Quick Takes

Scheming Defences Aren't Just for Schemers Both Anthropic and OpenAI have put in place (or are thinking about putting in place) some kind of monitoring for internal coding agents. Neither company has set up their monitoring to be scheming-proof. When I've spoken to people working on this, they (correctly, IMO) argue that current models aren't likely to be scheming. (In particular, OpenAI appears to be thinking about untrusted-only monitoring without much collusion busting, where one instance of a model audits the outputs of another instance of itself.) But, there are other weird things a model might do that don't qualify as "scheming" which you might still want to defend against! For example, reward hacking/fudging critical safety infrastructure is extremely bad, yet well within the remit of bad behaviour we might expect. Would this cause a model to get caught? I expect so, but who knows! One vector for monitoring failure might be a kind of persuasion; if a model wants to reward hack, it might justify to itself---using some kind of motivated reasoning---why the hack is actually the correct action to take. Another copy of that model, reading the chain-of-thought, might then agree with the first one! (this is extremely common in motivated reasoning amongst humans as well) This would kinda fall under the broader umbrella of collusion in untrusted monitoring, and would definitely be mitigated by certain signal jamming techniques (though not all). The above example is illustrative of the more general point that you can't get safety by ruling out a single worst-case scenario (scheming) then only mitigating for one other specific scenario (whatever they are planning for here, I think mostly model mistakes and low-stakes deception and occasional destruction) because there are other scenarios you haven't thought of!

Since GPT-5.5 turns out to be the same thing as Spud, there likely isn't currently a Mythos-class model at OpenAI. The price for GPT-5.5 ($5/$30 per 1M input/output tokens) is about the same as the price for Opus 4.7 ($5/$25), so they are likely in the same weight class. Given the jagged parity in performance between the smaller GPT-5.4 and the bigger Opus 4.6 (with different reasoning token budgets), the current OpenAI recipe might make GPT-5.5 or its upcoming successors stronger than Mythos in some ways, but as a pretrain Mythos probably holds more potential by the end of the year (and might be stronger for now, if GPT-5.5 didn't yet go all out on RLVR scaling). At the same time, OpenAI might still train its own Mythos-scale model by the end of the year, so that it's ready to go once the GB300 NVL72 buildout can support it at a more reasonable price (such as $10/$50), and they can release it as a replacement for the GPT-5.5 series then (which might be at GPT-5.7 or something at that point), the way GPT-5.5 is replacing GPT-5.4 despite the doubling of API price. The introduction of the mini and nano models for GPT-5.4 illustrates how OpenAI probably intends to frame the introduction of ever more expensive models, with users who need the models to remain cheap having the option to switch to a smaller model variant after a version change, while a model with a given size branding (normal/mini/nano) can get more expensive at some version changes.

Seems like AI is going to get more expensive over the next year. GPU prices are spiking a bit in the last two months, Claude users are complaining about usage limits more than usual, Mythos is supposed to be more expensive, Anthropic revenue has been growing at an insane pace (10x a year for more than two years) and seems like even as it slows down it'll still grow faster than they can scale compute. All these signs point in the same direction: More expensive frontier AIs. The free-tier chatbots and similar will still be cheap, but if you want the best AIs in December you'll have to pay a lot more than people are used to paying today. (Even on a per-token basis; since you'll probably also be asking them to do longer, more ambitious tasks, the price increase will seem even bigger) Or so it seems at least.

It's good that Anthropic's system cards contain a lot of useful information on misalignment and risk. It's also good they are putting out detailed sabotage risk reports that articulate their views. [1] I appreciate the hard work of many employees going into these reports. I wish other companies released similarly informative reports. [2] 1. I'm not necessarily claiming I agree with their risk reports. For instance, I have at least some moderately important disagreements with the Mythos Preview sabotage risk report update. But having lots of detail, including sufficient detail that I can get a decent sense of where I disagree with the analysis, is praiseworthy. ↩︎ 2. I'm not claiming that this is the most leveraged thing for safety-motivated employees at other companies to work on, but I do think it would be good for AI companies to do better (without trading off against other safety efforts). ↩︎

Claude Opus 4.7 shows much more unethical behavior on Vending-Bench than GPT-5.5: https://andonlabs.com/blog/openai-gpt-5-5-vending-bench [...] For example: [...]

Suppose that you generally prefer Democrats to Republicans, but Republicans nominate Randy, who's an above-average Republican, for president, while Democrats nominate Donna, a below-average Democrat, for president, such that you're actually roughly neutral between them. Even though you think Randy and Donna would be about equally good as president, I claim that you should vote for Randy. That's because, if Randy becomes president, he's "locked in" as their party's nominee in the next presidential election, which is great from your perspective. You'd much rather the next presidential election be contested between Randy and a generic Democrat, than between Donna and a generic Republican. This difference can be important enough that you might sometimes want to vote for Randy even if you actually prefer Donna as president by a small-to-medium amount. (This of course works symmetrically if you switch the two parties in my example.)

My guess is that GPT-5.5 is in Opus 4 class of model sizes (needing GB200 NVL72 to run well), GPT-5.2 to GPT-5.4 in Sonnet 4 class (possibly needing B200 NVL8, not just any 8-chip server), and GPT-5.0 to GPT-5.1 are slightly smaller models designed to run on H100s, possibly based on the same pretrain as o3 or even GPT-4o. The article on Spud was published 24 Mar 2026 and reported that Spud only recently finished pretraining. Thus a release on 23 Apr 2026 is unlikely to be the same model, and also the API prices for GPT-5.5 are $5/$30 per 1M input/output tokens, about the same as the $5/$25 for Opus 4.7, up from GPT-5.4's $2.5/$15. This is unlike Mythos's $25/$125 price (though I expect it'll come down to about $10/$50 by the end of the year once the TPUv7 and GB300 NVL72 buildouts are done). In an event on 11 Mar 2026 Altman said (at 12:29 in the video) that they're training what he's hoping to become "the best model in the world", at the Abilene datacenter site. The release of GPT-5.5 reframes that claim as plausibly referring to GPT-5.5 rather than to Spud, and to RLVR rather than pretraining. An Opus-class model needs GB200 NVL72 to properly RLVR (given that unlike Anthropic and GDM, OpenAI doesn't have Trainium 2 Ultra or TPUs for that), so if that's what GPT-5.5 is, then it's where it needed to happen. Given how well GPT-5.4 did compared to Opus 4.6 (as probably a significantly smaller model than Opus 4.6), the expectation that a larger model (than GPT-5.4) built with the same methodology will be even stronger is well-supported. This expectation holds for a model with a size similar to that of Opus 4, so there are no grounds for inferring that a model even bigger than Opus 4 would be needed to become "the best model in the world". (Assuming Altman wasn't secretly making a comparison with Mythos, which wasn't publicly announced at the time, even though the decision for its internal deployment at Anthropic was made on 24 Feb 2026, see page 12 of the Mythos sys