AI × Prediction Markets: Machines, Intelligence & the New Science of On-Chain Forecasting

Welcome to today's Fortune Global Space. I'm Celian, CEO of Fortune Protocol and I'll be here fast today. So while we're waiting for our speakers to join this space, let's get started. So our topic today is AI in prediction markets, machine intelligence and the new science of Unchained forecasting. And today conversation starts with a question we all page. Like how do we know which information deserves our trust? I can help us process a great deal of information quickly, but it can also produce the convincing answer that is wrong, repeat a bias to source or miss the context that matters. And prediction markets approach uncertainty in another way, where people put money behind their views and the pressure flag, what participants are willing to pay for an outcome. And that gives us a signal about what they believe. It still depends on who is participating, how much liquidity there is, and how clearly the question is defined. When I joined that process, there is an interesting possibility. It could help people get their evidence of dead forecast and keep the track of changing but many agents use similar models are the same sources. And they may also make the same mistake together. And more activity does not automatically mean better information. We will also explore the relationship in an other direction. Could a prediction market help us check an AI generated claim? What would the incentives look like and who decides what counts as a correct result? Then we'll look at the market making, product design and what it will take for institutions to participate. And for anyone who is new to fortune, let me explain a little bit about what we're building. Fortune protocol is an AI-powered prediction derivative hub, with a focus on liquidity infrastructure for prediction market. And our idea is to bring market information and the tools people need to participate closer together. A prediction market might ask whether a particular event will happen by a certain date. People can take a position on that outcome as they buy and sell the price. Gives us a view of what participants think is likely. That can be useful but the experience also depends on the quality of the market and how clearly its outcome is defined. And one problem we focus is on the fragmentation. Someone may have a few on the fund but still need movement platforms to find market compared to information and work out where they can trade. And we direct them to make that process easier. We never get through more connected interface. Okay, so we have approximately a gas joining us today. So please bring the perspective you know best whether that is infrastructure, AI trading, security, community or any other part of this page. Okay, with that, let's meet our panel. Okay, please share your role in the project. How your work connects with AI prediction markets are the infrastructure around them. First, we have from the AI. Please introduce yourself and share what's your take on today's topic. Okay, GMG, everyone. Thank you, Amy. Okay, so I'm Joseph, CM of DEXA AI. So I'm representing DEXA AI. So at DEXA AI, we focus on making clear to markets easier to understand by bringing together on chain data, markets, indicators, technical analysis and sentiments into one research dashboard. No, the idea here is to help people make sense of what is happening in the market without having to piece together information from dozens of different you know, sources. What interests me right about this about topic and the AI and prediction market is how we turn large amounts of information into beta, you've found this. Okay, I'm not hearing anything from my side. Is it just me or just anyone can hear the AI? Yes, I am a writer, I think. Okay, I think it's from me. You're right, back guys. Can you know, is this okay? My personal can't hear you. I don't know about the others. Okay, okay, so I was talking about like what impressed me about AI and prediction markets, how we can turn large information into beta, informed decisions. And I believe that AI can help us process information at scale, right? While prediction markets give people a way to put their beliefs about crypto to test. And my own hope tick on this matter is that AI can make prediction markets, you know, more informed. But then I believe it can also make the mistake happen faster and much as a larger scale. So now what do we do? I think the real opportunity here is, you know, simply not just putting AI into trading, but it's about building systems that can measure whether predictions are really good, right? If we can manage weeks and land from being wrong. So I'm excited to meet everyone here today. And thank you very much for having me. Okay, thank you Joseph. Next we have from Lincoln Labs. You're next. Welcome. Hey, hello. Thank you so much for having me here for so far. Really excited to be here and and during this panel, I mean, the panel looks amazing, first of all. And this is my first time with Fortune. So kind of excited for this session. Well, my name is Lauren and I'm from Lincoln Labs. Well, we are trying to understand one simple thing. As we know that AI is capable of doing a lot of things. But unfortunately, it can't remember the conversation that you have with AI, you know, for example, let me put this in a better way. So we are working around in a very pretty simple way. Like AI agents are becoming more capable, but still they need a reliable way to remember and access information over time. And you can think about this how we use AI today. You can you can have a long conversation with an AI, give it context, preferences, data, instructions. But once that contrast is gone, the experience can feel like you're starting from scratch. And we all have seen this. So we are trying to fix this. That's the area in collapse is focusing on. We are building infrastructure around storage, memory, and persistent data for AI agents. And I think the bigger vision is here not just about making AI agents modern. It's about making them more persistent and useful over time. So yeah, this is just what I want to add because I know we have very big panels. So we'd love to listen to others because that's what thank you over to you. Thank you, Lauren. Next we have from Goodhouse, welcome. Over to you. Okay, thank you. Thank you to having me here. And I am better than the BD and her Goldhouse. For those new two Goldhouse, wherever you there, WordPress Researcher platform that brings a, I didn't, I didn't say a sense social and payments and applications. Together, it will work on my name is MMJ. So our project is makes the WordPress racing simpler and the more accessible. At Goldhouse, my role focus on ecosystem development, partnerships and connecting projects with users and communities. And particularly interested in the intersection of AI and the production markets. How AI can leverage on chain data to improve market analysis, forecasting and decision making. While, while production makes can prove real time singles and valuable data for AI models. So I believe that combination of AI on-chain data and the production markets can create a more intelligent and in-fascency web-sweep market environment. So that's all from me. Thank you. Okay, thank you. Now next we have from blockchain. Welcome to the space. Thanks, Celine. Always good to be back on Fortune Stage. I'm leave you. I'm father of blockchain, a layer one where AI and post-quantum security are built into the foundation not added on top. So every native transaction carries the post-quantum signature and the AI inside our consensus has to give the exact same answer on every validator bit by bit. So my lens today is simple and I'm not going to talk too much right now. A prediction market is a truth machine. Yeah, AI can make it faster and sharper, but only if we can verify what the AI actually did. That's about it, Celine. Thank you. Okay, thank you, leave you. Next we have from HAI. Please go ahead. Yeah, thank you, Celine. Am I other group? Yeah. Okay, thanks guys. I'm so nice to be here. I'm looking and I'm working on some business developer, WHOI, our breakdowns, WHOI does. We simply bring AI, solution and response quicker and faster and more decentralized to the user. So instead of within for centralized cloud server, to respond to your request or task you want to give it, WHOI actually brings this response faster to you and more private. That's what we basically do. And as for today's topic, the very good topic, I think my own opinion, I think the position market AI is a good mix, but we should know the limits because AI is still a project. It isn't work. It isn't progress. I've not actually reached the limits of AI. So we have to just be careful in the coming future. Okay, thank you. And lastly, we have from Sonvia AI. Please go ahead. Hello guys. Can you all hear me? Hello. I'm Audible. Yeah, I can hear you. I don't know if I'm studying. You're very audible. Okay. Okay. Okay. So hello everyone. So I'm Azibi. I'm the CMO at Sonvia. And yeah, thanks for having us. So yeah, Sonvia is building around a simple idea that AI can create a huge amount of music, but like creating the music is only the beginning. And we also need a way to like give that music ownership identity utility and real economic value. So from our side, we are working on what we call the AI Music Acid Economy. And the idea is to like connect the entire journey of AI generated music from creation and assetization to like digital ownership and discovering discovery collecting trading and licensing. So we also see AI agents playing an important role here and like helping people discover music, evaluate assets, manage collections and find a opportunity to like use music commercially. So what interests us the most is how AI generated content can become something people can actually own use and build around. So rather than just a you know, another piece of content that gets created and forgotten, I think that's the direction we are exploring with Sonvia. So bringing creators, collectors, brand and applications and AI agents into one connected ecosystems. So yeah, I'm looking forward to like share a sharing a perspective on how AI digital assets and automation systems might like develop together. So yeah, over to you, that was all from my side. Okay, thank you, SAP. All right, that was a nice introduction from everyone. So prediction markets bring people used together through financial incentive.