Comment by johnfn
7 hours ago
Time and time again that I observe it is the AI skeptic that is not reacting with curiosity. This is almost fundamentally true, as in order to understand a new technology you need to be curious about it; AI will naturally draw people who are curious, because you have to be curious to learn something new.
When I engage with AI skeptics and I "ask these people what they're really thinking, and listen" they say something totally absurd, like GPT 3.5-turbo and Opus 4.6 are interchangeable, or they put into question my ability as an engineer, or that I am a "liar" for claiming that an agent can work for an hour unprompted (something I do virtually every day). This isn't even me picking the worst of it, this is pretty much a typical conversation I have on HN, and you can go through my comment history to verify I have not drawn any hyperbole.
AI will naturally draw people who are lazy and not interested in learning.
It's like flipping through a math book and nodding to yourself when you look at the answers and thinking you're learning. But really you aren't because the real learning requires actually doing it and solving and struggling through the problems yourself.
This is just completely inaccurate. There is more to learn now than ever before, and I find myself spending more and more time teaching myself things that I never before would have been able to find time to understand.
This is just completely inaccurate. There's the same amout of information available as before. It's not like LLMs provide you with information that isn't available anywhere else.
But I agree that it can serve as a tool for a person who it's interested in learning but I bet you that for every such person there's 10x as many who are happy to outsource all their thinking to the machine.
We already have reports from basically every school in the world struggling with this exact problem. Students are just copy pasting LLMs and not really learning.
I'm sorry you've had that experience, and I agree there are a good share of "skeptics" who have latched on to anecdata or outdated experience or theorycrafting. I know it must feel like the goalposts are moving, too, when someone who was against AI on technical grounds last year has now discovered ethical qualms previously unevidenced. I spend a lot of time wondering if I've driven myself to my particular views exclusively out of motivated reasoning. (For what it's worth, I also think "motivated reasoning" is underrated - I am not obligated to kick my own ass out of obligation to "The Truth"!)
That said, I _did_ read your comments history (only because you asked!) and - well, I don't know, you seem very reasonable, but I notice you're upset with people talking about "hallucinations" in code generation from Opus 4.6. Now, I have actually spent some time trying to understand these models (as tool or threat) and that means using them in realistic circumstances. I don't like the "H word" very much, because I am an orthodox Dijkstraist and I hold that anthropomorphizing computers and algorithms is always a mistake. But I will say that like you, I have found that in appropriate context (types, tests) I don't get calls to non-existent functions, etc. However, I have seen: incorrect descriptions of numerical algorithms or their parameters, gaslighting and "failed fix loops" due to missing a "copy the compiled artifact to the testing directory" step, and other things which I consider at least "hallucination-adjacent". I am personally much more concerned about "hallucinations" and bad assumptions smuggled in the explanations provided, choice of algorithms and modeling strategies, etc. because I deal with some fairly subtle domain-specific calculations and (mathematical) models. The should-be domain experts a) aren't always and b) tend to be "enthusiasts" who will implicitly trust the talking genius computer.
For what it's worth, my personal concerns don't entirely overlap the questions I raised way above. I think there are a whole host of reasons people might be reluctant or skeptical, especially given the level of vitriol and FUD being thrown around and the fairly explicit push to automate jobs away. I have a lot of aesthetic objections to the entire LLM-generated corpus, but de gustibus...
Your response is definitely on the top 5% of reasonableness from AI skeptics, so I appreciate that :-)
But, if you don't mind me going on a rant: the hallucinations thing. It kind of drives me nuts, because every day someone trots out hallucinations as some epic dunk that proves that AI will never be used in the real world or whatever. I totally hear you and think you are being a lot more reasonable than most (and thank you for that) -- you are saying that AI can get detail-oriented and fiddly math stuff wrong. But as I, my co-workers, and anyone who seriously uses AI in the industry all know, hallucinations are utterly irrelevant to our day-to-day.
My point is that hallucinations are irrelevant because if you use AI seriously for a while you quickly learn what it hallucinates on and what it does not, you build your mental model, and then you spend all your time on the stuff it doesn't hallucinate on, and it adds a fantastic amount of value there, and you are happy, and you ignore the things it is bad at, because why would you use a tool on things it is bad at? Hearing people talk about hallucinations in 2026 sounds to me like someone saying "a hammer will never succeed - I used it to smack a few screws and it NEVER worked!" And then someone added Hammer-doesnt-work-itis to Wikipedia and it got a few citations in Arxiv now it's all people can say when they talk about hammers online, omfg.
So when you say that I should spend more time asking "what do they see that I don't" - I feel quite confident I already know exactly what you see? You see that AI doesn't work in some domains. I quite agree with you that AI doesn't work in some domains. Why is this a surprise? Until 2023 it worked in no domains at all! There is no tool out there that works in every single domain.
But when you see something new, the much more natural question than "what doesn't this work on" is "what does this work on". Because it does work in a lot of domains, and fabulously well at that. Continuously bringing up how it doesn't work in some domain, when everyone is talking about the domains it does work, is just a non-sequitur, like if someone were to hop into a conversation about Rust and talk about how it can't solve your taxes, or a conversation about CSS to say that it isn't turing complete.