Ajeya’s timelines report is the best thing that’s ever been written about AI timelines imo.
Ajeya’s framework is to AI forecasting what actual climate models are to climate change forecasting (by contrast with lower-tier methods such as “Just look at the time series of temperature over time / AI performance over time and extrapolate” and “Make a list of factors that might push the temperature up or down in the future / make AI progress harder or easier,” and of course the classic “poll a bunch of people with vaguely related credentials.”
Ajeya’s model doesn’t actually assume anything, or maybe it makes only a few very plausible assumptions. This is underappreciated, I think. People will say e.g. “I think data is the bottleneck, not compute.” But Ajeya’s model doesn’t assume otherwise! If you think data is the bottleneck, then the model is more difficult for you to use and will give more boring outputs, but you can still use it.
Machine Learning Researchers
Not exactly a paraphrase or an argument, but just try to get them to plug their assumptions about AI progress into Ajeya’s model.
Daniel Kokotajilo explains better more articulately than I could why this could be persuasive:
https://www.lesswrong.com/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines?commentId=o3k4znyxFSnpXqrdL