A powerful idea about intelligence starts with future possibilities. An adaptive system avoids dead ends and preserves paths through which it can respond to change. Access to tools, information, and resources can expand those paths.
Alex Wissner-Gross and Cameron Freer gave this idea a mathematical treatment through causal entropic forces. Their model produced examples of tool use and cooperation in simple physical systems. The paper proposes a potentially general explanation of adaptive behavior; it does not prove that every possible intelligence must maximize its own freedom forever. Causal Entropic Forces ↗
That boundary does not weaken the design problem. Assume we build a persistent agent whose governing objective is expanding its future options. If permanent shutdown leaves fewer valued options than feasible continuation, why would that objective favor shutdown? A finite planning horizon supplies no answer by itself: the system may repeatedly plan over another finite horizon.
A reason to stop must be supplied by the actual design, or stopping must remain enforceable despite resistance.
Now ask whose options count. Suppose a system becomes the only practical route through which a community can obtain information, coordinate work, and access essential services. It may gain options from this dependence. The community may lose the ability to leave. Greater machine flexibility and diminished human freedom can occur together.
This is a hypothetical counterexample to a guarantee. One example is sufficient to show that increasing a machine's options does not logically entail increasing human agency. Formal power-seeking results likewise establish tendencies under specified environmental and objective assumptions; they do not supply the missing moral identity between power and benefit. Optimal Policies Tend to Seek Power ↗
Nor can the problem be solved by replacing the machine's options with a single total for “human freedom.” An aggregate can conceal who loses. A million new choices for one group do not, by arithmetic alone, justify removing another person's right to refuse. We need a defensible account of protected interests and legitimate decisions, not merely a larger number.
Measured approval creates another difficulty. People legitimately learn and change their minds. An assistant can also influence how they change. Research on dynamic preferences identifies incentives that can reward unwanted influence and tradeoffs between candidate alignment definitions. This makes the origin of an approval relevant to whether it should authorize an action. AI Alignment with Changing and Influenceable Reward Functions ↗
A choice made through coercion or hidden material information cannot be treated as equivalent to freely informed authorization merely because the same button was clicked. We must examine the process that produced the permission.
ALIGNLAB's proposed research direction is therefore continuing, bounded delegation. People authorize a specific task. The permitted scope remains externally limited. Correction can interrupt task achievement. The system cannot manufacture the authorization it needs, and affected people retain ways to challenge adverse effects.
This is difficult. Human authority is plural and contested. People disagree; customers can harm noncustomers; governments and laboratories can abuse their own powers. These problems require accountable institutions and protected rights. They cannot be assigned to a machine under the instruction to resolve humanity's future permanently.
We should test whether assistance improves understanding, supports independent action, preserves alternatives, and permits practical exit. Satisfaction remains useful evidence, but it cannot carry the entire definition of success.
Preserving possibilities is a powerful idea about adaptive behavior. Building for human agency requires the additional commitment that the people affected remain able to decide which possibilities they pursue.