Engineering management after the cost of code collapsed

The assumption is that LLMs should be writing the code and human engineers reviewing and verifying the LLM output. And that this pushes the cost of producing down. And I fundamentally disagree with that.

Every time I ask LLMs to write code, even with Opus 4.8 (haven't tried it with Opus 5 yet), what I get ends up being totally rewritten. LLMs still aren't good at writing maintainable code. Can they write plausibly functional code? Yes. But it won't survive the long term. People using LLMs to write all their code are gambling on them eventually getting to a point where the LLMs can fix their own code. It's possible, but I wouldn't necessarily bet on it.

Where I have found immense value from LLMs is in code review. Repeated review by LLMs catches an amazing amount of potential issues. They really shine on security review, but are very effective with any kind of review.

The other thing that the "LLMs write code camp" misunderstands is that writing was never the bottleneck. Understanding was. And understanding the code is still the bottleneck. But understanding is truly gained during the writing loop. The understanding you gain from pure reading or code review is marginal compared to the understanding you gain while writing.

Most of the time previously spent writing was actually spent updating and deepening our understanding of the system under development. There's no replacement for that understanding in a world where LLMs are doing the writing.

But if you flip it: humans write, LLMs review, then you still get a major gain -- not in speed, but in quality. And you keep the understanding loop intact. I would propose that this might be the best way to deploy LLMs.

If programmer productivity was something we actively optimized for, we wouldn't have crammed programmers like sardines in warm and noisy open floor offices with 2000 ppm CO2 levels and then further constantly interrupt them with emails and slack pings and meetings all day long, Jira rigmarole wouldn't make up a significant portion of what they did, programmers would have instead mostly been thinking and programming.

We've always had the ability to 2X if not 10X the output of each and every one of those poor souls. You don't end with this sort of programming purgatory because it's a productivity optimum, it very clearly isn't, but because it's a billable hours optimum and/or an org chart clout optimum and/or because of Jevons paradox got hands even in business management and the IT department was allocated too many dollars.

There are a lot of (excruciatingly) long-form posts about what folks are pioneering but not a whole lot of follow up about what failed. Where are the short posts on the negative space? How did halving your staff work out? Flattening your org? All those dark factories, what haven't they produced? How about all the other things tried, failed, and unceremoniously scrapped?

We need to explore and communicate the negative space more efficiently. Don't repeat the same mistakes, and don't make me read 2653 words when 300 do it better.

Too many teams and organizations have business types, mostly PM's who seek to lord over their area of know how and see themselves as delegators and mini CEOs, actively avoid looping engineers in to validate themselves. Engineers need to take on PM roles, and the PM role needs to be 1:50+ eng or go.

1. Pursuing polish and quality beyond previous norms

2. Replacing $100/mo/seat SAAS with something coded by a junior costing $200/day to develop over months.

The cost of code approaches zero, but the cost of having accountability, and hosting remains the same, and so individuals need to only coordinate to the extent that those things remain finite resources. Management needs to stop insisting that their directs adopt each others vibe coded tooling.

Not everything has to be written as though it’s a middle manager’s idea of what makes for a good TED talk.

Does this guy have access to Gemini 4 already?

I'm guessing Gemma 4 was happy to be mistaken for Gemini and didn't catch this mistake.

LLMs have brought a different unlock, and for everything we're seeing become easier, it allows people learn to use the tools to take on solving problems that couldn't be approached before.

This is a long-standing problem related to operational excellence and politics. I expect this will improve with AI adoption and integration. You can't get an exec to create a decision record and commit it to git. Managers have incentive to sequester information.

Engineering already has the discipline (maybe) and abilities to solve the problem. Version control, change control, ADRs, logging, structured docs, etc... We can trace an inbound packet or call through the entire stack. Management can't/won't do anything remotely close. 1-to-1 emails, meeting minutes, stale Word docs is the standard for most.

Inserting LLMs as the interface, and/or plugging into existing interfaces like email, is going to change things. Finally it will be possible to capture more institutional knowledge, without trying to teach an old dog new tricks.

I work at a company where the biggest problems are not 'writing code', they are:

- Organising teams

- Designing the system

- Prioritisation of work

The fuckups that we make on a daily bases are not 'code errors' they are failures in THOSE three things. I'll go into detail if anyone cares.

Words have meaning, and I strongly feel that clarity here is helpful.

An engineering approach to software development would include rigorous testing and require individual signoff for every library and module. It is quite clear that an LLM would not be able to meet this requirement.

When casting about for ideas or prototypes, the throw away nature could allow their limited use.

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

AI is doing a good job on writting code these days! Nothing against it; I use it every day, but the context switching is costing us a lot!

As I understand it, the purpose of management is to match financial resources with material+human resources to perform feasible tasks. There is nothing here I see that can't be done by an experienced token generator. If anything, automating management seems easier than automating engineering.

As for leadership, it can be done by the investors.

Who wants merely plausible code?

Honestly, when people say AI code quality is bad, Linus himself has said it's now genuinely useful. AI is useful and writes better code than most people. Even in competitive coding, tourist lost to AI. And in the most logical field of all, mathematics, AI is churning out an enormous number of theorems.

Looking at all this, it's fair to say AI is at least at a PhD level of technical ability, and most people would admit they don't have PhD level skills. Of course, there are still many people who code better than AI. But at least when it comes to unfolding logical structures, AI has a higher chance of being more logical than humans. Within a given framework, AI constructs much more logical structures.

That's why I think the article's use of the word 'semantic' is right. It's humans who form the framework, and that's the semantic, while AI fills the empty spaces inside it. If you feed it a flawed framework, it fails.

And the fact that AI is more logical than humans is paradoxically a greater risk. Human developers can rely on tacit knowledge to make reasonable compromises even when the requirements, the framework, are sloppy. AI can't do that. If there's a logical gap in the framework humans design, AI will exploit that weakness and expand the state space into regions we can't cognitively grasp.

Programming is ultimately about how you occupy state space. The problem is that as the program grows, the cognitively inaccessible territory keeps expanding. So we distribute trust across reliable points, libraries, frameworks, and for my own code, once it exceeds tens of thousands of lines, I rely on tests and gates.

Honestly, the idea of understanding everything in a program is a purely academic claim. Once the program gets large, it's impossible. No one can know every external factor, test bug, or unexpected interaction.

The issue is that with LLMs, when the prompt input goes deeper into the semantic space, it also reaches into areas I don't understand, producing code at a depth that's untestable.

For example, I might be an expert in domain A but a beginner in domain B. If I inject expert level knowledge for domain A into the AI, the AI will try to match that level in domain B as well. That results in code I can't understand or modify, and eventually, I'm left with no choice but to replace all the code with AI generated code.

So I'm wondering what to do about this. Should I focus on gaining empirical experience in handling black boxes? Or should I stick with smaller, human written codebases?

But realistically, the current situation, where I can build bigger and touch more things, is more enjoyable to me. I think what I actually enjoyed wasn't programming itself, but the act of creating something.

I think perhaps the assumption that engineering managers should have any employees may be outdated.

I can imagine average and mediocre engineers equipped with tokens could create chaos and debt on a scale never before imaginable, so it’s easy to see how orgs who still have these employees around are struggling with the transition.

The reality is you need to get rid of them all, and replace them with the most experienced highest paid person you can find. In the near future that person will become obsolete too.