The governance frameworks examined throughout this chapter provide essential tools for managing AI risks, but tools alone don't determine outcomes. Success requires choosing the right priorities, building necessary capabilities, and maintaining frameworks that evolve with the technology.
Technical expertise in government needs dramatic expansion across every major economy. The UK and US AI Safety Institutes demonstrate what's possible with sufficient resources and political support (Dafoe, 2020). This requires competitive compensation to attract top talent, career paths that value public service, exchange programs with industry and academia, and protection from political interference (Zaidan & Ibrahim, 2024). Currently, properly aligning advanced AI systems with human values will require resolving many uncertainties related to the psychology of human rationality, emotion, and biases, and most government agencies lack even basic technical literacy about AI systems (Irving & Askell, 2019).
Audit and assessment capabilities must professionalize into a distinct field. As AI systems become more complex, evaluation requires specialized expertise that goes beyond traditional software testing (Anderljung et al., 2023). Building this profession involves developing certification programs for AI auditors, creating standard methodologies and tools, establishing professional organizations and ethics codes, and ensuring independence from both developers and regulators (Schuett, 2023).
International coordination mechanisms need dedicated resources and authority. Current efforts rely heavily on voluntary participation and limited budgets (Ho et al., 2023). Effective coordination requires dedicated secretariats with technical expertise, funding for participation from developing countries, translation and communication services, and infrastructure for secure information sharing (Maas & Villalobos, 2023).
Governance frameworks must evolve as fast as the technology they govern. Static regulations will quickly become either irrelevant or obstructive (Casper, 2024). Building adaptive capacity into governance systems is essential for long-term effectiveness (Anderljung et al., 2023). This means mandatory annual reviews of capability thresholds, evaluation methodologies, enforcement priorities, and lessons from incidents (McKernon et al., 2024).
Scenario planning helps prepare for discontinuous change in AI development. Current governance assumes relatively continuous AI progress, but development could accelerate suddenly through algorithmic breakthroughs, decelerate due to technical barriers, or bifurcate with different regions pursuing incompatible approaches (Grace et al., 2024). Governance systems need contingency plans for rapid capability jumps, major AI accidents, breakdown of international cooperation, and emergence of artificial general intelligence (Cotra, 2022).
Learning from implementation enables continuous improvement over the critical next few years. The coming period will generate enormous amounts of data about what works in AI governance (Dafoe, 2020). Systematic learning requires tracking governance interventions and outcomes, sharing best practices across jurisdictions, acknowledging and correcting failures, and updating frameworks based on evidence (Cihon, 2019). The temptation will be to lock in current approaches - we must resist this in favor of evidence-based evolution (Dafoe, 2018).
The choices made in the next few years will shape humanity's relationship with artificial intelligence for decades to come. As AI capabilities advance and become more deeply embedded in critical systems, retrofitting governance becomes increasingly difficult (Anderljung et al., 2023). We have the tools, knowledge, and warning signs needed to build effective governance (Bengio et al., 2025). What remains is the collective will to act before events force our hand (Dafoe, 2018).
The path forward requires acknowledging uncomfortable truths: voluntary corporate measures won't suffice for systemic risks (Papagiannidis, 2025), national approaches need unprecedented coordination despite geopolitical tensions (Ho et al., 2023), and international governance faces enormous technical and political challenges (Maas & Villalobos, 2024). Yet history shows that humanity can rise to meet technological challenges when the stakes become clear and immediate (Maas, 2019).
With AI, the stakes could not be higher, and the timeline could not be shorter (Kokotajlo et al., 2025). The question is not whether we need comprehensive governance: the evidence presented throughout this chapter makes that case definitively. The question is whether we'll build it in time, with the technical sophistication and institutional authority required to govern humanity's most powerful technology, and the window for answering that question is narrowing with each new model release.