AI is becoming a financial engineering business

What happens when large language models become commodities? Competitive advantage moves from the model itself to the balance sheet behind it.

AI is now reversing 20 years of technology economics, turning what was once a software business into a capital-intensive industry.

For much of the past two decades, investors rewarded asset-light software companies that needed little capital and generated fat margins.

Today, however, those same companies are spending at a scale the tech sector has never seen.

Since the AI boom began in 2023, Amazon , Microsoft , Alphabet and Meta have together poured $1.1 trillion into AI infrastructure.

The four “hyperscalers” plan to invest another $745 billion this year alone.

Capital intensity is, clearly, no longer something Big Tech can avoid.

It has in fact become the cost of competing in the AI race.

But there is a far bigger shift under way: as AI models become increasingly interchangeable, competitive advantage will depend less on the models themselves than on who can finance, build and run the infrastructure behind them the most cheaply.

Which also helps to explain why Microsoft boss Satya Nadella said recently that “every model is substitutable” and Amazon chief Andy Jassy predicted that there will soon be “at least half a dozen” comparably good AI models.

That changes the basis of competition itself.

Rather than betting the house on a single winning model, the hyperscalers are building more of the infrastructure capable of supporting many of them.

And as that happens, financing and scale begin to matter more than owning the frontier model itself.

Yet one question still hangs over the investment cycle: whether the models themselves ultimately generate enough value to justify the trillions still being committed.