Jeff Dean: The 1% Rule for Building in AI

**Diana Hu** (0:07)
All right, should we get started, Jeff?

**Jeff Dean** (0:10)
Sure, sounds great.

**Diana Hu** (0:11)
All right, Jeff, welcome. And again, thank you so much for being here, especially, you just got a cold and thank you for being here.

**Jeff Dean** (0:17)
Yeah, I'm afraid I've lost my voice. I don't normally sound quite like this, but we'll do what we can.

**Diana Hu** (0:23)
So you built MapReduce, Bigtable, TensorFlow, the TPU, Gemini, we could spend a whole hour on all the things you've done. But what I love is that you're still making bold predictions in public. Last year, yes, last year in May 2025 at AI Ascent, you said that AI is at the level of a junior engineer. That was about a year ago.
Sven, how close are we to that prediction?

**Jeff Dean** (0:57)
Yeah, I mean, I feel like the models have been getting a lot better at sort of agent-based, longer running coding tasks. And it seems pretty clear that they are now actually pretty capable. And depending on exactly your definition of junior engineer, it seems pretty spot-on, I would say.

**Diana Hu** (1:15)
What did you underestimate from that prediction?

**Jeff Dean** (1:21)
I mean, I think the ability to do more and more complex tasks has been growing faster than I thought.
And I also think outside of coding, these agent-based systems are really starting to shine in other domains. And I think that's going to be an important trend in the future.

**Diana Hu** (1:44)
So give us another bold prediction. What do you think is going to be the 2027 edition?

**Jeff Dean** (1:51)
I think you will see a lot more automation of ML systems themselves.
Basically, getting ML systems to improve their capabilities by running lots of experiments, breaking things down into sub-problems, running those sub-problems in a tight automatic experimentation loop, putting the results together and being able to then get some improved system out from that fully automated problem decomposition and automated experimentation loop. I think that's going to be really exciting. I think that also applies not just to ML, but also to other fields of science and engineering. Basically, anything where you can have a measurable objective, I think you can actually make a lot of progress these days.

**Diana Hu** (2:39)
Now, let's go back to a little bit in history. Back in, way back in 2001, Google search used to run on hard drives.

**Jeff Dean** (2:49)
Yep.

**Diana Hu** (2:50)
You and Sanjay did the math and realized that at some point, the whole search index would finally fit in all of the RAM of all the computers you have running. And you made that radical realization. And you basically in a few days, with Sanjay, shipped in production a whole new search version that worked in RAM rather than hard drive. And that was the thing that got Google to be so fast, Google searches. So, history tends to remix.
What is the, it fits the memory moment right now in 2026 that everyone in this room is still, should be thinking about and designing?

**Jeff Dean** (3:36)
Yeah. I mean, it's a little different, but I think you're going to see more and more high performance and low energy inference hardware systems. Because I think everyone is now realizing that inference is the key to making these agent-based systems be available to more and more people, and that latency is really important, and that specialization of the hardware is a really key way you can make things that are more energy efficient and lower latency than more general purpose computational devices like say GPUs or TPUs.

**Diana Hu** (4:16)
Because I think everyone here is used to waiting for responses on models. Yeah.

**Jeff Dean** (4:22)
Waiting is no fun.

**Diana Hu** (4:25)
Master of Speed. So you're saying, what if we don't have to wait anymore?

**Jeff Dean** (4:30)
Yeah. I mean, I think we'll imagine what you could do with something where the latency is 50x better.

**Diana Hu** (4:38)
Interesting thought. Now, what's one assumption that perhaps 6,000 people in this room hold as already about AI?

**Jeff Dean** (4:50)
Yeah, that's a good question. I mean, I think probably one thing is people don't quite realize how possible it is to have agent-based systems that can run not just for an hour or two hours on a problem you care about, but for some problem domains and with highly capable models underlying them, you can get them to run for days or weeks and do really, really complicated tasks. I think some people are starting to see inklings of this, but I don't think everyone has really internalized this, and that's going to be really a pretty big deal.