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Krishna Rao - Anthropic's CFO on Compute, Scaling to $30B ARR, and the Returns to Frontier Intelligence - [Invest Like the Best, EP.472]

Invest Like the Best with Patrick O'Shaughnessy1h 16m

Krishna Rao, CFO of Anthropic, discusses the company's compute strategy, explosive revenue growth from $9B to $30B ARR in a single quarter, and the thesis that returns to frontier AI intelligence are exceptionally high in enterprise. He explains how Anthropic uses three chip platforms fungibly, navigates a 'cone of uncertainty' in forecasting, and why the company's culture of intellectual humility and collaboration has been a key competitive advantage.

Summary

Krishna Rao, CFO of Anthropic, sits down with Patrick O'Shaughnessy to discuss running the financial operations of one of the fastest-growing companies in history. The conversation centers on compute as the foundational resource of the business — Rao describes it as 'the canvas on which everything else gets built.' Anthropic uses three chip platforms (Amazon Trainium, Google TPUs, and NVIDIA GPUs) fungibly across three workloads: model training/development, internal use by employees, and serving customer inference demand. This flexibility, built over multiple years, is considered one of Anthropic's key competitive advantages and required building custom compilers and orchestration layers down to the chip level.

Rao introduces the concept of the 'cone of uncertainty' to describe how exponential business growth makes forecasting extremely difficult. Small differences in weekly or monthly growth rates compound into wildly different outcomes, requiring Anthropic to think in scenario ranges rather than point estimates and to build flexibility into compute procurement contracts. He notes that the company has signed deals totaling over $150 billion in compute commitments with Google/Broadcom (5 gigawatts of TPUs starting 2027) and Amazon (5 gigawatts of Trainium), while also opportunistically sourcing near-term compute such as the newly announced partnership with SpaceX's Colossus facility in Memphis.

A core thesis of the conversation is that 'returns to frontier intelligence are extremely high, especially in enterprise.' Rao argues that each new model generation doesn't just improve raw intelligence scores but unlocks new use cases and longer-horizon agentic tasks, driving a Jevons paradox effect where lower prices or better capabilities lead to dramatically higher consumption. This is evidenced by Anthropic growing from approximately $9B to $30B in annualized run rate revenue in a single quarter. The company serves nine of the Fortune 10, has a net dollar retention rate over 500%, and recently lowered Opus pricing, which caused consumption to increase far more than proportionally.

Rao discusses how Anthropic internally uses its own models extensively — over 90% of code is written by Claude/Claude Code, and the finance team has built over 70 Claude-powered skills including automated monthly financial reviews and statutory financial statement generation. He frames this internal use as essential: foregoing potential revenue from that compute is justified because it accelerates model development and compound productivity gains.

On the platform vs. application question, Rao says Anthropic is primarily a horizontal platform company (analogous to early AWS), but will build vertical applications like Claude Code where it has unique insight into model trajectories or can demonstrate value for the ecosystem. He describes the vision as enabling a 'virtual collaborator' that can use company-specific tools, maintain memory, and work on long-horizon tasks — targeting the estimated $40 trillion annual knowledge work market.

Regarding the competitive and regulatory environment, Rao notes that Mythos was released in a phased way due to its exceptional cybersecurity capabilities, which Anthropic views as a template for responsible release of highly capable models. He discusses Anthropic's collaborative relationship with hyperscalers as multifaceted partnerships involving chip roadmap influence, capacity planning, and distribution — not just procurement. On talent retention, despite large competing offers from companies like Meta, Anthropic reportedly lost only two technical employees, which Rao attributes to the company's distinctive culture of intellectual humility, radical transparency, collaborative decision-making, and mission alignment around safe AI development.

The interview closes with Rao identifying biotechnology and drug discovery as the area he is most excited about, envisioning AI dramatically accelerating the lab throughput for molecular research and potentially enabling cures for diseases within patients' lifetimes.

About this episode

My guest today is Krishna Rao, the CFO of Anthropic. The center of our conversation is how he navigates the decision around procuring and allocating compute, which he describes as the canvas on which everything else gets built. We talk about what he calls the cone of uncertainty, the three chip platforms Anthropic uses fungibly across Trainium, TPUs, and GPUs, and the daily meetings they run to allocate compute between model development, internal use, and serving customer demand. He explains why the returns to frontier intelligence keep getting higher, especially in enterprise, and how Anthropic thinks about the line between platform and application and why they choose to build their own products like Claude Code. Krishna has such a unique seat watching one of the fastest growing businesses in history, and he is generous in sharing what he has learned since joining the company two years ago. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp’s⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Episode Intro: Krishna Rao (00:03:14) Compute as Anthropic's Lifeblood (00:05:17) Three Fungible Chip Platforms (00:07:31) The Cone of Uncertainty (00:09:08) Competing Ways to Allocate Compute (00:10:36) What Drives Compute Efficiency (00:12:38) Why Frontier Returns Are So High (00:16:32) How Claude Code Writes Its Own Code (00:18:46) Will Talent Become Obsolete? (00:20:07) How Scaling Laws Are Holding (00:21:54) Exponential Thinking (00:23:17) The Layer Cake of Compute (00:26:36) How Anthropic Deploys New Compute (00:27:53) Platform v. Application Layer (00:32:42) Why Model Pricing Has Stayed Stable (00:35:26) Measuring Return on Compute (00:37:22) Working With Chip Providers (00:38:32) How Anthropic's Finance Team Uses Claude (00:41:32) The Jevons Paradox for Labor (00:43:08) Anthropic's Fundraising & Growth Journey (00:47:31) The Exponential Revenue Curve (00:49:02) The Hardest Thing to Explain to Investors (00:52:15) AI's Public Perception Problem (00:55:38) Mythos (00:57:31) Relationship With Government (00:58:51) Inside Anthropic's Culture (01:03:48) The Next Frontier: Virtual Collaborators (01:06:22) How Leaders Scale With a Business (01:10:55) The Biggest Risks to Continued Progress (01:12:09) What Krishna is Excited About  (01:13:45) The Kindest Thing

Key Insights

  • Rao argues that compute is so central to Anthropic that buying too much causes bankruptcy while buying too little prevents staying at the frontier — there is no safe middle ground, making procurement the most consequential decision in the company.
  • Anthropic uses three different chip platforms (Trainium, TPUs, GPUs) fungibly across training, internal use, and customer inference — a capability Rao claims took years to build and makes them the most efficient compute users among frontier labs.
  • Rao claims that each new model generation produces simultaneous improvements in both capability and inference efficiency, unlike the car analogy where better performance means worse fuel economy — meaning customers get more intelligence at lower cost with each release.
  • Anthropic grew from approximately $9B to $30B in annualized run rate revenue in a single quarter (Q1), which Rao attributes primarily to model intelligence leaps unlocking new enterprise use cases rather than sales team expansion.
  • Rao states that Anthropic's net dollar retention rate exceeds 500% annualized, indicating that existing enterprise customers dramatically increase their spend as they find more use cases — driven by the Jevons paradox effect of better, cheaper models.
  • Rao reveals that over 90% of Anthropic's internal code is written by Claude Code, and that a significant portion of Claude Code's own code is written by Claude Code — describing this as the practical reality of recursive model-assisted development.
  • Anthropic has committed to over $150 billion in future compute purchases (5 gigawatts each from Google/Broadcom and Amazon), with Rao framing these as long-horizon investments managed alongside near-term opportunistic compute like the SpaceX Colossus partnership.
  • Rao argues that Anthropic's investment in AI safety research (interpretability and alignment science) was motivated by mission but produced an unexpected business benefit: enterprise customers entrusting sensitive workloads prefer a company with a demonstrable safety track record.
  • When Anthropic lowered prices on the Opus model family, consumption increased far more than expected — a Jevons paradox effect Rao says validates their thesis that accessible pricing to unlock enterprise TAM is more valuable than higher per-token margins.
  • Rao describes Anthropic's culture as one where a candidate can excel on every technical dimension but be rejected for failing the culture interview — citing that when Meta made large competing offers to AI talent, Anthropic lost only two people while other labs lost dozens.
  • Rao frames Anthropic's strategy as primarily horizontal platform (building the intelligence layer and tools/SDKs for others to build on), with selective vertical applications like Claude Code built only where Anthropic has unique foresight into model trajectory or ecosystem demonstration value.
  • Rao identifies the biggest investor misconception about Anthropic as treating compute as a separable variable cost analogous to traditional software R&D, when in reality compute is a single fungible resource simultaneously driving near-term revenue, long-term model development, and internal productivity — making ROI on the full compute envelope the correct measuring stick.

Topics

Compute procurement and allocation strategyCone of uncertainty in exponential business forecastingReturns to frontier AI intelligence in enterpriseMulti-chip platform fungibility (Trainium, TPUs, GPUs)Revenue growth from $9B to $30B ARR in one quarterPlatform vs. application strategyInternal AI usage and productivity accelerationTalent retention and company cultureResponsible AI release (Mythos case study)Investor education on AI business modelsBiotechnology and healthcare as future opportunityRecursive self-improvement and scaling laws

Transcript

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