If we want to do prediction, let's just get a ton of features and use that, like they do in machine learning. Why fixate on one number?

Because it makes sense for many different people to study the same number.

In the last month I talked two times about Gottman. The guy got couples into his lab and observed them for 15 minutes while measuring all sorts of variables. Afterwards he did a mathematical model and found that the model has a 91% success rate in predicting whether newly-wed couples will divorce within 10 years.

The problem? The model is likely overfitted. Instead of using the model he generated in his first study I uses a new model for the next study that's also overfitted. If he would have instead work on developing a Gottman metric, other researcher could research the same metric. Other researcher could see what factors correlate with the Gottman metric.

In the case of IQ, IQ is seen as a robust metric. The EPA did studies to estimate how much IQ point are lost due to Mercury pollution. They priced IQ points. The compared the dollar value of the lost IQ points due to Mercury pollution with the cost for filters that reduce Mercury pollution.

That strong datadriven case allowed the EPA under Obama to take bold steps to reduce Mercury pollution. The Koch brothers didn't make a fuss about but payed for the installation of better filters. From their perspective the statistics were robust enough that it doesn't make sense to fight the EPA in the public sphere on the mercury regulation backed up by data driven argument.

The EPA can only do that because IQ isn't a metric that they invented themselves where someone can claim that the EPA simply did p-hacking to make it's case.