> I've seen demonstrations of AIs accomplishing [...] tasks that would take humans months to years. Due to this, I tentatively believe that (as of March 1st) the well-elicited 50% reliability time-horizon on ESNI tasks (using only publicly available models) is somewhere between a month and several years Once you are talking about scaling to arbitrary inference costs, I think the relevant notion of time horizon is closer to: "For what T could you solve this task using an arbitrary supply of humans each of whom only works on the project for T hours?" On this framing I think that lots of easily-checkable tasks have much shorter horizons than "amount of time they would take a human to do." That is, I think that you could successfully solve them by delegating to a giant team of low-context humans each of whom makes marginal improvements, and the work from those humans would look quite similar to the AI work. This is particularly true for reimplementation tasks with predefined tests + a reference implementation. In that setting a human can pick a specific failing test, use the reference implementation to understand the desired behavior, and then implement it while using tests to make sure they don't break anything else. For the projects you are describing it seems plausible that a human with a few days could use that workflow to reliably make forward progress and get the project done in a moderate number of steps. (I would expect a giant human team using that methodology to make a huge number of mistakes for anything not covered by the tests, and to write code that is low quality and hard to maintain. But I think we're currently seeing roughly that pattern of failures from AI reimplementation.) It's also very true of vulnerability discovery, since different humans can look in different places and then iteratively zoom in on whatever leads are most promising. So I'm not sure how much of the phenomenon you're observe is "there is a way longer horizon for these tasks" rather than "a more careful definition of horizon is more important for these tasks." Probably some of both, but It seems quite possible that for the tasks you are describing the horizon length is really more like a few days or a week than months or a year. I think it's helpful to try to pull these effects apart, particularly when reasoning about the likely effects of continuing RL schlep: I think that you are seeing good performance on these tasks partly because they are easy to decompose and delegate, and not entirely because AI developers are able to do RL on them. For the very "long horizon" tasks you cite, I think that's the dominant effect. So I wouldn't expect performance to be as impressive on tasks where it would be hard to delegate them to a giant team of low-context humans. I still agree that recent public development seem like evidence for shorter timelines, and I'm not saying my bottom line numbers differ much from yours. Even a more conservative extrapolation of time horizons would suggest more like 2-4 years until you can strictly outperform humans for virtual projects (with human parity somewhat before that).

About 1e27 FLOPs seems reasonable for Mythos and Spud, though a bit more might also be possible. Late 2025 compute is about 1-2 GW per AI company (in total, inference plus R&D), 300 MW is about 300K chips of Trainium 2 or 200K chips of H100, which is about 4e20 FP8 FLOP/s for either Trainium 2 or H100 (1.3e15 FP8 FLOP/s per chip for Trainium 2, 2e15 FP8 FLOP/s per chip for H100). Which in 3 months at 40% utilization gives 1.3e27 FLOPs. (Given the Nvidia bet on FP8 in Rubin, significantly increasing the FP8-to-BF16 performance ratio compared to earlier chips, FP8 is likely the mainsteam pretraining precision for frontier models now.) The important thing about Mythos and Spud compared to earlier models is that they are plausibly the first models big enough in total params to have enough active params to use 1e27 FLOPs. At 120 tokens/param maybe being compute optimal given modest sparsity, 1e27 FLOPs ask for 1.2T active params, which probably makes sense for at least 5T-10T total params or more, while models smaller in total params would also have fewer active params, and so won't be taking full advantage of 1e27 FLOPs in pretraining. Opus 4 probably targets Trainium 2 Ultra (6 TB of HBM per rack), so might be a 3T total param model, and GPT-5 probably targets 8-chip Nvidia servers, including H100 (0.64 TB of HBM per server), so might be a 300B-900B total param model (it had to compete with Opus 4, so even with longer reasoning traces it probably needed to be bigger than H100s want). But Mythos plausibly targets TPUv7 (9216 chips per pod, 192 GB of HBM per chip), so as a step up from Opus it might be a 10T total param model (there's a new announcement that Anthropic's access to TPUs will scale in 2027+ beyond the 1 GW of 2026). And Spud likely targets GB200/GB300 Oberon racks (14/20 TB of HBM per rack), so it might be a 6T-7T total param model (unless they go NVFP4 and take full advantage of that, but 12T-15T total params seems too sudden after GPT-5, there should be an intermediate step). Thus both Mythos and Spud might be at the right scale to fit 1.2T active params, though it's still probably a bit less, maybe 800B active params, to optimize input token cost.