> But if he is, he's missing that we do understand at a fundamental level how today's LLMs work.
No we don't? We understand practically nothing of how modern frontier systems actually function (in the sense that we would not be able to recreate even the tiniest fraction of their capabilities by conventional means). Knowing how they're trained has nothing to do with understanding their internal processes.
I'm pretty sure at this point more than half of Anthropic's new production code is LLM-written. That seems incompatible with "these agents are not up to the task of writing production level code at any meaningful scale".
It's pretty surprising that we don't have a good idea of how one of the most common (classes of) disease in the world spreads. This reviews the literature and does a bit of synthesis. (The conclusion is "probably mostly large particle aerosols, for adult-to-adult transmission, but more research needed to be confident".)
I have no idea what you think you're responding to. I use LLMs frequently in both professional and personal contexts and find them extremely useful. I am making a different, more specific claim than the thing you think I am saying. I recommend reading my comment more carefully.
Posting (unmarked) LLM-generated content on public discussion forums is polluting the commons. If I want an LLM's opinion on a topic, I can go get one (or five) for free, instantly. The reason I read the writing of other people is the chance that there's something interesting there, some non-obvious perspective or personal experience that I can't just press a button to access. Acting as a pipeline between LLMs and the public sphere destroys that signal.
For the benefit of external observers, you can stick the comment into either https://gptzero.me/ or https://copyleaks.com/ai-content-detector - neither are perfectly reliable, but the comment stuck out to me as obviously LLM-generated (I see a lot of LLM-generated content in my day job), and false positives from these services are actually kinda rare (false negatives much more common).
But if you want to get a sense of how I noticed (before I confirmed my suspicion with machine assistance), here are some tells:
"Large firms are cautious in regulatory filings because they must disclose risks, not hype." - "[x], not [y]"
"The suggestion that companies only adopt AI out of fear of missing out ignores the concrete examples already in place." - "concrete examples" as a phrase is (unfortunately) heavily over-represented in LLM-generated content.
"Stock prices reflect broader market conditions, not just adoption of a single technology." - "[x], not [y]" - again!
"Failures of workplace pilots usually result from integration challenges, not because the technology lacks value." - a third time.
"The fact that 374 S&P 500 companies are openly discussing it shows the opposite of “no clear upside” — it shows wide strategic interest." - not just the infamous emdash, but the phrasing is extremely typical of LLMs.