Summary
Tiny slot, but two genuinely consequential signals. @nottombrown (Anthropic) shills the Krishna Rao CFO podcast with a list of eye-watering numbers (cluster of 2): run-rate revenue $9B to $30B in one quarter, reportedly on pace for $50B by next month, NDR over 500% annualized, and over 90% of internal Anthropic code now written by Claude Code. AWS CEO @mattsgarman announces the $110M Build on Trainium program funding university researchers (Berkeley, MIT, CMU) on Trainium/Neuron, a clear hardware-stack play to widen the non-NVIDIA developer pool. @MillionInt offers a one-liner musing on how hard "vibes" are to encode in silicon, philosophical filler.
Posts
- Anthropic CFO podcast: $9B to $30B in one quarter, 90% of internal code is Claude Code (cluster of 2) (@nottombrown · @nottombrown intro). Krishna Rao's first podcast appearance. Stated stats: NDR over 500% annualized, first dollar of revenue in March 2023, run-rate $9B to $30B in one quarter with $50B trajectory by next month, ~$75B raised, Cowork growing faster than Claude Code did at the same point, head of tax is the heaviest token user on the finance team. If even half-true these are the most aggressive enterprise numbers ever publicly attributed to an AI lab and reinforce the Anthropic-overtakes-OpenAI B2B thread. The Trainium/TPU/GPU compute allocation discussion is also a routing-adjacent infrastructure signal worth a listen.
- AWS $110M Build on Trainium program for university AI researchers (@mattsgarman · aboutamazon.com). Berkeley, MIT, CMU and others get dedicated Trainium access; all research is open-source so kernel and compiler improvements flow back to the broader Neuron ecosystem. Same playbook NVIDIA ran with CUDA in the 2010s: subsidize the next cohort of researchers onto your stack so the tooling gap closes from the bottom up. Sits naturally next to the Amazon-Anthropic capital concentration thread and the broader GPU-alternative push covered in gpu-kernels.
- "Vibes are hard to replicate in silicon" (@MillionInt). One-liner observation that humans reason well over uncertain nonquantifiable qualities and that this remains hard for models. Reflection, no data. Skim.