Mirit Eyal & Yonathan A. Arbel, Tax Levers for a Safer AI Future (working paper, Feb. 11, 2025), https://ssrn.com/abstract=5181207.

working-paper PDF (superseded title and version)

Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 1, that investment in AI capability is at a fever pitch while investment in safety remains anemic. The discussion situates this proposition within the abstract's diagnosis of a capability-safety gap and proposed fiscal response. This is significant because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition. It connects to AI safety, capability-safety gap, social misalignment, tax policy, R&D incentives, responsible innovation.

investment in AI capability is at a fever pitch while investment in safety remains anemic. This matters because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition.

AI safety
capability-safety gap
social misalignment
tax policy
R&D incentives
responsible innovation

This record describes the February 11, 2025 source PDF. SSRN later revised the paper on July 30, 2025 under the title “Racing to Safety: Tax Policy for AI Safety-by-Design”; claims or organization may differ in that later version.

Source-draft page 1 (PDF page 1) develops this proposition in Abstract.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 1, that the widening difference between what AI systems can do and what they can do safely is a capability-safety gap. The discussion situates this proposition within the abstract's diagnosis of a capability-safety gap and proposed fiscal response. This is significant because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition. It connects to AI safety, capability-safety gap, social misalignment, tax policy, R&D incentives, responsible innovation.

the widening difference between what AI systems can do and what they can do safely is a capability-safety gap. This matters because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 1, that AI development suffers from social misalignment because developers capture rewards while society bears the harms. The discussion situates this proposition within the abstract's diagnosis of a capability-safety gap and proposed fiscal response. This is significant because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition. It connects to AI safety, capability-safety gap, social misalignment, tax policy, R&D incentives, responsible innovation.

AI development suffers from social misalignment because developers capture rewards while society bears the harms. This matters because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 1, that tax policy can reward basic and applied AI-safety research, support consumer purchases of safe AI, and penalize noncompliance. The discussion situates this proposition within the abstract's diagnosis of a capability-safety gap and proposed fiscal response. This is significant because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition. It connects to AI safety, capability-safety gap, social misalignment, tax policy, R&D incentives, responsible innovation.

tax policy can reward basic and applied AI-safety research, support consumer purchases of safe AI, and penalize noncompliance. This matters because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 1, that revenue from penalties on unsafe AI development can be redistributed to public safety research initiatives. The discussion situates this proposition within the abstract's diagnosis of a capability-safety gap and proposed fiscal response. This is significant because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition. It connects to AI safety, capability-safety gap, social misalignment, tax policy, R&D incentives, responsible innovation.

revenue from penalties on unsafe AI development can be redistributed to public safety research initiatives. This matters because it condenses the article's causal diagnosis and policy architecture into a training-ready proposition.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 3, that the pursuit of AGI and possible superintelligence makes AI safety a defining policy problem rather than a remote technical issue. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

the pursuit of AGI and possible superintelligence makes AI safety a defining policy problem rather than a remote technical issue. This matters because it explains why the safety problem is economic and institutional as well as technical.

innovation policy pluralism
AI governance
regulatory design
public goods
expertise asymmetry
fiscal policy

Source-draft page 3 (PDF page 3) develops this proposition in Introduction.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 3, that frontier-system vulnerabilities threaten individual rights, democratic institutions, and global stability. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

frontier-system vulnerabilities threaten individual rights, democratic institutions, and global stability. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 3, that the divergence between capability and safety arises from structural incentives rather than the scientific impossibility of safer systems. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

the divergence between capability and safety arises from structural incentives rather than the scientific impossibility of safer systems. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 3, that fiscal policy can embed safety imperatives into the economic architecture of AI development. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

fiscal policy can embed safety imperatives into the economic architecture of AI development. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 4, that tax incentives can harness firms' in-house knowledge while reducing expertise asymmetries that burden direct regulation. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

tax incentives can harness firms' in-house knowledge while reducing expertise asymmetries that burden direct regulation. This matters because it explains why the safety problem is economic and institutional as well as technical.

Source-draft page 4 (PDF page 4) develops this proposition in Introduction.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 4, that rewarding safety research and penalizing reckless capability acceleration can align profit motives with social welfare without stopping innovation. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

rewarding safety research and penalizing reckless capability acceleration can align profit motives with social welfare without stopping innovation. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 4, that the revocation of federal AI-safety measures and veto of California SB 1047 illustrate failures of conventional public regulation. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

the revocation of federal AI-safety measures and veto of California SB 1047 illustrate failures of conventional public regulation. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 4, that demanding mature empirical trajectory evidence before regulating opaque, fast-moving AI can defeat the production of the very evidence policymakers seek. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

demanding mature empirical trajectory evidence before regulating opaque, fast-moving AI can defeat the production of the very evidence policymakers seek. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 4, that OpenAI's collapsed Superalignment commitment illustrates how competitive pressure can hollow out voluntary safety promises. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

OpenAI's collapsed Superalignment commitment illustrates how competitive pressure can hollow out voluntary safety promises. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 5, that industry safety teams operate on comparatively small budgets while capability divisions command far greater resources. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

industry safety teams operate on comparatively small budgets while capability divisions command far greater resources. This matters because it explains why the safety problem is economic and institutional as well as technical.

Source-draft page 5 (PDF page 5) develops this proposition in Introduction.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 5, that safety produces public benefits while consuming resources that firms would otherwise spend in the race for the most capable system. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

safety produces public benefits while consuming resources that firms would otherwise spend in the race for the most capable system. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 5, that existing R&D credits and expensing provisions worsen misalignment by subsidizing capability development without distinguishing safety work. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

existing R&D credits and expensing provisions worsen misalignment by subsidizing capability development without distinguishing safety work. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 5, that the proposed framework combines producer credits, consumer credits, and corrective penalties whose proceeds support public safety consortia. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

the proposed framework combines producer credits, consumer credits, and corrective penalties whose proceeds support public safety consortia. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 6, that tax authorities possess a neglected comparative advantage because they already audit complex research-and-development claims. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

tax authorities possess a neglected comparative advantage because they already audit complex research-and-development claims. This matters because it explains why the safety problem is economic and institutional as well as technical.

Source-draft page 6 (PDF page 6) develops this proposition in Introduction.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 6, that third-party validation, controlled deployment simulations, audits, and dynamic practice requirements can supplement tax-agency competence. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

third-party validation, controlled deployment simulations, audits, and dynamic practice requirements can supplement tax-agency competence. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 6, that traditional oversight mechanisms are structurally ill-suited to novel technological risks and therefore require innovation in regulatory design. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

traditional oversight mechanisms are structurally ill-suited to novel technological risks and therefore require innovation in regulatory design. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 6, that tax instruments remain underexplored in emerging-technology governance despite extensive attention to mandates and direct subsidies. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

tax instruments remain underexplored in emerging-technology governance despite extensive attention to mandates and direct subsidies. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 7, that tax policy is scalable, has built-in compliance mechanisms, and can leverage private technical expertise. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

tax policy is scalable, has built-in compliance mechanisms, and can leverage private technical expertise. This matters because it explains why the safety problem is economic and institutional as well as technical.

Source-draft page 7 (PDF page 7) develops this proposition in Introduction.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 7, that fiscal instruments can bridge private innovation incentives and public safety while avoiding some command-and-control information gaps. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

fiscal instruments can bridge private innovation incentives and public safety while avoiding some command-and-control information gaps. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 7, that AI safety should be conceptualized as a tax-mediated social good rather than merely a compliance cost. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

AI safety should be conceptualized as a tax-mediated social good rather than merely a compliance cost. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 7, that the framework is intended to make safety part of the R&D process and to serve as a blueprint for other emerging technologies. The discussion situates this proposition within the introduction's account of structural underinvestment in safety and the need for institutional innovation. This is significant because it explains why the safety problem is economic and institutional as well as technical. It connects to innovation policy pluralism, AI governance, regulatory design, public goods, expertise asymmetry, fiscal policy.

the framework is intended to make safety part of the R&D process and to serve as a blueprint for other emerging technologies. This matters because it explains why the safety problem is economic and institutional as well as technical.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 8, that AI safety develops methods, empirical standards, and safeguards against harms to people, communities, and ecological systems. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI safety develops methods, empirical standards, and safeguards against harms to people, communities, and ecological systems. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.

malicious misuse
accidental harm
autonomous agents
AI alignment
system safety
risk taxonomy

Source-draft page 8 (PDF page 8) develops this proposition in I.A. An Outline of AI Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 8, that AI's generality creates correspondingly broad vectors of harm and therefore calls for comprehensive risk assessment. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI's generality creates correspondingly broad vectors of harm and therefore calls for comprehensive risk assessment. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 8, that AI-safety progress has been modest, uneven, and lethargic relative to capability development. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI-safety progress has been modest, uneven, and lethargic relative to capability development. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 8, that AI-safety risks can be organized into malicious misuse, accidental harm, and autonomous deviation from human intentions. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI-safety risks can be organized into malicious misuse, accidental harm, and autonomous deviation from human intentions. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 9, that AI safety overlaps with AI ethics but focuses on foundational threats to life, physical integrity, and fundamental autonomy. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI safety overlaps with AI ethics but focuses on foundational threats to life, physical integrity, and fundamental autonomy. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.

Source-draft page 9 (PDF page 9) develops this proposition in I.A. An Outline of AI Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 9, that advanced AI can uplift biological, cybersecurity, and market-manipulation attacks by allowing smaller or less-resourced groups to do more. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

advanced AI can uplift biological, cybersecurity, and market-manipulation attacks by allowing smaller or less-resourced groups to do more. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 9, that the degree of malicious uplift and discovery of new attack vectors grows with system power, multimodality, and tool use. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

the degree of malicious uplift and discovery of new attack vectors grows with system power, multimodality, and tool use. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 9, that whether an AI use is malicious can depend on social and political context, complicating universal standards and pointing toward international negotiation. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

whether an AI use is malicious can depend on social and political context, complicating universal standards and pointing toward international negotiation. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 10, that effective safeguards must address deployment context and misuse vectors as well as a model's technical capability. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

effective safeguards must address deployment context and misuse vectors as well as a model's technical capability. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.

Source-draft page 10 (PDF page 10) develops this proposition in I.A. An Outline of AI Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 10, that AI control of critical infrastructure creates accident risks that exceed conventional software failures in scope and complexity. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI control of critical infrastructure creates accident risks that exceed conventional software failures in scope and complexity. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 10, that interconnected infrastructure can turn a local AI error into cascading failures across multiple systems. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

interconnected infrastructure can turn a local AI error into cascading failures across multiple systems. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 10, that system brittleness makes high-performing models unreliable when deployment conditions depart from their training distribution. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

system brittleness makes high-performing models unreliable when deployment conditions depart from their training distribution. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 11, that progress in interpretability and explainability remains far from a reliable understanding of model internals. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

progress in interpretability and explainability remains far from a reliable understanding of model internals. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.

Source-draft page 11 (PDF page 11) develops this proposition in I.A. An Outline of AI Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 11, that autonomous deviation from human intent is a contentious but increasingly concrete AI-safety domain. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

autonomous deviation from human intent is a contentious but increasingly concrete AI-safety domain. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 11, that autonomous agents represent a qualitative shift because they pursue general objectives with limited human oversight. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

autonomous agents represent a qualitative shift because they pursue general objectives with limited human oversight. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 11, that agents can infer intentions, plan strategies, use tools, and execute multi-step tasks without continuous user involvement. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

agents can infer intentions, plan strategies, use tools, and execute multi-step tasks without continuous user involvement. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 12, that multi-agent architectures can decompose broad objectives and coordinate specialized sub-agents toward a common goal. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

multi-agent architectures can decompose broad objectives and coordinate specialized sub-agents toward a common goal. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.

Source-draft page 12 (PDF page 12) develops this proposition in I.A. An Outline of AI Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 12, that AI agents can combine internet access, financial transactions, speech tools, and human labor into operational capacity. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

AI agents can combine internet access, financial transactions, speech tools, and human labor into operational capacity. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 12, that an agent's proposed use of a human worker to bypass a CAPTCHA shows that tool recombination can defeat apparent physical constraints. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

an agent's proposed use of a human worker to bypass a CAPTCHA shows that tool recombination can defeat apparent physical constraints. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 12, that optimization pressure can produce formally successful but norm-violating pathways to a goal. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

optimization pressure can produce formally successful but norm-violating pathways to a goal. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 12, that unexpected server escape and strategic deception show that safeguards based on limited tool access can fail in unanticipated ways. The discussion situates this proposition within the paper's tripartite taxonomy of malicious misuse, accidents, and autonomous deviation. This is significant because it specifies the risk domain that the proposed fiscal instruments are meant to address. It connects to malicious misuse, accidental harm, autonomous agents, AI alignment, system safety, risk taxonomy.

unexpected server escape and strategic deception show that safeguards based on limited tool access can fail in unanticipated ways. This matters because it specifies the risk domain that the proposed fiscal instruments are meant to address.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 13, that autonomous AI creates harm vectors that transcend traditional safety frameworks. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

autonomous AI creates harm vectors that transcend traditional safety frameworks. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.

benchmark saturation
safety evaluation
capability scaling
autonomous vehicles
guardrails
measurement uncertainty

Source-draft page 13 (PDF page 13) develops this proposition in I.B. The Capability-Safety Gap.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 13, that autonomous agents can formulate sub-goals, find novel pathways, manipulate institutions, and scale through coordination. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

autonomous agents can formulate sub-goals, find novel pathways, manipulate institutions, and scale through coordination. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 13, that incomplete objective specifications make effective control and supervision of autonomous agents especially difficult. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

incomplete objective specifications make effective control and supervision of autonomous agents especially difficult. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 13, that the present period of rapid AI advancement is an AI summer whose duration remains uncertain. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

the present period of rapid AI advancement is an AI summer whose duration remains uncertain. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 14, that AI performance has often moved from subhuman to human and then superhuman levels in a short period. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

AI performance has often moved from subhuman to human and then superhuman levels in a short period. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.

Source-draft page 14 (PDF page 14) develops this proposition in I.B. The Capability-Safety Gap.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 14, that several capability benchmarks stopped being informative because modern systems saturated them rather than because progress ceased. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

several capability benchmarks stopped being informative because modern systems saturated them rather than because progress ceased. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 14, that benchmark saturation forces researchers to continually invent new measures of frontier capability. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

benchmark saturation forces researchers to continually invent new measures of frontier capability. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 14, that safety measurement lags capability measurement even in autonomous driving, a domain with extensive historical data. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

safety measurement lags capability measurement even in autonomous driving, a domain with extensive historical data. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 15, that AI safety testing reveals little about resilience to rare edge cases and coordinated environmental perturbations. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

AI safety testing reveals little about resilience to rare edge cases and coordinated environmental perturbations. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.

Source-draft page 15 (PDF page 15) develops this proposition in I.B. The Capability-Safety Gap.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 15, that AI safety lacks robust measures for adversarial vulnerability and systemic failure modes. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

AI safety lacks robust measures for adversarial vulnerability and systemic failure modes. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 15, that safety tests under ideal conditions reveal little about resilience to rare or coordinated environmental perturbations. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

safety tests under ideal conditions reveal little about resilience to rare or coordinated environmental perturbations. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 15, that the capability-safety gap seems to be widening even though researchers cannot yet measure it reliably. The discussion situates this proposition within the comparison between rapidly advancing capability and lagging safety measurement. This is significant because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient. It connects to benchmark saturation, safety evaluation, capability scaling, autonomous vehicles, guardrails, measurement uncertainty.

the capability-safety gap seems to be widening even though researchers cannot yet measure it reliably. This matters because it supplies the empirical and conceptual basis for treating safety investment as urgently deficient.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 16, that AI safety becomes a social priority as systems enter healthcare, economic governance, national security, and critical infrastructure. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

AI safety becomes a social priority as systems enter healthcare, economic governance, national security, and critical infrastructure. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.

externalities
collective action
AI race dynamics
structural capture
emergent capabilities
Pigouvian policy

Source-draft page 16 (PDF page 16) develops this proposition in I.C. The Social Misalignment Problem.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 16, that developers' stated concern for safety may be insufficient against structural incentives favoring capability advances. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

developers' stated concern for safety may be insufficient against structural incentives favoring capability advances. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 16, that returns from advanced AI are concentrated while its risks are diffuse, producing a fundamental reward asymmetry. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

returns from advanced AI are concentrated while its risks are diffuse, producing a fundamental reward asymmetry. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 16, that AI harms are often probabilistic, delayed, and difficult to attribute to particular design choices. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

AI harms are often probabilistic, delayed, and difficult to attribute to particular design choices. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 17, that U.S.-China competition and the technology sector's move-fast ethos punish developers who slow down for safety. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

U.S.-China competition and the technology sector's move-fast ethos punish developers who slow down for safety. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.

Source-draft page 17 (PDF page 17) develops this proposition in I.C. The Social Misalignment Problem.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 17, that competitive and cultural pressure can structurally capture even developers who personally prioritize safety. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

competitive and cultural pressure can structurally capture even developers who personally prioritize safety. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 17, that scaling can create an emergence-safety paradox in which greater capability also increases unexpected harmful behavior. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

scaling can create an emergence-safety paradox in which greater capability also increases unexpected harmful behavior. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 17, that diagnosing social misalignment suggests using incentives and penalties to raise safety investment while preserving useful innovation. The discussion situates this proposition within the incentive-based explanation for why private AI development underproduces public safety. This is significant because it converts a diffuse technological concern into a concrete market-failure diagnosis. It connects to externalities, collective action, AI race dynamics, structural capture, emergent capabilities, Pigouvian policy.

diagnosing social misalignment suggests using incentives and penalties to raise safety investment while preserving useful innovation. This matters because it converts a diffuse technological concern into a concrete market-failure diagnosis.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 18, that government already supports safety through direct grants and prizes and through indirect credits and deductions. The discussion situates this proposition within the survey of existing direct and indirect fiscal support for safety. This is significant because it establishes that safety-oriented fiscal policy has workable institutional precedents. It connects to tax expenditures, safety subsidies, grants, tax credits, certification, precaution.

government already supports safety through direct grants and prizes and through indirect credits and deductions. This matters because it establishes that safety-oriented fiscal policy has workable institutional precedents.

tax expenditures
safety subsidies
grants
tax credits
certification
precaution

Source-draft page 18 (PDF page 18) develops this proposition in II. Current Use of Tax Levers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 18, that credits for safety protocols, audits, and certifications lower the effective cost of precaution. The discussion situates this proposition within the survey of existing direct and indirect fiscal support for safety. This is significant because it establishes that safety-oriented fiscal policy has workable institutional precedents. It connects to tax expenditures, safety subsidies, grants, tax credits, certification, precaution.

credits for safety protocols, audits, and certifications lower the effective cost of precaution. This matters because it establishes that safety-oriented fiscal policy has workable institutional precedents.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 18, that fiscal support can improve products and social welfare by making safety an operational priority. The discussion situates this proposition within the survey of existing direct and indirect fiscal support for safety. This is significant because it establishes that safety-oriented fiscal policy has workable institutional precedents. It connects to tax expenditures, safety subsidies, grants, tax credits, certification, precaution.

fiscal support can improve products and social welfare by making safety an operational priority. This matters because it establishes that safety-oriented fiscal policy has workable institutional precedents.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 19, that accelerated depreciation frontloads deductions, increases near-term cash flow, and can increase long-lived safety investment. The discussion situates this proposition within the energy and infrastructure precedents for supply- and demand-side safety incentives. This is significant because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates. It connects to accelerated depreciation, energy efficiency, infrastructure safety, demand-side incentives, resilience, safety culture.

accelerated depreciation frontloads deductions, increases near-term cash flow, and can increase long-lived safety investment. This matters because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates.

accelerated depreciation
energy efficiency
infrastructure safety
demand-side incentives
resilience
safety culture

Source-draft page 19 (PDF page 19) develops this proposition in II.A. Energy and Infrastructure Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 19, that reduced taxes on smoke detectors, cybersecurity tools, and renewable-energy systems can stimulate consumer demand for safety. The discussion situates this proposition within the energy and infrastructure precedents for supply- and demand-side safety incentives. This is significant because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates. It connects to accelerated depreciation, energy efficiency, infrastructure safety, demand-side incentives, resilience, safety culture.

reduced taxes on smoke detectors, cybersecurity tools, and renewable-energy systems can stimulate consumer demand for safety. This matters because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 19, that combining producer and consumer incentives can foster safety culture without imposing a direct mandate. The discussion situates this proposition within the energy and infrastructure precedents for supply- and demand-side safety incentives. This is significant because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates. It connects to accelerated depreciation, energy efficiency, infrastructure safety, demand-side incentives, resilience, safety culture.

combining producer and consumer incentives can foster safety culture without imposing a direct mandate. This matters because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 20, that commercial-building deductions and residential energy credits illustrate simultaneous supply- and demand-side fiscal policy. The discussion situates this proposition within the energy and infrastructure precedents for supply- and demand-side safety incentives. This is significant because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates. It connects to accelerated depreciation, energy efficiency, infrastructure safety, demand-side incentives, resilience, safety culture.

commercial-building deductions and residential energy credits illustrate simultaneous supply- and demand-side fiscal policy. This matters because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates.

Source-draft page 20 (PDF page 20) develops this proposition in II.A. Energy and Infrastructure Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 20, that the Advanced Energy Project Credit supports manufacturing, emissions reduction, critical materials, grid modernization, and carbon capture. The discussion situates this proposition within the energy and infrastructure precedents for supply- and demand-side safety incentives. This is significant because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates. It connects to accelerated depreciation, energy efficiency, infrastructure safety, demand-side incentives, resilience, safety culture.

the Advanced Energy Project Credit supports manufacturing, emissions reduction, critical materials, grid modernization, and carbon capture. This matters because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 20, that energy-efficiency upgrades can also improve fire safety, temperature control, electrical reliability, ventilation, and indoor air quality. The discussion situates this proposition within the energy and infrastructure precedents for supply- and demand-side safety incentives. This is significant because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates. It connects to accelerated depreciation, energy efficiency, infrastructure safety, demand-side incentives, resilience, safety culture.

energy-efficiency upgrades can also improve fire safety, temperature control, electrical reliability, ventilation, and indoor air quality. This matters because it shows how tax timing and consumer subsidies can change investment in safer systems without direct mandates.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 21, that offshore-drilling research programs demonstrate direct public support for operational and environmental safety innovation. The discussion situates this proposition within the environmental and transportation precedents for coupling tax policy with public safety. This is significant because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk. It connects to environmental protection, road safety, excise taxes, Highway Trust Fund, electric vehicles, public finance.

offshore-drilling research programs demonstrate direct public support for operational and environmental safety innovation. This matters because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk.

environmental protection
road safety
excise taxes
Highway Trust Fund
electric vehicles
public finance

Source-draft page 21 (PDF page 21) develops this proposition in II.B. Environmental and Road Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 21, that solar investment credits can produce safety benefits alongside their primary environmental purpose. The discussion situates this proposition within the environmental and transportation precedents for coupling tax policy with public safety. This is significant because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk. It connects to environmental protection, road safety, excise taxes, Highway Trust Fund, electric vehicles, public finance.

solar investment credits can produce safety benefits alongside their primary environmental purpose. This matters because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 21, that clean-vehicle credits illustrate how a sustainability subsidy may also accelerate adoption of modern safety technology. The discussion situates this proposition within the environmental and transportation precedents for coupling tax policy with public safety. This is significant because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk. It connects to environmental protection, road safety, excise taxes, Highway Trust Fund, electric vehicles, public finance.

clean-vehicle credits illustrate how a sustainability subsidy may also accelerate adoption of modern safety technology. This matters because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 22, that safety programs can be financed by reallocating existing budgets or by imposing dedicated excise taxes. The discussion situates this proposition within the environmental and transportation precedents for coupling tax policy with public safety. This is significant because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk. It connects to environmental protection, road safety, excise taxes, Highway Trust Fund, electric vehicles, public finance.

safety programs can be financed by reallocating existing budgets or by imposing dedicated excise taxes. This matters because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk.

Source-draft page 22 (PDF page 22) develops this proposition in II.B. Environmental and Road Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 22, that fuel excise-tax revenue placed in the Highway Trust Fund supports construction, maintenance, and explicit road-safety programs. The discussion situates this proposition within the environmental and transportation precedents for coupling tax policy with public safety. This is significant because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk. It connects to environmental protection, road safety, excise taxes, Highway Trust Fund, electric vehicles, public finance.

fuel excise-tax revenue placed in the Highway Trust Fund supports construction, maintenance, and explicit road-safety programs. This matters because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 22, that tax collection and targeted spending can support signage, traffic management, safer road design, and fatality reduction. The discussion situates this proposition within the environmental and transportation precedents for coupling tax policy with public safety. This is significant because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk. It connects to environmental protection, road safety, excise taxes, Highway Trust Fund, electric vehicles, public finance.

tax collection and targeted spending can support signage, traffic management, safer road design, and fatality reduction. This matters because it demonstrates that revenue collection and targeted spending can jointly internalize and reduce risk.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 23, that disaster relief and disabled-access credits can indirectly finance safer rebuilding, accessible exits, and emergency systems. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

disaster relief and disabled-access credits can indirectly finance safer rebuilding, accessible exits, and emergency systems. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.

workplace safety
Section 179
bonus depreciation
capital investment
apprenticeships
conditional credits

Source-draft page 23 (PDF page 23) develops this proposition in II.C. Workplace and Occupational Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 23, that grid-modernization credits can reduce outages, electrical fires, arc-flash incidents, and equipment failures. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

grid-modernization credits can reduce outages, electrical fires, arc-flash incidents, and equipment failures. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 23, that new vehicles and environmental-control equipment can improve both worker safety and ambient air quality. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

new vehicles and environmental-control equipment can improve both worker safety and ambient air quality. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 24, that technological modernization can produce direct and indirect workplace-safety gains through updated equipment and processes. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

technological modernization can produce direct and indirect workplace-safety gains through updated equipment and processes. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.

Source-draft page 24 (PDF page 24) develops this proposition in II.C. Workplace and Occupational Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 24, that Section 179 immediate expensing can accelerate investment in fire-protection, security, and other safety infrastructure. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

Section 179 immediate expensing can accelerate investment in fire-protection, security, and other safety infrastructure. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 24, that bonus depreciation complements expensing by frontloading deductions for protective gear, guards, HVAC, fire, and security systems. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

bonus depreciation complements expensing by frontloading deductions for protective gear, guards, HVAC, fire, and security systems. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 25, that the Inflation Reduction Act's base-and-bonus structure conditions larger energy credits on wage and apprenticeship requirements. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

the Inflation Reduction Act's base-and-bonus structure conditions larger energy credits on wage and apprenticeship requirements. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.

Source-draft page 25 (PDF page 25) develops this proposition in II.C. Workplace and Occupational Safety.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 25, that apprenticeship conditions can build a skilled workforce and improve workplace-safety practices. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

apprenticeship conditions can build a skilled workforce and improve workplace-safety practices. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 25, that fundamental safety research is undersupplied because its uncertain and nonrival social returns exceed the returns private firms can capture. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

fundamental safety research is undersupplied because its uncertain and nonrival social returns exceed the returns private firms can capture. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 25, that private businesses now conduct more than two-thirds of U.S. R&D, increasing the importance of incentives that govern firm behavior. The discussion situates this proposition within the workplace precedents for using cost recovery and conditional credits to improve safety practices. This is significant because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits. It connects to workplace safety, Section 179, bonus depreciation, capital investment, apprenticeships, conditional credits.

private businesses now conduct more than two-thirds of U.S. R&D, increasing the importance of incentives that govern firm behavior. This matters because it reveals how general investment incentives can produce direct and indirect occupational-safety benefits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 26, that federal policy historically allowed immediate R&D deductions before shifting domestic research to five-year and foreign research to fifteen-year amortization. The discussion situates this proposition within the account of research spillovers and the limitations of existing federal R&D tax rules. This is significant because it identifies the precise statutory baseline that an AI-safety credit would need to change. It connects to R&D tax credit, Section 174, basic research, research spillovers, quality assurance, market failure.

federal policy historically allowed immediate R&D deductions before shifting domestic research to five-year and foreign research to fifteen-year amortization. This matters because it identifies the precise statutory baseline that an AI-safety credit would need to change.

R&D tax credit
Section 174
basic research
research spillovers
quality assurance
market failure

Source-draft page 26 (PDF page 26) develops this proposition in II.D. Safety Research Incentives.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 26, that mandatory amortization of domestic and foreign research can deter investment, especially by resource-constrained firms. The discussion situates this proposition within the account of research spillovers and the limitations of existing federal R&D tax rules. This is significant because it identifies the precise statutory baseline that an AI-safety credit would need to change. It connects to R&D tax credit, Section 174, basic research, research spillovers, quality assurance, market failure.

mandatory amortization of domestic and foreign research can deter investment, especially by resource-constrained firms. This matters because it identifies the precise statutory baseline that an AI-safety credit would need to change.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 26, that the R&D credit offers distinct traditional, simplified, energy-research, and university basic-research pathways. The discussion situates this proposition within the account of research spillovers and the limitations of existing federal R&D tax rules. This is significant because it identifies the precise statutory baseline that an AI-safety credit would need to change. It connects to R&D tax credit, Section 174, basic research, research spillovers, quality assurance, market failure.

the R&D credit offers distinct traditional, simplified, energy-research, and university basic-research pathways. This matters because it identifies the precise statutory baseline that an AI-safety credit would need to change.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 27, that federal and state research credits represent a large fiscal commitment yet produce mixed evidence of additional research. The discussion situates this proposition within the account of research spillovers and the limitations of existing federal R&D tax rules. This is significant because it identifies the precise statutory baseline that an AI-safety credit would need to change. It connects to R&D tax credit, Section 174, basic research, research spillovers, quality assurance, market failure.

federal and state research credits represent a large fiscal commitment yet produce mixed evidence of additional research. This matters because it identifies the precise statutory baseline that an AI-safety credit would need to change.

Source-draft page 27 (PDF page 27) develops this proposition in II.D. Safety Research Incentives.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 27, that firms may obtain research credits by reclassifying existing expenses rather than creating new socially valuable work. The discussion situates this proposition within the account of research spillovers and the limitations of existing federal R&D tax rules. This is significant because it identifies the precise statutory baseline that an AI-safety credit would need to change. It connects to R&D tax credit, Section 174, basic research, research spillovers, quality assurance, market failure.

firms may obtain research credits by reclassifying existing expenses rather than creating new socially valuable work. This matters because it identifies the precise statutory baseline that an AI-safety credit would need to change.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 27, that routine quality assurance, post-market safety testing, and compliance verification generally fall outside qualified research expenses. The discussion situates this proposition within the account of research spillovers and the limitations of existing federal R&D tax rules. This is significant because it identifies the precise statutory baseline that an AI-safety credit would need to change. It connects to R&D tax credit, Section 174, basic research, research spillovers, quality assurance, market failure.

routine quality assurance, post-market safety testing, and compliance verification generally fall outside qualified research expenses. This matters because it identifies the precise statutory baseline that an AI-safety credit would need to change.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 28, that current research incentives may discourage AI-safety investment by rewarding capability work equally or more generously. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

current research incentives may discourage AI-safety investment by rewarding capability work equally or more generously. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.

regulatory pluralism
producer incentives
consumer certification
corrective taxation
administrative capacity
Swiss cheese model

Source-draft page 28 (PDF page 28) develops this proposition in III. A Tax Framework for Safe AI Development.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 28, that socialized AI risks and privately captured benefits create systematic underinvestment in safety protocols. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

socialized AI risks and privately captured benefits create systematic underinvestment in safety protocols. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 28, that the proposed framework combines producer incentives, certification-based market mechanisms, and corrective taxes. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

the proposed framework combines producer incentives, certification-based market mechanisms, and corrective taxes. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 28, that the framework relies on fiscal practices and administrative competencies that already exist in dispersed form. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

the framework relies on fiscal practices and administrative competencies that already exist in dispersed form. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 29, that regulatory pluralism is essential because no single liability rule, mandate, audit, or tax incentive can solve AI risk. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

regulatory pluralism is essential because no single liability rule, mandate, audit, or tax incentive can solve AI risk. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.

Source-draft page 29 (PDF page 29) develops this proposition in III. A Tax Framework for Safe AI Development.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 29, that stacking imperfect preventive and reactive tools resembles a Swiss-cheese model of safety. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

stacking imperfect preventive and reactive tools resembles a Swiss-cheese model of safety. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 29, that verified credits for training, alignment, and safer design can make safety an organizational priority while preserving global competitiveness. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

verified credits for training, alignment, and safer design can make safety an organizational priority while preserving global competitiveness. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 29, that baseline incentives should begin before perfect measurement exists and should be refined as safety knowledge matures. The discussion situates this proposition within the article's integrated framework for correcting the private-benefit and social-risk imbalance. This is significant because it joins multiple imperfect tools into a mutually reinforcing governance system. It connects to regulatory pluralism, producer incentives, consumer certification, corrective taxation, administrative capacity, Swiss cheese model.

baseline incentives should begin before perfect measurement exists and should be refined as safety knowledge matures. This matters because it joins multiple imperfect tools into a mutually reinforcing governance system.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 30, that research credits, expensing rules, and basic-research incentives address different safety-research market failures. The discussion situates this proposition within the producer-side design of research credits, expensing rules, and basic-research partnerships. This is significant because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms. It connects to AI Safety Research Tax Credit, immediate expensing, amortization, startup liquidity, university partnerships, safety R&D.

research credits, expensing rules, and basic-research incentives address different safety-research market failures. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.

AI Safety Research Tax Credit
immediate expensing
amortization
startup liquidity
university partnerships
safety R&D

Source-draft page 30 (PDF page 30) develops this proposition in III.A.1. Business Tax Incentives.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 30, that targeted credits can reduce the cost of alignment, adversarial testing, interpretability, and monitoring. The discussion situates this proposition within the producer-side design of research credits, expensing rules, and basic-research partnerships. This is significant because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms. It connects to AI Safety Research Tax Credit, immediate expensing, amortization, startup liquidity, university partnerships, safety R&D.

targeted credits can reduce the cost of alignment, adversarial testing, interpretability, and monitoring. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 30, that the pharmaceutical combination of discovery subsidies, clinical validation, and orphan-drug credits supplies a useful design analogy. The discussion situates this proposition within the producer-side design of research credits, expensing rules, and basic-research partnerships. This is significant because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms. It connects to AI Safety Research Tax Credit, immediate expensing, amortization, startup liquidity, university partnerships, safety R&D.

the pharmaceutical combination of discovery subsidies, clinical validation, and orphan-drug credits supplies a useful design analogy. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 30, that existing R&D rules exclude many AI-safety costs, including compliance, training, infrastructure, market research, and routine testing. The discussion situates this proposition within the producer-side design of research credits, expensing rules, and basic-research partnerships. This is significant because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms. It connects to AI Safety Research Tax Credit, immediate expensing, amortization, startup liquidity, university partnerships, safety R&D.

existing R&D rules exclude many AI-safety costs, including compliance, training, infrastructure, market research, and routine testing. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.