Jeff Dean

speaker

343 appearances 1 recordings 1 series first heard Aug 2026 last heard 1 Aug

Jeff Dean’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.

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Y Combinator Startup Podcast · Jeff Dean: The 1% Rule for Building in AI · 1 Aug 2026 podcast

the foundation of the scientific method of you propose an experiment, you implement what you need to run the experiment, and you evaluate the experiment, and then you get results from that.

And I think there are more and more problems that are now possible to implement where that whole loop of running not just a few experiments, but running many, many experiments, because you're able to automate that loop and make the latency of that loop extremely low,

is going to be really, really important.

It's going to enable us to tackle lots of different problem domains in science and engineering and machine learning, model design itself, and also in engineering tasks like designing chips.

And so if you can actually do those things in an automated way,

and have some orchestration framework that can take very high level objectives and break them down into sub-problems.

And each of those sub-problems can be one of these automated loop that is exploring the best way to solve that sub-problem.

And then a orchestration framework that can put together sub-problem solutions into the overall solution for the higher level problem.

that's going to be really impactful and it's really, really important.

And I think it'll enable us to do, you know, accelerate machine learning progress.

It'll enable us to accelerate science and enable us to accelerate engineering.

And I think that's going to be amazing.

Yeah, I think in a lot of cases,

sometimes your evaluators need to be made much faster.

So as an example, my colleagues did some work maybe a decade ago on some problems in quantum chemistry where you're trying to understand the properties of a particular molecule and you can

generate some molecule configuration and then you want to understand what properties it has.

And so you can run a very computationally intensive density functional theory simulator, which is something that might take like a night of computation to tell you the answer for one thing.

But what my colleagues did was take a bunch of output from those simulation runs, the input molecule configurations and the outputs of the

the expensive simulator and then use it to train a neural approximation to the simulator.

So this is now a validation device, but instead of it taking a night, they made something that was 300,000 times faster and nearly as accurate as running the full scale simulator.

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