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.

The one thing a bot can’t consume, even if it wanted to pay for it: a donut wall. 🍩🍩🍩Great catching up with the Fastly team at Xcelerate in London. Glad to see the conversation moving past ‘AI is coming’ to the real questions - new business models (with Supertab as part of it), bot traffic, securing the stack. @fastly #xcelerateldn2026 @deflatermouse

No, I want to enable anyone to go to the Moon or Mars so that we secure the future of consciousness

Major AI licensing deals expire end of 2026. Content providers are slamming the gates with bot blocks. Will unauthorized scraping become more expensive than actually paying for it? If yes, the entire economics of AI flip. The winners won’t be those who scrape the hardest - they’ll be the ones who build sustainable relationships with creators while delivering value to users in simple, affordable ways. linkedin.com/pulse/new-econo…

Can Anthropic just CHILL for like a day so we can process this stuff? Shitting hell man

Show up always.

Subscriptions are a tax on curiosity. Maine Trust tested pay-as-you-go: $1 for 1 hour, $2.50 for a day, $5 for a week - and got 2,800 short-term pass customers, with 70 converting to full subscribers. Turns out: give people choice → they buy. nationaltrustforlocalnews.or…

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

Because it remains important to show the world what Russia looks like, the country that continues to spent hundreds of millions of dollars every night trying to freeze and terrorize the Ukrainian population, I have compiled a "best of" from my guides through Russia's cities.🧵

“The best people in your life are the ones who see potential in you that you didn't see in yourself.” — Brian Chesky, Airbnb CEO

David Friedberg: California’s “Billionaire Tax” is a Trojan Horse to Go After the Middle Class's Private Assets @friedberg: “The reason they're calling it a billionaire tax is to make it easier for people to vote for it, and sign up to this entirely new tax system that they're proposing to put on all Americans at some point, and for the first time ever degrading our private property rights.” “Forget about how much wealth you have, forget about how rich you are, forget about the term billionaire, millionaire, whatever it is.” “We're creating, or proposing the creation, of a new tax system that allows the government for the first time ever to come in and audit everything you own.” “All the jewelry your grandma gave you, the value of all the couches in your house, the value of your car, the value of all your stocks and bonds, and the government can come in, and for the first time, look through the veil into your personal property.” “And say, ‘Here's how much all this stuff is worth. I'm charging you a percentage of that. That's what I need to get paid.’ And it doesn't matter that it starts with billionaires. What matters is that we're giving the government the right to look into our private property and take a percentage of it every year.” “The total net worth of billionaires in the US is $8 trillion.” “The net worth of the US, the middle class, and everyone else is $170 trillion, compared to $8 trillion of the billionaires.” @chamath: “They need a way to open the door so that they can go after the real honey pot.” “The real honeypot is not 200 people.” @friedberg: “Just so everyone understands the real goal of this is not to tax billionaires, because there are other ways to tax billionaires.” “Charge them a capital gains tax if they borrow against their assets that they haven't paid capital gains tax on. Very simple, that can resolve this.” “Another thing you can do, you can raise the capital gains tax rate. Sounds unpopular. I don't agree with that, but that's another way to deal with this, which is to take the capital gains tax rate from 20% to 30%. You could do that.” “The real goal of this is to create, for the first time in American history, a private property asset seizure tax. Because they're going after the $170 trillion, not the $8 trillion that the billionaires have.”

Ab wann soll Journalismus auf der Homepage eigentlich Werbung querfinanzieren - statt umgekehrt? Und warum gibt es trotz all der Werbung für Nutzer fast immer nur die Wahl zwischen „Abo oder nichts“? @MEEDIA

Google cooked so hard. Not gonna lie, this feels like the future is here. Now develop Google Glasses with enough battery power, a good chip, and a look like Ray-Bans, and you'll have an instant hit. 100%.

Orthopedic surgeon Dr. Vonda Wright explains how muscles can save your life 💯👇

Marc Andreessen's rule of thumb for a successful tech sector: "The principle is just 'Do the opposite of the EU.'" David Sacks: " When they talk about AI leadership, what they mean is that they're taking the lead in defining the regulations." "They get together in Brussels and figure out what all the rules should be, and that's what they call 'leadership.'" @pmarca @DavidSacks

Just got off the phone with an ambitious, smart 25 year old who asked for a career consult session. The main thing I said over and over again: -------> Get technical <------- Learn how LLMs actually work. Build a simple real-world product on top of the APIs and ship it. Don't be one of the people who say "I can't code". All those folks are getting laid off or will be. Everyone said coding agents = you no longer need to code. It's the exact opposite. The devs are going to control the world even more than before. Become a dev.

Anthropic CEO Dario Amodei on Open-Source AI Models. "I don't think open source works the same way in AI that it has worked in other areas. Primarily because with open source you can see the source code of the model. Here we can't see inside the model, it's often called open weights instead of open source to kind of distinguish that. But a lot of the benefits, which is that many people can work on it and that it's kind of additive, don't quite work in the same way. So I've actually always seen it as a red herring. When I see a new model come out I don't care whether it's open source or not. If we talk about Deep Seek I don't think it mattered that Deep Seek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That's the only thing that I care about. It actually doesn't matter either way. Because ultimately you have to host it on the cloud. The people who host it on the cloud do inference. These are big models, they're hard to do inference on. When I think about competition I think about which models are good at the tasks that we do. I think open source is actually a red herring. It's not free. You have to run it on inference and someone has to make it fast on inference." --- From 'Alex Kantrowitz' YT channel

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Jensen Huang: People with high expectations have low resilience.

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