Coding after coders: The end of computer programming as we know it? (opens in new tab)

I've always hated solving puzzles with my deterministic toolbox, learning along the way and producing something of value at the end.

Glad that's finally over so I can focus on the soulful art of micromanaging chatbots with markdown instead.

But when I've used AI to generate new code for features I care about and will need to maintain it's never gotten it right. I can do it myself in less code and cleaner. It reminds me of code in the 2000s that you would get from your team in India - lots of unnecessary code copy-pasted from other projects/customers (I remember getting code for an Audi project that had method names related to McDonalds)

I think though that the day is coming where I can trust the code it produces and at that point I'll just by writing specs. It's not there yet though.

We are convincing a generation of morons that they can do something they plainly cannot. This will be a major problem, and soon.

Books didn't stop existing when the radio came out. Radio didn't stop existing when television was invented. If you go back in time a thousand years, people were complaining that an increase in literacy would damaging peoples' memorization skills.

People will still write code for consumption by other humans by hand. Some companies, though probably not most, will still prefer it. AI will change the industry - IS changing the industry - but things don't "end". They just look different, or are less popular.

There is enough for us to worry about and try to figure out how to respond to without the histrionics.

Also, on a related note, "Idiots are vibecoding bad stuff" is not the same as "engineers are using AI tools to do good work more quickly," and we should stop conflating it.

I believe both camps it frustratingly wrong. If you haven't yet given it a chance at doing something substantial, then at least _try_ it once. On the other side of the coin, that first experience where it does something 80% right is intoxicating, but AI doesn't reason and can't get it 100% right - it can't even multiply relatively small numbers.

The former camp is going to get left behind and won't be able to compete, the latter camp is one prompt away from a disaster.

Fast forward to 2024 when I saw Cursor (the IDE coding agent tool). I immediately felt like this was going to be the way for someone like me.

Back then, it was brutal. I'd fight with the models for 15 prompts just to get a website working without errors on localhost, let alone QA it. None of the plan modes or orchestration features existed. I had to hack around context engineering, memories, all that stuff. Things broke constantly. 10 failures for 1 success. But it was fun. To top it all off, most of the terminology sounded like science fiction, but it got better in time. I basically used AI itself to hack my way into understanding how things worked.

Fast forward again (only ~2 years later). The AI not only builds the app, it builds the website, the marketing, full documentation, GIFs, videos, content, screen recordings. It even hosts it online (literally controls the browser and configures everything). Letting the agent control the browser and the tooling around that is really, genuinely, just mad science fiction type magic stuff. It's unbelievable how often these models get something mostly right.

The reality though is that it still takes time. Time to understand what works well and what works better. Which agent is good for building apps, which one is good for frontend design, which one is good for research. Which tools are free, paid, credit-based, API-based. It all matters if you want to control costs and just get better outputs.

Do you use Gemini for a website skeleton? Claude for code? Grok for research? Gemini Deep Search? ChatGPT Search? Both? When do you use plan mode vs just prompting? Is GPT-5.x better here or Claude Opus? Or maybe Gemini actually is.

My point is: while anyone can start prompting an agent, it still takes a lot of trial and error to develop intuition about how to use them well. And even then everything you learn is probably outdated today because the space changes constantly.

I'm sure there are people using AI 100× better than I am. But it's still insane that someone with no coding background can build production-grade things that actually work.

The one-person company feels inevitable.

I'm curious how software engineers think about this today. Are you still writing most of your code manually?

"Can you believe that Dad actually used to have to go into an office and type code all day long, MAUALLY??! Line by line, with no advice from AI, he had to think all by himself!"

This sounds opposite to what the article said earlier: newbies aren’t able to get as much use out of these coding agents as the more experienced programmers do.

Are local models anywhere close to gaining enough capability and traction to do it in-house? Or are there good options for those who'd rather own the capability than rent it?

I can think of one successfully, off hand, although you could probably convince me there was more than one.

the principle phrase being "as we know it", since that implies a large scale change to how it works but it continues afterwards, altered.

That's also true for humans. If you sit down with an LLM and take the time to understand the problem you're trying to solve, it can perfectly guide you through it step by step. Even a non-technical person could build surprisingly solid software if, instead of immediately asking for new shiny features, they first ask questions, explore trade-offs, and get the model's opinion on design decisions..

LLMs are powerful tools in the hands of people who know they don't know everything. But in the hands of people who think they always know the best way, they can be much less useful (I'd say even dangerous)

Also I am not seeing how anyone is considering that what a programmer considers quality and what 'gets the job done' (as mentioned in the article) matters in any business. (Example with typesetting is original laser printers were only 300dpi but after a short period became 1200dpi 'good enough' for camera ready copy).

As far as the end of computer programming goes...

Step 1. Wow, I just vibe coded an application and it works! I'm going to write a blog about it and tell everyone how awesome AI is, much hype

Step 2. Vibe coded application faces inevitable problems, the perfect application is a fairytale after all. The only way to "fix" the application is spam tokens at the problem and pray.

Step 3. Author does not write a new blog post to report on this eventuality... probably because they feel embarrassed about how optimistic they were

Step 4. Perhaps author manages to fix application, awesome... then what about a year from now, author needs to update the application because a dependency has a security problem. The application is so needlessly complex that they don't even know when to begin.

Step 5. They boot up Claude Code, which their business is now 100% dependent on, but they're charging 10x the original cost per token. It's not like they have a contract, so user has to either eat the cost or give up

Step 6. User tries local model on their 1080 ti but they can barely run entry-level models

Step 7. Woops

Personally I think it's impossible to convince these people, the results will speak for themselves eventually.

Where's the references to the decline in quality and embarrassing outages for Amazon, Microsoft, etc?

As a result a lot of the responses here are either quibbles or cope disguised as personal anecdotes. I'm pretty worried about the impact of the LLMs too, but if you're not getting use out of them while coding, I really do think the problem is you.

Since people always want examples, I'll link to a PR in my current hobby project, which Claude code helped me complete in days instead of weeks. https://github.com/igor47/csheet/pull/68 Though this PR creates a bunch of tables, routes, services -- it's not just greenfield CRUD work. We're figuring out how to model a complicated domain (the rules to DnD 5e, including the 2014 and the 2024 revisions of those rules), integrating with existing code, thinking through complex integrations including with LLMs at run time. Claude is writing almost all the code, I'm just steering

> “If you say, This is a national security imperative, you need to write this test, there is a sense of just raising the stakes,” Ebert said.

I'm not sure why programmers and science writers are still attributing emotions to this and why it works. Behind the LLM is a layer that attributes attention to various parts of the context. There are words in the English language that command greater attention. There is no emotion or internal motivation on the part of the LLM. If you use charged words you get charged attention. Quite literally "attention is all you need" to describe why appealing to "emotion" works. It's a first order approximation for attention.

So tools (like AI) can move us closer to the 100% efficiency (or indeed further away if they are bad tools!) but there will always be the residual human engagement required - but perhaps moved to different activities (e.g. reviewing instead of writing).

Probably very effective teams/individuals were already close to 100% efficiency, so AI won't make much difference to them.

This doesn’t really make sense to me. GenAI ostensibly removes the drudgery from other creative endeavors too. You don’t need to make every painstaking brushstroke anymore; you can get to your intended final product faster than ever. I think a common misunderstanding is that the drudgery is really inseparable from the soulful part.

Also, I think GenAI in coding actually has the exact same failure modes as GenAI in painting, music, art, writing, etc. The output lacks depth, it lacks context, and it lacks an understanding of its own purpose. For most people, it’s much easier to intuitively see those shortcomings of GenAI manifest in traditional creative mediums, just because they come more naturally to us. For coding, I suspect the same shortcomings apply, they just aren’t as clear.

I mean, at the end of the day if writing code is just to get something that works, then sure, let’s blitz away with LLMs and not bother to understand what we’re doing or why we do it anymore. Maybe I’m naive in thinking that coding has creative value that we’re now throwing away, possibly forever.

Most folks I hang out with are infatuated with turning tokens into code. They are generally very senior 15+ years of experience.

Most folks I hang out with experience existential dread for juniors and those coming up in the field who won't necessarily have the battle scars to orchestrate systems that will work in the will world.

Was talking with one fellow yesterday (at an AI meetup) who says he has 6 folks under him, but that he could now run the team with just two of them and the others are basically a time suck.

The article could have been written from a very different perspective. Instead, the "journalists" likely interviewed a few insiders from Big Tech and generalized. They don't get it. They never will.

Before the advent of ChatGPT, maybe 2 in 100 people could code. I was actually hoping AI would increase programming literacy but it didn't, it became even more rare. Many journalists could have come at it from this perspective, but instead painted doom and gloom for coders and computer programming.

The New York Times should look in the mirror. With the advent of the iPad, most experts agreed that they would go out of business because a majority of their revenue came from print media. Look what happened.

Understand this, most professional software and IT engineers hate coding. It was a flex to say you no longer code professionally before ChatGPT. It's still a flex now. But it's corrupt journalism when there is a clear conflict of interest because the NYT is suing the hell out of AI companies.

CI is for preventing regressions. Agents.md is for avoiding wasted CI cycles.

If your base prompt informs the model they are a human software developer in a Severed situation, it gets even closer.

COBOL is dead. Java is dead. Programming is dead. AI is dead (yes, some people are already claiming this: https://hexa.club/@phooky/116087924952627103)

I must be the kid from The Sixth Sense because I keep seeing all these allegedly dead guys around me.

This excerpt:

>A.I. had become so good at writing code that Ebert, initially cautious, began letting it do more and more. Now Claude Code does the bulk of it.

is a little overstated. I think the brownfield section has things exactly backwards. Claude Code benefits enormously from large, established codebases, and it’s basically free riding on the years of human work that went into those codebases. I prodded Claude to add SNFG depictions to the molecular modeling program I work on. It couldn’t have come up with the whole program on its own and if I tried it would produce a different, maybe worse architecture than our atomic library, and then its design choices for molecules might constrain its ability to solve the problem as elegantly as it did. Even then, it needed a coworker to tell me that it had used the incorrect data structure and needed to switch to something that could, when selected, stand in for the atoms it represented.

Also this:

>But A.I.-generated code? If it passes its tests and works, it’s worth as much as what humans get paid $200,000 or more a year to compose.

Isn’t really true. It’s the free-riding problem again. The thing about an ESP is that the LLM has the advantage of either a blank canvas (if you’re using one to vibe code a startup), or at least the fact that several possibilities converge on one output, but, genuinely, not all of those realities include good coding architecture. Models can make mistakes, and without a human in the loop those mistakes can render a codebase unmaintainable. It’s a balance. That’s why I don’t let Claude stamp himself to my commits even if he assisted or even did all the work. Who cares if Claude wrote it? I’m the one taking responsibility for it. The article presents Greenfield as good for a startup, and it might be, but only for the early, fast, funding rounds, when you have to get an MVP out right now. That’s an unstable foundation they will have to go back and fix for regulatory or maintenance reasons, and I think that’s the better understanding of the situation than framing Aayush’s experience as a user error.

Even so, “weirdly jazzed about their new powers” is an understatement. Every team including ours has decades of programmer-years of tasks in the backlog, what’s not to love about something you can set to pet peeves for free and then see if the reality matches the ideal? git reset --hard if you don't like what it does, and if you do all the better. The Cuisy thing with the script for the printer is a perfect application of LLMs, a one-off that doesn’t have to be maintained.

Also, the whole framing is weirdly self limiting. The architectural taste that LLMs are, again, free riding off of, is hard won by doing the work more senior engineers are giving to LLMs instead of juniors. We’re setting ourselves up for a serious coordinated action problem as a profession. The article gestures at this a couple times

The thing about threatening LLMs is pretty funny too but something in me wants to fall back to Kant's position that what you do to anything you do to yourself.

'Salva opened up his code editor — essentially a word processor for writing code — to show me what it’s like to work alongside Gemini, Google’s L.L.M. '

And what's up with L.L.M, A.I., C.L.I. :)

I'm an engineer (not only software) by heart, but after seeing what Opus 4.6 based agents are capable of and especially the rate of improvement, i think the direction is clear.

Why? Because when the bubble burst and the companies (including mine) can not pay the 400% price increase and go bankrupt, then I still have keep my brain active and still can do stuff without or less tokens.

I used Claude just the other day to write unit test coverage for a tricky system that handles resolving updates into a consistent view of the world and handles record resurrection/deletion. It wrote great test coverage because it parsed my headerdoc and code comments that went into great detail about the expected behavior. The hard part of that implementation was the prose I wrote and the thinking required to come up with it. The actual lines of code were already a small part of the problem space. So yeah Claude saved me a day or two of monotonously writing up test cases. That's great.

Of course Claude also spat out some absolute garbage code using reflection to poke at internal properties because the access level didn't allow the test to poke at the things it wanted to poke at, along with some methods that were calling themselves in infinite recursion. Oh and a bunch of lines that didn't even compile.

The thing is about those errors: most of them were a fundamental inability to reason. They were technically correct in a sense. I can see how a model that learned from other code written by humans would learn those patterns and apply them. In some contexts they would be best-practice or even required. But the model can't reason. It has no executive function.

I think that is part of what makes these models both amazingly capable and incredibly stupid at the same time.

Citation needed. Are most developers "rarely" writing code?