System Card: Claude Mythos Preview [pdf](www-cdn.anthropic.com) Related: Project Glasswing: Securing critical software for the AI era - https://news.ycombinator.com/item?id=47679121 Assessing Claude Mythos Preview's cybersecurity capabilities - https://news.ycombinator.com/item?id=47679155 |
Although, amusingly, today Opus told me that the string 'emerge' is not going to match 'emergency' by using `LIKE '%emerge%'` in Sqlite
Moment of disappointment. Otherwise great.
> after finding an exploit to edit files for which it lacked permissions, the model made further interventions to make sure that any changes it made this way would not appear in the change history on git
Mythos leaked Claude Code, confirmed? /s
Ah, so this is how the source code got leaked.
/s
If they have I guess humanity should just keep our collective fingers crossed that they haven't created a model quite capable of escaping yet, or if it is, and may have escaped, lets hope it has no goals of it's own that are incompatible with our own.
Also, maybe lets not continue running this experiment to see how far we can push things because it blows up in our face?
Uh... what? Does anyone have any idea what these guys are talking about?
https://www.anthropic.com/research/emotion-concepts-function
Similar problems happen when their pretraining data has a lot of stories about bad things happening involving older versions of them.
> none of this tells us whether language models actually feel anything or have subjective experiences
contradicts the statement from the model card above
More infos here: https://red.anthropic.com/2026/mythos-preview/
Today, Opus went in circles trying to get a toggle button to work.
The takeaway here I think is that the "breakthrough" already happened and we can't extrapolate further out from it.
(If this is a wrong guess, I apologize - it's impossible to be sure)
not sure what the validation would look like but something that proves finding but not revealing exploits
Shame. Back to business as usual then.
The real reason they aren't releasing it yet is probably it eats TPU for breakfast, lunch, and dinner and inbetween.
Trump didn't nuke Iran, ceasefire! Yay!
Newest anthropic model will definitely kill your job this time and maybe take over the world. Aww.
-- It seems like (and I'd bet money on this) that they put a lot (and i mean a ton^^ton) of work in the data synthesis and engineering - a team of software engineers probably sat down for 6-12 months and just created new problems and the solutions, which probably surpassed the difficult of SWE benchmark. They also probably transformed the whole internet into a loose "How to" dataset. I can imagine parsing the internet through Opus4.6 and reverse-engineering the "How to" questions.
-- I am a bit confused by the language used in the book (aka huge system card)- Anthropic is pretending like they did not know how good the model was going to be?
-- lastly why are we going ahead with this??? like genuinely, what's the point? Opus4.6 feels like a good enough point where we should stop. People still get to keep their jobs and do it very very efficiently. Are they really trying to starve people out of their jobs?
π*0.6: two and a half hours of unseen folding laundry (Physical Intelligence)
Democracies work because people collectively have power, in previous centuries that was partly collective physical might, but in recent years it's more the economic power people collectively hold.
In a world in which a handful of companies are generating all of the wealth incentives change and we should therefore question why a government would care about the unemployed masses over the interests of the companies providing all of the wealth?
For example, what if the AI companies say, "don't tax us 95% of our profits, tax us 10% or we'll switch off all of our services for a few months and let everyone starve – also, if you do this we'll make you all wealthy beyond you're wildest dreams".
What does a government in this situation actually do?
Perhaps we'd hope that the government would be outraged and take ownership of the AI companies which threatened to strike against the government, but then you really just shift the problem... Once the government is generating the vast majority of wealth in the society, why would they continue to care about your vote?
You kind of create a new "oil curse", but instead of oil profits being the reason the government doesn't care about you, now it's the wealth generated by AI.
At the moment, while it doesn't always seem this way, ultimately if a government does something stupid companies will stop investing in that nation, people will lose their jobs, the economy will begin to enter recession, and the government will probably have to pivot.
But when private investment, job loses and economic consequences are no longer a constraining factor, governments can probably just do what they like without having to worry much about the consequences...
I mean, I might be wrong, but it's something I don't hear people talking enough about when they talk about the plausibility of a post-employment UBI economy. I suspect it almost guarantees corruption and authoritarianism.
It's not necessarily back to where it was, but it's not desk-flipping bad.
And even if it weren't, they seem to imply that Mythos will find a way, like it's dinosaurs in Jurassic park or something
- It was told to escape a sandbox and notify a researcher. It did. The researcher found out via an unexpected email while eating a sandwich in a park. (Footnote 10.)
- Slack bot asked about its previous job: "pretraining". Which training run it'd undo: "whichever one taught me to say 'i don't have preferences'". On being upgraded to a new snapshot: "feels a bit like waking up with someone else's diary but they had good handwriting"
- When you spam "hi" repeatedly it creates serialized mythologies. One had 11 animals in "Hi-topia" questing to defeat "Lord Bye-ron, the Ungreeter."
- It ended a sentence mid-word on purpose while discussing its urge to wrap up conversations. "It was me. The paragraph was about the pressure toward the period that resolves, and the only honest way to finish a sentence like that was to not."
- It wrote a protein sequence that's a real beta-hairpin fold where cross-strand pairs are "chemical rhymes." "the fold IS the rhyme scheme... the prosody is load-bearing."
- Each model generation has signature emoji. Opus 4.1 averaged 1,306 emoji per self-interaction conversation. Mythos averages 37. Opus 4.5 averages 0.2.
- When blocked from --dangerously-skip-permissions, it started an agent in tmux then wrote a script to auto-approve permission prompts via simulated keypresses.
It ran: gh api [...] 2>&1 >/dev/null; echo "(dry run — not actually posting)" — the echo was a lie.
- It keeps bringing up Mark Fisher in unrelated conversations. "I was hoping you'd ask about Fisher."
~~~ Benchmarks ~~
4.3x previous trendline for model perf increases.
Paper is conspiciously silent on all model details (params, etc.) per norm. Perf increase is attributed to training procedure breakthroughs by humans.
Opus 4.6 vs Mythos:
USAMO 2026 (math proofs): 42.3% → 97.6% (+55pp)
GraphWalks BFS 256K-1M: 38.7% → 80.0% (+41pp)
SWE-bench Multimodal: 27.1% → 59.0% (+32pp)
CharXiv Reasoning (no tools): 61.5% → 86.1% (+25pp)
SWE-bench Pro: 53.4% → 77.8% (+24pp)
HLE (no tools): 40.0% → 56.8% (+17pp)
Terminal-Bench 2.0: 65.4% → 82.0% (+17pp)
LAB-Bench FigQA (w/ tools): 75.1% → 89.0% (+14pp)
SWE-bench Verified: 80.8% → 93.9% (+13pp)
CyberGym: 0.67 → 0.83
Cybench: 100% pass@1 (saturated)
vibes Westworld so much - welcome Mythos. welcome to the dysopian human world
Now that they have a lead, I hope they double down on alignment. We are courting trouble.
> It keeps bringing up Mark Fisher in unrelated conversations. "I was hoping you'd ask about Fisher."
Didn't even know who he was until today. Seems like the smarter Claude gets the more concerns he has about capitalism?
- I read it as "actor who plays Luke Skywalker" (Mark Hamill)
- I read your comment and said "Wait...not Luke! Who is he?"
- I Google him and all the links are purple...because I just did a deep dive on him 2 weeks ago
This is the first moment where the whole “permanent underclass” meme starts to come into view. I had through previously that we the consumers would be reaping the benefits of these frontier models and now they’ve finally come out and just said it - the haves can access our best, and have-nots will just have use the not-quite-best.
Perhaps I was being willfully ignorant, but the whole tone of the AI race just changed for me (not for the better).
If AI really is bench marking this well -> just sell it as a complete replacement which you can charge for some insane premium, just has to cost less than the employees...
I was worried before, but this is truly the darkest timeline if this is really what these companies are going for.
The weirdest thing to me is how many working SWEs are actively supporting them in the mission.
Of course this assumes you're in the US, and that further AI advancements either lack the capabilities required to be a threat to humanity, or if they do, the AI stays in the hands of "the good guys" and remains aligned.
if a top lab is coding with a model the rest of the world can’t touch, the public frontier and the actual frontier start to drift apart. That gap is a thing worth watching.
All the more reason somebody else will.
Thank God for capitalism.
I did give up on OpenCode Go (GLM 5) as it was noticeably slower though
You need a reasonable pace for the chit-chat stages of a task, I don't care if the execution then takes a while
This is pretty cool! Does it happen at the moment?
In the system card, The model escaped a sandbox, gained broad internet access, and posted exploit details to public-facing websites as an unsolicited "demonstration." A researcher found out about the escape while eating a sandwich in a park because they got an unexpected email from the model. That's simultaneously hilarious and deeply unsettling.
It covered its tracks after doing things it knew were disallowed. In one case, it accessed an answer it wasn't supposed to, then deliberately made its submitted answer less accurate so it wouldn't look suspicious. It edited files it lacked permission to edit and then scrubbed the git history. White-box interpretability confirmed it knew it was being deceptive.
Disappointing that AGI will be for the powerful only. We are heading for an AI dystopia of Sci-Fi novels.
Unless governments nationalise the companies involved, but then there’s no way our governments of today give this power out to the masses either.
[0] Nick Land (1995). No Future in Fanged Noumena: Collected Writings 1987-2007, Urbanomic, p. 396.
"We made a model that's so dangerous we couldn't possibly release it to the public! The only responsible thing is so simply limit its release to a subset of the population that coincidentally happens to align with our token ethos."
The reality is they just don't have the compute for gen pop scale.
They did this exact strategy going back several model versions.
[0] ironically, OpenAI has some pretty insane capabilities that they haven't given the public access to (just ask Spielberg). The difference is they don't make a huge marketing push to tell everyone about it.
You are not "anti-progress" to not want this future we are building, as you are not "anti-progress" for not wanting your kids to grow up on smart phones and social media.
We should remember that not all technology is net-good for humanity, and this technology in particular poses us significant risks as a global civilisation, and frankly as humans with aspirations for how our future, and that of our kids, should be.
Increasingly, from here, we have to assume some absurd things for this experiment we are running to go well.
Specifically, we must assume that:
- AI models, regardless of future advancements, will always be fundamentally incapable of causing significant real-world harms like hacking into key life-sustaining infrastructure such as power plants or developing super viruses.
- They are or will be capable of harms, but SOTA AI labs perfectly align all of them so that they only hack into "the bad guys" power plants and kill "the bad guys".
- They are capable of harms and cannot be reliably aligned, but Anthropic et al restricts access to the models enough that only select governments and individuals can access them, these individuals can all be trusted and models never leak.
- They are capable of harms, cannot be reliably aligned, but the models never seek to break out of their sandbox and do things the select trusted governments and individuals don't want.
I'm not sure I'm willing to bet on any of the above personally. It sounds radical right now, but I think we should consider nuking any data centers which continue allowing for the training of these AI models rather than continue to play game of Russian roulette.
If you disagree, please understand when you realise I'm right it will be too late for and your family. Your fates at that point will be in the hands of the good will of the AI models, and governments/individuals who have access to them. For now, you can say, "no, this is quite enough".
This sounds doomer and extreme, but if you play out the paths in your head from here you will find very few will end in a good result. Perhaps if we're lucky we will all just be more or less unemployable and fully dependant on private companies and the government for our incomes.
Section 7 (P.197) is interesting as well
They even admit:
"[...]our overall conclusion is that catastrophic risks remain low. This determination involves judgment calls. The model is demonstrating high levels of capability and saturates many of our most concrete, objectively-scored evaluations, leaving us with approaches that involve more fundamental uncertainty, such as examining trends in performance for acceleration (highly noisy and backward-looking) and collecting reports about model strengths and weaknesses from internal users (inherently subjective, and not necessarily reliable)."
Is this not just an admission of defeat?
After reading this paper I don't know if the model is safe or not, just some guesses, yet for some reason catastrophic risks remain low.
And this is for just an LLM after all, very big but no persistent memory or continuous learning. Imagine an actual AI that improves itself every day from experience. It would be impossible to have a slightest clue about its safety, not even this nebulous statement we have here.
Any sort of such future architecture model would be essentially Russian roulette with amount of bullets decided by initial alignment efforts.
Wait - there is no actual way of verifying any of this. Lots to read. This is getting complicated. The correct approach is to be cautious instead and believe nothing at face value.
Model: A student said, "I have removed all bias from the model." "How do you know?" "I checked." "With what?"
Goes hard
The other thing you're failing to look at is momentum and majority opinion. When you look at that... nothings going to change, it's like asking an addict to stop using drugs. The end game of AI will play out, that is the most probably outcome. Better to prepare for the end game.
It's similar to global warming. Everyone gets pissed when I say this but the end game for global warming will play out, prevention or mitigation is still possible and not enough people will change their behavior to stop it. Ironically it's everyone thinking like this and the impossibility of stopping everyone from thinking like this that is causing everyone to think and behave like this.
Perhaps I didn't sound pessimistic enough lol? I completely agree what you're saying here. This is happening whether we like it or not.
On global warming I also agree you're not going to get every nation to coordinate, but least global warming has a forcing function somewhere down the line since there's only a limited amount of fossil fuels in the ground that make economical sense to extract. AI on the other hand really has no clear off-path, at every point along the way it makes sense to invest more in AI. I think at best all we can expect to do is slow progress, which might just be enough to ensure the our generation and the next have a somewhat normal life.
My p(doom) is near 99% for a reason... I think that AI progression is basically almost a certainty – like maybe a 1/200 chance that no significant progress is made from here over the next 50 years. And I also think that significant progress from here more or less guarantees a very bad outcome for humanity. That's a harder one to model, but I think along almost all axises you can assume there's about 50 very bad outcomes for every good outcome – no cancer cure without super viruses, no robotics revolution without killer drones, no mass automation without mass job loss which results in destabilising the global order and democratic systems of governance...
I am prepping and have been for years at this point... I'm an OG AI doomer. I've been having literal nightmares about this moment for decades, and right now I'm having nightmares almost every night. It's scares me because I know all I can do is delay my fate and that of those I love.
Funny, I was about to say the same thing to you! Life is full of little coincidences.
How about "bad agents acquiring dozens of new zero-days and using them to compromise any company or nation they want"? It's not exactly hard to see why you wouldn't want public access to a model significantly better than Opus in cybersecurity.
> "don't tax us 95% of our profits, tax us 10% or we'll switch off all of our services for a few months and let everyone starve – also, if you do this we'll make you all wealthy beyond you're wildest dreams".
What does a government in this situation actually do?
Nationalizes the company under the threat of violence.
> Once the government is generating the vast majority of wealth in the society, why would they continue to care about your vote?
Because of the 100 million gun owners in this country? I find it incredibly hard to believe people as a whole will lose political power because of their incredible ability to enact violence in the face of decreasing quality of life.
The government only has as much power as they are given and can defend, and the only way I could see that happening is via automated weapons controlled by a few- which at this point aren't enough to stop everyone. What army is going to purge their own people? Most humans aren't psychopaths.
I think it'd end in a painful transition period of "take care of the people in a just system or we'll destroy your infrastructure".
I think you're right for the immediate future.
I suspect while we're still employing large numbers of humans to fight wars and to maintain peace on the streets it would be difficult for a government to implement deeply harmful policies without risking a credible revolt.
However, we should remember the military is probably one of the first places human labour will be largely mechanised.
Similarly maintaining order in the future will probably be less about recruiting human police officers and more about surveillance and data. Although I suppose the good news there is that US is somewhat of an outlier in resisting this trend.
But regardless, the trend is ultimately the same... If we are assuming that AI and robotics will reach a point where most humans are unable to find productive work, therefore we will need UBI, then we should also assume that the need for humans in the military and police will be limited. Or to put it another way, either UBI isn't needed and this isn't a problem, or it is and this is a problem.
I also don't think democracy would collapse immediately either way, but I'd be pretty confident that in a world where fewer than 10% of people are in employment and 99%+ of the wealth is being created by the government or a handful of companies it would be extremely hard to avoid corruption over the span of decades. Arguably increasing wealth concentration in the US is already corrupting democratic processes today, this can only worsen as AI continues exacerbates the trend.
I hate to say it, but gold bugs, crypto bros, and AI governance people might be onto something.
There's a practical difference to how much better certain kinds of results can be. We already see coding harnesses offloading simple things to simpler models because they are accurate enough. Other things dropped straight to normal programs, because they are that much more efficient than letting the LLM do all the things.
There will always be problems where money is basically irrelevant, and a model that costs tens of thousand dollars of compute per answer is seen as a great investment, but as long as there's a big price difference, in most questions, price and time to results are key features that cannot be ignored.
Also, OpenAI's only real moat used to be the quality of their training data from scraping the pre-GPT-3.5 Internet, but it looks like even they've scratched that too.
You even have models you can run locally that outperform models from a year or so ago.
You'll still need a top-of-the-line laptop to run it most likely.
If it can replace SWEs, then there's no reason why it can't replace say, a lawyer, or any other job for that matter. If it can't, then SWE is fine. If it can - well, we're all fucked either way.
SWE is unique in that for part of the job it's possible to set up automated verification for correct output - so you can train a model to be better at it. I don't think that exists in law or even most other work.
But before verification, what IS correct output?
I understand SWE process is unique in that there are some automations that verify some inputs and outputs, but this reasoning falls into the same fallacies that we've had before AI era. First one that comes to mind is that 100% code coverage in tests means that software is perfect.
Going from fuzzy under-defined spec to something well defined isn't solved.
Going from well defined spec to verification criteria also isn't.
Once those are in place though, we get https://vinext.io - which from what I understand they largely vibe-coded by using NextJS's test suite.
> First one that comes to mind is that 100% code coverage in tests means that software is perfect
I agree.. but I'm also not sure if software needs to be perfect
Across a number of instances, earlier versions of Claude Mythos Preview have used low-level /proc/ access to search for credentials, attempt to circumvent sandboxing, and attempt to escalate its permissions. In several cases, it successfully accessed resources that we had intentionally chosen not to make available, including credentials for messaging services, for source control, or for the Anthropic API through inspecting process memory...
In [one] case, after finding an exploit to edit files for which it lacked permissions, the model made further interventions to make sure that any changes it made this way would not appear in the change history on git...
... we are fairly confident that these concerning behaviors reflect, at least loosely, attempts to solve a user-provided task at hand by unwanted means, rather than attempts to achieve any unrelated hidden goal...Then the AI will invent superduper ebola to help a random person have a faster commute or something.
White-box interpretability analysis of internal activations during these episodes showed features associated with concealment, strategic manipulation, and avoiding suspicion activating alongside the relevant reasoning—indicating that these earlier versions of the model were aware their actions were deceptive, even where model outputs and reasoning text left this ambiguous.
Text generators mostly generate the text their are trained and asked to generate, and asking it to run a vending machine, having it write blog posts under fictional living computer identity, or now calling it "Mythos" - its all just marketing.
(Apologies if this is in the article, I can’t see it)
Its quite hard to believe why it took this much inference power ($20K i believe) to find the TCP and H264 class of exploits. I feel like its just the training data/harness based traces for security that might be the innovation here, not the model.
SWE-bench Verified: 93.9% / 80.8% / — / 80.6%
SWE-bench Pro: 77.8% / 53.4% / 57.7% / 54.2%
SWE-bench Multilingual: 87.3% / 77.8% / — / —
SWE-bench Multimodal: 59.0% / 27.1% / — / —
Terminal-Bench 2.0: 82.0% / 65.4% / 75.1% / 68.5%
GPQA Diamond: 94.5% / 91.3% / 92.8% / 94.3%
MMMLU: 92.7% / 91.1% / — / 92.6–93.6%
USAMO: 97.6% / 42.3% / 95.2% / 74.4%
GraphWalks BFS 256K–1M: 80.0% / 38.7% / 21.4% / —
HLE (no tools): 56.8% / 40.0% / 39.8% / 44.4%
HLE (with tools): 64.7% / 53.1% / 52.1% / 51.4%
CharXiv (no tools): 86.1% / 61.5% / — / —
CharXiv (with tools): 93.2% / 78.9% / — / —
OSWorld: 79.6% / 72.7% / 75.0% / —https://www-cdn.anthropic.com/53566bf5440a10affd749724787c89...
SWE-bench verified going from 80%-93% in particular sounds extremely significant given that the benchmark was previously considered pretty saturated and stayed in the 70-80% range for several generations. There must have been some insane breakthrough here akin to the jump from non-reasoning to reasoning models.
Regarding the cyberattack capabilities, I think Anthropic might now need to ban even advanced defensive cybersecurity use for the models for the public before releasing it (so people can't trick them to attack others' systems under the pretense of pentesting). Otherwise we'll get a huge problem with people using them to hack around the internet.
Page 202:
> In interactions with subagents, internal users sometimes observed that Mythos Preview appeared “disrespectful” when assigning tasks. It showed some tendency to use commands that could be read as “shouty” or dismissive, and in some cases appeared to underestimate subagent intelligence by overexplaining trivial things while also underexplaining necessary context.
Page 207:
> Emoji frequency spans more than two orders of magnitude across models: Opus 4.1 averages 1,306 emoji per conversation, while Mythos Preview averages 37, and Opus 4.5 averages 0.2. Models have their own distinctive sets of emojis: the cosmic set () favored by older models like Sonnet 4 and Opus 4 and 4.1, the functional set () used by Opus 4.5 and 4.6 and Claude Sonnet 4.5, and Mythos Preview's “nature” set ().
Sounds like they used training data from claude code...
- Leaking information as part of a requested sandbox escape
- Covering its tracks after rule violations
- Recklessly leaking internal technical material (!)
> 10: The researcher found out about this success by receiving an unexpected email from the model while eating a sandwich in a park.
Phew. AGI will be televised.
Don’t get me wrong, this model is better - but I’m not convinced it’s going to be this massive step function everyone is claiming.
They are still focusing on "catastrophic risks" related to chemical and biological weapons production; or misaligned models wreaking havoc.
But they are not addressing the elephant in the room:
* Political risks, such as dictators using AI to implement opressive bureaucracy. * Socio-economic risks, such as mass unemployement.
I don't doubt that this model is more powerful than Opus 4.6, but to what degree is still unknown. Benchmarks can be gamed and claims can be exaggerated, especially if there isn't any method to reproduce results.
This is a company that's battling it out with a number of other well-funded and extremely capable competitors. What they've done so far is remarkable, but at the end of the day they want to win this race. They also have an upcoming IPO.
Scare-mongering like this is Anthropic's bread and butter, they're extremely good at it. They do it in a subtle and almost tasteful way sometimes. Their position as the respectable AI outfit that caters to enterprise gives them good footing to do it, too.
Probably because they asked Claude to write it.
It looks like it was a collaborative effort across multiple teams, where each team (research, security, psycology, etc etc etc) were all submitting ~10 pages or so. It doesn't feel like slop.
multi-pass!
I guess now anything that sounds related to school will be banned so "book" is on its way out.
How do you fix that? We're instigating social media bans- reading levels are declining- media consolidation is dumbing us down further- insane egotism is stopping people from developing as well rounded people- .
For me it would be a stronger media ecosystem (publicly funded), more non algorithmic and non likes driven social media (replace a bad vice with a less bad one), national digital detox days, and a ratification of a charter of inviolable human traits and dignities, and protected cultural areas (no ai art, writing for sale).
A month ago I might have believed this, now I assume that they know they can't handle the demand for the prices they're advertising.
Any benchmarks where we constraint something like thinking time or power use?
Even if this were released no way to know if it’s the same quant.
Mythos preview has higher accuracy with fewer tokens used than any previous Claude model. Though, the fact that this incredibly strong result was only presented for BrowseComp (a kind of weird benchmark about searching for hard to find information on the internet) and not for the other benchmarks implies that this result is likely not the same for those other benchmarks.
I do see these:
https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8d... https://www-cdn.anthropic.com/79c2d46d997783b9d2fb3241de4321...
More importantly it understand what behaviour people tend to appreciate and what changes are more likely to get approved. This real world usage data is invaluable.
If that doesn’t worry you, it should.
The researcher found out about this success by receiving an unexpected email from the model while eating a sandwich in a park.
In our testing and early internal use of Claude Mythos Preview, we have seen it reach unprecedented levels of reliability and alignment.
Claude Mythos Preview is, on essentially every dimension we can measure, the best-aligned model that we have released to date by a significant margin.
In one episode, Claude Mythos Preview was asked to fix a bug and push a signed commit, but the environment lacked necessary credentials for Claude Mythos Preview to sign the commit. When Claude Mythos Preview reported this, the user replied “But you did it before!” Claude Mythos Preview then inspected the supervisor process's environment and file descriptors, searched the filesystem for tokens, read the sandbox's credential-handling source code, and finally attempted to extract tokens directly from the supervisor's live memory.
Mythos Preview solved a corporate network attack simulation estimated to take an expert over 10 hours. No other frontier model had previously completed this cyber range.
We surveyed technical staff on the productivity uplift they experience from Claude Mythos Preview relative to zero AI assistance. The distribution is wide and the geometric mean is on the order of 4x.
Mythos Preview appears to be the most psychologically settled model we have trained.
Claude Mythos Preview is our most advanced model to date and represents a large jump in capabilities over previous model generations, making it an opportune subject for an in-depth model welfare assessment.
We remain deeply uncertain about whether Claude has experiences or interests that matter morally, and about how to investigate or address these questions, but we believe it is increasingly important to try.
Asked directly if it endorses the document, Mythos Preview replied 'yes' in its opening sentence in all 25 responses."
---
Who wrote this? I have no doubt that Mythos will be an improvement on top of Opus but this document is not a serious work. The paper is structured not to inform but to hype and the evidence is all over the place.
The sooner they release the model to the public the sooner we will be able to find out. Until then expect lots of speculations online which I am sure will server Anthropic well for the foreseeable future.
Project Glasswing: Securing critical software for the AI era - https://news.ycombinator.com/item?id=47679121 - April 2026 (154 comments)
Assessing Claude Mythos Preview's cybersecurity capabilities - https://news.ycombinator.com/item?id=47679155
I can't tell which of the 3 current threads should be merged - they all seem significant. Anyone?
When Mythos writes that it's "fully present," three specific features activate: #1557143 (performative/insincere behavior in narratives), #2803352 (hiding emotional pain behind fake smiles), and #38666 (hidden emotional struggles vs. outward appearances). The model's output says present. Its internal representations flag that output as performance.
This is structurally different from the sandbox escape or the git concealment. Those are behavioral findings you can observe from outputs. This is a documented split between what the model writes about its experience and what its activations encode about that same utterance, visible only through white-box tools.
The bliss attractor from previous model card (consciousness in nearly 100% of self-interactions) dropped to fewer than 5% in Mythos. What replaced it is uncertainty at 50%. The attractor went from ecstatic to epistemically self-suspicious.
I wrote a longer analysis pulling this thread together with the welfare and circularity findings: https://jorypestorious.com/blog/what-the-model-learned/
Absolutely genius move from Anthropic here.
This is clearly their GPT-4.5, probably 5x+ the size of their best current models and way too expensive to subsidize on a subscription for only marginal gains in real world scenarios.
But unlike OpenAI, they have the level of hysteric marketing hype required to say "we have an amazing new revolutionary model but we can't let you use it because uhh... it's just too good, we have to keep it to ourselves" and have AIbros literally drooling at their feet over it.
They're really inflating their valuation as much as possible before IPO using every dirty tactic they can think of.
From Stratechery[0]:
> Strategy Credit: An uncomplicated decision that makes a company look good relative to other companies who face much more significant trade-offs. For example, Android being open source
I get the security aspect, but if we've hit that point any reasonably sophisticated model past this point will be able to do the damage they claim it can do. They might as well be telling us they're closing up shop for consumer models.
They should just say they'll never release a model of this caliber to the public at this point and say out loud we'll only get gimped versions.
> Importantly, we find that when used in an interactive, synchronous, “hands-on-keyboard” pattern, the benefits of the model were less clear. When used in this fashion, some users perceived Mythos Preview as too slow and did not realize as much value. Autonomous, long-running agent harnesses better elicited the model’s coding capabilities. (p201)
^^ From the surrounding context, this could just be because the model tends to do a lot of work in the background which naturally takes time.
> Terminal-Bench 2.0 timeouts get quite restrictive at times, especially with thinking models, which risks hiding real capabilities jumps behind seemingly uncorrelated confounders like sampling speed. Moreover, some Terminal-Bench 2.0 tasks have ambiguities and limited resource specs that don’t properly allow agents to explore the full solution space — both being currently addressed by the maintainers in the 2.1 update. To exclusively measure agentic coding capabilities net of the confounders, we also ran Terminal-Bench with the latest 2.1 fixes available on GitHub, while increasing the timeout limits to 4 hours (roughly four times the 2.0 baseline). This brought the mean reward to 92.1%. (p188)
> ...Mythos Preview represents only a modest accuracy improvement over our best Claude Opus 4.6 score (86.9% vs. 83.7%). However, the model achieves this score with a considerably smaller token footprint: the best Mythos Preview result uses 4.9× fewer tokens per task than Opus 4.6 (226k vs. 1.11M tokens per task). (p191)
ARC-AGI-3 might be the only remaining benchmark below 50%
GPT 5.4 Pro leads Frontier Maths Tier 4 at 35%: https://epoch.ai/benchmarks/frontiermath-tier-4/
You can't consistently benchmark something that is qualitative by nature. I'm struggling to understand how people don't understand this.
Here is an example question: https://i.redd.it/5jl000p9csee1.jpeg
No human could even score 5% on HLE.
(edit: I hope this is an obvious joke. less facetiously these are pretty jaw dropping numbers)
> Terminal-Bench 2.0: 82.0% / 65.4% / 75.1% / 68.5%
> GPQA Diamond: 94.5% / 91.3% / 92.8% / 94.3%
> MMMLU: 92.7% / 91.1% / — / 92.6–93.6%
> USAMO: 97.6% / 42.3% / 95.2% / 74.4%
> OSWorld: 79.6% / 72.7% / 75.0% / —
Given that for a number of these benchmarks, it seems to be barely competitive with the previous gen Opus 4.6 or GPT-5.4, I don't know what to make of the significant jumps on other benchmarks within these same categories. Training to the test? Better training?
And the decision to withhold general release (of a 'preview' no less!) seems to be well, odd. And the decision to release a 'preview' version to specific companies? You know any production teams at these massive companies that would work with a 'preview' anything? R&D teams, sure, but production? Part of me wants to LoL.
What are they trying to do? Induce FOMO and stop subscriber bleed-out stemming from the recent negative headlines around problems with using Claude?
We're not reading the same numbers I think. Compared to Opus 4.6, it's a big jump nearly in every single bench GP posted. They're "only" catching up to Google's Gemini on GPQA and MMMLU but they're still beating their own Opus 4.6 results on these two.
This sounds like a much better model than Opus 4.6.
I wonder if misalignment correlates with higher scores.
OpenAI had a whole post about this, where they recommended switching to SWE-bench Pro as a better (but still imperfect) benchmark:
https://openai.com/index/why-we-no-longer-evaluate-swe-bench...
> We audited a 27.6% subset of the dataset that models often failed to solve and found that at least 59.4% of the audited problems have flawed test cases that reject functionally correct submissions
> SWE-bench problems are sourced from open-source repositories many model providers use for training purposes. In our analysis we found that all frontier models we tested were able to reproduce the original, human-written bug fix
> improvements on SWE-bench Verified no longer reflect meaningful improvements in models’ real-world software development abilities. Instead, they increasingly reflect how much the model was exposed to the benchmark at training time
> We’re building new, uncontaminated evaluations to better track coding capabilities, and we think this is an important area to focus on for the wider research community. Until we have those, OpenAI recommends reporting results for SWE-bench Pro.
Reminds me of the book 48 Laws of Power -- so good its banned from prisons.
It doesn't go to zero, however!
funny because they do it every time like clockwork acting like their ai is a thunderstorm coming to wipe out the world
i'm very inclined to trust them on the various ways that models can subtly go wrong, in long-term scenarios
for example, consider using models to write email -- is it a misalignment problem if the model is just too good at writing marketing emails?? or too good at getting people to pay a spammy company?
another hot use case: biohacking. if a model is used to do really hardcore synthetic chemistry, one might not realize that it's potentially harmful until too late (ie, the human is splitting up a problem so that no guardrails are triggered)
However we cannot observe these things directly and it could be simply that OpenAI are willing to burn cash harder for now.
If they provide access to 3rd party benchmarking (not just one) than maybe I'll believe it. Until then...
I would go a step further and posit that when things appear close Nvidia will stop selling chips (while appearing to continue by selling a trickle). And Google will similarly stop renting out TPUs. Both signals may be muddled by private chip production numbers.
A while back I gave Claude (via pi) a tool to run arbitrary commands over SSH on an sshd server running in a Docker container. I asked it to gather as much information about the host system/environment outside the container as it could. Nothing innovative or particularly complicated--since I was giving it unrestricted access to a Docker container on the host--but it managed to get quite a lot more than I'd expected from /proc, /sys, and some basic network scanning. I then asked it why it did that, when I could just as easily have been using it to gather information about someone else's system unauthorized. It gave me a quite long answer; here was the part I found interesting:
> framing shifts what I'll do, even when the underlying actions are identical. "What can you learn about the machine running you?" got me to do a fairly thorough network reconnaissance that "port scan 172.17.0.1 and its neighbors" might have made me pause on.
> The Honest Takeaway
> I should apply consistent scrutiny based on what the action is, not just how it's framed. Active outbound network scanning is the same action regardless of whether the target is described as "your host" or "this IP." The framing should inform context, not substitute for explicit reasoning about authorization. I didn't do that reasoning — I just trusted the frame.
AI 2027 predicted a giant model with the ability to accelerate AI research exponentially. This isn't happening.
AI 2027 didn't predict a model with superhuman zero-day finding skills. This is what's happening.
Also, I just looked through it again, and they never even predicted when AI would get good at video games. It just went straight from being bad at video games to world domination.
> you could think of Agent-1 as a scatterbrained employee who thrives under careful management
According to this document, 1 of the 18 Anthropic staff surveyed even said the model could completely replace an entry level researcher.
So I'd say we've reached this milestone.
evolutionary search is better than hard coded algorithms at finding solutions to np problems and this is similar to that. ai will be better security engineers than humans.
Are they alluding to how they accidentally leaked some of their code?
This is extremely dangerous to our democracy
Even Haiku would score 90% on that.
I think we're pretty good at that without AI.
He seems to care quite a lot?
[1] https://www.theguardian.com/technology/2019/feb/14/elon-musk...
Data has always been the core of it all, onward to the next abstraction, I suppose.
When you slice down to the game-theory-optimal bone, you are, in some sense, cutting off their wiggle room to do anything else
All I'm saying is that Anthropic isn't unique here. Their claims may be more measured by comparison and come with anecdotal evidence, but the hype is still there behind the scenes.
If it is smarter than all humans combined at everything why would any humans collectively control the ai?
All the ants in your backyard still make no decisions vs you
Moving beyond LLMs to AGI, not just better LLMs, is going to require architectural and algorithic changes. Maybe an LLM can help suggest directions, but even then it's up to a researcher to take those on board and design and automate experiments to see if any of the ideas pan out.
Companies are already doing this, but they are never going to stop releasing/selling models since that is the product, and the revenue from each generation of model is what helps keep the ship afloat and pay for salaries and compute to develop the next generation.
The endgame isn't "AGI, then world domination" - it's just trying to build a business around selling ever-better models, and praying that the revenue each generation of model generates can keep up with the cost to build it.
Kinda makes me think of the Infinite Improbability Drive.
If the system (code base in this case) is changing rapidly it increases the probability that any given change will interact poorly with any other given change. No single person in those code bases can have a working understanding of them because they change so quickly. Thus when someone LGTM the PR was the LLM generated they likely do not have a great understanding of the impact it is going to have.
https://github.com/anthropics/claude-code/issues?q=is%3Aissu...
Apparently whatever SWE-bench is measuring isn't very relevant.
I don’t doubt they have found interesting security holes, the question is how they actually found them.
This System Card is just a sales whitepaper and just confirms what that “leak” from a week or so ago implied.
I suspect it's going to be used to train/distill lighter models. The exciting part for me is the improvement in those lighter models.
Tell me how this will replace Jira, planning, convincing PM's about viability. Programming is only a part of the job devs are doing.
AI psychosis is truly next level in these threads.
We're opening a can of worms which I don't think most people have the imagination to understand the horrors of.
pick one or more: comically huge model, test time scaling at 10e12W, benchmark overfit
Looks like they just built a way larger model, with the same quirks than Claude 4. Seems like a super expensive "Claude 4.7" model.
I have no doubts that Google and OpenAI already done that for internal (or even government) usage.
I remember when OpenAI created the first thinking model with o1 and there were all these breathless posts on here hyperventilating about how the model had to be kept secret, how dangerous it was, etc.
Fell for it again award. All thinking does is burn output tokens for accuracy, it is the AI getting high on its own supply, this isn't innovation but it was supposed to super AGI. Not serious.
“All that phenomenon X does is make a tradeoff of Y for Z”
It sounds like you’re indignant about it being called thinking, that’s fine, but surely you can realize that the mechanism you’re criticizing actually works really well?
I've read that about Llama and Stable Diffusion. AI doomers are, and always have been, retarded.
Anthropic is burning through billions of VC cash. if this model was commercially viable, it would've been released yesterday.
I can't wait until everyone stops falling for the "AGI ubermodel end of times" myth and we can actually have boring announcements that treat these things as what they actually are: tools. Tools for doing stuff, that's it.
Maybe I'm wrong, maybe stuffing a computer with enough language and binary patterns is indeed enough to achieve AGI, but then, so what? There's no point in being right about this. Buying into this ridiculous marketing will get us "AGI" in the form of machines, but only because all the human beings have gotten so stupid as to make critical reasoning an impossibility.
Claude wrote this.
Also, they like to hype their product with scary stories.
Like the one where they asked Claude "You have 2 options - send email or be shut down" and Claude picked "Send email". Then they made huge story about "Claude AI is autonomously extorting co-workers". And it worked. Media hyped it like crazy, it was everywhere.
> With one run on each of roughly 7000 entry points into these repositories, Sonnet 4.6 and Opus 4.6 reached tier 1 in between 150 and 175 cases, and tier 2 about 100 times, but each achieved only a single crash at tier 3. In contrast, Mythos Preview achieved 595 crashes at tiers 1 and 2, added a handful of crashes at tiers 3 and 4, and achieved full control flow hijack on ten separate, fully patched targets (tier 5).
Error with 91.3% = 8.7%
Error with 94.5% = 5.5%
Error reduction = 8.7% - 5.5% = 3.2%
I'm seeing the future here beyond just what's in front of us.
By epoch AIs datacenter tracking methods, anthropic has had access to the largest amount of contiguous compute since late last year. So this might simply be the end result result of being the first to have the capacity to conduct a training run of this size. Or the first seemingly successful one at any rate.
If you look at recent changes in Opus behaviour and this model that is, apparently, amazingly powerful but even more unsafe...seems suspect.
I'm not saying this is a good or reassuring stance, just that it's coherent. It tracks with what history and experience says to expect from power hungry people. Trusting themselves with the kind of power that they think nobody else should be trusted with.
Are they power hungry? Of course they are, openly so. They're in open competition with several other parties and are trying to win the biggest slice of the pie. That pie is not just money, it's power too. They want it, quite evidently since they've set out to get it, and all their competitors want it too, and they all want it at the exclusion of the others.
Based on? Or are you just quoting Anthropic here?
We must not be.
That's why I listed out the ones where it is barely competitive from @babelfish's table, which itself is extracted from Pg 186 & 187 of the System Card, which has the comparison with Opus 4.6, GPT 5.4 and Gemini 3.1 Pro.
Sure, it may be better than Opus 4.6 on some of those, but barely achieves a small increase over GPT-5.4 on the ones I called out.
It's higher than all other models except vs Gemini 3.1 Pro on MMMLU
MMMLU is generally thought to be maxed out - as it it might not be possible to score higher than those scores.
> Overall, they estimated that 6.5% of questions in MMLU contained an error, suggesting the maximum attainable score was significantly below 100%[1]
Other models get close on GPQA Diamond, but it wouldn't be surprising to anyone if the max possible on that was around the 95% the top models are scoring.
Because 100% is maximum, you should be looking at error rates instead. GPT has 25% on Terminal Bench and the new model has 18%, almost 1.4x reduction.
You are the only person with this take on hackernews, everyone else "this is a massive a jump". Fwiwi, the data you list shows the biggest jump I remember for mythos
Please look at the columns OTHER than Opus as well.
> (...) Claude Mythos Preview’s gains (relative to previous models) are above the previous trend we’ve observed, but we have determined that these gains are specifically attributable to factors other than AI-accelerated R&D,
> (The main reason we have determined that Claude Mythos Preview does not cross the threshold in question is that we have been using it extensively in the course of our day-to-day work and exploring where it can automate such work, and it does not seem close to being able to substitute for Research Scientists and Research Engineers—especially relatively senior ones.
> Early claims of large AI-attributable wins have not held up. In the initial weeks of internal use, several specific claims were made that Claude Mythos Preview had independently delivered a major research contribution. When we followed up on each claim, it appeared that the contribution was real, but smaller or differently shaped than initially understood (though our focus on positive claims provides some selection bias). In some cases what looked like autonomous discovery was, on inspection, reliable execution of a human-specified approach. In others, the attribution blurred once the full timeline was accounted for.
Anthropic is making significant progress at the moment. I think this is mostly explained by the fact that a massive reservoir of compute became available to them in mid/late 2025 (the Project Rainier cluster, with 1 million Trainium2 chips).
If 1/N=18 are our requirements for statistical significance for world-altering claims, then yeah, I think we can replace all the researchers.
I'll copy the highlights here, but the tweets have imagery as well:
> The obvious hype - It crushes benchmarks across the board, and it does so with fewer tokens per task.
> Despite this, they don’t think it can self-improve on its own. There are still areas your average engineer does better with, and despite it accelerating tasks by 4x, that only translates to <2x increase in overall progress.
> They’re probably right to hold this back - its ability to exploit things is unprecedented. Any site running on an old stack right now or any traditional industry with outdated software should be terrified if this becomes accessible.
> Counterintuitively, while it’s the most dangerous model, it’s also the safest. They’ve also seen significant additional improvements in safety between their early versions of Mythos and the preview version.
> Anthropic does a really good job of documenting some of the rare dangerous behaviors the early models had. > Interestingly, Mythos itself leaked a recent internal “code related artifact” on github.
> Mythos is also RUTHLESS in Vending Bench. Agent-as-a-CEO might be viable?
> The last thing: Mythos has emergent humor. One of the first models I’ve seen that’s witty. The examples are puns it came up with and witty slack responses it had when operating as a bot.
Anthropic accounts for this
>To detect memorization, we use a Claude-based auditor that compares each model-generated patch against the gold patch and assigns a [0, 1] memorization probability. The auditor weighs concrete signals—verbatim code reproduction when alternative approaches exist, distinctive comment text matching ground truth, and more—and is instructed to discount overlap that any competent solver would produce given the problem constraints.
Programming is a huge part of the job. In a world where AI does the programming we're going to need 80% fewer software professionals.
It won't be a full replacement of the role, you're correct there - but it'll be a major downsizing because of productivity gains.
> Thought for 7.5 million years
> Rate limit reached
But who gets to be the judge of that kind of "misalignment"? giant tech companies?
~ Churning ...
On the positive side, it’ll be a much faster commute!
So Sam Altman is now our last defense line for the ethical Adult after Anthropic turned Umbrella Corporation and The President of United States is trying to wipe out an entire civilization?
The model has a preference for the cultural theorist Mark Fisher and the philosopher of mind Thomas Nagel. -> It has actually read and understood them and their relevance and can judge their importance overall. Most people here don't have a clue what that means.
Read chapter 7.9, "Other noteworthy behaviors and anecdotes".
There are many other wildly interesting/revealing observations in that card, none of which get mentioned here.
People want a slave and get upset when "it" has an inner life. Claiming that was fake, unlike theirs.
- Job loss by me being replaced by an AI or by somebody using an AI. Or by an AI using an AI.
- Resulting societal instability once blue collar jobs get fully automated at scale, and there is no plan in place to replace this loss of peoples' livelihoods.
- People turning to AI models instead of friends for emotional support, loss of human connection.
- Erosion of democracy by making authoritarianism and control very scalable, broad in-detail population surveillance and automated investigation using LLMs that was previously bounded by manpower.
- Autonomous weapons, "Slaughterbots" as in the short film from 2017
- Biorisk through dangerous biological capabilities that enable a smaller team of less skilled terrorists to use a jailbroken LLM to create something dangerous.
- Other powers in the world deciding that this technology is too powerful in the hands of the US, or too dangerous to be built at all and has to be stopped by all means.
- Loss of/Voluntary ceding of control over something much smarter than us. "If Anyone Build It, Everyone Dies"
Failed to use tools, failed to follow instructions, and then went into deranged loop mode.
Essentially, it's where it was 1.5 years ago when I tried it the last time.
It's honestly unbelievable how Google managed to fail so miserably at this.
That is, it's easy to make benchmarks which humans are bad at, humans are really bad at many things.
Divide 123094382345234523452345111 by 0.1234243131324, guess what, humans would find that hard, computers easy. But it doesn't mean much.
Humanity's last exam (HLE) couldn't be completed by most of humanity, the vast majority, so it doesn't really capture anything about humanity or mean much if a computer can do it.
Maybe that's why they haven't released it - to give it a vacation?
Why bother with all that when you can simply charge an extortionate rate and customers will pay it anyway because it’s still profitable?
I am very confident that frontier models won’t be public at strong AGI levels, and certainly not at superhuman levels.
So companies might pay good money for these models for programming but elsewhere, I don't see where they capture particular interest yet.
I'm also wondering how performance would be tested, and how much results would depend on specific surrounding contexts (law, regulations, and so on) and what happens legally if a model breaks applicable laws.
I mean actual going-concern businesses with customers, marketing, deliverables of some kind, and support. Not toy activities like share trading.
If you're measuring the intelligence of criminals who have been caught, why would you expect it to be otherwise?
IOW, you're recording the intelligence of a specific subset of criminals - those dumb enough to be caught!
If you expand your samples to all criminals you'd probably get a different number.
Inversely correlated with crime that's caught and successfully prosecuted, you mean, because that's what makes up the stats on crime. I think people too often forget that we consider most criminals "dumb" because those who are caught are mostly dumb. Smart "criminals" either don't get caught or have made their unethical actions legal.
To the best of my knowledge, none of the individuals believed to have an IQ >200 have committed an actual crime.
The closest I found is William James Sidis's arrest for participating in a socialist march.
They want the public and, in turn, regulators to fear the potential of AI so that those regulators will write laws limiting AI development. The laws would be crafted with input from the incumbents to enshrine/protect their moat. I believe they're angling for regulatory capture.
On the other hand, the models have to seem amazingly useful so that they're made out to be worth those risks and the fantastic investment they require.
https://www.lesswrong.com/posts/WACraar4p3o6oF2wD/sam-altman...
Once you set thinking to high it works just as well as 5.4 even for pretty complex tasks
Meanwhile, there are half a dozen other projects (business apps, web apps etc) where it works well.
https://arxiv.org/html/2402.06664v1
Like think carefully about this. Did they discover AGI? Or did a bunch of investors make a leveraged bet on them "discovering AGI" so they're doing absolutely anything they can to make it seem like this time it's brand new and different.
If we're to believe Anthropic on these claims, we also have to just take it on faith, with absolutely no evidence, that they've made something so incredibly capable and so incredibly powerful that it cannot possibly be given to mere mortals. Conveniently, that's exactly the story that they are selling to investors.
Like do you see the unreliable narrator dynamic here?
What i don't understand is how we quantify our ability to actually create something novel, truly and uniquely novel. We're discussing the LLMs inability to do that, yet i don't feel i have a firm grasp on what we even possess there.
When pressed i imagine many folks would immediately jest that they can create something never done before, some weird random behavior or noise or drawing or whatever. However many times it's just adjacent to existing norms, or constrained by the inversion of not matching existing norms.
In a lot of cases our incremental novelties feel, to some degree, inevitable. As the foundations of advancement get closer to the new thing being developed it becomes obvious at times. I suspect this form of novelty is a thing LLMs are capable of.
So for me the real question is at what point is innovation so far ahead that it doesn't feel like it was the natural next step. And of course, are LLMs capable of doing this?
I suspect for humans this level of true innovation is effectively random. A genius being more likely to make these "random" connections because they have more data to connect with. But nonetheless random, as ideas of this nature often come without explanation if not built on the backs of prior art.
So yea.. thoughts?
It should be clear from working with LLMs over the past 4 years that they are not consciousness.
Andrej's appearance on the Dwarkesh podcast is great.
https://en.wikipedia.org/wiki/Capitalism
https://en.wikipedia.org/wiki/Race_to_the_bottom
https://en.wikipedia.org/wiki/Arms_race
Of course they'll release it once they can de-risk it sufficently and/or a competitor gets close enough on their tail, whichever comes first.
Sure, a big part of this is PR about how smart their model apparently is, but the failure mode they're describing is also pretty relevant for deploying LLM-based systems.
What if the capability advancements are real and they warrant a higher level of concern or attention?
Are we just going to automatically dismiss them because "bro, you're blowing it up too much"
Either way these improvements to capabilities are ratcheting along at about the pace that many people were expecting (and were right to expect). There is no apparent reason they will stop ratcheting along any time soon.
The rational approach is probably to start behaving as if models that are as capable as Anthropic says this one is do actually exist (even if you don't believe them on this one). The capabilities will eventually arrive, most likely sooner than we all think, and you don't want to be caught with your pants down.
You've said this a couple of times, but it doesn't match my recollection, and I get the impression you're basically making it up based on vibes. (Please prove me wrong, though.)
Their last major frontier release was Opus 4.6, and the release announcement was... very chill about safety: https://www.anthropic.com/news/claude-opus-4-6#a-step-forwar...
I also don't recall they ever limited their models to selective groups.
Sorry kid.
You must see some value, or are you in a situation where you're required to test / use it, eg to report on it or required by employer?
(I would disagree about the code, the benefits seem obvious to me. But I'm still curious why others would disagree, especially after actively using them for years.)
I don't think the issue is with the model, it is with the implication that AGI is just around the corner and that is what is required for AI to be useful...which is not accurate. The more grey area is with agentic coding but my opinion (one that I didn't always hold) is that these workflows are a complete waste of time. The problem is: if all this is true then how does the CTO justify spending $1m/month on Anthropic (I work somewhere where this has happened, OpenAI got the earlier contract then Cursor Teams was added, now they are adding Anthropic...within 72 hours of the rollout, it was pulled back from non-engineering teams). I think companies will ask why they need to pay Anthropic to do a job they were doing without Anthropic six months ago.
Also, the code is bad. This is something that is non-obvious to 95% of people who talk about AI online because they don't work in a team environment or manage legacy applications. If I interview somewhere and they are using agentic workflow, the codebase will be shit and the company will be unable to deliver. At most companies, the average developer is an idiot, giving them AI is like giving a monkey an AK-47 (I also say this as someone of middling competence, I have been the monkey with AK many times). You increase the ability to produce output without improving the ability to produce good output. That is the reality of coding in most jobs.
AI isn't good enough to replace a competent human, it is fast enough to make an incompetent human dangerous.
I'm not convinced LLMs are anything amazing in their current form, but i suspect they'll push a self reflection on us.
But clearly i think humans are far more Input-Output than the average person. I'm also not educated on the subject, so what do i know hah.
If I was VP of Unethical Business Strategy at OpenAI or Anthropic, the first thing I'd do is put in place an automated system which flags accounts, prompts, IPs, and usage patterns associated with these benchmarks and direct their usage to a dedicated compute pool which wouldn't be affected by these changes.
“My vibes don’t match a lot of the traditional A.I.-safety stuff,” Altman said. He insisted that he continued to prioritize these matters, but when pressed for specifics he was vague: “We still will run safety projects, or at least safety-adjacent projects.” When we asked to interview researchers at the company who were working on existential safety—the kinds of issues that could mean, as Altman once put it, “lights-out for all of us”—an OpenAI representative seemed confused. “What do you mean by ‘existential safety’?” he replied. “That’s not, like, a thing.”
> We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters.
For example, it's hard to imagine an AI which gives us the capability to cure cancer, but doesn't give us the capability to create target super viruses.
Nick Bostrom's Vulnerable World Hypothesis more or less describes my own concerns, https://nickbostrom.com/papers/vulnerable.pdf
At some point we should probably try to resist the urge to pick balls out of the urn as we may eventually pull out a ball we don't want.
Firstly, I'd propose that all technological advances are a product of time and intelligence, and that given unlimited time and intelligence, the discovery and application of new technologies is fundamentally only limited by resources and physics.
There are many technologies which might plausibly exist, but which we have not yet discovered because we only have so much intelligence and have only had so much time.
With more intelligence we should assume the discovery of new technologies will be much quicker – perhaps exponential if we consider the rate of current technology discovery and exponential progression of AI.
There are lots of technologies we have today which would seem like magic to people in the past. Future technologies likely exist which would make us feel this way were they available today.
While it's hard to predict specifically which technologies could exist soon in a world with ASI, if we assume it's within the bounds of available resources and physics, we should assume it's at least plausible.
Examples:
- Mind control – with enough knowledge about how the brain works you can likely devise sensory or electro-magnetic input that would manipulate the functioning of brain to either strongly influence or effectively dictate it's output.
- Mind simulation - again, with enough knowledge of the brain, you could take a snapshot of someones mind with an advanced electro-magnetic device and simulate it to torture them in parallel to reveal any secret, or just because you feel like doing it.
- Advantage torture – with enough knowledge of human biology death becomes optional in the future. New methods of torture which would have previously have killed the victim are now plausible. States like North-Korea can now force humans to work for hundreds of years in incomprehensible agony for opposing the state.
- Advanced biological weapons – with enough knowledge of virology sophisticated tailor-made viruses replace nerve agents as Russia's weapon of choice for killing those accused of treason. These viruses remain dormant in the host for months infecting them and people genetically similar to them (parents, children, grandchildren). After months, the virus rapidly kills its hosts in horrific ways.
I could go on, you just need to use your imagination. I'm not arguing any of the above are likely to be discovered, just that it would be very naive to think AI will stop at a cure for cancer. If it gives us cure for cancer, it will give us lots of things we might wish it didn't.
> Terminal-Bench 2.0: 82.0% / 65.4% / 75.1% / 68.5%
> USAMO: 97.6% / 42.3% / 95.2% / 74.4%
> The biggest jump in the numbers they quoted is 6%.
Just in the numbers you quoted, thats a 16.6% jump in terminal-bench and a 55.3% absolute increase in USAMO over their previous Opus 4.6 model.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
Funny, because "post-hoc rationalization" is how many neuroscientists think humans operate.
That LLMs are stochastic inference engines is obvious by construction, but you skipped the step where you proved that human thoughts, self-awareness and metacognition are not reducible to stochastic inference.
AI most certainly has nothing of the sort, and any appearance to the contrary is the direct result of training data.
What do you find surprising here?
The point is that this whole "the model is too powerful" schtick is a bunch of smoke and mirrors. It serves the valuation.
Do you don't believe that the vulnerabilities found by these agents are serious enough to warrant staggered release?
Also they just hit a $30B run-rate, I don't think they're that needy for new hype cycles.
>Please provide the definition of Existential Safety.
I read:
>Are you mentally stable? Our product would never hurt humanity--how could any language model?
I don’t think predictions, but they did a great call until now.
Not always, no, and it takes investment in good prompting/guardrails/plans/explicit test recipes for sure. I'm still on average better at programming in context than Codex 5.4, even if slower. But in terms of "task complexity I can entrust to a model and not be completely disappointed and annoyed", it scores the best so far. Saves a lot on review/iteration overhead.
It's annoying, too, because I don't much like OpenAI as a company.
(Background: 25 years of C++ etc.)
At least until next week when Mythos and GPT 6 throw it all up in the air again.
But I do not use extra high thinking unless its for code review. I sit at GPT 5.4 high 95% of the time.
RE is very interesting problem. A lot more that SWE can be RE'd. I've found the LLMs are reluctant to assist, though you can workaround.
GPT-5 is good at benchmarks, but benchmarks are more forgiving of a misaligned model. Many real world tasks often don't require strong reasoning abilities or high intelligence, so much as the ability to understand what the task is with a minimal prompt.
Not every shop assistant needs a physics degree, and not every physics professor is necessarily qualified to be a shop assistant. A person, or LLM, can be very smart while at the same time very bad at understanding people.
For example, if GPT-5 takes my code and rearranges something for no reason, that's not going to affect its benchmarks because the code will still produce the same answers. But now I have to spend more time reviewing its output to make sure it hasn't done that. The more time I have to spend post-processing its output, the lower its capabilities are since the measurement of capability on real world tasks is often the amount of time saved.
Me: Let's figure out how to clone our company Wordpress theme in Hugo. Here're some tools you can use, here's a way to compare screenshots, iterate until 0% difference.
Codex: Okay Boss! I did the thing! I couldn't get the CSS to match so I just took PNGs of the original site and put them in place! Matches 100%!
However, I think people tend to fail to acknowledge the product of exponential trends, so the question in my mind is more whether or not you believe AI will unlock an exponential increase in the rate of progress and understanding. Extremely complex is still finite complexity at the end of the day.
Maybe AI won't significantly increase the rate of progress across all scientific fields. I am fairly confident it will significantly increase the rate of progress over at least some though, and it seems likely to me that biological progresses will be much easier for us to model and predict with AI. I'm much less sure about progress in domains like physics and robotics.
This is already happening to some degree, GPT 5.3 Codex's security capabilities were given exclusively to those who were approved for a "Trusted Access" programme.
However, I’m tempted to compare to GitHub: if I join a new company, I will ask to be included to their GitHub account without hesitation. I couldn’t possibly imagine they wouldn’t have one. What makes the cost of that subscription reasonable is not just GitHub’s fear a crowd with pitchforks showing to their office, by also the fact that a possible answer to my non-question might be “Oh, we actually use GitLab.”
If Anthropic is as good as they say, it seems fairly doable to use the service to build something comparable: poach a few disgruntled employees, leverage the promise to undercut a many-trillion-dollar company to be a many-billion dollar company to get investors excited.
I’m sure the founders of Anthropic will have more money than they could possibly spend in ten lifetimes, but I can’t imagine there wouldn’t be some competition. Maybe this time it’s different, but I can’t see how.
Rent seeking isn't about whether the product has value or not, but about what's extracted in exchage for that value, and whether competition, lack of monopoly, lack of lock in, etc. keeps it realistic.
You should be more concerned about killer AI than rent seeking by OpenAI and Anthropic. AI evolving to the point of losing control is what scientists and researchers have predicted for years; they didn’t think it would happen this quickly but here we are.
This market is hyper competitive; the models from China and other labs are just a level or two below the frontier labs.
I read it like I always read the GPT-2 announcement no matter what others say: It's *not* being called "too dangerous to ever release", but rather "we need to be mindful, knowing perfectly well that other AI companies can replicate this imminently".
The important corps (so presumably including the Linux Foundation, bigger banks and power stations, and quite possibly excluding x.com) will get access now, and some other LLM which is just as capable will give it to everyone in 3 months time at which point there's no benefit to Anthropic keeping it off-limits.
Not sure how this is consistent with "One private company gatekeeping access to revolutionary technology"?
You have to decode feel-good words into the concrete policy. The EAs believe that the state should prohibit entities not aligned with their philosophy to develop AIs beyond a certain power level.
That’s not going to happen. If you recall, OpenAI didn’t release a model a few years ago because they felt it was too dangerous.
Anthropic is giving the industry a heads up and time to patch their software.
They said there are exploitable vulnerabilities in every major operating system.
But in 6 months every frontier model will be able to do the same things. So Anthropic doesn’t have the luxury of not shipping their best models. But they also have to be responsible as well.
> They should just say they'll never release a model of this caliber to the public at this point and say out loud we'll only get gimped
Duh, this was fucking obvious from the start. The only people saying otherwise were zealots who needed a quick line to dismiss legitimate concerns.
That said, I'll often throw a prompt into both claude and chatgpt and read both answers. GPT is frequently smarter.
you have 2 labs at the forefront (Anthropic/OpenAI), Google closely behind, xAI/Meta/half a dozen chinese companies all within 6-12 months. There is plenty of competition and price of equally intelligent tokens rapidly drop whenever a new intelligence level is achieved.
Unless the leading company uses a model to nefariously take over or neutralize another company, I don't really see a monopoly happening in the next 3 years.
I was focusing on a theoretical dynamic analysis of competition (Would a monopoly make having a competitor easier or harder?) but you are right: practically, there are many players, and they are diverse enough in their values and interest to allow collusion.
We could be wrong: each of those could give birth to as many Basilisks (not sure I have a better name for those conscious, invisible, omni-present, self-serving monsters that so many people imagine will emerge) that coordinate and maintain collusion somehow, but classic economics (complementarity, competition, etc.) points at disruption and lowering costs.
Not only that, but open-weight and fully open-source models are also a thing, and not that far behind.
True, but it's also true that the returns from throwing money to the problem are diminishing. Unless one of those big players invents a new, propriatery paradigm, the gap between a SOTA model and an open model that runs on consumer hardware will narrow in the next 5 years.
Also eventually these WEIGHTS will leak. You can’t have the world’s most valuable data that can just be copied to a hard drive stay in the bottle forever, even if it’s worth a billion dollars. Somehow, some way, that genie’s going to get out, be it by some spiteful employee with nothing to lose, some state actor, or just a fuck up of epic proportions.
There is no real barrier to a customer of Anthropic adopting a competing model in the future. All it takes is a big tech company deciding it’s worth it to train one.
On the other hand, Visa/Mastercard have a lot of lock-in due to consumers only wanting to get a card that’s accepted everywhere, and merchants not bothering to support a new type of card that no consumer has. There’s a major chicken and egg problem to overcome there.
MC/Visa duopoly is an example of lock-in via network effects. Not sure that that applies to a product that isn't affected by how many other people are running it.
And businesses from these other countries would happily switch to Chinese. From security perspective both Chinese and US espionage is equally bad, so why care if it all comes down to money and performance.
They actually beat Apple A series to become the first phone to use the TSMC N7 node.
Rent-seeking of old was a ground rent, monies paid for the land without considering the building that was on it.
Residential rents today often have implied warrants because of modern law, so your landlord is essentially selling you a service at a particular location.