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.
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?
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).
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.
I do love the appeal to bread making. It's a wonderful example. If people haven't made french bread by hand, it's a humbling exercise.
Recipes of course have evolved too. Old roman recipes were merely a list of ingredients. Water, flour, salt, yeast.
Written steps came after, then photos, videos, gradually replacing hands on training / kneading.
There are now recipes as code running sour dough assembly lines. Certainly capturing much more detail in technique than even a well made video. But I bet there is still human QA at the end judging "is this bread what folks expect?"
I suspect that in order of complexity you'll get "can I attempt to follow each step", "can I follow the intention of each step and understand if I've failed to meet it" (mitigated by using more specific and detailed steps) "can I follow the intention of the recipe itself - can I add or modify steps that are missing to give the ideal form of sour dough" (maybe you show a machine what good bread looks like, moisture content, crunch?) Those mostly overlap with the 3 you've called out. But I'd add "why would anyone make bread?" Why the heck are we still mixing flour and water together. Why does this recipe exist? Great crusty sourdough requires them all.
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.
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 my opinion, the bread example doesn't really work that well because it bridges into the physical domain which most cognitive systems don't have access to. That said, for grounding context and therefore creating truth having a version of a world model is very important (See Yan LeCun's work). My experience is that given the right world to operate in, an agent can indeed find flaws in recipes and fix it even though the agent has not been prompted explicitly to do it. This world, as far as I understand it now, is a combination of sequential (at which step am I in a process), conversational (what was talked about alread/ what had I done already), and context memory (what is the frame or reference/plane of existence).
In the section "Everyone should learn some coding":
> I would say that the major unlocks are at:
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).
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.
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?
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."
As a serial successful field-hopper, I agree that I'm not the right person to be making these estimates.
But the external view is that college courses roughly expect you to do what I'm claiming, in roughly the time investment that I'm claiming -- and undergrads are typically in 4+ classes at a time. So is it that the whole educational system is delusional? (I fully acknowledge it might be so!)
The reality is that most people write horrible code.
The programming knowledge of a university student that just completed their intro programming course is abysmal. The programming knowledge of a university student that just completed the 4 year degree, but didn't spend hundreds to thousands of additional hours working on programming outside of that is abysmal. College classes don't expect you to learn programming to any real extent, they expect you to learn computer science. And the rigor of most schools is even questionable there.
I've been programming for a long time and I'm still not sure if what I write is very good. I know it's better than a lot of what I see, but shiny trash is still trash. I've seen astoundingly bad production issues (bugs are sometimes an understatement) produced by senior engineers. Those people have years of experience and I wouldn't trust them to properly review my code, let alone LLM code.
I do think people should try and learn the basics of any and everything, and I mean everything literally. But if you know the basics of biology are you now able to credibly review ChatGPT's medical advice?
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.
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.
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.
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.
I should caveat my comment that this doesn't apply to pacemaker software and higher end software engineering
Welcome to the club, but remember: you break it, you own it. You will be expected to take part in incident response and explain why it broke and how to remediate it in the post-mortem.
Believe me, I know. I am completely an entirely responsible for a service that receives around 500 requests a second. AI assisted coding has really helped me get through a backlog of things I've wanted to do but never had the time because I was one man.
Hacker News is like an enclave of soldiers who haven't heard that the war is over. Those who don't make an effort to rejoin society will be left behind after "AI" companies bankrupt all of their customers and "AI" investors move on to the next fad.
The crowd who came for the love of computers either left or switched to be the ones who were fed up with building things out of thin air, and are fine with swiping their credit cards to have someone else do the work for them while they're micromanaging that someone else (AI).
... and so was the Big Data! (Remember 3xVs of it? I do – but most people who made a serial promos out of it don't. AI will have its winter. Again. Another one. Soon.)
And yet, some of the most important questions that come up with technology is which one should I use, how much does it cost, what are the best practices, what serious risks should I look out for, and that sort of thing. With generative LLms we are being told use the latest, or maybe the full super intelligence expected in a few months, pay whatever, best practices are just emerging but focus on not using it too much and avoiding overload, and the risks are seriously deteriorated skills and focus which don't matter anymore anyway. And while these critical questions lack compelling answers people are seeing kids who can't answer basic questions or think things through, data centers causing massive complications, and theft of intellectual property on a grand scale. Instead we go over and over about what you can do with this technology and what human employees are good for anyway.
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.
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.
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.”
The poster to whom I was replying was complaining about how the front page is dominated by a single topic. I don't see why this point is relevant to that.
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.
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:
Sir, is this really the right direction?
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.
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.
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.
I had to spoon feed it an algorithm - here's how you determine if a tile is on top of another one, etc. etc.
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.
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.
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.
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.
Srsly. Welcome to my day job. I can see that the LLMs' center of training is so far off from where I'd need it, I can accelerate auxillary stuff but prompt never beats the weights and it constantly pulls back into it's middle...
I don't see this discussed often enough but high school and universities need to adapt FAST, like yesterday, to the current reality.
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?
> More in-class study and "hands-on" work with proctored in-person exams.
If you move to in-class, hands-on work you don't need exams at all as you will see their performance develop in class as well. Exams are for things you can't see them actually use first hand.
I studied computer science and mathematics, not software engineering
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"
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".
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.
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.
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.
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.
"I suspect the biggest source of moral taboos will turn out to be power struggles in which one side only barely has the upper hand. That's where you'll find a group powerful enough to enforce taboos, but weak enough to need them."
Those on the left have been trying to advance their power through creating new taboos that cement their position. But they've misjudged. As a result Trump, by simply speaking to the resulting pain points, has been put on a potential course towards dictator. (Note, he doesn't have to do anything about it - just name the pain.) Will he succeed? Probably not, but he's certainly making a try of it.
Very few on the left are willing to engage in the self-reflection to realize how they have contributed to Trump's rise. It should be obvious - if Trump is an existential threat then you should reach out to people you dislike, who dislike Trump more than you. But no. We've been doubling down on ideological purity. And the horrible result is in the (currently partially demolished) White House.
100% agree. But this seems to be standard now for most political dialog. Either one group will call you a racist or the other group will call you communist. Or maybe antisemitic.
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
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.
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.
First off I don’t like the tone most people use when they say “trade school”. Most cs students go to a job out of school. Of the roughly 10% who go on to grad school, 10% will pursue a PhD.
So 99/100 students in undergrad will not be pursuing higher computer science. We should acknowledge that and the new circumstances where writing code by hand is harder to do in corporations who use AI.
Universities can provide a place to do so.
I also happen to think that writing a lot of code is an excellent way to prepare yourself for computer science theory.
“education” is not the same as “job training”. there’s more to education than learning skills you can apply at your job. it’s learning how to think critically, study literature, problem solve, collaborate with others, etc. etc. skills that I believe all humans could benefit from, irrespective of their job. yes, trade schools are more immediately valuable in the strict capitalist sense, but I wish we lived in a world where everyone could spare a few years to grow as a person, not immediately start optimizing for salary. alas, could be wishful thinking
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.
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.
Both are at the same levels at +5 years after high-school, but they leads to different career paths.
Residency programs, as in medicine. After completing your degree, you spend a few years working as a junior under formal supervision. The incentives are kind of bad but solvable.
If the underlying issue is that you need more skills to be worth hiring, it cannot be solved by shuffling the curriculum. The actual answer is more education and more training.
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.
I would have killed for access to an LLM during school. Not to do my homework (though that too, homework is an antipattern imo) but to fill my gaps at my own pace and level of patience. Just endlessly pestering the AI "ok, but why?" until I grokked it.
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.
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.
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.
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.
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 roughly 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
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.
With investing in people, it's harder to predict.
Industry does it by buying machines, now knowledge-based companies might do it with GPUs or tokens.
> 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.
I am struggling to interpret what they mean by "gap". Gap between what two things?
The gap between juniors and seniors?
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 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.
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".
I'm doing a degree (part-time) in Mathematics and Physics to get away from AI crap and challenge myself now in my early 30's. The client I work for now has deemed us "AI first"... Even though the back-end work and optimizations that I do are not that impacted I'm just fed up with colleagues that are not programmers (or are programmers that are not on my level) to tell me how to do stuff with Claude or GPT. It's all so tiresome.
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.
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.
Are 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.
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.
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.
This effect is certainly real, likely even the default, but it's not inevitable.
I had great results using AI to help my son study for his final exams in chemistry and math. We went through the review guide the teacher provided, he did the problems, I checked them, and I had Claude generate additional targeted problems as permutations of the ones he had difficulty with. He worked them and got more practice in exactly the areas he was weak.
I could have set these problems up myself, but it was much smoother to have Claude set them up and I validate them. It let him get a lot more reps in, in exactly the areas he _needed_ more practice, than he would have otherwise.
The key is that to learn you have to do the work. AI can help you figure out where you're weak and provide the wherewithal to get additional practice, and there's huge value there. But you have to lift the mental weights yourself.
I can (and do!) pay the power company a pittance to run a surprisingly strong local model on a little box next to my keyboard, that does a fine job. Maybe not as fine as the billionaires thinking machine, but good enough and often better. Given that fact, I consider reliance on LLMs as much of an "issue" as reliance on a computer.
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?
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).
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.
Recipes of course have evolved too. Old roman recipes were merely a list of ingredients. Water, flour, salt, yeast.
Written steps came after, then photos, videos, gradually replacing hands on training / kneading.
There are now recipes as code running sour dough assembly lines. Certainly capturing much more detail in technique than even a well made video. But I bet there is still human QA at the end judging "is this bread what folks expect?"
I suspect that in order of complexity you'll get "can I attempt to follow each step", "can I follow the intention of each step and understand if I've failed to meet it" (mitigated by using more specific and detailed steps) "can I follow the intention of the recipe itself - can I add or modify steps that are missing to give the ideal form of sour dough" (maybe you show a machine what good bread looks like, moisture content, crunch?) Those mostly overlap with the 3 you've called out. But I'd add "why would anyone make bread?" Why the heck are we still mixing flour and water together. Why does this recipe exist? Great crusty sourdough requires them all.
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).
Self-correcting agents are already here: https://jdsemrau.substack.com/p/hyperagents-and-self-correct...
> I would say that the major unlocks are at:
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.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?
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."
"How much could a bunch of bananas cost. $30?"
This strikes me as someone who has lost touch with how much time and effort that building real expertise takes.
As a serial successful field-hopper, I agree that I'm not the right person to be making these estimates.
But the external view is that college courses roughly expect you to do what I'm claiming, in roughly the time investment that I'm claiming -- and undergrads are typically in 4+ classes at a time. So is it that the whole educational system is delusional? (I fully acknowledge it might be so!)
The programming knowledge of a university student that just completed their intro programming course is abysmal. The programming knowledge of a university student that just completed the 4 year degree, but didn't spend hundreds to thousands of additional hours working on programming outside of that is abysmal. College classes don't expect you to learn programming to any real extent, they expect you to learn computer science. And the rigor of most schools is even questionable there.
I've been programming for a long time and I'm still not sure if what I write is very good. I know it's better than a lot of what I see, but shiny trash is still trash. I've seen astoundingly bad production issues (bugs are sometimes an understatement) produced by senior engineers. Those people have years of experience and I wouldn't trust them to properly review my code, let alone LLM code.
I do think people should try and learn the basics of any and everything, and I mean everything literally. But if you know the basics of biology are you now able to credibly review ChatGPT's medical advice?
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
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.
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.
I should caveat my comment that this doesn't apply to pacemaker software and higher end software engineering
"I'm tired of all this internet talk" in 1990s?
People say AI is the new internet. I say AI is the new tobacco.
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.
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.”
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:
Sir, is this really the right direction?
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.
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.
Actually I tried that and you are correct about this.
With Claude it took me hundreds of iterations and I'm still not happy.
I had to spoon feed it an algorithm - here's how you determine if a tile is on top of another one, etc. etc.
Anything that involves, well, "3d space" they don't seem to do very well on it at all (which makes sense, of course)
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.
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.
Srsly. Welcome to my day job. I can see that the LLMs' center of training is so far off from where I'd need it, I can accelerate auxillary stuff but prompt never beats the weights and it constantly pulls back into it's middle...
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?
If you move to in-class, hands-on work you don't need exams at all as you will see their performance develop in class as well. Exams are for things you can't see them actually use first hand.
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"
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.
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.
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.
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.
The essay https://www.paulgraham.com/say.html captures the problem perfectly. I think that Paul Graham was completely correct when he said:
"I suspect the biggest source of moral taboos will turn out to be power struggles in which one side only barely has the upper hand. That's where you'll find a group powerful enough to enforce taboos, but weak enough to need them."
Those on the left have been trying to advance their power through creating new taboos that cement their position. But they've misjudged. As a result Trump, by simply speaking to the resulting pain points, has been put on a potential course towards dictator. (Note, he doesn't have to do anything about it - just name the pain.) Will he succeed? Probably not, but he's certainly making a try of it.
Very few on the left are willing to engage in the self-reflection to realize how they have contributed to Trump's rise. It should be obvious - if Trump is an existential threat then you should reach out to people you dislike, who dislike Trump more than you. But no. We've been doubling down on ideological purity. And the horrible result is in the (currently partially demolished) White House.
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.
So 99/100 students in undergrad will not be pursuing higher computer science. We should acknowledge that and the new circumstances where writing code by hand is harder to do in corporations who use AI.
Universities can provide a place to do so.
I also happen to think that writing a lot of code is an excellent way to prepare yourself for computer science theory.
Both are at the same levels at +5 years after high-school, but they leads to different career paths.
If the underlying issue is that you need more skills to be worth hiring, it cannot be solved by shuffling the curriculum. The actual answer is more education and more training.
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'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.
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.
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.
With investing in people, it's harder to predict.
Industry does it by buying machines, now knowledge-based companies might do it with GPUs or tokens.
I am struggling to interpret what they mean by "gap". Gap between what two things?
The gap between juniors and seniors?
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?
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".
> I currently work on AI agents at Jane Street in NYC.
Are 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.
It's however built on years and years of grinding through hands-on experience, that the junior will not have.
A high schooler can become an expert very quickly with AI, that used to require years and years of education and experience.
but the real expertise still will be to translate real world problems to technical solutions and iterate on design.
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.
https://arxiv.org/abs/2506.08872
I had great results using AI to help my son study for his final exams in chemistry and math. We went through the review guide the teacher provided, he did the problems, I checked them, and I had Claude generate additional targeted problems as permutations of the ones he had difficulty with. He worked them and got more practice in exactly the areas he was weak.
I could have set these problems up myself, but it was much smoother to have Claude set them up and I validate them. It let him get a lot more reps in, in exactly the areas he _needed_ more practice, than he would have otherwise.
The key is that to learn you have to do the work. AI can help you figure out where you're weak and provide the wherewithal to get additional practice, and there's huge value there. But you have to lift the mental weights yourself.