A Tale of Two Timelines: The Slow Scenario and The Fast Scenario

If models continue to fall short in one or two respects, AI’s increasing array of superhuman strengths – in speed, breadth of knowledge, ability to take 1000 attempts at a problem, and so forth – may be able to compensate. But if progress on multiple indicators is slow and unreliable, that will constitute strong evidence that AGI is not around the corner.

We may see nontechnical barriers to AI adoption: inertia, regulatory friction, and entrenched interests. This would not necessarily indicate evidence of slow progress toward AGI, so long as these barriers are not posing a significant obstacle to the ongoing development of AI itself. In this scenario, AI adoption in the broader economy might lag until AI capabilities start to become radically superhuman, at which point there would be strong incentives to circumvent the barriers. (Though if inertia specifically is a major barrier to adoption, this might constitute evidence that AI is still not very flexible, which would suggest slow progress toward AGI.)