John Platt (Google): AI for Climate and Science

John Platt is a Google Fellow and leads the Applied Science team at Google Research, the group working on climate and science. He's also the person behind SMO and Platt scaling. In this episode, we talk with him about how Google decides which scientific problems are worth its time, and what happens when AI meets real physical systems. A big part of the conversation is contrails. A small share of flights creates most of the warming from contrails, which together add up to roughly 1% of human-caused warming. John explains how his team detects contrails from weather satellites and predicts where they will form. In trials with American Airlines, rerouting flights cut contrail formation by 62% with almost no extra fuel. We also get into ERA, Google's empirical research assistant, and where AI agents actually help scientists. John sees them taking over the drudgery of building models and munging data, while humans keep the taste and the rigor. He shares why he's skeptical of gradient-free optimization, why the bitter lesson runs into energy and data center limits, and why physical AI that makes manufacturing cheaper could matter a lot for climate. We finish with FireSat, a satellite constellation (about 50 satellites at full scale) designed to catch wildfires while they're still small enough to put out.

Timeline

00:00 Intro
01:09 How Google picks science problems: comparative advantage
04:15 Projects that didn't work: RNA aptamers and tool/problem fit
05:46 DNA, RNA and XNA
07:41 Contrails and why they warm the planet
13:01 Three AI systems for avoiding contrails
15:23 Cloud seeding and why cloud physics is hard
18:17 Airline trials: 62% fewer contrails
19:46 Navier-Stokes, wind tunnels and where simulation still fails
25:07 Will agents replace scientists? ERA and scorable tasks
31:36 Overfitting, Kaggle and the half-pixel bug
33:41 Agents that exploit bugs: Lean and Goodhart's law
36:45 Gradient-free optimization
38:53 SMO, SVMs and hinge loss in neural networks
42:12 The bitter lesson and the limits of scaling
43:36 Where agents will help science in the next 12 months
45:13 Robotics and making climate tech cheaper to build
48:22 AI risk and misuse
50:51 Climate outlook: past 2°C
54:39 Why AI won't just solve climate change
57:04 Room-temperature superconductors, fusion and HVDC
1:02:25 FireSat: detecting wildfires from orbit
1:05:16 Why existing satellites aren't enough
1:07:53 The economics of 50 satellites
1:09:34 False positives
1:11:16 Closing

Topics

• Comparative advantage as the test for which science problems Google takes on
• Contrails: the physics, the AI pipeline, and the American Airlines trials
• ERA and AI agents for scientific software
• What scientists still do that agents can’t: taste, creativity, rigor
• Overfitting, Goodhart’s law, and agents exploiting bugs
• Gradient-free methods, SMO, and hinge loss
• The bitter lesson and the physical limits of scaling
• Climate outlook, fusion, and physical AI
• Room-temperature superconductors and long-distance power transmission
• FireSat and early wildfire detection

Music

- "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0