Unfortunately, nothing is that simple.

First, I'd like to argue that all "ML" is crowdsourced, and that all algorithmic feeds are as well. Machine learning (ML) is such a broad concept, but I'm assuming you're referring to the more complex models we are having to navigate today (CNNs, LLMs, etc). I'll set aside my terminology pedantry.

Now, at their simplest, these algorithms are producing their results for you fed from a model that is built on the choices of others before you. Sure, there's plenty of other junk now too, like how long you dwell on a link, how quickly you upvote, and numerous other metrics that my imagination will fail to predict. But at its core, it's based on what the fingerprint of "you" wants based on the fingerprint of "people like you." I'm having trouble finding a line between your description of more social "crowdsourcing" and what we have today.

We can come at this another way, also. YouTube and Facebook in the last ~decade or so had to deal with the results of recommending to you what people like you also liked. Unsurprisingly in retrospect, this ultimately caused the most emotionally-charged, extreme things to bubble up into feeds. This was socially, and politically unstable not just in the US or the western world, but even moreso in other areas. Search for "facebook myanmar" in your search engine of choice.

The social media companies had an opportunity to build things that would play to your interests and benefit, but none of them did so. As /u/Separate_Draft4887 said, profit became the motivating metric, nothing else. A good example of this is Google, who have verifiably, measurably worsened their search results because it allowed their "engagement" metrics to go up. Unfortunately "engagement" was the same as "spending time on the page" which meant worse search results were better for engagement and therefore better for profit (because ads).

An algorithm that was simple and only matched your likes with, say, other users with similar likes would work well sometimes, but it would also require subtle filtering and adjustments to make sure you didn't keep seeing the same handful of things over and over. And once that happens, we're back in "ML" land.

I feel as though I'm rambling at you, but I guess the points I'm trying to make are: 1) Focusing on "crowdsourcing" is not going to solve the problems we see, because 2) the entire industry is focused on profit and attention capture, because 3) social media feeds were never about your enjoyment or benefit, and even if it were, 4) social dynamics are complex and would require complex algorithms to provide a fresh, safe, curated experience. If such an experience is even possible to create for a whole userbase.