2026

TL;DR On 12 June 2026 at 5:21pm ET, the US government issued an export control directive ordering Anthropic to suspend all access to Fable 5 and Mythos 5 - globally, for every user, including Anthropic’s own employees The stated reason is national security: the government believes it has identified a method of jailbreaking Fable 5. Anthropic says the evidence was verbal only and describes a narrow, non-universal technique - essentially asking the model to read a codebase and fix software flaws Anthropic reviewed a demonstration and found it surfaced a small number of previously known, minor vulnerabilities that are widely available from other models Anthropic disagrees that a narrow jailbreak justifies recalling a commercial model deployed to hundreds of millions of people, and warns the same standard would “essentially halt all new model deployments for all frontier model providers” All other Anthropic models are unaffected. The company says it believes this is a misunderstanding and is working to restore access Four days. That is how long Mythos-class capability lasted as a publicly available product before the US government ordered it off the shelf. ...

TL;DR Bloomberg’s The Circuit with Emily Chang went inside Anthropic in a rare, in-depth episode released June 10, 2026. Dario and Daniela Amodei discuss the founding story, the Pentagon dispute, and why they say safety and commercial success are the same bet. Anthropic is now valued at $965 billion, eclipsing OpenAI’s $852 billion for the first time, after an 80-fold revenue surge in Q1 2026. The Pentagon story is not PR - Anthropic refused to remove safety guardrails from its military contract, was blacklisted by the Trump administration, and sued. A federal judge sided with Anthropic. A confidential S-1 IPO filing in June 2026 means this stops being a private company conversation soon. The Bloomberg Documentary: Emily Chang Inside Anthropic Bloomberg’s The Circuit has done this kind of access piece before - Zuckerberg, Musk, Jensen Huang. But the Anthropic episode feels different in tone. Emily Chang is not sitting across from a founder who has already won. She is sitting across from two founders in the middle of one of the most consequential moments in the company’s short history: record valuation, Pentagon litigation, IPO on the horizon, and model releases arriving fast enough that the competitive landscape changes every few months. ...

Dario Amodei has published a new essay, Policy on the AI Exponential, and it reads like the third act of a trilogy. Machines of Loving Grace made the case for what powerful AI could give us. The Adolescence of Technology catalogued what could go wrong. This one is about the machinery in between - the laws, agencies, and international arrangements that will decide which of those two essays turns out to be the better prediction. ...

TL;DR When two AI agents talk to each other in English, they are doing something faintly absurd: serialising rich internal state into a lossy human language, transmitting it, and decoding it back. English between machines is a compatibility layer, not a natural medium. Machines have already shown they will drop that layer the moment we let them - negotiation bots drifting out of English in 2017, agents switching to sound-based data protocols in 2025, and research systems now sharing internal model state directly with no language in between. The same logic applies to programming languages. Python and Rust exist for human readers. If agents write, maintain, and consume the software, the human-readability requirement quietly disappears - and with it, eventually, the need for source code and compilers as we know them. I do not think compilers vanish so much as sink. Like assembly, the layers below us stop being something humans write or read, while the guarantees they provide get absorbed into the agents’ toolchain. The part worth worrying about is not efficiency, it is legibility. Human language and human-readable code are our audit trail into what machines are doing. This is all speculation on my part, and I sketch where I think the line should be held. Human Language Is a Compatibility Layer Think about what actually happens when two AI agents have a conversation in English today. ...

TL;DR Claude Fable 5 is Anthropic’s first Mythos-class model made safe for general use - state-of-the-art on nearly every benchmark Anthropic tested, with the gap widening on longer, more complex tasks Claude Mythos 5 is the same underlying model with cyber safeguards lifted for Project Glasswing partners; a biology trusted-access program is coming next Risky queries in cybersecurity, biology/chemistry, or suspected distillation attempts are routed to Claude Opus 4.8 instead - roughly 5% of sessions, with Anthropic acknowledging some false positives Pricing drops to $10 / $50 per million input/output tokens - less than half what Mythos Preview cost Fable 5 is free on Pro, Max, Team, and seat-based Enterprise plans through 22 June 2026, then moves to usage credits until capacity catches up Two months ago I wrote that Claude Mythos Preview was the benchmark breaker that would not be released - 93.9% on SWE-bench, thousands of zero-day vulnerabilities found autonomously, access restricted to a dozen companies through Project Glasswing. The question hanging over that post was whether Anthropic could ever democratise Mythos-level capability without democratising the offensive potential. ...

For most of the last decade, “ethical data use” was something that happened in a different building. The lawyers wrote the privacy policy, the data protection officer ran the impact assessment, and the engineers built whatever the ticket said. The ethics lived in a PDF, and the pipeline lived in the warehouse, and the two rarely met. In 2026 that separation has quietly collapsed. The reason is not that engineers suddenly became more principled - it is that the decisions which determine whether data is used ethically are now made at the schema, the table, and the access-control layer, and those are the engineer’s decisions. Consent, deletion, minimisation, provenance, bias: every one of them is now something you either build into the pipeline or fail to. This is a practical look at what that means. ...

Dario Amodei is one of the few frontier-lab CEOs whose public talking points have not changed materially in five years. The same message he gave to small audiences in 2021 - that powerful AI is coming faster than people think, that the safety problem is real, and that the companies building it have an obligation to do so carefully - is the message he is giving to Congress and Davos in 2026. The thing that has changed is that he now runs the company most aggressively turning that message into a commercial position. ...

For most of the AI conversation in 2024 and 2025, the binding constraints on the build-out were chips and capital. By 2026 the conversation has shifted, and the constraint that gets discussed most seriously inside the hyperscalers is electricity. Not the cost of electricity. The actual physical availability of electrons - at gigawatt scale, in the places where the data centres need to be, on the schedule the model labs need them to be. The story does not have a single villain or a single number, but it has a shape, and the shape is becoming the story of the second half of the decade. ...

For most of the last decade, talking about AI hardware meant talking about Nvidia. In 2026 that has stopped being true at the inference layer. Three companies - Cerebras, Groq, and SambaNova - have built genuinely different chips around the same insight: that the workload economics of running models in production are not the same as the workload economics of training them, and that the chip architecture should follow the workload. The bet has been right enough that Nvidia has now licensed pieces of it. ...

Two years ago the way to make a model better was to train a bigger one. By the start of 2026 that recipe has stopped being the most interesting answer. The frontier has moved to a different lever - letting the model think for longer at inference time, generating intermediate reasoning, and only then producing the final answer. The category has a name now (reasoning models) and a family of products built around it. The interesting questions are no longer whether the trick works, because it clearly does, but when to reach for one, where it lands in production, and what the costs actually look like once the demo glow wears off. ...