Also see FutureSearch’s report on a plausible breakdown for how OpenBrain hits $100B ARR by mid-2027.
I think if you condition on the capability progression in the scenario and look at existing subscription services generating in the $100B range, it feels very plausible intuitively, independently from the ‘tripling’ extrapolation.
romeo
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Seconding Daniel, thanks for the comment! I decided to adjust down the early numbers to be below the human professional range until Dec 2025[1] due to agreeing with the considerations you raised about about longer horizon tasks which should be included in how these ranges are defined.
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Note that these are based on internal capabilities, so that translates to the best public models reaching the low human range in early-mid 2026.
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Thanks for the comment Vladimir!
[...] for the total of 2x in performance.
I never got around to updating based on the GTC 2025 announcement but I do have the Blackwell to Rubin efficiency gain down as ~2.0x adjusted by die size so looks like we are in agreement there (though I attributed it a little differently based on information I could find at the time).
So the first models will start being trained on Rubin no earlier than late 2026, much more likely only in 2027 [...]
Agreed! I have them coming into use in early 2027 in this chart.
This predicts at most 1e28 BF16 FLOPs (2e28 FP8 FLOPs) models in late 2026
Agreed! As you noted we have the early version of Agent-2 at 1e28 fp16 in late 2026.
Rubin Ultra is another big step ~1 year after Rubin, with 2x more compute dies per chip and 2x more chips per rack, so it’s a reason to plan pacing the scaling a bit rather than rushing it in 2026-2027. Such plans will make rushing it more difficult if there is suddenly a reason to do so, and 4 GW with non-Ultra Rubin seems a bit sudden.
Agree! I wrote this before knowing about the Rubin Ultra roadmap, but this part of the forecast starts to be affected somewhat by the intelligence explosion. Specifically an urgent demand for research experiment compute and inference specialised chips for running automated researchers.
Good point, thanks. Previously I would have pretty confidently read “100K GB200 GPUs,” or “100K GB200 cluster” as 200K B200s (~= 500K H100s) but I can see how it’s easily ambiguous. Now that I think of it, I remembered this Tom’s Hardware article where B200 and GB200 are mistakenly used interchangeably (compare the subtitle vs. the end of the first paragraph)...
That’s indeed inconvenient. I was aware of NVL2, NVL4, NVL36, NVL72, but I was under the impression that ‘GB200’ mentioned on its own always means 2 Blackwells, 1 Grace (unless you add on a ‘NVL__’). Are there counterexamples to this? I scanned the links you mentioned and only saw ‘GB200 NVL2,’ ‘GB200 NVL4,’ ‘GB200 NVL72’ respectively.
I was operating on this pretty confidently but unsure where else I saw this described (apart from the column I linked above). On a quick search of ‘GB200 vs B200’ the first link I found seemed to corroborate GB200 = 2xB200s + 1xGrace CPU. Edit: second link also says: “the Grace-Blackwell GB200 Superchip. This is a module that has two B200 GPUs wired to an NVIDIA Grace CPU...”
I think ‘GB200’ refers to this column (2 Blackwell GPU + 1 Grace CPU) so 16K GB200s ~= 32K B200s ~= 80K H100s. Agree that it is still very low.
My guess is that Bloomberg’s phrasing is just misleading or the reporting is incomplete. For example, maybe they are only reporting the chips Oracle is contributing or something like that. I’d be very surprised if OpenAI don’t have access to >200K GB200s ~= 1M H100s by the end of 2025. For reference, that is only ~$20B capex (assuming $100k total cost of ownership per GB200) or roughly 1⁄4 of what Microsoft alone plan to invest this year.
Once they have just 100K GB200s, that should train 2e27 FLOP in 4 months.[1]- ^
There’s a nice correspondence between H100s and FLOP/month (assuming 40% utilisation and 16-bit precision) of 1e21 FLOP/month/H100. So since 100K GB200s = 500K H100s, that’s 5e26 FLOP/month.
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Thanks Vladimir, this is really interesting!
Re: OpenAI’s compute, I inferred from this NYT article that their $8.7B costs this year were likely to include about $6B in compute costs, which implies an average use of ~274k H100s throughout the year[1] (assuming $2.50/hr average H100 rental price). Assuming this was their annual average, I would’ve guessed they’d be on track to be using around 400k H100s by now.
So the 150k H100s campus in Phoenix might be only a small fraction of the total compute they have access to? Does this sound plausible?
The co-location of the Trainium2 cluster might give Anthropic a short term advantage, though I think its actually quite unclear if their networking and topology will fully enable this advantage. Perhaps the OpenAI Phoenix campus is well-connected enough to another OpenAI campus to be doing a 2-campus asynchronous training run effectively.- ^
$6e9 / 365.25d / 24h / $2.5/hr = 274k
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Thanks for the detailed comments! We really appreciate it. Regarding revenue, here’s some thoughts:
AI 2027 is not a median forecast but a modal forecast, so a plausible story for the faster side of the capability progression expected by the team. If you condition on the capability progression in the scenario, I actually think $140B in 2027 is potentially on the conservative side. My favourite parts of the FutureSearch report is the examples from the ~$100B/year reference class, e.g., ‘Microsoft’s Productivity and Business Process segment.’ If you take the AI’s agentic capabilities and reliability from the scenario seriously, I think it feels intuitively easy to imagine how a similar scale business booms relatively quickly, and i’m glad that FutureSearch was able to give a breakdown as an example of how that could look.
So maybe I should just ask whether you are conditioning on the capabilities progression or not with this disagreement? Do you think $140b in 2027 is implausible even if you condition on the AI 2027 capability progression?
If you just think $140B in 2027 is not a good unconditional median forecast all things considered, then I think we all agree!
We aren’t forecasting OpenAI revenue but OpenBrain revenue which is different because its ~MAX(OpenAI, Anthropic, GDM (AI-only), xAI, etc.).[1] In some places FutureSearch indeed seems to have given the ‘plausible $100B ARR breakdown’ under the assumption that OpenAI is the leading company in 2027, but that doesn’t mean the two are supposed to be equal neither in their own revenue forecast nor in any of the AI 2027 work.
The exact breakdown FutureSearch use seems relatively unimportant compared to the high level argument that the headline (1) $/month and (2) no. of subscribers, very plausibly reaches the $100B ARR range, given the expected quality of agents that they will be able to offer.
I don’t think a monopoly is necessary, there’s a significant OpenBrain lead-time in the scenario, and I think it seems plausible that OpenBrain would convert that into a significant market share.
Not exactly equal since maybe the leading company in AI capabilities (measured by AI R&D prog. multiplier), i.e., OpenBrain, is not the one making the most revenue.