[oliver101]

> 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.

[mlthoughts2018]

Great way of putting it. The trouble there is that it takes an exceptional kind of senior ML person to basically wear a product manager hat all the time and press to translate project success criteria into revenue impact or cost reduction terms.

Having these “glue people” that connect ML engineering to product management is probably the most important thing to running an ML organization.