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  • OpenCode CEO Jay V on 20x Growth in 6 Months: 13 Million Users, 7 Trillion Tokens a Day, the Anthropic Block That Backfired, and the 16-Year Road to Overnight Success

    In this episode of Y Combinator’s Lightcone podcast, Jay V, founder and CEO of OpenCode, the open-source coding agent that works with any model, walks through one of the wildest growth stories in developer tools: 650,000 monthly active users in January to roughly 13 million by June, 7 trillion tokens processed per day, and a business that went from zero to a $40 million revenue run rate in about eight months. He also tells the part almost nobody knows: the company behind this “overnight success” is a 16-year-old legal entity that applied to Y Combinator nine times before getting in.

    TLDW

    Jay V explains how OpenCode grew 20x in six months to around 13 million monthly active users and 4.6 million weekly actives, processing 7 trillion tokens a day (more than OpenRouter’s entire volume), with an inference business annualizing near $40 million plus 160,000 subscribers worth another $18 million. The inflection point came when Anthropic started blocking Claude Code subscriptions inside OpenCode by rejecting requests whose system prompt contained the words “open code,” which backfired by equating the two products and sending curious users flooding in, shortly after which OpenAI’s Codex officially supported OpenCode. The conversation covers OpenCode’s public usage data (DeepSeek Flash dominating token volume despite GLM hype), a global user base led by China at 17% with heavy usage in Indonesia, Brazil, and Vietnam, Fortune 500 companies discovering thousands of employees already using the tool, the shift from ad-based CAC to token-based CAC, the flat 24-hour GPU utilization curve that comes from serving the whole planet, the “betting the field” marketplace thesis on model commoditization, and the founder’s 16-year, nine-application journey from a Waterloo dorm through SST, OpenNext, and selling coffee over SSH to finally catching lightning.

    Thoughts

    The Anthropic block is the most instructive growth story in the episode, because it is a perfect modern Streisand effect. Anthropic had a defensible reason to stop subsidized Claude Code subscriptions from flowing through a third-party harness, but the implementation (rejecting any request whose system prompt literally contained “open code”) turned a quiet policy decision into a public endorsement. As Jay puts it, the block placed OpenCode on the same pedestal as Claude Code in the minds of developers who had never heard of it. The hosts’ Instacart comparison is apt: when Amazon bought Whole Foods, the “death of Instacart” meme drove every grocer in America into Instacart’s arms. Incumbents keep learning this lesson the hard way. You cannot block a product without simultaneously advertising that it matters.

    The deeper story is geographic. Silicon Valley talks about coding agents as if the $200-per-month power user is the market, and Jay’s data says the opposite. China alone is 17% of OpenCode’s usage, with Indonesia, Brazil, and Vietnam each carrying meaningful share, places where a frontier subscription costs more than rent. OpenCode’s $10 Go plan, running DeepSeek and GLM instead of Sonnet and Opus, is how billions of developers will actually have their first coding-agent moment. There is also a hard operational edge hiding in that distribution: because the East works while the West sleeps, OpenCode’s GPU utilization runs a nearly flat 24-hour cycle, which quietly improves unit economics in a way no single-market competitor can match. Serving the whole planet is not just a mission statement. It is a margin strategy.

    OpenCode’s neutrality is turning into one of the most valuable datasets in AI. Because the product is a harness over every model rather than a storefront for one lab, opencode.ai/data shows what developers actually run when they are spending their own money, and it routinely contradicts the Twitter narrative. GLM was supposedly eating DeepSeek’s lunch; the token-volume charts show DeepSeek Flash dipping and then bouncing right back. Users are not loyal, they are rational: they ride frontier limits until they hit caps, then switch to models cheap and fast enough to finish the day’s work. That behavioral reality, boring cost optimization rather than fandom, is what the model market actually looks like once the marketing fog clears, and only a neutral aggregator gets to see it.

    The business model inversion deserves more attention than it usually gets. In the last era, customer acquisition cost meant ads. In this one, it means tokens: the free tier is the marketing budget, spent on giving people the aha moment, and the payoff comes when a fraction of those users become whales paying per token, where OpenCode’s volume discounts become margin. This is the same funnel Anthropic and OpenAI run, except the frontier labs subsidize with investor billions while OpenCode rides the falling cost curve of open-weight models. The enterprise motion follows the same bottoms-up physics: no procurement dance, just inbound emails saying thousands of our employees are already using you, please sign the security questionnaire. That is the purest product-market-fit signal that exists.

    And then there is the 16-year overnight success. Same legal entity since 2010, same two founders from a Waterloo dorm room, nine YC applications and four interviews before acceptance in 2021, years of living with parents and running out of money, a serverless framework, a coffee shop that ran over SSH. Every “dead end” turns out to have been training: the consumer company taught metrics discipline, SST taught open source and building in public, the terminal storefront taught terminal-UI craft that made OpenCode instantly credible with the Neovim crowd. The hosts land the right conclusion: lightning did strike, but the founders spent a decade positioning the bottle. In an industry currently obsessed with six-month-old unicorns, this episode is a useful reminder that most of them are carrying more history than the headline suggests.

    Key Takeaways

    • OpenCode ended June 2026 at roughly 13 million monthly active users and 4.6 million weekly actives, close to Codex’s numbers, a 20x increase from about 650,000 monthly actives at the start of the year.
    • The platform now processes around 7 trillion tokens per day, more than OpenRouter’s total of roughly 6 trillion, up from about 300 billion per day at the beginning of the year.
    • The pay-per-token inference business, launched around late September 2025, annualizes to $31-33 million on June data and $38-40 million on the most recent week, roughly eight months from zero.
    • The subscription product launched in late February has grown to about 160,000 monthly subscribers, roughly $18 million in annualized revenue on top of inference.
    • A Codex lead engineer publicly noted that about 5% of all Codex subscribers use OpenCode as their main harness, and OpenAI officially supports Codex subscriptions inside OpenCode.
    • In the first week of January, Anthropic began blocking Claude Code subscriptions in OpenCode by rejecting any request whose system prompt contained the words “open code.”
    • Jay concedes the block made business sense (Anthropic subsidizes that usage) but says it inadvertently equated OpenCode with Claude Code and drove waves of new users to investigate the product.
    • The hosts compare it to Amazon buying Whole Foods: the “death of Instacart” meme drove every grocer in America to sign with Instacart, fueling its growth instead of killing it.
    • The founding premise is that most people in the world still have not experienced the magic of a coding agent, and frontier per-token prices put that moment out of reach for much of the globe.
    • When OpenCode launched in June 2025 the pitch was using your Claude Code subscription in a better terminal UI; by August and September the first credible open-source models (GLM, Kimi, MiniMax) arrived, roughly six months behind the frontier.
    • February 2026 marked the first four-week span in OpenCode’s data where users ran Gemini more than the Anthropic models (Sonnet plus Opus combined), which convinced the team the non-Anthropic models were ready for real work and triggered the subscription launch.
    • OpenCode publishes its usage data at opencode.ai/data, covering the Go plan where $10 a month buys access to open-source models.
    • DeepSeek Flash leads token volume per day, with the two DeepSeek models plus GLM as the top three, despite social media chatter suggesting GLM had overtaken DeepSeek.
    • By unique users the top models run DeepSeek Flash at about 38,000, DeepSeek Pro at 31,000, and GLM 5.2 near 30,000.
    • A key usage pattern: as users approach daily or weekly limits on premium models, they switch to very cheap models like DeepSeek Flash to finish their work, extending how much coding-agent time their budget buys.
    • Speed matters too: some open models are hosted with far higher tokens-per-second than alternatives, making the agent feel near real time, and users perceive quality niches, like GLM 5.2 being better at front-end design.
    • China is OpenCode’s largest market at 17% of usage, which the hosts note may make it the only YC company in history with meaningful usage in China, partly because Chinese developers want to run Chinese models and OpenCode gives them that choice.
    • Developing countries are huge: Indonesia at 4% of traffic, Brazil at 5%, plus Vietnam and similar markets where a $200-a-month Claude Code subscription is prohibitively expensive.
    • The US, which the team was not even targeting with the Go plan, is growing strongly anyway, which Jay reads as a broader vibe shift toward token budgeting even among Americans.
    • Large US companies with effectively unlimited token budgets also adopted OpenCode early because they did not want to be locked into a specific model or harness.
    • Dozens of forward-leaning Fortune 500 companies have significant OpenCode footprints, often discovered when the company itself emails saying thousands of employees are already using it.
    • Enterprise inbound has inverted the old SaaS procurement dance: companies beg OpenCode to fill out security questionnaires so they can officially use a product their engineers already adopted.
    • Enterprise pull comes in four flavors: officially blessing developer usage, extending the tool to non-technical employees, embedding the agent loop inside their own products, and managing token spend by routing teams to cheaper models.
    • One enterprise asked for deep visibility into exactly what every employee does with the tool, which the team flagged as a should-we-even-build-this question.
    • Ramp built a Slack bot running OpenCode’s embeddable server (the agent loop that works behind the UI) before OpenCode had built anything similar internally, publishing a blog post about it in December.
    • OpenCode is architected as a two-part product: the terminal UI you interact with, and a separately embeddable server that runs the agent loop and calls the LLM.
    • The new CAC is tokens, not ads: the free tier exists to give people the magic moment, the subscription converts them to real work, and whales paying per token feed directly into margin via OpenCode’s volume discounts on inference.
    • The episode references Dylan Patel’s podcast claim that Anthropic reached roughly $50 billion annualized revenue at around 70% margin in Q2, proof that the subsidize-then-harvest funnel can cross into profitability.
    • Global usage produces a nearly flat 24-hour GPU utilization curve (the East works while the West sleeps), improving unit economics versus competitors serving one region.
    • Jay describes OpenCode as a marketplace that showcases model diversity: competition among labs benefits consumers, while vendor lock-in mostly benefits vendor margins.
    • OpenCode is now the largest customer by token volume for most open-source model labs, making the relationship symbiotic: the strategy is not picking a winning lab but betting the whole field.
    • Every bump in OpenCode’s monthly actives traces back to a corresponding release in the open-source model market, making its growth a proxy for open-model progress.
    • The name OpenCode was deliberate positioning: when a market has one or two dominant players, the rest coalesces around an open alternative, and whoever occupies that position first is very hard to displace.
    • To support 70+ models and providers at launch, the team built models.dev, an open-source database of models and providers that Jay calls probably the best such dataset in the world.
    • The origin moment: when Claude Code appeared in February 2025, the team (Neovim users unimpressed by its terminal UI) decided to build a coding agent that met the standard of modern terminal tools, credibility that resonated instantly with the core developer audience.
    • The team had form here: co-founder Dax had built terminal.shop, a complete storefront for buying coffee over SSH, the kind of eccentric-taste project the hosts argue pulls founders toward outlier outcomes.
    • The company is one 16-year-old legal entity, incorporated in 2010, founded by Jay and his college roommate Frank after a Waterloo co-op term convinced Jay he never wanted a normal job.
    • Jay applied to YC nine times between 2016 and 2021 with four interviews before getting in, with his first interview dating back to the era when Paul Graham ran them and an Airbnb founder was hanging around the waiting room.
    • The 2021 YC idea was a serverless platform, Heroku for AWS, which became SST, the team’s first big open-source project and the on-ramp to building in public.
    • Building in public became core identity after co-founder Dax observed that if all your code is public and you work in public, staying silent about it is a disservice to the product; the community now follows the company like a reality TV show.
    • Jay credits survival to stubbornness, visible forward progress, and cheap burn (living with parents after running out of money), while warning founders: don’t try this at home.
    • The hosts’ framing of the whole arc: it took ten years of grinding to get to zero-to-$30-million in eight months, and catching lightning in a bottle requires positioning the bottle correctly first.

    Detailed Summary

    The Numbers: 20x in Six Months

    OpenCode began the year around 650,000 monthly active users and ended June near 13 million, with 4.6 million weekly actives that put it in the same conversation as OpenAI’s Codex. Token throughput grew from roughly 300 billion per day to 7 trillion, a volume larger than all of OpenRouter. The money followed two tracks: a pay-per-token inference business launched in the fall that annualizes near $40 million on recent weeks, and a subscription product launched in late February that reached 160,000 monthly subscribers and about $18 million annualized. Codex officially supporting OpenCode, with around 5% of Codex subscribers choosing it as their harness, added a second frontier on-ramp right as the Anthropic controversy peaked.

    The Anthropic Block That Backfired

    Using a Claude Code subscription inside OpenCode was one of the most common usage patterns until Anthropic moved to stop it in early January, rejecting requests whose system prompt mentioned “open code.” Jay is gracious about the logic (Anthropic subsidizes subscription usage and wants it inside its own product) but the effect was the opposite of containment. The block put the scrappy open-source harness on the same pedestal as the category leader, told every developer who had not tried it that it was worth investigating, and kicked off the year’s 20x run. The hosts draw the Instacart parallel: a supposed death blow that functioned as the best marketing campaign the company never paid for.

    A Global User Base the Valley Doesn’t See

    The product premise is that the coding-agent aha moment is a once-a-generation experience most of the world cannot afford at frontier prices. The Go plan ($10 a month for open-source models) was built for that global audience, and the geography shows it: China leads at 17%, with Indonesia at 4%, Brazil at 5%, and Vietnam prominent, markets where $200 a month is simply not a consumer price point. Two surprises followed. Chinese developers use OpenCode partly to run their own country’s models, which no US-locked product lets them do. And the US, never the target for Go, is growing fast anyway, which Jay reads as the token-budgeting vibe shift reaching even the throw-money-at-it crowd, helped by moments like GLM 5.2’s popularity making the plan the easiest way to try it.

    What the Usage Data Really Shows

    OpenCode publishes per-model usage at opencode.ai/data, and because every data point is an actual end user rather than aggregated API traffic, it is arguably the cleanest picture of what working engineers really run. DeepSeek Flash dominates token volume, the two DeepSeeks plus GLM hold the top three, and the market-share graph shows DeepSeek dipping when GLM launched and then bouncing back, contradicting the Twitter narrative of a GLM takeover. By unique users, Flash leads at 38,000 with DeepSeek Pro at 31,000 and GLM 5.2 near 30,000. The behavioral driver is pragmatic: cheap, fast models let users keep working after they hit premium limits, hosted speeds make some models feel real time, and perceived niches (GLM for front-end design) steer specific workloads.

    Enterprises Arriving Through the Back Door

    Before the open-model wave, companies adopted OpenCode to avoid lock-in to any single model or harness. Now dozens of forward-thinking Fortune 500 companies have significant footprints, and the procurement process has inverted: instead of sales outreach, OpenCode receives DMs saying a few thousand employees are already using the product, please sign the security questionnaire, and often, please don’t tell anyone. Once inside, enterprises pull in predictable directions: extend access to non-technical staff, embed the agent loop in their own products, and manage token spend by restricting expensive frontier models to teams that need them. Ramp exemplified the embedding path, running a Slack bot on OpenCode’s server component before OpenCode itself had tried it. One request, total visibility into employee activity, raised the harder question of what the company is willing to build.

    Token Economics: CAC Is Now Paid in Tokens

    The episode’s sharpest business insight is that customer acquisition cost has migrated from ads to tokens. Becoming skilled enough with coding agents to justify heavy spend is itself expensive, a chasm most individuals and companies cannot cross unaided. Anthropic and OpenAI solve this by subsidizing subscriptions until a percentage of users become whales, and per Dylan Patel’s numbers cited in the episode, that funnel has carried Anthropic to roughly $50 billion annualized at 70% margins. OpenCode runs the same funnel without frontier-scale subsidies: the free tier delivers the magic moment, the $10 plan makes real work affordable on open models, and whales paying per token convert OpenCode’s volume discounts into margin. The flat 24-hour GPU utilization curve from serving every timezone compounds the advantage.

    Betting the Field: The Marketplace Thesis

    Jay frames OpenCode as a marketplace where users pick models by attribute and cost, which keeps labs honest and passes competitive gains to consumers instead of vendor margins. Every bump in OpenCode’s growth traces to a release in the open-model market, so the company is explicitly not picking a winning lab; it is betting the field. That bet has made OpenCode the largest customer by token volume for most open-source model labs, a symbiosis where each side needs the other. On commoditization, Jay’s view is nuanced: the intelligence market is so large that labs will carve defensible niches along the quality-cost-performance axes, the way DeepSeek deliberately owns the cost corner. The positioning strategy has deep roots: as with the team’s earlier OpenNext project, when a market has two dominant players, the rest coalesces around an open alternative, and OpenCode raced to become that default, building models.dev along the way just to support 70+ providers at launch.

    Sixteen Years to Overnight Success

    The backstory reframes everything. Jay started the company after a discouraging Waterloo co-op term in 2006-2007, incorporated with college roommate Frank in 2010, and spent the next decade shipping products that did “reasonably well” while applying to YC nine times across 2016-2021, with four interviews, all as the same legal entity, the same founders, and a rotating cast of ideas. His first YC interview was with Paul Graham, in a waiting room shared with an Airbnb founder. Acceptance finally came in 2021 with the serverless platform that became SST, the team’s gateway into open source and building in public, a practice pushed by YC’s Dalton and crystallized by co-founder Dax’s observation that public code deserves public storytelling. When Claude Code landed in February 2025, the team’s terminal-UI taste (honed on projects as eccentric as coffee-over-SSH) told them exactly what to build. The hosts close on the honest version of the lightning-in-a-bottle myth: ten years of grinding taught the team consumer metrics, open source, marketing, and positioning, so when the strike came, the bottle was already in place.

    Notable Quotes

    “Most people in the world still haven’t experienced the magic of a coding agent.”

    Jay V, on the founding premise of OpenCode

    “You really know you have product market fit when like enterprises are bugging you to sign the security agreement so they can use your product.”

    Lightcone host, on OpenCode’s inverted enterprise sales motion

    “It’s not that we’re picking a winner in terms of a model lab. We’re just betting the field. We just think the rest of the field is going to do well.”

    Jay V, on OpenCode’s strategy toward the model market

    “With these open-source models, we’re the largest customer for most of them.”

    Jay V, on OpenCode’s token volume relative to open-model labs

    “When you’ve got a dominant or in this case two dominant players in the market, the rest of the market coalesces around an open alternative. And picking that position ends up being really valuable because if you pick it, it’s very hard for somebody else to displace you.”

    Jay V, on the deliberate positioning behind the OpenCode name

    “This is just an unprecedented market, like the market for intelligence has not existed before, everybody should be thinking in a positive-sum grow-the-pie mentality.”

    Lightcone host, on why labs should welcome OpenCode’s growth

    “Look, you know, all your code is public. You work basically in public. If you don’t talk about it publicly, you’re probably doing yourself a disservice and your product a disservice.”

    Jay V, recounting co-founder Dax’s case for building in public

    “It was really more a journey that took 10 years to get to 0 to 30 million in 8 months.”

    Lightcone host, reframing the overnight-success narrative

    “To catch the lightning in the bottle, you actually like have to sort of position the bottle correctly and be ready for it and know what to do with it.”

    Lightcone host, closing the episode on preparation meeting luck

    Watch the full conversation here.

    Related Reading

    • OpenCode the open-source coding agent discussed throughout the episode, including its public usage data.
    • models.dev the open-source database of AI models and providers the team built to support 70+ providers at launch.
    • SST the serverless framework that got the company into YC and established its open-source, build-in-public roots.
    • Terminal the coffee-over-SSH storefront that proved the team’s terminal-UI chops before OpenCode existed.
    • Y Combinator the accelerator behind the Lightcone podcast, which Jay applied to nine times before getting in.

  • Whale Rock Capital Founder Alex Sacerdote on S-Curve Investing, Why Anthropic Is His Highest Conviction Bet, and the Decommoditization of AI Hardware

    Alex Sacerdote built Whale Rock Capital into one of the most respected technology hedge funds in the world by treating markets through a single disciplined lens: the technology adoption S-curve. In this long conversation on Invest Like the Best with Patrick O’Shaughnessy, he lays out the full framework that has carried him through internet 1.0, mobile, cloud, e-commerce, and now AI, and he explains why Anthropic became his highest conviction position, why his fund went net short application software, and why the least glamorous corner of the market, the hardware and chips that build out data centers, may be one of the best ways to play artificial intelligence right now. What follows is the working theory of a money manager who has spent twenty years trying to think exponentially while the rest of the market thinks one quarter at a time.

    TLDW

    Sacerdote walks through Whale Rock’s three-part investment framework: find the right part of an S-curve, identify the company with a durable competitive advantage, and buy when long-term earnings power is underappreciated. He tells the story of investing in Anthropic at a 180 billion dollar valuation in August 2025 after Claude Code made coding the true unlock of AI, and frames the foundational model market as a three-horse race between Anthropic, OpenAI, and Google that resolved from sixty startups into an oligopoly. He argues enterprise AI is less than 1 percent penetrated, calls the adoption shape an L curve rather than an S-curve, and warns there is not enough compute in the world. He explains why he sold almost all of his application software and went net short, why he loves the decommoditization of AI hardware (Celestica, Corning, Elite Materials, Delta, Advanced Energy, high bandwidth memory, 40-layer PCBs), introduces a modified rule of 40 for chip investing, surveys the moats that let leaders win (network effects, industry standard, scale, critical IP, brand, recursive self-improvement), discusses moving from public markets into private deals like Stripe and Anthropic, lays out Whale Rock’s fund products including the new Mega Cap Tech Fund, defends old-fashioned scuttlebutt research in an AI age, and closes on the kindest thing anyone ever did for him, his father joining the firm after 41 years at Goldman Sachs.

    Thoughts

    The most useful idea in this conversation is not the bullishness on AI, which is everywhere now, but the discipline underneath it. Sacerdote’s framework forces a separation that most investors collapse. A great market is not a great investment. A great company is not a great investment. You need a tall S-curve, a company with a moat that survives the curve, and a price that does not yet reflect the earnings power. He says the quiet part out loud: he has repeatedly bought the best companies in the world at four or five times earnings precisely because the market refuses to extrapolate exponential growth. Nvidia at four times earnings in 2023, Tesla at five times in 2019, Amazon where AWS came free. The edge is not information, it is the willingness to underwrite two to four years out when the consensus cannot see past the next quarter.

    The Anthropic story is the framework applied in real time, and it is worth noting how late and how cautious he was. Whale Rock passed on the 60 billion dollar round because gross margins were negative and coding had not yet exploded. They only got conviction once Claude Code flipped from autocomplete to agentic work, once they heard Anthropic engineers were burning 100 dollars a day in tokens, and once the math on twenty million coders implied a half trillion dollar market from coding alone. The lesson he repeats throughout, that it is okay to be late, that you can miss the first 100 percent if the curve is tall enough, is a direct rebuke to the fear of missing out that drives most AI investing. He waited for the moat to be visible before he paid up.

    His most contrarian and most actionable call is on hardware. The consensus reflex is that chips and components are commodities that get competed to zero. Sacerdote argues the opposite is happening: AI workloads growing 10x a year are pushing every layer of the server to its physical limits, and that pressure is decommoditizing the entire stack. A liquid-cooled AI server is a 300,000 dollar piece of critical infrastructure, not a 5,000 dollar throwaway box, which means the supplier becomes a permanent fixture like a parts vendor on a plane. The Celestica example is the template: a contract manufacturer left for dead since 1999 that turned out to be the sole supplier of Google’s TPU server and a leader in liquid cooling and Ethernet switching, trading at eight times earnings. If he is right that we are 30 percent short on DRAM, NAND, and PCBs, the picks-and-shovels trade has years left to run regardless of which model company wins.

    The software bear case deserves the most scrutiny because it is the most consequential and the least certain. Going from 40 to 50 percent of the portfolio in software to net short is a violent reallocation, and his reasons are layered: AI products that nobody will pay for, CIO budgets being raided to fund Anthropic tokens, pricing power evaporating, and the long-term threat that AI-native startups rebuild incumbents from scratch. But he is honest that the bull case is real too, that old technology is sticky, that companies prefer to buy rather than build, and that AI might actually make platforms like Slack or CRM more important if agents end up operating inside them. This is the genuine uncertainty in the whole AI trade. The bottom of Jensen’s cake, chips and models, is where the value has accrued so far, but historically the application layer captured most of the market cap. Sacerdote is betting that this time the infrastructure and model layers hold the value longer, and he admits the application ecosystem is still unclear and a little bit dangerous. That admission is more valuable than any of his confident calls.

    Finally, the section on research in an AI age is a quiet refutation of the idea that this work automates away. Sacerdote runs a Philip Fisher scuttlebutt operation, 2,500 to 3,000 face-to-face management meetings a year, two decades of compounding relationships, the tripod of conviction where he, his analyst, and a respected outsider all independently like an idea. AI writes better notes now, but the paragraph on top, the wisdom about what it means and how it fits the thesis, is still human. The durable moat in his own business is the same one he looks for in the companies he buys: an accumulated advantage that newcomers cannot replicate quickly. That consistency between how he invests and how he operates is the most credible thing in the interview.

    Key Takeaways

    • Whale Rock’s framework has three legs: identify the right part of a technology S-curve, find the company with a powerful competitive advantage, and invest when long-term earnings power is underappreciated.
    • The core insight is exponential, not linear. Strong tech business models grow earnings exponentially, and because the market refuses to extrapolate, you can buy elite companies at very low multiples.
    • Concrete examples of buying exponential growth cheaply: Nvidia at four times earnings in 2023, Tesla at five times in 2019, Apple at four times, and Amazon where AWS was effectively free.
    • When ChatGPT launched in November 2022, Whale Rock did a firm-wide deep dive and chose to invest in chips and infrastructure first, because demand arrives there first and the winners are knowable regardless of who wins the model layer.
    • The foundational model market went from roughly 60 startups to a three-horse race: Anthropic, OpenAI, and Google. Most startups died, Amazon never showed up, and Meta faltered and had to reboot.
    • Anthropic was the dark horse that focused purely on enterprise while OpenAI won consumer. Whale Rock made it their highest conviction position.
    • Coding is the true unlock of AI. The progression went from Microsoft Copilot at 20 dollars a month (fixing grammar, finding a bug) to Claude running agentically and writing most of the code.
    • The market math: Anthropic engineers were reportedly spending 100 dollars a day on tokens, roughly 20 to 30 thousand dollars a year, and with about 20 million coders in the world that implies a half trillion dollar market from coding alone.
    • Whale Rock invested in Anthropic at the 180 billion dollar valuation in August 2025, when the company hoped to reach 9 billion in revenue and nobody yet knew what 2026 could be.
    • Andrej Karpathy and Linus Torvalds both flipped on AI coding. Karpathy went from 80 percent handwritten code to writing almost no code except in English.
    • Models are not pure commodities. There is real differentiation: Anthropic is strong for private equity and finance, Google is strong at ingesting PDFs, and routers that switch between models mask but do not erase that differentiation.
    • Anthropic is building an ecosystem around the API (SDK, orchestration, the harness, tools), echoing how AWS built lock-in with products around commodity servers starting in 2013.
    • The 800 million people using AI are mostly using AI 1.0, a search engine on steroids. Sundar Pichai estimated only about 10 basis points of knowledge workers are truly using AI’s new capabilities.
    • Enterprise AI is less than 1 percent penetrated. Whale Rock calls the adoption shape an L curve or backwards L curve because it goes straight up, unlike the slower 30 to 50 percent growth of cloud and SaaS.
    • There is not enough compute in the world. Anthropic reportedly has half of what it needs, and Marc Andreessen said the one thing he is sure of is that there will not be enough compute for the next four years.
    • The infrastructure S-curve is only about 10 percent penetrated and remains one of the best ways to play AI.
    • Getting into private deals requires a double opt-in. Whale Rock did a 90-page deck (built with Claude Code) on the coding market to win their Anthropic allocation, and their first private was Stripe in 2020 at a 35 billion dollar valuation.
    • The unicorn private market is now bigger than most European stock markets, larger than Germany or the UK individually. Whale Rock does 2,500 to 3,000 management meetings a year, 10 to 15 percent with privates.
    • S-curves come in two sizes: mega S-curves (internet, mobile, cloud, e-commerce, AI) and sub S-curves within them. AI is the biggest of all and each curve builds on the last.
    • Adoption inflects when barriers fall. Steve Jobs cut the smartphone price to 200 dollars on a 3G touchscreen, Elon cut the EV price to 40,000 with 300-mile range and a working supply chain. Remove the barriers and you get the tornado of demand.
    • Knowing how tall the curve is tells you when to sell. Growth stops being exponential around 30 to 40 percent penetration, when the sell side catches up and big beats end. EVs hit a wall at 10 to 15 percent instead of the expected 40 to 50 percent.
    • Selling Apple in 2012 at roughly 50 percent US smartphone penetration was a mistake, because the moat let it keep compounding around 20 percent even after the explosive phase ended.
    • At strategic inflection points you cannot trust the data (Andy Grove). The signal is intuition and anecdote: a 12-year-old in China on a giant phone playing a real game, or standing-room-only sessions at the Gartner IT Symposium for AWS, VMware, and Splunk.
    • Adoption slope varies. The radio curve hit near-full penetration in about 7 years, while B2B and infrastructure (the dishwasher that has to be plugged in) take far longer. AI is fast because you just open a browser.
    • The moats that let leaders win: network effects, becoming an industry standard, rapid scale, critical intellectual property, brand, and platform lock-in. Anthropic appears to have critical IP, enterprise brand, escape velocity, and recursive self-improvement from using its own code on its own models.
    • On the internet, the leader usually goes bigger, faster, and wins, and compounds on itself (Amazon, Shopify). Exceptions come at paradigm shifts, like AOL failing to make the dialup-to-broadband transition.
    • Whale Rock went from 40 to 50 percent in software five years ago to net short entering this year, which helped performance in the first quarter. AI products were not good enough to charge for and were not moving the needle.
    • Software faces a stack of headaches: falling priority on CIO to-do lists, budget pressure from token spend, lost pricing power, hiring freezes that hurt seat-based models, and the long-term threat of AI-native replacements.
    • The classic rule of 40 is growth rate plus operating margin. Whale Rock’s modified rule of 40 for chip investing is percent of sales that are AI plus market share in that category. Software AI exposure is still only 1 to 2 percent.
    • AI may make some platforms more important. The first thing you do with Claude is plug it into Slack, which could make Slack a permanent repository, and agents may end up operating inside incumbent tools like CRM, solidifying rather than killing them.
    • The data center stood still for 40 years on Intel x86, with every component commoditized. AI changed that. Workloads growing 10x a year are driving the decommoditization of the hardware industry.
    • Celestica is the template: a contract manufacturer left for dead since 1999, sole supplier of the Google TPU server, strong in liquid cooling and Ethernet white-box switching, with 50 to 60 percent share of the cloud Ethernet switch market, once trading at eight times earnings.
    • The whole supply chain is rerating: high bandwidth memory stacked 10 chips high, 40-layer PCBs (versus 10 for a normal server), Elite Materials copper clad laminate, Corning fiber (enough to circle the world four and a half times in one Microsoft data center), and Delta and Advanced Energy power supplies seeing ASPs rise 40 percent a year.
    • Networking has three layers: scale out (racks together), scale across (data centers together), and scale up (every GPU in a rack, currently copper, eventually fiber). The copper-to-fiber shift could two-to-three-x Corning’s opportunity.
    • Whale Rock estimates the market is roughly 30 percent short on DRAM, NAND, and PCBs even at today’s 10 basis points of real AI usage.
    • Rate of change matters more than absolute level. When Claude plotted market share data it missed the rate of change, the thing that drives accelerating growth and margins as a company moves from 10 to 30 percent share.
    • Key risks: public and government negativity toward AI (Maine reportedly banned data centers, only 20 percent of people are optimistic), models hitting a wall and letting open source catch up into a race to the bottom, and a major player faltering and stranding compute.
    • Chip companies do not care who wins the token war, which makes them a relatively safe way to play AI. Jensen Huang actively wants open source to take off.
    • Research is still human work. Whale Rock runs a Philip Fisher scuttlebutt process, the tripod of conviction (Alex, the analyst, and a respected outsider), and 20 years of compounding knowledge. AI writes better notes but cannot supply the wisdom paragraph on top or pick stocks.
    • The firm’s product evolution: 15 years as a long short fund, a long only fund in 2020 that is now larger than the long short, opt-in privates formalized around 2015 and activated in 2020, an 80 percent privates hybrid fund in 2021, and the new Whale Rock Mega Cap Tech Fund.
    • The Mega Cap Tech Fund thesis: endowments are structurally underweight the largest tech companies because they believe there is no alpha in large cap. Whale Rock takes the top 30 global market caps and picks the best 12 or 13, arguing it takes 100 diversified PMs to realize Google is a winner.
    • The kindest thing anyone ever did for Sacerdote: his father, after 41 years at Goldman Sachs, joined Whale Rock as chairman and the gray hair for six years until he passed away in 2011.

    Detailed Summary

    The Anthropic Investment and the Three-Horse Race

    When ChatGPT launched in November 2022, Whale Rock immediately took its 10-person team and ran a firm-wide deep dive. Sacerdote’s first principle is that every new compute paradigm creates a new stack with new winners and losers, and in this stack the layers run from power and chips at the bottom, to the clouds, to the foundational models, to the applications on top. In early 2023 the firm deliberately positioned in chips and infrastructure first, reasoning that demand arrives there first and the winners are knowable no matter who wins above. At an April 2023 webinar they framed the model layer as a coin flip between winner-take-all, total commodity, a race to zero, or an oligopoly of three or four. Over the next three years the answer became clear: of roughly 60 startups, almost all died, Amazon never really showed up, Meta came in strong then faltered and rebooted, and Anthropic emerged as the dark horse focused purely on enterprise while OpenAI won consumer and Google remained a perennial threat. The result looked like the cloud market, where three companies underpin the entire SaaS world with excellent businesses.

    The decisive factor was code. Sacerdote says the firm was initially skeptical AI could replace labor, given the negative corporate feedback on early models. That changed in 2025 when Claude Code and the agentic coding tools exploded. The progression ran from Microsoft Copilot at 20 dollars a month, which could improve coding grammar or find a bug, to Claude running agentically and doing far more. The token economics were staggering: Anthropic engineers reportedly spending 100 dollars a day, which annualizes to 20 to 30 thousand dollars, and with 20 million coders worldwide that implied a half trillion dollar market from coding alone, on technology that was only 7 to 9 months old. Whale Rock made the investment at the 180 billion dollar valuation in August 2025, writing in their letter that the company hoped to reach 9 billion in revenue, with growth like nothing they had ever seen, 100 million to a billion on the way to 9 billion, and no one yet knowing what 2026 could bring.

    Why the Models Are Not Commodities

    Everyone expected the foundational models to be pure commodities, but Sacerdote argues there is tremendous differentiation within them. Different training methods produce different skills: Anthropic excels at anything touching private equity and finance, Google is strong at ingesting PDFs. Routers that switch between models make them look like commodities but mask genuine, critical IP. Beyond the model itself, Anthropic is building a whole ecosystem around the API: the SDK, the orchestration layer, the tools, and the harness, the software wrapped around the API that gets the most out of the model. He compares this directly to AWS in 2013, when people dismissed cloud as commodity servers in a warehouse and missed that Amazon was inventing products that slowly built lock-in. The open-source risk from China is real, but Sacerdote got comfortable that leading-edge token quality is superior, because going from 80 to 85 percent of benchmark performance is a huge unlock and the open-source players lack the compute to leapfrog the frontier.

    The S-Curve Framework in Full

    Whale Rock’s whole edge is thinking exponentially when the world thinks linearly. Sacerdote argues very few people believe you can accurately predict two, three, or four years out, but if you understand the S-curve, the moats, and how to model, you can. Every technology follows the same pattern: it exists hidden for years (smartphones 10 years before the iPhone, the internet 20 years before Netscape, EVs 15 years before Tesla went vertical in 2019) until the barriers to adoption fall and demand inflects into a tornado. Knowing how tall the curve is tells you when to sell, because exponential growth stops around 30 to 40 percent penetration when the sell side catches up. Curves can also be dynamic: AWS turned out to address a far larger TAM than expected once it became clear cloud was not actually deflationary. There are mega S-curves (internet, mobile, cloud, e-commerce, AI) and sub S-curves within them. AI is the biggest. And slope varies enormously by the nature of the technology, the radio curve hitting full penetration in 7 years, B2B and infrastructure taking decades because, like a dishwasher, they have to be plugged into existing systems.

    On timing, Sacerdote is relaxed about being late. Citing Peter Lynch, who mentored him at Fidelity and told him to white out the chart because it is all about the future, he argues it is fine to miss the first one, two, or three years and even the first 100 percent if the top of the curve is half a trillion. At strategic inflection points, per Andy Grove, you cannot trust the data, so the firm relies on intuition and anecdote: a 12-year-old in China playing a real video game on a huge phone, or the AWS session at the Gartner IT Symposium that was standing-room-only at 9, 10, and 11 in the morning. Spotting the leader pulling away matters because, on the internet, the leader usually goes bigger, faster, and wins, compounding on itself, with exceptions only at paradigm shifts like AOL missing the move from dialup to broadband.

    The Software Bear Case

    Five years ago Whale Rock had 40 to 50 percent of its portfolio in software. Their April 2023 thesis was that incumbents with huge sales forces and proprietary data would take the AI APIs and build great products. Instead, the AI products were not good enough to charge for and did not move the needle, so the firm sold almost all of its application software and entered this year net short, which helped in the first quarter. The bear case is layered: software has fallen down the CIO priority list, budgets are being raided to fund Anthropic tokens with faster ROI, annual price increases look risky, and hiring freezes hurt seat-based models. The deeper threat is that AI-native startups could rebuild any incumbent from scratch, obviating the data advantage. The bull case is genuine too: old tech is sticky (mobile games did not kill consoles, tablets did not kill the PC), companies prefer to buy rather than build, and an ERP is hard to replace. Sacerdote also floats an optimistic twist, that AI could make platforms like Slack more important as agent repositories, and that agents operating inside CRM could solidify rather than destroy it, even as the bear case is that CRM goes headless and gets relegated to a database.

    The Decommoditization of AI Hardware

    This is Sacerdote’s most differentiated call. For 40 years nothing changed in the data center; Intel x86 became the standard, compute grew 25 to 40 percent a year in line with Moore’s law, and every component, from the printed circuit board to memory to enclosures to networking, commoditized. AI broke that. Workloads now grow 10x a year and push every aspect of the hardware to its physical limits, creating both tremendous unit growth and what Whale Rock calls the decommoditization of the hardware industry. He cites Sean Maguire wishing he could run a hardware hedge fund because all the companies are public with powerful IP, and compares it to Sequoia’s best early hardware investments in Apple and Cisco. The economics flip because an AI server is a liquid-cooled, 200 to 300 thousand dollar piece of critical infrastructure where a single failure brings the whole thing down, so suppliers become permanent like a critical part on a plane.

    Celestica is the marquee example: a contract manufacturer that had been a disaster industry since 1999 and went offshore to China, but kept its IBM supercomputing heritage and talent, became the sole supplier of the Google TPU server, and was trading at eight times earnings three years ago. It turned out to be excellent at liquid cooling where others failed, holds 50 to 60 percent share of the crucial cloud Ethernet switch market, and its engineers helped write the open-source SONiC software, working closely with Broadcom. The same dynamic runs up and down the chain: high bandwidth memory stacked 10 chips high that took Samsung years to master, 40-layer PCBs versus 10 for a normal server with very few suppliers able to make them, Elite Materials supplying the copper clad laminate, and Corning’s fiber, thinner and more bendable, with enough in a single Microsoft data center to circle the world four and a half times. Networking splits into scale out, scale across, and scale up, with the eventual copper-to-fiber shift in scale up potentially two-to-three-x-ing Corning’s opportunity. Power supplies from Delta and Advanced Energy are seeing ASPs rise 40 percent a year at higher margins because each Nvidia rack uses 50 to 125 percent more power. Visibility has gone from we’ll call you next week to design this roadmap with us for four years, turning 5 percent low-margin businesses into 35 to 50 percent topline growers with rising margins, and the whole market is roughly 30 percent short on DRAM, NAND, and PCBs.

    Private Markets, Risks, and the Research Machine

    Moving from public markets into privates meant adapting to a double opt-in, where the company has to choose to let you in. Whale Rock won its Anthropic allocation partly by building a 90-page deck with Claude Code scouring the internet for feedback on the coding market. Their first private was Stripe in April 2020 at a 35 billion dollar valuation, which they could only underwrite because they knew the public comp Adyen cold, and they upsized to a 100 million dollar block. The unicorn market is now bigger than most European stock markets combined. On risk, Sacerdote worries about public and government negativity (Maine reportedly banning data centers, only 20 percent of people optimistic), the possibility that models hit a wall and open source catches up into a race to the bottom, and a major player faltering and stranding compute, though he notes someone else (like Meta stepping into a cancelled Oracle deal) would likely absorb it, and that chip companies benefit regardless of who wins the token war. He explains his caution on the application layer by noting it always comes later, the iPhone took years to spawn its app economy, and the ecosystem is still unclear and a little dangerous, while pointing to Brett Taylor’s Sierra as the kind of company that could prove it out.

    On the research itself, Sacerdote insists AI has not supplanted the analyst. Whale Rock runs the scuttlebutt approach straight out of Philip Fisher’s Common Stocks and Uncommon Profits, doing 2,500 to 3,000 face-to-face management meetings a year and talking to suppliers, customers, and competitors. AI now writes much better notes and gets the team up to speed quickly on complex areas like ABF substrates, but there must be a wisdom paragraph on top, and it cannot pick stocks or replicate the work two analysts did building conviction in AppLovin and a relationship with Adam Foroughi. He calls the firm the Whale Rock learning machine, a group of 10 highly experienced people compounding knowledge for 20 years, with the tripod of conviction (himself, his analyst, and a respected outside investor all liking an idea) as the test. The firm’s products evolved from a 15-year long short fund to a 2020 long only fund now larger than the original, opt-in privates, an 80 percent privates hybrid in 2021, and the new Mega Cap Tech Fund built on the thesis that endowments are structurally underweight the largest tech companies because they wrongly believe large cap has no alpha. He closes on his father, who left Goldman after 41 years to join Whale Rock as chairman and the gray hair until his death in 2011, a mentor remembered by countless people for his humility and grace.

    Notable Quotes

    “When you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, your earnings don’t grow linearly, they grow exponentially.”

    Alex Sacerdote, stating the core of the Whale Rock investment framework

    “The world doesn’t think exponentially. Very few people believe you can accurately predict two, three, four years out. But if you follow and understand the S-curve and you know the moats and you know how to model, you really can predict these great things.”

    Alex Sacerdote, on why the market consistently underprices long-term earnings power

    “The enterprise AI or enterprise application AI market is less than 1 percent penetrated, and we’ve never seen, you know, we talk about S-curves, we call this an L curve, just straight up.”

    Alex Sacerdote, on why AI adoption looks different from every prior technology curve

    “We’re at 10 basis points of people really using AI and we’re already sold out. There’s not enough compute in the world. So Anthropic has half of what they need right now, and that’s before this huge takeup.”

    Alex Sacerdote, on the scale of the compute shortage relative to actual adoption

    “It’s okay to be late. It’s okay to miss the first one, two, three years in a lot of cases, because if the top of the S-curve is half a trillion, the growth can go on for a long time. It’s okay to miss the first 100 percent.”

    Alex Sacerdote, on why fear of missing out is the wrong instinct in a tall S-curve

    “The old way of software is like using a pen and paper or a horse and buggy. The new way of software is like a jet engine or frankly like the transporter from Star Trek. It’s so revolutionary it feels like it has to be disruptive.”

    Alex Sacerdote, explaining why Whale Rock went net short application software

    “You become like critical infrastructure, like selling a critical part on a plane. You’ll never get swapped out.”

    Alex Sacerdote, on how liquid-cooled AI servers turned commodity hardware suppliers into permanent fixtures

    “Why do you tell everyone your secret? It’s like why does the casino teach people how to play blackjack? It’s harder. It’s really hard to do.”

    Alex Sacerdote, quoting his mother on why a public framework does not erase the edge

    “He said, you know, I’ve been at Goldman for 41 years. How about I come and join you? I’ll be the gray hair. I’ll be the oversight. I’ll be the chairman. You do what you do.”

    Alex Sacerdote, recalling his father joining Whale Rock, the kindest thing anyone ever did for him

    Watch the full conversation here: Whale Rock Capital Founder on Investing in the Age of Exponential AI.

    Related Reading

  • 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.

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

    TL;DR:

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

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

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

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

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

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

    Harnessing Majorana Particles for Stable Qubits

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

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

    Scalability: From Eight Qubits to One Million

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

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

    Microsoft’s Partnership with DARPA

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

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

    Beyond AGI: AI’s Role in Economic Expansion

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

    Key takeaways from Nadella’s discussion:

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

    AI’s Impact on Enterprise and SaaS Markets

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

    Microsoft’s AI-Powered Gaming Evolution

    Muse: The World Model for Next-Gen Gaming

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

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

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

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