Learning Programming in an Age of LLMs

> Do I think that AI enables people to develop faster than they can keep up?

Absolutely. That's the core of this person's email, and everyone else who asks similar questions. Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP. Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.

I don't think anyone has clear answers to all the questions brought up in this email. I think people can learn faster than they used to, because they can make connections between different areas faster than they used to. But it requires skill and discipline in how you learn, and how you work. You have to intentionally build your understanding as you build your projects.

Now ask yourself a question, what language do you want to maintain the programs in? Do you think natural language is going to be easier and more maintainable than formal logic?

The answer is no. So you need programmers, people who can read the formal description and adapt it to new requirements.

LLMs are amazing technology, but the truth is - natural language just kinda sucks. Therefore, you don't really need them (see also https://en.wikipedia.org/wiki/AI_effect ).

I think people love LLMs for the same reasons they love magicians. But just like the magician employs a hidden trick, LLM just runs some algorithm you don't see or understand.

So worrying about LLMs taking programming job is kinda like worrying that a magician will take a warehouse worker job, because they can levitate stuff. Meanwhile, we already have automated programmer - it's called a compiler.

China's entry into the WTO is really not a good evidentiary example for AI causing mass unemployment. Unemployment in the U.S. had already been increasing at the time, peaked soon after, decreased to well below the point it had been at China's entry, and only went up again during the Great Financial Crisis, which had nothing -- or at least very little -- to do with competition from China. That's not to say that jobs weren't lost, even en masse, but they were replaced, and U.S. unemployment has been near record lows in recent years. China's WTO entry is a supporting point, not a counterpoint, to the idea that jobs lost to AI will be replaced by new ones.

https://fred.stlouisfed.org/series/UNRATE

A while back Claude went down in the middle of a somewhat frantic initial deployment of a product to production at a company where a friend of mine works. And suddenly nobody was able to do anything. Because nobody had actually read the code and had no idea how it worked.

So essentially: much of their day to day work now depends entirely on the availability of a couple of frontier LLMs.

I read the document and sometimes use LLM as a quick search engine because I am tired of every query on google that use AI to summarize

The project goes slowly but seems the basics I grasped over the years help a lot

So perhaps it still worth to learn by hand with trial and fail

I agreed with the author that one must learn deep above the abstraction and I truely think programming still a thing even the agentic coding is getting powerful

So I would say it accentuates the gap between good developers and bad ones.

A sharp sense for logic and causality etc is what differentiates.

I feel like that about a lot of code i did myself; If you don't structure things very logically and really think about your comments; A few months or years will leave you with a hell of a learning curve to understand what you created.

AI actually helps with this, if you have the right prompt injections. I feel like the correct way to handle AI is to take a step back in abstracting problems.

I'm very use to collapsing subroutines to make things readable, maybe even further back from this though, the issue is words become too vague to be useful at these scales.

This is the most obvious thing so many people who claim "new jobs were created" miss.

Yeah sure, new jobs will be created. But not for 90% of those who lost them (and most likely not for the one who claims this). You either won't have time, or energy or else to learn it and compete etc. Sure a small % of people will transition, a massive amount will not.

[1]https://www.gatesnotes.com/home/home-page-topic/reader/a-tur...

None of us know how to farm, not even the chefs who cook for us at a restaurant or fast food joint, but we eat every day and nobody's going around making people feel guilty about not knowing how to till soil and sow seeds..

In programming and other creativity, most people's skills will [have to] change/evolve into managing, directing, dictating, knowing what you want, describing it, and focusing on the end product and iterating,

instead of wrestling with why the f is a string a pointer to a pointer to a character

just like we don't track the phases of the moon and seasonal rainfall before we can have a nice salad to eat.

But realistically speaking, choosing LLM programming ultimately means pouring out an enormous amount of code, and it's difficult to verify all of it. Common sense says that if you produce 10,000 lines in an hour, you can't read all of it, and even if you do read it, you'd have to rewrite it. The problem is that LLM code differs from human abstraction. Or more precisely, it lacks a programmer's habits, so it's hard for me to maintain.

Clearly, programming in the LLM era will be different. The problem is that I can't get a sense of what that way of doing things actually is.

I think that low-priority frontend work will probably be handled by LLMs, while only complex animation work will be handled by humans, and humans will end up working only on things like payment modules, which are hard to fix if something actually goes wrong.

LLMs are now better at optimization than most people.

Those sound like hobbies? Outside of apocalyptic/utopian scenarios that is.

My guess is it will probably take some time to incorporate LLM use into education. People who are graduating right now have a problem, being between two worlds. Those graduating in a few years might have chance to figure out what to do.

> I may have built a system that is above my own level of understanding

If I venture into an unknown area, I end up where the letter-writer ends up when he is visiting programming. Suppose I am curious about an advanced math topic, like Navier-Stokes. The LLM's answer to the news about the new advance last week is strewn with words I don't understand. Asking about anything produces another essay with more things, a loop that never closes. If it were my specialist area, I imagine I would eventually hit some point where the explanation connects to something familiar.

I think this is the wall people run into when they don't have the fundamentals. You eventually get to a point where the machine is asking you for decisions that you won't know the consequences of, and when you are trying to clarify, you end up in a massive rabbit hole. It's not that different from asking a real expert about their area, they will eventually ask you to clarify something that means something to them, but not to you.

I learned programming the slow way. I would run into phrases like "memory barrier" or "green thread" and find an article using the keywords, which led to more searches, which led to more...

There are also many false dawns. Early on, after some success writing some trading strategies, I thought I had it, in the sense that I would be able to write any program required. But it wasn't true, I would run into an iceberg from time to time. Huge areas of knowledge that I hadn't come across.

Obviously I'm not claiming I finally know everything, but LLMs have arrived at a very convenient time for me. For the things I build, there is rarely anything that I don't understand at a fundamental level. When it asks me something, it's an incidental question: what decision should we make? What are the superficial changes that are needed to fit the architecture to the desired product?

I am essentially using LLM as a very quick junior, who knows how the OS works well enough to compile things and analyze logs. These are things that would take a lot of attention in the old days because they can break on very small errors, but the direction was known from the start, and thus for me (having paid the learning cost already) it is just a matter of waiting for the AI to get the code into the desired state.

I have a somewhat usable experience. I was asked to build a trading system a few years ago, which would connect to certain exchanges and show an orderbook. This kind of thing is bread and butter, but writing it up at a new firm would still take weeks.

In recent engagements, I've simply declaratively told the LLM what properties I wanted to see in the solution, waited, and answered a few questions. Since the architecture is the same, there weren't a lot of real decisions. The time difference is immense.

Mostly humans are replaced at physical labor (although even then not entirely). Human computers and punchcard operators, switchboard and telegraph operators, typesetters/letterpress/linotype operators, draftsmen, photo retouchers, film developers and projectionists, pneumatic tube operators, record-pressing/mastering engineers, the horse-drawn transportation industry, handloom weavers and embroiderers, coopers, wheelwrights, blacksmiths, key cutters, bookkeepers, payroll clerks, proofreaders... This is just a tiny list.

Technology replaces the least efficient parts first, and humans remain to do things that're harder or more expensive to automate safely or reliably. A human who used to build something by hand, transitions to a human who operates a machine to build, then designs or prepares work for the machine, and finally maintains the machine that does it all. For many automated jobs, people now perform maintenance, operations, or design work, that could be done by machine, but we either require or prefer a human do it.

For those remaining jobs, you often still need special skills. But it's no longer a herculean task to perform the work, and the jobs are more specialized and less skilled in general. We still need those jobs, or those automated things simply wouldn't function. Someone has to build them, someone has to maintain them, and someone has to operate them, and each of those requires skill.

So human programmers, systems engineers, designers, architects, operators, etc, will all be very necessary over the next 50 years. You will still need to know the languages, compilers, networks, computers, etc work. You just won't be manually typesetting anymore, or manually weaving the digital cloth. Someone will need to get into the guts of the machine from time to time.

It's comical how these people claim first person: "I built". Look: having a LLM shit you some code is in no way different than paying some third world country dude on Upwork 5 bucks to build you "a Facebook clone" or whatever preposterous claim of grand software. In fact at this point it's cheaper to pay that third world country team than a LLM.

And yet before the advent of LLMs noone ordering a job on Upwork was delusional enough to claim "I built it". Although it's the same magical process, like the magic ring in fairy stories. You put the ring on your finger, rotate it and make a wish and the ring makes it appear. Well, for 5 bucks or something.

But nowadays every half witted retard with 50 bucks to spend goes to a LLM and has some "Facebook clone" spitted out and claims "I BUILT THIS!". You haven't built shit, and you know nothing!

Fortunately, reality strikes sooner or later but boy am I tired of Lord of The rings claims.