Sam: Anthropic just closed a $30 billion funding round — its second in three months — putting its valuation at $900 billion and annualized revenue near $45 billion. That revenue number is what matters: it means the frontier AI market is real, large, and concentrating fast.

Priya: Welcome to AI Revolution's Week in Review. I'm Priya Nair, here with Sam Kim, and it's Saturday, May 16th. This was a week where the money got serious, the infrastructure cracks got louder, and the legal and regulatory pressure on AI companies went from background noise to front-page. We're going to cover four themes today: the competitive reshuffling happening at the top of the AI industry, the physical infrastructure strains that are now creating real community harm, a genuinely interesting research result on model efficiency, and a cluster of legal and liability stories that are quietly building the rules AI is going to have to live by.

Sam: Let's start with the industry picture, because this week it looked different than it did a month ago. Anthropic raising another $30 billion three months after raising $30 billion is the kind of thing you'd normally dismiss as financial engineering. But the revenue figure is hard to dismiss. A fivefold increase in annualized revenue since the end of 2024 — approaching $45 billion — means enterprise customers are actually deploying Claude at scale and paying real money for it. The valuation crossing OpenAI's for the first time is a milestone, but the operating story underneath it is what's worth watching.

Priya: And OpenAI is having a complicated week on several fronts simultaneously. The Musk v. Altman trial wrapped its third week with the jury now deliberating. Whatever you think of the personalities involved, the legal question is structurally important: can a nonprofit-originated AI company convert to for-profit, and under what constraints? The jury's answer will have implications beyond just OpenAI. Meanwhile, OpenAI is reportedly preparing legal action against Apple over the ChatGPT integration that was supposed to drive subscriber growth and didn't. A judge has already ordered Apple to disclose internal communications about the deal. When a partnership between two of the most valuable companies in tech ends up in litigation, it tells you something about how hard AI distribution is, even when you have the best-known brand in the space.

Sam: And then on Friday, OpenAI reorganized again — Greg Brockman taking over all product, the company explicitly framing 2026 as the year they go all-in on agents rather than chat. ChatGPT and Codex are being consolidated into a single product experience. That's a meaningful strategic signal. The underlying logic is that agents — systems that take multi-step actions autonomously rather than just responding to queries — require a different product architecture than a chat interface. Combining the coding-focused Codex product with consumer ChatGPT suggests they're converging on a single agentic runtime.

Priya: There's a data point this week that gives that agent bet some texture. A three-person team running an open-source project called OpenClaw is keeping roughly 100 Codex instances running simultaneously, spending $1.3 million a month on OpenAI API calls. The founder frames it as research into what software development looks like when token costs aren't a constraint. That's an extreme case, but it's a preview of the economics. Before cost curves compress — and they will — this is what agent-heavy workflows actually cost. The fact that a small team can even structure an experiment like this says something about where tooling has gotten to.

Sam: And OpenAI also launched ChatGPT personal finance this week — Pro users in the US can now connect bank accounts through Plaid, get a dashboard of spending and portfolio performance, all running on the GPT-5.5 Thinking model. OpenAI is careful to say it's not a licensed financial advisor. But connecting an LLM to live financial account data is a qualitatively different kind of deployment than answering questions about budgeting. The liability surface is genuinely new.

Priya: Which brings us to the infrastructure theme, because the physical reality of AI at scale is becoming impossible to ignore. Three stories this week, and they fit a pattern. In the Lake Tahoe area, an energy supplier is prioritizing data center contracts over the 49,000 California residents it's also supposed to serve. People competing with server farms for electricity isn't hypothetical anymore — it's happening. At xAI's Colossus 2 data center in Mississippi, nearly 50 gas turbines are running as permanent power infrastructure while being classified as "mobile" units — a classification that lets them sidestep standard environmental permitting. There's now a lawsuit over that. And Nvidia is piloting roughly 25 micro data centers placed directly at utility substations, sized between 5 and 20 megawatts each, designed to shift compute dynamically based on where power is available at any given moment.

Sam: That last one is worth dwelling on technically. The conventional model for data centers is: find a large parcel of land, build massive power infrastructure, run it continuously. Nvidia's approach inverts that — instead of routing power to computation, route computation to power. If you have grid-scale storage or renewable generation that's geographically variable, you want your workloads to follow the electrons rather than the electrons following the workloads. The pilot is across five US utilities, and it's early, but if it works it addresses a real constraint. Inference is the right workload to try this on, because unlike training, many inference jobs aren't time-critical in the same way.

Priya: The xAI turbine story is the other end of that spectrum. Running industrial gas turbines under a regulatory classification designed for temporary construction equipment is a regulatory arbitrage move. The lawsuit will determine whether that holds up. But these three stories together — residential displacement, permitting evasion, novel grid architecture — they're all responses to the same underlying fact: AI's power demand is growing faster than the grid was designed to accommodate.

Sam: Now, there was one research result this week I want to make sure we actually explain properly rather than just summarize. Allen Institute for AI and UC Berkeley published work on a model they call EMO — Expert-specialized Mixture of Experts. To understand why this matters, a quick primer on mixture-of-experts architecture: instead of every parameter in a model being active for every token, you divide the model into "experts" — subnetworks — and a routing mechanism picks a small subset of experts to activate for each token. This dramatically reduces the compute per forward pass while keeping total parameter count high.

Priya: The typical routing strategy routes based on something like word type or syntactic context. EMO routes based on content domain — the topic or subject matter of what's being processed. And what they found is that when experts specialize by domain rather than syntax, you can prune three-quarters of the experts and lose roughly one percentage point of performance. That's a very different trade-off than you'd get with conventional routing. A one-point performance drop in exchange for operating at 12.5 percent of the expert footprint is potentially the difference between a model that requires data center hardware and one that can run in memory-constrained environments — edge devices, local deployments, scenarios where you want the model close to data rather than in the cloud.

Sam: The mechanism makes intuitive sense once you see it: if an expert has learned to handle medical literature and you're not processing medical text, activating it adds noise rather than signal. Domain-based routing makes the sparsity meaningful rather than just computational. Whether this generalizes to very large models and diverse production workloads is still an open question, but as an architectural finding it's clean.

Priya: Now, the legal and liability picture this week. Several stories, different domains, but they're building something. The Anthropic copyright settlement — $1.5 billion to authors — is stalled. A judge delayed approval after authors pushed for higher payouts and raised concerns that lawyers rushed the settlement to capture $320 million in fees. The structure of how training data compensation works for AI has not been resolved by that settlement. It may be resolved by whatever comes out of the litigation over it.

Sam: And a lawsuit filed this week alleges that a teenager died after using ChatGPT for drug safety guidance. The chat logs apparently show the teen asking whether a combination would be safe and trusting the AI's response. This is the third or fourth case in roughly this category — AI systems giving consequential guidance in high-stakes personal situations, without appropriate uncertainty or appropriate referral. Each case is building a factual record. Courts haven't established AI product liability standards yet, but the cases required to establish them are accumulating.

Priya: There's also the Hugging Face malware incident, which is an AI supply chain story rather than a liability story. A malicious repository posed as an OpenAI model release and delivered infostealer malware to Windows machines — roughly 244,000 downloads before it was removed, though that number may be inflated by the attackers. Model repositories are becoming an attractive attack surface for the same reason package managers were: developers pull from them with high trust and often without verification. The HiddenLayer research pointing this out is worth reading if you work anywhere near ML infrastructure.

Sam: And arXiv this week announced it will ban researchers for submitting papers with incontrovertible evidence of unchecked LLM generation — hallucinated references, LLM meta-comments left in the text. It's a narrow standard, deliberately so. But it's the first time a major preprint server has said there will be punitive consequences, not just rejection. That matters because the research record is foundational to the whole field. If the preprint layer gets polluted with AI slop, every downstream system that uses research literature — including AI training pipelines — degrades.

Priya: So what does this week mean? When we step back and look at it together, a few things stand out to me. The Anthropic revenue number is the biggest single signal. $45 billion annualized, 5x growth in roughly five months — that's enterprise deployment at real scale, not pilot programs. The frontier lab market is not a science project anymore.

Sam: For me, the infrastructure cluster is what I keep coming back to. The grid is a real constraint now, not a theoretical one. Residential customers in California are competing with data centers. A major AI company is running industrial turbines under a classification designed for construction equipment. And Nvidia is piloting compute infrastructure that physically routes itself to available power. All three of those things being true simultaneously tells you the pace of deployment has outrun the supporting infrastructure, and the next few years are going to be defined partly by how that resolves.

Priya: Heading into next week, I'm watching the Musk v. Altman jury. Whatever they decide has structural implications for how AI's most important governance questions get settled. And I'm watching whether the ChatGPT-Apple legal situation develops further — platform distribution for AI has always seemed like it would get complicated, and it has.

Sam: I'm watching the EMO results and whether we see more domain-routing work follow from it. The efficiency gains there, if they generalize, change the deployment math in ways that matter.

Priya: That's the week. Thanks for listening to AI Revolution's Week in Review. You can find show notes and links to every story we covered today at cleartext.fm. We're back Monday with the daily show. Have a good weekend.