Dwarkesh Patel: belief

Transcript context

…That’s an interesting question. I don’t think we’re currently seeing that with radiology. I think radiology is not a good example. I don’t know why Geoff Hinton picked on radiology because I think it’s an extremely messy, complicated profession. I would be a lot more interested in what’s happening with call center employees today, for example, because I would expect a lot of the rote stuff to be automatable today. I don’t have first-level access to it but I would be looking for trends of what’s happening with the call center employees. Some of the things I would also expect is that maybe they are swapping in AI, but then I would still wait for a year or two because I would potentially expect them to pull back and rehire some of the people. There’s been evidence that that’s already been happening generally in companies that have been adopting AI, which I think is quite surprising. I also found what was really surprising. AGI, right? A thing which would do everything. We’ll take out physical work, but it should be able to do all knowledge work. What you would have naively anticipated is that the way this progression would happen is that you take a little task that a consultant is doing, you take that out of the bucket. You take a little task that an accountant is doing, you take that out of the bucket. Then you’re just doing this across all knowledge work. But instead, if we do believe we’re on the path of AGI with the current paradigm, the progression is very much not like that. It does not seem like consultants and accountants are getting huge productivity improvements. It’s very much like programmers are getting more and more chiseled away at their work. If you look at the revenues of these companies, discounting normal chat revenue—which is similar to Google or something—just looking at API revenues, it’s dominated by coding. So this thing which is “general”, which should be able to do any knowledge work, is just overwhelmingly doing only coding. It’s a surprising way that you would expect the AGI to be deployed. There’s an interesting point here. I do believe coding is the perfect first thing for these LLMs and agents. That’s because coding has always fundamentally worked around text. It’s computer terminals and text, and everything is based around text. LLMs, the way they’re trained on the Internet, love text. They’re perfect text processors, and there’s all this data out there. It’s a perfect fit. We also have a lot of infrastructure pre-built for handling code and text. For example, we have Visual Studio Code or your favorite IDE showing you code, and an agent can plug into that. If an agent has a diff where it made some change, we suddenly have all this code already that shows all the differences to a code base using a diff. It’s almost like we’ve pre-built a lot of the infrastructure for code. Contrast that with some of the things that don’t enjoy that at all. As an example, there are people trying to build automation not for coding, but for slides. I saw a company doing slides. That’s much, much harder. The reason it’s much harder is because slides are not text. Slides are little graphics, they’re arranged spatially, and there’s a visual component to it. Slides don’t have this pre-built infrastructure. For example, if an agent is to make a change to your slides, how does a thing show you the diff? How do you see the diff? There’s nothing that shows diffs for slides. Someone has to build it. Some of these things are not amenable to AIs as they are, which are text processors, and code surprisingly is.…

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