Introduction

How many years will pass before transformative AI is built? Three people who have thought about this question a lot are Ajeya Cotra from Open Philanthropy, Daniel Kokotajlo from OpenAI and Ege Erdil from Epoch. Despite each spending at least hundreds of hours investigating this question, they still still disagree substantially about the relevant timescales. For instance, here are their median timelines for one operationalization of transformative AI:

Median Estimate for when 99% of currently fully remote jobs will be automatable
Daniel	4 years
Ajeya	13 years
Ege	40 years

You can see the strength of their disagreements in the graphs below, where they give very different probability distributions over two questions relating to AGI development (note that these graphs are very rough and are only intended to capture high-level differences, and especially aren't very robust in the left and right tails).

In what year would AI systems be able to replace 99% of current fully remote jobs?

In what year will the energy consumption of humanity or its descendants be 1000x greater than now?

Median indicated by small dotted line. Note that Ege's median is outside of the bounds at 2177

So I invited them to have a conversation about where their disagreements lie, sitting down for 3 hours to have a written dialogue. You can read the discussion below, which I personally found quite valuable.

The dialogue is roughly split in two, with the first part focusing on disagreements between Ajeya and Daniel, and the second part focusing on disagreements between Daniel/Ajeya and Ege.

I'll summarize the discussion here, but you can also jump straight in.

Summary of the Dialogue

Some Background on their Models

Ajeya and Daniel are using a compute-centric model for their AI forecasts, illustrated by Ajeya's draft AI Timelines report, and Tom Davidson's takeoff model where the question of "when transformative AI" gets reduced to "how much compute is necessary to get AGI and when will we have that much compute? (modeling algorithmic advances as reductions in necessary compute)".

Whereas Ege thinks such models should have a lot of weight in our forecasts, but that they likely miss important considerations and doesn't have enough evidence to justify the extraordinary predictions it makes.

Habryka's Overview of Ajeya & Daniel discussion

Ajeya thinks transl

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