The "too dangerous to release" line was definitely a marketing stunt.

OpenAI already used the same playbook with GPT-2 in 2019, and some of the same people involved back then are now doing it again at Anthropic with Mythos.

Same safety-branding DNA, different company, and people are falling for it again.

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I guess it was too dangerous to even read the article

Mythos had to silence you apparently

Silenced immediately.

My thinking is that if it really was super duper then Anthropic could charge eye watering amounts and have willing customers and set up expectations going forward that SOTA costs a lot to use.

That they don’t suggests that really it is only incrementally better than Opus 4.7 and that the market won’t bear a price increase that makes it economical to serve let alone profit from serving.

So the cynical me imagines execs sitting around the table and worrying that releasing it at anywhere close to break even would risk actually hurting the brand instead of setting them up as a premium company, and this at a time just before ipo when they can ill afford that rumour.

So they wonder what to do, and think playing national security card is the obvious way out. It’s incrementally better enough to find bugs that previous sota missed, it doesn’t get used widely so it’s cheap to serve and they get the good publicity without the economic scrutiny?

Making a loss selling to a small number of users using it in a limited way is entirely affordable. Making a loss selling it at scale is correspondingly unaffordable?

It all sounds a bit too marketing-ey to me “we have this amazing model that is too good to release” but the goal is still AGI? Ok right.

For marketing purposes it is always too dangerous, not saying it is safe

AI has always been dangerous, but not existentially dangerous.

Mythos is dangerous but it's not going to Skynet us.

Just the same as the military drone using some sort of OpenCV library and target prioritisation loop isn't going to turn evil on us.

This lengthy article by a self-described "AI enthusiast" muddies the waters. Yes, Anthropic has capacity constraints, which is why they rented Colossus from Musk despite the danger of being distilled.

The real reason is that the hype around Mythos has already gone quiet because it does not find more than other models. That is, nothing at all in most open source projects. If you hide the model, embarrassing statistics will not be posted.

The thought of this didn't even cross my mind until yesterday. I previously figured the hype was primarily around marketing, but after watching this Primagen video, I have the same suspicion.

https://www.youtube.com/watch?v=zaGOKd4jqEk

It's probably a little of both: dangerous and expensive. This article makes a good case that the cost is at least part of the reason.

I wish the article could have been a lot tighter and shorter. This is not earth shattering information that requires a New Yorker length piece of investigative journalism.

My guess is they are still in the "fake it till you make it" phase. There's no Mythos, it's just a hype machine fueled by a hot air.

When your logo is AI, your illustrations are AI, and you profile pic is AI, I'm going to assume the text is AI too and won't read it.

It's probably not much more dangerous than all the AI security patching being done without it, CVE rate is approaching a straight line up

My posts* got to the first spot on hackernews couple of times. Never once it broke down like that. And why would it, it's just a bunch of html and css files served through (free) vercel (don't think it matters). I wonder what do people run their blogs these days, so they fail under the pressure so easily.

* https://news.ycombinator.com/from?site=yanist.com

The real Mythos was the friends we made along the way.

Opus Fast Mode is 30$/150$/M Input/Output cost. Mythos's pricing (from model card) is 25$/125$ Input/Output cost.

Based on this I doubt that Mythos pro is too dangerous to release or provides significantly more value.

I'd be tempted to offer this as a consultant service were I at Anthropic.

It feels like an AI tool that needs professionals to interface with it. Get some of those professionals, have them work with clients in a targeted way. It helps reduce the exposure the tool has to bad actors, and reduces the amount of resource usage that it will incur, because it's being used only by trained individuals.

Use what you learn from the experience to further refine its operation and make it less expensive to operate.

Conclusion: both are true which makes sense. The KV cache scaling yields both the emergent power and requires the enormous capacity.

(I work at Anthropic) We have publicly stated[1] that our goal is to deploy Mythos-class models at scale when we have the requisite safeguards for offensive cyber risks in place. As a reminder, Mythos is a general frontier model, not a cyber-specific model. So, there are many reasons why we think our users will benefit from access (with the aforementioned safeguards in place) in due course. Compute has also not factored into our decision[2] to rollout the model in a limited fashion to defenders. We'll be sharing more soon on the first month or so of the project and rollout.

[1] https://www.anthropic.com/glasswing#:~:text=deploy%20Mythos%...

[2] https://x.com/logangraham/status/2054613618168082935

You don't have to look much further than marketing...

It's pretty clear at this point that Mythos' capability to discover and exploit zero-day vulnerabilities at scale is but an incremental improvement over existing models like ChatGPT Plus/Pro.

Anthropic tries to create marketing hype around Mythos using two psychological tricks.

1. Put large numbers in the headlines.

"Mythos discovered 271 vulnerabilities in Firefox" makes the model seem extremely capable to the uninitiated.

But it's actually meaningless as a measure of capability _improvement_.

Anthropic gave away $100mil specifically as Mythos credits to these projects and companies (that's $2.5mil per project). Spending the same exorbitant amount of compute analyzing the same codebases in an older model like ChatGPT Plus would have turned up 260 of these vulnerabilities, or could even have turned up more than 271 ones.

No need to speculate, since this is exactly what we saw in the few code bases where we have such comparisons (like in the curl codebase). Supposedly weaker models, working with a much lower budget, turned up dozens of vulnerabilities. Mythos turned up only one, which ended up as a low severity CVE.

2. Do the whole "too dangerous to release" shtick. This is one of Dario Amodei's favorite moves. When he was vice president of research at OpenAI, he declared GPT-3 (which wasn't able to produce coherent text beyond 3-4 sentences at the time) too dangerous [1] as well.

Long story short, it's the ChatGPT 4.5 situation again: a company trained a model that's too slow and expensive, but not much more capable than what came before. It therefore requires these marketing stunts.

[1] https://www.itpro.com/technology/artificial-intelligence-ai/...