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.
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".
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.
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?
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.
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.
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.
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.
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.
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.
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).
"I'm tired of all this internet talk" in 1990s?
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
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.
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.
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.
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)
Both are at the same levels at +5 years after high-school, but they leads to different career paths.
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.
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.
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.
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.
2026-05-12
Tagged: llms
Does it make sense to hire junior engineers in the age of coding agents?
Junior engineers are expensive, both in salary and seniors engineers’ time. This cost was partially recouped through code contributions, but today, it’s more effective to directly maximize the output of your senior engineers. The hiring market reflects this trend: senior engineers have an easy time finding jobs, while fresh CS grads are having their worst years ever. And yet, OpenAI, Anthropic, and many top companies continue to compete fiercely for junior talent. What’s going on?
In this essay, I’ll explore the changing nature of expertise in the age of AI.
I think it helps to think about the impact of AI in terms of math, which had its AI moment half a century ago.
There used to be a job called “calculator”, which was a human who could do math calculations accurately and quickly. These people balanced books, calculated artillery firing angles based on distance and wind adjustments, calculated optimal hull shapes for ships and aircraft bodies, and so on. This job doesn’t exist anymore, and the last serious use of abaci and slide rules was in the 1970s, due to the invention of the scientific calculator. Calculators have only become more sophisticated over time, with today’s numerical modeling software running full scale physics and engineering simulations. (For the purpose of this essay, I’ll use “calculator” to mean everything from basic calculators to modeling software.)
Despite the existence of calculators, we teach and expect people to learn algebra, geometry, and calculus in high school. Continuing into the college level, we expect STEM majors to learn multivariable calculus, ODEs, PDEs, statistics, and linear algebra. Upon graduation, the vast majority of them use calculators every day and forget how to do all but the most basic mental math.
There are two basic explanations for this discrepancy:
As a formerly strong believer of the signaling hypothesis, I am now increasingly buying the skills hypothesis (let’s say ~50% attribution to each cause). It’s clear that senior engineers today are far more capable of using coding agents than their junior counterparts, and a large portion of this is due to having struggled through 5+ years of writing code manually.
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.
In between coding agent improvements and natural variation in learning aptitude, maybe 50% of new CS graduates will not be able to catch up, ever. Some senior engineers will also eventually fall behind the curve despite their head start.
To answer the opening question of the essay: only some junior engineers are worth hiring, specifically, the ones who are good enough to reach some useful threshold of “coding intuition” within ~2-3 years of having graduated. Since there are not very many of these graduates, a small number of elite companies compete fiercely for this talent.
The second-class tier of software consultants will continue growing, expanding the total size of the job market, but I don’t anticipate that their salaries will grow anywhere close to as rapidly as today’s senior engineers.
Even as the bar to get into software engineering rises, I still think everyone should learn some coding. Too often, I see people treat computers as appliances - capable of doing what they were built to do, but nothing more. If you don’t think of computers as scriptable or programmable, then you won’t ever think to ask AI to automate something for you! The same is also true for many other fields, too! Math, law, taxes, medicine, DIY home repair, etc… Abundant and cheap expertise is now available for just $20/month, if only you know how to ask.
I would say that the major unlocks are at:
If you’re already a software engineer, you might consider dabbling in data science, frontend, backend, security, and performance optimization/profiling – all of which are distinct skillsets.
Here’s a data science example of a “how + when + correctness”: A coworker was running some correlational analysis on a dataset and found it difficult to understand what was going on. I suggested he literally ask Claude to “make it prettier using NMF” – and all of a sudden, useful clusters started appearing.
(The expanded version of this prompt: NMF on the pairwise distance matrix gives k cluster centroids and cluster membership scores. Reordering the original distance matrix according to argmax(cluster score) highlights the clusters. The “how” here is knowing the keyword “NMF”; the “when” is “clustering on distance matrices”, and the “correctness” is knowing the preconditions for using it.)
Do your homework! One weirdly common and nihilistic take on AI is that you should stop trying so hard, and just use AI to speedrun your classes. I think this is probably the worst possible response. Doing the work is the best way to build mastery, and just like you weren’t allowed to use a calculator on your middle school math classes, you should hold off on using AI to do your classwork. The calculator advice sounded condescending when I was a kid, and this AI advice probably sounds the same – but I really do believe it’s for your own good. This advice continues to hold after you graduate, too. Don’t use AI until you’ve done it by hand at least once.