If LLMs aren’t going to reach a point where they outperform a junior developer who needs too much micromanaging to be a net gain to productivity, then AI’s not going to be a net gain to productivity, and the only productive way to use it is to fight its adoption, much like the only way to productively use keyboards that had a bunch of the letters missing would be to refuse to use them. It’s not worth worrying about obsolescence until such a time as there’s some evidence that they’re likely to be better, just like how it wasn’t worth worrying about obsolescence yet when neural nets were being worked on in the 80s.
You’re not wrong, but in my personal experience AI that I’ve used is already at the level of a decent intern, maybe fresh junior level. There’s no reason it can’t improve from there. In fact I get pretty good results by working incrementally to stay within its context window.
I was around for the dotcom bubble and I expect this to go similarly: at first there is a rush to put AI into everything. Then they start realizing they have to actually make money and the frivolous stuff drops by the wayside and the useful stuff remains.
But it doesn’t go away completely. After the dotcom bust, the Internet age was firmly upon us, just with less hype. I expect AI to follow a similar trend. So, we can hope for another AI winter or we can figure out where we fit in. I know which one I’m doing.
There’s a pretty good reason to think it’s not going to improve much. The size of models and amount of compute and training data required to create them is increasing much faster than their performance is increasing, and they’re already putting serious strain on the world’s ability to build and power computers, and the world’s ability to get human-written text into training sets (hence why so many sites are having to deploy things like Anubis to keep themselves functioning). The levers AI companies have access to are already pulled as far as they can go, and so the slowing of improvement can only increase, and the returns can only diminish faster.
I can only say I hope you’re right. I don’t like the way things are going, but I need to do what I can to adapt and survive so I choose to not put my hopes on AI failing anytime soon.
By the way, thank you for the thoughtful responses and discussion.
If LLMs aren’t going to reach a point where they outperform a junior developer who needs too much micromanaging to be a net gain to productivity, then AI’s not going to be a net gain to productivity, and the only productive way to use it is to fight its adoption, much like the only way to productively use keyboards that had a bunch of the letters missing would be to refuse to use them. It’s not worth worrying about obsolescence until such a time as there’s some evidence that they’re likely to be better, just like how it wasn’t worth worrying about obsolescence yet when neural nets were being worked on in the 80s.
You’re not wrong, but in my personal experience AI that I’ve used is already at the level of a decent intern, maybe fresh junior level. There’s no reason it can’t improve from there. In fact I get pretty good results by working incrementally to stay within its context window.
I was around for the dotcom bubble and I expect this to go similarly: at first there is a rush to put AI into everything. Then they start realizing they have to actually make money and the frivolous stuff drops by the wayside and the useful stuff remains.
But it doesn’t go away completely. After the dotcom bust, the Internet age was firmly upon us, just with less hype. I expect AI to follow a similar trend. So, we can hope for another AI winter or we can figure out where we fit in. I know which one I’m doing.
There’s a pretty good reason to think it’s not going to improve much. The size of models and amount of compute and training data required to create them is increasing much faster than their performance is increasing, and they’re already putting serious strain on the world’s ability to build and power computers, and the world’s ability to get human-written text into training sets (hence why so many sites are having to deploy things like Anubis to keep themselves functioning). The levers AI companies have access to are already pulled as far as they can go, and so the slowing of improvement can only increase, and the returns can only diminish faster.
I can only say I hope you’re right. I don’t like the way things are going, but I need to do what I can to adapt and survive so I choose to not put my hopes on AI failing anytime soon.
By the way, thank you for the thoughtful responses and discussion.