I think this is unlikely because of the nature of the problem. It's kind of like when Paul Graham said consulting is fundamentally not scalable. Throwing more p

I think this is unlikely because of the nature of the problem. It's kind of like when Paul Graham said consulting is fundamentally not scalable. Throwing more people at a problem like this one only works if you have dedicated consultants for every bespoke business case, because there's no one-size-fits-all model, and the way that performance limitations interacts with which kind of face detection models you can use is very complicated. Companies would end up paying huge premia to Amazon essentially to have Amazon build in-house teams that would be equivalent, only much more expensive, than the companies own in-house team just building an in-house solution. With some forms of consulting this can make sense, because the consulting services are temporary and should set the company in a position such that for the long run, the company can maintain the solution cost-effectively. But for something like face detection, assuming it's a pivotal service you'll need on an on-going basis, this would become the worst sort of vendor lock-in imaginable, combined with the fact that if you don't appear to be creating growth for Amazon, you're liable to be deprioritized at any point, and at the mercy of Amazon's choices regarding when and how to address your bespoke need. For these specialized machine learning services it doesn't fit the same kind of commodity model that AWS infrastructure uses, though that's currently how Amazon is pursuing it. Don't get me wrong, Amazon is very good at making money and convincing nervous management to buy their brand. I suspect they will find ways to sell these services despite the services not being cost-effective or customizeable for bespoke customer situations. But when I point out that Amazon's machine learning services don't offer cost-effective performance, it's very different from a "just throw more devs at it" kind of problem, fundamentally.