The rapid advancement of AI has created a pressing need for well-crafted policies to address risks like misuse, unintended consequences, and ethical dilemmas. However, the talent pipeline for AI policy—especially in the U.S.—remains underdeveloped, with most programs targeting late-career professionals or aligning with specific ideological communities. This leaves gaps in early-career development and political diversity, potentially limiting the effectiveness and balance of future AI policies.

Bridging the Gaps in AI Policy Development

One way to address this challenge could be through programs designed to nurture early-career talent and foster bipartisan engagement. For example, mentorship initiatives, internships, or training programs could help undergraduates and recent graduates build practical policy skills like drafting legislation or regulatory analysis. Simultaneously, tailored outreach to conservatives and Republicans—who are often underrepresented in AI policy discussions—could encourage diverse perspectives and reduce ideological polarization. These efforts might operate independently of existing ideological brands to broaden their appeal.

Building a Sustainable Framework

An MVP could start with a small-scale mentorship program pairing 10-20 early-career individuals with experienced AI policy professionals. If successful, this could expand into workshops on specific topics (e.g., AI in national security) or a fellowship targeting conservatives. Over time, a standalone organization or network might sustain these efforts. Key assumptions—like demand from early-career professionals and conservatives—could be tested through surveys or pilot programs before scaling.

Aligning Incentives and Opportunities

Stakeholders stand to benefit in several ways:

• Early-career professionals gain skills and networks in a high-impact field.
• Policymakers access a wider range of informed perspectives.
• Conservative groups get a seat at the table in shaping AI policy.

Revenue could come from grants, government contracts for training, or membership fees for premium features like exclusive networking events.

By focusing on early talent and political diversity, this approach could strengthen the AI policy ecosystem while mitigating risks through careful, phased execution.