Here is the fundamental issue. We use the word "intelligence" for different things. Can you follow a recipe for making sour dough bread? Pretty easy. Can you make sour dough bread? Not so easy. Does following a recipe require "intelligence"? Yes. If something can follow a recipe can it also make bread? Not necessarily.<p>And another question, perhaps the most important. Can you determine that a recipe is flawed? In immediate terms, if I tell you to feed your sour dough starter every day, can you determine why, how or if that might be bad advice?<p>My conjecture is that there are at least three types of intelligence, as outlined above. And you have to remember that AI is by definition "artificial". Not in the sense of being unnatural but in the sense of artificial sour dough bread. It is not the real thing. (at least for two out of the three definitions of intelligence).<p>This is not to argue that AI is not useful and extremely beneficial in some contexts. Unfortunately our whole system of education has trained us to be "follow the recipe" kind of people. Uh Oh! So if your only skill and ability is to follow recipes, you might want to focus on developing your other kinds of intelligence.
Reminds me of Moravec's paradox, that it is easy to get computers to ace complex math tests but difficult to teach them to walk. We are very excited that computers have mastered the "know the recipe" step and are underplaying the complexity of actual intelligence required to really replace people.<p>My fear in your above example would be that we offload more and more of the "know the recipe" intelligence to computers and humans are slotted in as replaceable manual labor and are left arguing with a computer about whether the starter needs to be fed or not (or whatever equivalent scenario).
In the section "Everyone should learn some coding":<p>> I would say that the major unlocks are at:<p><pre><code> 1-2 weeks: Basic understanding of what the field is about and what general words to use when asking the AI to do something.
1-2 months: Basic understanding of how and when to ask the AI something.
4-6 months: Ability to check the output for correctness (using external sources as needed).
</code></pre>
1-2 weeks for enough of an understanding to appropriately use terms? No way. Using Harvard CS50 as a reference, it takes until week 2 to learn about arrays.<p>4-6 months to check output for correctness? Are we trusting fresh bootcampers in their first week at their first job to do prod code reviews now?<p>You can learn a LOT in a short period of time, but it would take much more than casual time investment. This is insane advice on the level of telling blue collar workers to just "learn to code."
> Currently, the level of computing intuition needed to additively prompt the coding agents sits at roughly 5 years’ experience level. Today’s seniors were lucky enough to get paid to build their computing intuition, but the gap grows as coding agents continue to improve.<p>I am struggling to interpret what they mean by "gap". Gap between what two things?<p>The gap between juniors and seniors?<p>The gap between ${AI + 0 YOE} and ${AI + N YOE}? Where N is the growing "gap"? Eg, as AI gets better, you need more and more YOE to justify throwing a salaried human into the loop?
I don't see this discussed often enough but high school and universities need to adapt FAST, like yesterday, to the current reality.<p>More in-class study and "hands-on" work with proctored in-person exams. There is no incentive for students to go through their courses "the honest way" and build this intuition themselves. Can you blame them?
I studied computer science and mathematics, not software engineering<p>Could've used a better software engineering class but I use the more abstract knowledge regularly and I think it would be a disadvantage to strip that out and just go straight to "here's how to prompt"<p>Sorry if I'm straw manning your comment, I do think that the abstract stuff is more important than ever, and would also like to see more philosophy and such required for eng/science/math degrees.
Schools have always lagged and can barely keep up. Books once printed on any tech topic is almost always outdated by the time it reaches students. Anecdotally, I went through high school being told over and over that I wouldn't always have a calculator in my pocket. I think the messaging they conveyed was done poorly, and should have said "you need to understand the fundamentals and why the calculator gave you the answer".
Are proctored in-person exams not the default for most places anyway?
Sorry for the off-topic comment, but what happened to the front page? At the time I’m writing this, 11/30 submissions are related to AI. Maybe my comment is cliché too, but I’m honestly tired of all the AI stuff.
Everyone is quite worried of their job. Many of us have made coding/IT our personality, what we were proud of, what the society made us feel valued. It's a big change in life... and there is no solution yet.<p>So everyone feels the needs to talk about it, to either get rid of this anxiety by ranting or trying to prove that it would be an opportunity, or a non-event depending on the point of view, etc
Going off this thought tangent a bit, I think many engineers could be gatekeepers because it's a pretty hard industry to get into and it's just not everybody's cup of tea. Now that AI is assisting people who wouldn't necessarily make it in the old world, it turns out business just cares about results and the gatekeepers don't matter as much anymore. It's creating quite a big split between the old guard and people who just get stuff done even if it creates 10 times the bloat.<p>I've always been on the get it done side to the chagrin of my peers but I've also never impressed anyone with what I've came up with so who knows.<p>My personal opinion is that if you don't get with the program, you're probably going to get left in the dust or going to have to split off and do your own thing where you can control what's going on but I think in general in a capitalistic society, the business just wants to get to the next thing to make more money and subpar or middling quality is good enough.<p>I should caveat my comment that this doesn't apply to pacemaker software and higher end software engineering
I don't think you can ignore it. It's the biggest change to tech in 30 years I'd say.<p>"I'm tired of all this internet talk" in 1990s?
Even worse it that it's the same few talking points repeated over, and over, and over again - re-spun with AI
Been away for a while? It's been like this for at least a year.
AI is the most important, impactful and disruptive technology of today. It makes sense to be talking about it.
because this is a billboard for YC companies and YC cultural psyops, this isn't an organic forum
5 years ago it was web3. 5 years before that it was Haskell and monads. It’s how things work
Maybe hit this fellow up with a feature request to add a regex filter, etc: <a href="https://github.com/IronsideXXVI/Hacker-News" rel="nofollow">https://github.com/IronsideXXVI/Hacker-News</a><p>Very nice HN client and he was responsive to ideas. I was thinking of same to filter out "Democrat" "Republican" "Trump" and "Musk", partly due to upcoming elections in November.
Personalization would solve that.
Honestly, at this point it's an accurate reflection of the reality of tech.
Welcome to the next wave blogspam campaign for LLMs. Plenty of popular HN blogs have gotten good notoriety by writing about LLMs even if the content is a nothingburger. Now everyone is jumping on that trend to try to continue to normalize it.<p>Part 1 was flood with AI content. Now Part 2 is walk back bold claims made in Part 1 (call it a fast moving landscape) and have the evangelists flood with AI content. Extra points if you can wax on something and try to redefine it as a pro for LLMs. “What is expertise? Did you have it before? Well now it’s faster with LLMs! Forget about all the efficiency claims, expertise is the real benefit you get with statistically incorrect LLMs.”
It's polluting the world, gobbling up hardware, and making us dumber. And HN and LinkedIn just can't get enough.<p>People say AI is the new internet. I say AI is the new tobacco.
Actually - to disillusion yourself from AI, try dabbling into something you do not know. Try writing a production quality 3D engine. Trust me, a 3D engine has its own domain knowledge besides just graphics. No, seriously. And then see how helpless you feel when you yourself do not have the expertise to judge whether the direction being taken is the right or wrong.<p>At that time, you wish if there were some pipe through which you could reach John Carmack, Tim Sweeney, Gabe Nawell, Jonathan Blow some Casey Muratori and just ask one thing:<p>Sir, is this really the right direction?<p>These tools feel good when you yourself are a domain expert. I have written backend systems and designed REST APIs all my life in multiple languages in Java, Python, Go, Ruby for multiple verticals I'd say I am damn expert at API design including all the layers that go under it and I can confidently give a shut up call to an LLM knowing what I know.<p>Fuck the bean counters and the greedy parasite execs and VPs. Hug a junior today, society will need them tomorrow because I was a clueless junior once and my seniors were very kind to me that I am able to put bread for my family on the table.
Yeah ok. First of all, just because it sucks now, it doesn't mean we're still safe in just 24 months. Everyone was mocking AI 24 months ago.<p>Second, most of the work out there is not at all about "production quality 3D engine," that's the whole point. Most of us have been doing the same repetitive work for decades. Move this button here. Fix the bug here.<p>Sure it's not as easy as it looks, but if the average guy can spit out an acceptable app/page in 60 seconds, most people won't even be able to tell the difference.
> Try writing a production quality 3D engine.<p>Actually I tried that and you are correct about this.<p>With Claude it took me hundreds of iterations and I'm still not happy.
Yes, for fun I tried to make a Mahjong solver and NONE of the SOTA frontier models could understand what they were looking at to determine tile occlusion/geometry to build the DAG.<p>I had to spoon feed it an algorithm - here's how you determine if a tile is on top of another one, etc. etc.<p>Anything that involves, well, "3d space" they don't seem to do very well on it at all (which makes sense, of course)
I'm curious. Is one able to actually land a job like this? Or at least some interesting opportunity? I'm fullstack dev "Enterprise" and it's not just boring but also kinda problematic in the future.
I tried it with embedded programming, and failed miserably.
I think that the universities have an opportunity here to be the places where manual code is written so that juniors can gain the coding expertise necessary to become effective with AI.<p>Many universities are not set up to take advantage of this opportunity because they lean heavily into theory and look down on coding, but some departments will make the pivot well. I hope that ours (Montana State) is one of them.
The argument for universities to be a place to learn to think critically and not learn specific skills is an even stronger value prop in an era where useful skills likely change rapidly.
There needs to be a realization of how important communication skills are to develop and possess. The act of disagreement has skill levels that do not trigger emotional responses, and cause cross understanding to occur. Learning how to convey understanding and gain understanding from others becomes more and more important in a landscape of rapid change. Which we are collectively terrible at, with most companies being miscommunication circuses, with all the stress that generates, needlessly.
The problem is that professors say "learn to think critically", then actually just want the students to learn to sound like them, and agree with them. Actual critical thought has been on the decline for some time.<p>This is especially true in the humanities and the social sciences. Where truth is hard to ascertain, and therefore it is easier to substitute political correctness for critical thought.
Some will probably dismiss your comment as partisan but it is very hard to (honestly) argue that this isn’t the case. “Think critically…” but only about the cliché punching bags of academia: capitalism, Western culture, American foreign policy, The Patriarchy, etc. I didn’t witness any college classes that encouraged us to think critically about socialism, or think critically about Islam, or think critically about non-Western countries’ foreign policy aims, or think critically about third-wave feminism’s impact on society. Instead, even questioning any of those sacred cows usually brands you as “far right” and professors sometimes even get fired for making others “feel unsafe” if they even try.<p>Note: you can still be an avowed and serious leftist and have my respect if you allow your ideas to be questioned, hold yourself to a standard of proof, and tolerate dissent. What I’m criticizing is the way especially in universities, people jump right to “You’re a Nazi/fascist and the only acceptable response is to shut you down and eject you from the community” if someone doesn’t embrace all the same political dogma as you.
so universities become trade schools? one concern is where does one get theoretical knowledge required for e.g. going to graduate school and then doing research to push the state of the art. that's one of the reasons universities emphasize theory: it's seen as the first step on the academic ladder, not as a trade school
I hear you, there certainly is a huge value in understanding the theory, including in computer science. I don’t mean to put words in someone else’s mouth, but I think perhaps in the future, writing code yourself, unaided by any AI, may be thought of as more of an exercise in theory than a practical skill in its own right. Kind of like doing math “by hand” is absolutely key for someone in college to study math, whereas after graduation in a job outside of academia, the same person would have every reason to avail themselves of software that automates the same thing.
The majority of undergrads are at university because a degree is the qualification they need to get a job. They are not there as the first step on a path to grad school and a Ph.D. and a lifetime of deep expertise, teaching, and learning in a field that they are passionate about.<p>So, yes. Universities are trade schools for the white collar world. Have been for quite a while. Never mind that most companies could spend 2-4 years running high school grads through an apprenticeship type of program and probably come out with better results.
Good question! Maybe a scheme like in France: we generally separate engineering schools, which teach a mix of theory and knowledge, for getting a white collar hob; and the masters, which teach mostly pure theoretical learning, which leads to an academical career.<p>Both are at the same levels at +5 years after high-school, but they leads to different career paths.
LLM are quite a good learning opportunity, mostly in classes where learning is sequential/needs building blocks, like mathematics, where if you miss a trimester, it's finished. Here it's like a free and immediately accessible private tutor. It would be great for computer sciences classes indeed.
They don't know it yet but universities have a role to curate training data, so we can have trustable models.
Agreed, but I can immediately see how painful it will be to monitor whether the work is actually done by the student.
At some point we will have to stop treating universities as tests to pass, and actually what they claim to be: places to learn. Ultimately it needs to be on the student to want to learn.<p>Obviously this would be easier if our entire school system before university wasn't seemingly designed too destroy every last ounce of a child's curiosity.
I wish you were not right. Every single positive experience with learning for me was only related to my school was incidental and more based on luck associated with the fact that I had access to at least two very curious minds that, unlike school, showed me actual use for, among other things, proportions. It feels highly unfair that your entire life can effectively boil down to whether you meet at least one person, who can make it relatable to you.
I think 'expertise' is a bit of a red herring when what is being discussed is experience.<p>I've always believed that coding and development is an art and something analogous is the experience of a visual arts student. There's a level of experience required when one applies to an art school. The student builds a portfolio of passion projects and demonstrates a passion and skill along with creativity and other beneficial traits. If they are accepted, they learn the deeper theory, techniques, and more that will aide them in their career. This increases their exposure and overall experience.<p>Experience for a young developer is going to start with passion projects and be supplemented and bolstered through education in a similar way. You can take shortcuts as an arts student or a developer but you really just end up hurting yourself.
AI is cheap right now. Let's re-ask this question when it's priced to recover profit and ROI.
What’s built with all that VC money is already built though; I don’t foresee a future a few years out where we don’t have access to an open-source model <i>roughly</i> as good as the current flagship models for the cost of the compute itself.
Variable costs - electricity etc. Current model is very resource intensive. You know when they build all those Olympic Venues and then once the Olympics is done the ongoing cost is too expensive and then they become derelict buildings.... like that...
It's like the rail industry analogy: we got a big bubble, but the rails are still there. Now with llm, we can just distill expensive one to create cheap open-source ones indeed
I have the theory (not tested, subjective) that current economy prefers buying capital (broadly here defined as machine/tools) than having to pay workers salaries, even if both have the same level of competitivity<p>Capital expenditures are easy to calculate, and it's easy to help raising money. As the current economical system is based on debts, it works quite well: if a company knows that productivity output will raise by 15% over the next year if they spend X dollars, it's easy to get investments (investments firms themselves are relying heavily on private credits, which more and more is coming from bank too). With a system based on debts, they care less about the amount spent, than the yield generated.<p>With investing in people, it's harder to predict.<p>Industry does it by buying machines, now knowledge-based companies might do it with GPUs or tokens.
I get the analogy of the calculator. The thing however, is that in college, we had dedicated time to learn how to not use it: classes without it, exams without it, etc.<p>In current job market and pressure, we doesn't have time anymore. You need to be constantly delivering the new jira ticket, and the time expected to perform a task now decreased, as it's expected of the workers that now they are "more productive with AI".
Nice to see that HN is coming to its senses and people are realizing the flaws and BS in AI / LLMs. We are past-peak Bitcoin / NFT on the curve and I can't wait for this wave to end and move to the next thing.
hiring top junior talent is more competitive than it's ever been!
I don't understand why so many people think that true expertise would become less valuable in the age of AI. How would a non-technical person, who doesn't know the difference between HTTP and HTTPS, have what it takes to build anything serious? I mean, how would you even know to ask the AI for everything that your system needs to be doing, without understanding the concepts?
> And yet, OpenAI, Anthropic, and many top companies continue to compete fiercely for junior talent.<p><i>Are</i> they? I would imagine they have the luxury to pick the brightest candidates, and set them to work on jobs for which their models don't have training data for, such as developing new models. Not writing React code.
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Don't post generated comments or AI-edited comments. HN is for conversation between humans. <a href="https://news.ycombinator.com/newsguidelines.html#generated">https://news.ycombinator.com/newsguidelines.html#generated</a>
The thing is that it's easy for us senior to spot bugs, because your brain develop a subconscious instinct on finding where bugs might be hiding.<p>It's however built on years and years of grinding through hands-on experience, that the junior will not have.
This is a valuable insight, Seniors engineers have often built a career out of building intuitions around when to trust people, not AI.
AI compresses the time to acquire expertise.<p>A high schooler can become an expert very quickly with AI, that used to require years and years of education and experience.<p>but the real expertise still will be to translate real world problems to technical solutions and iterate on design.
not really sure how you're imagining AI sidesteps education and experience
This is already studied, people do not retain knowledge when learning with AI. Learning with AI only creates the most mediocre of people, I've witnessed this myself over and over and over again over the last couple of years.<p>Read a book, write, think and you'll be fine. Use LLM and your brain is going to become completely reliant on its ability to access some billionaires thinking machine in order to read and write. You will be a second class citizen who has no differentiating skills. You will end up not being able to write anything on your own or solve problems independently without paying a billionaire, just like how nobody can navigate without Google Maps anymore.<p><a href="https://arxiv.org/abs/2506.08872" rel="nofollow">https://arxiv.org/abs/2506.08872</a>
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