At a time of rapid and transformative revolutions, academia serves a crucial role: to identify emerging shifts, highlight their implications, and provide the critical analysis needed to navigate their consequences. Academic researchers, drawing on their deep expertise and broad experience, have the unique ability to uncover issues that might be too complex for others to recognize, too difficult to analyze, or too fragmented to address through isolated efforts. By combining knowledge across disciplines and fostering collaboration, scholars can bridge gaps, challenge prevailing narratives, and illuminate pathways that might otherwise remain obscured. This is precisely what the authors of Tax Levers for a Safer AI Future are doing by applying their distinct expertise in tax law and artificial intelligence policy to offer a novel framework for aligning artificial intelligence development with public safety imperatives.

As AI development accelerates, it often becomes increasingly difficult to determine where we truly stand on the key issues surrounding this transformative technology. The pace of innovation is relentless, with breakthroughs in natural language processing, autonomous systems, and predictive modeling occurring almost daily. Yet one thing is becoming clearer as time goes on: a critical gap is emerging between what these systems can do and what they can do safely. While investors and companies pour billions into advancing AI capabilities, building ever more powerful models capable of reshaping industries, economies, and societies, investment in safety remains minimal, limited to underfunded research initiatives and superficial safety-washing efforts that prioritize optics over substance. The article frames this problem not only as a technological challenge but also as an economic one, where the rewards of powerful AI systems are privatized, accruing to the developers and investors who create and deploy them, while the risks, such as systemic failures, unintended biases, or catastrophic misuse, are borne by society as a whole. The authors identify this as the social misalignment problem and propose a novel solution: leveraging tax policy to realign private incentives with public safety imperatives. Rather than relying on traditional regulations, which are often slow to adapt, reactive in nature, and susceptible to industry influence through lobbying or regulatory capture, the authors argue that targeted tax mechanisms can integrate safety directly into the economic framework of AI development, offering a proactive and adaptable approach to a pressing global challenge.

The proposed framework is built on four key elements that work together to align economic incentives with AI safety, each designed to address a different facet of the misalignment problem. The first component involves tax incentives specifically designed to encourage investment in AI safety research. The authors propose that these incentives should be directly tied to verifiable safety expenditures, such as workforce training to enhance technical expertise in secure AI design, alignment research to ensure systems behave as intended, adversarial robustness testing to protect against malicious exploits, and the development of interpretability frameworks to make AI decision-making transparent and accountable. By making safety-related research financially attractive through mechanisms like R&D tax credits, accelerated expensing for safety-focused investments, and direct research subsidies, companies would have stronger motivation to allocate resources toward developing secure and reliable AI systems rather than focusing solely on expanding capabilities to gain market share or competitive advantage.

Drawing inspiration from the Orphan Drug Tax Credit, which was introduced to address market failures in pharmaceutical research by incentivizing drug development for rare diseases that would otherwise be unprofitable, the authors argue that a similar approach can address the systematic underinvestment in AI safety. Under their proposal, companies that dedicate resources to AI safety research would benefit from enhanced deductions and credits, lowering the financial barriers to conducting this critical work and encouraging a shift in corporate priorities. The framework emphasizes that while precise metrics for AI safety investment remain an evolving challenge, unlike pharmaceutical outcomes with clear regulatory endpoints, waiting for perfect measurement tools would only serve to privilege capability development over safety, further exacerbating the gap. Instead, the authors advocate for a pragmatic approach, starting with broad but verifiable categories of safety expenditure and refining them as the field matures.

The second element of the framework introduces consumer tax credits to promote the adoption of safe AI products. These credits would be available to individuals and businesses purchasing AI systems that meet established safety standards, such as those set by NIST or ISO, which might include robustness against adversarial attacks, transparency in decision-making processes, and resilience against failure in high-stakes applications. By offering tax credits similar to those provided for energy-efficient appliances or electric vehicles, which have successfully shifted consumer behavior toward sustainable choices, the authors aim to create market demand for safer AI technologies, encouraging developers to prioritize safety features in their products and rewarding consumers for choosing reliability over raw power.

The third component focuses on enforcement through escalating tax penalties for companies that fail to meet AI safety standards. These penalties would include direct tax surcharges for preventable safety risks, such as deploying systems that cause foreseeable harm to critical infrastructure or public welfare, and the recapture of previously granted tax benefits if companies’ AI systems result in significant safety failures after receiving incentives. Modeled as Pigouvian taxes, which are designed to correct market failures by imposing costs on activities with negative externalities, these penalties aim to ensure that companies internalize the societal risks of unsafe AI rather than externalizing them onto the public.

The fourth and final element ties the framework together by redirecting revenue from tax penalties into funding for independent AI safety research and the development of robust safety benchmarks. This reinvestment strategy ensures that the financial consequences of non-compliance contribute directly to improving AI safety, creating a feedback loop that strengthens the ecosystem over time. It transforms punishment into progress, using industry failures as a resource to build better tools, standards, and knowledge for the future.

The authors acknowledge that implementing this framework comes with significant challenges. Verifying compliance with AI safety standards is inherently complex due to the lack of standardized, widely accepted metrics, making it difficult to distinguish genuine efforts from superficial ones. This places a substantial administrative burden on tax authorities, who may lack the technical expertise to evaluate AI-specific claims and could require extensive retraining or collaboration with external experts. Industry resistance is another hurdle, as companies may lobby against penalties, argue for weaker standards, or exploit loopholes in the tax code to minimize their obligations. Additionally, the global nature of AI development raises concerns about international enforcement, as firms could relocate to jurisdictions with laxer regulations to avoid penalties, undermining the framework’s effectiveness unless coordinated internationally.

Despite these challenges, the authors present tax policy as a pragmatic and viable tool for addressing the AI safety crisis. By leveraging existing tax infrastructure and drawing on successful precedents like the Orphan Drug Tax Credit and energy efficiency incentives, their framework offers a scalable approach that integrates safety into the economic incentives driving AI development. While not a complete solution and requiring complementary measures like regulation and international cooperation, it represents a significant step toward closing the capability-safety gap and mitigating the social misalignment problem.

While challenges remain, particularly in measuring safety compliance and ensuring global enforcement, the authors present a pragmatic alternative to rigid regulations. By embedding safety into financial incentives rather than relying solely on reactive policy interventions, this approach may offer the best chance of aligning AI development with public safety in a rapidly evolving landscape.