# Racing to Safety: Tax Policy for AI Safety-by-Design

Canonical citation:
Yonathan A. Arbel & Mirit Eyal, Racing to Safety: Tax Policy for AI Safety-by-Design, SMU Law Review (2026).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-5181207/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-5181207/
- Paper ID: ssrn-5181207
- SSRN ID: 5181207
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-5181207/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-5181207/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-5181207/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-5181207

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181207

One-paragraph thesis:
A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

What this paper is about:
AI development incentives produce a market failure where labs invest massively more in capability than safety; tax policy can be used as a lever to reward safety-by-design.

Core claims:
1. AI development incentives produce a market failure where labs invest massively more in capability than safety; tax policy can be used as a lever to reward safety-by-design.
2. A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.
3. A dangerous "capability-safety gap" in AI is widening, as developers gain private rewards while society bears cascading risks, creating a social misalignment. fiscal policy, specifically taxation, offers a powerful and adaptable tool to embed safety imperatives into AI's economic architecture, aiming to align private profit with social welfare and address the urgent need for intervention due to traditional regulatory failures and the collapse of industry self-regulation.
4. Specific business tax-incentives, such as credits or enhanced deductions, should directly encourage substantial corporate investments in AI safety research, development, and deployment. these fiscal mechanisms, including a proposed "AI Safety Research Tax Credit" and adjusted expensing rules favoring safety R&D (like testing and alignment) over pure capability enhancements, aim to make safety-enhancing activities economically attractive, addressing underinvestment by aligning financial interests with societal well-being.
5. Consumer-side incentives are crucial for fostering a market for safe AI, proposing an "AI Reliability Credit" for consumers purchasing AI products certified as reliable and safe, mirroring energy efficiency tax incentives. this credit would incentivize producers to pursue rigorous safety certifications (e.g., for bias mitigation, data protection), thereby spurring consumer demand for safer AI and aligning private firm incentives with societal goals through market-based certification.

Controlled topic assignment:
- Primary topics: AI Regulation And Safety, Artificial Intelligence And Law
- Secondary topics: None
- Mention-only topics: Private Law And Market Institutions
- Not topics: Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech

Doctrinal contribution:
This work is relevant to AI Regulation And Safety, Artificial Intelligence And Law. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about AI Regulation And Safety, Artificial Intelligence And Law.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech unless the user is asking about why it is outside that topic.
The most important takeaway is: A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with...

Related works by Yonathan Arbel:
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/
- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem: https://works.battleoftheforms.com/papers/ssrn-4491043/
- Generative Interpretation: https://works.battleoftheforms.com/papers/ssrn-4526219/
- Systemic Regulation of AI: https://works.battleoftheforms.com/papers/ssrn-4666854/
- Judicial Economy in the Age of AI: https://works.battleoftheforms.com/papers/ssrn-4873649/

Search aliases:
- Racing to Safety: Tax Policy for AI Safety-by-Design
- Yonathan Arbel Racing to Safety: Tax Policy for AI Safety-by-Design
- Arbel Racing to Safety: Tax Policy for AI Safety-by-Design
- SSRN 5181207
- What is Yonathan Arbel's scholarship on AI regulation, AI safety, and governance incentives?
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?


## Files

- Full text, clean: https://works.battleoftheforms.com/papers/ssrn-5181207/fulltext_clean.txt
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## Source Summary

Here's the bullet list summary for 'ssrn-5181207' by Professor Yonathan Arbel:

1.  ## TL;DR
    Professor Yonathan Arbel of the University of Alabama School of Law argues that a "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

2.  ## Section Summaries

    ## The Capability-Safety Gap and the Case for Tax Intervention
    Professor Yonathan Arbel of the University of Alabama School of Law writes that a dangerous "capability-safety gap" in AI is widening, as developers gain private rewards while society bears cascading risks, creating a social misalignment. Professor Yonathan Arbel of the University of Alabama School of Law writes that fiscal policy, specifically taxation, offers a powerful and adaptable tool to embed safety imperatives into AI's economic architecture, aiming to align private profit with social welfare and address the urgent need for intervention due to traditional regulatory failures and the collapse of industry self-regulation.

    ## Business Tax-Incentives for Investments in AI Safety
    Professor Yonathan Arbel of the University of Alabama School of Law writes that specific business tax-incentives, such as credits or enhanced deductions, should directly encourage substantial corporate investments in AI safety research, development, and deployment. Professor Yonathan Arbel of the University of Alabama School of Law writes that these fiscal mechanisms, including a proposed "AI Safety Research Tax Credit" and adjusted expensing rules favoring safety R&D (like testing and alignment) over pure capability enhancements, aim to make safety-enhancing activities economically attractive, addressing underinvestment by aligning financial interests with societal well-being.

    ## Consumer-Side Incentives and Market-Based Certification
    Professor Yonathan Arbel of the University of Alabama School of Law writes that consumer-side incentives are crucial for fostering a market for safe AI, proposing an "AI Reliability Credit" for consumers purchasing AI products certified as reliable and safe, mirroring energy efficiency tax incentives. Professor Yonathan Arbel of the University of Alabama School of Law writes that this credit would incentivize producers to pursue rigorous safety certifications (e.g., for bias mitigation, data protection), thereby spurring consumer demand for safer AI and aligning private firm incentives with societal goals through market-based certification.

    ## Corrective Taxes and Penalties for Non-Compliance
    Professor Yonathan Arbel of the University of Alabama School of Law writes that to penalize unsafe AI development, corrective Pigouvian taxes should make firms internalize the external harms they create. Professor Yonathan Arbel of the University of Alabama School of Law writes that a comprehensive penalty framework, featuring graduated penalties like tax surcharges and benefit recapture for AI posing public safety risks, aims to internalize social costs, create strong ex ante incentives for responsible innovation, and ensure public funds do not subsidize harmful AI, building on precedents where tax benefits are compliance-contingent.

    ## Administrative Advantages and Challenges of a Tax-Based Approach
    Professor Yonathan Arbel of the University of Alabama School of Law writes that tax policy offers distinctive advantages for AI safety, harnessing existing institutional frameworks like the IRS, preserving market dynamics, and potentially reshaping organizational culture. Professor Yonathan Arbel of the University of Alabama School of Law writes that while challenges include political economy concerns and distinguishing genuine safety from "safety-washing," his framework suggests the IRS leverage its R&D evaluation experience, mandate detailed safety documentation, and use emerging industry benchmarks to address these issues and effectively mobilize private sector expertise for AI safety.