> It won't be able to self-drive cars due to all the edge cases, it won't be able to perform surgeries because it might kill people, etc.

You literally just cherry-picked the most difficult applications of AI. The vast majority of peoples' jobs don't involve life or death, and thus are ripe for automation. And even if the life or death jobs retain a human element, they will most certainly be augmented by AI agents. For example a surgery might still be handled by a human, but it will probably become mandatory for a doctor or nurse to diagnose a patient in conjunction with an AI.

> We went through a similar bubble a few years ago with chatbots

Are you honestly comparing that to now? ChatGPT got to 100 million users in a few months and everyone and their grandma has used it. I wasn't even aware of any chatbot bubble a few years ago, it certainly wasn't that significant.

> even the most useful AI tools (Copilot, etc.) are marginally useful at best

Sure, but you're literally seeing them in their worst versions. ChatGPT has been a life-changer for me, and it doesn't even execute code yet (Code Interpreter does though, which I haven't tested yet)

By 2030 humans probably won't be typing code anymore, it'll just be prompting machines and directing AI agents. By then most peoples' jobs will also be automated.

AI isn't just some fad, it's going to change literally every industry, and way faster than people think. The cynicism here trying to dismiss the implications of AI by comparing it to the metaverse are just absurd and utterly lacking in imagination. Yes there is still a lot of work that needs to be done, specifically in the AI agent side of things, but we will get there, probably way faster than people realize, and the implications are enormous.

Eventually, perhaps. But by 2023? Definitely not.

I think both you and the GP are at opposite ends of the extreme and the reality is somewhere in that gulf in between

That said, AlphaGo went from "hallucinating" bad moves to the best player in the world in a fairly short period of time. If this is at all doable for language models, GPT-x may blow all this out of the water.

I think the state space when looking at something like Go v. natural language (or even formal languages like programming languages or first/second order logic) is not even remotely comparable. The number of states in Go is 3^361. The number of possible sentences in English, while technically infinite, has some sensible estimates (Googling shows the relatively tame 10^570 figure).

Hard disagree. A very clear counterexample from my usage:

Gpt-4 is phenomenal at helping a skilled person work on tangential tasks where their skills generally translate but they don’t have strong domain knowledge.

I’ve been writing code for a decade, and recently I’ve been learning some ML for the first time. I’m using gpt-4 everyday and it’s been a delight.

To be fair, I can see one might find the rough edges annoying on occasion. For me, it’s quite manageable and not much of a bother. I’ve gotten better at ignoring or working around them. There is definitely an art to using these tools.

I expect the value provided to continue growing. We haven’t plucked all of the low-hanging or mid-hanging fruit yet.

I can share chat transcripts if you are interested.

A key difference is that these things, no matter how impressive their technical merits, required people to completely reshape whatever they were doing to get the first bit of benefit.

Modern AI (and really, usually LLMs) has immediate and broad applicability across nearly every economic sector, and that's why so many of us are already building and releasing features with it. There's incredible value in this stuff. Completely world-changing? No. But enough to create new product categories and fundamentally improve large swaths of existing product capabilities? Absolutely.