> now that they have people who built services on their API

People really can’t wait to be the next Zynga

switching models is insanely cheap compared to token cost on anything signficant, this is a take so cynical it misses the reality

in any corporate or half compliance-relevant setting switching isn't trivial. new DPA, subprocessor notifications, TIA, procurement review, security questionnaires, plus re-running your evals because prompts don't transfer 1:1. token cost is just one of the line items.

no it really not, even the soggiest bank has multiple api vendors atm.

I agree with parent. I'm not sure where your stance is coming from.

From what I hear, most enterprise AI deployments are seat-based subscriptions with annual commitments.

50K FTE global firm. We’re still piloting ChatGPT. AI is a four-letter word and there are ridiculous ceremonies and hundreds of hours of overhead for every trivial use case.

Amusingly, Enterprise credits are more expensive than just paying a zero-commitment on-demand API fee. Personal accounts are still the best value.

Yes, I work at a 50 person startup and even here switching from CC to codex or cursor would be non-trivial for multiple reasons - not just the annual commitment.

I don't doubt you but it's amazing how much easier things get when there's another option at 20% of the price, and that's what's going to happen here if these American companies keep trying to squeeze the prices up.

my stance is coming from working at one of the soggiest banks and having access to 3 with 2 more coming, and knowing the same is true at 2 of our large competitors.

I think the big 3 are cartelizing and starting to ratchet up costs. GPT5.5 is not easily distinguishable from 5.1. I would it be shocked if we hit the ceiling and everyone is quietly positioning for the exit.

I don't understand why everyone thinks there is a ceiling below human-level intelligence, when we have an existence proof that human-level intelligence is possible.

for LLMs as implemented today?

This is very napkin math, but the human brain has about 100 trillion parameters. Even the biggest models today top out at 10 trillion parameters. I think it's reasonable to assume that models need to be at least an order of magnitude bigger to capture the complexity of human intelligence, and probably a lot more.