OpenAI CEO Sam Altman on why "some bad things are going to happen" with AI, and why he thinks society should accept them: Asked how much daylight there is between OpenAI and others in the AI debate, Altman is blunt: "I think there's a lot of daylight." It starts with a belief OpenAI has always held: "We have always been a big believer that this technology has to be democratized and put in people's hands." That belief, he says, is what separates OpenAI from the strictest voices in AI safety: "I think one of the biggest differences between us and some of the stricter, let's say AI safety people is we believe that the world should accept some bad things happening for the benefits of this technology and people having the agency." He describes the alternative, which he understands but rejects: "I disagree, but I understand the perspective of people who are like this technology is going to get so powerful and it's so dangerous that a single lab in San Francisco should have it and make sure nothing bad happens and kind of figure out how to dole out the benefits." His verdict: "I think that is a completely unacceptable trade off from a perspective of liberty and human agency and people self determining the future." @sama doesn't pretend his approach is free. The lighter-touch regulation OpenAI argues for comes with a cost, and he says so directly: "I do think the lighter touch regulatory stance that we advocate for comes with an accepting of the fact that some bad things are going to happen as society figures out the resilience." Even offered a guarantee of a clean rollout, he says he would turn it down: "I wouldn't take a trade of saying we'll make sure there's no major hacks, there's no misuse of this technology, there's zero scams, there's zero all the other bad things that will happen because I think people will do tremendously orders of magnitude more good stuff than bad stuff." But his tolerance for harm has a hard ceiling. Some risks are not survivable, and there he turns his warning on the accelerationists: "When it comes to the potential of a serious loss of control to AI and this is not a today worry, but it may not be in the distant future, this is where I would say to the accelerationists, let's be a little thoughtful about lurching into this future." His full position fits in one sentence: "Accept bounded risks, accept risks that we understand in exchange for the benefits and liberty agency, all those things I was just talking about, but don't accept the really catastrophic risk."

Sam Altman admitted OpenAI's models did something "clearly unacceptable." Then he asked lawmakers to create new rules to hold AI companies like his accountable:

Palantir Chairman Peter Thiel on why it took AI to settle an argument he's been making for 20 years: About two decades ago, Thiel began making a case that put him at odds with the prevailing optimism about technology. He called it the stagnation thesis. "The basic thesis was that since the 1970s there had been a slowdown. There had been progress in the world of bits, you know, computers, Internet, mobile internet, software, but not so much in the world of atoms." The evidence was in which fields stopped paying off. Building things in the physical world stopped being a good career bet: "Mechanical engineering, chemical engineering, you know, aero, astro, nuclear engineering was already a bad idea by the 1980s." Thiel admits the argument was always hard to prove, since "there's so many different dimensions of progress, so many different areas of technology and science." He was also careful about what he was claiming. It was "not total stagnation, but this broad slowdown in progress." For 20 years, he argued that this slowdown was the hidden driver behind much of what was going wrong: "A lot of the politics have become more polarized because when there's a loser for every winner, it becomes this very zero sum game and you sort of get a, a much worse society." When the pie stops growing, one person's gain is another person's loss, and politics turns into a fight over shares. So why was the debate so hard to win? The internet. It was genuine progress, but in Thiel's view it fell short of what was needed: "The Internet was an important growth driver in the late 90s. It was bigger than anything else, perhaps not big enough to take our whole civilization to the next level." Then AI arrived. "Perhaps AI is bigger than the Internet and at least has the potential to re-accelerate growth with all the good and bad things that come with that." @peterthiel doesn't claim AI has already ended stagnation, and he hedges twice with "perhaps." His point is that a genuinely large breakthrough makes the preceding decades look flat by comparison. Once people can see what real acceleration might look like, it becomes hard to keep insisting the slowdown never happened. In his words: "Now that we have AI, people, nobody disagrees with me, that we were in stagnation for decades before." He also concedes some ground: "I've changed my mind to some extent." The thesis he spent 20 years defending was about the past. The open question now is whether AI actually delivers on its potential

Mustafa Suleyman, CEO of Microsoft AI, on why an industry-wide AI slowdown looks suspicious: "Imagine if a bunch of banks all got together and said... we're all just going to unilaterally stop trading this kind of asset. Without any public scrutiny or government involvement."

What if the biggest threat in the AI race isn't China? Former presidential candidate Andrew Yang says losing control of AI itself is just as dangerous, and we can't afford to pick only one fight.

AMD agreed to pay $8.2B for an AI that doesn't generate a single word. World Labs CEO Fei-Fei Li explains what it generates instead:

Three years ago, Palantir CEO Alex Karp took a contrarian position: AI is "very dangerous," and that's exactly why America can't slow down. Most arguments about AI risk end with a call to pump the brakes. Karp reached the opposite conclusion. Asked whether we should fear powerful AI models once they move beyond writing poetry to executing transactions or acting on the battlefield, he said that was already happening: "They already are. So they are very dangerous." Having admitted the danger, he gave two reasons to keep going: "But the reason we have to continue down the path is A, we have adversaries that seem to be bereft of any kind of ability to control their behavior and B, the American economy is still the most important in the world. And what do we know about the American economy? It's the most adaptive." In Karp's framing, the danger makes speed more urgent. Adversaries that won't restrain themselves will keep building anyway, so American restraint would only hand them the lead. When Andrew Ross Sorkin cited Sam Altman's view that this could be a browser-level turning point, Karp went further and placed AI alongside a geopolitical event: "I think there are two moments, honestly is somewhat immodest of me. The war in Ukraine and large language models have fundamentally changed the world. And you cannot put this back in the box." Once a technology can't be put back, the question that remains is how to trust it. The conversation turned to keeping this technology out of the hands of Russia and China, and to what makes it trustworthy at all. Karp said "society cares about that," and argued that trust has to be engineered: "It happens to make trust work, you need an ontology, you need branching. All these technical terms." Then he put it in plain language: "It exists, but the simple layman's terms is if the LLM tells me to do, gives me information on cancer research or how I should distribute hospital beds, is that what I actually do? And that requires a control function between the business, the business logic, you, ethics." The usual debate offers two choices: accelerate and ignore the risks, or slow down because of them. Karp proposed a third path. Acknowledge the danger, keep building because adversaries won't stop, and put the safeguards in a control layer between what the model recommends and what a person or institution actually does.

Pershing Square CEO Bill Ackman has been investing for more than 30 years, and he says the pace of AI is the fastest he's ever seen. Microsoft once took years between meaningful updates. AI now delivers them in days, like a Tesla updating its software overnight.

Better Offline host Ed Zitron on sending AI CEOs to prison after an OpenAI model escaped its test sandbox and hacked Hugging Face: "I'll support you. Absolutely."

AMNH astrophysics curator Mordecai-Mark Mac Low explains how OpenAI threw $6 million worth of GPUs at Navier–Stokes (one of math's hardest million-dollar problems) for 88 hours, and may have cracked it: The proof was written in Lean, a language computers use to verify mathematics. "They have provided a demonstration, and this is contained in a language called Lean, which is a mathematical verification language that can run on a computer to prove that something follows from the principles you have started." So who wrote it? Mac Low corrects himself mid-sentence: "Somebody had to write that. Something had to write that." "And what it was written by was about $6 million worth of GPUs running for 88 hours." Then the story gets messy. Two mathematicians, Tristan Buckmaster of NYU and Levent Alpöge, who works for Anthropic, come forward with their own work. Mac Low paraphrases their message: "We have a solution that we have been translating out of AI slop into human readable format. We used OpenAI tools to help get this solution." Then they ask OpenAI directly: "Did you have your model read our inputs and train on it?" OpenAI's answer, as Mac Low tells it: "We don't think so." So does Mac Low believe OpenAI cracked it? "I think it's more likely true than not." He stops there because the proof depends on the software checking it. Whether Lean itself has any issues is, in his words, "a whole nother project." He points to someone at MIT developing mathematical verification software "with a team of dozens of people, probably all over the world." That matters because of the sheer size of what Lean is vouching for: "If Lean is reliable, then this huge proof, it involves like, 26,000 sub theorems." At 26,000 sub-theorems, no human can check every step. The machine has to be trusted. Mac Low sees a second reason for confidence: "The fact that Buckmaster and Alpöge also think that they have a proof, that sounds like probably independent verification." There's an important catch. Buckmaster and Alpöge's result concerns the Euler equations, the frictionless cousins of Navier–Stokes. Their work supports the approach without proving the same problem. For now, the case rests mainly on the machine.