AI is real. But the LLM-token economy still looks like a bubble. The money flow is simple: Enterprise → LLM → GPU → Memory Investors are moving upstream in search of certainty. No one knows which AI app will win, so they buy the LLM labs. No one knows whether OpenAI, Anthropic, Google, Meta, or xAI will win, so they buy NVIDIA. No one knows how durable GPU demand is, so they buy memory. Every layer looks safer than the one below it. But that certainty is an illusion. The entire chain is still funded by the layer closest to real ROI. Enterprises pay for tokens because they are trying to prove LLM adoption works. But so far, LLM productivity gains have not clearly translated into revenue growth. Consumers are not obviously buying more. Expenses are not obviously falling either. Most AI-related layoffs look more like companies using LLMs to rationalize previous overhiring. The layoffs truly driven by “AI efficiency” often create backlash, operational problems, or quality issues. When hallucination is still unsolved, critical work still needs human supervision. So the economics are awkward: no obvious revenue lift, no obvious expense reduction, and a new token bill on top. Meanwhile, customers are not receiving much of the surplus. They are not getting better products at lower prices. They are getting higher prices, worse content, and weaker job security. Personally, I hate this most when I see gaming consoles getting more expensive, PCs getting more expensive, Macs getting more expensive, and the internet filling up with lower-quality AI slop. If consumers do not spend more, the companies selling to them cannot justify ever-growing LLM expenses. And enterprises do not need to abandon LLMs for the chain to break. They only need to slow the growth of LLM spending.

Tesla Robotaxi was at least ~1.81x safer than humans in April 2026. With the latest NHTSA crash report, and thanks to Robotaxi Tracker, I can finally report that Tesla Robotaxi in Austin surpassed average human safety in April 2026 (last month). 1. According to NHTSA, I estimate the human crash rate, including both reported and non-reported crashes, is ~249K miles per accident. 2. I collected all Tesla-reported mileage, as well as 7-day active fleet size from Robotaxi Tracker, and found the mileage is pretty predictable. This allows me to accurately estimate monthly Robotaxi mileage in Austin with <5% error. 3. Using the latest NHTSA crash report, I calculated the 3-month rolling crash rate, since Tesla sometimes has 0 monthly crashes. It clearly shows that in April, the 3-month rolling crash rate surpassed humans for the first time.