| And on the other side, to detractors, AI and LLMs cannot ever succeed. There's always another goalpost to shift. If it seems to work well, it's because it's copying training data. Or it sometimes gets something wrong, so it's unreliable. If they say it boosts their productivity, they're obviously deluded as to where they're _really_ spending time, or what they were doing was trivial. If they point to improvements in benchmarks, it's because model vendors are training to the tests, or the benchmarks don't really measure real-world performance. If the improvements are in complex operations where there aren't benchmarks, their reports are too vague and anecdotal. The exciting part about this belief system is how little you have to investigate the actual products, and indeed, you can simply rely on a small set of canned responses. You can just entirely dismiss reports of success and progress; that's completely due to the reporter's incompetence and self-delusion. |
This is definitely something that biases me against AI, sure. Seeing how the sausage is made doesn't help. Because it's really a lot of offal right now especially where I work.
I'm a very anti-corporate non-teamplayer kinda person so I tend to be highly critical, I'll never just go along with PR if it's actually false. I won't support my 'team' if it's just wrong. Which often rubs people the wrong way at work. Like when I emphasised in a training that AI results must be double checked. Or when I answered in an "anonymous" survey that I'd rather have a free lunch than "copilot" and rated it a 2 out of 5 in terms of added value (I mean, at the time it didn't even work in some apps)
But I'm kinda done with soul-killing corporatism anyway. Just waiting for some good redundancy packages when the AI bubble collapses :)