- Is fundamentally tied to data that is proprietary and difficult to gather.
- Is intrinsically extremely complex to build and maintain.
- Can only be built by a diverse team which is difficult to gather.
Then... well, yes, I guess you could say that those of good metric for determining if a product can easily be replicated.
...but that's not the same thing as it being useful or profitable.
It just means that the team has a bit more time to try to figure out those other two important things before someone else comes along and copies what they've done.
> My claim is that Vertical AI startups are inherently defensible.
Putting 'vertical' in front of 'AI' doesn't magically make things better than just 'AI'.
The problem with these products is you can't take a trivial 'proof of concept' or MVP, pitch it and then roll it out 'into production'.
This isn't some 'smoke and mirrors' jazzy demo of a website & app combo you can go away and implement properly later... the proof of concept you build may not scale. Like... it may actually not be possible to scale. Maybe it takes too much compute to train; maybe it takes data you don't have; maybe it turns out your data isn't suitable.
You think building a business and getting users and sorting your workers and so on isn't hard enough?
Try adding a product that may or may not actually ever work into the mix. Sound scary yet? It should sound scary. That's the sound of money draining out of a hole in the floor.
...and sure, you argue, these models do work and they are good; but there's the catch right there... :)
...if you use a model that does work and isn't risky and does use available data... then you lose all the points at the top that made it an interesting business to invest in.