# Fast learned evaluators can change the pace of science

Markdown copy of https://realalpha.app/s/fast-learned-evaluators-can-change-the-pace-of-science/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Jeff Dean](https://realalpha.app/builders/jeff-dean/) · Jeff Dean is Chief Scientist at Google. · Co-founded AI startup Discovery Loop; previously Google chief scientist and co-creator of MapReduce, Bigtable.
- Host: [Diana Hu](https://realalpha.app/people/diana-hu/) · Managing partner, Y Combinator · CTO of Escher Reality; after Niantic bought it, ran AR for Pokémon GO.
- Show: Lightcone
- Episode: [Jeff Dean: The 1% Rule for Building in AI](https://realalpha.app/v/CxXgV54KzpQ/), Jul 30, 2026
- Moment: 42:04 to 46:12 of the episode (4 min), playing on https://realalpha.app/s/fast-learned-evaluators-can-change-the-pace-of-science/; the episode from there: https://realalpha.app/v/CxXgV54KzpQ/?t=2524

## Quote

> “they made something that was **300,000 times faster** and nearly as accurate as running the full-scale simulator.”
> — [Jeff Dean](https://realalpha.app/builders/jeff-dean/) at [45:17](https://realalpha.app/v/CxXgV54KzpQ/?t=2717) · Lightcone

## Takeaways

1. Automated experiment loops can accelerate science and engineering.
2. A learned approximation can stand in for a slow simulator.
3. Faster evaluation makes it practical to screen far more candidates.

## About this segment

Jeff Dean describes automating scientific experiments and speeding up evaluation with a learned approximation of a costly simulator. One example ran 300,000 times faster while staying nearly as accurate.

From “Jeff Dean: The 1% Rule for Building in AI”, Jul 30, 2026. [Watch the full episode](https://realalpha.app/v/CxXgV54KzpQ/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[42:09](https://realalpha.app/v/CxXgV54KzpQ/?t=2529) Now, let's go back to, you talked a bit about your interest right now in working on a lot of customized hardware. So right now, Alpha Chip lays out chips. Now you also got Alpha Evolved that proposes solutions, evaluates them, and keeps all the ones at work. Seems like you're starting to build all these systems that can compound and build AI that builds AI. Yeah. I mean, I think more generally, there's this sort of 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 you know 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

[43:06](https://realalpha.app/v/CxXgV54KzpQ/?t=2586) low is going to be really really important it's going to enable us to tackle you know lots of different problem domains and 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 loops that is exploring the best way to solve that sub-problem, and then an 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.

[44:00](https://realalpha.app/v/CxXgV54KzpQ/?t=2640) 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. That sounds awesome. I mean, it sounds like a lot of fields basically where you can have very good evaluators and maybe adjacent to basically things that can be formally verified, right? Those are ripe for AI systems that can self-improve. 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.

[44:47](https://realalpha.app/v/CxXgV54KzpQ/?t=2687) 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 expensive simulator, and then use it to train a neural approximation to the simulator.

[45:17](https://realalpha.app/v/CxXgV54KzpQ/?t=2717) 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. So now that completely changes how you would do science, right? Because now you have 10 million things to screen. You know, you could do that while you go to lunch, rather than it being a six-month endeavor where you could try to scrape together enough compute to run all these simulations. And I think there's a lot of room in a lot of domains for much faster validation models, possibly learned validation models that can, you know, get you an approximation to the true answer much more rapidly. And that changes how those experimental loops can be thought of and how quickly you can go around those loops.