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Ansem explains the problem with the trenches and how to fix it "Everybody's been trading these $10K market cap coins and not holding anything. The average hold time has been like 30 seconds" "The way you actually make money during bull markets is holding things for an extended period...

30,872 Aufrufe • vor 8 Tagen •via X (Twitter)

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I have a friend who doesn't read anything published in the past 50 years, and the more I think about it, the more I think he's onto something. The reason is that time is the best filter we have for quality. People are bad at judging quality in the moment but very good at getting rid of junk over time. — — "History is not very good at capturing all that is great in art. It is not good at that. There are many great symphonies that have been lost permanently, there are many great painters that died unknown and their paintings are gone, there's novels that have been written that no one will ever read. So history is not good at capturing all this great art. But history is very good at discarding all that is mediocre. And the amount of time that that takes, it's something like 50 years. So over the course of 50 years, what will happen is a lot of stuff that was prominent will be re-filtered and re-filtered and re-filtered, and you'll end up with a smaller group of things which have survived that test of time. So if you think about it right now, if you go back and look at the bestseller lists for 1974, 1973, there's a lot of that that would have been highly regarded at the time, which people do not read anymore for a variety of reasons, and there's some that has survived, and that's a very telling distinction. So in a world where I'm turning 60 this year, you have a limited amount of time, all four of us have active lives, we want to make sure that if we're going to sit down, we're going to read carefully, we're going to meet and we're going to discuss it in detail, we want to make sure that the work is rewarding. And the best way to ensure that is by drawing from the past." amor towles

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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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