Arvind Narayanan

Major allegations about an Apple employee who joined OpenAI in Jan 2026, kept a laptop, found an exploit in Apple's systems and stole files over a multi-week time. This is similar to what Anthony Levandowski was accused of doing by Google back in 2018

unsolicited advice to college students and recent grads, after lots of recent coffees w/ people entering the job market: the most accomplished person you know may not have the most relevant career advice for you. they can offer immense wisdom ofc, but you should also seek out successful people in their late 20s / 30s who’ve been successful in the current job market. for example, students often tell me that their parents or an older, tenured professor have encouraged them to pursue academia because they’re bookish, intellectually curious, etc. and that may be sincere advice! — but it’s coming from someone who hasn’t confronted the relevant job market in decades. you can’t look at someone’s linkedin trajectory and assume trying to replicate it would pan out the same way now. ok good luck and godspeed.

Companies check their own work through various internal but independent functional units: QA, security red teams, model risk management in banks. **I think it’s time for AI evaluation to become one such unit.** Orgs deploying AI should stand up cross-functional eval teams with their own reporting line. Many reasons: 1) Evals as IP / moat. It’s now widely recognized that evals are the new IP. So it makes sense to have teams whose primary focus is on creating and widening this moat. 2) Evals are harder than you think. This is less well recognized but as someone whose research centers on AI evals this has been my consistent experience. It can't be an afterthought and must be a center of excellence. 3) Evals are inherently cross-functional and require a distinct set of skills. They are judgment heavy, require both AI expertise and deep domain expertise, as well as customer understanding and sophisticated thinking about risk. To do them well, you need competence in data science & stats, business operations, product/customer experience, IT, risk management, and even compliance (depending on the sector). 4) In-house but independent eval teams keep companies honest. A climate where teams are getting top-down mandates to hit deployment targets and show results has resulted in a culture of companies fooling themselves. It is extremely easy to knowingly or unknowingly to do evals poorly, making your AI deployment look much more successful than it is. Eval teams who don’t share the deploying teams’ KPIs are the best defense against this.

At the start of my research career I operated in a deadline-driven mode because that's what most researchers seemed to do. Gradually I discovered the value-driven way of working. I'm glad I had a supportive advisor who didn't make me chase deadlines. It took me 20 years to fully embrace the switch — it requires developing a long-term vision, willpower to create structure without deadline pressure, a theory of value, project management skills, good taste, the willingness to turn projects down, brutal honesty about whether our work is any good (even if it gets published), and a lot more. But there is no going back!

At the start of my research career I operated in a deadline-driven mode because that's what most researchers seemed to do. Gradually I discovered the value-driven way of working. I'm glad I had a supportive advisor who didn't make me chase deadlines. It took me 20 years to fully embrace the switch — it requires developing a long-term vision, willpower to create structure without deadline pressure, a theory of value, project management skills, good taste, the willingness to turn projects down, brutal honesty about whether our work is any good (even if it gets published), and a lot more. But there is no going back!

One question I'm sometimes asked is how my research group picks problems. Do I come up with most of the ideas for new papers, or do the students? Neither! I strongly believe that research is more effective if we pick projects, not problems. What's the difference? - Projects are long-term research agendas that last 3-5 years or more. A productive project could easily produce a dozen or more papers (depends on the field, of course — in some fields papers represent a lot more work than in others). - Projects are defined not by a research question but by a change we want to see in the world. For example, the goal of a current project in my group is to make AI more reliable. We may or may not succeed, but the point is that this is a much more ambitious scope than can be tackled in a single paper. (Some fields have a norm that their job is only to describe the world, not change it. This is culturally jarring to me but even in that case I think projects are better defined in terms of a change you want to see in the research community, if not the external world.) - Projects are best executed by a core team that stays together and provides intellectual continuity but with a diverse and varying set of collaborators for individual papers which helps constantly bring in new perspectives. Why pick projects instead of problems? If your method is to jump from problem to problem, you face a tradeoff. You could pick small problems that you can tackle in a month or two, but in that case the resulting papers may not have much impact. Or your can go deep into a topic for many years (essentially what I've described as a project, but structured as a single paper), but that's extremely risky. In my experience, once a research team is committed to a project, generating the research questions that individual papers in the project will tackle is fairly straightforward. Each paper in the project naturally generates a bunch of new questions and directions for future work. So generating new ideas is not the hard part, rather it is the profusion of ideas. How to select among them? Ideally some combination of intellectual curiosity and whatever best furthers the project's overall goals and vision.

Tenure helps. As does explicit development of one's own "Band Manager" skills — executive function, that is. You can grow in this area if you try, which is a great blessing!