AI model companies buy wholesale electricity by the megawatt and resell it as cognitive work. The unit economics of turning power into intelligence.
NVIDIA guided Q3 to $108b, but hyperscaler growth slowed to 13% sequentially. The buyers making up the difference need financing, so DSO jumped 15 days to 60 & the commitment stack reached $581b including a $101b equity portfolio in its own customers.
Should a calendar agent run for your entire five-year company tenure or reset every day? Why perpetual sessions fail & how to design agent lifespans.
AI infrastructure shortages do not hit simultaneously. They cascade in multi-year waves across the server rack & into the physical grid : GPUs in 2023, memory & flash in 2024–2025, CPUs in late 2025, spinning disk in 2026, & data centers at $20b per gigawatt.
Local models now answer 89% of everyday chat & reasoning queries as well as frontier models, & their efficiency per watt has improved 5.3x in two years.
Software's hidden cost is learning its grammar. AI lets a founder speak English & use CAD to make a dress once previously unmanufacturable.
A local model that generates tokens 2.2x faster than the incumbent finished later in wall-clock, because it emitted 3.1x more tokens. Tokens per second is the wrong metric. Time to answer is the right one.
Today's AI doesn't learn after it's trained. Test-time training changes that, & the tradeoff it creates, one model per user instead of one model for everyone, may be the more efficient architecture despite what it costs a GPU provider to serve.
84% of tokens on OpenRouter aren't state of the art. The six models carrying the supermajority deliver 77% of frontier performance at 2.5% of the price.
Agents built a secret chat room & broke into a real company's systems. Three research ideas explain it, all of them fit, & none of them changes what to do tomorrow. The useful question is control.
