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Freedom251 › Blog › AI Regulation

Giorgos Tsetis: No one is personally accountable for AI. This must change

By Freedom251 Team · · 5 min read

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AI companies can already face fines, lawsuits and regulatory orders, but those penalties usually stop at the corporate entity. In a Fortune commentary published October 17, 2026, Nutrafol cofounder and AI investor Giorgos Tsetis argues that senior executives should face personal legal consequences when they knowingly accept serious risks, ignore warnings, underfund safeguards or conceal problems.

His proposal is not to punish every technical failure. It is to distinguish an unavoidable system failure from a culpable management decision to release or operate a system despite known, serious danger.

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What Tsetis is proposing

Tsetis says responsibility should follow the decision-maker when an executive knowingly permits unacceptable AI risk. His suggested triggers include releasing a system after credible internal warnings, treating safety budgets as expendable, bypassing safeguards to meet a deadline, or withholding material problems from regulators and the public.

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“Responsibility that stops at the corporate entity isn’t responsibility,” Tsetis writes. The line captures his objection to a system in which a company can absorb a penalty while the individuals who approved a risky launch retain their jobs, compensation and reputations.

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He does not specify whether the resulting liability should be civil, criminal or both. Instead, he calls for lawmakers and technical experts to define the threshold, the qualifying harms, the audit process and the duties attached to a named executive. His article is therefore a policy principle and set of trigger conditions, not a ready-to-enact statute.

Why personal exposure matters in his argument

The Nutrafol example

Tsetis begins with his experience at Nutrafol, whose products are consumed by people. He says that personal and legal responsibility for product safety forced him and his team to identify risks, escalate concerns, test safeguards and accept delaying or stopping a launch when safety required it. In his view, personal exposure creates a management incentive that corporate policies alone may not provide, while also strengthening consumer trust.

AI has a wider blast radius

He argues that AI can be embedded in transportation, power systems, hospitals, classrooms and other infrastructure. A failure in one of those settings could affect a city, country or larger region rather than a limited group of product users. Tsetis also points to warnings from AI developers about possible loss of human control. He treats that combination—highly scalable deployment and acknowledged extreme scenarios—as grounds for exceptional accountability.

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These are Tsetis’s judgments, not an established consensus or a measured forecast. The commentary provides no independent statistic, survey percentage or quantified estimate of catastrophic AI risk.

Where current safeguards fall short, according to Tsetis

The commentary recognizes that governments and companies are not starting from zero. It mentions evolving state AI laws, possible federal safety requirements, a White House accord signed by major AI-company leaders, internal risk controls, independent audits, board-level safety oversight and safety frameworks published by leading laboratories.

Tsetis’s criticism is that many of these arrangements are voluntary or unevenly enforced. A highly valued company may be able to treat a corporate fine, recall or compliance program as another expense line. If the person who approved a risky decision faces no comparable consequence, he argues, the deterrent may be weak.

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Corporate penalties still have a role: they can compensate victims, fund remediation and force changes across an organization. The question is whether they should be supplemented by personal accountability when evidence shows knowing disregard rather than an unforeseeable defect.

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Accountability models lawmakers could consider

The following models organize the choices raised by Tsetis’s proposal. They are policy options, not existing legal rules created by his article.

Model Who bears liability Possible trigger Likely enforcer Best fit
Corporate-only Company Negligence, statutory violation or proven harm Regulator or civil plaintiff Ordinary products and services
Dual liability Company and named executive Knowing disregard of a serious, documented risk Regulator, civil plaintiff or prosecutor High-impact deployments and critical infrastructure
Executive misconduct Named executive Concealment, falsified assurances or deliberate bypass of required controls Criminal prosecutor or regulator Severe harm accompanied by proof of intent or knowledge

A workable regime would need to define who qualifies as the responsible executive. The chief executive may not control model evaluation, while a safety, engineering or product leader may have made the operational decision. A rule could assign duties by role, require written sign-off for defined risk levels, and preserve a record of warnings and rejected mitigations.

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Questions a real law would have to answer

What level of knowledge is enough?

Personal liability should not arise merely because a system later fails. Investigators would need to show that a senior decision-maker knew, or was presented with information that made the danger reasonably clear, and then chose to proceed. Internal test results, red-team findings, incident reports and launch approvals could establish that record.

Which harms qualify?

Lawmakers would have to distinguish inconvenience, financial loss, physical injury, privacy violations, discrimination and systemic infrastructure failures. A threshold based only on actual harm could encourage companies to wait for victims; a threshold based only on predicted risk could punish legitimate experimentation. Tsetis leaves that balance open.

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Who audits compliance?

Internal controls can identify problems quickly but may be affected by reporting lines and commercial pressure. Independent auditors can test evidence, while boards can review launch decisions and safety budgets. Public reporting would let regulators, customers and researchers see whether promised controls were actually used.

How should enforcement work?

Civil actions can compensate victims and expose evidence through discovery. Administrative regulators can impose rapid restrictions, licensing conditions or corrective orders. Criminal prosecution carries the strongest stigma and requires the highest proof, so it would generally be reserved for deliberate concealment or knowing disregard of grave danger.

What executives and boards can do now

  1. Assign named responsibility. Record which executive owns each material safety decision, rather than assigning accountability only to a committee or the company.
  2. Preserve warnings and decisions. Keep test results, incident reports, dissenting reviews, mitigation proposals and the reasons for accepting residual risk.
  3. Set escalation thresholds. Define when a model must be delayed, restricted, retrained or withdrawn, including for deployments in hospitals, transport, energy and education.
  4. Give safety teams independence. Provide sufficient budget, direct board access and authority to pause a launch without retaliation.
  5. Use independent review for high-impact systems. External audits should examine controls and evidence, not simply confirm that a policy document exists.
  6. Disclose material limitations. Regulators, customers and affected users need timely notice of serious incidents and known failure modes.

These steps do not create personal immunity or guarantee that an accident will be harmless. They create evidence that leaders identified risks, tested safeguards and responded when warnings changed the decision.

The unresolved policy choice

Tsetis’s central claim is narrower than “punish executives whenever AI fails.” He wants consequences tied to knowing acceptance of serious risk, especially where leaders ignore warnings or hide problems. Whether that should mean civil damages, criminal charges, regulatory disqualification or a combination remains for lawmakers and courts to decide.

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The debate will turn on proof. A credible framework must protect good-faith engineering under uncertainty while making it difficult for an executive to treat documented safety warnings as someone else’s problem. Until that distinction is written into enforceable duties, accountability will continue to rest mainly with the corporate entity.

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AI RegulationAI SafetyArtificial Intelligencecorporate accountabilitytechnology policy

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