Been spending most of my time trying to actually build and ship AI workflows lately, and the gap between online discussions and reality is getting more evident. A lot of the conversations about AI we see online still revolves around stuff like model comparisons, reasoning capabilities, benchmarks, and every new release cycle. Meanwhile, I keep finding myself buried in questions around reliability, evaluation, observability, governance, and what it actually takes to run these systems in production. I spent a good chunk of last week going down a rabbit hole on agent operations and monitoring, and it made me wonder whether we're collectively underestimating that side of the stack.
So I'd like to know your opinions about these two things:
>What's a topic you think deserves significantly more attention right now?
>And what's something the AI community spends far too much time debating?
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