Engineering management after the cost of code collapsed

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.

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.

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 communication, 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.

> 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.

Pangram reports this post was 100% AI generated.

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?

Code was never expensive.

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.

[flagged]

[dead]

[dead]