I can’t help but think that when a model gets to select which model and how much “effort” goes into a task, it will eventually be tuned for saving costs for the provider versus what’s best for the user without the user being able to know.
If we don't know because it's good optimization that does not impact us in a noticeable way, then that seems like a fine trade-off.
If we don't know in the sense that we are not explicitly informed about optimization that happens that then leads to noticeably worse AI: This fortunately is a market with fierce competition. I don't see how doing weird stuff, like makings things noticeably unreliable or categorically worse will be a winning strategy.
In either case "not knowing" is really not an issue.
Same problem as ai safety, but the actual problem is now the corporate greed of humans behind the ai rather than an actual agi trying to manipulate you.
We don’t know what we don’t know, we can’t always judge what is categorically right or wrong to make an informed decision. What we can do is decide who we want to ask a question based on competence.
What's the idea? How does creeping, far reaching incompetence continually get past all of us?
The idea would/could be not intentional dissemination of missinformation, but purely financial. Models are expensive to run, hardware, rack space and power limited and making newer releases seem more robust subjectively can be a powerful incentive.
With prior models we already have seen quantization post release and it’s been a personal pet peeve of mine that this should be communicated via a changelog, with the router there is one more quite powerful, potentially even less transparent way for providers to put their thumb on the scale. For now, GPT-5 does very impressively in my limited use cases and testing, especially considering pricing, but the concern that this may (and past experience tells me likely) change soon enough remains.
prob different incentives at each