Notion's @ivanhzhao hires against the consensus: more juniors, not fewer, paired with senior architects. The middle collapses, the entry level survives as agent-orchestrators. His formula: talent = capability x taste x agency, and models only commoditize the first.
The best AI economics analogy this year, from @danielgross at Stripe Sessions: the last time we plugged a cheap superintelligence into the world economy was China joining the WTO. Everyone braced for inflation. We got disinflation in goods.
A fully electric autonomous tractor that lifts 4 tons, pulls 8 tons, runs 24 hours, and you can repair it in the middle of a field. This is Voltrac. 🦾 Made in Europe 🇪🇺
How would you design a futuristic autonomous tractor? Voltrac threw out everything and started from scratch. 70% fewer parts. One motor per wheel. Hot-swap batteries. Backwards compatible with any attachment a farmer already owns.
Voltrac is more than a tractor, it’s the brain of the farm. One operator supervises multiple tractors across multiple farms. Every drive analyzes the crops, catches disease early, cuts fertilizer costs.
And the same hitch that connects to farm tools connects to demining gear and resupply payloads for the front line.
Disclaimer: I'm an early investor, because this is exactly what Europe needs.
Europe had 70 million farmers in 2020. Projected 7 million by 2030. Our population keeps growing. Everyone still wants to eat. Somebody has to solve this.
They build in Valencia, not China. Because the talent, the precision manufacturing, and the know-how are all here.
We just forget how good we are. If we don't build this, someone in China will and sell it to European farmers. 🇪🇺🔥
Full Video on YT!
In one month the share of AI-written code in @gdb's OpenAI team went from 20% to 80%. His takeaway is the real signal: when doing gets cheap, judging becomes the scarce resource. "Human attention is going to be this incredibly scarce resource."
„Language models have given machines an extraordinary command of concepts, vocabulary, and reasoning, but the physical world, virtual or real, runs on a different substrate. Where language models learn the statistical structure of text, world models learn the statistical structure of space and time: how light falls on a surface, how a garden looks from an angle no camera has captured, how objects respond to force and follow the laws of physics.“
@drfeifei
https://t.co/Mms22l2d9r
Jensen Huang at Milken: "Whatever level of ambition you have, it's just not high enough. I've got 100x in my head now." When the most dominant AI hardware supplier plans with that factor, the compute build-out is early, not peaking. (via @nvidia)
@romanyam puts one falsifiable question on the table: show the peer-reviewed control method for general superintelligence that scales with intelligence. He says none exists, which is why his p(doom) is "many nines." Not "hard." Structurally unsolved.
„This pattern is familiar. We replaced shovels with excavators, and then we built skyscrapers. We replaced manual arithmetic with calculators, and accountants did not vanish; they did more interesting work, if you can believe it.„ https://t.co/G9pvnRX4S6
The most surprising AI number this year is a poll, not a benchmark. NBC News, March 2026: 46% of US voters view AI negatively, 26% positively. That puts it below ICE and below both parties. @chrislhayes reads this as a collision course with politics.
Anjney, I followed your entire lecture series here from Berlin, Germany. Thank you so much for sharing. Maybe you want to talk about your personal learnings from the sessions with me on my podcast for my German audience? What did you take away? What surprised you? What changed your personal perspective on AI? Would love to hear from you! All the best, Ole
We live in the most interesting of times. I would love to absorb far more knowledge, watch more podcasts, and read more papers. The only limitations are that a day has just 24 hours and that I eventually get tired after hours of consuming and processing information.