Existing apps could give AI a path into customer service

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 1:06:19

   What's XAI's plan to stay on the compute ramp of that all the labs are doing right now? The labs are on track to spend over like 50 to 100 million dollars. You mean the corporations? Sorry, sorry, sorry, yeah. Corporations. The labs are at universities and they're really like a snail. They're not sending you $50 million. You mean the revenue-maximizing corporations? The revenue-maximizing corporations that call themselves labs.

2. 1:06:41

   Are making like 20 to 10 billion, depending, like OpenAI is making 20B revenue, Anthropics, like 10B. Close to a maximum profit AI. XAI is reportedly at like 1B. Like what's the plan to get to their compute level, get to their revenue level and stay there as things get started? Yeah. So as soon as you unlock digital human, you basically have access to trillions of dollars for revenue. So, in fact, you can really think of it like the most valuable companies currently by market cap, their output is digital.

3. 1:07:22

   So, NVIDIA's output is FTPing files to Taiwan. It's digital. Now, those are very, very difficult. Yeah, high value files. They're the only ones that can make files that good. But that is literally their output, the FTP files to Taiwan. Do they FTP them? I believe so. I believe that is the file transfer protocol, I believe. I could be wrong. But either way, it's a bit stream going to Taiwan. Apple doesn't make phones.

4. 1:07:55

   They send files to China. Microsoft doesn't manufacture anything. Even for Xbox, that's outsourced. Again, their output is digital. Meta's output is digital. Google's output is digital. So if you have a human emulator, you can basically create one of the most valuable companies in the world overnight. And you would have access to trillions of dollars revenue. It's not like a small amount. Okay, I see.

5. 1:08:29

   You're saying basically like revenue figures today are just like so, like they're all rounding errors compared to the actual TAM. So just like focus on the TAM and how to get there. I mean, if you take something as simple as say customer service, if you have to integrate with the APIs of existing corporations, many of which don't even have an API, so you've got to make one and you've got to wade through legacy software, that's extremely slow.

6. 1:08:57

   However, if AI can simply take whatever is given to the outsourced customer service company that they already use and do customer service using the apps that they already use, then you can make tremendous headway in customer service, which is, I think, 1% of the world economy or something like that. It's close to a trillion dollars all in for customer service. and there's no barriers to entry. You can just immediately say, we'll outsource it for a fraction of the cost and there's no integration needed.

7. 1:09:31

   You can imagine some kind of categorization of intelligence tasks where there is breadth, where customer service is done by very many people, but many people can do it. And then there's difficulty where there's a best-in-class turbine engine. Presumably, there's a 10% more fuel-efficient turbine engine that could be imagined by an intelligence, but we just haven't found it yet, or GLP-1s are just a few bytes of data.

8. 1:09:57

   Where do you think you want to play in this? Is it a lot of reasonably intelligent intelligence, or is it the very pinnacle of cognitive tasks? Well, I was just using customer service as something that's a very significant revenue stream, but one that is probably not super difficult to solve for. So if you can emulate a human at a desktop, that's just literally what customer service is. And, you know, it's people of average intelligence.

9. 1:10:33

   It's not like, you know, you don't need, like, somebody who's spent many years. You don't need, like, you know, sort of several Sigma good engineers for that. But obviously, as you make that work, you can then, once you have computers working, effectively digital optimists working, you can then run any application. Like, let's say you're trying to design chips. So you could then run your conventional apps, you know, like stuff from Cadence and Synopsys and whatnot.

10. 1:11:08

    And you can say, you can run 1,000 simultaneously or 10,000 and say, okay, given this Antbook, I get this output for the chip. And at some point, you can say, OK, you're actually going to know what the chip should look like without using any of the tools. So basically, you should be able to do a digital chip design. You can do chip design. You watch up the difficulty curve. You could be able to do CAD.

11. 1:11:47

    So you could use NX or any of the CAD software to design things. OK. So you think you started the simplest tasks and walk your way up the different level. Yeah.