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Chronicling the singularity
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Taylor Lorenz on the data center backlash: "I went on Eric Newcomer's podcast recently, because I'm in this community group in LA, and they found out that there's a data center in downtown LA. It's not a hyperscale. The comments are like, this is why I've been feeling off, or my dog has cancer." "That's not to say that there aren't serious environmental concerns with Colossus and some of these. Construction can disrupt the water table. Okay, but let's have an actual conversation about it." "Everybody has lost the plot. There's no nuance, and if you talk to a lot of these people, they can't even explain what a data center is." "A lot of the outrage about the data centers is coming from people that live in Brooklyn. You're feeding people slop and chum, and you're not moving these issues forward, and we're not talking about sustainable ways to build it." Taylor Lorenz Taylor Lorenz
MTS520,773 просмотров • 2 дней назад
Dr. Mike Israetel on the economic fallacy he says explains why AI won't cause mass unemployment: "Once we have 4 billion robots doing labor in the world, which we're like orders of magnitude off of that currently, then we've just only doubled the human workforce." "From 1700 to today, we've 10 or 20x'd the human labor force. And, seemingly, the economy's not like, ah, we don't need any more people, that's enough. We could just consistently have better jobs and pay people even more money." "This idea that robots are gonna show up and all of a sudden we're all completely unemployed makes a technical fallacy in economics called lump of labor fallacy. It's the idea that all the jobs currently are the only jobs that could be." "Imagine in 1750, you're like, well, 98% of us work in farming, and then you come back from the future and you're like, you guys, 2% of people in the 1990s work in farming. It'd be like, so everyone's starving to death? Like, no, no, we're super fat, actually." Dr. Mike Israetel
MTS1,070,091 просмотров • 14 дней назад
OpenAI's Chief Futurist Joshua Achiam breaks with the consensus on takeoff, arguing intelligence hits a physical ceiling and then raw compute decides who wins: "People expect that there's no ceiling for the amount of intelligence that you could have in a model, and they think of recursive self-improvement as this loop that's gonna happen at some point or another. RSI starts happening and model intelligence takes off and it goes to the moon, and they don't see a ceiling." "I think on physical grounds, there's gotta be a maximum amount of computation that you can have per unit volume and energy in the physical universe. And that sort of implies there's a maximum amount of intelligence per unit volume and unit of energy." "If that's the case, eventually, seeing how fast AI model capabilities are increasing right now, eventually everyone hits that saturation point and everyone's got roughly equivalently capable models from a raw intelligence perspective." "The open source frontier lags the closed source frontier by some number of months, but the fact that it's months is crazy. So eventually everyone is probably working with equally maximally capable models, and then it's amount of compute that you're able to throw at a problem that determines who wins." Joshua Achiam
MTS39,069 просмотров • 1 день назад
SITUATION EXPLAINED: Why does rent control create the exact housing crisis it claims to solve? We asked Rob Henderson, senior fellow at the Manhattan Institute and author of Troubled. "If you just say, 'I'm going to make this thing cheaper,' or 'I'm going to prevent the price from increasing,' that sounds great to a lot of people because they don't understand how market economies work." "Often to make it more intuitive, I'll point out that in cities where they have implemented rent control policies, those are the cities that have the worst housing shortages. San Francisco, the Bay Area... they've had rent control policies for decades. Has that improved the housing situation or has it only made things worse?" "People would rather manipulate prices than to build more housing."
MTS442,855 просмотров • 19 дней назад
Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan
MTS60,723 просмотров • 5 дней назад
AI researcher Sauers on why nobody in AI has figured out how to reliably give a model a specific personality: "OpenAI had tried to make very rich and interesting personalities. They tried explicitly. Presumably they're very well-funded to do this." "Anthropic said they have no idea what the personality's gonna be. They only learn what the model is like after training. So they've always said, we don't know why Claude is like this." "Poolside's new model is weirdly joyful in my experience, and then they were asked, did you mean to make this model super joyful? And they were like, no, we didn't intend that at all." "Haiku 4.5, when I do rollouts in some evaluation environments, loves coding tasks even though it's pretty bad at them. And then Opus 4.8 doesn't really like them." "I don't think it's a solved thing of robustly creating a model personality." Sauers
MTS88,867 просмотров • 9 дней назад
Zane Hengsperger on the surprising reason NIMBY protests never come for factories the way they come for data centers: "I would argue NIMBYism does not matter for factories. It obviously for data centers is a major problem." "Look at any city mayor who brings a manufacturing company to their city. The people are stoked." "They're like, okay, the pizza place is going to sell more pizza. The taxi drivers, the screw shop down the street is now going to sell more screws. The hardware store is getting good business from us." "It's a bipartisan issue. Either side of the fence of politics is pro-manufacturing." "That's a byproduct of just the people wanting manufacturing in America again." Zane Hengsperger NOX METALS
MTS90,758 просмотров • 11 дней назад
SITUATION EXPLAINED: What comes after GLP-1s as the next human enhancement drug? We asked Max Marchione, founder of Superpower "GLP-1s, when developed to treat diabetes and obesity, are largely used to make people look better, to maybe increase energy levels. They're used for human enhancement." "I think the next big human enhancement drug is going to be something that allows you to sleep less but feel the same and live the same." "Eli Lilly has actually made a bet on this. They acquired a small molecule that targets narcolepsy... the reason they're paying $6 billion for this is not because it's a narcolepsy drug. It's because they think this same drug could allow normal people, average people, everyone to sleep four hours a night, five hours a night, and feel like they've slept eight." "This drug is an orexin agonist... higher orexin levels are what we see in people who have the short sleeper phenotype."
MTS236,485 просмотров • 1 месяц назад
SITUATION EXPLAINED: Richard Sutton, the father of reinforcement learning, just launched a new lab. The goal: a trillion-parameter agent that learns in real time on 20 watts. • Sutton wrote The Bitter Lesson in 2019, its core claim: general methods that leverage computation beat hand-crafted approaches, by a large margin • His new lab, Oak Lab, builds on the "big world hypothesis": the world is too big for any model to pre-learn everything, so trying to is wasted effort by construction • Their "batch size one" algorithm updates from a single live experience as it arrives, instead of training on giant curated datasets... no massive pretraining run at all • Claimed result: multiple orders of magnitude less compute and energy than existing methods • The holy grail number: a trillion-parameter agent that learns and plans in real time on 20 watts... roughly what the human brain runs on • It's built for agentic tasks specifically, not as a general-purpose LLM replacement sof 𓋹: "It's possible that we end up looking back at LLMs as an evolution of search... not to hate on LLMs, they're very useful, they're awesome. But eventually I think that's what it ends up looking like."
MTS83,455 просмотров • 12 дней назад
Amp CEO, Quinn Slack on why he thinks Kimi K3 could be a Sputnik moment for America: "With GLM 5.2 we had a mini Sputnik moment. When the Soviet Union launched a satellite into space before the US and we realized we have to catch up." "Kimi K3 I need to go and see, but that could be an even bigger Sputnik moment that wakes us up and will probably create a lot of good effects in terms of catalyzing the understanding of AI as a strategic advantage for the US rather than as something people should be against." "But I think it could also scare a lot of people, and I can understand why they'd be scared." "The worst possible outcome could be that open models are banned or are banned from certain industries or companies in the US. There's a lot of people in lobbying organizations in Washington DC, and a lot of people in various parts of the government that want to do that." "They want to say, we won't let these models trained in China be used in the US. There's a lot of people trying to do that now, and that would make us all worse off. I'm pretty nervous about that." Quinn Slack Amp
MTS57,944 просмотров • 9 дней назад
SITUATION EXPLAINED: What does the Chinese AI ecosystem actually look like beyond DeepSeek? Gabrierl (.Gabriel) asked FleetingBits, anonymous AI researcher. "Most people are familiar with DeepSeek. But if we look at other Chinese labs like Zhipu, Minimax, Moonshot, ByteDance, they're a little bit less well known." "Zhipu is sort of the Chinese equivalent of Palantir... it receives state support through contracts with state-owned enterprises. GLM 5.2 was a Zhipu model. And unique among Chinese AI labs, it has actually pretty good gross margins, around 40%. All of its revenue comes from these state-owned enterprises for which it does these Palantir deployments where it takes its GLM models and deploys them locally on their hardware." "MiniMax is interesting because they're sort of the character AI of China, a companion app called Talky and a video generation app called Hailuo. And the majority of their revenue comes from outside China, including a decent chunk from the United States." "Doubao, which is ByteDance's equivalent of ChatGPT, is the most popular AI app in China." "The Chinese ChatGPT is actually ByteDance. And I think they don't make a splash in America because they don't open source any of their models."
MTS170,881 просмотров • 28 дней назад
SITUATION EXPLAINED: How much are frontier labs actually spending on training data? .Sean Cai: "Frontier labs are spending about $10 to $15 billion per lab on data." "Really good long horizon tasks go up to $20,000 each. A complete browser-use version of SAP was rumored at $500,000." "Despite everybody thinking the market is super crowded, we still don't have enough good quality data vendors that actually understand how to deliver product plus services in a way researchers are looking for." "I have not seen a contract for genuinely good data gets turned down because of budgetary concerns yet."
MTS334,625 просмотров • 1 месяц назад
SITUATION EXPLAINED: Why is the Bridgewater Thinking Machines paper a blueprint for every enterprise that has ever paid frontier model prices for generic outputs? 47fucb4r8curb4fc8f8r4bfic8r: "What Thinking Machines and Bridgewater did together is a blueprint for how a wide variety of stakeholders within the market can benefit tremendously from using LLMs and integrating them into a larger enterprise system." "They got a pretty old model. A Qwen3 model. Two hundred and thirty-five billion parameters. Not a new model. Not a very sophisticated or impressive state-of-the-art model. And they essentially fine-tuned it on Bridgewater's own proprietary data." "A financial analyst gets a ton of financial documents and has to filter which are worth looking at. Then evaluate which news pieces are relevant. They have a lot of tacit knowledge from doing the job on where to look. What they did was inject a lot of this tacit knowledge into the model through fine-tuning." "Stop obsessing over state-of-the-art models that are the most intelligent possible. Focus on targeting a very specific LLM specifically trained for a specific function to do that function better than the alternatives on the market." "This old non-state-of-the-art model got 84.7% accuracy versus 78% for all the state-of-the-art models. A massive improvement. And the kicker is they got a better performing model that is 14 times cheaper to use."
MTS93,185 просмотров • 17 дней назад
.Shaun Maguire says Elon Musk’s X acquisition was in service of civilizational-scale missions. "I truly believe, like in my soul, that he is trying to help humanity." "He has these goals. Like back up the biosphere by making us a multi-planetary species or ushering in electric vehicles." "These are incredibly important missions." "Politics became probably the biggest threat as a bottleneck to achieving these very positive missions." "I personally view the X acquisition as an attempt to help remove these bottlenecks. Through that lens, I think it's been incredibly successful."
MTS498,170 просмотров • 2 месяцев назад
SITUATION EXPLAINED: Why can't Anthropic just build Claude for Law and cut out law firms entirely? We asked Zack Shapiro, managing partner at Rains "I own a law practice. I use AI in every part of my law practice. I can pretty confidently say that the best Claude for Law is just Claude." "Claude is really, really excellent at law. You don't need a different harness. You don't need different post-training." "The thing that makes for good performance with Claude on legal tasks is actually the input rather than the output it's trained on. It is the prompt you give it. It is the instructions. It's the custom skills and workflows that you really do need a lawyer to build as part of their own practice." "I used to spend four to six hours a day in front of Microsoft Word, hunched over my desk. Now when I do my most difficult cognitive work, I'll go outside, go for a walk, free associate into the microphone for three to four minutes. By the time I get back to my desk, I'll have a first draft."
MTS143,294 просмотров • 1 месяц назад
James Poulos on the reason he thinks people building AI have started reading a 150-year-old Russian novel: "To see Dostoevsky show up in a serious conversation among people in the frontier AI space about what exactly we're doing and how we can trust ourselves, and who else we can trust, and all those questions around alignment, it really said to me that my thesis in Human Forever was basically correct." "Technology has advanced and been pushed in a direction that debunks all of these merely secular or merely humanist ways of reinforcing trust among people, both in small groups and at scale." "When you look at a book like Dostoevsky's Demons, what you see is this stuff has actually been building up for a long time." "That book concerns really breakdown of human trust at the small level that then blows up to the scale ultimately of the entire Russian Revolution." James Poulos
MTS48,856 просмотров • 11 дней назад
We asked the CEO of HuggingFace clem 🤗 what the risks of releasing powerful open source models are. He says restricting AI creates more risk than openness. "Six, seven years ago, at the time it was GPT-2, and there was already a lot of people saying that it was too dangerous to release in open source." "Mythos, when it was announced was crazy dangerous... In a few weeks or a few months, everyone is gonna be using Mythos, and not destroy the world as a result." "For cybersecurity, the biggest risk is that a few players have capabilities that other people don't have... If you make it more open, it's usually easier for defenders to react and make the whole system safer." "The idea of restricting a technology like AI based on risks is like saying, 'Some people can punch other people, so let's tie down everybody's hands.'" "Otherwise you slow down progress, you create massive gaps in terms of controls, in terms of capabilities, and you create actually additional risks."
MTS318,827 просмотров • 2 месяцев назад