Marc Andreessen 🇺🇸

I used to like Nietzsche until I found out, from his habitual use of the em dash, that he was using ChatGPT to write his laconisms.

A ChatGPT automation just found ~$45K in erroneous invoices across 3 years of billing history that I've confirmed and already had resolved. My lifetime history for ChatGPT is ~$1,800, so it just paid for itself 25x over. I setup an automation with read-only access to my email, and tasked this one specifically with analyzing construction invoices. It has access to prior construction invoices, emails, meeting notes, etc. It produces a report and emails it to me (the only email its allowed to send, enforced by API token) whenever I receive a construction invoice. Across 3 years of construction projects, it found about $45K in issues. Some were wrong amounts, some were duplicate invoices, some were invoices addressed to the wrong person. I manually verified, emailed my GCs, and got refunded/credited. I get multiple construction bills each month and each bill is ~50 pages in a PDF of low-quality scanned paper. I do manually review each bill but its pretty hard to be right all the time. I do believe these were genuine mistakes and not done out of ill will just based on what the mistakes were. I don't want to share my full construction costs across the past few years, but $45K is a very small percentage of overall billed amounts. Pretty sweet.

Arm CEO Rene Haas makes a great point in the AI data center debate. The same factions who railed against the gutting of the U.S. manufacturing base to China risk repeating that mistake if the AI factory ecosystem gets blocked domestically. Tae: AI is going to happen. Do we want the jobs and manufacturing ecosystem here in the U.S. or not? Or are we going to be dumb and defeat ourselves again? Welp.

Why are politicians so dumb?

Neuroticism reads as conscientiousness to the neurotic

Today, we launched GPU compute forward curves derived from our prediction market prices. Forward curves are now available on Nvidia B200. H200, and A100 chips. Forward curves track implied future prices. They are how mature commodity markets form expectations, allocate capital, and manage risk. Energy, interest rates/SOFR, FX, metals, and agricultural markets all rely on market-implied forward prices. Despite becoming one of the key inputs in the global economy, compute has lacked that market-derived infrastructure. Compute right now is where oil was before NYMEX — traded only via OTC deals, just like oil used to trade OTC between producers and refiners. As compute becomes as fundamental to the economy as energy, the industry will need a similar derivative market to promote efficient price discovery. Prediction markets are uniquely suited to this problem. Compute is not one uniform commodity and spans many chips, grades, tenors, locations, and contract structures. A live prediction market can aggregate those dispersed views into transparent prices that reflect market expectations for different maturities. The opportunity is big. Hyperscalers are spending over $700B on compute this year and the market is expected to grow to $7-10T by 2030. If this market behaves like traditional commodity markets, a liquid derivative market could be 10-20x bigger than the underlying spot market. Compute is still not uniform enough, but this is a step towards standardization as forward curves will help us see the rise and fall of different model prices and how they correlate. The forward curve is a first step. Up next: futures and perps.

GPT-5.6 gets an initial IQ score of 136, which is smarter than 99% of humans. GPT-5.6 is the first model to score over 130.

Every once in a while you work at a company that only hires people who know what they're doing, and suddenly its 20 people doing the same work as 400 somewhere else. There's zero meetings, everyone talks once a week on slack, and you go huh, how much garbage is there actually.