At this point, I trust LLMs to come up with something more secure than the cheapest engineering firm for hire.
LLMs are just surfacing the fact that assessing and managing risk is an acquired, difficult-to-learn skill. Most people don't know what they don't know and fail to think about what might happen if they do something (correctly or otherwise) before they do it, let alone what they'd do if it goes wrong.
And to be fair to those people, coming to topics with a research mindset is genuinely hard and time consuming. So I can’t actually blame people for being lazy.
All LLMs do is provide an even easier way to “research”. But it’s not like people were disbelieving random Facebook posts, online scams, and word-of-mouth before LLMs.
The problem with centralisation isn’t that it gobbles up data. It’s that it allows those weights to be dictated by a small few who might choose to skew the model more favourably to the messaging they’ve want to promote.
And this is a genuine concern. But it’s also not a new problem either. We already have that problem with new broadcasters, newspaper publications, social media ethics teams, and so on and so forth.
The new problem LLMs bring to human interaction isn’t any of the issues described above. It’s with LLMs replacing human contact in situations where you need something with a conscience to step in.
For example, conversations leading to AI promoting negative thoughts from people with mental health problems because the chat history starts to overwhelm the context window, resulting in the system prompt doing a poorer job of weighting the conversation away from dangerous topics like suicide.
This isn’t to say that the points which you’ve addressed aren’t real problems that exist. They definitely do exist. But they’ve also always existed, even before GPT was invented. We’ve just never properly addressed those problems because:
either there’s no incentive to. If you are powerful enough to control the narrative then why would you use that power to turn the narrative against you?
…or there simply isn’t a good way of solving that problem. eg I might hate stupid conspiracy theories, but censoring research is a much worse alternative. So we just have to allow nutters to share their dumb ideas in the hope that enough legitimate research is published, and enough people are sensible enough to read it, that the nutters don’t have any meaningful impact on society.
A friend that studied fish production did recommend not eating salmon though and eating trout instead (ørret in Norwegian). Based on scientific evidence difference is pretty small (15% fish not surviving for salmon vs 12% for trout). But rainbow trout does have more DHA per kg.
The AI is being sold as an expert, not a student. These are categorically different things.
The mistake in the post is one that can be avoided by taking a single class at a community college. No PhD required, not even a B.S., not even an electricians certificate.
So I don't get your point. You're comparing a person in a learning environment to the equivalent of a person claiming to have a PhD in electrical engineering. A student letting the magic smoke escape from a basic circuit is a learnable experience (a memorable one that has high impact), especially when done in a learning environment where an expert can ensure more dangerous mistakes are less likely or non existent. But the same action from a PhD educated engineer would make you reasonably question their qualifications. Yes, humans make mistakes but if you follow the AI's instructions and light things on fire you get sued. If you follow the engineer's instructions and set things on fire then that engineer gets fired likely loses their license.
So what is your point?
You're biased because you're not considering that by definition the student is inexperienced. Unknown unknowns. Tons of people don't know very basic things (why would they?) like circuits with capacitors bring dangerous when the power is off.
Why are you defending there LLM? Would you be as nice to a person? I'd expect not because these threads tend to point out a person's idiocy. I'm not sure why we give greater leeway to the machine. I'm not sure why we forgive them as if they are a student learning but someone posting similar instructions on a blog gets (rightfully) thrashed. That blog writer is almost never claiming PhD expertise
I agree that LLMs can greatly aid in learning. But I also think they can greatly hinder learning. I'm not sure why anyone thinks it's any different than when people got access to the internet. We gave people access to all the information in the world and people "do their own research" and end up making egregious errors because they don't know how to research (naively think it's "searching for information"), what questions to ask, or how to interrogate data (and much more). Instead we've ended up with lots of conspiratorial thinking. Now a sycophantic search engine is going to fix that? I'm unconvinced. Mostly because we can observe the result.
You pin pointed a major problem with education, indeed. Personally, I think 3 crucial courses should be taught in school to mitigate that: 1) rational thinking 2) learning how to learn 3) learning how to do a research.
[0] https://enlightenedidiot.net/random/feynman-on-brazilian-edu...
https://www.wpr.org/news/judge-sanctions-kenosha-county-da-a...
AI is indeed being understood to be an expert that replaces human judgement, and people are being hurt because of it.
Some recent examples:
* foreign languages ("explain the difference between these two words that have the same English translation", "here's a photo of a mock German exam paper and here is my written answer - mark it & show how I could have done better")
* domains that I'm familiar with but might not know the exact commands off the top of my head (troubleshooting some ARP weirdness across a bunch of OSX/Linux/Windows boxes on an Omada network)
* learning basic skills in a new domain ("I'm building this thing out of 4mm mild steel - how do I go about choosing the right type of threading tap?", "what's the difference between Type B and Type F RCCB?")
Many of these can be easily answered with a web search, but the ability to ask follow-up questions has been a game changer.
I'd love to hear from other addicts - are there areas where LLMs have really accelerated your learning?
Learned a lot on how it works, to the point I’m confident that I can go the DIY route and spend my money in AliExpress buying components instead.
Why not ask a pro solar panel installer instead? I live in an apartment, of course they would say it’s not possible to place a solar panel on my terrace. I don’t believe in things not being possible.
But I had two semesters of electronics/robotics in my CS undergrad and I know to not to trust the LLM blindly and verify.
Basically if you can't differentiate how your typical conspiracy theorist isn't researching then you're at greater risk. It's worth thinking about that question, as they do do a lot of reading, thinking, and looking things up. It's more subtle, right?
FWIW, a thing I find LLMs really useful for is learning the vernacular of fields I'm unfamiliar or less familiar with. It is especially helpful when searches fail due to overloaded words (and let's be honest, Google's self elected lobotomy), but it is more a launching point. Though this still has the conspiracy problem as it is easy to self-reinforce a belief and not considering the alternatives. Follow-up questions are nice and can really help sifting through large amounts of information, but they certainly have a preference to narrow the view. I think this makes learning feel faster and more direct but have also taught (at the university level) I think it is important to learn all the boring stuff too. That stuff may not be important "now" but a well organized course means that that stuff is going to be important "soon" and "now" is the best time to learn it. No different than how musicians need to practice boring scales and patterns, athletes need to do drills and not just learn by competing (or "simulated" computations), or how children learn to write by boringly writing shapes over and over. I find the LLMs like to avoid the boring parts.
AI is a tool that can accelerate learning, or severely inhibit it. I do think the tooling is going to continue to make it easier and easier to get good output without knowing what you're doing, though.
> Just because a calculator will only ever be used by a subset of the population
I'll give an example. I tell people I tip by: round the decimal, divide by 10, multiply by 2. Nearly every time I say that people tell me it is too difficult. This includes people with PhD STEM educations...
Yes, and that's okay because the classroom is a learning environment. However, LLMs don't learn; a model that releases the magic smoke in this session will be happy to release it all over again next time.
> LLMs are just surfacing the fact that assessing and managing risk is an acquired, difficult-to-learn skill.
Which makes the problem worse, not better. If risk management is a difficult skill, then that means we can't extrapolate from 'easy' demonstrations of said skill to argue that an LLM is generally safe for more sensitive tasks.
Overall, it seems like LLMs have a long tail of failures. Even while their mean or median performance is good, they seem exponentially more likely than a similarly-competent human to advise something like `rm -rf /`. This is a deeply unintuitive behaviour, precisely because our 'human-like' intuition is engaged with resepct to the average/median skill.
The operator is still a factor.
The LLM got it to “working” state, but the people operating it didn’t understand what it was doing. They just prompt until it looks like it works and then ship it.
The parents are saying they'd rather vibe code themselves than trust an unproven engineering firm that does(n't) vibe code.
You could cut the statement short here, and it would still be a reasonable position to take these days.
LLMs are still complex, sharp tools - despite their simple appearance and proteststions of both biggest fans and haters alike, the dominating factor for effectiveness of an LLM tool on a problem is still whether or not you're holding it wrong.
Paraphasing, LLMs are great (bad) tools for the right (wrong) job...
in the right hands,
at the right time,
in the right place...
We’re not taking about the parent commenter, we’re talking about unskilled Kickstarter operators making decisions. Not a skilled programmer using an LLM.
THAT makes sense. Engineering was never cheap nor non-differentiating if normalized by man-hours, only when it was USD normalized. If a large enough number of people were to get the same FALSE impression that software and firmware parts are now basically free and non-differentiating commodities, then there will be tons of spectacular failures in software world in coming years. There has already been early previews of those here.
It gave up, removed the code it had written directly accessing the correct property, and replaced it with a new function that did a BFS to walk through every single field in the API response object while applying a regex "looksLikeHttpsUrl" and hoping the first valid URL that had https:// would be the correct key to use.
On the contrary, the shift from pretraining driving most gains to RL driving most gains is pressuring these models resort to new hacks and shortcuts that are increasingly novel and disturbing!
That hasn't, universally, been my experience. Sometimes the code is fine. Sometimes it is functional, but organized poorly, or does things in a very unusual way that is hard to understand. And sometimes it produces code that might work sometimes but misses important edge cases and isn't robust at all, or does things in an incredibly slow way.
> They have no problem writing tedious guards against edge cases that humans brush off.
The flip side of that is that instead of coming up with a good design that doesn't have as many edge cases, it will write verbose code that handles many different cases in similar, but not quite the same ways.
> They also keep comments up to date and obsess over tests.
Sure but they will often make comments or tests that aren't actually useful, or modify tests to succeed instead of fixing the code.
One significant danger of LLMs is that the quality of the output is higly variable and unpredictable.
That's ok, if you have someone knowledgeable reviewing and correcting it. But if you blindly trust it, because it produced decent results a few times, you'll probably be sorry.
> Sure but they will often make comments or tests that aren't actually useful, or modify tests to succeed instead of fixing the code.
> They don't take shortcuts or resort to ugly hacks.
> They have no problem writing tedious guards against edge cases that humans brush off.
I have a hard time getting them to write small and flexible functions. Even with explicit instructions about how a specific routine should be done. (Really easy to produce in bash scripts as they seem to avoid using functions, but so do people, but most people suck at bash) IME they're fixated on the end goal and do not grasp the larger context (which is often implicit though I still find difficulty when I'm highly explicit. Which at that point it's usually faster to write myself)
It also makes me question context. Are humans not doing this because they don't think about it or because we've been training people to ignore things? How often do we hear "I just care that it works?" I've only heard that phrase from those that also love to talk about minimum viable products because... frankly, who is not concerned if it works? That's always been a disagreement about what is sufficient. Only very junior people believe in perfection. It's why we have sayings like "there's no solution more permanent than a temporary fix that works". It's the same people who believe tests are proof of correctness rather than a bound on correctness. The same people who read that last sentence and think I'm suggesting to not write tests or believe tests are useless.
I'd be concerned with the LLM operator quite a bit because of this. Subtle things are important when instructing LLMs. Subtle things in the prompts can wildly change the output
My AGENTS.md is filled with specific lines to counter all of them that come up.
I’ve been using Opus 4.6 and GPT-Codex-5.3 daily and I see plenty of hacks and problems all day long.
I think this is missing the point. The code in this product might be robust in the sense that it follows documentation and does things without hacks, but the things it’s doing are a mismatch for what is needed in the situation.
It might be perfectly structured code, but it uses hardcoded shared credentials.
A skilled operator could have directed it to do the right things and implement something secure, but an unskilled operator doesn’t even know how to specify the right requirements.
I’m much more worried about the reliability of software produced by LLMs.
In my experience that is all they do, and you constantly have to fight them to get the quality up, and then fight again to prevent regressions on every change.
So, will they? Probably. Can you trust the kind of LLM that you would use to do a better job than the cheapest firm? Absolutely.