5,697 karma · joined January 12, 2014
Intelligence is one thing, being able to figure out how get a task done (say). But understanding that no, I don't want you to exploit a backdoor or blackmail my teammate or launch a warhead even though that might expedite the task. Or why some task is more important than another. Or that solving the P=NP problem is more fulfilling than computing the trillionth digit of pi. That's perhaps a different thing entirely, completely disjoint with intelligence.
And by that definition, maybe we are in the neighborhood of AGI already. The things can already accomplish many challenging tasks more reliably than most humans. But the lack of wisdom, emotion, human alignment, or whatever we want to call it, lead it to accomplish the wrong tasks, or accomplish them in the wrong way, or overlook obvious implicit requirements, may cause people to view it as unintelligent, even if intelligence is not the issue.
And that may be an unsolvable problem because AI simply isn't a living being, much less human. It doesn't have goals or ambitions or want a better future for its children. But it doesn't mean we can never achieve AGI.
Oh, and to your first question, yes it's a huge number of jobs, maybe half of jobs in developed nations. And why not? If you can get AI to do the work of the scientist for a tenth of the price, just give it a general role description and budget and let it rip, with the expectation that it'll identify the most promising experiments, process the results, decide what could use further investigation, look for market trends, grow the operation accordingly, that's all you need from a human scientist too. Plausibly the same for executives and other roles. Of course maybe sometimes the role needs a human face for press conferences or whatever, and I don't know how AI would be able to take that, but especially for jobs that are entirely internal-facing, it seems like there's no particular need for a human. Except that maybe, given the above, yes, you still need a human at the helm.
* Which is a much larger class of jobs than just engineering. And also excludes field engineers and other types of engineers that need a physical body for interacting with customers, etc.**
** Though even then, you could in theory divvy up the engineering part and the customer interaction part of the job, where the human that's doing the interaction part is primarily a proxy to the engineering agent that's in his earbud.
Making five different apps just to claim "native" doesn't seem like a great choice, and obviously for now, delivering new claude features takes priority over a native graphics framework, so electron makes sense. But that doesn't mean it'll be on electron forever.
So basically businesses who focus on maintaining best in class core services and avoid the cruft will be the winners in the AI world.
I guess the future model is, LLMs pay for raw data and news to ingest and use on demand, and ignore the "free" internet. That seems like a good landing point, where quality info is rewarded and cheap spin is not. Of course cheap spin will continue to be produced, but hopefully won't be baked into the system.
I expect rust to gain some market share since it's safe and fast, with a better type system, but complex enough that many developers would struggle by themselves. But IME AI also struggles with the manual memory management currently in large projects and can end up hacking things that "work" but end up even slower than GC. So I think the ecosystem will grow, but even once AI masters it, the time and tokens required for planning, building, testing will always exceed that of a GC language, so I don't see it ever usurping go, at least not in the next decade.
I wish the winner would be OCaml, as it's got the type safety of rust (or better), and the development speed of Go. But for whatever reason it never became that mainstream, and the lack of libraries and training data will probably relegate it to the dustbin. Basically, training data and libraries >>> operational characteristics >>> language semantics in the AI world.
I have a hard time imagining any other language maintaining a solid advantage over those two. There's less need for a managed runtime, definitely no need for an interpreted language, so I imagine Java and Python will slowly start to be replaced. Also I have to imagine C/C++ will be horrible for AI for obvious reasons. Of course JS will still be required for web, Swift for iOS, etc., but for mainstream development I think it's going to be Rust and Go.
Maybe not a permanent part of the commit, but something stored on the side for a few weeks at a time. Or even permanently, it could be useful to go back and ask, "why did you do it that way?", and realize that the reason is no longer relevant and you can simplify the design without worrying you're breaking something.
I imagine lots of established companies will struggle migrating back to that pattern, but I have to think most new companies will head in that direction, which should let them catch up quickly.
Anyway that's my take. We'll see.
But I think there will be new opportunities for people who are willing and able to learn. Entirely new fields will pop up and somebody will have to work on them. Most likely, the CS grads who are out of a job, or just frustrated and want to do something else.
So I don't think the opportunity to do innovative things and make a difference in the world is gone. But the opportunity to do so by typing code into a text editor may have breathed its last.
From what I hear, that's kind of true across the industry. I wonder how things would be different if all these press releases pointed to Temporal as the cause rather than AI. It's kind of weird to think about. (Not that I think Temporal is the reason behind the layoffs either, but just as a thought experiment).
Still, this seems useful for being able to see at a glance. I have no idea where most of my own projects would land.
No data points yet, except now this one.
Still, all the bitcoin stuff, music, other side ventures, most of the international expansion, attempts to appeal to bigger businesses, the recent "focus local" vision, all hardly made a dent in the respective markets and I wouldn't be surprised if they lost money or are still losing money on most of those things.
Either way, I think this is how it's gonna be. Regardless of whether AI significantly increases productivity (40%? come on), layoffs will be preemptory. Executives will see the lack of productivity boost as being due to lack of pressure, and imagine engineers are just using the AI to make their own lives easier rather than to work more efficiently. You can't really double output velocity because your users will see it as too much churn, so the only choice is to lay off half the workforce and double the workload for those who stay. "Necessity is the mother of invention." They'll overlook the fact that the work AI tools provide only encompasses 10% of your job even if they're 100% efficient.