Andrej Karpathy: prediction

Transcript context

…I have two answers to that. Number one, I’m almost tempted to reject the question entirely because I see this as an extension of computing. Have we talked about how to chart progress in computing, or how do you chart progress in computing since the 1970s or whatever? What is the y-axis? The whole question is funny from that perspective a little bit. When people talk about AI and the original AGI and how we spoke about it when OpenAI started, AGI was a system you could go to that can do any economically valuable task at human performance or better. That was the definition. I was pretty happy with that at the time. I’ve stuck to that definition forever, and then people have made up all kinds of other definitions. But I like that definition. The first concession that people make all the time is they just take out all the physical stuff because we’re just talking about digital knowledge work. That’s a pretty major concession compared to the original definition, which was any task a human can do. I can lift things, etc. AI can’t do that, obviously, but we’ll take it. What fraction of the economy are we taking away by saying, “Oh, only knowledge work?” I don’t know the numbers. I feel about 10% to 20%, if I had to guess, is only knowledge work, someone could work from home and perform tasks, something like that. It’s still a really large market. What is the size of the economy, and what is 10% or 20%? We’re still talking about a few trillion dollars, even in the US, of market share or work. So it’s still a very massive bucket. Going back to the definition, what I would be looking for is to what extent is that definition true? Are there jobs or lots of tasks? If we think of tasks as not jobs but tasks. It’s difficult because the problem is society will refactor based on the tasks that make up jobs, based on what’s automatable or not. Today, what jobs are replaceable by AI? A good example recently was Geoff Hinton’s prediction that radiologists would not be a job anymore, and this turned out to be very wrong in a bunch of ways. Radiologists are alive and well and growing, even though computer vision is really, really good at recognizing all the different things that they have to recognize in images. It’s just a messy, complicated job with a lot of surfaces and dealing with patients and all this stuff in the context of it. I don’t know that by that definition AI has made a huge dent yet. Some of the jobs that I would be looking for have some features that make it very amenable to automation earlier than later. As an example, call center employees often come up, and I think rightly so. Call center employees have a number of simplifying properties with respect to what’s automatable today. Their jobs are pretty simple. It’s a sequence of tasks, and every task looks similar. You take a phone call with a person, it’s 10 minutes of interaction or whatever it is, probably a bit longer. In my experience, a lot longer. e. It’s a sequence of tasks, and every task looks similar. You take a phone call with a person, it’s 10 minutes of interaction or whatever it is, probably a bit longer. In my experience, a lot longer. You complete some task in some scheme, and you change some database entries around or something like that. So you keep repeating something over and over again, and that’s your job. You do want to bring in the task horizon—how long it takes to perform a task—and then you want to also remove context. You’re not dealing with different parts of services of companies or other customers. It’s just the database, you, and a person you’re serving. It’s more closed, it’s more understandable, it’s purely digital. So I would be looking for those things. But even there, I’m not looking at full automation yet. I’m looking for an autonomy slider. I expect that we are not going to instantly replace people. We’re going to be swapping in AIs that do 80% of the volume. They delegate 20% of the volume to humans, and humans are supervising teams of five AIs doing the call center work that’s more rote. I would be looking for new interfaces or new companies that provide some layer that allows you to manage some of these AIs that are not yet perfect. Then I would expect that across the economy. A lot of jobs are a lot harder than a call center employee. With radiologists, I’m totally speculating and I have no idea what the actual workflow of a radiologist involves. But one analogy that might be applicable is when Waymos were first being rolled out, there’d be a person sitting in the front seat, and you just had to have them there to make sure that if something went really wrong, they’re there to monitor. Even today, people are still watching to make sure things are going well. Robotaxi, which was just deployed, still has a person inside it. Now we could be in a similar situation where if you automate 99% of a job, that last 1% the human has to do is incredibly valuable because it’s bottlenecking everything else. If it were the case with radiologists, where the person sitting in the front of Waymo has to be specially trained for years in order to provide the last 1%, their wages should go up tremendously because they’re the one thing bottlenecking wide deployment. Radiologists, I think their wages have gone up for similar reasons, if you’re the last bottleneck and you’re not fungible. A Waymo driver might be fungible with others. So you might see this thing where your wages go up until you get to 99% and then fall just like that when the last 1% is gone. And I wonder if we’re seeing similar things with radiology or salaries of call center workers or anything like that.…

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