At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It’s mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”

Video, full transcript, and links to learn more: https://80k.info/nn2

This means creating as many opportunities as possible for surprisingly good things to happen:

Write publicly.
Reach out to researchers whose work you admire.
Say yes to unusual projects that seem a little scary.

Nanda’s own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.

His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.

Most remarkably, he ended up running DeepMind’s mechanistic interpretability team. He’d joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it’s gone reasonably well.”

His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.

In this extended conversation, Neel and host Rob Wiblin discuss all that and some other hot takes from Neel's four years at Google DeepMind. (And be sure to check out part one of Rob and Neel’s conversation!)

What did you think of the episode? https://forms.gle/6binZivKmjjiHU6dA

Chapters:

Cold open (00:00:00)
Who’s Neel Nanda? (00:01:12)
Luck surface area and making the right opportunities (00:01:46)
Writing cold emails that aren't insta-deleted (00:03:50)
How Neel uses LLMs to get much more done (00:09:08)
“If your safety work doesn't advance capabilities, it's probably bad safety work” (00:23:22)
Why Neel refuses to share his p(doom) (00:27:22)
How Neel went from the couch to an alignment rocketship (00:31:24)
Navigating towards impact at a frontier AI company (00:39:24)
How does impact differ inside and outside frontier companies? (00:49:56)
Is a special skill set needed to guide large companies? (00:56:06)
The benefit of risk frameworks: early preparation (01:00:05)
Should people work at the safest or most reckless company? (01:05:21)
Advice for getting hired by a frontier AI company (01:08:40)
What makes for a good ML researcher? (01:12:57)
Three stages of the research process (01:19:40)
How do supervisors actually add value? (01:31:53)
An AI PhD – with these timelines?! (01:34:11)
Is career advice generalisable, or does everyone get the advice they don't need? (01:40:52)
Remember: You can just do things (01:43:51)

This episode was recorded on July 21.

Video editing: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Coordination, transcriptions, and web: Katy Moore

EP 191. Shaping the future of tertiary education data