Toby Ord

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1,415 appearances 1 recordings 1 series first heard Aug 2026 last heard 6 Aug

Toby Ord’s voice in public audio — every appearance, attributed to the second.

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80,000 Hours Podcast · #250 – Toby Ord on where AGI timelines go wrong · 6 Aug 2026 podcast

There could be different forms of governance or different kinds of rules applied at the labs in order to manage them.

So then a third one would be that when humans create a new technology, say guns, first we create muskets, and then we make rifles, and then eventually we've got handguns and machine guns and things like this.

And we've got this opportunity, though, to learn from the intermediate stages.

So we don't unleash the most powerful form of the technology on society all at once.

Or with cars, we had much slower cars before they could go at the current speeds.

And so that gave us opportunities to learn.

Also, there was only a few of them around at first instead of everyone having a car.

And so

Having those kinds of intermediate levels is extremely useful for society.

We're actually really quite bad at predicting what's going to happen if some new technology were to arrive and to regulate in advance.

But if we get to witness some of the ill effects on a smaller scale, then we can learn from that.

And so in this case, if things are going, let's say they're going five times faster, maybe instead of...

uh it being the model that would have been released in 2027 uh in 2027 we get the model that would have been released in 2032 or something um and so we have like some some really big jump uh up there uh if if we get that uh we don't get the opportunity to learn from these intermediate things and it could be that there's one that really would have created a lot of havoc but at a level that was ultimately manageable by society but we really learned our lesson and in this case we might not get to learn that lesson yeah is there a fourth one

Yeah.

So I think a fourth area is that if you kind of imagine, you know, the rate of progress just with human only research in AI, let's say there's kind of no ticking along.

And then imagine that there's a leading lab where their research is kind of improving, you know, their capabilities over time.

And then there's a trailing lab that's a year behind them.

The difference in the capabilities of the leading lab and the trailing lab, it's noticeable, but it's only so big.

Whereas if you have this recursive self-improvement and the capabilities really bend upwards steeply at some point, then the first lab to go through that process, it could be that

if we go to Tom Davidson's rule of thumb of five years of progress in one year, it will be then five years ahead on the old scheme of where the lab that was a year behind is.

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