Impact Science for people and planet

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! ...

This meeting remains the densest gathering of decision-makers anywhere: public, private, science, technology, and civil society, all compressed into one intense week. There’s nothing like Davos, and overall we do need Davos, imo. I was on the ground, but our whole team worked incredibly hard to make this week count. Between interviews, articles, meetings, panels, and strategy sessions, the pace was unrelenting. And yet, looking back at the blur of 72 calendar entries and the “who’s who”, it is hard for me not to feel the familiar tug of “more”. One more session. One more corridor conversation. One more unexpected connection. ...

I’ll miss bedtime stories next week. Not because I love the 75 events in my calendar at Davos, but because I’m carrying something personal with me. My kids are growing up in the EU, in Denmark, where safety, nature, and independence are built into everyday life. That kind of childhood isn’t magic. It’s the result of decades of public investment, data, and care. For most of the world, that model has simply been out of reach. At LGND AI and Clay we’re building extremely horizontal planetary intelligence: tools designed to understand the world at scale. Child infrastructure, air quality, climate risk, food security, disaster response. This is just one possible use. ...

Greenland is increasingly framed through geopolitics, resources, and strategic interest. But it is first and foremost a place of extraordinary beauty, culture, and people. In 2014, I had the opportunity to visit Greenland during a climate-change event and document the place from the air (planes, helicopters, and drones) and the ground (including the then-fancy Google Glass). Given the growing attention on the Arctic, I’ve uploaded hundreds of my photos and videos as open data (CC-BY) to support journalists and independent media who need high-resolution, reusable visuals to tell accurate and respectful stories about Greenland. ...

Eleven years ago, I wore a traditional Asturian dress to the World Economic Forum gala in Davos. At the Forum, I spoke on our work doing tech innovation at the World Bank. The outfit made more headlines than the work. Turns out symbols help translate complexity. The outfit helped connect elite spaces with grounded realities of my rural cultural roots. This January, I’m going back, for the 4th time, with the same mission. This time, the technology is AI — AI for Earth. At LGND AI (and Clay), we’re making Earth searchable: turning satellite and geospatial data into usable intelligence for climate, energy, infrastructure, risk, nature, and defense. The models work, the products are real. The hardest part is not building the AI. It’s conveying just how transformative AI for the physical world can be. We’re not alone in this work. But we do need help reaching out. ...

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. ...