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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.
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.
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)
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...
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".
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.
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.
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'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.
- also a calculator does not offload your critical thinking ability
- fun fact, i have stopped using calculators since the last 3 months or so as an experiment and guess what? I can subtract and add six digit numbers effortlessly now
- also a calculator is not subject to bias which the AI frontier model companies can most certainly push in your direction if they wanted to.
- So when I see people comparing AI with the dawn of calculators, i really sit and wonder how such a comparison even makes sense
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 currently work on AI agents at Jane Street in NYC.
I'm pretty sure we as a society have gone through periods before where we think oh what if we just get cheap laymen to do it!?
but then in the end, if you're able to get an expert vs. a non expert, and you still profit from the work they do, do you really want to gamble?
its like, we look at Google reviews and credentials for a reason, we want trust
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.
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.