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𝙸𝚗 𝚖𝚎𝚖𝚎𝚌𝚘𝚒𝚗 𝚝𝚛𝚊𝚍𝚒𝚗𝚐, 𝚑𝚊𝚟𝚒𝚗𝚐 𝚝𝚑𝚎 𝚜𝚔𝚒𝚕𝚕 𝚒𝚜 𝚘𝚗𝚎 𝚝𝚑𝚒𝚗𝚐. 𝙷𝚊𝚟𝚒𝚗𝚐 𝚎𝚗𝚘𝚞𝚐𝚑 𝚌𝚊𝚙𝚒𝚝𝚊𝚕 𝚝𝚘 𝚙𝚞𝚝 𝚝𝚑𝚊𝚝 𝚜𝚔𝚒𝚕𝚕 𝚝𝚘 𝚠𝚘𝚛𝚔 𝚒𝚜 𝚊𝚗𝚘𝚝𝚑𝚎𝚛. Memecoins offer some of the biggest opportunities in crypto, but they also come with extreme volatility, thin liquidity and brutal downside. A trader can have the strategy, experience and...

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.Naval: Marxism, besides denying human incentives, also has a problem where it just assumes that everything is finite and we're all just dividing up the same small set of things. Well, the cavemen didn't have color TVs, computers, cars, antibiotics, or medicine. They were not sitting around dividing up the same few things. The knowledge grows, and we create more. It also tends to assume that we can freeze frame at some point in society and say, 'Well, we have enough different kinds of sneakers, enough different kinds of housing, we just need to allocate it better.' And that is not how anything works. David Deutsch has a great definition of wealth, which he says is the set of physical transformations that we can affect. So, when you think about it that way, you realize that knowledge is not just stored capital in the classic Marxist sense (capital vs. labor), but it's also knowledge on what to do with that capital. The cavemen or Palaeolithic ancestors had access to all the same resources we did. They were living on the same Earth, and by the modern environmentalist arguments, they had a better Earth—they had more to do things with. But yet, they couldn't do anything. They were not wealthy by any stretch of the imagination. Why? Because of knowledge. Life is not a zero-sum game, it's a positive-sum game. But we are hardwired to think it's a zero-sum game because, for millions or billions of years, there was no such thing as wealth. There was no such thing as persistent knowledge creation in the environment. What you had was a small amount of resources being divided up, and most of the games were status games—'Which monkey outranks which other monkey?' And that decides which monkey gets to eat first. We played that game for a billion years. Now, we come onto a recent game where, actually, we can all eat, and the big problem is obesity. It's no longer starvation. The big problem is boredom. It's not actually work. There's enough work. So, in this environment, switching your evolved mindset from a zero-sum game to a positive-sum game where we can all win, if we create knowledge together and use the resources that we have to create more resources and more wealth, that's the game we all need to be playing now.

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At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

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83,572 Aufrufe • vor 5 Monaten