Rohin Shah
speaker
1,071 appearances
1 recordings
1 series
first heard Jun 2026
last heard 2 Jun
Rohin Shah’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jun 2026 with 1.
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Google has a deep think algorithm which applies even more inference scaling to get even better results.
I think this, you should basically expect this to continue.
And so like the picture of the first automated researcher might be something that's more like, you know, a relatively, uh, dumb system.
That's like not as smart, quote unquote, as an, as a human researcher, but spending like just tremendous amounts of time doing reasoning, exploring tons of dead ends, realizing their dead ends.
And then, uh,
coming back and trying something else, which a human would never have gone down because they would have known in advance it would be a dead end.
And that's how it does its automated research, such that it might even be more expensive than a human researcher would be.
And then we improve it over time, the cost goes down pretty quickly, as is usually the case in AI.
But that would suggest that it won't be that abrupt.
Like you'll...
The AI will reach cost parity with humans, then start becoming more cost effective than the humans, and that will start setting off the acceleration.
If you take it from the point at which the AI systems start being just barely, like just on par with existing human AI researchers, then yes, that's right.
That's why I would think it would take years rather than months.
But most of my timelines delay is just thinking that like even getting to that point will take quite a while.
But quite a while, like, you know, a decade maybe, which I feel like in past years would have been called extremely short timelines and nowadays gets called medium to long timelines.
I would guess probably the biggest difference is that I had a picture in my mind about how AI progress would happen.
has been reasonably close to it.
For example, I think the biggest timelines freak out was, the timelines freak out in January, let's say, was from the advent of reasoning models, 01 and then particularly 03, where I would say the key idea there is like, let's actually apply reinforcement learning to large language models.
And I think this has actually been like,
I have since, for quite a long time, I'd say probably at least 2019, but maybe even earlier than that, thought, yes, of course, we are going to need to use reinforcement learning in order to develop powerful AI systems.
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