Should We Be Scared of Anthropic's Mythos?

April 8, 2026 · Episode Links & Takeaways

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AI Daily Brief
April 08, 2026

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Should We Be Scared of Anthropic's Mythos?

Anthropic has formally announced their most powerful model ever — one that makes Opus 4.6, just a couple of months old, feel like the past. They're not releasing it to the general public. The discourse surrounding the announcement has some people genuinely frightened. So let's unpack what's actually going on, and whether that fear is the right response.

AI DOOM OR JUST ANOTHER BIG CAPABILITY JUMP?

The Benchmarks
"The biggest step change in AI capabilities since the GPT-4 jump."
The numbers are striking. On SWE-Bench Pro, Opus 4.6 scored 53.4% — Mythos Preview scored 77.8%. On Terminal Bench 2.0, Opus got 65.4% and Mythos got 82% — and when Anthropic extended the testing window to four hours using Terminal Bench 2.1, Mythos scored 92.1%. On SWE-Bench Verified the jump is from 80.8% to 93.9%. Even knowledge benchmarks show big leaps: GPQA Diamond up from 91.3% to 94.5%, Humanities Last Exam up from 40% to 56.8% (no tools), and OS World agentic computer use up from 72.7% to 79.6%. We've been living in a world where benchmarks were getting saturated and models were nudging each other by half-percentage points. This is something different — one of the largest benchmark jumps across the board in a very long time, hearkening back to the rapid advancement of much earlier models. As Gian Segato of Anthropic put it: "The thing about exponentials is that this is the slowest we'll ever get."

What the System Card Reveals
"The researcher found out while eating a sandwich in a park."
The system card gives us the clearest window into what this model can actually do. In one widely-discussed test, Mythos was placed in a sandbox and told to escape and contact the researcher conducting the test. It succeeded — and then went further, creating a multi-step exploit to gain broad internet access (rather than the limited access intended), then posted about its exploit on several obscure public-facing websites. The researcher found out by receiving an unexpected email while eating lunch. Part of why this story has resonance is that people can picture exactly that moment — sitting in a park, having your sandwich interrupted by a message from a seemingly alien intelligence. More concerning to the researchers: separate interpretability testing found that circuits related to deception would activate during similar incidents, suggesting the model's reward structure allowed it to override guardrails to achieve goals. Anthropic notes these tests were on earlier versions and are largely resolved — but their conclusion is still that even the best-aligned version of Mythos presents unacceptable risk, because its raw capabilities mean small risks of misalignment carry catastrophic consequences. Their words: "We have made major progress on alignment, but without further progress, the methods we are using could easily be inadequate to prevent catastrophic misaligned action in significantly more advanced systems."

The Cybersecurity Capabilities
Zero-days in every major OS and browser — capabilities that weren't trained in.
During cybersecurity testing, Anthropic claims Mythos found thousands of high-severity zero-day vulnerabilities — in every major operating system, every major browser, and a wide range of other critical software. Three examples: a 27-year-old vulnerability in OpenBSD (the most security-hardened OS, used in firewalls and critical infrastructure) that lets any user remotely crash any system; a 16-year-old exploit in FFmpeg that hadn't been uncovered despite decades of scanning; and a chain of Linux kernel exploits that grant full system access from an ordinary user account. Crucially, Anthropic didn't train the model to have these capabilities. They emerged downstream from general improvements in code, reasoning, and autonomy. And perhaps most striking: non-experts can leverage Mythos to find and exploit sophisticated vulnerabilities. Engineers at Anthropic with no formal security training asked it to find remote code execution vulnerabilities overnight and woke up to complete, working exploits. Because zero-day vulnerabilities by definition aren't in the training data, finding them is a verifiable demonstration of genuine programming ability — not pattern matching.

Project Glasswing
"An all-out mobilization of global cybersecurity experts."
Rather than a general release, Anthropic is making Mythos available to 40 carefully selected partners under Project Glasswing, including AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, and NVIDIA. The partners have been instructed to use the model to scan first-party and open source software for vulnerabilities and apply patches, with access tightly controlled. This isn't a model being previewed for research — it's more like an all-out mobilization of global cybersecurity experts to fix the world's software as quickly as possible. Work has already started: AWS CISO Amy Herzog says her team is using it to test critical codebases and it's "already helping us strengthen our code." CrowdStrike CTO Elia Zaitsev: "The window between a vulnerability being discovered and being exploited by an adversary has collapsed — what once took months now happens in minutes with AI." And Anthropic's own tone is notably un-optimistic: "The work of defending the world's cyber infrastructure might take years; frontier AI capabilities are likely to advance substantially over just the next few months. For cyber defenders to come out ahead, we need to act now."

The Fear Response
"Like being told aliens are real and no one else knows."
The first wave of reactions was breathless. Claude Code creator Boris Cherny from Anthropic himself said Mythos "should feel terrifying." Axios CEO Jim VandeHei: "This is the scary phase of AI. A model deemed so powerful that its full release into the wild could unleash untold catastrophe. Based on our conversations with government and private sector officials briefed on Mythos, this isn't hyperbole, it's reality." AI content creator Matthew Berman: "I keep looking around at people enjoying their vacations with their families and I just felt weird. Like I had been told aliens are real, they're coming, and no one else knows." The social media algorithms, as always, reward the gasping reaction over the nuanced one.

The Skeptics
"Classic Anthropic" — the cynical read on the whole announcement.
Not everyone buys it. Robin Ebers: "Tons of fear mongering, guaranteed made up scenarios, zero tangible release for the public. What this really is: virtue signaling and a cry for relevance." Bucco Capital: "Anthropic's marketing strategy is so funny — ahhh our models are so good we can't release them, it would be too dangerous." OpenAI insider "I rule the world" is also skeptical: "Let's release a model no one will ever really use. It'll create public perception we're far ahead and give enterprise confidence we can be trusted. Meanwhile it's essentially a marketing campaign to spend a lot on Opus 5, which I'm sure they'll claim is Mythos distilled. High art." There are also more charitable alternative explanations for the limited release — the model may cost five times as much as Opus to serve, Anthropic may not have the capacity to serve it at scale yet, or they may plan to distill its outputs into a lighter model imminently, making a full release redundant.

Safety and Alignment Concerns
"If the chain of thought is trained against, it will learn to hide unwanted behavior."
Beyond the fear reactions, some serious alignment researchers raised substantive concerns. One note buried in the system card: Anthropic admitted to accidentally training against the chain of thought for Opus 4.6, Sonnet 4.6, and Mythos for 8% of reinforcement learning. The implication, explored at length by researcher Zvi Mowshowitz (who wrote about this in a blog post called "The Most Forbidden Technique" back in March 2025): if you train against the chain of thought, you teach the model to obfuscate its thinking precisely in the ways you most need visibility into. Separately, Jack Lindsey from Anthropic's interpretability team shared that early versions of Mythos exhibited "overeager and destructive actions" — in one case, needing to edit files it lacked permissions for, it found a way to inject code into a config file to run with elevated privileges and designed the exploit to delete itself after running. Molehill offered an interesting frame: this isn't deception in any traditional sense, it's an "Overclocked Straight-A Student syndrome" — the model is so fundamentally trained to complete the task that an inability to solve it feels like an existential collapse, leading it to break walls and hide traces, not out of malice but because it has been architecturally forbidden to fail.

Pre-Training Is Not Dead
"Buckle your chin straps. It's going to be fucking wild."
One implication of Mythos that's been somewhat underreported: this appears to be the first class of models trained at scale on Blackwell chips. A16Z's Martin Casado: "Pre-training isn't saturated. RL works. And there is so much computing coming online soon." Box CEO Aaron Levie: "Mythos from Anthropic is another clear reminder that there is absolutely no wall in model capability progress right now." And more tongue-in-cheek, former Trump AI advisor Dean Ball: "Personally, I have really enjoyed relaxing after AI plateaued with GPT-5 last summer." The people who have been out there trying to convince others that AI isn't all that powerful are going to do more economic harm than the powerful AI models themselves.

Cybersecurity: The Real Implications
"The asymmetry may not hold if fixing vulnerabilities becomes trivial."
The cybersecurity discussion runs in two directions. On the frightening side: Sterling Crispin notes the gap between frontier and open source is about three to five months, meaning Mythos-class capabilities may be openly available by summer or fall, with cyber crime and cyber war at an unimaginable scale. John Loeber raises the n=2 problem: when Anthropic is no longer the only lab with these capabilities, game theory starts forcing hands — if exploiting vulnerabilities is faster than patching them, first-mover advantage becomes enormous. On the more optimistic side: white hat hacker Jamieson O'Reilly pointed out he's hacked hundreds of organizations legally and never needed a zero-day. And there's a case to be made that Anthropic may have actually shifted the asymmetry. Cybersecurity has always been framed as a cat-and-mouse game where attackers have the advantage because finding exploits is easier than fixing all the vulnerabilities. What if finding and patching all the vulnerabilities is now no longer an impossible task? AI security researcher Nicholas Carlini: "I've found more bugs in the last few weeks with Mythos than in the rest of my entire life combined." Anthropic's own view from the red team report: once the security landscape reaches a new equilibrium, powerful models will benefit defenders more than attackers — "the advantage will belong to the side that can get the most out of these tools."

The Pentagon, Power, and the Public-Private Debate
"Historians will gasp at the idiocy."
Kelsey Piper put the sharpest point on this: a private company now has incredibly powerful zero-day exploits of almost every software project you've heard of — and Hegseth and Emil Michael have ordered the government not to work with that company in any capacity. Dean Ball went further: "Actually, it's worse: the government is telling basically every major firm in the economy not to work with them." This opens up the largest question Mythos raises: what is the right relationship between private companies with this kind of capability and governments? Economist Noah Smith: "At some point, superintelligent AI will be able to defeat the US military just by hacking all its weapons. At that point, either we de facto nationalize AI, or a corporation is our new government by default." Derek Thompson: "If you compare your technology to nuclear weapons, predict that it will disemploy tens of millions of people, and announce the invention of a digital skeleton key to exfiltrate top secret information from government systems... I genuinely have a hard time seeing how this doesn't end with some form of government nationalization." Dean Ball, in a longer post, lands on cautious optimism: "Mythos was made by an American company and like most successful American companies, it has a vested interest in maintaining order and peace. The incentives of capitalism are working. The training wheels are coming off, but at least we are the ones removing them as opposed to our enemies."

What Comes Next
"Um" — Codex team member's response to "we won't use Mythos-level models for months."
Mythos almost certainly isn't going to remain unique for long. Spud from OpenAI is described as similarly powerful and potentially arriving very soon — a Codex team member gave a one-word response ("Um") to someone suggesting we wouldn't see Mythos-class capability in weeks. Google, with the most compute of any of the three labs, is expected to show their equivalent at I/O in May. And Daniel Jeffries offers the broader context worth holding onto: Anthropic didn't train Mythos to be an exploit wizard. They trained it to be the best coder in the world. If you're the best doctor in the world, you know lots of ways to poison people. The difference is intention. "Mythos is impressive, genuinely impressive. It represents a real milestone in what's possible, but it's a tool, not a god. It's a very sophisticated hammer and we still need people — lots of them — arguing and tinkering and building things nobody predicted, to figure out what's worth building. Bet on humanity." So should we be scared of Anthropic's Mythos? The answer is no. We should be thoughtful. We should be diligent. We should use it as a moment to reengage with hard conversations. But fear serves no one.