Code was very rarely the bottleneck in the first place.
If programmer productivity was something we actively optimized for, we wouldn't have cram 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.
> Code was very rarely the bottleneck in the first place.
I disagree. Kinda
What AI has made much simpler is that you don't have to waste time checking docs and have the best autocomplete system by a long shot - this was a bottleneck unless you were doing Java or some other language with "perfect" AC
What AI made "kinda easier": solving for usual problems. The stuff you would search Stack Overflow, or think a couple of minutes for an optimized solution - not a bottleneck but not 100% smooth neither
You still have to test and validate your code. AI made this easier-ish but this is still where I see manual work being needed (even if you are automating tests - you still have to think on what you want the code to do)
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.
You don't build credibility by putting out slop and correcting it every time someone points out that it's wrong. You think it's our job to proofread and fact-check?
A genuine mistake. I make these posts for myself, mainly to structure & share my thoughts. Let me know if you find other errors, I appreciate the feedback.
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.
Code was very rarely the bottleneck in the first place.
If programmer productivity was something we actively optimized for, we wouldn't have cram 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.
> Code was very rarely the bottleneck in the first place.
I disagree. Kinda
What AI has made much simpler is that you don't have to waste time checking docs and have the best autocomplete system by a long shot - this was a bottleneck unless you were doing Java or some other language with "perfect" AC
What AI made "kinda easier": solving for usual problems. The stuff you would search Stack Overflow, or think a couple of minutes for an optimized solution - not a bottleneck but not 100% smooth neither
You still have to test and validate your code. AI made this easier-ish but this is still where I see manual work being needed (even if you are automating tests - you still have to think on what you want the code to do)
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.
Pangram reports this post was 100% AI generated.
> 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.
“Helping” is doing some heavy lifting in that sentence! It appears to be 100% AI.
Yes correct, Gemma. Will correct it shortly.
You don't build credibility by putting out slop and correcting it every time someone points out that it's wrong. You think it's our job to proofread and fact-check?
A genuine mistake. I make these posts for myself, mainly to structure & share my thoughts. Let me know if you find other errors, I appreciate the feedback.
Code was never expensive.
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?
Ask it to do so, would love to know your results.
I did, the problem is that you need a sort of standardized feature request to compare similar repos.
Some code bases better than others but the top models can.
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.
I read this entire article and didn’t sniff AI, plus it had some good insights…?
Feels like a human-authored outline that was run through AI to make it into an article.
Well it contains a typo, so it made a cock up.
More than that, the AI disclaimer itself is a hallucination:
> Gemini 4 helped with the editing.
There is no Gemini 4, unless the author is writing from the future.
You are correct, my bad. I meant to write Gemma 4. I'll update.
I've written this myself, Gemini 4 did a bit of editing:
> What follows is a cleaned-up version of notes I accumulated over the past year. Gemini 4 helped with the editing.
The image is made by an AI image generator on fal.ai. It's better I spare you all my design skills :)
AI image, though.
I closed the tab after seeing the AI hero image. Good to know I didn't miss anything.
Reading the domain-name had the same effect for me.
You discount people's blogs based on them having a non-western name? Maybe I've misunderstood your comment, but the domain is just their name.
I can't change my name, but happy to change the hero picture :))
You did miss something. But the picture doesn’t help…why would you start an article with an image that telegraphs “low effort”?
Yes the article took a while to get going, but once it did it was thoughtful and well reasoned.
Thank you very much for the kind words :)
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.