It’s absolutely taking off in some areas. But there’s also an unsustainable bubble because AI of the large language model variety is being hyped like crazy for absolutely everything when there are plenty of things it’s not only not ready for yet, but that it fundamentally cannot do.
You don’t have to dig very deeply to find reports of companies that tried to replace significant chunks of their workforces with AI, only to find out middle managers giving ChatGPT vague commands weren’t capable of replicating the work of someone who actually knows what they’re doing.
That’s been particularly common with technology companies that moved very quickly to replace developers, and then ended up hiring them back because developers can think about the entire project and how it fits together, while AI can’t - and never will as long as the AI everyone’s using is built around large language models.
Inevitably, being able to work with and use AI is going to be a job requirement in a lot of industries going forward. Software development is already changing to include a lot of work with Copilot. But any actual developer knows that you don’t just deploy whatever Copilot comes up with, because - let’s be blunt - it’s going to be very bad code. It won’t be DRY, it will be bloated, it will implement things in nonsensical ways, it will hallucinate… You use it as a starting point, and then sculpt it into shape.
It will make you faster, especially as you get good at the emerging software development technique of “programming” the AI assistant via carefully structured commands.
And there’s no doubt that this speed will result in some permanent job losses eventually. But AI is still leagues away from being able to perform the joined-up thinking that allows actual human developers to come up with those structured commands in the first place, as a lot of companies that tried to do away with humans have discovered.
Every few years, something comes along that non-developers declare will replace developers. AI is the closest yet, but until it can do joined-up thinking, it’s still just a pipe-dream for MBAs.
But any actual developer knows that you don’t just deploy whatever Copilot comes up with, because - let’s be blunt - it’s going to be very bad code. It won’t be DRY, it will be bloated, it will implement things in nonsensical ways, it will hallucinate… You use it as a starting point, and then sculpt it into shape.
Yeah, but I don’t know where you’re getting the “never will” or “fundamentally cannot do” from. LLMs used to be only useful for coding if you ask for simple self-contained functions in the most popular languages, and now we’re here; most requests with small scope, I’m getting a result that is better written than I could have done myself by spending way more time, it makes way fewer mistakes than before and can often correct them. That’s with only using local models which became actually viable for me less than a year ago. So why won’t it keep going?
From what I can tell there is not very much actually standing in the way of sensible holistic consideration of a larger problem or codebase here, just context size limits and being more likely to forget things in the context window the longer it is, which afaik are problems being actively worked on where there’s no reason they would be guaranteed to remain unsolved. This also seems to be what is holding back agentic AI from being actually useful. If that stuff gets cracked, I think it’s going to mean things will start changing even faster.
It’s absolutely taking off in some areas. But there’s also an unsustainable bubble because AI of the large language model variety is being hyped like crazy for absolutely everything when there are plenty of things it’s not only not ready for yet, but that it fundamentally cannot do.
You don’t have to dig very deeply to find reports of companies that tried to replace significant chunks of their workforces with AI, only to find out middle managers giving ChatGPT vague commands weren’t capable of replicating the work of someone who actually knows what they’re doing.
That’s been particularly common with technology companies that moved very quickly to replace developers, and then ended up hiring them back because developers can think about the entire project and how it fits together, while AI can’t - and never will as long as the AI everyone’s using is built around large language models.
Inevitably, being able to work with and use AI is going to be a job requirement in a lot of industries going forward. Software development is already changing to include a lot of work with Copilot. But any actual developer knows that you don’t just deploy whatever Copilot comes up with, because - let’s be blunt - it’s going to be very bad code. It won’t be DRY, it will be bloated, it will implement things in nonsensical ways, it will hallucinate… You use it as a starting point, and then sculpt it into shape.
It will make you faster, especially as you get good at the emerging software development technique of “programming” the AI assistant via carefully structured commands.
And there’s no doubt that this speed will result in some permanent job losses eventually. But AI is still leagues away from being able to perform the joined-up thinking that allows actual human developers to come up with those structured commands in the first place, as a lot of companies that tried to do away with humans have discovered.
Every few years, something comes along that non-developers declare will replace developers. AI is the closest yet, but until it can do joined-up thinking, it’s still just a pipe-dream for MBAs.
Yeah, but I don’t know where you’re getting the “never will” or “fundamentally cannot do” from. LLMs used to be only useful for coding if you ask for simple self-contained functions in the most popular languages, and now we’re here; most requests with small scope, I’m getting a result that is better written than I could have done myself by spending way more time, it makes way fewer mistakes than before and can often correct them. That’s with only using local models which became actually viable for me less than a year ago. So why won’t it keep going?
From what I can tell there is not very much actually standing in the way of sensible holistic consideration of a larger problem or codebase here, just context size limits and being more likely to forget things in the context window the longer it is, which afaik are problems being actively worked on where there’s no reason they would be guaranteed to remain unsolved. This also seems to be what is holding back agentic AI from being actually useful. If that stuff gets cracked, I think it’s going to mean things will start changing even faster.