We are likely in a situation where the most powerful models in the world are internal models, and, here's a key distinction, potentially even models that are never intended to be released to the public (i.e. a model designed to provide internal uplift on capabilities during training but that is not intended to be / not particularly suitable for commercial purposes.)

Internal-external information and capability gaps can distort both public and regulatory perception of frontier capabilities and dangers. If the internal frontier is three or six months ahead, such a gap is worrisome if the labs are close to automating various bottlenecks to increased capabilities development, like automating AI R&D.

Some ways of mitigating these visibility gaps and communicating the existing data we do have are implementable over the short term and confer minimal cost for many actors relative to the advantages of visibility for policymakers.

What is a "holy shit" graph?

Many of my intuitions about the coming impacts of AI are shaped by "Holy shit" graphs. The METR time horizon is one such graph, but has been discussed to death, so let's talk about another three:

Internal spending by OpenAI researchers (at [...]

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Outline:

(01:02) What is a "holy shit" graph?

(01:18) Internal spending by OpenAI researchers (at API prices)

(02:25) General AI (as opposed to narrow AI) model revenues

(03:50) Extent of AI R&D automation

(08:43) Labor, relational and political

(09:17) This could be you!

The original text contained 11 footnotes which were omitted from this narration.

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First published:
October 18th, 2026

Source:
https://www.lesswrong.com/posts/GK9p6NHEk8bkZQxH4/holy-shit-graphs-and-how-to-get-more-of-them-1

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Narrated by TYPE III AUDIO.

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Images from the article:

Line graph showingGoogle search AI Overview stating global token demand grows roughly 10× per year, not monthly doubling.Area chartScatter plot titledStacked area chart titled

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