Sam: OpenAI's reasoning model just disproved a geometry conjecture that had been open since 1946. Independent mathematicians — the same ones who caught OpenAI making a false claim about math earlier this year — reviewed the proof and confirmed it's valid. That's a different category of result than scoring well on a benchmark. It's a novel mathematical discovery that human mathematicians hadn't managed in eight decades.

Priya: Welcome to AI Revolution for Thursday, May 21st, 2026. I'm Priya Nair, here with Sam Kim. Today is a dense one. We've got that math breakthrough, a major reveal from SpaceX's IPO filing that exposes just how expensive frontier AI has become, Anthropic's first profitable quarter, Nvidia's continued dominance plus a chip you may not have heard of, and DeepSeek entering the coding agent wars. Let's get into it.

Sam: So the math result first, because it deserves real attention. The conjecture in question is a geometry problem from 1946 — I won't pretend the specific topology is simple to explain in a minute, but the point is it had resisted eight decades of human mathematical effort. OpenAI's o-series reasoning model produced a disproof. Not an approximate answer, not a statistical guess — a formal mathematical argument that shows the conjecture is false.

Priya: And the verification matters here. OpenAI had an embarrassing episode earlier this year where they claimed a math result that turned out to be wrong. The mathematicians who caught that error are the same people who reviewed this new result and said it checks out.

Sam: Right. So the credibility signal is real. What's interesting technically is what kind of capability this requires. Formal mathematical reasoning means the model has to chain together logically dependent steps where any single error invalidates the whole thing. You can't hallucinate your way through a proof. The fact that a reasoning model can do this at all — and do it on a problem that was genuinely hard — tells us something about how far the extended chain-of-thought approach has come.

Priya: The open question is whether this is a one-off or whether we're entering a period where AI is regularly contributing to mathematical literature. Mathematicians I've spoken to are cautiously optimistic but want to see more volume before drawing big conclusions.

Sam: Fair. Now let's talk about the infrastructure story, because the numbers out of SpaceX's IPO filing are genuinely striking. Anthropic is paying SpaceX fifteen billion dollars a year for access to the Colossus data centers in Memphis. That's 1.25 billion dollars a month.

Priya: To put that in context — that's roughly what a mid-sized cloud provider generates in total annual revenue. Anthropic is spending it on compute access alone.

Sam: And it makes sense when you think about what frontier training actually requires. Training a top-tier model today means running tens of thousands of GPUs in tight coordination for months. The interconnect requirements, the power density, the cooling — you can't just rent a few racks somewhere. You need purpose-built infrastructure at enormous scale. Colossus was originally built for xAI's Grok training, so it's designed for exactly this kind of workload.

Priya: What I find structurally interesting here is SpaceX is now effectively a hyperscaler. You have AWS, Azure, Google Cloud — and now SpaceX is in that conversation for the most compute-intensive AI workloads. That's not a position anyone would have predicted for a rocket company five years ago.

Sam: The xAI angle has an environmental wrinkle too. Also buried in the SpaceX IPO filing: xAI is planning to spend 2.8 billion dollars on natural gas turbines over the next three years to power its data centers. They're already being sued over emissions from existing generators at the Memphis site. There's a real tension here between how fast these companies need to scale power and what's available on the grid.

Priya: And that tension is going to shape where data centers get built. States and municipalities that can offer grid capacity and favorable permitting are going to be in a different conversation with these companies than places that can't.

Sam: Now, the other Anthropic story. Despite spending fifteen billion a year on compute, Anthropic is telling investors it's about to have its first profitable quarter. Revenue is projected at around 10.9 billion for Q2, which would be more than double Q1.

Priya: That's a remarkable trajectory. A year ago people were seriously asking whether the frontier lab model was financially sustainable — whether the capital requirements would just keep growing faster than revenue. These numbers suggest the answer is no, the revenue can catch up.

Sam: The API business is clearly doing heavy lifting here. Enterprise adoption of Claude has accelerated significantly, and products built on the API are generating real recurring revenue. What this probably means going forward is that Anthropic has more flexibility — they can fund compute from operations rather than purely from fundraising rounds.

Priya: Okay, Nvidia. Record quarter, eighty-one-point-six billion in revenue, guidance of ninety-one billion for Q2. At this point the records are almost expected.

Sam: They are, but the guidance number is the interesting part. Wall Street had modeled eighty-six billion. Nvidia guided ninety-one. That's a five billion dollar beat on forward guidance, which suggests demand visibility is strong — customers are committing to purchases further out.

Priya: And buried in the earnings disclosure: Nvidia has forty-three billion dollars in startup equity holdings. They've been taking stakes in AI companies as a condition of supply allocations or as direct investments, and that portfolio is now enormous. They're not just the picks-and-shovels provider — they have skin in the game across the whole ecosystem.

Sam: The chip story that didn't get as much attention is Vera, Nvidia's CPU architecture. Jensen Huang is framing this as a two-hundred-billion-dollar market opportunity, and the reasoning is specific to AI agent workloads. Here's the technical logic: GPU-heavy inference makes sense for large model forward passes. But agentic systems — the ones that are running loops, calling tools, managing state, orchestrating subtasks — spend a lot of time on the CPU side. Scheduling, context management, tool dispatch. Vera is designed to handle that part of the workload efficiently.

Priya: So this is Nvidia saying: we see agents as the dominant deployment paradigm, and we want to own the full stack of that compute, not just the GPU portion.

Sam: Exactly. And if you look at where enterprise AI is heading, agents are the primary deployment model. It's not just chat interfaces anymore. So the addressable market claim isn't unreasonable.

Priya: Quickly on DeepSeek Code — they're building a dedicated team in Beijing to develop a coding agent that directly targets Claude Code, Codex, and Cursor. Job postings ask for expertise in agent loops, MCP protocol, and context engineering.

Sam: The MCP detail is worth pausing on. Model Context Protocol has become the standard for how agents connect to external tools and data sources. The fact that DeepSeek is explicitly hiring for MCP expertise tells you they're building something with real tool-use integration, not just a code-completion product. This is an agent play.

Priya: DeepSeek's track record on cost efficiency has been remarkable. If they bring that same approach to a coding agent, it could put pressure on pricing across the whole sector.

Sam: Brief note on Hark — Brett Adcock's new startup raised seven hundred million in a Series A at a six billion dollar valuation. The product is described as a "universal AI interface," which is deliberately vague. Adcock previously founded Figure Robotics, so he has credibility with investors. Seven hundred million at Series A before a product ships is a sign of how much capital is chasing the interface layer right now.

Priya: Okay, looking ahead. The Anthropic profitability story and the fifteen-billion-dollar compute deal exist in the same breath, and I think that's the dynamic to watch. The question isn't whether these companies can generate revenue — it's whether the revenue growth rate can stay ahead of the compute cost curve as models keep getting more capable.

Sam: On the math result, I'd watch for whether other labs start publishing similar results. If reasoning models are genuinely capable of novel mathematical discovery, there are implications for how research works in every formal discipline — not just pure math. Physics, cryptography, materials science — anywhere proofs matter.

Priya: And on Nvidia's Vera: the next twelve months will tell us whether the CPU play lands. If agentic deployments scale the way everyone expects, the bottleneck may genuinely shift from raw GPU throughput to the orchestration layer. That's a new problem, and Vera is a bet that it's Nvidia's problem to solve.

Sam: One to watch.

Priya: Thanks for listening to AI Revolution. Show notes and links to everything we covered today are at cleartext.fm. We're back tomorrow.

Sam: See you then.