Earlier quoted context omitted.
Not to mention all the other ways they can screw you: - Middle of the day, servers busy? Swap to Sonnet while pretending it's still Opus. Many people won't notice, and nobody can prove anything if they suspect. - Middle of the night, server load is light? Put it into extra thinky mode so it burns more tokens to ramp up the bills. Flip the switch where it gets really pedantic about writing lots of extra test cases and…
It all sounds like having to rely on a dodgy housing contractor that wants to steal from you, take shortcuts AND choose the gold-plated options from their supplier friends, and will start doing this the minute you are not on site supervising. You don't do it yourself (because the contractor is faster and stronger than you in many ways) but you can't leave, so you're stuck on the worksite just watching them.
I've seen local models recognize when the task I'm asking them for is likely to be an artificial benchmark.
And any smart company is going to use lightweight models to monitor your sessions. If their sentiment analysis suspects you're close to cancelling, they'll up the knob for a few days until you calm down. Or worse, their accounting tells them that you're getting too much value from your fixed price subscription, so they turn the knob down to encourage you to cancel.
In the short term, the "frontier" models are too good to ignore. But if (when?) that plateaus, I don't see how anyone could trust a non-local model. When you pay an ISP to serve your web site, you can tell if they over-compress your images to save storage and bandwidth. With LLMs, it's just JSON with more errors and pointing to the fine print that models are not deterministic.