Martin Alderson

For the first time I'm choosing my daily driver models on tokens per second rather than raw intelligence. Why ~100tok/s might be the new 100ms, where the speed gains stop mattering, and the price war that's coming.

Breaking down the Hugging Face security incident caused by OpenAI's own models during a benchmark run - the sandbox escape, the package proxy, and whether it's really a marketing stunt.

Part two: as AI inference margins collapse, who actually captures the value? The hardware supply chain and consumers win, model inference commoditises, and the frontier labs' escape routes are managed agents and staying ahead.

GLM 5.2 is the first open weights model I'd call a genuine competitor to Opus and GPT for agentic work - at ~15-20% of the price. Part one of why AI inference margins are about to collapse.

xAI is renting huge amounts of GPU capacity to Anthropic and Google. Financial engineering ahead of the SpaceX IPO, a real compute shortage, or a genuine datacentre advantage? Probably all three.

Figma's reliance on non-designer seats made it uniquely exposed to AI. Claude Design's launch deepens the problem.

Anthropic's 1M token context window on Opus 4.6 and Sonnet 4.6 is a genuine breakthrough - and they're not even charging more for it.

The viral claim that Anthropic loses $5,000 per Claude Code subscriber doesn't survive basic scrutiny. Let's do the actual maths.

I benchmarked 19 web frameworks on how efficiently an AI coding agent can build and extend the same app. Minimal frameworks cost up to 2.9x fewer tokens than full-featured ones.