I was a chef in Michelin-starred restaurants for 11 years. One of my favorite positions was washing dishes. The goal was always to keep the machine running on its 5-minute cycle. It was about getting the dishes into racks, rinsing them, and having them ready and waiting for the previous cycle to end—so you could push them into the machine immediately—then getting them dried and put away after the cycle, making sure the quality was there and no spot was missed. If the machine stopped, the goal was to get another batch into it, putting everything else on hold. Keeping the machine running was the only way to prevent dishes from piling up, which would end with the towers falling over and breaking plates. This work requires moving lightning fast with dexterity.

AI coding agents are analogous to the machine. My job is to get the prompts written, and to do quality control and housekeeping after it runs a cycle. Nonetheless, like all automation, humans are still needed... for now.

This reads like shilling/advertisement.. Coding AIs are struggling for anything remotely complex, make up crap and present it as research, write tests that are just "return true", and won't ever question a decision you make.

Those twenty engineers must not have produced much.

It's definitely scary in a way.

However I'm still finding a trend even in my org; better non-AI developers tend to be better at using AI to develop.

AI still forgets requirements.

I'm currently running an experiment where I try to get a design and then execute on an enterprise 'SAAS-replacement' application [0].

AI can spit forth a completely convincing looking overall project plan [1] that has gaps if anyone, even the AI itself, tries to execute on the plan; this is where a proper, experienced developer can step in at the right steps to help out.

IDK if that's the right way to venture into the brave new world, but I am at least doing my best to be at a forefront of how my org is using the tech.

[0] - I figured it was a good exercise for testing limits of both my skills prompting and the AI's capability. I do not expect success.

I think I've been using AI wrong. I can't understand testimonies like this. Most times I try to use AI for a task, it is a shitshow, and I have to rewrite everything anyway.

My experience with these tools is far and away no where close to this.

If you're really able to do the work of a 20 man org on your own, start a business.

That's kind of the point of the article, though.

Sure LLMs can churn out code, and they sort of work for developers who already understand code and design, but what happens when that junior dev with no hard experience builds their years of experience with LLMs?

Over time those who actually understand what the LLMs are doing and how to correct the output are replaced by developers who've never learned the hard lessons of writing code line by line. The ability to reason about code gets lost.

This points to the hard problem that the article highlights. The hard problem of software is actually knowing how to write it, which usually takes years, sometimes up to a decade of real experience.

Any idiot can churn out code that doesn't work. But working, effective software takes a lot of skill that LLMs will be stripping people of. Leaving a market there for people who have actually put the time in and understand software.

They do all those things you've mentioned more efficiently than most of us, but they fall woefully short as soon as novelty is required. Creativity is not in their repertoire. So if you're banging out the same type of thing over and over again, yes, they will make that work light and then scarce. But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.

I choose to look at it as an opportunity to spend more time on the interesting problems, and work at a higher level. We used to worry about pointers and memory allocation. Now we will worry less and less about how the code is written and more about the result it built.

I feel you, it's scary. But the possibilities we're presented with are incredible. I'm revisiting all these projects that I put aside because they were "too big" or "too much for a machine". It's quite exciting

Try have your engineers pick up some product work. Clients do NOT want to talk to bots.

There will be a need. Don't worry. Most people still haven't figured out how to properly read and interpret instructions. So they build things incorrectly - with or without AI

Seriously. The bar is that low. When people say "AI slop" I just chuckle because it's not "AI" it's everyone. That's the general state of the industry.

So all you have to do is stay engaged, ask questions, and understand the requirements. Know what it is you're building and you'll be fine.

Perfect economic substitution in coding doesn't happen for a long time. Meanwhile, AI appears as an amplifier to the human and vice versa. That the work will change is scary, but the change also opens up possibilities, many of them now hard to imagine.

Stop freaking out. Seriously. You're afraid of something completely ridiculous.

It is certainly more eloquent than you regarding software architecture (which was a scam all along, but conversation for another time). It will find SOME bugs better than you, that's a given.

Review code better than you? Seriously? What you're using and what you consider code review? Assume I could identify one change broke production and you reviewed the latest commit. I am pinging you and you better answer. Ok, Claude broke production, now what? Can you begin to understand the difference between you and the generative technology? When you hop on the call, you will explain to me with a great deal of details what you know about the system you built, and explain decision making and changes over time. You'll tell about what worked and what didn't. You will tell about the risks, behavior and expectations. About where the code runs, it's dependencies, users, usage patterns, load, CPU usage and memory footprint, you could probably tell what's happening without looking at logs but at metrics. With Claude I get: you're absolutely right! You asked about what it WAS, but I told you about what it WASN'T! MY BAD.

Knowledge requires a soul to experience and this is why you're paid.

>> From where I’m standing, it’s scary.

You are being fooled by randomness [1]

Not because the models are random, but because you are mistaking a massive combinatorial search over seen patterns for genuine reasoning. Taleb point was about confusing luck for skill. Dont confuse interpolation for understanding.

You can read a Rust book after years of Java, then go build software for an industry that did not exist when you started. Ask any LLM to write a driver for hardware that shipped last month, or model a regulatory framework that just passed... It will confidently hallucinate. You will figure it out. That is the difference between pattern matching and understanding.

[1] https://en.wikipedia.org/wiki/Fooled_by_Randomness

Yeah, it makes me wonder whether I should start learning to be a carpenter or something. Those who either support AI or thinks "it's all bullshit" cite a lack of evidence for humans truly being replaced in the engineering process, but that's just the thing; the unprecedented levels of uncertainty make it very difficult to invest one's self in the present, intellectually and emotionally. With the current state of things, I don't think it's silly to wonder "what's the point" if another 5 years of this trajectory is going to mean not getting hired as a software dev again unless you have a PhD and want to work for an AI company.

What doesn't help is that the current state of AI adoption is heavily top-down. What I mean is the buy-in is coming from the leadership class and the shareholder class, both of whom have the incentive to remove the necessary evil of human beings from their processes. Ironically, these classes are perhaps the least qualified to decide whether generative AI can replace swathes of their workforce without serious unforeseen consequences. To make matters worse, those consequences might be as distal as too many NEETs in the system such that no one can afford to buy their crap anymore; good luck getting anyone focused on making it to the next financial quarter to give a shit about that. And that's really all that matters at the end of the day; what leadership believes, whether or not they are in touch with reality.

Where the hell was all this fear when the push for open source everything got fully underway? When entire websites were being spawned and scaffolded with just a couple lines of code? Do we not remember all those impressive tech demos of developers doing massive complex thing with "just one line of code"? How did we not just write software for every kind of software problem that could exist by now?

How has free code, developed by humans, become more available than ever and yet somehow we have had to employ more and more developers? Why didn't we trend toward less developers?

It just doesn't make sense. AI is nothing but a snippet generator, a static analyzer, a linter, a compiler, an LSP, a google search, a copy paste from stackoverflow, all technologies we've had for a long time, all things developers used to have to go without at some point in history.

I don't have the answers.

Interesting concept, but as of now we don't apply this technologies as a new compounding layer. We are not using them after the fact we constructed the initial solution. We are not ingesting the code to compare against specs. We are not using them to curate and analyze current hand written tests(prompt: is this test any good? assistant: it is hot garbage, you are inferring that expected result equals your mocked result). We are not really at this phase yet. Not in general, not intelligently. But when the "safe and effective" crowd leave technology we will find good use cases for it, I am certain (unlike uml, VB and Delphi)

> One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline

It's another interesting attempt at normalising the bullshit output by LLMs, but NO. Even with the entshittified Boeing, the aviation industry safety and reliability records, are far far far above deterministic software (know for a lot of un-reliability itself), and deterministic, B2C software to LLMs in turn is what Boeing and Airbus software and hardware reliablity are for the B2C software...So you cannot even begin to apply aviation industry paradigms to the shit machines, please.

LLMs are Kraft Singles. Stuff that only kind of looks like cheese. Once you know it's in there, someone has to inspect, and sign-off on, the entire wheel for any credible semblance of safety.

The first part is surely true if you change it to "the hardEST part," (I'm a huge believer in "Programming as Theory Building"), but there are plenty of other hard or just downright tedious/expensive aspects of software development. I'm still not fully bought in on some of the AI stuff—I haven't had a chance to really apply an agentic flow to anything professional, I pretty much always get errors even when one-shotting, and who knows if even the productive stuff is big-picture economical—but I've already done some professional "mini projects" that just would not have gotten done without an AI. Simple example is I converted a C# UI to Java Swing in less than a day, few thousand lines of code, simple utility but important to my current project for <reasons>. Assuming tasks like these can be done economically over time, I don't see any reason why small and medium difficulty programming tasks can't be achieved efficiently with these tools.

Hardest part of programming is knowing wtf all the existing code does and why.

maybe not the MOST valuable part of prompting an LLM during a task, but one of them, is defining the exact problem in precise language. i dont just blindly turn to an LLM without understanding the problem first, but i do find Claude is better than a cardboard cutout of a dog

Aren't they also losing money on the marginal inference job?

Indeed, while DeepSeek 3.2 or GLM 4.7 are not Opus 4.5 quality, they are close enough that I could _get by_ because they're not that far off, and are about where I was with Sonnet 3.5 or Sonnet 4 a few months ago.

I'm not convinced DeepSeek is making money hosting these, but it's not that far off from it I suspect. They could triple their prices and still be cheaper than Anthropic is now.

Maybe we'll train the llms in our ways of using them, and the next generation of coding assistants will be another layer inbetween us and the code. You talk to the chief engineer llm who in turn talks to its cadre of claude code instances running in virtual tmux. \hj?

I think most of the progress is training by reinforcement learning on automated assessments of the code produced. So data is not really an issue.

That is a naive assumption. Or rather multiple naive assumptions: Developers mostly don’t move over to AI development, but integrate it into their workflow. Many of them will stay intellectually curious and thus focus their attention elsewhere; I’m not convinced they will just suddenly all stagnate.

Also, training data isn’t just crawled text from the internet anymore, but also sourced from interactions of millions of developers with coding agents, manually provided sample sessions, deliberately generated code, and more—there is a massive amount of money and research involved here, so that’s another bet I wouldn’t be willing to make.

But they're not just training off code and its use, but off a corpus general human knowledge in written form.

I mean, in general not only do they have all of the crappy PHP code in existence in their corpus but they also have Principia Mathematica, or probably The Art of Computer Programming. And it has become increasingly clear to me that the models have bridged the gap between "autocomplete based on code I've seen" to some sort of distillation of first order logic based on them just reading a lot of language... and some fuzzy attempt at reasoning that came out of it.

Plus the agentic tools driving them are increasingly ruthless at wringing out good results.

That said -- I think there is a natural cap on what they can get at as pure coding machines. They're pretty much there IMHO. The results are usually -- I get what I asked for, almost 100%, and it tends to "just do the right thing."

I think the next step is actually to actually make it scale and make it profitable but also...

fix the tools -- they're not what I want as an engineer. They try to take over, and they don't put me in control, and they create a very difficult review and maintenance problem. Not because they make bad code but because they make code that nobody feels responsible for.