This episode carries John Jumper's own address, with nobody on the show putting
questions to them. It still counts as said, and it is kept out of every score on their page.
Nobel Laureate and DeepMind researcher John Jumper reflects on compute allocation during ML research and model development.
“The real cost of compute is the cost of ideas that didn't work. All the things you had to do to get there.”
quote is from the automated transcript, cleaned for reading:
filler sounds and stutters are removed, nothing is rephrased. names can be misheard
(the analysis reads context, assessments check outside sources). how →
More from John Jumper
AssertionSupported
Jumper: Equivariance explains only 2-3 GDT of AlphaFold 2's 30-point gain
“The sixth row there, no IPA, invariant point attention, that removes all the equivariance in alpha fold, and it hurts a bit, but only a bit. Alpha fold itself on this GDT scale that you can see on the left graph, alpha fold two was about 30 GDT better than alp…”
John JumperJul 15, 2025▶ 15:34Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery · Y Combinator
Opinion
Jumper: AlphaFold made structural biology 5% to 10% faster
“I like to think that our work Made the whole field of what's called structural biology, biology that deals with structures, you know, five or 10% faster. But the amount to which that matters for the world is enormous.”
John JumperJul 15, 2025▶ 25:21Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery · Y Combinator
PredictionNot checkable as stated
Jumper: Scientific AI will eventually be driven by broad, general models
“I think we will start to see this on more general systems, be them LLMs or others, That we will find more and more scientific knowledge within them, and we'll use them for important, important purposes, and I think this is really where this is going, and I thi…”
John JumperJul 15, 2025▶ 26:49Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery · Y Combinator
AssertionSupported
Jumper: A two-day open-source hack created the best protein interaction predictor
“The tweet on the left from Yoshitaka Morawaki came out two days after our code was available. We had predicted the structure of individual proteins, but we considered, we were working on building a system that would predict how proteins came together. But, ah,…”
John JumperJul 15, 2025▶ 21:28Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery · Y Combinator
AssertionPartly supported
Jumper: AlphaFold 2's final model trained on 128 TPUv3s for two weeks
“The final model itself was a 128 TPU V three cores, roughly equivalent to a GPU per core for two weeks.”
John JumperJul 15, 2025▶ 11:46Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery · Y Combinator
AssertionSupported
Jumper: AlphaFold 2 architecture on 1% of data matched AlphaFold 1
“The Al Qureshi lab, did a very, ah, careful experiment, where they took AlphaFold II, the architecture, and they trained it on one percent of the available data. And they could show that alpha fold two trained on one percent of the data was as accurate or more…”
John JumperJul 15, 2025▶ 13:37Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery · Y Combinator
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