The rapid progress of artificial intelligence (AI) — especially in recent years — means the choices society makes about regulation, governance, and deployment of AI systems could shape the future in huge ways. The “AI governance & policy” career track emerges as a critical path for people who want to influence that future and steer it toward safety, fairness, and long-term benefit.
In short: as AI systems become more powerful and entrenched in society, the potential benefits and risks become enormous. Those who dedicate themselves to shaping AI policy could have more impact than many traditional career paths — but it requires care, responsibility, and deep understanding of both technical and societal dynamics.
What Is “AI Governance & Policy”?
“Governance” broadly refers to the structures, institutions, and mechanisms through which societies regulate powerful technologies — in this case, AI. It involves governments, international bodies, corporations, civil-society groups, and sometimes ad-hoc coalitions working to set rules, guardrails, and norms around development and deployment of AI.
Within AI governance, the work can aim to:
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Prevent deployment of AI systems that pose substantial catastrophic risk.
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Mitigate collateral harms — for example, making sure AI doesn’t amplify social inequality, worsen injustice, or enable misuse (e.g. in warfare, surveillance, misinformation).
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Guide integration of AI into economy and society in ways that maximize benefit and minimize harm — e.g. balancing innovation with safety, security, fairness, labor-market shifts.
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Limit competitive “arms races” among countries or companies seeking to gain advantage via unchecked AI advances — encouraging cooperation, shared safety standards, global coordination.
Because of this, contributions in AI policy — especially early on — can meaningfully influence whether AI becomes a force for good or a source of serious global risk.
Types of Roles in AI Policy & Strategy
People pursuing this career path can find themselves working in many different kinds of roles, depending on what they enjoy and what challenges they want to tackle. Some of the major categories include:
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Government / Public-sector work — contributing to national or regional regulation, shaping laws or regulatory frameworks around AI, working with governments or intergovernmental bodies.
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Research on AI policy & strategy — studying how AI development should be governed, analyzing societal risks, building proposals and frameworks for safe, equitable AI deployment.
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Industry internal policy & governance — working within AI companies to set internal safety standards, audit deployment practices, ensure corporate responsibility and compliance.
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Advocacy and lobbying — representing public interest, pushing for regulation, influencing public opinion, working with NGOs or civil-society groups to hold industry and government accountable.
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Third-party auditing and evaluation — independent assessment of AI systems, evaluating compliance with safety standards, transparency, ethical considerations; acting as watchdogs or advisory organizations.
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International coordination & diplomacy — given the global nature of AI risk, there’s a place for experts working on treaties, global agreements, cross-country collaborations to manage competition and promote cooperative safety standards.
Because the field is still relatively new and evolving, people often move between these categories — for example, from research → think-tank → government → NGO. The flexibility is helpful because there’s no fixed “roadmap” yet, but that also means uncertainty and the need for adaptability.
Skills, Experience & How to Get Started
Since AI policy lies at the intersection of technology, law, society, and ethics, people entering this field benefit from a diverse skill set. Useful background includes — but is not limited to — computer science, public policy, law, economics, international relations, ethics, or even social sciences.
That said, you don’t always need to have a policy degree. Technical or STEM backgrounds (e.g. a degree in computer science) are increasingly valuable — because many policymakers and institutions lack deep technical understandings, so individuals who can bridge both worlds are in high demand.
Typical steps to get started may include:
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Internship or entry-level roles at think tanks, NGOs, research organisations focused on AI or technology policy.
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Writing analyses — blog posts, research summaries, policy proposals — to build visibility and show ability to think critically about AI risk and governance.
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Pursuing higher studies (e.g. master’s or PhD) in relevant fields such as policy, public administration, international relations, tech governance, or ethics.
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Applying for government internships, fellowships, or fellow-ships in newly formed AI-safety institutes or regulatory bodies.
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Networking — talking to experts, joining communities concerned with AI governance, participating in public-policy discussions, attending conferences, collaborating with others across disciplines.
Because the field is evolving and many positions may not yet exist, accumulating what the review calls “career capital” — knowledge, skills, networks, track record — is often more important than aiming immediately for a top job.
What Kinds of Policies & Practices Could Make a Difference
Proposed governance and safety strategies for AI often focus on limiting risk while preserving beneficial innovation. Some of the recommended approaches include:
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Responsible scaling policies — internal frameworks within companies or industry standards that gradually tighten safety and compliance as AI capabilities increase, ensuring growth doesn’t outpace safeguards.
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Robust information-security standards — protecting access to sensitive data, AI model weights, ensuring secure development pipelines to avoid misuse by malicious actors or state-sponsored threats.
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Liability and regulatory law — updating existing liability frameworks so that companies are legally accountable if AI systems cause serious harm, which can incentivize safer development practices.
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Compute governance — regulating access to high-performance computing resources and AI-training infrastructure, making sure powerful AI doesn’t emerge without oversight or safety testing.
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International coordination & treaties — given that AI risks are global, international agreements and coordination (similar to arms-control treaties) might be necessary to avoid competitive “race to the bottom” among nations or companies.
These measures are not yet universally implemented — the field is young, ideas are debated, and there’s no consensus on the perfect roadmap. But thoughtful governance work now could shape long-term outcomes drastically.
Pitfalls and Risks — Why This Field Is Difficult
Working in AI policy and governance isn’t all idealism and high stakes; there are serious challenges, and this path isn’t right for everyone. The original review warns of several risks:
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Accidentally doing harm — poorly designed policies could backfire. For instance, regulation intended to slow AI development might push it underground, lead to black-market AI, or stifle beneficial innovation.
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Misplaced priorities — focusing on low-probability but high-salience risks while ignoring more immediate, concrete issues. Overemphasis on existential risk can overshadow real-world harms such as bias, inequality, job displacement, privacy.
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Burnout and frustration — policy work often involves bureaucracy, slow progress, unclear results, and long hours. Senior positions may take years to reach, and systemic change can be frustratingly slow.
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Uncertainty and lack of clear roadmap — because the field is new and evolving, there’s no guaranteed path to influence or impact. What seems like a promising avenue today may shift tomorrow.
Because of these pitfalls, people interested in this route are encouraged to move carefully, build broad-based “career capital,” stay flexible, and avoid overcommitting too early.
Who Should Consider a Career in AI Policy & Strategy?
You may be a good fit if you:
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Are deeply concerned about long-term global risks, especially those posed by powerful AI systems.
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Combine — or are willing to develop — both technical understanding of AI (or at least familiarity) and interest in policy, ethics, law, or governance.
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Enjoy research, writing, analysis, and advocacy.
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Are comfortable with ambiguity, long-term thinking, and open-ended problems.
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Want to contribute to shaping global norms and standards, rather than working purely in engineering or product development.
For such people, pursuing a path in AI governance might be one of the highest-impact choices in the coming decade.
AI is no longer a niche academic subject — it is rapidly becoming central to global industry, politics, security, and social change. As such, how we govern AI may determine whether it becomes a tool for progress or a source of risk.
Choosing a career in AI policy and strategy is not easy — it demands humility, willingness to learn, interdisciplinary thinking, and long-term dedication. But for those who engage sincerely, it offers a rare opportunity: to influence the trajectory of one of the most transformative technologies humanity has ever built.
If we invest now in thoughtful, robust policy frameworks, transparency, cooperation, and ethical governance — we may ensure that AI serves humanity broadly, safely, and fairly.
For anyone who cares about the long-term future — and who has the interest and resilience — AI policy isn’t just a career. It could be one of the most consequential jobs of our time.