Paradoxically, xAI might be in a better position as a result of having fewer users, and so they might be able to serve their 6T total param Grok 5 starting early 2026 at a reasonable price.
If compute used for RL is comparable to compute used for inference for GDM and Anthropic, then serving to users might not be that important of a dimension. I guess it could be acceptable to have much slower inference for RL but not for serving to users.
The AIs are obviously fully (or almost fully) automating AI R&D and we're trying to do control evaluations.
Looks like it isn't specified again in the Opus 4.5 System card despite Anthropic clarifying this for Haiku 4.5 and Sonnet 4.5. Hopefully this is just a mistake...
The capability evaluations in the Opus 4.5 system card seem worrying. The provided evidence in the system card seem pretty weak (in terms of how much it supports Anthropic's claims). I plan to write more about this in the future; here are some of my more quickly written up thoughts.
[This comment is based on this X/twitter thread I wrote]
I ultimately basically agree with their judgments about the capability thresholds they discuss. (I think the AI is very likely below the relevant AI R&D threshold, the CBRN-4 threshold, and the cyber thresholds.) But, if I just had access to the system card, I would be much more unsure. My view depends a lot on assuming some level of continuity from prior models (and assuming 4.5 Opus wasn't a big scale up relative to prior models), on other evidence (e.g. METR time horizon results), and on some pretty illegible things (e.g. making assumptions about evaluations Anthropic ran or about the survey they did).
Some specifics:
- Autonomy: For autonomy, evals are mostly saturated so they depend on an (underspecified) employee survey. They do specify a threshold, but the threshold seems totally consistent with a large chance of being above the relevant RSP threshold. (In particular the threshold is "A majority of employees surveyed think the AI can't automate a junior researcher job AND a majority think uplift is <3x". If 1/4 of employees thought it could automate a junior researcher job that would be a lot of evidence for a substantial chance it could!)
- Cyber: Evals are mostly saturated. They don't specify any threshold or argue for their ultimate judgement that the AI doesn't pose catastrophic cyber risk.
- Bio: To rule out the CBRN-4 threshold (uplift for moderately resourced state programs, e.g. North Korea), they seem to depend mostly on a text-based uplift trial. The model is extremely close to the relevant threshold and it's unclear how much confidence we should have in this uplift trial.
Generally, it seems like the current situation is that capability evals don't provide much assurance. This is partially Anthropic's fault (they are supposed to do better) and partially because the problem is just difficult and unsolved.
I still think Anthropic is probably mostly doing a better job evaluating capabilities relative to other companies.
(It would be kinda reasonable for them to clearly say "Look, evaluating capabilities well is too hard and we have bigger things to worry about, so we're going to half-ass this and make our best guess. This means we're not longer providing much/any assurance, but we think this is a good tradeoff given the situation.")
Some (quickly written) recommendations:
- We should actually get some longer+harder AI R&D/autonomy tasks. E.g., tasks that take a human a week or two (and that junior researchers at Anthropic can somewhat reliably do). The employee survey should be improved (make sure employees have had access for >1-2 weeks, give us the exact questions, probably sanity check this more) and the threshold should probably be lower (if 1/4 of the employees do think the AI can automate a junior researcher, why should we have much confidence that it can't!).
- Anthropic should specify a threshold for cyber or make it clear what they using to make judgments. It would also be fine for them to say "We are no longer making a judgment on whether our AIs are above ASL-3 cyber, but we guess they probably aren't. We won't justify this."
- On bio, I think we need more third party review of their evals and some third party judgment of the situation because we're plausibly getting into a pretty scary regime and their evals are extremely illegible.
We're probably headed towards a regime of uncertainty and limited assurance. Right now is easy mode and we're failing to some extent.
1
1I'm just literally assuming that Plan B involves a moderate amount of lead time via the US having a lead or trying pretty hard to sabotage China, this is part of the plan/assumptions.
I don’t believe that datacenter security is actually a problem (see another argument).
Sorry, is your claim here that securing datacenters against highly resourced attacks from state actors (e.g. China) is going to be easy? This seems like a crazy claim to me.
(This link you cite isn't about this claim, the link is about AI enabled cyber attacks not being a big deal because cyber attacks in general aren't that big of a deal. I think I broadly agree with this, but think that stealing/tampering with model weights is a big deal.)
The China Problem: Plan B’s 13% risk doesn’t make sense if China (DeepSeek) doesn’t slow down and is only 3 months behind. Real risk is probably the same as for E, 75% unless there is a pivotal act.
What about the US trying somewhat hard to buy lead time, e.g., by sabotaging Chinese AI companies?
The framework treats political will as a background variable rather than a key strategic lever.
I roughly agree with this. It's useful to condition on (initial) political will when making a technical plan, but I agree raising political will is important and one issue with this perspective is it might incorrectly make this less salient.
Not a crux for me ~at all. Some upstream views that make me think "AI takeover but humans stay alive" is more likely and also make me think avoiding AI takeover is relatively easier might be a crux.
1I expect a roughly 5.5 month doubling time in the next year or two, but somewhat lower seems pretty likely. The proposed timeline I gave consistent with Anthropic's predictions requires <1 month doubling times (and this is prior to >2x AI R&D acceleration, at least given my view of what you get at that level of capability).
Filler tokens don't allow for serially deeper cognition than what architectural limits allow (n-layers of processing), but they could totally allow for solving a higher fraction of "heavily serial" reasoning tasks [[1]] insofar as the LLM could still benefit from more parallel processing. For instance, the AI might by default be unable to do some serial step within 3 layers but can do that step within 3 layers if it can parallelize this over a bunch of filler tokens. This functionally could allow for more serial depth unless the AI is strongly bottlenecked on serial depth with no way for more layers to help (e.g., the shallowest viable computation graph has depth K and K is greater than the number of layers and the LLM can't do multiple nodes in a single layer [[2]] ).
Or at least reasoning tasks that seem heavily serial to humans. ↩︎
With a wide enough layer, you can technically do anything, but this can quickly get extremely impractical. ↩︎