I’d say it’s only slightly aligned with improving AI risk research abilities, but it’s moderate-to-highly aligned with career success in general.

The main benefit to a very high GPA (top 5-10%) is as a signal that you’re smart and diligent. If you’re going to a non-brand-name school, that can be useful for putting you within the same league as the applicants from brand-name schools for internships and job opportunities, including for AI risk research.

A secondary benefit is exercising the muscle of diligence and hard work. However, there are other ways you can do this which are more direct aligned with AI risk work, for example independent research, taking AI risk courses, etc.

The benefit to your relevant research abilities would only come from courses with practically relevant course material, like applied ML. I would definitely encourage you to give it your all on those courses. However, while theoretical CS is super cool, knowing it won’t matter for AI risk work. But as Tomasik and others write, college is deeply inefficient for learning, and with the proper effort you can easily learn way more than your undergrad degree will teach you by diligently gathering knowledge available online for free.

Edit: I’d also consider trying to graduate a year or two early if possible. You’ll learn way more by actually working in AI risk, it’s better on shorter timelines, you’ll get paid for time you otherwise would have spent paying for irrelevant courses, and it’s an even stronger signal of intelligence and diligence.

The signalling aspect is probably overwhelmingly important for career success and ultimately alignment. A high GPA will open up roles that you could otherwise not access. Unfortunately hiring processes often filter based on it. Top 100 is likely not good enough to merely rely on brand recognition.

 (I don’t work in this field but in a competitive & technical area and I’m sure that the recent graduate CVs I see are already pre-filtered by HR based on these kinds of blunt metrics.)