I've worked on "legacy systems" written 30 to 45 years ago (or more) and still running today (things like green-screen apps written in Pick/Basic, Cobol, etc.). Some of them were written once and subsystems replaced, but some of it is original code.

In systems written in the last.. say, 10 to 20 years, I've seen them undergo drastic rates of change, sometimes full rewrites every few years. This seemed to go hand-in-hand with the rise of agile development (not condemning nor approving of it) - where rapid rates of change were expected.. and often the tech the system was written in was changing rapidly also.

In hardware engineering, I personally also saw a huge move to more frequent design and implementation refreshes to prevent obsolescence issues (some might say this is "planned obsolescence" but it also is done for valid reasons as well).

I think not reading the code anymore TODAY may be a bit premature, but I don't think it's impossible to consider that someday in the nearer than further future, we might be at a point where generative systems have more predictability and maybe even get certified for safety/etc. of the generated code.. leading to truly not reading the code.

I'm not sure it's a good future, or that it's tomorrow, but it might not be beyond the next 20 year timeframe either, it might be sooner.

And for anything really serious? Opus 4.5 struggles to maintain a large-scale, clean architecture. And the resulting software is often really buggy.

Conclusion: if you want quality in anything big in February 2026, you still need to read the code.

We have been throwing away entire pieces of software forever. Where's Novell? Who runs 90s Linux kernels in prod?

Code isn't a bridge or car. Preservation isn't meaningful. If we aren't shutting the DCs off we're still burning the resources regardless if we save old code or not.

Most coders are so many layers of abstraction above the hardware at this point anyway they may as well consider themselves syntax artists as much as programmers, and think of Github as DeviantArt for syntax fetishists.

Am working on a model of /home to experiment with booting Linux to models. I can see a future where Python in my screen "runs" without an interpreter because the model is capable of correctly generating the appropriate output without one.

Code is ethno objects, only exists socially. It's not essential to computer operations. At the hardware level it's arithmetical operations against memory states.

Am working on my own "geometric primitives" models that know how to draw GUIs and 3D world primitives, text; think like "boot to blender". Rather store data in strings, will just scaffold out vectors to a running "desktop metaphor".

It's just electromagnetic geometry, delta sync between memory and display: https://iopscience.iop.org/article/10.1088/1742-6596/2987/1/...

I can't imagine not reading the code I'm responsible for any more than I could imagine not looking out the windscreen in a self driving Tesla.

But if so many people are already there, and mostly highly skilled programmers imagine in 2 years time with people who've never programmed!

That happens just as often without AI. Maybe the people that like it all thave experience with trashing multiple sets of products over the course of their life?

You have to remember that the number of software developers saw a massive swell in the last 20 years, and many of these folks are Bootcamp-educated web/app dev types, not John Carmack. They typically started too late and for the wrong reasons to become very skilled in the craft by middle age, under pre-AI circumstances and statistically (of course there are many wonderful exceptions; one of my best developers is someone who worked in a retail store for 15 years before pivoting).

AI tools are now available to everyone, not just the developers who were already proficient at writing code. When you take in the excitement you always have to consider what it does for the average developer and also those below average: A chance to redefine yourself, be among the first doing a new thing, skip over many years of skill-building and, as many of them would put it, focus on results.

It's totally obvious why many leap at this, and it's even probably what they should do, individually. But it's a selfish concern, not a care for the practice as-is. It also results in a lot of performative blog posting. But if it was you, you might well do the same to get ahead in life. There's only to so many opportunities to get in on something on the ground floor.

I feel a lot of senior developers don't keep the demographics of our community of practice into account when they try to understand the reception of AI tools.

https://www.linkedin.com/in/benshoemaker000/

Anyone overseeing work from multiple people has to? At some point you have to let go and trust people‘s judgement, or, well, let them go. Reading and understanding the whole output of 9 concurrently running agents is impossible. People who do that (I‘m not one of them btw) must rely on higher level reports. Maybe drilling into this or that piece of code occasionally.

The output of code isn't just the code itself, it's the product. The code is a means to an end.

So the proper analogy isn't the photographer not looking at the photos, it's the photographer not looking at what's going on under the hood to produce the photos. Which, of course, is perfectly common and normal.

It is right often enough that your time is better spent testing the functionality than the code.

Sometimes it’s not right, and you need to re-instruct (often) or dive in (not very often).

I can think of a few. The last 78 pages of any 80-page business analysis report. The music tracks of those "12 hours of chill jazz music" YouTube videos. Political speeches written ahead of time. Basically - anywhere that a proper review is more work than the task itself, and the quality of output doesn't matter much.

Almost everyone does this. Hardly anyone taking pictures understands what f-stop or focal length are. Even those who do seldom adjust them.

There dozens of other examples where people voluntarily move to a black box approach. How many Americans drive a car with a manual transmission?

I don't know... it depends on the use case. I can't imagine even the best front-end engineer ever can read HTML faster than looking at the rendered webpage to check if the layout is correct.

The AI also writes the black box tests, what am I missing here?

code is not the output. functionality is the output, and you do look at that.

To be fair - it is not accurate to say I absolutely never read the code. It's just rare, and it's much more the exception than the rule.

My workflow just focuses much more on the final product, and the initial input layer, not the code - it's becoming less consequential.

It's producing seemingly working code faster than you can closely review it.

You may read all the assembly that your compiler produces. (Which, awesome! Sounds like you have a fun job.) But I don't. I know how to read assembly and occasionally do it. But I do it rarely enough that I have to re-learn a bunch of stuff to solve the hairy bug or learn the interesting system-level thing that I'm trying to track down if I'm reading the output of the compiler. And mostly even when I have a bug down at the level where reading assembly might help, I'm using other tools at one or two removes to understand the code at that level.

I think it's pretty clear that "reading the code" is going to go the way of reading compiler output. And quite quickly. Even for critical production systems. LLMs are getting better at writing code very fast, and there's no obvious reason we'll hit a ceiling on that progress any time soon.

In a world where the LLMs are not just pretty good at writing some kinds of code, but very good at writing almost all kinds of code, it will be the same kind of waste of time to read source code as it is, today, to read assembly code.

What’s worth paying for is something that is trustworthy.

Claude code is a perfect example: They blocked tools like opencode because they know quality is the only moat, and they don’t currently have it.

Even in those environments, I'd argue that AI coding can offer a lot in terms of verification & automated testing. However, I'd probably agree, in high-stakes safety environments, it's more of a 'yes and' than an either/or.

Code health is a choice. We have power tools now. All you have to do is ask.

Agents are a quality/velocity tradeoff (which is often good), if you can't debug stuff without them that's a problem as you'll get into holes, but that doesn't mean you have to write the code by hand.

Is the image really not that clear? There are IDE-like tools that all are focusing on different parts of the Spec --> Agent --> Code continuum. I think it illustrates that all right.

I'm not trying to express that my particular flavor of career is safe. I think that the ability to produce software is much less about the ability to hand-write code, and that's going to continue as the models and ecosystem improve, and I'm fascinated by where that goes.