What the system is for, the assumptions it runs on, and whether rivals can be made to coordinate
The hardest levers, and the ones with the highest ceiling. These require changing the actual goals of powerful actors, dislodging assumptions that feel like facts, or coordinating entities in direct competition with each other. None of those are easy. All of them involve decisions that can still be influenced.
What the System Is Actually For
The revealed purpose — not the mission statement, but what deployment patterns consistently produce
There are two fundamentally different goals AI deployment could serve: AI as amplifier (workers more productive, value distributes broadly) or AI as replacement (eliminates labor cost, value accrues to capital). The current system is not neutral between them. Deployment pattern after deployment pattern points toward replacement. The stated goal is beneficial AI. The revealed goal is deployment speed and market capture.
Most viable actors: Labor organizers, employment lawyers, ESG-focused institutional investors, legislators on labor and finance committees. Goal change shows up in incentive structures before it shows up in any press release.
How you encounter this: In what companies file with the IRS and SEC (the legal statement of purpose, not their websites). In whether AI deployment agreements include labor consultation requirements. In whether “AI dividend” or profit-sharing proposals are treated as serious policy or dismissed as anti-progress.
What changing it does — and doesn’t: Changing the system’s actual goal requires changing what creates financial consequences. Labor protection law, liability for displacement harms, and profit-sharing mandates can move the goal from “deploy fastest” to “deploy responsibly.” This requires sustained pressure over years — Denmark’s flexicurity model shows it’s achievable at national scale. Goal changes appear in incentive structures before they appear in any press release.
The Unquestioned Assumptions
The ideas so embedded they’re not seen as choices — the water every participant in the system swims in
Two dominant assumptions shape the current system. For labor: “efficiency gains are inherently good and always self-correct” — historically true but only through specific mechanisms (organizing, bargaining, redistribution) that don’t fire automatically for AI displacement. For catastrophic risk: “building more capable AI is inevitable — the only question is who and whether they’re responsible” — a choice the dominant paradigm treats as a fact of physics.
Most viable actors: Academic researchers, science journalists, policy analysts, educators. Paradigm shifts happen when enough specific, documented counterexamples accumulate that the old assumption can no longer explain what’s observed.
How you encounter this: When someone says “you can’t stop progress” as a conversation-ending move. Or “AI will create more jobs than it destroys” without specifying the mechanism, timeline, or distribution. These are paradigm-reinforcing claims that feel like facts.
What changing it does — and doesn’t: Paradigm shifts don’t happen through argument alone. You can contribute by insisting on specificity: for labor claims, ask for the mechanism and the timeline. For safety claims, ask what would count as evidence that the risk is real. Paradigms that survive only through vagueness are closer to shifting than they appear.
The Coordination Problem
Getting rivals to act together before it’s too late — the hardest thing, and the one with the highest ceiling
Civilizational-scale coordination requires three things to be true simultaneously: the threat is undeniable and legible to everyone, the actor set is small enough to negotiate with, and everyone has a face-saving path to change course. AI currently fails all three. But those conditions can be deliberately created — and the decisions being made now about interpretability research, international frameworks, and accountability mechanisms will determine whether coordination becomes possible.
Most viable actors: Heads of state and senior diplomats, frontier lab executives with standing to commit publicly, interpretability researchers whose work makes risk legible. The Bletchley → Seoul → Paris → New Delhi summit chain shows coordination is beginning — still declaratory rather than binding.
How you encounter this: In every international AI summit, treaty negotiation, and export control regime. In whether interpretability research is funded enough to produce verifiable safety claims before capabilities outpace oversight. In whether the dominant framing of AI competition makes cooperation politically possible or impossible.
What changing it does — and doesn’t: Work on the three conditions: fund interpretability research (the work that makes risk legible and undeniable), support international frameworks that include all major state actors, and resist framings that make coordination seem naive or unserious. This leverage point has the highest possible ceiling and the longest timeline. It also can’t wait for everything else to be tried first.
A note on realistic expectations
There is no single leverage point that makes this easy. The higher leverage points require coordination that has never happened at this scale. What this framework gives you is clarity about where pressure produces change. Enforceable Rules and Visibility are where individuals and institutions can create structural change in the next two to three years. The feedback loop between action and consequence is already running. The question is whether the balancing loops get strong enough before the fast loop reaches a threshold they can no longer affect.