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The machine economy does not begin when robots get smarter. It begins when a machine can be paid to do something, and every party involved can prove what happened. Made with @FabricFND. Two systems meet in the middle of it: Agent Passport issues the agent a verifiable identity and a spending authority its owner defines, and RoboPay actuates a robot after the payment behind the request checks out. What happens before the robot moves: ▷ Authority is granted once, and it is bounded. The human signs a spending session with a passkey: a total budget, a per-transaction ceiling, the assets allowed, and an expiry. The agent holds no card number and no wallet key. It holds a delegation it cannot exceed. ▷ Two payments, because these are two different obligations. One settles with the merchant for the goods. A separate x402 payment pays the robot for the work of moving them. Buying a thing and hiring a machine to carry it are not the same transaction, and the receipt keeps them apart. ▷ Verification comes before motion. The request arrives with an x402 payment header. The facilitator checks the network, the price, the payee wallet, and the signed payload. Only then are the transaction details sealed onto the robot action event and the command published. Until that clears, the robot sits still. ▷ Every step leaves a receipt. Identity, scope, approval, both payments, verification, dispatch. When you need to know why a machine did something, the answer is a record rather than a guess. (This run was executed in a demo environment.) Agents have been paying for software for a while now. Paying for physical work is a harder problem, because a delivery cannot be rolled back. The guarantee has to sit in front of the action instead of behind it. Authorization before payment, payment before motion: that ordering is what makes it safe to let autonomous systems spend in the world we live in. It is also the layer the machine economy has to get right before anything else in it can work. Scoped by Kite. Verified by RoboPay. Delivered in the real world. 🪁

I listened to 85 minutes of The Economist’s interview of Elon so you don’t have to. Besides, it’s behind a paywall. Elon’s predictions: In five years, AI compute will exceed the sum of all human intelligence. In ten years, we will have reached the age of abundance. Money won’t matter. Everyone will have what they need or want (at least in economies that embrace AI). Ms. Beddoes tried to pin Elon down on how the economy will transform that way, but he wouldn’t get into specifics beyond noting that widespread AI robotics is a deflationary force. This means governments won’t need to raise taxes for universal basic income schemes, or, as Elon likes to call it, universal high income, since they will simply be able to print money to ward off deflation caused by the robot economy. She noted that Elon appears to have a more sanguine view of AI lately. He replied that he’s concluded superintelligent AI is now inevitable, so there’s no point trying to stop or slow it down, it can’t be done. We might as well enjoy the ride. The interviewer also noted that Mars no longer seems to be Elon’s overall ambition. He answered that his real mission was always to propagate and preserve human consciousness into the far future. Mars was just a vehicle for that. But now AI is a very important part of that goal. AI will necessarily be part of any future plan. And then came the oh-so-typical, increasingly tiresome part of most long journalist interviews: the interviewer constructs a straw-man version of Elon and argues against it. Elon carefully explained that he isn’t a raging far-right extremist, racist Nazi who kills puppies … and the journalist still didn't believe it. It is so effing tiresome. The lack of self-awareness on the part of journalists is off the charts. She complained about Elon’s supposed misperception of how dangerous London is, while remaining oblivious to the role she plays in creating the giant misperception of Elon as a person in her own writing. Elon defended his political views, saying he is for secure borders, locking up criminals, and balanced government spending, something even she had to admit didn’t sound crazy. And… that’s about it for an 85-minute interview. I couldn’t help but think that the next long-form interview Elon does should be conducted by an AI.

🚨 Elon Musk on DOGE: "I think the goal of DOGE was to try to do something about the deficit, which is massive, where the interest payments exceed the amount of money spent on the Department of War. Like basically, if you add up all the money spent on on what is now the Department of War and the and intelligence in the US, that is smaller than the interest payments on the debt. So we need to sort of take a close look at spending and make sure that money is being spent in sensible ways and that it is not wasted or spent on fraudulent activities. And I got a lot of flack from this for this, obviously. But the people don't understand just how simple it was for the DOGE team to approve payments. If we literally we were just asked to have the contact information of the recipients or some evidence that say money was going to its intended purpose. But over and over that we were told we need to wire money for some sort of important cause in Africa. And we're like, OK, but the wiring wiring instructions are to Deloitte and Touche in D.C., in Washington, D.C., not to Africa. So we would like to send the money to Africa. Can you please connect us with the recipients so we can send them the money directly? And then we get silence."

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@minchoi Our 2T model, which is better than our 1.5T in every way, will finish initial training next week. It might be able to exceed Kimi, but with speed and token efficiency close to our 1.5T (aka Grok 4.5).

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A few thoughts on the very near future First of all, what had previously been little more than a rumor has now been confirmed: GPT-5.6 had already been fully trained for two months and was available to selected users in early access. The obvious question is why it was not rolled out earlier. I do not think this was because OpenAI feared that the model might be overshadowed by Fable 5 or Mythos 5. Instead, OpenAI likely began working with government and regulatory authorities at a very early stage to ensure that the model could be released at all. Even after it had been previewed and announced, it still took some time before it could be rolled out publicly. That said, OpenAI clearly handled the rollout far better than Anthropic, which apparently did not have the same level of cooperation with government and regulatory authorities. Conversely, however, this also clearly means that future delays and increasingly strict model reviews will probably force us to wait longer for official releases. The next widely discussed rumor is that, within a few weeks, most likely no more than six, we will see either a preview or even the release of GPT-6. (Andrew Curran @AndrewCurran_ is one of the most reliable sources here on X, so I think that's very realistic.) The model has undergone entirely new pretraining, and the pace of releases is accelerating. The numbers are clear: Frontier labs are releasing more and better models at an increasingly rapid pace. Whereas we once had to wait months, quarters, or even half a year for major new releases, they are now arriving almost weekly. The latest frontier models may be more efficient in terms of intelligence per token, but they are also being deployed with much larger reasoning budgets. In practice, models such as Fable 5 and GPT-5.6 often consume considerably more tokens during complex or agentic tasks. This is not necessarily a sign of declining efficiency. Rather, it suggests that improvements in efficiency are being reinvested into deeper reasoning, longer trajectories and more capable agentic behavior. The result is that total compute consumption per task can continue to rise even as the underlying models become more efficient. Fable 5 and GPT 5.6 demonstrate just how intensive token usage has become. Although Sam Altman explicitly stated that GPT-5.6 is 54% more token-efficient (via CNBC), the fact remains that compute demand continues to increase, requiring more powerful and efficient computing infrastructure. Inference chips will probably become even more important as well. In summary, my initial conclusion from the latest releases is that compute demand will not merely continue to grow, but will probably exceed the available supply. This naturally means that energy demand will also increase, and, based on my initial assessment, probably more sharply than previously expected. This is likely to remain the largest bottleneck in the very near future. And this is important to me: there are bottlenecks. Not the training of the models, but besides compute, above all energy. This needs to be taken seriously! The US power grid, for example, is a major bottleneck, and the obvious question is how the necessary expansion can be achieved. Capital expenditure on data centers in the United States continues to rise sharply. This year, it exceeds 800 billion. It is not yet clear what the situation will look like in 2027, but I can hardly imagine investment declining or less CapEx being required. The reason lies precisely in the developments already mentioned: Demand is growing, particularly demand for energy. China clearly has an advantage here, a genuine moat, and I believe the West must be extremely careful not to fall behind because of the energy advantage China already possesses in practice. This could also help explain why, according to a recent Reuters report, China is considering restricting Western access to its frontier models. It may have concluded that it will win the long-term race. Unless there is a genuine breakthrough, whether in small modular nuclear reactors or fusion energy, I expect major problems to emerge over the coming years, for example by 2030. So far, I do not see any viable solutions. We can therefore clearly establish two points: Models are becoming larger, better, and increasingly useful for all users. There is no end to this development in sight. At the same time, the bottleneck appears to be growing increasingly severe, and this is already visible in practice. Regulation, energy demand, and compute demand could mean that, in the very near future, the release cadence will not accelerate as quickly as hoped or desired. This creates a clear contradiction. Thank you for coming to my TED Talk.