I think the machine learning problem is not data. It's not models. It's not compute.
It's annotation. That is a workforce problem. You want to automate contracts? You need attorneys. You want to automate radiology? You need radiologists. You want to automate driving? You need drivers.
This makes ML less like a SaaS business, more like a mining business. There's tons, literally tons, of data/ore for any interesting problem. That's why it's an interesting problem. There are buyers of iron, gold, and marble. There are buyers of driverless cars, physician decision support systems, and contract automation solutions. But recovering the data from the mine (digitization) and enriching it (annotation) cost money. So much that the market variation may make it lucrative at some times and not others. If you are near peak employment, the value of a model goes up, but the cost of annotators is high also.
I'm not sure how finance guys capture that problem: how do you make a profit when there's high demand on both sides at one time, and low demand on both sides at other times? I submit that when both sides are low is the time to do annotation, and the time with both sides are high is the time to sell models.
But then you need an investor who can ride out the market.