There are two big assumptions in your hypothesis that are incorrect in my opinion: 1. The prevalence if machine learning in trading. 2. That the game wasn't ri

There are two big assumptions in your hypothesis that are incorrect in my opinion: 1. The prevalence if machine learning in trading. 2. That the game wasn't rigged against retail at a point in the past. First, there is very little (almost none) sophisticated ML in trading. Sure, if you call linear regression or simple classification tools ML, then yes, there's plenty of ML in finance, and has been for decades. If you think that there are super sophisticated DNNs running the majority of trading today, you are very far from the truth. The vast majority of trading is based on fairly simple models. It happens mostly based on human insight and exploiting specific market flows. Sure, almost every firm has some token ML overlay for marketing purposes because it was the fad of the recent years. They all want the public to think that they have some super secret sophisticated AI that gives then an edge. It's all a smoke screen. Almost none of them mean it in earnest. In truth, it is incredibly difficult to build anything resembling a scalable and robust strategy with complex ML tools. The reasons are too numerous to describe here. a very small handful of firms have the capability and mandate to actually use these tools with success, but their market participation is minute, basically negligible. Second, the game was always rigged in a sense. Big money could always buy privileged access to liquidity, information and tools. Nothing has changed here. If anything, the commoditization of all three of those things has lowered the barrier of entry. You can run a successful quant fund with a couple of friends now. That was much more difficult before.