> This is the crux of the AI business dilemma. If the economics are a function of the problem – not the technology per se – how can we improve them?

The article focusses on the costs of resources to build a model (annotated data + compute) but the economics are also affected by the ongoing cost of making a prediction error. False positives and false negatives usually have a different cost and each user might have their own preferences:

e.g. "show me all the content that's a bit relevant" vs "show me just the content that's really relevant".

If you can write out the loss function in $$$ terms not just accuracy, then you're closer to either abandoning the problem or finding a profitable AI model.