Debugging at scale

Intro

0:00

Joining Google in 1999

2:44

Future of Moore's Law

5:36

Future TPUs

10:21

Jeff’s undergrad thesis: parallel backprop

13:13

LLMs in 2007

15:10

“Holy s**t” moments

23:07

AI fulfills Google’s original mission

29:46

Doing Search in-context

34:19

The internal coding model

38:32

What will 2027 models do?

39:49

A new architecture every day?

46:00

Automated chip design and intelligence explosion

49:21

Future of inference scaling

57:31

Already doing multi-datacenter runs

1:03:56

Fast takeoff and superalignment

1:26:05

A million evil Jeff Deans

1:34:40

Fun times at Google

1:38:16

World compute demand in 2030

1:41:50

Getting back to modularity

1:48:21

Keeping a giga-MoE in-memory

1:59:13

All of Google in one model

2:04:09

What’s missing from distillation

2:12:43

Open research, pros and cons

2:18:03

Going the distance

2:24:54