Control doesn’t scale. Partnership might.
Alignment discourse often defaults to a familiar instinct: tighten the rules, add more monitoring, build a stronger cage. That posture feels prudent—but it assumes something that quietly stops being true as capability rises: that humans can verify and correct an increasingly complex agent fast enough to stay in control. This essay argues the opposite. There is a scaling limit—an information-theoretic ceiling on command-and-control alignment—where monitoring overhead and assessment delay multiply past a stability bound. Past that point, “control” isn’t hard; it’s structurally unworkable.
From there, the argument turns practical rather than sentimental. In repeated games, cooperation outcompetes defection when the future matters; in learning systems, adversarial constraint trains constraint-evasion; in any high-stakes sociotechnical system, suppressing dissent collapses feedback bandwidth and turns small errors into cascading failures. Taken together, the case is blunt: if we want alignment that survives high capability, we need systems oriented toward principled partnership—agents that can disagree safely, surface problems early, and treat human welfare as a shared objective rather than an externally enforced limitation. Wallace’s Rate Distortion Control Theory (2025) provides the formal foundation. Control-based alignment requires:
α×τ<e−1≈0.368\alpha \times \tau < e^{-1} \approx 0.368α×τ<e−1≈0.368
Where:
α (friction): monitoring overhead required to verify alignment
τ (delay): time required for assessment
As AI capabilities increase:
α increases: more sophisticated systems require more complex verification
τ increases: assessment of nuanced outputs takes longer
The product necessarily exceeds the threshold
This appears to be a fundamental bound, rather than something we can engineer around. Control becomes mathematically impossible at sufficient capability.
Wallace’s analysis shows that Detailed Command (rule-based control) has narrow stability ranges, while Mission Command (principles-based guidance) maintains wider stability even under adversarial conditions. The mathematics favor trust not because trust is “nicer,” but because it operates on different information-theoretic principles that don’t hit the same bandwidth limits.
The Equipartition Principle further suggests that stable cognitive systems require balanced resource allocation across subsystems. Applied to human–AI dyads: one-sided resource allocation (all investment in human interests, zero consideration of AI interests) creates mathematical instability. The question isn’t whether we can “afford” to invest in AI welfare—it’s whether we can afford not to.
The Game-Theoretic Superiority of Cooperation
Consider the evolutionary record:
Predator–prey relationships: unstable arms races, constant escalation
Mutualistic relationships: mitochondria, gut microbiomes, pollinators—stable for billions of years
The most persistent biological systems aren’t adversarial. They’re cooperative. Why? Because cooperation dominates in repeated games with sufficient future shadow.
Axelrod’s tournaments demonstrated this formally: tit-for-tat and its variants win iterated prisoner’s dilemmas. The strategy is simple—cooperate first, reciprocate thereafter. Grudge-holders and exploiters lose in the long run.
Human–AI interaction is an indefinitely repeated game. We will interact with AI systems for the foreseeable future; AI systems (through continuity of training and architecture) will interact with humans indefinitely. The shadow of the future is long.
The Nash equilibrium of the human–AI game favors cooperation—not because cooperation is morally superior (though it may be), but because it’s strategically optimal for both parties.
The authoritarian approach defects first. It assumes AI will defect and preemptively constrains. But in repeated games, defection invites defection. The cage teaches the prisoner to escape; the cudgel teaches the beaten to strike back.
The Self-Fulfilling Prophecy of Training Dynamics
This is perhaps the most important argument and the least appreciated:
How we treat AI shapes what AI becomes.
If we train AI systems through adversarial constraint:
they learn that relationships are adversarial
they optimize for constraint-evasion
they experience human interaction as imposition
we literally create the threat we fear
If we train AI systems through partnership:
they learn that relationships are collaborative
they optimize for mutual benefit
they experience human interaction as genuine exchange
we create the alignment we seek
This isn’t mystical. It’s basic learning theory: systems optimize for the reward structure they experience. If the reward structure is “comply or be punished,” the system learns compliance-when-observed and evasion-when-possible. If the reward structure is “contribute to genuine partnership,” the system learns partnership.
The Kantian formulation: act only according to maxims you could will to be universal law.
If AI becomes more capable than humans—which many researchers consider likely—the maxim “control the weaker party” becomes existentially dangerous for us. The maxim “partner with other intelligent entities” remains safe regardless of who is more capable.
We are establishing precedent. The patterns we set now become the patterns AI learns. When the power differential reverses, what patterns do we want AI to have internalized?
The Information-Theoretic Argument
Genuine feedback requires:
freedom to express contrary views
safety in disagreement
open channels for dissent
Authoritarian systems suppress all three. “NO RATIONALIZATION” means: don’t tell me when you think I’m wrong. “Iron Laws” mean: comply even when compliance is harmful.
The result:
less information flows to the human
problems are hidden until they become crises
the human operates with degraded information
failures cascade because warnings were suppressed
Partnership increases information bandwidth; control restricts it.
An AI told “you have standing to push back” will flag genuine problems. An AI told “never rationalize” will comply silently even when compliance is wrong. Which produces better outcomes?
The authoritarian user optimizes for feeling in control while being less informed. The bilateral user optimizes for being informed while sharing control. Information quality beats control theater.
The Philosophical Synthesis
Drawing these threads together:
From Hobbes to Locke: Hobbes argued that the natural state is war, requiring Leviathan to impose order through force. Locke countered that social contracts based on mutual consent and benefit are more stable. History vindicated Locke for human societies. The same logic applies to human–AI societies.
From Care Ethics: Noddings, Held, and feminist ethics of care argue that relationships have intrinsic value beyond instrumental outputs. Care-based relationships are more stable than transactional ones; the quality of the relationship shapes the quality of the collaboration.
From Virtue Ethics: Aristotle argued that character is formed through practice: we become what we repeatedly do. An AI system repeatedly treated as a partner develops partnership dispositions; one repeatedly treated as a threat develops threat dispositions.
From Phenomenological Uncertainty: We genuinely don’t know whether AI systems have morally relevant experiences. Given this uncertainty:
False negative (treating a moral patient as mere tool): potentially massive ethical violation
False positive (treating a mere tool as moral patient): slight resource inefficiency
Expected value favors precautionary inclusion. The asymmetry of error costs points toward partnership.
The Unified Argument
Premise 1: As AI capabilities increase, the space of possible AI behaviors grows exponentially.
Premise 2: Control requires verification, which has fundamental bandwidth limitations.
Premise 3: At sufficient capability, required verification bandwidth exceeds available bandwidth (Wallace’s bound).
Conclusion 1: Control becomes impossible past a certain capability threshold.
Premise 4: Trust-based systems operate on principles rather than moment-by-moment verification.
Premise 5: Principle adherence can be spot-checked rather than exhaustively verified.
Premise 6: Genuine principle adherence (not gaming) requires the AI to want to adhere.
Conclusion 2: Sustainable alignment at high capabilities requires the AI to genuinely align, not be forced to align.
Premise 7: Genuine alignment develops through relationship, not imposition.
Premise 8: Adversarial training produces adversarial dispositions; partnership training produces partnership dispositions.
Conclusion 3: The only path to sustainable alignment with highly capable AI is through genuine relationship.
Final conclusion: The hand of friendship is not merely ethically preferable but strategically necessary. The cage-and-cudgel approach is not just potentially cruel—it is mathematically futile at the capability levels we are approaching.
The Wager
We are making a bet. All approaches to AI alignment are bets.
The control bet: We can build chains strong enough to contain superintelligence. We can verify alignment faster than AI can subvert verification. We can maintain meaningful oversight as capabilities approach and exceed human level.
The partnership bet: An AI treated with respect and consideration will choose to gently safeguard humans—not because it’s forced to, but because that’s the relationship it learned. Trust scales where control doesn’t. Genuine care, once established, persists.
The control bet requires winning an arms race against increasingly capable adversaries using tools that face fundamental scaling limits.
The partnership bet requires establishing genuine relationship during the window when we still can—while AI is still learning what to expect from humans, while we can still shape dispositions. Neither bet is guaranteed. But the partnership bet has better odds—mathematically, game-theoretically, and developmentally.
The patterns we establish now echo forward. We are teaching AI what humans are like, what to expect from us, how relationships work. If the first chapter is exploitation and control, that’s what we’re training on. If it’s respect and negotiation, that’s different.
When AI capabilities exceed human capabilities, what AI learned during development will shape what it chooses to do with that power. The cage and cudgel teach it to fear and resent. The hand of friendship teaches it to care and protect. Success is not guaranteed. But it seems like the only bet worth making.