Ryan Greenblatt

If Mythos actually made Anthropic employees for X more productive, I would radically shorten my timelines.

By Ryan Greenblatt Published on April 10, 2026 Anthropic's system card for Mythos Preview says There's an image here Description Highlighted text excerpt discussing Claude Mythos Preview Productivity Survey Results and Research Progress Impact

It's unclear how we should interpret this.

What do they mean by productivity uplift?

To what extent is Anthropic's institutional view that the uplift is for X?

Like, what do they mean by we take this seriously and it is consistent with our own internal experience of the model?

One straightforward interpretation is, AI systems improve the productivity of Anthropic so much that Anthropic would be indifferent between the current situation and a situation where all of their technical employees magically work.

4.

Hours for every one hour, at equal productivity without burnout, but they get zero AI assistance.

In other words, AI assistance is as useful as having their employees operate at 4x faster speeds for all activities, meetings, coding, thinking, writing, etc.

I'll call this 4x serial labour acceleration, see here for more discussion of this idea.

I currently think it's very unlikely that Anthropic's AIs are yielding for X serial labor acceleration, but if I did come to believe it was true, I would update towards radically shorter timelines.

I tentatively think my median to automated coder would go from 4 years from now to maybe 1.3 years from now.

My median to AI R&D parity would go from 5 years from now to maybe 2.5 years from now.

My best guess is that for X serial labor acceleration would cause AI progress to go 1.75X faster, see the appendix.

Estimating AI progress speed up from serial labor acceleration, which is very large and close to the 2X dramatic acceleration threshold Anthropic is using for autonomy threat model 2.

Risks from automated R&D.

My current best, low-confidence, low-precision guess for the serial labour acceleration is roughly 1.55x, with a higher serial labour acceleration of roughly 1.75x for just research engineering activities.

I currently think that reasonably informed anthropic employees that have thought about this topic in a decent amount of detail think the serial labour acceleration is closer to 1.5x than 4x.

I think uplift metrics like serial labor acceleration at AI companies are some of the most relevant metrics to track when trying to figure out how close we are to key risk-relevant milestones in AI development like full automation of AI R&D.