Jeff Dean

7 statements across 1 episodes · 3 bullish · 0 bearish · 1 people on the record · first statement Jul 30, 2026 by Jeff Dean · said 2 times in 2 episodes since 2026 · across every show →

On the record as a speaker too: Jeff Dean's record, appearances and statements → this page counts the times other people say the name.

Mentions by year

brought up most by Garry Tan (1), Demis Hassabis (1)

tap a year for its mentions
0011222026episodesmentions
0122026episodes it came up in
000.51122026episodesmentions per episode

every mention, scene by scene, with the transcript →

Everything said about Jeff Dean, oldest first

Jul 30, 2026
Insight
Dean: 1,000x energy penalty for moving data forces machine learning batching
“I mean, I think the example you raised of a thousand X difference in bringing moving data versus actually computing on it in, in terms of energy is, is a pretty significant one. And it shapes a lot of aspects of what we do in machine learning. Because if you d…”
Jeff Dean Jul 30, 2026 ▶ 12:52 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Jul 30, 2026
Assertion Supported
Dean: Sanjay Ghemawat and I published a 30-page code optimization guide
“Oh we actually published the document maybe a few months ago called performance hints that Sanjay and I wrote. That's like a 30 page document about, you know, various kinds of performance tricks and Some people have taken that and then given it in summarized f…”
Jeff Dean Jul 30, 2026 ▶ 21:40 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Jul 30, 2026
Insight
Dean: In-context data is clearer to models than pretraining parameters
“And the nice thing about that is that information is really clear to the model. Unlike the training data, the model was trained on where it's all kind of like trillions of tokens stirred together into a soup of hundreds of billions or trillions of parameters, …”
Jeff Dean Jul 30, 2026 ▶ 17:14 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Jul 30, 2026 positive
Opinion
Jeff Dean: AI models have reached junior engineer capability level
“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 sa…”
Jeff Dean Jul 30, 2026 ▶ 0:58 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Jul 30, 2026
Insight
Dean: AI startups should target tasks with 1% model success, not 20%
“If they're completely failing, that's probably a good sign. If they're kind of able to do some of it, but not very well, That's maybe not a great sign because that's probably a sign that the capability is starting to be present in those models and with more tr…”
Jeff Dean Jul 30, 2026 ▶ 28:26 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Jul 30, 2026 bullish
Insight
Jeff Dean: AI agents can run autonomously for days or weeks
“Probably one thing is people don't quite realize how possible it is to have, you know, 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, …”
Jeff Dean Jul 30, 2026 ▶ 4:56 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Jul 30, 2026 bullish
Prediction Not checkable as stated
Dean: Specialized inference hardware will surpass general GPUs and TPUs
“I think, ah, you're gonna see more and more, ah high performance and low energy inference hardware systems, because I think everyone is now realizing that inference is the key to making, you know, these agent-based systems be available to more and more people,…”
Jeff Dean Jul 30, 2026 ▶ 3:37 Jeff Dean: The 1% Rule for Building in AI · Y Combinator
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 300 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.