So, Y Combinator announced in a blog post on Sunday
that it’s going to select a group of artificial intelligence startups as part of the upcoming batch of companies in its popular accelerator/seed funding program. If you’re inclined to see bubbles whenever a space gets really frothy with investment, this is as good a sign as any to start preparing for a pop
However, YC is doing at least one particularly smart thing with its call for AI startups: it’s looking for startups targeting vertical industries, rather than just AI startups in general. If there’s something seemingly everyone agrees on right now, it’s that most successful AI companies will be those building vertical products rather than horizontal platforms or general-purpose AI software. Most companies have neither the expertise to manage any sort of AI system, nor, frankly, the desire to learn an entirely new field of computer science.
Several years ago, everyone was supposed to invest in big data. A few years ago, they were all supposed to hire data scientists to manage those systems and build applications. How many companies outside of Silicon Valley and Wall Street actually did that? I would venture to guess it was very few, relatively speaking.
So here we are with AI and there is, understandably, a fair amount of skepticism from the companies that actually have to buy what Silicon Valley is building with unbridled optimism.
However, the good news for everybody is that there’s plenty of good AI advice already out there for startups and software companies that are willing to listen. Most of it starts with the aforementioned advice to build a targeted application. I would take things even further and suggest having at least one founder who has actually worked deeply in the industry you’re trying to target—AI right now is going to be best at optimizing specific tasks and workflows for day-to-day practitioners rather than revolutionizing an entire industry.
Lots of folks are also offering up advice on how to think about generating and analyzing data in AI startups, and figuring out which approaches to use. We barely scratched the surface of tried-and-true machine learning approaches before diving whole hog into neural networks; it’s possible that “AI” might not actually be the best fit right now. Further, it’s possible that no matter what approach you take, focusing sales and marketing around a product’s “intelligence” will be a recipe for disaster if it can’t live up to someone’s preconceived (and possibly lofty) expectations of what that means.
With that in mind, here’s the best advice I’ve seen this week about starting companies that do AI: