I'm surprised how long the "solution in search of a problem" trend has dominated tech product design, despite obvious and repeated failures of this approach to produce results.
AI/ML products fundamentally don't make sense compared to products that happen to use some AI/ML to aid in solving a problem.
It's sort of like loving to use redis (which I do) and thinking you want to found a company based on using redis in the product, or start a redis product team, dedicated to shipping products that use redis.
It's one thing if you want to host redis as your business, which is solving a problem involving redis, but if your aim is to use redis to solve a problem then you're going to be in trouble.
Imagine a PM on the "use redis" team rejecting a great idea for customers because it could be more efficiently solved using a traditional database, or forcing the use of redis when a cheaper, easier solution already works just as well if not better. This is actually the case on AI/ML teams.
GPT startups that will thrive are the ones that aren't GPT startups, but instead solving some other, real, problem that happens to only be solvable in a post-GPT word.
I'm not too surprised the trend persists, if I just follow the money. People like to bet on new(-ish) technologies and hypes, hoping to be the first in some brave new world - like with personal computers, the internet and mobile.
Didn't quite work for crypto or VR. I think that's partly because the economy changed: I don't remember such an insane amount of companies chase money so quickly with those earlier examples. For a long time there were lots of people just playing and tinkering with the new thing for fun, not immediately trying to get rich (even mobile, I fondly remember having PDAs long before there was an iPhone, tinkering with them and hanging out on XDA developers). If everyone is primarily trying to make money off something right from the get go instead of primarily trying to build something useful, I can see how adoption suffers. Bit of a catch 22.
AI/ML products fundamentally don't make sense compared to products that happen to use some AI/ML to aid in solving a problem.
It's sort of like loving to use redis (which I do) and thinking you want to found a company based on using redis in the product, or start a redis product team, dedicated to shipping products that use redis.
It's one thing if you want to host redis as your business, which is solving a problem involving redis, but if your aim is to use redis to solve a problem then you're going to be in trouble.
Imagine a PM on the "use redis" team rejecting a great idea for customers because it could be more efficiently solved using a traditional database, or forcing the use of redis when a cheaper, easier solution already works just as well if not better. This is actually the case on AI/ML teams.
GPT startups that will thrive are the ones that aren't GPT startups, but instead solving some other, real, problem that happens to only be solvable in a post-GPT word.