I like the hard-nosed business evaluation of this article. However, the difficulties of a successful “AI” company are the same difficulties a “web” company had

I like the hard-nosed business evaluation of this article. However, the difficulties of a successful “AI” company are the same difficulties a “web” company had in the 90s. How many “World Wide Web” (back when we used that phrase) products survived the bubble? And how many people made a lot of money while creating software of very little value? People understood, correctly, that the “Net” (sorry, can’t help my nostalgia) was a breakthrough technology that would change everything. But very few people understood how to use it effectively in that moment to provide business or consumer value. The same is true today for deep learning models. The tech has advanced to the point where there are clearly products waiting to be built upon advances that are largely open-sourced (if not pretrained). But building a product that is useful hasn’t changed from being a very difficult task to easy simply because a new technology exists. Moreover, as the article points out, costs in terms of dollars and manpower are currently higher than a making another web or mobile app. All this should make hackers salivate. The higher than challenge and the more advanced the stack, the greater an opportunity becomes. A useful way of thinking about this moment is that we have another platform to launch products from: first desktop, then web, then mobile, now “AI”. Each platform enabled a new kind of product. Unlike the other platforms, which were essentially hardware mechanisms of software delivery, ML/DL is a software mechanism of delivering software. It is a fundamentally different way of creating software that produces fundamentally different kinds of software — the kind you couldn’t make any other way. The article is right to throw the cold-water of reality on the hype of AI. But the backdrop is one of breakthrough technologies that are just waiting to be leveraged.