Sam: OpenAI just doubled the price of its most capable API. And the framing — "a new class of intelligence" — is easy to dismiss as marketing. But there's something technically real underneath it. GPT-5.5 is designed from the ground up for agentic workloads, meaning it's built to autonomously orchestrate multiple tools across complex, multi-step tasks. That's a different architecture target than a chat model. And almost simultaneously, DeepSeek dropped a 1.6 trillion parameter model for nearly nothing. These two releases, side by side, tell you almost everything about where the AI industry is right now.

Priya: Welcome to AI Revolution. I'm Priya Nair, here with Sam Kim, and it is Friday, April 24th, 2026. Today we have a genuinely packed show. GPT-5.5 and the economics of frontier AI. DeepSeek's latest volley in the cost wars. Meta quietly buying millions of CPUs — not GPUs — for agentic workloads, which is a story that deserves more attention than it's getting. Anthropic's Claude Mythos and what autonomous vulnerability discovery actually means for cybersecurity. And Yann LeCun raising a billion dollars for twelve people to build AI that isn't an LLM. Let's get into it.

Sam: So GPT-5.5. OpenAI's core claim is that this model is purpose-built for agentic use — it autonomously decides which tools to invoke, in what order, and when to hand off between them during a complex task. That's meaningfully different from a model that just has tools available. The distinction is orchestration logic being internalized rather than externally scripted. In previous setups, you'd write orchestration code that told the model when to call a web search, when to write code, when to query a database. GPT-5.5 is supposed to reason about that itself, dynamically, mid-task.

Priya: And the double price point is the honest signal here. When OpenAI prices something at 2x, they're telling you who the customer is — it's not hobbyists or startups doing cheap RAG pipelines. It's enterprises running long-horizon agentic workflows where the model is genuinely doing consequential autonomous work. The question I keep turning over is whether "new class of intelligence" is doing real technical work as a description, or whether it's positioning language. My read: the agentic orchestration piece is real. The framing around it is optimistic.

Sam: What makes it technically interesting is the inference profile. Agentic tasks require the model to maintain coherent state across many more reasoning steps than a single-turn exchange. That puts pressure on context management, on the model's ability to not lose the thread across long sequences of tool calls. Whether GPT-5.5 has actually solved that well — we'll know from production deployments over the next few weeks.

Priya: And right as OpenAI pushes prices up, DeepSeek ships V4-Pro and V4-Flash. V4-Pro is 1.6 trillion parameters total, but it's a mixture-of-experts architecture, so active parameters per forward pass are a fraction of that. One million token context window. And the pricing is, by any measure, dramatically below what OpenAI, Google, or Anthropic are charging for comparable capability tiers.

Sam: The technical paper is worth reading. DeepSeek is transparent about using distillation techniques — training smaller or more efficient models by learning from larger ones — and they're explicit about their hardware constraints given export controls on high-end NVIDIA silicon. What's impressive is that the architectural efficiency work they've been forced into is producing genuinely competitive models. The constraint is driving innovation.

Priya: The pattern keeps repeating. DeepSeek ships something that matches frontier capability at a fraction of the cost. Western labs respond by emphasizing capability differentiation at the top. The market bifurcates — premium agentic models for high-stakes workflows, near-free models for high-volume commodity tasks. Both can be true simultaneously.

Sam: Now let's talk about the Meta-Amazon chip story, because I think this is genuinely underappreciated. Meta signed a deal for millions of Amazon's custom CPUs — not GPUs — specifically for agentic AI workloads. That's a striking architectural statement.

Priya: The GPU-centric view of AI infrastructure has been dominant because training large models is massively parallelizable, and GPUs are designed for exactly that kind of workload — thousands of cores doing the same floating point operation simultaneously. But agentic inference looks different. You have a model making sequential decisions, calling tools, waiting for results, branching based on outputs. That's a much more irregular, latency-sensitive compute pattern.

Sam: CPUs handle irregular branching, low-latency I/O, and complex control flow far better than GPUs. If agentic AI workloads really do have a fundamentally different compute profile — more sequential orchestration, less pure matrix math — then the infrastructure stack needs to reflect that. Meta buying millions of Amazon's custom CPUs is a bet that this is true at scale.

Priya: And it opens a new front in the chip competition. The GPU wars between NVIDIA, AMD, and the hyperscaler custom silicon shops have been the dominant narrative. But if agentic inference is CPU-bound in ways we're just starting to quantify, that reshapes who the relevant players are and what the build-out economics look like.

Sam: Which connects to the NVIDIA-Google announcement. At Google Cloud Next, they detailed new A5X bare-metal instances on NVIDIA's Vera Rubin NVL72 rack-scale systems, claiming up to 10x lower inference costs through hardware-software codesign. The Vera Rubin architecture is designed with inference economics in mind — not just raw training throughput. Getting inference costs down by an order of magnitude is what makes high-volume agentic deployments economically viable at all. The Meta CPU story and the NVIDIA-Google story are two different bets on what the agentic compute stack actually looks like in practice. Both might be right for different parts of the workload.

Priya: Let's spend real time on Anthropic's Claude Mythos, because this one deserves careful treatment. Two weeks ago, Anthropic announced that Mythos Preview can autonomously discover software vulnerabilities and turn them into working exploits — without expert human guidance. The vulnerabilities it found were in operating systems and internet infrastructure software. Code that thousands of professional developers had been working on and auditing for years.

Sam: The way this works, mechanically: the model is given access to a codebase and essentially does autonomous static and dynamic analysis. It builds a mental model of the code's logic, identifies where assumptions might break down, generates candidate exploit paths, tests them, refines. What used to require a skilled security researcher with deep domain knowledge — weeks of focused work — this model is doing autonomously.

Priya: This is AI finding real, exploitable vulnerabilities — a meaningful step beyond benchmark results on CTF challenges. The IEEE Spectrum analysis emphasizes that the vulnerabilities weren't toy examples. They were in critical infrastructure software. And Anthropic is restricting access to a limited number of companies because of the obvious dual-use implications.

Sam: The dual-use tension here is as sharp as it gets. The same capability that lets a defender find and patch a zero-day before an attacker exploits it also, in the wrong hands, is a zero-day factory. Anthropic's access restrictions are a reasonable response, but they're also a stopgap. Once the capability exists at this level, the question of who has it and under what conditions becomes a policy and governance question as much as a technical one.

Priya: And the asymmetry is what keeps me up at night. Defenders need to patch everything. Attackers need to find one thing. If autonomous vulnerability discovery gets commoditized — and given the DeepSeek trajectory on costs, that's not a distant scenario — that asymmetry gets a lot worse before the defensive tooling catches up.

Sam: Quick note on OpenAI's Privacy Filter — they've open-sourced a dedicated model for detecting and redacting PII from text. This is unglamorous but genuinely useful. One of the persistent friction points in enterprise AI adoption is feeding data through LLMs without leaking sensitive information. Having a standardized, open-source tool for PII redaction in the pipeline removes a real barrier. It's the kind of infrastructure-layer release that doesn't make headlines but gets quietly adopted everywhere.

Priya: And fast one on Yann LeCun's AMI Labs — a billion dollars, twelve employees, and a mandate to build AI that fundamentally isn't an LLM. LeCun has been consistent for years that autoregressive language models have architectural limitations that prevent them from achieving robust world models or reliable causal reasoning. AMI Labs is his funded bet that the field needs a different path. A billion dollars for twelve researchers is essentially saying: we're funding a paradigm shift attempt, not a product company.

Sam: Whether he's right is genuinely open. The LLM scaling curve has surprised almost everyone who predicted its limits. But the fact that serious investors are funding a credible alternative research program is healthy for the field. Monocultures in research are bad. Having well-resourced teams pursuing genuinely different architectures keeps the option space open.

Priya: And on Tesla committing to Intel's unfinished 14A process for custom AI silicon — the notable thing is the desperation signal. Non-NVIDIA players are so constrained on advanced chip manufacturing capacity that Tesla is betting on a process node that doesn't exist yet. That's how tight the advanced semiconductor supply situation remains.

Sam: Looking ahead — the theme threading through today is agentic AI arriving as an infrastructure problem, not just a model problem. GPT-5.5 is priced for it, Meta is rearchitecting compute for it, NVIDIA and Google are repricing inference for it, and Mythos is showing us what it enables in security. The question now open is how the ecosystem around agentic orchestration matures — the monitoring, the guardrails, the audit trails for what an autonomous model actually did and why.

Priya: And the cost compression story isn't slowing down. DeepSeek keeps demonstrating that the gap between frontier and near-frontier closes fast. That changes who can deploy what, which changes the threat landscape, which changes what defensive AI tooling needs to look like. These threads are all connected.

Sam: That's our show for Friday. Have a good weekend — and if you're poking at any of the DeepSeek V4 technical paper details or the Mythos access program, we'd genuinely love to hear what you find.

Priya: AI Revolution is produced for technical professionals who want the real picture. We'll be back Monday. Thanks for listening.