Depends on the metric. Humans who up-voted that material clearly thought it was worth. The problem is distinguishing the various reasons people think something

Depends on the metric. Humans who up-voted that material clearly thought it was worth. The problem is distinguishing the various reasons people think something is worth and using the right context. That requires a lot of intelligence. The fact that modern language models are able to model sentiment and sarcasm as well as they do is a remarkable achievement. Sure there is a lot of work to be done to improve that, especially at scale and in products where humans are expecting something more than a good statistical "success rate", but they actually expect the precision level they are used from professionally curated human sources.