> The role of the human engineer […] has been to reduce risk in the face of ambiguity, constraints, and change. That responsibility not only endures in a world of Write-Only Code, if anything it expands.

> The next generation of software engineering excellence will be defined not by how well we review the code we ship, but by how well we design systems that remain correct, resilient, and accountable even when no human ever reads the code that runs in production.

As a mechanical engineer, I have learned how to design systems that meet your needs. Many tools are used in this process that you cannot audit by yourself. The industry has evolved to the point that there are many checks at every level, backed by standards, governing bodies, third parties, and so on. Trust is a major ingredient, but it is institutionalized. Our entire profession relies on the laws of physics and mathematics. In other words, we have a deterministic system where every step is understood and cast into trust in one way or another. The journey began with the Industrial Revolution and is never-ending; we are always learning and improving.

Given what I have learned and read about LLM-based technology, I don't think it's fit for the purpose you describe as a future goal. Technology breakthroughs will be evaluated retrospectively, and we are in the very early stages right now. Let's evaluate again in 20 years, but I doubt that "write-only code" without human understanding is the way forward for our civilization.

I am old. This immediately triggered "Perl!?" in me...

Joke aside: Programming languages and compilers are still being optimized until the assembly and execution match certain expectations. So prompts and whatever inputs to AI also will be optimized until some expectations are met. This includes looking at their output, obviously. So I think this is an overblown extrapolation like many we see these days.

I’ve started doing a quick background check on authors before I dive into their content. This piece starts with the assumption that the writer is closely involved in engineering, but a little research reveals they don't actually work in active software development.

I’ll pass on this.

p.s. I’m happy to read authors with opposing views. Issue is with people who make claims, without having recent direct experience.

> Write-Only Code is not a prediction about what we should want. It is a description of what happens when software production scales beyond human attention.

Have we considered whether it's even a good idea to produce software at scales beyond human attention? I'm beginning to suspect that, in terms of the net amount of economic effort and sheer quantity of software produced, we are already creating simply too much software relative to the amount of economic effort we put into hardware, construction, and human capital. Most human needs and desires can only be met through manipulation of atoms, and it seems as though we've largely refocused on those which can be met through manipulation of numbers and symbols - not because anyone really wants their life to revolve around them to the exclusion of everything else - but because they're the easiest markets to profitably scale for the least amount of capital input.

I helped updating ancient fortran to slightly less ancient C once. The company that depended on the ancient fortran no longer had any fortran programmers.

The resulting software upgrade was a nightmare that nearly killed that company. I shudder if someone needs to fix 20 year old AI write only code and I feel for the poor AI that has to do it. Because an AI 'intelligent' enough to do that deserves holidays and labor rights.

I remember when everything was "machine learning" as opposed to the current LLM stuff. Some of the machine learning techniques involve training and using models that are more or less opaque, and nobody looked at what was inside those models because you can't understand them anyway.

Once LLM generated code becomes large enough that it's infeasible to review, it will feel just like those machine learning models. But this time around, instead of trying to convince other people who were downstream of the machine learning output, we are trying to convince ourselves that "yes we don't fully understand it, but don't worry it's statistically correct most of the time".

We tried adoption of Claude Code at my last job as our PM was quite excited about its potential and I saw some horrors that were raised during code review (where I thankfully read the code):

- Using private undocumented APIs subject to breaking changes;

- Removing a call to produce a message to an external topic, with no internal references besides tests, declaring it as "redundant" (it was very much not redundant);

- Repeated duplication of formatting, calculation, and permission logic, causing inconsistencies and bugs.

They met whatever goal the agent created for itself, but they would've broken several features in ways that would've been difficult to test for, either today or in the future.

He is trying to use a different phrase “write-only code” to define exactly the same thing Karpathy defined last year as “vibe coding”.

For what it is worth, in my experience one of the most important skills one should strive to get much better at to be good at using coding agents is reading and understanding code.

This future may come someday, but it's not here yet, not even with the post-late-'25 models. It assumes that agents can write reasonable code reliably, which they're currently far from doing (even 95% isn't enough, and I don't think we're at 95%).

Anthropic's C compiler experiment showed that even in a situation where people give the agent every imaginable advantage (above and beyond what they can reasonably do in most projects), i.e provide not only a very precise specificaton but also thousands of tests, a reference implementation to use as an oracle, and have the model trained on the reference implementaition - years of "preparation" effort - and all the agent has to do is just code, it still fails on a task that's certainly not trivial but also by no means monumental.

A lot of writing about agentic coding seems to assume that today's agent have coding down whereas the experience of anyone using them across different kinds of software work as well as tests by the labs themselves show that this is not yet true.

We already have effectively write-only code. It's all the assembly language and eventually machine code that we produce from other languages.

The problem with "write-only code" as it relates to LLMs is that we don't have a formal definition of the input the the LLM, nor do people typically save the requirements, both implicit and explicit, that were given the LLM to generate the code. English language will never be a formal definition, of course, but that obviously doesn't prevent the creation of a formal definition from English nor reduce the value of the informal description.

This is very similar to the problem of documentation in software development. It is difficult to enumerate all the requirements, remember all the edge cases, recall why a certain thing was done in a certain way. So computer programs are almost never well documented.

If you knew that you currently have a bunch of junior developers, and next year you will replace all of them with senior developers who could rewrite everything the junior developers did, but taking only a day, how would that affect your strategy documenting the development work and customer/technical requirements? Because that's what you have with current LLMs and coding agents. They are currently the worst that they'll ever be.

So there are two compelling strategies:

1) business as usual, i.e. not documenting things rigorously, and planning to hack in whatever new features or bugfixes you need until that becomes unsustainable and you reboot the project.

2) trying to use the LLM to produce as much and as thorough documentation and tests as possible, such that you have a basis to rewrite the project from scratch. This won't be a cheap operation at first, (you will usually do strategy #1), but eventually the LLMs and the tooling around managing them will improve such that a large rewrite or rearchitecture costs <$10k and a weekend of passive time.

> In the Write-Only Code story, that same engineer becomes a systems designer, a constraint writer, and a trade-off manager.

This is also what I see my job to be shifting towards, increasingly fast in recent weeks. I wonder how long we will stay in this paradigm, I dont know.

> “AI writes the code” is already true inside many enterprise teams

I'm highly doubtful this is true. Adoption isn't even close to the level necessary for this to be the case.

> LLMs are clearly a massive productivity boost for software developers, and the value of humans manually translating intent into lines of code is rapidly depreciating.

This take is so divorced from reality it's hard to take any of this seriously. The evidence continues to show that LLMs for coding only make you feel more productive, while destroying productivity and eroding your ability to learn.

This article assumes that AI bubble will never break, and the prices will never go up. While the major LLM providers are located in USA and China, but mostly USA I can imagine the service becoming a hostage for some political interests, and being blocked for a country or more... This also can become a weapon as creation of invisibly malicious code might be advised by LLMs or even executed by LLMs running loose... So I'd say, copying some other author which I read here, that LLM is an exoskeleton and we're not going anywhere, just being strengthened by it and sped up, and constantly held accountable for the code we together with LLM generate

Write-only blog posts.

write-once read-never?

something that was not perl ;)

in ~2005 i lead a team to build horse-betting terminals for Singapore, and there server could only understand CORBA. So.. i modelled the needed protocol in python, which generated a set of specific python files - one per domain - which then generated the needed C folders-of-files. Like 500 lines of models -> 5000 lines 2nd level -> 50000 lines C at bottom. Never read that (once the pattern was established and working).

But - but - it was 1000% controllable and repeatable. Unlike current fancy "generators"..

There will be more of it where it does not matter. Maybe eventually with times. At the moment, in my experience, most systems rely on hyperlinear semantics. Especially scalable ones. Current llms cannot physically handle this at the moment. Maybe with biological or quantum (sic) computing.

But even then it is quite impressive.

Concretely in my use case, off of a manual base of code, having claude has the planner and code writer and GPT as the reviewer works very well. GPT is somehow better at minutiae and thinking in depth. But claude is a bit smarter and somehow has better coding style.

Before 4.5, GPT was just miles ahead.

Is that really true though? Are people really not saving design documentation to the code repository along with the code? And is it really too much to ask in a prompt to make the LLM document aggressively? Do the LLMs you use complain about being asked to write lots of comments in their code? Is it a token cost thing? It seems ridiculous not to just start with #2 but I might be spoiled by not knowing how much my token usage costs.

It will never work. Code is the documentation. The difference between Code -> IR -> binary is that the transition is deterministic. Doc -> Code is non-deterministic asf. And will never be. The only reason it works and we have senior, middle, junior engineers is the fact that assumptions on that path that senior engineers make are better than the ones junior engineers make. LLMs cannot make any assumptions though, as they have zero real world knowledge and thus zero common sense.

Re productivity: the METR study is seriously flawed overall, and:

1. if you disaggregate the highly aggregated data, it shows that the slowdown was highly dependent on task type, and tasks that required using documentation or novel tasks were possibly sped up, whereas ones the developers were very experienced with were slowed down, which actually matched the developers' own reports

2. developers were asked to estimate time beforehand per-task, but estimate whether they were sped up or slowed down only once, afterwards, so you're not really measuring the same thing

3. There were no rules about which AI to use, how to use it, or how much to use it, so it's hard to draw a clear conclusion

4. Most participants didn't have much experience with the AI tools they used (just prompting chatbots), and the one that did had a big productivity boost

5. It isn't an RCT.

See [1] for all.

The Anthropic study was using a task far too short to really measure productivity (30 mins), and furthermore the AI users were using chatbots, and spent the vast majority of their time manually retyping AI outputs, and if you ignore that time, AI users were 25% faster[2], so the study was not a good study to judge productivity, and the way people quote it is deeply misleading.

Re learning: the Anthropic study shows that how you use AI massively changes whether you learn and how well you learn; some of the best scoring subjects in that study were ones who had the AI do the work for them, but then explain it afterward[3].

[1]: https://www.fightforthehuman.com/are-developers-slowed-down-... [2]: https://www.seangoedecke.com/how-does-ai-impact-skill-format... [3]: https://www.anthropic.com/research/AI-assistance-coding-skil...

That’s the conclusion you get when you sit in the board of 20 companies where all the CEOs are telling you the same thing but you don’t understand that you are all just selling the same golden shovel to each other. Obviously this can also be backed by their own experiences too: 100% of code is written by AI, because last time they actually wrote code was in 2010.

Your comment is so divorced from reality...