THE AI COMMODITY TRAP: Why Smarter Models Aren't Making Money!

The market currently treats AI as a horse race based on which model is smartest, but this is a flawed strategy. Historical tech cycles show that revolutionary technology eventually becomes commoditized as businesses shift focus to price and efficiency.

Data shows that AI prices are dropping while usage diversifies. Businesses are moving away from relying on a single 'best' model, instead using routers to select the most cost-effective tool for specific tasks, leading to budgetary pressures.

Capital is increasingly flowing into the 'layer underneath' the models: power grids, data centers, and physical compute infrastructure. Private equity is investing in these physical bottlenecks because they are essential and harder to disrupt than software.

Investors should evaluate AI companies based on four criteria: the specific layer they occupy, whether they own or rent the infrastructure, whether they control the customer, and the source of their funding. Owning the physical stack results in long-term depreciation, but ultimately provides more stability than model development.