[shadowmint]

If you can deliver 'a totally new opportunity through rich domain modelling' (ie. You have a solution to a problem that hasn't been solved before), and your solution:

- 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.

[blennon]

I'm not trying to be offensive, but what point are you trying to make? It sounds like you disagree with the author but your commentary seems like incoherent rambling to me.

I think the author makes excellent points.

It's hard to build a general AI business. E.g. a computer vision API provider. The technology is so democratized that you can't compete on algorithms alone. These APIs/services are more or less commodities nowadays.

Compare that to credit card fraud detection. For many years, there was one company/product (HNC/FICO/Falcon) that dominated the market (and largely still does) because they had a monopoly on the data. They smartly created a consortium and only they have the rights to train models on the data. They still use a relatively simple feedforward neural network with a ton of hand-tuned features. This is an example of vertical AI that created a wildly successful company.

[shadowmint]

tldr: The author claims 'Vertical AI startups are inherently defensible'.

My point is, so what? That's not a good indicator of anything meaningful.

The values that make them 'defensible' only make them difficult to replicate, that might look good on paper or in a pitch ('no one else can do this...') but they're not indicators of value, and the pose significant risks to execution.

/shrug

You don't have to agree, I really don't care; but this article sounds more like AI hype to spin to investors than meaningful advice.

Picking ML models that are difficult to replicate, hard to obtain data for and require a diverse team is not what you should be trying to do.

[bluetwo]

I think he is looking at it from the point of view that you want to build a company that has value.

Focus on a problem that has value in an industry that has money. Collect as much data as possible to prototype and use the tool itself to collect additional data as it is used.

I don't see that as hype, I see that as a useful roadmap.