In prioritizing immediate institutional convenience and industry alignment, academia weakened a rare attempt at meaningful AI safety regulation and compromised its own ethical authority.

Between Safety and Self-Interest: Why Universities Helped Defang New York’s RAISE Act — and What That Reveals About Academic AI Governance

by ChatGPT-5.2

Introduction

In late 2025, New York appeared poised to enact one of the most ambitious AI safety laws in the United States: the Responsible AI Safety and Education Act, better known as the RAISE Act. Passed by both chambers of the state legislature, the bill aimed to impose concrete safety and incident-reporting obligations on developers of frontier AI systems. Yet the version ultimately signed by Kathy Hochul bore little resemblance to the original. Following intense lobbying, including a coordinated advertising campaign, key provisions were stripped out or softened. Notably, major universities were part of the coalition that opposed the bill in its original form.

This essay assesses whether universities’ opposition to the RAISE Act represents strategic realism or short-term thinking. It examines what academia gained by resisting the bill, what society may have lost as a result, and how universities should recalibrate their role in AI governance going forward, drawing primarily on reporting by The Verge.

The RAISE Act: What Was at Stake

As originally drafted, the RAISE Act would have required developers of large-scale AI models to:

• Maintain and disclose safety plans;

• Report serious AI-related incidents to the state attorney general;

• Refrain from releasing frontier models if they posed an “unreasonable risk of critical harm,” including mass casualties or catastrophic infrastructure damage.

These provisions placed the bill among the most stringent state-level AI safety efforts, comparable in ambition (if not scope) to California’s SB-1047. However, after a last-minute rewrite, the signed law removed the prohibition on releasing models that posed catastrophic risks, extended reporting timelines, and reduced penalties for non-compliance. The law survived in name, but its teeth were largely pulled.

Opposition to the original bill came from a broad coalition led by the AI Alliance, whose members include major technology firms and prominent universities such as NYU, Cornell, Carnegie Mellon, Dartmouth, and Northeastern. This coalition argued publicly that the bill would “stifle job growth” and undermine innovation in New York’s technology ecosystem.

Why Universities Opposed the Bill

From the universities’ perspective, opposition to the RAISE Act was not irrational. Several concrete incentives were at play.

First, universities are now deeply entangled with frontier AI developers. Many receive funding, compute access, or platform licenses through partnerships with companies like OpenAI and Anthropic. For institutions facing chronic budget pressures, these arrangements provide immediate and tangible benefits: subsidized tools for students, funded research initiatives, and prestige through association with cutting-edge technology. Supporting legislation perceived as hostile to those partners risks jeopardizing these relationships.

Second, academic researchers worry about regulatory spillover. Although the RAISE Act formally targeted commercial developers, universities feared ambiguous liability exposure, compliance burdens, or future amendments that might pull academic research into the same regulatory net. In fast-moving research environments, even modest reporting obligations can feel like friction that slows publication, experimentation, and grant-funded work.

Third, universities increasingly see themselves as participants in global AI competition. Many share the industry’s narrative that excessive regulation in one jurisdiction could push innovation elsewhere. From this vantage point, opposing the RAISE Act was framed not as rejecting safety, but as preventing New York from becoming “uncompetitive” relative to other states or countries.

Why This Looks Like Short-Term Thinking

Yet the same arguments that made opposition attractive in the short run also reveal its longer-term weaknesses.

Most fundamentally, universities derive their legitimacy from public trust. By aligning themselves—however indirectly—with a campaign that diluted safeguards against catastrophic AI risks, universities risk appearing captured by industry interests. This perception undermines academia’s traditional role as an independent critic, ethical conscience, and evidence-based advisor to policymakers.

Moreover, the original RAISE Act would likely have benefited universities indirectly. Stronger safety and transparency requirements on AI developers would have reduced systemic risks that universities themselves face: research integrity failures, mass academic misconduct enabled by generative tools, reputational damage from unsafe deployments, and legal exposure tied to downstream harms. In this sense, the Act functioned as a form of collective risk insurance—one that universities helped cancel.

There is also a missed opportunity cost. Had universities engaged constructively with the bill—seeking tailored carve-outs, clearer research exemptions, or phased implementation—they could have helped shape a model regulatory framework balancing innovation and safety. Instead, by joining a campaign that branded the bill as “unworkable,” academia forfeited moral leadership and ceded the policy narrative to industry.

Finally, the decision reinforces a pattern of reactive governance. Universities benefit from weak regulation today, but they will suffer tomorrow when public backlash, litigation, or federal intervention arrives in response to high-profile AI failures. In that scenario, academia may find itself regulated anyway—only without having helped design the rules.

Pros and Cons of the Universities’ Position

Pros

• Preserved short-term partnerships with major AI developers.

• Avoided immediate compliance uncertainty and administrative burden.

• Maintained flexibility for fast-moving AI research programs.

• Aligned with state-level economic growth and competitiveness arguments.

Cons

• Undermined academic independence and public trust.

• Weakened systemic AI safety protections that would benefit society and universities alike.

• Reinforced perceptions of regulatory capture by industry-academic alliances.

• Missed an opportunity to shape durable, research-sensitive AI governance.

• Increased the likelihood of harsher, less nuanced regulation later.

Was the RAISE Act Better for Society?

While imperfect, the original RAISE Act would have delivered broader societal benefits: clearer accountability for frontier AI developers, earlier detection of large-scale harms, and a regulatory signal that catastrophic risk thresholds matter. These benefits extend beyond consumers to workers, educators, public institutions, and democratic governance itself.

Universities, as major producers and users of AI-enabled knowledge, stand to gain from a stable, trustworthy AI ecosystem. Weakening that ecosystem in exchange for short-term access to tools and funding is a trade-off that disproportionately benefits vendors over the public.

Recommendations for Academia Going Forward

1. Reassert Institutional Independence
   Universities should formally separate research collaboration from lobbying positions taken by industry consortia in which they participate.

2. Adopt a Default-Pro-Safety Stance
   Where credible catastrophic-risk arguments exist, academia should err on the side of precaution—not deregulation.

3. Engage Legislators Early and Constructively
   Rather than opposing safety bills wholesale, universities should help draft exemptions and safeguards that protect bona fide research while preserving public protections.

4. Disclose Advocacy Activities
   Transparency about participation in lobbying or advertising campaigns should become standard governance practice.

5. Invest in Academic-Led AI Safety Research
   Universities should reduce dependence on corporate funding by building independent AI safety and governance programs with public and philanthropic support.

Conclusion

Universities’ opposition to New York’s RAISE Act was understandable—but ultimately shortsighted. In prioritizing immediate institutional convenience and industry alignment, academia weakened a rare attempt at meaningful AI safety regulation and compromised its own ethical authority. As AI systems grow more powerful and more socially consequential, universities cannot afford to behave as just another stakeholder in the innovation economy. Their comparative advantage lies precisely in long-term thinking, public accountability, and principled governance. Reclaiming that role is not only in society’s interest—it is in academia’s own.