There are valuable uses of learning models, but I'd say they all have the following constraints:
- The relation between input and output is at most 1:1. So the output does not contain any information that cannot be derived from the input.
- The scope is sufficiently constrained so that the error rate can be meaningfully quantified.
- Dealing with the errors (including verifying that there are errors, if needed) takes less effort than just doing everything manually.
Seems to me that the "market performance ratio" should weigh a lot heavier. The whole thing that makes something a bubble is that a lot of money is being put into it while very little is coming is coming out and there being very few prospects of that changing in the near enough future outside of religious conviction, yet this metric is the only one suggesting that investments creating real value should matter and it only accounts for 7.5% of the whole score. Then again, the site doesn't actually properly define what "market performance ratio" means and doesn't state its sources beyond a vague description.
Also, the person who made this, Mert Demirdelen, is "head of growth and product" at Mobiversite, an AI app maker. His skills listed on LinkedIn include "AI" and "Blockchain". So maybe not someone who is completely devoid of the desire to invoke a particular impression of the state of the AI economy.