Kevin Frazier (University of Texas) posted this to LinkedIn, and with his permission I am copying it over here.
I invited Kevin to draft a short intro. Here is his text. The LI post follows.
There’s a shortage of scholars–junior and senior–studying AI governance. Many of the leading minds in the academy–both in law and more generally–have opted for careers in the private sector. They will undoubtedly do great work for these AI companies but their presence will be missed on campus. How best to make up for their absence is an open question that demands at least partial answers in the short run as another academic year looms.
One answer is to encourage students who are already interested in this space to lean into that curiosity. They should be directed toward classes, clubs, and community groups that may be able to support their investigation of the many legal and policy matters related to AI. Yet, those opportunities may not always be readily available, what then?
When and if you learn of such a student, it’s best not to be caught flatfooted but instead have something to point to — this is my own list of recommendations that I have started sharing when students from across the country and around the world ask for guidance on how to get involved. Please feel free to copy it, share it, amend it, etc. You can also feel free to put a student in touch with me, I’m keen to fan the flames of their AI interest!
From LinkedIn:
Receiving a steady stream of emails from “non-technical” law students asking for guidance on how to get involved in AI policy – here’s my **concise** list of recommendations. Please add to it (h/t Ben Brooks for Recommendation 18):
(Full list appears below the jump)
(1) we’re all technical now. Write that on a sticky note and put it on your mirror.
Stop selling yourself short.
Fake it until you make it.
You can and must dive into the technical weeds to truly add value to this space. Folks who can “speak AI” and “speak law” will be in high demand for the foreseeable future.
(2) treat learning about AI like learning another language – immerse yourself. Take a BlueDot Impact course, read through Google‘s free materials on AI, subscribe to Nathan Lambert‘s substack as well as Sebastian Raschka, PhD‘s and read everything they write. When you do not understand something, chase down whatever information you need to learn to grasp it.
(3) use the tools.
(4) use the tools.
(5) use the tools.
(6) talk about the tools with other people (professors, students, tech folks in your community) and learn what they are building. Then go build it for yourself.
(7) repeat steps 3-5.
(8) follow Helen Toner, Janet Egan, Anton L., Dean Ball, Jack Clark, and anyone they follow and retweet.
(9) read every blog post from OpenAI, Anthropic, and Google DeepMind (especially the really technical ones)
(10) listen to Scaling Laws (Yes, I’m the co-host (as well as a shameless academic)) (link to pod in comments).
(11) apply to join or take courses from GovAI, Institute for Law & AI (LawAI), Center for Security and Emerging Technology (CSET), Horizon Institute for Public Service, Vista Institute for AI Policy, or any org in that universe. Get your foot in the door.
(12) write about AI.
(13) critically analyze AI policy. Then compare and contrast what Charlie Bullock and Adam Thierer had to say about it.
(14) call me. Better yet, come to Austin and I’ll buy you breakfast tacos.
(15) join the AI Opportunity Inventory and help analyze AI tools intended to solve public policy problems (link in comments).
(16) join an AI club or start one.
(17) pat yourself on the back because you’re asking the right questions! Stay relentlessly curious. Just start doing stuff. Consider this your invitation to join everyone trying to figure this all out.
(18) “Own an outcome, don’t just navel gaze. That could be an advocacy outcome, technical outcome, or business outcome. Nothing crystallizes AI policy like actually having to deliver for a community you care about.”
(19) from Nathan Labenz and the Cognitive Revolution pod: remember that AI defies all binaries!
