This is AI's "dialup era" (pre-56k, maybe even the 2400 baud era).
We've got a bunch of models, but they don't fit into many products.
Companies and leadership were told to "adopt AI" and given crude tools with no instructions. Of course it failed.
Chat is an interesting UX, but it's primitive. We need better ways to connect domains, especially multi-dimensional ones.
Most products are "bolting on" AI. There are few products that really "get it". Adobe is one of the only companies I've seen with actually compelling AI + interface results, and even their experiments are just early demos [1-4]. (I've built open source versions of most of these.)
We're in for another 5 years of figuring this out. And we don't need monolithic AI models via APIs. We need access to the AI building blocks and sub networks so we can adapt and fine tune models to the actual control surfaces. That's when the real take off will happen.
This is AI's Segway era. Perfectly functional device, but the early-2000s notion that it was going to become the primary mode of transportation was just an investor-fueled pipe dream.
AI is going to be bigger than Segway / personal mobility.
I think dialup is the appropriate analogy because the world was building WebVan-type companies before the technology was sufficiently wide spread to support the economics.
In this case, the technology is too concentrated and there aren't enough ways to adapt models to problems. The models are too big, too slow, not granular enough, etc. They aren't build on a per-problem domain basis, but rather a "one-size fits all" model.
This is AI's "dialup era" (pre-56k, maybe even the 2400 baud era).
We've got a bunch of models, but they don't fit into many products.
Companies and leadership were told to "adopt AI" and given crude tools with no instructions. Of course it failed.
Chat is an interesting UX, but it's primitive. We need better ways to connect domains, especially multi-dimensional ones.
Most products are "bolting on" AI. There are few products that really "get it". Adobe is one of the only companies I've seen with actually compelling AI + interface results, and even their experiments are just early demos [1-4]. (I've built open source versions of most of these.)
We're in for another 5 years of figuring this out. And we don't need monolithic AI models via APIs. We need access to the AI building blocks and sub networks so we can adapt and fine tune models to the actual control surfaces. That's when the real take off will happen.
[1] Relighting scenes: https://youtu.be/YqAAFX1XXY8?si=DG6ODYZXInb0Ckvc&t=211
[2] Image -> 3D editing: https://youtu.be/BLxFn_BFB5c?si=GJg12gU5gFU9ZpVc&t=185 (payoff is at 3:54)
[3] Image -> Gaussian -> Gaussian editing: https://youtu.be/z3lHAahgpRk?si=XwSouqEJUFhC44TP&t=285
[4] 3D -> image with semantic tags: https://youtu.be/z275i_6jDPc?si=2HaatjXOEk3lHeW-&t=443
edit: curious why I'm getting the flood of downvotes for saying we're too early. Care to offer a counter argument I can consider?