alt Hacker NewsEngineering management after the cost of code collapsed
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Code was very rarely the bottleneck in the first place.
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.
What is the right, best software organization in the current era of AI coding? This question is critical and wholly unanswered in comprehensive research along the same axis as Accelerate (2018, Forsgren, Humble, Kim).
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.
The cost of code actually increased; code debt is being accumulated faster than we can clean it up.
> Sorting requires a model of how your org actually behaves: trust relationships, hallway knowledge, the consequences of past decisions. Almost none of this is written down.
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.
Author works at parity.io, which sells AI website builders:
https://www.parity.io/blog/playground-dot-what-190-people-bu...
Every single time. Remember 2022/2023 when we discussed actual technology?
"Shield the team from the business." The assumption underneath this one is that attention is finite and context switching is expensive. That assumption is intact. What changed is the cost of starving the team of context. Engineers prompting AI tools without business context just produce fluent, plausible, wrong work, at scale."
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.
In my experience, management is mostly pissing away the gains made by AI by either:
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.
I don't think it changes much for good managers. It should always be able setting people and processes up so the team can land durable measurable impact. The managers that thought the job of software engineers was to write code were bad managers. PRs or LoC were never good metrics.
AI reduces the cost of writing code, but it makes adding things that nobody uses even cheaper. The bottleneck then becomes deciding what deserves to exist—and having the discipline to remove the rest. So this will imply more time spent in code reviews that lead to more iterations in PR's.
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!
> Gemini 4 helped with the editing.
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.
My take, as a non-coder (well, not software engineering, I write 'code' but it's infra, and utilities in go/bash/pythong)...
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.
I’m struggling to imagine a project that would require more than one talented engineer and a bucket of tokens anymore.
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.
Can a llm predict the price of a change to a codebase in tokens and predict the origin of the price, aka cam it see good and bad architecture?
Gemini 4 eh????
Coding used to be an expensive task. Seeing how quickly repositories have grown with the rise of AI coding, it's clear how much people wanted to build things but were thirsting for the means to do so. Even languages like R, which were mostly used by graduate students and experts, have seen a massive increase in usage since vibe coding became popular.
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.
The worse problem is blog posts after the cost of writing collapsed.
Not everything has to be written as though it’s a middle manager’s idea of what makes for a good TED talk.
Software development has always evolved. Sometimes slowly, sometimes quicker.
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.
Engineers do not need management. Investors do.
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.
> The cost of producing plausible code has collapsed
Who wants merely plausible code?
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I think most of this is correct, in spite of potentially being built on a bad assumption.
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.