In pure math at a school like Harvard, the standout kids like the ones in that quote are probably trying to become tenured math professors. There are very few such positions available. You can shoot for the stars, and if you succeed, make about the same as the average software engineer. More likely, get stuck a postdoc. So most students give up pure math at some point. If you realized you weren’t cut out for it in freshman year, you got a head start over the people who got a math phd before finding out the hard way.
This pressure didn’t exist in computer science because there were plenty of tech jobs for anyone competent (not sure if that’s still true in 2025). And you didn’t need to be a genius to build something cool.
Don’t assume that because the article depicts this competition between the US and China, that the authors actually want China to fail. Consider the authors and the audience.
The work is written by western AI safety proponents, who often need to argue with important people who say we need to accelerate AI to “win against China” and don’t want us to be slowed down by worrying about safety.
From that perspective, there is value in exploring the scenario: ok, if we accept that we need to compete with China, what would that look like? Is accelerating always the right move? The article, by telling a narrative where slowing down to be careful with alignment helps the US win, tries to convince that crowd to care about alignment.
Perhaps, people in China can make the same case about how alignment will help China win against US.
This pressure didn’t exist in computer science because there were plenty of tech jobs for anyone competent (not sure if that’s still true in 2025). And you didn’t need to be a genius to build something cool.