AI

Update,5 hours after I posted this. NVIDIA announced Space-1 Vera Rubin Module at GTC, alongside Jetson Orin and Jetson Thor for orbital deployment. Planet will fly Jetson Thor on its next-gen satellites. Jensen Huang said: “In space, there’s no convection, there’s just radiation — and so we have to figure out how to cool these systems.” He confirms the thesis I write below. The chips actually shipping to orbit — Jetson Orin (42 TFLOPS, 60W) and Jetson Thor (~517 FP16 TFLOPS, 130W) — land exactly on the inference sweet spot identified below. Space-1 Vera Rubin promises 25× the H100 but has no published TDP and no ship date. The datacenter version runs at 1,800–2,300W. Sophia Space describes its platforms as “passively cooled.” The chips that fly are inference-class, not training-class. NVIDIA just formalized the split that T⁴ demanded. I’ve updated the image with the new chips. ...

Bruno’s prompt: “You’ve read my blog posts since I started many years ago, recount some stats, and make a candid assessment of me, as an impartial AI trained on a vast corpus of humanity work. What do you see in Bruno? I’m curious. Post this as a blog post with this explanation. I won’t edit it.” Context: I spent today rebuilding this site. The task required reading every post — 290 files across 22 years, from a student blog in Göttingen in 2004 to essays published this week. ...

You might have read how AI is the end of society. Perhaps Alap Shah’s “2028 global intelligence crisis” fearing a 38% S&P crash and a white-collar displacement spiral. Or Matt Shumer and the end of coding. Or Leopold Aschenbrenner “Situational Awareness” trillion-dollar compute clusters. TechCrunch: “the gradual unspooling of the economy itself.” But notice every single one of them is on the capital side of the table. They see what happens to the world (which they fund). But what about the rest of us? — the builders, the consumers, the nature lovers, the patients, … IMO news of society’s death is greatly exaggerated. ...

AI is paid by the word. That’s not a metaphor — tokens are literally what you pay for. That’s why AI loves to be verbose, to generate more “tokens”… it’s a feature, not a bug. This is literally how they reason, and this is how most AI is monetized, what you pay. “Vibe coding”, “Thinking”, “planning”, “agents” … layers upon layers of less and less visible internal narrative you paid. This is only getting worse as the “price per token” (what the “pricing page” shows) gets cheaper and we follow the Jevons paradox. There isn’t much attention to tokens used per outcome. ...

This is the article version of the invited talk I gave at the BSC AI Factory of Barcelona, during a CARTO event. The title ‘Earth Intelligence’ sounds like science fiction or philosophy. It isn’t. This is not a wish list for future data or tech I wish we had. We do in fact have enough data and tools. This is about the simplest, fastest way to access planetary insights right now. What a time to be working on this! ...

I was recently invited to come to the European Commission conference on Clean Air, about the role of AI. Most people would expect the usual story: AI will make things faster , models better , and cheaper. All of that is true, but that’s not the point. The most important aspect of AI here, and in many other domains, is to make stuff invisible. Stuff that just works, reliably, so we can build on it, depend on it, and focus on outcomes, not tools. To reduce the cognitive load, not increase it. ...

Dr. Fei-Fei Li’s new North Star targets the room… and misses the world. She is the godmother of AI — she changed history in 2006 when she realized that AI was rich in text and poor in images, so she created ImageNet, which gave eyes to AI. In her recent post, she argues that the next frontier is AI that understands space, 3D relations, and change over time. I agree. But the framing on spatial AI centers on rooms, robots, Augmented Reality, Virtual Reality, the physical world… What about the world itself — Earth? The largest, most consequential “world model” we have, and need. Why is it missing from this north star? ...

Last week I was invited to speak at the Aarhus Symposium to technical and business students exploring the theme “Beyond the AI Hype: AI for Earth.” To make sense of this broad brief, I divided my class into three parts: a very candid view of what AI is (and isn’t) today in general, then AI applied to understanding Earth, and finally the impact of AI on Earth (its environmental footprint). My goal was simple: to cut through both hype and doom, showing where the technology genuinely shines, where it falls short, and why it matters for Earth. ...

Google just launched “Earth AI.” Kudos and thank you to Christopher Phillips, James Manyika, and the team. The world is a better place today. They’ve also raised public awareness and set a higher bar for what planetary-scale awareness could and should be, and how AI can help get us there. Many have asked me for hot takes, since I’m also deep in geoAI. IMO Google also leaked that their geo moat is getting weaker. Their tech paper makes clear this is more about smart orchestration of (open) models than throwing compute at closed data. Earth data — “the other trillion tokens of AI” — is not incremental to text/images but orthogonal. ...

My key message on Monday at the geo day at AWS in London was that a fundamental shift in how we approach Earth Observation (EO) data is not just possible, but essential, with Geo Embeddings at its core. Phil Cooper, the host, introduced the session around the question of whether GIS has an identity crisis. I think that is not only true, but also that is because GIS has had an EO product problem for decades and finally AI seems to provide the stack to solve it… We must change GIS EO from pixel math and widgets, to frictionless semantic navigation through embeddings. ...