Geoffrey Hinton says a big language model runs on about 1% of your brain's connections and still ends up knowing more than you: "So in your brain, you have a hundred trillion connections, roughly speaking. Okay. That's a lot. And you only live for about two billion seconds. That's not much." "If you compare how many seconds you live for, with how many connections you've got, you have a whole lot more connections than experiences." "Now with these neural nets, it's sort of the other way round. They only have of the order of a trillion connections. So like 1% of your connections, even in a big language model, many of them fewer, but they get thousands of times more experience than you." "So the big language models are solving the problem with not many connections, only a trillion. How do I make use of a huge amount of experience?" "And back propagation is really, really good at packing huge amounts of knowledge into not many connections." "But that's not the problem we're solving. We've got huge numbers of connections, not much experience. We need to sort of extract the most we can from each experience." Two to three billion seconds is the whole budget. Everything you know, you learned inside it. So evolution built you to squeeze a lot out of very little. Hinton's point is that a language model has the opposite problem and the opposite fix, and backprop turned out to be extremely good at that fix. Worth noticing what this predicts about failure. A system running on 1% of your wiring and thousands of times your experience is not going to fail the way you do. You fail from having seen too few examples. It fails from compressing too many into too little, and the compression is where the errors get made. That is a strange thing to be deploying into hospitals and courts with no way to inspect it. We test these systems by asking them questions, which tells you what came out. Nobody can yet look at a trillion connections and say what got packed in. - Geoffrey Hinton, Nobel laureate and Turing Award winner, on StarTalk (@StarTalkRadio) with Neil deGrasse Tyson.

Between endless social media scrolling and asking AI every question (including many we should still work through ourselves to keep our brains sharp), I'm seeing a worrying trend: almost everyone seems constantly distracted, juggling multiple streams of information and behaving as if they have perpetual ADHD-like attention. AI can make us feel like experts on everything. Social media can make us feel more successful, influential, or validated than reality through likes and comments. Together, they create an illusion of competence and self-worth while quietly eroding deep thinking, attention, and learning. The consequences are becoming hard to ignore. I'm seeing 10-year-olds in California who struggle with basic math after spending an entire summer scrolling YouTube and TikTok instead of exercising their minds. Technology should amplify human intelligence—not replace curiosity, focus, and the discipline of learning.

Sam Altman reveals the benchmark that may matter more than model IQ: 54% better token efficiency on agentic coding "5.6 Sol, I think, is not only the best model in the world for most people." "It's also much more efficient than other models out in the world." "So it's 54% more token efficient on agentic coding tasks and also as good or better as the other best models out there." "And we're really seeing people now start to care about efficiency, understand their spend, get a great ROI." "So this is a great step forward for us." The model race is moving from leaderboard intelligence to cost per completed task. The same agentic work at radically lower token cost changes product margins, rate limits, and how much autonomy an enterprise can afford. The next moat may not be the smartest model. It may be the system that turns each dollar of inference into the most reliable work. - Sam Altman (@sama), CEO of OpenAI, on @CNBC

Call for Academic and Industry papers at our Inaugural Trust and Governance Summit @Stanford Faculty Club. Link below: @karlmehta @odbmsorg @IEEEorg @ACMawards Please spread the word

Satya Nadella explains the 4 billion typist fallacy: treating AI agents as a separate labor pool repeats the mistake people made about computers in the 1980s "So that's why I kind of still think that, you know, going and thinking of these as somehow living outside of the realm of human agency is probably not the right way to think about it." "In fact, the way to perhaps conceive it, like let's say in early 80s, if somebody had come to us and said, well, 4 billion people are gonna wake up every morning and start typing, you would have said, why, right?" "You know, we have a typist pool that's good enough. We don't need 4 billion people." "But that's what happened. Like we invented this entire class of thing called knowledge work." The PC did not scale by preserving a small typist pool. It made typing universal and created knowledge work on top. Satya's edge is that agents may follow the same path. The bottleneck moves from performing every task to supplying intent, judgment, and accountability. - Satya Nadella (@satyanadella), Chairman and CEO of Microsoft, at the World Economic Forum (@wef)

Dario Amodei explains the Amdahl's law trap: 5x more code only reveals what breaks next "That one is interesting, and again we would go back to our old friend Amdahl's law." "Which is, you know, we've found with the internal model acceleration, you can write two times as many, four times as many, five times as many." "You know, we just, you see this within the company." "But then you see what breaks." Teams think a 5x coding gain means a 5x company. Amdahl's law says the untouched parts of the system become the ceiling. The winners will not be the teams that generate the most code. They will be the ones that find each new bottleneck before it becomes the constraint. - Dario Amodei (@DarioAmodei), CEO of Anthropic, at Code with Claude (@AnthropicAI)

Satya Nadella reveals why every company may need its own AI model: the model becomes the new company database. "To me, a model is like the database market." "A firm should be able to take the tacit knowledge it has and embed it inside weights in a model that they control." "When somebody asks me how many models should there be, I'll say as many models as firms in the world." The contrarian part: the value may not sit in one universal frontier model. It sits in each company turning its private operating knowledge into a controlled model.