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.
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.