What Jeff Dean thinks about scaling laws

@jeff-dean · 22 positions · On ppll ↗

Chief Scientist at Google DeepMind and Google Research, at Google since 1999, where he co-created MapReduce, Bigtable, TensorFlow and the TPU.

Jeff Dean did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

Filter scaling laws · Newest first

Topic

All 22AI agents 4coding agents 3energy 3LLMs 3scaling laws 3semiconductors 2taste 2AI and jobs 1AI and science 1

4 more topics

design 1protein structure prediction 1recursive self-improvement 1startups 1

Sort

NewestOldest

Only Jeff Dean on scaling laws. See everyone on scaling laws

3 dated positions, 2026, in their own words. Our reading of what Jeff Dean has said — not written or endorsed by them.

3 positions so far — this page is not yet offered to search engines.

1. 30 Jul 2026

   Our readingSpending compute at inference time to search over plausible solutions is the general fix for unreliable long agent runs.

   Their wordsUm, 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 longunning agent flows.

   ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 11th of 22 in this recording

3. 30 Jul 2026

   Our readingBefore building on a gap in the general models, work out whether that gap survives six months or three years.

   Their wordsUm but I 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 uh 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 years or three years? And you know, you you want to weigh that as you're as you're deciding what to work on.

   ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 12th of 22 in this recording

5. 30 Jul 2026

   Our readingBuild where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving.

   Their wordsAnd 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 a probably a a sign that the capability is starting to be present in those models and with more training data or larger scale models or or whatever it's likely to get better. So um you know look for something where the model succeeds 0% or 1% of the time not not 20%.

   ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 13th of 22 in this recording