Toby Ord

This creates an extra multiplier for the value of direct work in these areas now, and in some cases is a larger effect than the chance your work comes to fruition after transformative AI.

Overall, I think that longer-term projects do get downweighted by these considerations, but their advantages sometimes outweigh that, especially if they are shooting for a very big payoff.

I'd guess that if someone looked at their options and thought the best option was one that took 5 to 10 years to pay off, then about half the time it would remain their best option even after taking AI timelines into consideration.

After all, it is not uncommon for your best option to be several times better than your second best.

So I think the community of people working on transformative AI are likely underrating types of work that need 5 or more years in order to pay off.

The ideal portfolio of activities aimed at making the AI transition go well should include a number of things that really help us succeed in worlds where we get longer to try.

But I want to stress that none of this implies we can slack off.

We're in a race against AI timelines.

It is just that we don't know if that race is a sprint or a marathon.

In either case, time is of the essence.

Heading Conclusions We have seen that there is substantial disagreement and uncertainty about when AI will start having transformative impacts on the world.

This is because there just isn't enough evidence to pin it down.

My claim is that for the purposes of planning we should adopt neither short nor long timelines, but broad timelines.

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The correct epistemic response to the lasting expert disagreement is to have a broad distribution over AI timelines.

End quote.

Given this deep uncertainty we need to act with epistemic humility.

We have to take seriously the possibility it will come soon and hedge against that.

But we also have to take seriously the possibility that it comes late and take advantage of the opportunities that would afford us.

The world at large is doing too little of the former, but those of us who care most about making the AI transition go well might be doing too little of the latter.