Eric B @provingwork

x.com/i/article/2065…

Elon Musk: There are no lords and peasants at Tesla. Everyone eats at the same table. “I actually know the people on the line, because I worked on the line, I walked the line, I slept in the factory, and I worked beside them. So, I'm no stranger to them. There are many people at Tesla who have gone from working on the line to being in senior management. There are no lords and peasants. Everyone eats at the same table. Everyone parks in the same parking lot. At GM, there's a special elevator only for senior executives. We have no such thing at Tesla. We give everyone stock options. Many people who are just working the line, who didn't even know what stocks were, we've made them millionaires. And I just want to say that I'm incredibly appreciative of those who build the cars, and they know it.” New York Times DealBook Summit, 2023

Today feels like a good day to reshare this clip of Ariane Aerospace’s CEO calling SpaceX’s reusability plans “a dream”

x.com/i/article/2065…

LinkedIn, X, Instagram, Reddit

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 Spurs know how to TAKE the lead, they just don't know how to HOLD the lead. And that's really the most important part of the lead: the holding

i got 3 clankers running frontier models monitoring the situation 500+ global sources analyzed every five minutes no ads, no paywalls, no clickbait made it for myself, yall are welcome to use it for free citadelwire.com

I'm finally reading Dune. This quote, which is in the first few pages, hits hard: "Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them."

i should not have left x created a vacuum for grifters i was wrong, that’s on me elon wanted $50k for my deleted account so i made a fresh handle and kept the bitcoin

🚨 We may be looking at the rarest market setup in 50 years. The S&P 500's four historic drawdowns since 1972: – 1973 Inflation: -43% – 1987 Liquidity: -30% – 2000 Tech: -47% – 2008 Credit: -55% Each one was driven by ONE dominant risk. Right now, all four are present at the same time. 1. INFLATION A commodity supercycle. Energy, metals, agriculture all in multi-year base breakouts. The Fed's preferred inflation gauge has been above 2% for 18 of the last 24 months. 2. LIQUIDITY The largest equity supply shock since 2000. SpaceX, OpenAI, Anthropic raising ~$275B combined. Google flipping from $60B/year buybacks to $80B net issuance. Over $1 trillion of IPO and lockup supply hitting the Russell 3000 in 2026. 3. TECH Semiconductors trading 73% above their 200-day moving average – the largest stretch since March 2000. Climax run signals across the AI complex. Micron, Palantir, SMCI, the SOX index, all showing the textbook O'Neil sell pattern. 4. CREDIT Apollo, KKR, BlackRock, Blue Owl, Cliffwater, Partners Group – all gating redemptions on their evergreen funds in the last 90 days. The private credit machine is freezing in real time. Never in 50 years have all four risks been simultaneously present. But here's the part nobody talks about While the AI Big 10 has gone vertical, quality stocks have been left for dead. – Berkshire Hathaway: trailing the S&P 500 by hundreds of basis points – Coca-Cola, Procter & Gamble, Pepsi: trading at multi-year relative lows – HEICO, Union Pacific, MSCI: making boring new highs while everyone watches Nvidia – Healthcare vs. S&P 500: 25-year relative low The last time this happened? December 1999. Barron's ran a cover titled "What's Wrong, Warren?" – mocking Buffett for being a dinosaur, for missing the internet, for refusing to pay for growth at any price. Berkshire was down 19% in 1999 while the Nasdaq was up 85%. What followed: – Berkshire +29% over the next 24 months – Nasdaq -78% over the next 30 months The setup today Four historic risks stacked simultaneously, while the boring, durable, cash-flowing businesses that always survive these regimes have been treated like dead money for years. The math doesn't get more asymmetric than this. Quality stocks aren't out of style. They're being orphaned. That's when generational positions are built. The boring stuff hasn't worked for a long time. History suggests that's exactly the moment it starts to.

The world is hitting tank‑bottom oil inventories by September. Oil inventories work like blood in the human body: you can donate a bit, but below a certain level your blood pressure tanks and organs start failing. You don’t die because blood hits zero, you die because circulation collapses That’s where we are with oil. We began the year with more than 8 billion barrels in storage, yet only around 10% was actually usable without pushing the system into stress... that safety margin has now been drawn down. The next stage is dropping to operational floor levels, where pipelines and refineries start failing – that’s the real tank bottom.

Anthropic is questioning whether AI may turn out to be altogether useless. This is the single most honest thing Anthropic has ever written. “But achieving recursive improvement alone does not suggest an immediate change in how industrial production occurs, societies organize, or markets function. More intelligence can’t learn what a drug does over decades of use, can’t hold elections sooner than a constitution dictates, and can’t turn a stranger into an old friend in a weekend. For most people, the felt pace of this future will still be set by the bottlenecks, even if the laboratory upstream runs at the speed of compute. That collision, where recursive intelligence building itself ever faster meets the world of humans, relationships, and governance, is another part of this future we can’t predict.”

Anthropic @AnthropicAI

2 weeks ago

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

2K 5K 29K 18.5M 15K

NEW: $12.6 trillion Charles Schwab explains why their "entire framework" for investing in Bitcoin is because it's backed by energy 🤯

Equity markets at all time highs with the highest degree of concentration in a few stocks ever Bitcoin is down 50% from all time highs and precisely zero of it's fundamentals have changed It's so obvious If you don't take this trade you are not a contrarian You're the heard

AI related equities are irrationally exuberant while bitcoin is irrationally pessimistic. A prudent contrarian investor would be selling the former to buy the latter.

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening - Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs - For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval - On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect - This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API I shared more on this here: x.com/Shaughnessy119… - And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed) - Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1. More on open source inference provider raises here: x.com/Shaughnessy119… - And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th - Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free - This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models - Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months - So go forward, what happens? - I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter) - It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122% - With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it) - The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses - Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more - This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

Hedgie @HedgieMarkets

2 weeks ago

🦔GitHub Copilot switched to token-based billing this morning and users are already out of credits. Pro+ subscribers paying $39 a month are reporting 60% of their credits gone in two hours of normal use. One user lost 20% of their allowance from a single file review with no code

209 589 4K 842K 1K