Jeff Dean: The 1% Rule for Building in AI
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What is the main topic discussed in this episode?
All right, should we get started, Jeff?
Sure, sounds great.
All right, Jeff, welcome. And again, thank you so much for being here. Especially, I just got a cold, and thank you for being here.
Yeah, I'm afraid I've lost my voice. I don't normally sound quite like this, but we'll do what we can.
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. How close are we to that prediction?
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.
What did you underestimate from that prediction?
I mean, I think the... 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 gonna be an important trend in the future.
So give us another bold prediction. What do you think is gonna be the 2027 edition?
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 sort of fully automated problem decomposition and automated experimentation. 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.
Now let's go back to a little bit in history. Way back in 2001, Google Search used to run on hard drives. And you and Sanjay did the math and realized at some point the whole search index would finally fit in all of the RAM of all the computers you had 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 with 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?
How does Jeff Dean assess current AI capabilities compared to a junior engineer?
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.
Because I think everyone here is used to waiting for responses on models.
Yeah, waiting is no fun.
Master of speed. So you're saying, what if we don't have to wait anymore?
Yeah, I mean, I think, well, imagine what you could do with something where the latency is, you know, 50x better.
Interesting thought. Now, what's one assumption that perhaps 6,000 people in this room hold that's already false about AI?
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.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:07–3:36
2
How does Jeff Dean assess current AI capabilities compared to a junior engineer?
3:36–18:16
3
What 'memory moment' from 2001 is Jeff Dean comparing to 2026 and why does it matter?
18:16–24:30
4
Why does Jeff Dean predict specialized, low-energy inference hardware will be the next focus?
24:30–41:49
5
How did Jeff Dean’s napkin math lead to the creation of the TPU and what was the insight?
41:49–57:06