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However I think it's aggrandizing what human engineers actually do with remarks like "Engineers own tradeoffs." My experience is that certainly less than half of the employed software engineers don't actually give a real analysis to questions like:
"Given these constraints, this team, this business, this infrastructure, this budget, these risks, and the expected evolution of the product, what is the most appropriate way to implement X, today?"
Thus I think AI is more able to replace the average engineer more than this article admits, however the inadequacy of "average engineering" will be much more apparent now: codebases can become large/complex enough to be unwieldy in months now when it used to take 5 years [a timescale where accountability is effectively impossible].
These get overlooked so often. The way you build software if you’re at the helm vs the way you need to build it when dealing with a more/less capable team and business, especially if someone else will be doing the deployment and will need lots of consultations, is way different.
Do you think this is where it stops? This is where it begins.
Machines will be good at managing complexity too. You can't draw a line and say improvement stops here, because everything we've seen so far flies in the face of that.
I shudder to think what these models will be capable of in 24 months.
No, this is pretty much where it stops.
The models are good enough for the average coding task, and the slop they produce often is in the category of what a bad or careless dev that's being contracted out might produce.
Yea, they'll get better, but not in next-level sort of way.
The limitation is not the models or intelligence, it's the human in the loop. We're still stuck on stupid human issues ranging from usability bugs, to figuring out what the product should be, to how we should program in the first place.
I know the models are capable of sorting out issues it gets stuck on because it's writing error handling in the wrong way, or just it doesn't have the right abstractions, because we can't settle on the right way to program. I still see people arguing about dyanmic vs. static typing.
And obviously, there is a next level, but that's real singularity, and we're all out of jobs.
There are things an engineer can do to flatten the curve - that is OP's complexity management idea - but complexity growth can never be linear as long as you are adding to the software.
I made a model/theorem for this that I posted on X: https://x.com/i/status/2027771813346820349
Code generation has exposed that verification is the central problem of software engineering. And I think it always has been.
Defining what is "correct" can be hard enough, let alone building a system that lends itself to verification, let alone spending the time to verify. Releasing software and letting users find bugs is therefore a very efficient strategy, because it spreads the burden. But you have to ride the line between losing users and getting enough feedback to find and fix the bugs that matter.
As we confront whether AI might take our jobs, I take some comfort in the idea that the world might be too complex for even the largest, best trained AI we can imagine. At a certain point, you need to simulate the whole world (or some substantial portion of it) and the cost/benefit of trying to do all that with compute may not be worth it versus using the real world (that is, humans) as your verifier.
The two tasks of writing code and engineering software cannot be separated without damaging the integrity of the mental model of the engineer. Having architects who didn't interact with the code always produced map/territory mismatches.
Problem with the analogy is that the strain in software engineering is necessary for an in depth understanding of the code.
The question is whether that depth of knowledge is ultimately more helpful than the speed that we can build with AI.
I love to say that
No? Why?
Because software languages are a pretty good abstraction.
To the extent that good abstractions are in place, you can avoid looking at code specifically.
Those don't perfectly well exist, so it takes a lot of self discipline and the right tools/methods, but invariably, AI will produce better systems.
That said, its very easy to produce slop, so well see much more of it.
But mostly, it will be AI from here on in, as a matter of productivity. There are some arguments on the margins but those will fade over the next few years.
'At minimum' - the 'power tools' are here to stay.
An LLM translating a prompt to to high-level code has a much lower degree of predictability. To say an LLM prompt is a comparable abstraction is unfair, though I admit it's getting very close.
But yes, it has to fulfill some kind of contract defined by the absraction.
It's less a problem of the LLM, and more so how we use them, and the inherent tooling around it.
Looking at the compiler output is a totally valid concept, but it's definitely a niche case.
> No? Why?
> Because software languages are a pretty good abstraction.
No, it’s because compilers produce deterministic output. I am so tired of this argument.
If I’m not concerned with the performance of my code, I can be 100% confident that that exact code will produce the correct assembly every time. That’s why I don’t read it. Not because I don’t care.
It's entirely the nature of the abstraction.
You want it to work as expected, it does not have to produce the same thing each time.
This is true to such an extent that I have to question the overall competence of anybody who makes the comparison. It's an enormous red flag.
I'd recommend reading Joel spolsky's leaky abstractions essay coz while it applies less and less 20 years later to things like kernel abstractions it explains very well why treating the LLM as a compiler sets you up for abject failures.
It's not as good at system design as writing code, yet. But it feels like it's better than most of my coworkers.
I think in a few months, system architecture will have its Claude Code moment, and humans will be outclassed.
A fun little exercise you can do is design a system and write some code and then ask LLM to explain why you wrote it that way. Results are varied and interesting but in my experience rarely capture the actual why behind decisions.
As an software engineer, I will readily admit that LLMs have greatly increased my output - especially on the menial work.
But now we have leadership telling everyone to "use moar AI" on everything, everywhere. I literally have observed folks dropping into incident Slack chats saying things like, "hey all - i asked Claude about this issue and then i had it write a solution. here's the PR." This feels like the kind of thing that should be a fire-able offense, but instead they're getting shout-outs from the CEO.
Hell, the next time I go on vacation, I think I could put Claude Code on YOLO mode for 2 weeks and I'd probably come back to find I'd been promoted.
I do not know how this is going to end, but I have a feeling it's going to get way darker before it gets better.
I personally recommend, but I understand many people do not want to be pushed back by something they see as little more than a servant.
This setup does work to also have agents argue with each other. That can be very interesting, though you have to set them up to be very skeptical. Otherwise they will tend to read another agents assertion as authoritative off the bat.
I am convinced much of the harness/prompt engineering we are doing now will also be automated away. Within 5 years the best practices for the most popular use cases will have been found, automated and fully baked in.
How often do engineers get a say in product direction?
Every one keeps saying that AI isnt moving the needle on the bottom line.
Well duh, code doesn't move the bottom line, features do, products do.
If you're building all the wrong things faster, all your doing is performing a speed run to a legacy code base.
The site just goes into a reload loop on iOS?
People are shouting “yeah the hard part was never writing code, it was managing complexity” as a sort of last hurrah before AI engulfs them.
This is reality: not only can AI write code. It can manage complexity.
Prompt: read the article in this thread then execute its principles on my codebase. Write a harness and programmatic procedures that will trigger you to respond with the articles philosophy to code changes. Be vigilant and monitor every aspect constantly.
I would say for the above prompt, AI is about 60 to 70 percent as a good as a human now. A year ago it was 20 percent. The gap is closing.
Even a human could not “manage complexity” if it’s not in the right context. This is not about AI vs. human capabilities.
It's less about writing code now but we're lying if we try to pretend it was a distraction and not a big part of the real work.
And every claim about what the job actually is or was all along has an implied (for now) at the end of it.
Which is why the productivity of people of people has never been correlated with typing speed.
In other jobs productivity is correlated with typing speed and in those jobs a typing speed like 60wpm is part of the job requirements.
I've been hearing this "writing code is not what being an enginner is" mantra for years like some sort of gotcha. (It was prevalent even before AI, and I think people underestimated a lot how many people were simply incapable of writing code even given all the specs and design choices.)
> To systematically investigate the role of end-user semantics of derivational traces, we set up a controlled study where we train transformer models from scratch on formally verifiable reasoning traces and the solutions they lead to. We notice that, despite gains over the solution-only baseline, models trained on entirely correct traces can still produce invalid reasoning traces even when arriving at correct solutions. More interestingly, our experiments also show that models trained on corrupted traces, whose intermediate reasoning steps bear no relation to the problem they accompany, perform similarly to those trained on correct ones, and even generalize better on out-of-distribution tasks.
https://arxiv.org/abs/2505.13775
Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
Here’s an example: back in the 2000s, everyone was afraid programming was going to get outsourced to India or other countries. It didn’t happen, Sillicon Valley continues to spend billions to import engineers to work in person even though it’s 10x cheaper to hire remote outsourcers in India who are just as skilled programmers. Why do they spend 10x to move physical bodies to the office? Because it’s impossible to write good software without being in the physical context of the problem domain and team.
Similarly you cannot outsource to AI, because it cannot have complete context. No matter how good AI is, the problem is not mechanically solvable.