Asklemmy

I'm not really losing any sleep over this myself. Current approach to machine learning is really no different from a Markov chain. The model doesn't have any understanding in a meaningful sense. It just knows that certain tokens tend to follow certain other tokens, and when you have a really big token space, then it produces impressive looking results.

However, a big part of the job is understanding what the actual business requirements are, translating those to logical steps, and then code. This part of the job can't be replaced until we figure out AGI, and we're nowhere close to doing that right now.

I do think that the nature of work will change, I kind of look at it as sort of doing a pair programming session. You can focus on what the logic is doing, and the model can focus on writing the boilerplate for you.

As this tech matures, I do expect that it will result in less workers being needed to do the same amount of work, and the nature of the job will likely shift towards being closer to a business analyst where the human focuses more on the semantics rather than implementation details.

We might also see new types of languages emerge that leverage the models. For example, I can see a language that allows you to declaratively write a specification for the code, and to encode constraints such as memory usage and runtime complexity. Then the model can bang its head against the spec until it produces code that passes it. If it can run through thousands of solutions in a few minutes, it's still going to be faster than a human coming up with one.

I am om the product side of things and have created some basic proof of concept tools with AI that my bosses wanted to sell off. No way no how will I be able to sevrice or maintain them. It's incredibly impressive that I could even get this output.

I am not saying it won't become possible, but I lack the fundamental knowledge and understanding to make anything beyond the most minor adjustments and AI is still wuite bad at only addressing specific issues or, good forbid, expanding code, without fully rewriting the whole thing and breaking everything else.

For our devs I see it as a much improved and less snide stackoverflow and Google. The direct conversational nature really speeds things up with boilerplate code and since they actually know what they are doing, it's amazing. Not only that but we had devs copy paste from online searches withoout fully understanding the snippets. Now the AI can explain it in context.

It doesn't matter what you think about AI. It's very clear that this technology is here to stay and will only improve. From this point on AI will become deeply integrated into human culture and technology, after all we've been fetishizing it for almost 100 years now. Your only logical option as a developer is to learn how to use it and abuse it. Choosing not to do so is career suicide, possibly even societal suicide depending on how quickly adoption happens.

You're probably right, in the near future people that can't use it will be fired. To that point they should be fired. Why the fuck would I allow my accounts to do their financal work on paper when Excel exists?

Welcome to the future.

To answer your question directly: The debate has been going on in the broader public since ChatGPT 3 dropped

To answer how you’re feeling: that’s valid, because a lot of big pockets seem to not care at all about the ethical considerations.

I think your job in your current form is likely in danger.

SOTA Foundation Models like GPT4 and Gemini Ultra can write code, execute, and debug with special chain of thought prompting techniques, and large acale process verification on synthetic data and RL search for correct outputs will make this 10x better. The silver lining to this is that I expect this to require an absolute shit ton of compute to constantly generate LLM output hundreds of times for each internal prompt over multiple prompts, requiring immense compute and possibly taking longer than an ordinary software engineer to run. I suspect early full stack developer LLMs will mainly be used to do a few very tedious coding tasks and SWEs will be cheaper for a fair length of time.

I expect it will be 2-3 years before this happens, so for that short period I expect workers to be "super-productive" by using LLMs in the coding process, but I expect the crossover point when the LLM becomes better is quite soon, perhaps in the next 5 years as compute requirements go down.

I disagree with the other posts here that you're overreacting. I think that AI will replace most jobs (maybe as high as 85% at some point). Consider becoming a plumber or an electrician. Until the robots will become commonplace in 20 years from now, you will have a job that AI won't be able to touch much. And people won't run out of asses or gaming. So they'll be stable professions for quite a while. You can still code in your free time, as a hobby. And don't cry for the lost revenue of being a programmer, because that will happen to everyone who will be affected by AI. You'll just have another job while the others won't. That's the upside.

I understand that this comment is not what people want to hear with their wishful thinking, so they'll downvote it. But I gotta say it how I see it. AI is the biggest revolution since the industrial revolution.

🙄 no I'm sure you're the only one