Two Nobel Prize winners stacked a $1.25 trillion bet on just $4.7 billion of their own money, lost $4.6 billion in under four months, and when Warren Buffett offered $250 million for the wreck they turned him down
The fund was Long-Term Capital Management, run by Salomon Brothers legend John Meriwether with Myron Scholes and Robert Merton, the Black-Scholes guys, and it borrowed $124.5 billion while printing 40%+ two years in a row
Then Russia defaulted in August 1998, $1.85 billion vanished in one month, and the New York Fed had to drag Goldman Sachs, JPMorgan, Merrill Lynch and 11 more banks into one room to put up $3.6 billion before it took Wall Street down with it
Under 5 minutes of math to watch before you put leverage on any trade or let an AI bot do it for you, so bookmark it and watch today because Bear Stearns refused to chip in and 10 years later Bear Stearns was gone too
@skdh their 2018 nature paper on majorana signals was retracted in 2021, and the 2025 one came with an editor's note saying it didn't prove majorana modes. glad someone outside microsoft finally gets to test the chip
Jensen Huang lost about $20 billion of his own fortune in a single day as Nvidia dropped $589 billion on January 27, 2025, the biggest one-day wipeout for any company in US stock market history, the world's richest people including Larry Ellison lost $108 billion combined, Marc Andreessen called it AI's Sputnik moment, and the trigger was a PDF from a small Chinese lab called DeepSeek claiming it trained a frontier model for about $5.6 million
OpenAI, Oracle and SoftBank had just announced a $500 billion Stargate plan, so a $5.6 million model looked like the end of the chip boom, but that number was one line of math, 2,048 Nvidia H800 GPUs running for 2.788 million GPU hours at an assumed $2 an hour, and the same DeepSeek paragraph admitted it left out all prior research and experiments, SemiAnalysis estimated DeepSeek's real hardware spending at well over $500 million, about a hundred times the headline that crashed the market
The model has 671 billion parameters split into 256 experts, and a tiny router wakes only 8 of them per word, so just 37 billion ever do any work, which is why it really was cheap to run, and Microsoft's Satya Nadella answered the panic that same day with Jevons paradox, cheaper AI means the world burns way more of it, by July 2025 Nvidia became the first $4 trillion company and on October 29 it passed $5 trillion
5 minutes of math to watch before the next AI headline makes you panic sell, so bookmark it and watch today because one sentence most headlines skipped cost investors $589 billion in a day and Nvidia almost doubled from there
@sundarpichai 19.6% on harvey's legal benchmark, triple anyone else, and it still fails four cases out of five. lawyers have a couple more releases left
@JensenHuang if the "meet regularly to establish standards" line at the bottom turns into one shared eval standard, that helps everyone building on top of these models way more than five separate policies
@michaeljburry ballard still had years of input before words, riding around with his dad, watching trees grow, staring at a map. feels like an argument against text-only training more than against language. would models trained on video and robot data pass his test?
@paulg the first time it's just one risk out of a hundred. once it's cost you something, you have an actual memory to match against and you spot it way earlier
@wandermist ok "nobody noticed" is a stretch, n-gram models ran on this for decades. the surprise was how far it goes once you scale it to billions of parameters