I would rather make N bad prototypes to understand the feasibility of solving N problems than trying to write beautiful code for one misguided problem which may turn out to be a dead end.
There are a few orders of magnitude more problems worth solving than you can write good code for. Your time is your most important resource, writing needlessly robust code, checking for situations that your prototype will never encounter, just wastes time when it gets thrown away.
A good analogy for this is how we built bridges in the Roman empire, versus how we do it now.
From the other side, the vast majority of customers will happily take the cheap/free/ad-supported buggy software. This is why we have all these random Google apps, for example.
Take a look at the bug tracker of any large open source codebase, there will be a few tens of thousands of reported bugs. It is worse for closed corporate codebases. The economics to write good code or to get bugs fixed does not make sense until you have a paying customer complain loudly.
The real money we used to get paid was for business success, not directly for code quality; the quality metrics we told ourselves were closer to CV-driven development than anything the people with the money understood let alone cared about, which in turn was why the term "technical debt" was coined as a way to try to get the leadership to care about what we care about.
There's some domains where all that stuff we tell ourselves about quality, absolutely does matter… but then there's the 278th small restaurant that wants a website with a menu, opening hours, and table booking service without having e.g. 1500 American corporations showing up in the cookie consent message to provide analytics they don't need but are still automatically pre-packaged with the off-the-shelf solution.
One ironic thing about LLM-generated bad code is that churning out millions of lines just makes it less likely the LLM is going to be able to manage the results, because token capacity is neither unlimited nor free.
(Note I’m not saying all LLM code is bad; but so far the fully vibecoded stuff seems bad at any nontrivial scale.)
This is like dissing software from 2004 because it used 2gb extra memory.
In the last year, token context window increased by about 100x and halved in cost at the same time.
If this is the crux of your argument, technology advancement will render it moot.
So? It's nowhere close to solving the issue.
I'm not anti-LLM. I'm very senior at a company that's had an AI-centric primary product since before the GPT explosion. But in order to navigate what's going on now, we need to understand the strengths and weaknesses of the technology currently, as well as what it's likely to be in the near, medium, and far future.
The cost of LLMs dealing with their own generated multi-million LOC systems is very unlikely to become tractable in the near future, and possibly not even medium-term. Besides, no-one has yet demonstrated an LLM-based system for even achieving that, i.e. resolving the technical debt that it created.
Don't let fanboism get in the way of rationality.
If you have a concrete way to pose this problem, you'll find that there will be concrete solutions.
There is no way to demonstrate something as vague as "resolving the technical debt that it created".