LLMS are charging like $5 per million of tokens. And even if it is subsidized 100x it is still cheaper an order of magnitude than an overseas engineer.
Not to mention speed. An LLM will spit out 1000 lines in seconds, not hours.
https://www.folklore.org/Negative_2000_Lines_Of_Code.html
> When he got to the lines of code part, he thought about it for a second, and then wrote in the number: -2000
But in retrospect it’s absolutely baffling that mixing raw SQL queries with HTML tag soup wasn’t necessarily uncommon then. Also, I haven’t met many PHP developers that I’d recommend for a PHP job.
That's not how I read it. I would say that it's more like "If a human no longer needs to read the code, is it important for it to be readable?"
That is, of course, based on the premise that AI is now capable of both generating and maintaining software projects of this size.
Oh, and it begs another question: are human-readable and AI-readable the same thing? If they're not, it very well could make sense to instruct the model to generate code that prioritizes what matters to LLMs over what matters to humans.
has your experience been otherwise?
I remember when Gemini Pro 3 was the latest hotness and I started to get FOMO seeing demos on X posted to HN showing it one shot-ing all sorts of impressive stuff. So I tried it out for a couple days in Gemini CLI/OpenCode and ran into the exact same pain points I was dealing with using CC/Codex.
Flashy one shot demos of greenfield prompts are a natural hype magnet so get lots of attention, but in my experience aren't particularly useful for evaluating value in complex, legacy projects with tightly bounded requirements that can't be easily reduced to a page or two of prose for a prompt.
By getting the LLM to keep changes minimal I’m able to keep quality high while increasing velocity to the point where productivity is limited by my review bandwidth.
I do not fear competition from junior engineers or non-technical people wielding poorly-guided LLMs for sustained development. Nor for prototyping or one offs, for that matter — I’m confident about knowing what to ask for from the LLM and how to ask.
But if today it’s so cheap to generate new code that meets updated specs, why care about the quality of the code itself?
Maybe the engineering work today is to review specs and tests and let LLMs do whatever behind the scenes to hit the specs. If the specs change, just start from scratch.
Let's assume the LLM agents can write tests for, and hit, specs better and cheaper than the outsourced offshore teams could.
So let's assume now you can have a working product that hits your spec without understanding the code. How many bugs and security vulnerabilities have slipped through "well tested" code because of edge cases of certain input/state combinations? Ok, throw an LLM at the codebase to scan for vulnerabilities; ok, throw another one at it to ensure no nasty side effects of the changes that one made; ok, add some functionality and a new set of tests and let it churn through a bunch of gross code changes needed to bolt that functionality into the pile of spaghetti...
How long do you want your critical business logic relying on not-understood code with "100% coverage" (of lines of code and spec'd features) but super-low coverage of actual possible combinations of input+machine+system state? How big can that codebase get before "rewrite the entire world to pass all the existing specs and tests" starts getting very very very slow?
We've learned MANY hard lessons about security, extensibility, and maintainability of multi-million-LOC-or-larger long-lived business systems and those don't go away just because you're no longer reading the code that's making you the money. They might even get more urgent. Is there perhaps a reason Google and Amazon didn't just hire 10x the number of people at 1/10th the salary to replace the vast majority of their engineering teams year ago?
> let LLMs do whatever behind the scenes to hit the specs
it gets me thinking: if anyone can vibe from spec, whats stopping company a (or even user a) from telling an llm agent "duplicate every aspect of this service in python and deploy it to my aws account xyz"...
in that scenario, why even have companies?