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

So one is give the model skills and hints that kind of tend to keep it in the sort of more brightly lit path of things it does know how to do.

I think having multi-agent systems where you have multiple agents trying different approaches and you can evaluate, you have maybe another model or another agent that's evaluating which ones of those seem promising is another way to kind of

in some sense, search the path, search the space of possible solutions

and stick to the ones that seem most promising and discard the ones that didn't seem to work or maybe that went off the rails or whatever.

And that's a very, very useful general technique is, you know, inference time compute to perform search over plausible ways of solving the problem that can get much, much higher performance or much more reliability in long running agent flows.

Yeah, I mean, we have harnesses and then we have a whole set of skills, particularly in the internal Google development environment.

We have skills so that the agents can know how to use lots of our internal tooling for coding or for code reviews or for measuring performance or

you know, fetching log files.

And those are just skills that you can add to make the base model more capable, even though it hasn't necessarily been trained on exactly the way that, you know, Google internal engineers would fetch log files from our, you know, proprietary system with the right kind of skill definition, you can actually get it to work.

And that improves the usefulness of the agents.

Yeah, I mean, I think obviously Google and our Gemini models and our hardware infrastructure are really trying to build very general models that can do almost anything.

But in a lot of cases, that means that we don't have a lot of attention on particular domains where perhaps a really well-designed surface and maybe a model and set of skills or maybe a specialized model that isn't in sort of the general mix of things that our models do well.

can actually have a significant advantage because you can build something delightful and really high accuracy, really high quality for a domain that you are really passionate about.

And I think that's where the two or three people in a room building that that they're really excited about can have an advantage.

But I would also caution that the general models are definitely getting better at a broader and broader range of things.

So you have to figure out, you know, is that thing you're working on, is that going to be a durable thing?

Or do you think the models at the forefront are going to get better at that in the next six months or 12 months?

Or is it something they're not going to be able to do for a couple of years or three years?

And, you know, you want to weigh that as you're deciding what to work on.

Yeah, I mean, the most important thing is to pick something you're super excited about and want to build and you think would be useful in the world.

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