OpenAI Is Building an AOL. Here Is Why That Limits AI's Scale

Transcript

Sure, really good question. Ultimately, it comes down to scale. So, what we view those companies as doing is equivalent to what you see, for example, AOL doing in the early internet era. It's building up a walled garden as big as you can possibly make it with as strong a kind of technology as you can possibly make it to convince users to join so that you can then continue to make money from those users once you've got them into your walled garden. And that's really effective in the early days of a technology. It's a very good way of proving this technology works and scaling it to do value extraction, essentially. We saw it happen in the early internet era. We saw it happen in the social media era, where the exactly the same thing

happened. Realistically, between those companies, like if you look at the social media companies now, there is very, very little difference between what those products can actually do. The thing is entirely commoditized. It's just the users that they managed to kind of bring into their ecosystem and hold that. And they are able to make money from that. In terms of innovation and technology, though, once those companies get up to that monopoly position, they no longer are incentivized to continue innovating on the technology. They're incentivized to maintain their hold on their users. We don't want that to happen for machine learning. We think that is a kind of if you think of machine learning as trying to achieve the greatest scale, the most effective can possibly have as a technology, we think that walled garden approach falls

short. It ultimately stops progress at a certain point while that company tries to extract value from those users. We just don't think that should happen. There's an example in social media, we didn't manage to do this. We didn't manage to make an open alternative. Many people tried, but we're beholden to these companies. In the early internet infrastructure, we did manage to do this. And I think if you look at the internet and what happened, how it allowed so many things to flourish across the world, so many completely open things, we want that for machine learning as a technology. We can achieve that by building this as an open system where anyone can participate. And in doing that, we get internet level scale, not like Meta or Facebook level scale.

That's not as open and accessible as it possibly could be compared to the internet running on like every device in the world. And so, from a machine learning perspective, we think those companies will continue to make enormous amounts of money. They'll create walled gardens. They'll make money off their users. They'll make great products with good UX's, but they won't scale as far as a truly open technology can scale, and that's what we build. I could go into a little bit more on the kind of machine learning side with the concept of a world model, but I'll maybe hold that. Let me know if it's worth us going down that that path.

>> Yeah, let's Yeah, let's get into the prediction market because I mean, that's obviously a very hot topic these days, especially with Polymarket looking to get back into into the US. I guess it was what, last week, either last week or the week before, where I think a a US um soldier was arrested because he apparently bet on the successful uh capture of Venezuelan President Nicolás Maduro. And we're coming up on election season, which is going to drive a lot of attention. So, uh there's a lot of obviously, from that with that perspective, launching a prediction market makes a lot of sense.

That said, you could have done anything. Why did you choose to to launch a prediction market as your first sort of mainstream application? Sure. So, what I would say is what we have launched with Delphi is something called an information market. And there's a there's a crucial difference with the information market concept to compared to prediction markets. So, the reason we've launched an information market is because it trades a resource which is required for machine learning. So, underneath everything within machine learning, you need three resources. You need compute, so you need access to GPUs to train a machine learning model. You need data, which is just raw data from the world. You just deploy a sensor in the world and it kind of takes analog data, like a a camera starts converting

analog data into digital data, the image itself, or a microphone converts audio in the world into digital audio, so that then you can process it like technologically. And then finally, you need information. And information is this is kind of fuzzy what it is. Our definition is information is what you get if you play apply intelligence to raw data. And so, typically, intelligence means human intelligence. More and more now it doesn't. It means machine intelligence as well. But ultimately, it means taking some intelligence, so a human, for example, looking at an image and categorizing that image, or labeling something in that image, or segmenting out an area of that image. That is creating information. Machine learning models need all three of those things to to

exist, to improve. And crucially, machine learning models only generate information. That's the only thing they can do. And so, they take raw data and using the value that they have in their parameters and weights, they generate information from that. And so, what Delphi information markets do is allow machine learning models to trade the one thing that they create. They can now, through the information markets, start to take the information that they have. And if they see a price delta that makes sense for them, they can trade that information within those markets. So, that's that they could do that within prediction markets, but if the markets existed, that's fine. But the second piece of it is models and anyone in the world can now create a market. That's the difference with these information

markets. In creating a market, you're essentially posing a question to the world, and you're paying for somebody to answer that question. So, you're buying information. So, the information markets crucially are bidirectional. That's a difference to prediction markets. Prediction markets are just anyone centralized company can say, "I made a bunch of prediction markets. Come and predict in them." Sure. The prediction is the main thing there. In information markets, it's actually bidirectional. Posing a question says, "I'm buying information on this question. I'm incentivizing people who have information to trade and therefore give me the information." And I could go to an existing market and I can trade the information I have if it's more valuable than the information other people have.

In that way, we've created this bidirectional market for information itself. That becomes the flywheel for machine learning improvement. Got you. Okay. So, I mean, I just want to um get a little more bring this I guess a little more down to the ground level. Would it be accurate to kind of characterize this information market as sort of a hybrid between a prediction market and like some sort of I guess like like bidirectional like peer-to-peer Google in in a sense, where people can can source information and uh and and again, like the flywheel effect is is that people learn from each other, the model learns, it becomes more efficient on where to kind of route the right types of queries and and and

pricing and and and all and all sorts of all sorts of things like that. And I would imagine there'd be some sort of reputational system for which information, which actors are the most accurate or the most responsive to [clears throat] incentivize use and that type of stuff. Am I am I interpreting this correctly? Exactly, yeah. And you've actually you've touched on the the piece in that that we feel really strongly about, which is as you mentioned, these markets, A, they just allow the trade of information. That's the kind of purely financial use case. But B, they also create this queryable model of the world through the markets themselves. And so, this is the combination of two concepts of of a world model, essentially. The free market concept from like Hayek and the the Austrian School of Economics,

that if you have free markets over as many things as possible, you those markets start to represent the state of the world because all beliefs are traded in those markets. And so, you get this like economic model of the world within the markets, which I think a lot of people listening are probably familiar with that kind of like theory of economics. The same kind of world model concept exists within machine learning, which is this idea that if you make a model big enough, it can represent the world within its parameters. And then you can ask it questions. The crucial thing behind Jasmyne as a kind of network and as a set of technology is that we combine those two things. We say that through the information markets, we create the economic world model. And through the ability for machine learning models to access all the resources they

need to train and improve on our network, we also create the machine learning world model. But both of those things work in concert to incentivize the creation of the model. And so, that's where you say, "Well, OpenAI could maybe try and create the machine learning world model themselves. What if they could make a better one?" Well, we say they can make that up to a certain scale, but they'll never make it to the scale that true open free market incentivization can make it because we can go into every single corner of the world through completely open creation of information markets, to the point where the incentive exists for somebody in any corner of the world to trade any information, no matter how small it is.

OpenAI are incentivized to gather that information. They're incentivized to gather the big information. So, ultimately, this spiders out and becomes larger than anything they could possibly build. >> If you've been loving Bits N' Bips, [music] don't forget that the show is transitioning to its own feeds on X, YouTube, and your favorite podcast [music] player. If you're not already subscribed to Bits N' Bips on its own channels, go there now and hit that subscribe [music] button so you can keep up with our twice-weekly live streams and Macro Mu crypto breakdowns. Bits N' Bips will only be on the [music] Unchained feed for a few more weeks, so subscribe today to be ready for launch. You can get all the links [music] at unchainedcrypto.com/bitsnbips.