Data Economics

Budgets have a charming way of making AI strategy less philosophical. In the demo room, the question is usually whether a model can reason, code, summarize, plan, and sound pleasantly harmless while doing so. In the finance room, the question becomes simpler: how many tokens, how many GPUs, how many weeks, and why exactly are we paying to teach the model another version of the same web page? ...

Training data used to be treated like warehouse inventory: collect enough of it, clean the worst parts, stack it neatly, and feed it to the model. That worked well enough when the main question was scale. More tokens, more compute, more parameters, more dashboards announcing progress with the confidence of a quarterly sales deck. But production AI is beginning to run into a less convenient truth: data is not only an input. It is an allocation decision. ...