Comment by sillyfluke

I find it best to first consider the software sector since this is where Big AI seems to make a lot of their money from.

There been many a capable dev who have come out and said they haven't been coding for 6 months or more in their jobs. So if there were no alternatives then Big AI would clearly be too big to fail at its current state because the collective reaction from software devs would be akin to having a bad drug withdrawl and they wouldn't know what to with themselves. They have been pushed to be more dependent on AI by the companies they work for. The more time passes, the less people there will be who will be proficient at coding.

At the moment Big AI is operating at a loss, if they are actually too big to fail and they've destroyed all the alternatives, then there is no stopping them from colluding with each other and jacking up the prices to whatever number they want. They can even eventually jack up the prices to exceed human salaries, because humans will have suffered skill degradation in that same time period. Hence the software dev companies will shrink and shrink since all the work can be done by Big AI.

I don't think brand strength is as much as a thing on the tech side. The criteria is, "How close is this to the capability of the model my competitors use, is it good enough for the works I do, and is it the cheapest." As long as the capability is there people jump to the cheapest model. There are people that have claimed that they have created workflows that have allowed them to do most of their work with open models. As long as this threat to Big AI exists it will prevent them from exceeding a certain threshold with respect to pricing, and restricting their ability to build datacenters en masse will prevent them from outpricing smaller models.

Any fallout from not being able to train on customer data is a secondary issue and can be addressed once open models start taking over and Big AI's strength diminishes. People can train their models on their own code at least. It can also go back it how it was, where people buy each others source code or open source it in a way to allow it to be trained by open models.

"Inference at scale" is less of a problem when people are self-hosting their small models.