I agree that the problem is volume, even more so than correctness.
All that LLMs and other generative models have done is enable an order of magnitude more stuff to be created cheaply. This then puts the onus and cost on the consumer of that output, hence why everyone is exhausted after a day of work that just involves looking over output. This volume of output will cause people to stop looking at all of the output and just trust the randomly generated code, and in time the quality will suffer.
I'm just saying that I already see that people are outsourcing all the thinking to the models - not only code generation and reviews, but even design - the part that "senior engineers" without imagination think only they are capable of doing.
It's worrying how much trust is being put in those systems. And my worry is not about the job anymore, but our future in general.
You could say the same thing about compiled code, actually it's worse because anything a compiler spits out is very hard to understand even for those who understand assembly.
You don't need to look at the entire program at the assembly level to figure out parts that you want to optimise or prove for correctness. You do need to look at all the code the LLM generates in order to understand it.
You can learn to understand the patterns that compilers spit out and there are many tools out there to aid in that understanding. You can't learn to understand what an LLM spits out because by design it is non-deterministic and will vary in form and function for each pull of the lever.
You can learn to understand how high level concepts in code map down to assembly language and how compilers transform constructs in one language to another. You can't know that about LLMs because they generate non-deterministic output based on processing of huge low-precision tables.
Have you tried to shift through a whole lot of vibe coded slop? It’s really mentally draining to see all of the really bad techniques they fall back on just to brute force a solution.
Nope, not anymore. Many already forgot how to do that and it's not a joke.
And putting aside the vanishing skill, there is also an issue of volume.
I agree that the problem is volume, even more so than correctness.
All that LLMs and other generative models have done is enable an order of magnitude more stuff to be created cheaply. This then puts the onus and cost on the consumer of that output, hence why everyone is exhausted after a day of work that just involves looking over output. This volume of output will cause people to stop looking at all of the output and just trust the randomly generated code, and in time the quality will suffer.
So... Our jobs are safe then? I mean, assuming we don't also atrophy to the same extent as the 'many'?
It's the "our jobs are lost" attitude that is part of problem. Is not about that. Is more quality thinking, is daring, not fearing or hoping
1 reply →
I'm just saying that I already see that people are outsourcing all the thinking to the models - not only code generation and reviews, but even design - the part that "senior engineers" without imagination think only they are capable of doing.
It's worrying how much trust is being put in those systems. And my worry is not about the job anymore, but our future in general.
4 replies →
You could say the same thing about compiled code, actually it's worse because anything a compiler spits out is very hard to understand even for those who understand assembly.
You don't need to look at the entire program at the assembly level to figure out parts that you want to optimise or prove for correctness. You do need to look at all the code the LLM generates in order to understand it.
You can learn to understand the patterns that compilers spit out and there are many tools out there to aid in that understanding. You can't learn to understand what an LLM spits out because by design it is non-deterministic and will vary in form and function for each pull of the lever.
You can learn to understand how high level concepts in code map down to assembly language and how compilers transform constructs in one language to another. You can't know that about LLMs because they generate non-deterministic output based on processing of huge low-precision tables.
It's not even a close comparison.
1 reply →
Have you tried to shift through a whole lot of vibe coded slop? It’s really mentally draining to see all of the really bad techniques they fall back on just to brute force a solution.
for now. some people seem to think we should make ai native programming languages and just let them be black boxes. which is a bad idea imo
How can you read a language you didn't learn?
Unless people can't think without the AI.
here's a tip, it would really help if you put yourself into a Ralph loop before posting comments.