My previous post aimed to make AI believers’ key ideas accessible, given that they are not always made explicit. I see these ideas as “fuel” that prime the community to expect imminent transformative AI in response to events.

In this post, I will discuss recent events from that perspective. What has happened in recent years, and how does it fit AI believers’ models of how transformative AI may come about? In short, current trends are consistent with a certain variant of the basic compute extrapolation model. This is consistent with many future trajectories, but is consistent with both a high importance of compute and a high rate of growth in AI impacts.

I very much hope for economists and AI believers to be able to productively discuss their expectations for AI capabilities. In my next post, I will make some suggestions for how this can be done.

Compute extrapolation predicts a transition period

In my previous post, I outlined the broad family of compute extrapolation models that has anchored the thinking of AI believers.

However, compute extrapolation models have a critical ambiguity. Sufficiently long before the threshold of human equivalence is reached, they predict that AI should have negligible impact; long afterwards, AI should have a transformative impact. But in the middle, there must be a transition. They say nothing about how that transition should look.

AI believers had widely divergent intuitions about transition dynamics. Eliezer Yudkowsky believed in a “fast takeoff”, while others, most notably Robin Hanson, believed in “slow takeoff”. For a long time, this “takeoff debate” could not be meaningfully settled, as AI was simply not “taking off” in any meaningful way.

The AI transition can now be measured

The modern Scaling Era of AI has provided the AI community with a wealth of data that was unavailable to the old AI believers.

The AI community is now able to estimate all of the following:

The stock of compute: The amount of AI compute in existence, in FLOPS.
Compute efficiency growth: The rate at which the FLOPS required to achieve a given level of AI capability falls.
Effective compute: The product of the above two quantities. This metric has been widely popularized by the AI forecasting organization Epoch AI (1, 2).
AI capabilities: Performance on benchmarks.
AI revenues: The total revenue earned by all AI services (e.g., ChatGPT, C

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AI Believers