Yishan@yishan
A while ago I made a startup prediction that every AI application startup was likely to fail due to the rapid expansion of the foundational model providers.
I'd like to elaborate on that, since many people interpreted it in only one way - and there are TWO main ways they are likely to be killed.
The first one is like the quoted post below: a foundational model provider releases a new feature that does what the startup does, and the startup dies.
Many people interpreted it that way, and so plenty of people offered their objections citing examples in the past where nimble startups had disrupted platform incumbents.
The second cause is more subtle: the rapid expansion of capabilities by foundational model providers causes a roiling sea of constant market chaos that disrupts the orderly growth of small- and medium-sized startups.
Large ships can survive a huge storm, small ships stand no chance just because the waves are too high. Some of the storm is caused by the large ships, but not necessarily even in a way that benefits them. They are struggling to keep up in their own race.
Growing businesses prefer stability (or at least orderly chaos), and my claim is that the level of economic chaos and its rate of change is too high for application startups to survive. I mean, we're literally entering the Singularity now.
The foundational model providers are throwing off so much overhang (untapped value) that individual programmers - their numbers unleashed by vibe coding - will be able to constantly disrupt growing startups as those startups try to make their way from small to medium and beyond: after their initial founding moment and achieving PMF, they now have to stabilize and scale by building distribution channels. This is where they will be constantly vulnerable. (God help them if they aren't unit profitable, and many AI startups have huge capex)
One counter-argument was startups who represent a unique set of valuable training data. The thesis is that if you can discover, encode, and label a set of valuable training data that no one else has, you can create a service for that. Maybe.
The question will be whether that's enough to sustain becoming a generational company. You might still be better selling out in 18 months for the bag of cash.