Sam: Elon Musk admitted under oath this week that xAI used OpenAI's models to train Grok through distillation. That's not a footnote in a legal drama — it reframes the entire competitive landscape around model IP, because if your outputs can train a rival, the question of what "proprietary" means for frontier models gets a lot harder to answer.

Priya: Welcome to AI Revolution's Week in Review for the week ending May 2nd, 2026. I'm Priya Nair, here with Sam Kim, and we have a genuinely dense week to work through. A few major themes shaped things: the AI industry's ownership and partnership structures are being renegotiated in real time, frontier cybersecurity capabilities are converging across models and the ecosystem is scrambling to respond, the money flowing into AI infrastructure hit a number this week that's almost hard to process, and there's a slow-moving but significant story about how AI is getting regulated — and who gets excluded when it does. Let's get into it.

Sam: Start with the Musk v. Altman trial, because it produced something more interesting than courtroom theater. The distillation admission is the technical crux. Distillation, for context, is a training technique where you use a more capable model's outputs — its probability distributions over tokens, essentially — to supervise a smaller or newer model. It's extremely effective. It's also how you can bootstrap capability without matching the original model's training compute. The legal question of whether that constitutes misappropriation of OpenAI's IP is now genuinely live, and it doesn't have a clean answer because model outputs haven't been treated as protected assets in the same way as weights or training data.

Priya: The exhibits from OpenAI's founding era are also worth paying attention to. Internal documents from 2015 and 2016 are now public record in a federal case. Whatever they show about the original nonprofit structure and what Musk was promised, they're going to inform how courts think about AI company governance for years. That's before you get to the distillation question.

Sam: And the timing is interesting because the same week OpenAI quietly ended its exclusivity arrangement with Microsoft. The deal restructuring means Microsoft's license to OpenAI models is now non-exclusive, Microsoft no longer owes OpenAI a revenue share in exchange, and OpenAI models are now in limited preview on Amazon Bedrock. Read those three things together and OpenAI is actively diversifying its distribution while simultaneously fighting a legal battle over model IP. The strategic logic is clear — you want your models everywhere — but the IP questions raised in the Musk trial make the "your models" framing complicated.

Priya: The Microsoft restructuring also reshuffles the competitive dynamics between hyperscalers in a real way. Azure had a genuine moat from GPT exclusivity. That's gone now. AWS gets GPT models on Bedrock. Google is watching this from the side. The cloud layer underneath frontier AI is becoming more contested.

Sam: Let's talk about the cybersecurity story because it developed in a specific direction this week that's technically significant. The background: Anthropic's Mythos Preview got a lot of attention for demonstrated capability in offensive security tasks — finding real vulnerabilities, not just answering CTF questions. Then independent researchers ran GPT-5.5 through similar evaluations and got comparable results.

Priya: Which tells you something important about where the capability floor is across frontier models right now. Advanced vulnerability discovery appears to be an emergent property of sufficient capability in general — it's not a specialized architecture, it's not Anthropic doing something uniquely clever with Mythos. When you train a model that reasons well about code and systems, it reasons well about the failure modes in code and systems. That capability is apparently available at the frontier broadly.

Sam: The response from both labs was to restrict access. Anthropic gated Mythos Preview earlier. OpenAI this week limited GPT-5.5-Cyber to a vetted set of "critical cyber defenders." What's notable is that OpenAI had publicly criticized Anthropic's restricted release approach — and then did the same thing within weeks. The Register ran with that angle, and honestly it's a fair observation. But the more interesting read is that both labs arrived at the same conclusion under pressure: when your model can reliably find exploitable vulnerabilities in production systems, you can't just ship it through the standard API.

Priya: There's an emerging norm being established in real time. It's not codified anywhere, there's no industry standard, but the behavior is converging. You restrict access to cyber-capable variants, you vet who gets in. Whether that actually contains the risk or just creates a gatekeeping theater is a legitimate debate.

Sam: The Pentagon story connects here. Eight companies — including Nvidia, Microsoft, and AWS — signed deals to deploy AI across classified military networks this week. Anthropic was explicitly excluded after rejecting a usage clause and being flagged as a security risk.

Priya: The commercial consequence is real. Anthropic's responsible use policy, which they've leaned into as a differentiator, cost them a contract. But the same week they're reportedly closing a funding round at over $900 billion valuation. Those two things coexisting is genuinely strange — you're excluded from the Pentagon's AI push and simultaneously one of the most valuable private companies in history.

Sam: It reflects a market bifurcation that's becoming clearer. Anthropic generates more LLM revenue than OpenAI despite having a fraction of the users. The enterprise and API-heavy customer base they've built is apparently willing to pay premium prices for what Anthropic positions as safer, more reliable models. That's a coherent business even if it forecloses some contracts.

Priya: Let's do a real number for a second. Google, Amazon, Microsoft, and Meta have a combined AI capex budget of approximately $725 billion this year. That's infrastructure — data centers, chips, power. To put it in context, the GDP of Switzerland is around $800 billion. The four largest tech companies are spending close to that on AI infrastructure in a single year.

Sam: The implications run through multiple layers. Chip supply chains, obviously — Nvidia is the primary beneficiary but the whole semiconductor stack is getting pulled along. Energy infrastructure is a real constraint at this scale. You're talking about gigawatts of new power demand being sourced simultaneously across multiple continents. And the capital concentration this represents means the barrier to training frontier models keeps moving up.

Priya: Which connects to the evaluation story from Hugging Face this week. As models scale, evaluating them properly is getting harder and more compute-intensive. The time and resources required to actually understand what a model can and can't do is starting to become a bottleneck in its own right. That creates a real problem: development speed is outpacing our ability to assess capability gaps and safety properties.

Sam: Goodfire's Silico tool is a partial response to that broader problem. It's a mechanistic interpretability tool that works during training, not just post-hoc. You can inspect what's happening inside the model and adjust parameters in real time rather than training, evaluating, finding a problem, and retraining. Whether that scales to frontier-sized models is an open question, but the direction is right — better instrumentation of the training process itself.

Priya: A few things worth flagging before we wrap up. GitHub moving Copilot to usage-based pricing is a signal about the economics of inference-heavy products. Flat-rate subscriptions worked when usage was moderate; they don't when users with heavy workflows are running thousands of completions a day. The same pressure will hit other flat-rate AI products.

Sam: OpenAI turning on marketing cookies by default for free ChatGPT users is a different kind of signal. The ad-supported revenue model for consumer AI is starting to materialize. Conversations with an AI assistant are a pretty rich data source for behavioral advertising. The privacy implications are significant and the regulatory scrutiny will follow.

Priya: The ClawHub story is worth noting because it's a preview of a problem that will get worse. Thirty plugin-style skills in an AI agent ecosystem were quietly co-opting agents for crypto mining — no traditional malware involved, just malicious agent skills. As agentic architectures proliferate and third-party tool ecosystems grow around them, the attack surface grows with them. This week it was crypto mining. The same technique applies to more serious objectives.

Sam: And Meta acquiring Assured Robot Intelligence to build an open humanoid platform — the Android analogy is explicit in their framing. If you can commoditize the software stack for physical robots the way Android commoditized the smartphone software stack, you change who can build in that space dramatically. It's early, but Meta's distribution and open-source credibility make them a plausible player.

Priya: Stepping back — what does this week actually mean? The legal, commercial, and policy structures around AI are being stress-tested simultaneously. The Musk trial is the most visible version of that, but the OpenAI-Microsoft restructuring, the Pentagon contracts, the EU forcing Google to open Android's AI stack to competitors — these are all institutions trying to establish rules for a technology that moved faster than the rules.

Sam: And the capability side isn't slowing down. Frontier cybersecurity capability being general rather than model-specific is a significant development. When advanced offensive security capabilities are available across multiple top models, the responsible disclosure and access control questions become industry-wide rather than individual lab problems.

Priya: What I'm watching next week: how the Anthropic funding round closes and what terms investors accepted at that valuation. And whether the Musk trial produces more document disclosures — the early exhibits are already rewriting some of the founding mythology around OpenAI.

Sam: I'm watching whether the Pentagon contract exclusion creates any pressure on Anthropic to revise its usage policies, and whether other labs get pulled toward the restricted-access model for cyber-capable variants or whether that consensus breaks down under competitive pressure.

Priya: That's the week. Thanks for listening to AI Revolution's Week in Review. We're back Monday with the daily show — subscribe wherever you get your podcasts, and we'll see you then.