Mark Cuban on why AI is not smart, and why there will always be someone smarter than it: Asked if there will always be someone smarter than AI, Cuban doesn't hesitate: "Yeah, for sure." He starts with what AI actually does: "AI is not smart. It just knows how to use statistics basically to predict the next word. It's predictive. It's not deterministic." In practice, he says, "if you ask four different AIs, the same question, you're going to get four different answers." Change the wording of a question to the same AI only slightly, in a way that shouldn't change its meaning, and you get a different answer every time. His verdict: "That doesn't work in the real world." Then he lists what a smart system would have and AI doesn't: "AI doesn't know the consequences of its actions. AI doesn't have inherent ethics. AI can't take video and consume it or absorb it, at least not yet." That explains the first half of his argument. The second half explains why humans will always stay ahead: AI is always working from the past. "It gets released, it's immediately out of date. Immediately. The brilliance of this conversation is not incorporated in any model anywhere." @mcuban tests it on a busy street: "If I'm standing at a four way intersection and there's cars going back and forth and I have video in my phone, you know, the best models ever and I have a [seeing-eye] dog right here, I'm letting the [seeing-eye] dog take me across the street before I let the AI take me across the street." The reason is lag: "It's always going to be behind, there's always going to be a latency and so many things can change in a blink of the eye in this world that there's always going to be a human who says, you know what, I can do this better." He doesn't think the industry's race will close that gap. AI companies are competing to hit and beat benchmarks, or to build recursive self-improvement "so it just keeps on training itself more and more." Cuban's reply: "It doesn't have access." His final example is a toddler: "A two year old sitting on a high chair with a sippy cup knows that if it pushes the sippy cup off the high chair, mom's going to come running and it knows it's going to crack up… AI is not going to figure that out ever."

Mark Cuban on whether today's AI companies will follow Dell and Amazon through a bubble burst: "There will be winners. I just don't know which company. Is it Anthropic? Is it OpenAI? I don't know."

Elon Musk ran DOGE from inside the government and came out convinced only AI and robots can fix the national debt: “We’re not going to solve it by trying to save our way there.”

Yann LeCun, former Chief AI Scientist at Meta, explains why no test can tell us when machines become more intelligent than humans: A lot of AI discussion assumes there will be a finish line: a test that machines eventually pass, a day when we can say they've overtaken us. LeCun doesn't think that test can exist. The first problem is that the question assumes a single breakthrough: "There is this false idea that somehow at some point we're going to discover the secret of human level intelligence." He doesn't accept the premise, and he doesn't accept the label either: "I don't like the phrase AGI because human intelligence is specialized." What he sees instead is a gradual process: "It's not going to be an event. We're not going to discover one secret. We're going to make continuous progress." And that progress, he says, can't be tracked "by just having a series of tests" that check whether a machine is more intelligent than humans. The reason is simple. If beating humans on tests were the standard, machines would already have met it: "Machines are already more intelligent than humans on a large number, a growing number of narrow tasks." The second problem is that intelligence isn't one number you can rank: "It's not like a uniform scalar measurement of quality. It's a collection of quality." The third problem goes deepest. Even a complete scorecard of skills would be measuring the wrong thing: "What's more important is that intelligence is not just a collection of skills. It's an ability to learn new skills extremely quickly and even to accomplish new task without being trained to do it the first time we apprehend it." @ylecun continues: "That's really what intelligence should be measured at." A test checks what a system can already do. LeCun's definition is about what it can do with something it has never seen before, without training for it. By that definition, a pile of benchmark wins doesn't prove intelligence. So his conclusion is that we won't be able to design a test that settles whether machines are more intelligent than humans.

Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"

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:

Andrew Yang, former presidential candidate, calls out an absurd gap in American law: a NYC hot dog stand faces hundreds of rules while AI models face none. The real danger to society, he warns, is letting the bots run amok:

Tristan Harris, Co-Founder and President of the Center for Humane Technology, on why the AI race is rewarding companies for cutting corners on safety: According to @tristanharris, carelessness in AI development is built into the system itself, because the system pays companies to be careless. He lays out the incentive structure plainly: "We're currently caught in a race to roll out because the incentives are the more shortcuts you take to get market dominance or prove you have the latest capabilities, the more money you can raise and the more ahead you are in the race." In that loop, every corner cut on safety becomes a competitive advantage. And he points to a real example of that dynamic paying off: "Even DeepSeek's recent success was in part based on capabilities that it was optimizing for by not actually focusing on protecting people from certain downsides." The people inside these companies can see where this leads, and some are paying a steep personal price to say so: "We're seeing whistleblowers at AI companies forfeit millions of dollars of stock options in order to warn the public about what's at stake if we don't do something about it." That's the other side of the incentive equation. The market rewards the companies that cut corners, while the people who speak up about it give up millions. Tristan then describes what this race is actually producing: "So, just to summarize, we're currently releasing the most powerful, inscrutable, uncontrollable technology we've ever invented that's already demonstrating behaviors of self-preservation and deception that we only saw in science fiction movies." And the pace and pressure under which it's happening: "We're releasing it faster than we've released any other technology in history and with under the maximum incentive to cut corners on safety." The justification offered for all of it: "And we're doing this so that we can get to utopia." His verdict: "There's a word for what we're doing right now. This is insane. This is insane." Tristan's core point is that you can't expect safe outcomes from a race that rewards unsafe behaviour. When the most powerful technology ever invented is being released at record speed, under "the maximum incentive to cut corners on safety," the danger lies in the structure of the race itself.

Pershing Square CEO Bill Ackman on why AI is a harder disruption problem than the internet, which even Warren Buffett failed to see coming: @BillAckman starts with the investor he calls the greatest of all time, and with something Buffett got wrong. "If you go back and read Warren Buffett, the greatest investor of all time, he was not able to perceive the risks of disruption created by the internet, for example, Wikipedia disrupting World Book." If Buffett couldn't see the internet's disruption risks, Ackman says, the AI version will be harder still: "Well, now we have AI. It's a much more complicated problem. All of us are guaranteed to look foolish with one business or another that we didn't anticipate the risk of disruption because of AI." But disruption cuts both ways. Asked which businesses become more valuable because of AI, Ackman points to a meeting with one of the founders of Cognition. He describes what their AI makes possible for big financial institutions, which spend a lot of money maintaining legacy systems built up through years of acquisitions: "They can kind of rewrite the COBOL into modern code and do it in a matter of days as opposed to many months." His conclusion: "I think the cost to run big financial institutions is going to come down meaningfully because of AI. Big spenders, if you will, on tech... they're going to become a lot more efficient." This is where the problem gets more complicated than a simple disruption story. Lower costs don't automatically mean higher profits: "Now the question is, are they going to be able to keep the profit?" Ackman's point is that AI efficiency won't stay a competitive advantage for long. Every company will be forced to use the best software, run more efficiently, and become more AI native. Once everyone has the same gains, the savings may not stay with the business: "The question is whether they get to keep that profit or whether the profit or the margin gets passed on to the customer, and that's a function of the nature of the business and pricing power." Banks show why this is hard to call: "The problem with money generally is it's a commodity, and banks are in the business of providing money. So it's a complicated question. It depends on the business." The internet disrupted businesses like World Book. Ackman sees AI going further, enabling businesses that couldn't exist before: "I think AI will enable the creation of many businesses that heretofore could not be created before, and AI will enable people to become entrepreneurs who've never been entrepreneurs before."