Everyone has been stressing over losing their job because of AI. I'm genuinely starting to think this will end in 5x more work needing to clean up the mess caused. Who's going to maintain all this generated code?
Similarly, Human-in-the-loop utilization of AI/ML tooling in software development is expected and in fact encouraged.
Any IP that is monetizable and requires significant transformation will continue to see humans-in-the-loop.
Weak hiring in the tech industry is for other reasons (macro changes, crappy/overpriced "talent", foreign subsidies, demanding remote work).
AI+Competent Developer paid $300k TC > Competent Developer paid $400k TC >>> AI+Average Developer paid $30k TC >> Average Developer paid $40k TC >>>>> Average Developer paid $200k TC
Huh?
A Coding copilot subscription paired with a competent developer dramatically speeds up product and feature delivery, and also significantly upskills less competent developers.
That said, truly competent developers are few and far between, and the fact that developers in (eg.) Durham or remote are demanding a SF circa 2023 base makes the math to offshore more cost effective - even if the delivered quality is subpar (which isn't neccesarily true), it's good enough to release, and can be refactored at a later date.
What differentiates a "competent" developer from an "average" developer is the learning mindset. Plenty of people on HN kvetch about being forced to learn K8s, Golang, Cloud Primitives, Prompt Engineering, etc or not working in a hub, and then bemoan the job market.
If we are paying you IB Associate level salaries with a fraction of the pedigree and vetting needed to get those roles, upskilling is the least you can do.
We aren't paying mid 6 figure TC for a code monkey - at that point we may as well entirely use AI and an associate at Infosys - we are paying for critical and abstract thinking.
As such, AI in the hands of a truly competent engineer is legitimately transformative.
Tl;dr - Mo' money, Mo' expectations
Not that old big non-AI software doesn't have similar maintainability issues (I keep posting this example, but I don't actually want to callthat company out specifically, the problem is widespread: https://news.ycombinator.com/item?id=18442941).
That's why I'm reluctant to complain about the AI code issues too much. The problem of how software is written, on the higher level, the teams, the decisions, the rotating programmers, may be bigger than that of any particular technology or person actually writing the code.
I remember a company where I looked at a contractor job, they wanted me to fix a lot of code they had received from their Eastern European programmers. They complained about them a lot in our meeting. However, after hearing them out I was convinced the problem was not the people generating the code, but the ones above them who failed to provide them with accurate specs and clear guidance, and got surprised at the very end that it did not work as expected.
Similar with AI. It may be hard to disentangle what is project management, what is actually the fault of the AI. I found that you can live with pockets of suboptimal but mostly working code well enough, even adding features and fixing bugs easily, if the overall architecture is solid, and components are well isolated.
That is why I don't worry too much about the complaints here about bad error checks and other small stuff. Even if it is bad, you will have lots of such issues in typical large corporate projects, even with competent people. That's because programmers keep changing, management focuses on features over anything else (usually customers, internal or external, don't pay for code reorg, only for new features). The layers above the low level code are more important in deciding if the project is and remains viable.
From what the commenters say, it seems to me the problem starts much higher than the Claude code, so it is hard to say how much at fault AI generated code actually is IMHO. Whether you have inexperienced juniors or an AI producing code, you need solid project lead and architecture layers above the lines of code first of all.
I'd much rather make plans based on reality
Other AI agents, I guess. Call Claude in to clean up code written by Gemini, then ChatGPT to clean up the bugs introduced by Claude, then start the cycle over again.
I've had one llm one-shot a codebase. Then I use another one to review (with a pretty explicit prompt). I take that review and feed it to another agent to refactor. Repeat that a bunch of times.
If the code is cheap (and it certainly is), then tossing it out and replacing it can also be cheap.