AI-Driven Cost Reduction

Act One of generative AI was about model capabilities and the infrastructure to serve them. Act Two is about applications that generate sustainable revenue. We are now seeing the first generation of AI-native applications reach $100M ARR, and they share common traits: they solve specific workflows, they improve with usage data, and they create switching costs through accumulated context.

Microsoft's 365 Copilot has generated roughly $2B in annualised revenue, well below the $12B that Wall Street analysts projected. The gap highlights a persistent challenge: enterprises are willing to pilot AI tools but slow to roll them out across entire organisations. Usage data shows that only 15-20% of licensed Copilot users are active on a weekly basis.

We are seeing inference costs drop 10x every 18 months. That changes the economics of every AI deployment. The question is no longer whether you can afford to use AI, but whether you can afford not to. The companies building on this cost curve are pulling away from those still doing manual processes.

A startup founder told us he replaced his three junior developers with an AI coding agent. The agent costs $500 a month total, versus $30,000 a month for the team. The code quality is comparable. We explored both the exciting and deeply uncomfortable implications of this shift.

The most important chart in AI right now: inference cost per million tokens has dropped 95% in 18 months. This is not incremental improvement. This is a phase change. Every business case that did not work at $10/M tokens works at $0.50/M tokens. Every automation that was too expensive is now cheap. 1/12