Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 31, that AI systems generate externalities affecting users and nonusers, giving AI-safety research a broader impact radius than ordinary product research. 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.

AI systems generate externalities affecting users and nonusers, giving AI-safety research a broader impact radius than ordinary product research. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.

Source-draft page 31 (PDF page 31) develops this proposition in III.A.1. Business Tax Incentives.


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working-paper PDF (superseded title and version)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 31, that an AI Safety Research Tax Credit should expand eligible activities to red teaming, explainability, training monitoring, and guardrail development. 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.

an AI Safety Research Tax Credit should expand eligible activities to red teaming, explainability, training monitoring, and guardrail development. 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

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.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 31, that higher credit rates for startups and for late-stage quality assurance could target liquidity constraints and the importance of validation. 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.

higher credit rates for startups and for late-stage quality assurance could target liquidity constraints and the importance of validation. 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 31, that a redesigned Basic Research Credit could support academic, nonprofit, and industry collaboration on safety protocols and robustness. 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.

a redesigned Basic Research Credit could support academic, nonprofit, and industry collaboration on safety protocols and robustness. 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 32, that immediate expensing of safety work and extended amortization of pure capability work would change the relative price of the two activities. 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.

immediate expensing of safety work and extended amortization of pure capability work would change the relative price of the two activities. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.

Source-draft page 32 (PDF page 32) 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 32, that qualifying safety expenditures should include testing frameworks, alignment research, monitoring systems, software, prototypes, licenses, and patents. 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.

qualifying safety expenditures should include testing frameworks, alignment research, monitoring systems, software, prototypes, licenses, and patents. 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 32, that longer amortization for model-size and compute investments can act as a subtle brake on move-fast-and-break-things development. 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.

longer amortization for model-size and compute investments can act as a subtle brake on move-fast-and-break-things development. 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 32, that certified safe AI software should receive immediate expensing even though custom-developed software is ordinarily amortized as an intangible. 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.

certified safe AI software should receive immediate expensing even though custom-developed software is ordinarily amortized as an intangible. 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 33, that a refundable payroll-tax offset would give safety credits immediate value to startups with little income-tax liability. 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.

a refundable payroll-tax offset would give safety credits immediate value to startups with little income-tax liability. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.

Source-draft page 33 (PDF page 33) 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 33, that startup eligibility could be widened by increasing payroll offsets, relaxing gross-receipts limits, and extending the claim period. 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.

startup eligibility could be widened by increasing payroll offsets, relaxing gross-receipts limits, and extending the claim period. 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 33, that the Basic Research Credit is structurally suited to foundational safety inquiry with long horizons, uncertainty, and diffuse benefits. 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 Basic Research Credit is structurally suited to foundational safety inquiry with long horizons, uncertainty, and diffuse benefits. 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 33, that written partnerships between companies and qualified research institutions can create formal channels for safety knowledge production. 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.

written partnerships between companies and qualified research institutions can create formal channels for safety knowledge production. 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 34, that industry-university partnerships combine firms' practical expertise and compute with academia's theory and commitment to open inquiry. 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.

industry-university partnerships combine firms' practical expertise and compute with academia's theory and commitment to open inquiry. This matters because it translates AI-safety goals into administrable changes to familiar business-tax mechanisms.

Source-draft page 34 (PDF page 34) 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 34, that current eligibility rules exclude ethical studies, foreign collaboration, routine data work, capital assets, training, implementation, and many administrative costs. 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.

current eligibility rules exclude ethical studies, foreign collaboration, routine data work, capital assets, training, implementation, and many administrative costs. 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 34, that those exclusions prevent a comprehensive safety framework from qualifying under existing research-credit rules. 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.

those exclusions prevent a comprehensive safety framework from qualifying under existing research-credit rules. 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 34, that targeted safety credits can be built within existing fiscal architecture despite real administrative and operational challenges. 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 safety credits can be built within existing fiscal architecture despite real administrative and operational challenges. 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 35, that an AI Reliability Credit could subsidize household purchases of products certified as safe and reliable. The discussion situates this proposition within the proposed consumer credit and certification market for reliable AI products. This is significant because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers. It connects to AI Reliability Credit, consumer demand, product certification, safety labels, NIST, ISO standards.

an AI Reliability Credit could subsidize household purchases of products certified as safe and reliable. This matters because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers.

AI Reliability Credit
consumer demand
product certification
safety labels
NIST
ISO standards

Source-draft page 35 (PDF page 35) develops this proposition in III.A.2. Consumer Demand.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 35, that a 30 percent credit for qualified consumer AI could reward bias mitigation, data protection, transparency, and reliability. The discussion situates this proposition within the proposed consumer credit and certification market for reliable AI products. This is significant because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers. It connects to AI Reliability Credit, consumer demand, product certification, safety labels, NIST, ISO standards.

a 30 percent credit for qualified consumer AI could reward bias mitigation, data protection, transparency, and reliability. This matters because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 35, that manufacturer certification and an AI Reliability label could mirror the Energy Efficient Home Improvement Credit. The discussion situates this proposition within the proposed consumer credit and certification market for reliable AI products. This is significant because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers. It connects to AI Reliability Credit, consumer demand, product certification, safety labels, NIST, ISO standards.

manufacturer certification and an AI Reliability label could mirror the Energy Efficient Home Improvement Credit. This matters because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 36, that purchase documentation and a capped per-product or household credit could make reliability salient without unlimited fiscal exposure. The discussion situates this proposition within the proposed consumer credit and certification market for reliable AI products. This is significant because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers. It connects to AI Reliability Credit, consumer demand, product certification, safety labels, NIST, ISO standards.

purchase documentation and a capped per-product or household credit could make reliability salient without unlimited fiscal exposure. This matters because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers.

Source-draft page 36 (PDF page 36) develops this proposition in III.A.2. Consumer Demand.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 36, that credible certification can redirect consumer demand and thereby pressure domestic and international producers toward stronger safety standards. The discussion situates this proposition within the proposed consumer credit and certification market for reliable AI products. This is significant because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers. It connects to AI Reliability Credit, consumer demand, product certification, safety labels, NIST, ISO standards.

credible certification can redirect consumer demand and thereby pressure domestic and international producers toward stronger safety standards. This matters because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 36, that consumer incentives should rely on standards established by independent organizations working with public regulators. The discussion situates this proposition within the proposed consumer credit and certification market for reliable AI products. This is significant because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers. It connects to AI Reliability Credit, consumer demand, product certification, safety labels, NIST, ISO standards.

consumer incentives should rely on standards established by independent organizations working with public regulators. This matters because it uses buyer demand to reward verifiable safety and transmit incentives upstream to producers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 37, that Pigouvian taxes can force developers to internalize risks they would otherwise externalize to others. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

Pigouvian taxes can force developers to internalize risks they would otherwise externalize to others. This matters because it complements subsidies with credible downside consequences for preventable safety failures.

Pigouvian tax
tax surcharge
credit recapture
optimal deterrence
harm calibration
safety compliance

Source-draft page 37 (PDF page 37) develops this proposition in III.A.3. Penalizing Unsafe AI.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 37, that corrective taxation preserves firms' choice over whether and how to engage in risky activity, unlike a flat command-and-control prohibition. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

corrective taxation preserves firms' choice over whether and how to engage in risky activity, unlike a flat command-and-control prohibition. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 37, that graduated AI penalties should combine direct tax surcharges with recapture of prior credits and expensing benefits. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

graduated AI penalties should combine direct tax surcharges with recapture of prior credits and expensing benefits. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 38, that recapture ensures that public funds do not continue subsidizing AI systems that cause preventable severe harm. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

recapture ensures that public funds do not continue subsidizing AI systems that cause preventable severe harm. This matters because it complements subsidies with credible downside consequences for preventable safety failures.

Source-draft page 38 (PDF page 38) develops this proposition in III.A.3. Penalizing Unsafe AI.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 38, that penalty revenue should finance public development of AI-safety standards and research. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

penalty revenue should finance public development of AI-safety standards and research. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 38, that tax law already makes benefits contingent on compliance in housing, energy, employment, insurance, and extractive industries. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

tax law already makes benefits contingent on compliance in housing, energy, employment, insurance, and extractive industries. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 38, that AI firms could be required to allocate a minimum share of development spending, illustratively 25 percent, to safety before receiving tax benefits. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

AI firms could be required to allocate a minimum share of development spending, illustratively 25 percent, to safety before receiving tax benefits. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 38, that validating safety spending is difficult because firms possess more information than regulators. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

validating safety spending is difficult because firms possess more information than regulators. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 38, that initial claims can use relatively permissive standards while post-incident investigations and targeted audits apply stricter scrutiny. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

initial claims can use relatively permissive standards while post-incident investigations and targeted audits apply stricter scrutiny. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 39, that tax law's mix of bright-line triggers and flexible administrative standards offers a model for technically complex AI-safety oversight. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

tax law's mix of bright-line triggers and flexible administrative standards offers a model for technically complex AI-safety oversight. This matters because it complements subsidies with credible downside consequences for preventable safety failures.

Source-draft page 39 (PDF page 39) develops this proposition in III.A.3. Penalizing Unsafe AI.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 39, that ex ante investment requirements and calibrated enforcement can bridge private incentives and public safety imperatives. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

ex ante investment requirements and calibrated enforcement can bridge private incentives and public safety imperatives. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 39, that tax policy can preserve market flexibility while aligning organizations with public welfare more dynamically than rigid commands. The discussion situates this proposition within the corrective-tax and recapture mechanisms for internalizing unsafe-development costs. This is significant because it complements subsidies with credible downside consequences for preventable safety failures. It connects to Pigouvian tax, tax surcharge, credit recapture, optimal deterrence, harm calibration, safety compliance.

tax policy can preserve market flexibility while aligning organizations with public welfare more dynamically than rigid commands. This matters because it complements subsidies with credible downside consequences for preventable safety failures.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 40, that tax law's mix of bright-line triggers and flexible standards offers a model for technically complex AI-safety oversight. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

tax law's mix of bright-line triggers and flexible standards offers a model for technically complex AI-safety oversight. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.

organizational culture
private expertise
public goods
knowledge sharing
market flexibility
regulatory ecosystem

Source-draft page 40 (PDF page 40) develops this proposition in III.E. The Case for Fiscal Levers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 40, that tax incentives can reshape organizational culture by making safety research, governance, and compliance financially advantageous. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

tax incentives can reshape organizational culture by making safety research, governance, and compliance financially advantageous. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 40, that fiscal policy can mobilize private expertise and partnerships while leaving firms ownership over how they implement safety. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

fiscal policy can mobilize private expertise and partnerships while leaving firms ownership over how they implement safety. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 40, that treating AI safety as a public good spreads costs across taxpayers while concentrating resources on safety-enhancing work. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

treating AI safety as a public good spreads costs across taxpayers while concentrating resources on safety-enhancing work. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 41, that public-private safety partnerships can share research insights while protecting proprietary information. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

public-private safety partnerships can share research insights while protecting proprietary information. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.

Source-draft page 41 (PDF page 41) develops this proposition in III.E. The Case for Fiscal Levers.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 41, that credits can support precompetitive alliances, third-party certification, open-source tools, and knowledge sharing without requiring disclosure of all proprietary information. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

credits can support precompetitive alliances, third-party certification, open-source tools, and knowledge sharing without requiring disclosure of all proprietary information. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 41, that consumer and producer incentives can foster a sustainable safety ecosystem in which domain experts retain implementation flexibility. The discussion situates this proposition within the comparative case for fiscal instruments within a broader regulatory ecosystem. This is significant because it identifies the institutional advantages that could make tax policy useful despite its limits. It connects to organizational culture, private expertise, public goods, knowledge sharing, market flexibility, regulatory ecosystem.

consumer and producer incentives can foster a sustainable safety ecosystem in which domain experts retain implementation flexibility. This matters because it identifies the institutional advantages that could make tax policy useful despite its limits.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 42, that subsidizing a profitable technology sector creates political and distributional objections because foregone revenue must be financed elsewhere. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

subsidizing a profitable technology sector creates political and distributional objections because foregone revenue must be financed elsewhere. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.

political capture
safety-washing
IRS audits
documentation
pre-certification
output-based incentives

Source-draft page 42 (PDF page 42) develops this proposition in III.F. The Administrative Challenge.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 42, that complex R&D incentives impose documentation, professional-advice, audit, and compliance burdens that may overwhelm their benefits. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

complex R&D incentives impose documentation, professional-advice, audit, and compliance burdens that may overwhelm their benefits. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 42, that tax benefits may fail to influence strategy if they remain nonsalient or peripheral to senior executives. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

tax benefits may fail to influence strategy if they remain nonsalient or peripheral to senior executives. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 43, that the relevant reform can redirect existing capability subsidies toward safety rather than necessarily creating new net benefits. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

the relevant reform can redirect existing capability subsidies toward safety rather than necessarily creating new net benefits. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.

Source-draft page 43 (PDF page 43) develops this proposition in III.F. The Administrative Challenge.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 43, that distributed oversight across tax incentives, grants, exemptions, and partnerships can limit any single agency's discretion. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

distributed oversight across tax incentives, grants, exemptions, and partnerships can limit any single agency's discretion. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 43, that initial eligibility should focus on foundational research and model training because upstream safety advances generate ecosystem-wide spillovers. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

initial eligibility should focus on foundational research and model training because upstream safety advances generate ecosystem-wide spillovers. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 44, that safety-washing can disguise capability investment as safety and therefore requires a constrained, verifiable definition of qualifying work. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

safety-washing can disguise capability investment as safety and therefore requires a constrained, verifiable definition of qualifying work. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.

Source-draft page 44 (PDF page 44) develops this proposition in III.F. The Administrative Challenge.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 44, that the Scott Aaronson episode illustrates how a prestigious safety appointment can serve reputational goals without producing broad safety reform. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

the Scott Aaronson episode illustrates how a prestigious safety appointment can serve reputational goals without producing broad safety reform. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 44, that the IRS can adapt its existing experience evaluating technical experimentation and uncertainty rather than creating an entirely new regulator. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

the IRS can adapt its existing experience evaluating technical experimentation and uncertainty rather than creating an entirely new regulator. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 45, that AI developers should document alignment tests, adversarial evaluations, red-team results, training behavior, failure modes, and mitigation work. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

AI developers should document alignment tests, adversarial evaluations, red-team results, training behavior, failure modes, and mitigation work. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.

Source-draft page 45 (PDF page 45) develops this proposition in III.F. The Administrative Challenge.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 45, that emerging NIST, ISO, IEEE, OECD, European, and industry standards can supply concrete qualification metrics. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

emerging NIST, ISO, IEEE, OECD, European, and industry standards can supply concrete qualification metrics. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 45, that standardized eligibility, pre-certification, automated records, and output-based incentives can reduce administrative burden and gaming. The discussion situates this proposition within the paper's treatment of political economy, verification, safety-washing, and administrative burden. This is significant because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking. It connects to political capture, safety-washing, IRS audits, documentation, pre-certification, output-based incentives.

standardized eligibility, pre-certification, automated records, and output-based incentives can reduce administrative burden and gaming. This matters because it sets boundaries and safeguards needed to prevent a nominal safety subsidy from becoming corporate rent seeking.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 46, that regular review should keep fiscal incentives effective and manageable as AI safety knowledge changes. The discussion situates this proposition within the conclusion's synthesis of the fiscal framework and its broader implications. This is significant because it clarifies both the proposal's ambition and its role as only one layer of technology governance. It connects to responsible innovation, AI safety, tax policy, market failure, technology governance, institutional design.

regular review should keep fiscal incentives effective and manageable as AI safety knowledge changes. This matters because it clarifies both the proposal's ambition and its role as only one layer of technology governance.

responsible innovation
AI safety
tax policy
market failure
technology governance
institutional design

Source-draft page 46 (PDF page 46) develops this proposition in Conclusion.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 46, that implementation is unavoidably difficult, but existing tax tools offer a comparatively practical path for building regulatory capacity. The discussion situates this proposition within the conclusion's synthesis of the fiscal framework and its broader implications. This is significant because it clarifies both the proposal's ambition and its role as only one layer of technology governance. It connects to responsible innovation, AI safety, tax policy, market failure, technology governance, institutional design.

implementation is unavoidably difficult, but existing tax tools offer a comparatively practical path for building regulatory capacity. This matters because it clarifies both the proposal's ambition and its role as only one layer of technology governance.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 46, that the framework addresses social misalignment through producer incentives, consumer credits, and corrective penalties rather than a new standalone agency. The discussion situates this proposition within the conclusion's synthesis of the fiscal framework and its broader implications. This is significant because it clarifies both the proposal's ambition and its role as only one layer of technology governance. It connects to responsible innovation, AI safety, tax policy, market failure, technology governance, institutional design.

the framework addresses social misalignment through producer incentives, consumer credits, and corrective penalties rather than a new standalone agency. This matters because it clarifies both the proposal's ambition and its role as only one layer of technology governance.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 47, that the fiscal blueprint can extend to biotechnology, nanotechnology, and other domains where private innovation incentives diverge from public welfare. The discussion situates this proposition within the conclusion's synthesis of the fiscal framework and its broader implications. This is significant because it clarifies both the proposal's ambition and its role as only one layer of technology governance. It connects to responsible innovation, AI safety, tax policy, market failure, technology governance, institutional design.

the fiscal blueprint can extend to biotechnology, nanotechnology, and other domains where private innovation incentives diverge from public welfare. This matters because it clarifies both the proposal's ambition and its role as only one layer of technology governance.

Source-draft page 47 (PDF page 47) develops this proposition in Conclusion.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 47, that tax incentives cannot guarantee safe AI and should operate as one component of a broader regulatory ecosystem. The discussion situates this proposition within the conclusion's synthesis of the fiscal framework and its broader implications. This is significant because it clarifies both the proposal's ambition and its role as only one layer of technology governance. It connects to responsible innovation, AI safety, tax policy, market failure, technology governance, institutional design.

tax incentives cannot guarantee safe AI and should operate as one component of a broader regulatory ecosystem. This matters because it clarifies both the proposal's ambition and its role as only one layer of technology governance.


Professors Mirit Eyal and Yonathan Arbel claim, in the article “Tax Levers for a Safer AI Future” on page 47, that market mechanisms and firm knowledge give tax policy comparative value in fast-moving fields marked by regulatory information and expertise asymmetries. The discussion situates this proposition within the conclusion's synthesis of the fiscal framework and its broader implications. This is significant because it clarifies both the proposal's ambition and its role as only one layer of technology governance. It connects to responsible innovation, AI safety, tax policy, market failure, technology governance, institutional design.

market mechanisms and firm knowledge give tax policy comparative value in fast-moving fields marked by regulatory information and expertise asymmetries. This matters because it clarifies both the proposal's ambition and its role as only one layer of technology governance.