[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.