joey

Software engineering is the dominant token-consuming use case (70%+ of Anthropic API ARR), with top 1% of enterprise users spending ~$100K/year per employee on AI — clear ROI evidence as budgets only cut non-ROI projects, sustaining net new ARR growth for both Anthropic and OpenAI.

Token-as-a-service (Bedrock, Foundry, Vertex, Together, Fireworks) is the fastest-growing consumption model; AWS/Azure dominate regulated enterprise via existing cloud estates, while neoclouds capture startup volume but face structural margin compression as labs internalize optimization.

Google I/O's disappointing reception highlights a structural tension: major tech conferences are scheduled years in advance, but frontier model training runs have uncertain completion dates. This forces companies to ship incremental updates (Gemini 3.5 Flash) rather than step-function improvements, while independent labs (OpenAI, Anthropic, xAI) release when ready, gaining narrative advantage.