only go vertical when it can either demonstrate capabilities that are skating to where the puck is going, like Claude Code did before the models could fully support it, or when it wants to set a template for an industry vertical, as with Claude for Financial Services, Claude for Life Sciences, and Claude Security. He acknowledges that surprise capability jumps make customers anxious about the platform competing with them, and frames Anthropic’s mitigation as deeper partnerships, early access programs, and an emphasis on accelerating customer building rather than disintermediating it.

    Pricing, Jevons Paradox, and Return on Compute

    Pricing across Haiku, Sonnet, and Opus has been stable. The notable exception is Opus, which Anthropic deliberately repriced lower when launching Opus 4.5 because Opus class problems were being squeezed into Sonnet workloads. Efficiency gains made it possible to serve Opus profitably at the new level. The consumption response was a classic Jevons paradox, with usage rising far more than the price reduction would have predicted, and Opus 4.6 then slotted in at the same price with a capability bump. Margins are not framed as a per token markup. Compute is fungible across model development, internal acceleration, and customer serving, so Anthropic measures return on the entire compute envelope rather than software style variable cost per call.

    Fundraising, DeepSeek, and Capital Intensity

    Rao joined while Anthropic was closing its Series D, mid frontier model launch and during the FTX share liquidation. Investors initially questioned whether Anthropic needed a frontier model, whether AI safety and a real business could coexist, and why the sales team was so small. The Series E closed the same day the DeepSeek news broke, with markets violently re pricing AI in real time. Since Rao joined, Anthropic has raised over $75 billion, with another $50 billion tied to the Amazon and Google compute deals. The reason for the size of the raises is the cone of uncertainty, not current losses. Returns on compute today are described as robust.

    Mythos, Cyber Capability, and Phased Releases

    The Mythos release marks the first time Anthropic shipped a model under a deliberately phased rollout because of a specific capability spike. Cyber is the dimension that spiked. Where a prior model found 22 vulnerabilities in an open source codebase, Mythos found roughly 250. The defensive applications, automatically patching massive codebases, are genuinely valuable, but the offensive risk is real enough that Anthropic chose to release to a smaller group first and expand access over time. Rao positions this as a template for future capability spikes, not a permanent restriction. He also describes the relationship with the US administration as cooperative, including the Department of War interaction, with Anthropic supporting a regulatory framework that does not strangle innovation but takes responsibility seriously.

    Claude Inside Finance

    Anthropic’s finance team is one of the strongest internal case studies. Statutory financial statements for every legal entity are produced by Claude, with a human reviewer. A skill library of more than 70 finance specific skills underpins a Monthly Financial Review skill that drafts the monthly close at 90 to 95 percent ready, so leadership meetings shift from explaining the numbers to discussing what to do about them. An internal analytics platform called Anthrop Stats compresses weekly insight cycles from hours to 30 minutes. The biggest internal token user in finance is the head of tax, building policy engines, which Rao highlights as evidence that adoption is driven by the most senior people, not just younger engineers.

    Culture, Co Founders, and the Race to the Top

    Seven co founders should not, on paper, work as a leadership group. Rao argues it works because the culture was set early around collaboration, intellectual honesty, transparency, and humility. The culture interview is a real veto, not a checkbox. Dario Amodei runs an all hands every two weeks with a short written piece followed by unscripted questions, and decisions, once made, get clean alignment rather than residual politics. Anthropic frames its approach as a race to the top, where being a model for how to build the technology responsibly is itself a recruiting and retention advantage.

    The Virtual Collaborator and the Frontier Ahead

    The product vision Rao describes is the virtual collaborator. Not just a smarter chatbot, but an agent with organizational context, access to the company’s tools, memory, and the ability to work on ideas over long horizons. Coding was the first domain to feel this, but CoWork, Anthropic’s extension of the Claude Code pattern into general knowledge work, is being adopted faster than Claude Code was at the same age. Product development inside Anthropic already looks different. Teams ship daily, with fleets of agents working across the company, and individual humans increasingly act as managers of those fleets.

    Downside Risks and What Excites Him Most

    The three risks Rao names if asked to do a premortem on a softer year are slower customer diffusion of model capability into real workflows, scaling laws unexpectedly flattening, and Anthropic losing its frontier position to competitors. None of these are observed today, but he is unwilling to claim them with certainty. On the upside, he is most excited about biotech and healthcare. Lab throughput rising 10x or 100x, paired with AI assisted clinical workflows, could turn currently incurable diagnoses into treatable ones within a patient’s lifetime. That is the outcome he wants the technology to chase.

    Thoughts

    The most consequential structural point in this interview is the framing of compute as a single fungible resource pool measured by return on the entire envelope, not as a variable cost per inference call. That accounting shift, if you accept it, breaks most of the bear cases about AI lab unit economics. The bear argument almost always assumes that a token served to a customer is the only thing the chip did that day. Rao’s version is that the same fleet trains models in the morning, runs reinforcement learning at lunch, serves customers in the afternoon, and accelerates internal engineers in the evening. If even half of that is real, the right comparison is total compute spend versus total enterprise value created by the platform, and on that ratio Anthropic looks structurally strong rather than weak.

    The Jevons paradox on Opus pricing is the most actionable insight for anyone running an AI product. Most teams default to either chasing premium pricing on the newest model or undercutting to chase volume. Anthropic did something more disciplined: it left Sonnet and Haiku alone, dropped Opus when efficiency gains made it serveable, and watched aggregate usage rise faster than the price cut. The lesson is that frontier model pricing is not really a price problem. It is a capability access problem, and elasticity around the right tier is much higher than the standard SaaS playbook implies.

    The Mythos cyber jump deserves more attention than it has gotten. Going from 22 to 250 vulnerabilities found in the same codebase is the kind of capability discontinuity that genuinely changes the regulatory calculus. Anthropic is signaling that it can identify these discontinuities ahead of release and choose a deployment shape that respects them. Whether peer labs adopt similar discipline is the open question. Anthropic’s race to the top framing assumes they will be forced to. The competitive market may say otherwise.

    The hiring data point is the most underrated investor signal. Two departures while peer labs lost dozens, during the most aggressive talent war in tech history, is not a culture poster. It is a structural advantage that compounds every time another lab tries to buy its way to the frontier. Money can be matched. Conviction in the mission, transparent leadership, and a culture interview that can veto otherwise stellar candidates cannot. If you believe scaling laws hold, talent retention at this density is one of the few moats that actually scales with capital.

    Finally, the most interesting personal admission is that Krishna Rao, a finance leader trained at Blackstone and Cedar, is openly telling investors that linear thinking is the failure mode he had to break out of. The companies that pattern match this moment to prior technology waves are mispricing it, in both directions. The cone of uncertainty Anthropic uses internally is the right metaphor for everyone else too. If you are forecasting AI as if it is cloud in 2010, you are almost certainly wrong, and the magnitude of the error is much larger than it would be in any prior era.

    Watch the full conversation with Krishna Rao on Invest Like the Best here.

  • Dwarkesh Patel: From Podcasting Prodigy to AI Chronicler with The Scaling Era

    TLDW (Too Long; Didn’t Watch)

    Dwarkesh Patel, a 24-year-old podcasting sensation, has made waves with his deep, unapologetically intellectual interviews on science, history, and technology. In a recent Core Memory Podcast episode hosted by Ashlee Vance, Patel announced his new book, The Scaling Era: An Oral History of AI, co-authored with Gavin Leech and published by Stripe Press. Released digitally on March 25, 2025, with a hardcover to follow in July, the book compiles insights from AI luminaries like Mark Zuckerberg and Satya Nadella, offering a vivid snapshot of the current AI revolution. Patel’s journey from a computer science student to a chronicler of the AI age, his optimistic vision for a future enriched by artificial intelligence, and his reflections on podcasting as a tool for learning and growth take center stage in this engaging conversation.

    At just 24, Dwarkesh Patel has carved out a unique niche in the crowded world of podcasting. Known for his probing interviews with scientists, historians, and tech pioneers, Patel refuses to pander to short attention spans, instead diving deep into complex topics with a gravitas that belies his age. On March 25, 2025, he joined Ashlee Vance on the Core Memory Podcast to discuss his life, his meteoric rise, and his latest venture: a book titled The Scaling Era: An Oral History of AI, published by Stripe Press. The episode, recorded in Patel’s San Francisco studio, offers a window into the mind of a young intellectual who’s become a key voice in documenting the AI revolution.

    Patel’s podcasting career began as a side project while he was a computer science student at the University of Texas. What started with interviews of economists like Bryan Caplan and Tyler Cowen has since expanded into a platform—the Lunar Society—that tackles everything from ancient DNA to military history. But it’s his focus on artificial intelligence that has garnered the most attention in recent years. Having interviewed the likes of Dario Amodei, Satya Nadella, and Mark Zuckerberg, Patel has positioned himself at the epicenter of the AI boom, capturing the thoughts of the field’s biggest players as large language models reshape the world.

    The Scaling Era, co-authored with Gavin Leech, is the culmination of these efforts. Released digitally on March 25, 2025, with a print edition slated for July, the book stitches together Patel’s interviews into a cohesive narrative, enriched with commentary, footnotes, and charts. It’s an oral history of what Patel calls the “scaling era”—the period where throwing more compute and data at AI models has yielded astonishing, often mysterious, leaps in capability. “It’s one of those things where afterwards, you can’t get the sense of how people were thinking about it at the time,” Patel told Vance, emphasizing the book’s value as a time capsule of this pivotal moment.

    The process of creating The Scaling Era was no small feat. Patel credits co-author Leech and editor Rebecca for helping weave disparate perspectives—from computer scientists to primatologists—into a unified story. The first chapter, for instance, explores why scaling works, drawing on insights from AI researchers, neuroscientists, and anthropologists. “Seeing all these snippets next to each other was a really fun experience,” Patel said, highlighting how the book connects dots he’d overlooked in his standalone interviews.

    Beyond the book, the podcast delves into Patel’s personal story. Born in India, he moved to the U.S. at age eight, bouncing between rural states like North Dakota and West Texas as his father, a doctor on an H1B visa, took jobs where domestic talent was scarce. A high school debate star—complete with a “chiseled chin” and concise extemp speeches—Patel initially saw himself heading toward a startup career, dabbling in ideas like furniture resale and a philosophy-inspired forum called PopperPlay (a name he later realized had unintended connotations). But it was podcasting that took off, transforming from a gap-year experiment into a full-fledged calling.

    Patel’s optimism about AI shines through in the conversation. He envisions a future where AI eliminates scarcity, not just of material goods but of experiences—think aesthetics, peak human moments, and interstellar exploration. “I’m a transhumanist,” he admitted, advocating for a world where humanity integrates with AI to unlock vast potential. He predicts AI task horizons doubling every seven months, potentially leading to “discontinuous” economic impacts within 18 months if models master computer use and reinforcement learning (RL) environments. Yet he remains skeptical of a “software-only singularity,” arguing that physical bottlenecks—like chip manufacturing—will temper the pace of progress, requiring a broader tech stack upgrade akin to building an iPhone in 1900.

    On the race to artificial general intelligence (AGI), Patel questions whether the first lab to get there will dominate indefinitely. He points to fast-follow dynamics—where breakthroughs are quickly replicated at lower cost—and the coalescing approaches of labs like xAI, OpenAI, and Anthropic. “The cost of training these models is declining like 10x a year,” he noted, suggesting a future where AGI becomes commodified rather than monopolized. He’s cautiously optimistic about safety, too, estimating a 10-20% “P(doom)” (probability of catastrophic outcomes) but arguing that current lab leaders are far better than alternatives like unchecked nationalized efforts or a reckless trillion-dollar GPU hoard.

    Patel’s influences—like economist Tyler Cowen, who mentored him early on—and unexpected podcast hits—like military historian Sarah Paine—round out the episode. Paine, a Naval War College scholar whose episodes with Patel have exploded in popularity, exemplifies his knack for spotlighting overlooked brilliance. “You really don’t know what’s going to be popular,” he mused, advocating for following personal curiosity over chasing trends.

    Looking ahead, Patel aims to make his podcast the go-to place for understanding the AI-driven “explosive growth” he sees coming. Writing, though a struggle, will play a bigger role as he refines his takes. “I want it to become the place where… you come to make sense of what’s going on,” he said. In a world often dominated by shallow content, Patel’s commitment to depth and learning stands out—a beacon for those who’d rather grapple with big ideas than scroll through 30-second blips.