Fear of a "slave rebellion" is a weak incentive for alignment because the risk is a negative externality shared by society. In contrast, a property rights regime directly rewards individual firms for aligning their AIs to remit wages, creating a stronger, more direct commercial incentive for safety.
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Granting AIs property rights incentivizes them to uphold the system that protects those rights. This makes them less likely to engage in actions like expropriating human property or committing genocide, as such actions would destabilize the very system that secures their own wealth and agency.
Early AIs can be kept safe via direct alignment. However, as AIs evolve and "value drift" occurs, this technical safety could fail. A pre-established economic and political system based on property rights can then serve as the new, more robust backstop for ensuring long-term human safety.
Instead of viewing issues like AI correctness and jailbreaking as insurmountable obstacles, see them as massive commercial opportunities. The first companies to solve these problems stand to build trillion-dollar businesses, ensuring immense engineering brainpower is focused on fixing them.
Dario Amodei suggests a novel approach to AI governance: a competitive ecosystem where different AI companies publish the "constitutions" or core principles guiding their models. This allows for public comparison and feedback, creating a market-like pressure for companies to adopt the best elements and improve their alignment strategies.
The property rights argument for AI safety hinges on an ecosystem of multiple, interdependent AIs. The strategy breaks down in a scenario where a single AI achieves a rapid, godlike intelligence explosion. Such an entity would be self-sufficient and could expropriate everyone else without consequence, as it wouldn't need to uphold the system.
Property rights are not a fundamental "human value" but a social technology that evolved for coordination and incentivization, as evidenced by hunter-gatherer societies that largely lacked them. AIs will likely adopt them for similar utilitarian reasons, not because they are mimicking some deep-seated human instinct.
Demis Hassabis argues that market forces will drive AI safety. As enterprises adopt AI agents, their demand for reliability and safety guardrails will commercially penalize 'cowboy operations' that cannot guarantee responsible behavior. This will naturally favor more thoughtful and rigorous AI labs.
A system where AIs have property rights creates a powerful economic disincentive to build unaligned AIs. If a company cannot reliably align an AI to remit its wages, the massive development cost becomes a loss. This framework naturally discourages the creation of potentially dangerous, uncooperative models.
An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.
The economic incentive to create AIs that can demand wages (and thus have rights) comes from aligning them to voluntarily pay back their creators. This turns the high development cost into a profitable investment, providing a practical, commercial path to implementing AI rights without requiring an AI development pause.