(1) OP's strategy performs worse than the alternative (2) They already do this, and have resources that allow them to outperform OP at their own strategy
If the returns are really meaningful, i.e. better Sharpe ratio than just holding $SPY or some dead simple strategy like that, then (2) must be true at least _somewhere_.
Obviously this is true, but I think you're missing the point. Trading with ML on price data is a strategy that literally anyone can reproduce and, as is evident by reading the comments in this thread, is something that many people have tried to replicate. In that context, everyone using that strategy is effectively acting as a large fund. Further, a large fund or prop shop can deploy small-scale strategies, I think the limiting factor really tends to be leverage. But if they are just trying to make 5% returns for example, they can deploy a lot of small strategies that make ~5% returns. And that's not mentioning the countless tiny shops operating under the radar trading <10-50 million AUM (really, I think the average fund is much smaller than what you would imagine). What I'm getting at is that there are a lot more market players than the "big guys" and they will either have an equivalent strategy to you or will be better equipped to take advantage of that same alpha because of more capital/better data sources/smarter stats. With that in mind, it seems insane to suggest that you can find significant alpha in such a low-hanging fruit.
Remember that you are trading during one of the longest bull markets in history. It's not hard to make good returns, but it is hard to analyze risk. There are a million and one ways to make 100% y/y, but a fraction of a percent of those will continue to work in the long-term. With a black-box model you cannot properly assess risk. Even with well-understood models, this is something that real industry players struggle with: backtrading alpha != simulation alpha != profitable alpha != long-term alpha.
Their returns kind of suck, but it's more to do with their trading frequency and correlations than anything else.
Another thing to look at is correlation to the market. The less correlated to the market your strategy is, the more valuable it is. This is because investors like uncorrelated strategies. For example, lets say you have n strategies, each with a volatility of sigma and mean return of mu. Allocating all of your money to one strategy or two or all of them won't change your return, it will still be mu. But if the strategies are uncorrelated, and you equal weight each one, your vol will be sigma/sqrt(n) and your return will be mu. This is the essence of Modern Portfolio Theory (MPT): add as many uncorrelated assets that you can.
In no particular order, here's a list of things that matter when evaluating an (equity) strategy: turnover, size of alpha being exploited, Sharpe ratio, correlation to market and sector, correlation to style factors (value, momentum, oil, etc), and net exposure (long or short).
Apologies if I misinterpreted your comment, just my thoughts when reading it.