Don't You Mean Extinct? Evolving as a Programmer in the Age of LLMs
Drawing parallels between the CGI revolution that replaced stop-motion in Jurassic Park and today's rise of LLMs, I argue that programmers must evolve to avoid obsolescence. By mastering new tools like Andrej Karpathy's resources and John Carmack's insights, we can shift focus from typing code to solving problems. This means demanding higher quality in code reviews, writing better tests, and embracing smaller teams to build elegant software.
Coding was never the source of value, and people shouldn't get overly attached to it. Problem solving is the core skill.
- ChiperSoft
This analogy has layers that OP probably doesn't recognize.
The film industry loves cgi because none of the digital vfx houses are unionized and they can treat the artists like crap. It severely devalued a ton of skilled labor around miniature and set design.
Now, after 20 years of hard swing into cgi, people are starting to recognize just how much better movies from the practical era looked, and there is a push back towards it. Project Hail Mary was predominantly practical effects, for example. Stop motion animation is coming back, and theres a push back into hand cell animation.
- singpolyma3
I mostly like this article but
> Those who refuse to use an LLM will fall behind because they won't be able to produce as much
Seems like a silly and needlessly aggressive take.
Fall behind what? Able to produce "as much" what? I've never been evaluated on volume in my life. Nor have co workers who were severely "behind" ever feared for their jobs.
- lnrd
> Writing every line by hand is no longer the norm. Those who refuse to use an LLM will fall behind because they won't be able to produce as much
> It remains important to be able to read the code and understand the architecture. As a result, I reduce my velocity by iterating over my PR until it reaches the same level of quality I would have produced "by hand"
I do that too and when I do it I'm not sure anymore if I'm "producing as much more" than if I was doing it by hand. I need to spend time to read the code, break down the flow so that it clicks in my head and so that I'm 100% sure that I understand what is going on and what every line does. And then I still test it (executing it), because that's where you notice the edge cases anyways.
Once I understand it and test it, the part where I iterate or fix small quirks and hallucinations is the smallest part of the job and is irrelevant if i do it by myself or ask the LLM to make the change.
I'm still not convinced that I'm faster with an LLM at all, since I add this new bottleneck (the time spent understanding every line). If I do it by hand it already clicks in my head, so it's faster for me to test it, find unaddressed edge cases and then confidently ship it. Maybe the LLMs gains are not in this at all and writing every line by hand will still be the norm for a long time.
Still, LLMs make me insanely faster in: finding something in the codebase, recostructing a flow and understanding the architecture, triaging a bug (somet […]
- tambourine_man
Did Tippett enjoy working on the Dinosaur Input Device as much as he did with his go-motion technique?
I see employability being discussed far more often than joy.
If your motivation was selling as many clothes as possible, then the industrial textile revolution was miraculous.
If you enjoyed knitting threads together, it was the crushing victory of mediocrity.
- pocksuppet
It's probably best to learn about LLMs, and then don't use them most of the time. It's much harder to justify not even knowing how the new thing works, than to justify not using it because the old thing is better.
- bryanlarsen
> I asked an LLM to write a Levenshtein distance function instead of adding a dependency to my project.
Which you likely failed to review thoroughly, so may be subtly wrong.
- amluto
> Writing tests used to be a pain. This is no longer the case. It is ok to request unit tests/CI tests for each PR. These have never been more important since large refactors are becoming increasingly common. Human and LLM review may miss stuff but good tests should catch breakages.
The default behavior is not necessarily good. You end up with tests that match the code, but you don’t necessarily end up with tests that test the behavior you care about. And then, if the LLM tries to decide whether the code is correct, it can conclude “it matches the test so it must be right” regardless of whether it’s actually right.
TDD can help but is not a panacea.
- overgard
I think this is a flawed analogy. In the past when we had a new way of doing something that obsoleted the old way, it replaced it because it was an obvious improvement. I mean, stop motion is cool, but obviously there are limitations.
The deal GenAI offers is: the result will be mediocre at best, on average it will be slop, but it will do it much faster. Ok, that's a fair value proposition in certain contexts. We've always had a need to prototype things fast, and the tradeoff with a prototype is always quality.
However, we're living in an age where we have WAY TOO MUCH in the way of information byproducts, even before AI. How many people do you meet that are like "God, I just wish I had more software in my life!" Most people don't want more software, they want less software that works better. They want more quality and less quantity. It's like this in almost everything digital now. I sign onto Netflix and I can't find anything to watch, even though there's more to watch than I could consume in a lifetime. I live in abundance but I don't want any of it.
GenAI offers us an abundance of stuff we don't want or need (lots of bad code, lots of bad writing, lots of bad illustrations, lots of bad videos) at a cost of stuff we do not have in abundance (energy, attention, natural resources, jobs). It strikes me as a bad trade: lets transform the stuff we need into stuff nobody wants, while decimating our culture in the process.
Anyway, FWIW I do agree with his point that the job has al […]