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It's because they don't know data structures. "Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious." - Fred Brooks, The Mythical Man-Month (1975). and essentially the same sentiment, three decades later: "Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds, git mailing list, 2006. These things have not changed even though everything else is topsy-turvy. As-of current writing, I have yet to see an LLM make good data structure choices; they go for something that is superficially plausible but profoundly ill-considered (or rather, not considered at all), and then commonly burn tokens treating this implementation detail as a design invariant and trying to deal with the consequences by writing more code, instead of iterating directly upon the ill-fitting data at the root its problems. If you're wondering, "does he mean the schema of let's say a db or other persistent store, or does he mean abstract/algebraic structures", the answer is yes to both, I think coding models are today shockingly weak when it comes to design reasoning in both domains. Fortunately, their suggestibility means the same models will readily accept direction on the matter (perhaps even more so than on the structure of code), so I recommend doing just that, and (bonus!) this means your CS degree is still relevant.

I think it's not only a matter of token efficiency. If you don't know what you are doing development will eventually crawl to a halt invariably. It's the compound counter-probability of success, so even a 99% efficient model will in time accumulate so much error that without conscious cleanup and steering, it becomes really unlikely really fast that anything could be changed in the code without affecting something else, no matter how many tokens you throw at it. It's the collapse of a complex system under the weight of sheer uncertainty of what the system actually does.

I think this is valid now, but not guaranteed to be valid forever. For engineers, there was a period where more checks, more tests, more auto code reviews improved results quite a bit. People were consuming tokens like crazy (including me). Then things improved via better effort/thinking levels, where you could see repeated code reviews plateaued, so now people don't really do that quite as much. There was also a period where specifically OpenAI models would always have to comment something in code review and the builders were agreeable up to listening to each nitpick. If you'd have a loop of build->review->build->review, it would take maybe 5-7 rounds for it to 'settle' and not find the smallest nitpicks to argue about. Tried it this week with Astra reviewer and it's about 0-2 review loops (never had a LLM accept a change without nitpicking first try before Astra). There was also a period where you'd have to give quite specific instructions for agents to keep iterating, but now agent are pretty proactive and try to finish tasks you give them unsurprisingly most of the time. So, while there's a shortcoming of LLM+harness and engineers observe more tokens improve things even logarithmicly, you'll see more tokens seemingly abused by engineers.

For my normal work I take ownership of the code, and end up with the exact code I want. I still have it go off and do a good amount of work a lot of the time, still queue up multiple tasks at the same time a lot of the time. Sometimes I throw it all away and re-prompt once it's time to commit to it, sometimes edit what it made, sometimes have it edit what it made etc. For all of my side projects I'm full-on vibe. Well, almost: I do have opinions on what kinds of code it should write and set up my projects to get that. But I don't LOOK at the code. I use a LOT more tokens on my side projects. I can have it working more or less constantly and it doesn't take up that much of my attention, but it is FAR less token efficient.

Because that "vibe coders" didn't know and go through the fundamentals, thus they're wasting tokens with probably continuing the AI hallucination suggestions. I've seen bunch of persons like this and that's kinda stupid because they're just blindly following AI's "suggestions" while they actually don't know what they're doing, then results on terrible code and architecture with "if it works, it works" mentality.