2025 marks a historic inflection point in AI economics: inference revenue has officially surpassed training revenue. This isn’t just an accounting change—it represents a fundamental restructuring of how AI companies make money and where competitive advantages lie.
The Two Different Economic Models framework explains why this shift matters:
The numbers tell the story: training a frontier model costs ~$150M as a one-time investment. But inference at scale generates $2.3B annually—15x the training cost, recurring every year.
This has massive competitive implications. The shift favors purpose-built inference chips (TPUs, custom ASICs) over general-purpose GPUs. Midjourney’s switch to TPUs cut their costs by 65%. Companies optimizing for the old paradigm (training) are building the wrong capabilities for the new paradigm (inference).
For strategists, the question becomes: are you building for episodic training economics or continuous inference economics? The companies that dominate the next decade will be those that master the inference game.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
