1) It's plain by quickly looking at the allocation of capital in investment firms, that AUM is not made by performance; it's marketing. At best people invest when they believe a person is connected to inside information. Saying you have an ML advisor is really just a pre-req to these people.
2) Is that allocation stupid? No, it's not, because actually the powers of mathematics and by extension ML are intrinsically limited for investment returns because they are fat-tailed </Taleb>. For example this author quotes a realistic sharpe (0.8), but didn't calculate the standard deviation in his sharpe, which I would bet a large sum was _at least_ 0.8. Ie: he doesn't really know what his sharpe is. This is because equity assets behave like a student-t distributions with a degree-of-freedom parameter ~2 or less </Mandlebrot, /Bergomi, /Gatheral etc.>. Ie: higher moments such as uncertainty in sharpe, literally do not exist or converge and are unknowable. The only exception is if your strategy explicitly cuts off tails.
Once you understand 2) you begin to understand that there's no such thing as a real quant fund (ie a fund which truly makes money predictably using models) which doesn't trade a liquidity limited book that has quite advanced hedging. Wealthy people are aware of this, which is why the author can't market this product.
If you're doing something silly like holding equities without tail risk control, you literally cannot be quantitatively investing. You are just slowly rediscovering what Kelly, Bergomi, Mandlebrot, Bernay's etc. realized with a little deep thought over pen and paper (while clumsily writing boilerplate software.) That markets are entropy machines rougher than a normal distribution, and any gains come directly from information. (see: Kelly: "a novel interpretation of the information rate".)
For a high latency (ms) market data feed, the returns on information are very very small. Markets are efficient.