lol shut the fuck up NYT
So much cope. Everyone is in denial. It strikes deep, I know.
Ignoring the truth won’t help you deal with it.
As in: you agree with the article’s assertion?
I don’t write code anymore. No one I work with does either.
Same here. None of my coworkers write code.
But then again, I’m a truck driver.
I work with a lot of productive software developers. They all write code. They also leverage AI to do all the boring stuff.
They’re wasting time cause the AI can also do the non-boring stuff.
Trust me… it’s getting better, but it can’t do the non-boring stuff in a way that’s ISO compliant. Yet.
I’m not a coder, so I can’t speak to the quality of code generated by these models. I am a lawyer, and every time I see stuff that lay people think is impressive in my field, I can’t help but guffaw and think “none of this is going to function, and no one will know for years. We’re so fucked…and then one day we’ll have to clean all this up and it’s gonna be so much work.” I kind of assume it’ll be similar for code? Like…it’ll obviously be somewhat better because there is a lot of testing you can actually do, whereas in law “testing” takes many years…and by the time you find out something doesn’t work, the burden of having done it wrong all this time, thinking it was right is catastrophic (which is why lawyers are so conservative about language that they “know works.”
I can see how little features can get added and these tools can deliver on those projects fast…but like…can they do bigger things with consistency? Can they like…set things up well? I’m not saying it’s impossible, but…I guess i’m thinking about Go. It took a long time for neural networks to get to be good at 19 x 19. They got good at 9 x 9 pretty fast. But as the game gets more complicated, it’s way WAY harder to do good long-term strategy. And the machines got there, no doubt. But the entire universe of Go is a 19x19 grid, on which the spaces are black or white or empty. How much more complicated is a language? Even a programming language? infinitely more complex, of course!
So I worry that we’re going to have individual features that work well, but systems that cannot function…looking like the uhhh…weasley house in Harry Potter…but without the magic to hold it up lol.
I think the difference is that the LLMs can read all the context of your project and figure out what will work. If you want to add a feature, it will do so in a way that won’t break other things or offer you options if you can’t make that change without breaking something.
Also, LLMs are super fast compared to humans so even when it’s slightly wrong, it can be fixed with another prompt. People act like the LLM doing something wrong makes using LLMs pointless, but they are ignoring the fact that the LLM can always take another prompt and keep working until it gets it right, which is usually immediately once the issue is recognized.
You can even automate the feedback loop by describing the test scenarios and then having it run those tests, see the failures, and fix the code all by itself.
I get LLMs might not work as well for law at this point, but they do work for coding.
I’ll have to take your word for it! “figuring out” sounds like a higher-order process than a large language model is capable of to me, but if what they do is as good, then great.
I think I’m just skeptical because of how horrendously bad LLM output is in my field of expertise (despite looking fine to a lay person), so I immediately analogize that to other areas. The output of law and coding are both really about language, and the process of creating that output on the part of a lawyer or coder are really about language, so I can see how one might think LLMs would be able to recreate what lawyers and coders do. But boy it doesn’t strike me as remotely plausible that LLMs will ever get there, at least for law. I have no doubt some yet-unimagined technology could get us there, but “next word prediction” just isn’t gonna be it.
The current state of LLMs is financially and environmentally unsustainable. I’m sure that in short time, additional technologies like neurosymbolic AI will prevent hallucinations and improve efficiency. But will they help AI vendors become profitable?
They AI bubble might pop or fizzle, but we’ll see what developers do with their code bases when they don’t have their toys at their disposal anymore.
