I’m frequently asked for advice on how to break into the industry I’m in or how to achieve a position I’ve held. I’ve been privileged to serve in many interesting and varied roles across sectors, from small nonprofits and garage startups to huge multinationals and even the US Federal Government. While I personally owe a lot of my journey to privilege, luck, and the good graces of others, I know that “be lucky” isn’t particularly useful advice. In the spirit of providing something actionable, I’ve gathered here some reflections on things that I think have served me in my journey. I hope these reflections will be helpful for others on their journey as well, and I encourage those with experience in this space to share more about their experiences, lessons learned, and advice as well!

Note: For the purpose of this post, I’ll be talking about “getting into AI policy” and offering some specific examples, but most of these suggestions should hold regardless of what it is specifically you want to do. For more on entering the tech policy field, see my Emerging Technology Policy Careers profile.

This series comes in five parts:

1. Mindset
2. Be perceived
3. Be the reply guy you want to see in the world
4. Don’t wait to do the work
5. Pay it forward (don’t skip this!)

Once all five parts are posted, I'll make it possible to read them all in one place, if you're into that kinda thing.

---

Be perceived

All right, so you (still) want to get into AI policy! The first question you should ask yourself when trying to move into a new domain is “how would anyone know that this is something I am trying to do?”  

For the purposes of this section, I’ll mostly be talking in terms of getting a paying job in AI policy, but a lot of this will apply even if you aren’t looking for paid employment.

Even though the AI policy market is competitive, there are still many opportunities. Many of those opportunities may not be accessible, however, through public channels (like on job boards). Many jobs are never listed publicly or are published only as a formality once a candidate has already been selected. The world is not a meritocracy, and often “who you know” does make quite a bit of difference, even if people might pretend otherwise.

Don’t despair! Meritocracy actually sucks in a lot of ways, and you can navigate this reality. You can become legible to your prospective collaborators, employers, and funders. You just need to help them to help you. You need to be perceived. 

Whether you are already deep in this field or just getting started, you can make it easier for other people to find out how interested and passionate you are about AI policy. I am not talking about just adding the words “AI Policy Enthusiast” to your LinkedIn headline. Like the classic writing advice instructs: show, don’t tell. There are lots of people that say they want to work on these topics, but what are you doing (or what have you done) that demonstrates to people that you care about AI policy?

Here are some examples of things I have done that helped demonstrate my interest and expertise in this space:

• Created an “AI study group” to audit free online machine learning courses with a group of coworkers
• Followed the hashtags of conferences on social media and engaged in conversations about the presentations (even if I wasn’t physically present!)
• Participated in and helped host meetups and events focused on tech and social impact
• Shared articles about tech policy that I thought were interesting or informative
• Joined a machine learning research paper discussion group
• Publicly asked questions and shared reflections about topics I was learning about

The things you do don’t need to be particularly grand or consequential. They don’t need to be fancy or “official” or especially advanced. Sure, it’s great if you can run a public campaign or draft a new AI policy strategy from scratch, but you can also show your commitment and interest just by starting a three-person reading group with some friends. You can commit to summarizing one article per week on LinkedIn. You can maintain a running list of your favorite lectures on AI on a personal website. There are many ways to demonstrate your interest, growth, and commitment. You just need to give other people the chance to learn about it.

You do not have to wait until you are an “AI policy expert” to do this, either! Rather, it is incredibly useful to “learn out loud” by sharing your questions and journey as you learn. The first item on my list (created an “AI study group”) was something that I did knowing full-well that most, if not all, of the people who joined the study group would know more about the topic than I did. I scheduled the meetings, reserved the room, and handled the logistics, but often I was the one asking the most—and often the most basic—questions during our meetings.

Despite this, not only was I learning, but the other people were learning, too. So they kept showing up. Through my “learning out loud,” my coworkers got answers to questions they might never have asked and got to practice explaining topics that they thought they understood. All the while, whether I knew it or not, I was establishing myself, even if just within my company, as someone who cares deeply about these topics.

I was PERCEIVED even to the extent that people started sending me unsolicited links to articles or invites to events based on my clear interest. If people in your friend group or at work are sending interesting AI policy stuff to you, that’s a good sign that you have effectively indicated that this is a thing you care about.

Step one: Mindset

Step two: Be percieved ✅

Next up: Be the reply guy you want to see in the world