As Tao points out, merely suggesting new open questions isn't really sufficient. Part of what gives these problems their fame is their notoriety, their difficulty, the fact that many prodigious mathematicians have spent an evening or week or month or several years studying it.

It wouldn't be as interesting if it had just been solved by the fifth random mathematician who considered it

Notably, gardening a new field of study in math is somewhat nontrivial. You have to introduce the field, illustrate some relevance or connections, and then - and this is key - not solve all of the low-hanging fruit yourself! Because you need somebody else to become an expert in that particular field.

The analog in programming is: if a large company merely open sources a product that's decent but not great and in a language nobody wants to maintain, but they don't commit to maintaining it themselves.

Suddenly there's a bit of a vacuum because in order to provide something of value, you either need to:

1. Implement something more complete than was initially open sourced

2. Or maintain something in a horrendous language while incrementally improving it and keeping it relevant

3. Or rewrite it into a tolerable and maintainable modern language.

What the large company has done is create a vacuum in the tool space where you now require extreme motivation to get someone else to step in.

Note that in this scenario, in 2026, it's actually not such a big deal. I think several recent models could happily translate it into a more maintainable language themselves or happily maintain it in the original crufty one. And so the question is: which parts of this analogy are true in math, too?