I have a new kind of big button that I can press for Codex. Over the next 100 days, we will select one person per day who does impressive or incredibly useful work with Codex and give them 10X usage limits for a month to see what they can do with it. First one tomorrow.

You have noticed it. ChatGPT feels dumber than it used to. Your prompts that worked six months ago produce worse results now. The writing sounds flatter. The ideas sound safer. The internet itself feels like it is shrinking. Every article reads the same. Every email sounds the same. Every answer sounds like it was written by the same voice. You thought it was you. It is not you. Researchers at Oxford and Cambridge published a paper in Nature proving what is happening. They call it Model Collapse. Here is the mechanism in one sentence. AI trained on AI-generated data gets dumber every generation until it forgets what real human data looked like. The internet is filling with AI-generated content. Blog posts. Articles. Reviews. Comments. Social media. AI companies scrape the internet to train the next generation of models. Which means the next generation of AI is being trained on the output of the current generation. Each cycle loses information. Not randomly. It loses the rarest, most unusual, most creative parts first. The researchers call these the "tails of the distribution." The weird ideas. The unexpected perspectives. The things that made the internet feel human. Those disappear first. What remains is the average. The safe. The expected. The bland. Then the next generation trains on that. And loses more. And the next generation trains on that. And loses more. The researchers proved this is not a slow decline. Major degradation happens within just a few iterations. Even when some of the original human data is preserved. They tested it on large language models. On image generators. On statistical models. The pattern was the same every time. The output converges toward a narrow, flattened version of reality that looks nothing like the original data. The lead researcher put it plainly. "Large language models are like fire. A useful tool. But one that pollutes the environment." The pollution is invisible. You cannot see which sentence on the internet was written by a human and which was written by AI. Neither can the AI that is about to train on it. And once the tails are gone, they do not come back. The damage is irreversible. This is not a prediction anymore. It is a diagnosis. The internet you grew up on was built by humans writing things no algorithm would have written. Strange, personal, imperfect, alive. That internet is being diluted. One generation of AI at a time. And the models trained on what remains are learning a smaller and smaller version of the world. Model Collapse is not a technical problem. It is a cultural one. The thing that made the internet worth reading is the thing that disappears first.

Good news! D-Wave has unveiled a new roadmap aiming to build a 100 logical-qubit fault-tolerant quantum computer capable of over 1 million operations by 2032. Instead of chasing massive qubit counts, D-Wave is focusing on a new dual-rail superconducting architecture that can detect roughly 90% of qubit errors directly at the hardware level. Company believes this approach could dramatically reduce the number of physical qubits needed for error correction, one of the biggest challenges in quantum computing today. After acquiring Quantum Circuits Inc., D-Wave is now pursuing both quantum annealing and gate-model quantum computing, making it one of the few companies competing across both major quantum architectures. This technology could accelerate breakthroughs in drug discovery, materials science, chemistry and quantum AI.

Reminder For more than 60 years, Faster than light particles called tachyons have been dismissed because they appear to break causality and create time travel paradoxes. Now, researchers have developed a new framework called Covariant Quantum Field Theory of Tachyons that challenges some of the biggest objections to their existence. The team showed that long standing issues such as negative-energy instabilities, vacuum instability and observer-dependent inconsistencies can be resolved within a mathematically consistent relativistic framework. One of the most striking findings is that faster-than-light particles may not automatically break causality in the way physicists once believed. The work also uncovers deep connections between quantum mechanics, relativity and the fundamental nature of time. To be clear, no tachyons have been detected and this is not a demonstration of time travel. But it represents a significant advance in the theoretical study of superluminal physics and reopens scientific discussions that many considered closed. In other words, a concept that spent decades on the fringes of theoretical physics has just received one of its strongest mathematical foundations yet.