It's worse though, because you can't really watch them at all. It's very difficult to get quantitative numbers for quality. Even within the same model family, same tokenizer, and complete control over the weights and logits, perplexity and KL-divergence isn't really what you want. Now put it behind an HTTP endpoint, and it's just opaque.

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.