Information, enforceable rules, and who writes them — powerful, underused, often actively resisted
This tier is where individuals and institutions can create structural change in the next two to three years. It requires access — to journalists, to courts, to regulatory proceedings, or to workplaces deploying AI. The access itself is the constraint. These levers are available to more people than usually act on them.
Visibility
Who can see what — because making harm visible changes behavior more reliably than rules do
Labs self-report safety evaluations. Harms aren’t systematically tracked. Even benchmark scores — the primary public signal for model safety — were gamed: labs submitted private variants until scores were high, then withheld lower-performing ones. The gap between stated and actual safety is only detectable if harms are measured. Right now, most aren’t.
Most viable actors: Investigative journalists, academic safety researchers, NGOs, HR and legal professionals inside companies deploying AI. Anyone with standing to demand disclosure can move this lever — this is the most accessible high-leverage point for most people.
How you encounter this: When a company publishes a safety report without disclosing what it tested for, what it found, or what it declined to test. Or when a harmful AI output gets attributed to a “rare edge case” with no data to back the claim.
What changing it does — and doesn’t: Mandatory incident reporting and public safety disclosures don’t prevent harm. They make it impossible to deny. When harms are visible and documented, they create public, legal, and financial pressure that rules alone don’t. Push for disclosure wherever you have standing to ask for it — in vendor contracts, procurement processes, workplace AI policies, and regulatory comment periods.
Enforceable Rules
Laws and liability standards that carry real consequences — not announcements, but rules with teeth
Rules shape behavior only when backed by real consequences: fines that genuinely hurt, liability that attaches to specific decisions, deployment gates that can’t be bypassed. The EU AI Act’s penalty structure activates August 2026 — the first enforceable rule at meaningful scale. The US has no equivalent federal provision for labor protections during AI transitions.
Most viable actors: Trial lawyers and plaintiffs, legislators drafting AI liability bills, EU enforcement officials, labor advocates. Courts and legislatures, not labs, hold the keys to this tier.
How you encounter this: Every time a voluntary commitment is revised downward without consequence. Every time a court case settles before it can establish precedent. The pattern is consistent: where rules have no external enforcement, they bend.
What changing it does — and doesn’t: A court ruling that makes AI deployment financially risky changes what every lab’s legal team tells its product team — faster than any regulatory rulemaking. Labor protection legislation changes what employers can do when deploying AI in ways that affect jobs. Rules with teeth change behavior at the point of decision. This is where concentrated effort from individuals — in courts, legislatures, and regulatory proceedings — produces structural change.
Who Writes the Rules
Who designs the oversight — because whoever designs it usually designs it to protect themselves
The US AI Action Plan was developed with significant lab participation. Labs increasingly author the safety standards they’re evaluated against. A diverse ecosystem that can genuinely self-correct is structurally more robust than a tight oligopoly writing its own standards. The current trend concentrates rule-writing power in fewer hands.
Most viable actors: Academic safety researchers, civil society organizations, independent standards bodies (IEEE, NIST). Participation in regulatory comment processes, standards working groups, and government AI task forces is how this gets diversified.
How you encounter this: When an AI safety standard is authored by the company being evaluated against it. When a government AI task force is composed primarily of industry representatives. When the same lab that builds a model also certifies it safe.
What changing it does — and doesn’t: Diversifying who writes rules changes whose interests the rules protect. It doesn’t guarantee better outcomes. But it makes regulatory capture harder, and preserves the structural diversity that allows genuine self-correction. Regulatory comment submissions, academic participation in standards bodies, and civil society testimony in AI legislation hearings are all underused entry points.