Instead of trying to anticipate every potential harm, AI regulation should mandate open, internationally consistent audit trails, similar to financial transaction logs. This shifts the focus from pre-approval to post-hoc accountability, allowing regulators and the public to address harms as they emerge.

Frontier AI labs now actively call for third-party verification. This is a strategic response to a significant public "trust deficit" and the realization they cannot self-certify their way to broad adoption and social license.

When buying AI solutions, demand transparency from vendors about the specific models and prompts they use. Mollick argues that 'we use a prompt' is not a defensible 'secret sauce' and that this transparency is crucial for auditing results and ensuring you aren't paying for outdated or flawed technology.

As AI becomes more integrated into pharma, a need for validation will emerge. AI models used for medical affairs or commercial tasks will likely require accreditation from a neutral third party, similar to a 'certified pre-owned' car, to ensure reliability, compliance, and effectiveness.

In regulated industries like finance, the primary barrier to full AI automation is often regulation, not just user trust. It is the technology provider's responsibility to prove AI's reliability and safety to regulators, much like the industry did to legitimize e-signatures over a decade ago.

New technologies like electricity, cars, and now AI gain societal trust through a reinforcing cycle. Industry standards create a safety baseline, third-party audits verify compliance, and insurance covers the remaining residual risk, creating a powerful adoption flywheel.

The AI auditing field risks a race to the bottom, where firms offer cheap, superficial audits. To ensure accountability, legislation must require auditors to publicly post their methodologies and code, allowing the community to scrutinize their work and establish robust standards.

Major AI companies are not solely seeking to stifle competition with regulation. They are also signaling an inability to self-regulate amidst intense competitive pressure, effectively asking external bodies to impose a mandatory safety floor that applies to everyone.

A pilot AI certification program revealed that even simplified criteria were interpreted inconsistently. This proves AI systems are too dynamic for static, checklist-based certification. The solution is to empower auditors with discretion and focus heavily on their specialized training and education.