Yesterday I had the privilege of attending a New York State forum about the future of tech careers and how schools statewide might consider to expanding upon K-12 computing education. Hosted by the Center for an Urban Future in Albany, it included two complementary panels: One featuring social impact tech leaders and a second featuring key policymakers in New York State.

The panelists started by anchoring the room in real-time labor market stats about the way AI is upending jobs: 200,000 more jobs in AI are expected by 2030, and 39% of all core jobs will be disrupted by AI. And yet, still only 52% of high schools in New York State offer any computer science.

The two biggest problems that rose to the surface for me were:

Some of the policy ideas surfaced by the group included dedicated leadership and support for CS education at the state level, improved teacher training programs, and more cross-sector collaboration between the public and private sectors in this work.

I think these are all really good ideas. But in hearing the discussion, I also noticed how the panelists spoke about how tough it is to learn as fast as what's required to simply "keep up" with today's tech education. One panelist rightly pointed out that curriculum can in no way keep up at the rate that things are change in industry. This made me wonder if there are a couple of other alternative and creative options for how we might address both of these points.

I've been actively involved in tech workforce development initiatives in New York since 2014 and continue to support this work in my daily practice today.

While I recognize there is a lot more nuance and complexity to all of the issues surfaced, here are two thoughts that surfaced for me during yesterday's discussion that I thought I'd share to keep the conversation going.

The panelists spoke a lot about the importance of tracking outcomes with data. Today, those metrics have largely included things like measuring school participation, student enrollment, program completion rates, and quality benchmarks. While important, these metrics primarily help us track activity, not necessarily the long-term impact. I'm really curious to explore new metrics for this new era.

I'm particularly interested in how any K-12 CS education initiatives ultimately help drive toward the real-time learning behaviors that I see among my successful peers in the tech industry today. And just like the panelists observed, one thing I'm seeing loud and clear is that the people who are most valuable to the industry are those who can keep up.

I asked a question in session that posed a provocative thought: Can we combine the problem of the teacher shortage with the labor market displacements and consider alternative ways of teaching some of these core areas?

Interestingly, the room thought I was suggesting that we substitute teachers for AI tools, which was very clearly rejected in hand. (Alas, yet another side effect of walking around the world wearing AI glasses...) But what I was really curious to ask about was something much simpler (and hopefully less controversial): How can we lean into the changing labor market and get more people to become teachers?

Even from where I sit in industry, one of the most powerful ways that I've learned how to learn as an adult is not only by building in industry, but by teaching back as I learn. This is why throughout the past 10 years I've been so actively involved in so many workforce development initiatives with high school and college students. It's also why, even at my current company's stage as a 1-person organization, I'm already partnering with over a dozen interns, both in high school and college, to help me ideate on important foundational concepts.

I think there's a really interesting white space to explore how we could invite more of these studio-style collisions, and also lean into the zeitgeist of needing teachers more than ever to consider a different type of nationwide upskilling effort to get more teachers (even if the role looks a little different).

From assembly lines to cubicle farms, the way we work has already changed so much in the last century. And we don't yet have a good enough sense of what skills we'll all need to thrive in this AI-native era. But one thing is clear: We need to learn how to keep learning.

As somebody building in the K-12 education space (even while I'm not building a product designed for in-classroom experiences) it's imperative that I have an understanding of the dialogue, the discourse, and the leading figures in the conversation. I showed up to remind myself that I'm not building in a vacuum, but alongside the same moment in time that a lot of this important discourse is shaping our next steps.

I'm excited to see how the conversation continues to evolve in the months and years ahead and to keep engaging in real-time with students, teachers, and policymakers on some of these open questions. And I'd love to hear what else I'm not thinking about yet too.