It’s a strange time we live in. The price of a single AI calculation has plummeted, yet we see companies bleeding millions every month in operating costs.
At Aixia, we call it the inference paradox. It’s that uncomfortable point where efficiency meets volume, and volume wins.
The pattern that repeats itself
We see a pattern repeating itself. In the test phase, everything feels calm; the costs are barely noticeable. But fast forward 12 months. Suddenly, AI operations are the organization’s biggest expense item. Especially when the systems start chugging along around the clock, fully autonomous, without waiting for anyone to push a button.
It’s not the training that costs anymore. It’s life itself in production.
Nordic advantage
Here in the Nordics, we are actually sitting on a goldmine, if we play our cards right. With fossil-free energy and natural cooling, we have a structural advantage that is hard to match. But the question is how we manage it.
Our analysis shows that the tipping point for building your own AI infrastructure often comes earlier than you think – around 60-70% of the cloud cost. Then you not only control the money, but also the data and the geopolitical risk.
The question is not if, but how
The question is no longer about whether you should scale your AI. It’s about how you will afford to keep the lights on once you do.
It is time for a comprehensive economic analysis that goes beyond the next quarter.
