By any chance, is the following true: you had no empirics in the loop. It couldn’t validate any of its theories by experimentation. If the solution requires being able to make actual changes in order to gather more information and it is not allowed to, and this is the only way to solve the problem, by definition it couldn’t do it, nor could you. Or was it something that could be worked out entirely on an a priori basis from the available data?

And while we’re talking about hilarious delusions, perhaps you should look at the current capability curve of AI and weigh it against the constant stream of arguments for why it couldn’t have continued at every point and yet has.

i’m not an anti-AI believer. And our career is changing dramatically no doubt. i just think people overestimate short term gains and underestimate long term gains. (applicable to your comment)

In this case i had no empirics in the loop. The scenario was only reproducible under high api load. I could load test, but management isn’t eager to spend prod-like costs in staging (requires scaling opensearch a lot in stage). What can i say.

> in the loop

Yes, the solution is to just burn more tokens.