The Ouroboros of Optimization

Game Theory, Industrial Systems, and the Political Economy of Large Language Models

Author's Note

This essay explains patterns. It does not advocate positions, predict specific outcomes, or assign moral judgment.

The analysis belongs to a tradition of skeptical scholarship that examines how systems produce outcomes that no participant intends. The method is game theory—not as cynicism, but as a descriptive framework for understanding strategic interaction under uncertainty.

Readers across the political spectrum may draw different conclusions from the same structural analysis. That is appropriate. The essay provides orientation, not prescription.

I. Familiar Advice in Unfamiliar Times

"Learn to code."

For a decade, this was the answer. Displacement from manufacturing, from retail, from administrative work—the response was consistent. Acquire technical skills. Adapt. The economy is changing; change with it.

The advice was not wrong in isolation. Many who followed it found employment. The labor market did shift toward technical roles. The guidance reflected real dynamics.

Then the dynamics shifted again.

"Join the trades."

The new advice arrived with equal confidence. Electricians cannot be automated. Plumbers must be present. The physical trades offer stability that knowledge work cannot guarantee. Learn to work with your hands.

This advice, too, reflects real dynamics. It, too, is not wrong in isolation.

What deserves attention is not the content of the advice but its structure.

Notice that the advice flows downward. It addresses individuals, not institutions. It prescribes adaptation by workers, not restructuring by systems. It locates responsibility for navigating economic disruption with those who bear displacement, not those who benefit from it.

Notice that the advice recurs. Each wave of technological change produces a new variant. The specifics differ—learn to code, join the trades, develop soft skills, embrace lifelong learning—but the form is constant. Individuals must adapt because the system will not.

Notice that the advice is confident. Each iteration arrives as though the answer has finally been found. The previous advice is quietly retired. The new advice is presented without reference to the pattern it continues.

These narratives are not solutions. They are coordination mechanisms. They absorb the political cost of structural change by distributing adjustment across millions of individual decisions. They preserve the underlying system by channeling disruption into personal responsibility.

This is not conspiracy. It is equilibrium behavior. The advice flows downward because that is the path of least institutional resistance. The pattern recurs because the structural conditions that produce it recur.

Game theory offers a framework for understanding why such patterns persist—and why they are accelerating.

II. Why Game Theory Is the Correct Lens

Most discourse about artificial intelligence fails because it asks the wrong questions.

Is this technology good or bad? Are the people deploying it ethical? Will the benefits outweigh the harms? These questions invite answers that depend on values, predictions, and moral frameworks about which reasonable people disagree.

Game theory asks a different question:

What strategies are stable when multiple actors optimize under uncertainty?

This question has answers that do not depend on moral agreement. The answers emerge from structure, not intent. They describe what happens when rational actors pursue their objectives within competitive systems, regardless of whether those objectives are admirable.

The framework has three foundational premises.

First, actors are rational in the technical sense: they pursue their objectives given their beliefs and constraints. This does not mean actors are wise, benevolent, or farsighted. It means they respond to incentives. When incentives change, behavior changes. When incentives do not change, behavior does not change.

Second, actors operate under uncertainty. They do not know others' capabilities, intentions, or future actions with certainty. They must act on beliefs that may be mistaken. Errors are endemic, not exceptional.

Third, actors are interdependent. Each actor's outcomes depend not only on their own choices but on others' choices. Strategy is not optimization against a static environment; it is optimization against other optimizers.

From these premises, game theory derives results about which outcomes are stable—which configurations of strategy can persist because no actor can improve their position by unilaterally changing behavior.

The results are often counterintuitive. Rational actors can converge on outcomes that all of them would prefer to avoid. Cooperation can fail even when all parties would benefit from it. Escalation can proceed even when all parties would prefer restraint. The invisible hand does not always guide markets toward efficiency; sometimes it guides competitors toward mutual ruin.

Thomas Schelling, whose work on strategic interaction earned a Nobel Prize, emphasized that conflict often emerges from coordination failure rather than incompatible objectives. Actors who share interests can nonetheless produce outcomes that serve none of them. The problem is not disagreement about goals but inability to coordinate on achieving them.

Schelling's phrase—"the threat that leaves something to chance"—captures the essence of strategic instability. When outcomes depend on risk, on uncertainty, on the possibility of miscalculation, actors face pressure to act preemptively, to signal commitment, to remove their own capacity for restraint. The logic can produce escalation that no participant desires.

This framework applies to large language models not because LLMs create new games but because they change how existing games are played.

LLMs do not introduce new incentives. They reduce the cost, latency, and visibility of strategic moves—forcing actors into well-known game-theoretic traps faster than institutions can respond.

III. LLMs as Optimization Accelerants

Large language models are optimization technology.

They reduce the cost of activities that were previously expensive: planning, analysis, persuasion, iteration, content generation, scenario exploration. They compress decision cycles. They remove friction from processes that friction once constrained.

This is not a claim about artificial general intelligence, consciousness, or superhuman capability. It is a claim about economics. Activities that required hours now require minutes. Activities that required teams now require individuals. Activities that required expertise now require access.

The effects are structural:

Cost reduction. Strategic planning that required analyst teams can be approximated by individuals with model access. Influence campaigns that required creative staffs can be generated at scale. Surveillance that required human review can be automated. The barriers to strategic activity fall.

Cycle compression. When planning is cheaper and faster, more planning occurs. When iteration is cheaper and faster, more iterations occur. Decision cycles that once required weeks can complete in days. Response times shorten across the board.

Friction removal. Many strategic activities were constrained not by capability but by effort. The friction of production, coordination, and execution limited how far actors could pursue their objectives. LLMs reduce this friction without supplying alternative constraints.

Authorship diffusion. When content and strategy emerge from human-model interaction, attribution becomes ambiguous. "The system suggested this approach" is not a lie, but it is also not clear accountability. The line between human decision and model output blurs.

These effects are not specific to any particular use case. They apply to beneficial applications and harmful applications alike. The technology is neutral in the sense that amplification is neutral: it amplifies whatever it is applied to.

The question is not whether LLMs will be used for optimization. They will. The question is what happens when optimization proceeds faster than governance can adapt—when the friction that made previous games survivable is removed without replacement.

The following sections examine specific game-theoretic dynamics that this acceleration intensifies. Each game describes a different failure mode. Each failure mode is well-documented in theory and history. Each is made more dangerous by the same technological pattern.

IV. Prisoner's Dilemma — Why Restraint Is Unstable

The Prisoner's Dilemma is not a puzzle. It is a proof.

Two actors face a choice. If both cooperate, both benefit. If one defects while the other cooperates, the defector gains at the cooperator's expense. If both defect, both lose—but less than the cooperator who trusted a defector.

The structure is simple. The logic is merciless. Defection dominates.

Not because actors are malicious. Not because they lack foresight. But because cooperation requires trust that cannot be verified. In a single encounter between strangers, restraint is a gift to whoever chooses not to give it.

Game theorists have documented this for seventy years. The lesson is not that cooperation is impossible—only that it is unstable without enforcement, repetition, or payoff restructuring. Remove those stabilizers and the dilemma reasserts itself with mechanical predictability.

This matters because "responsible AI" discourse is, at its core, an attempt to solve a Prisoner's Dilemma through voluntary restraint.

The Verification Problem Is Structural

Consider what restraint would require.

A firm pledges not to use LLMs for behavioral manipulation. A government pledges not to deploy influence models against its own population. A research lab pledges to delay capability releases until safety is established.

Each pledge is costless to make and impossible to verify.

The problem is not dishonesty. The problem is that capability cannot be audited from outside. Outputs can be inspected. Policies can be reviewed. But no external observer can confirm what a model is capable of until it acts, and no external observer can confirm intent at any point.

This is not a contingent limitation awaiting better auditing tools. It is structural. The nature of general-purpose optimization is that capability ceilings are discovered, not declared. A model's designers may not know what it can do. Its operators certainly do not.

When verification is impossible, promises are signals—not constraints.

Historical Precedent: Interwar Arms Limitation

The 1920s and 1930s offer a direct parallel.

After the catastrophe of World War I, the major powers attempted to prevent rearmament through treaty. The Washington Naval Treaty of 1922 limited battleship tonnage. The Geneva Protocol of 1925 prohibited chemical weapons. The Kellogg-Briand Pact of 1928 renounced war as an instrument of policy.

These were not cynical gestures. They were sincere attempts to restructure incentives. Statesmen understood that arms races produced outcomes no one wanted. The treaties represented genuine coordination toward mutual restraint.

They failed.

Not because signatories were villains, but because verification could not keep pace with capability development. Germany rearmed in secret. Japan exceeded tonnage limits. Chemical weapons research continued in ambiguous categories. Each actor faced the same calculus: if others defect and we restrain, we lose catastrophically; if we defect and others restrain, we gain; if all defect, we are no worse off than if we alone had cooperated.

The defection was gradual, then sudden. By 1939, the treaties were historical artifacts.

What matters is not the specific failures but the pattern: voluntary restraint collapses when verification fails and competitive pressure persists. The actors were not irrational. The structure was.

The Corporate Parallel

The same logic appears in peacetime economic competition, stripped of existential stakes but identical in form.

Consider environmental self-regulation before binding law. Firms understood that collective pollution harmed everyone, including themselves. Industry associations drafted voluntary standards. Corporate responsibility reports proliferated.

Compliance was uneven. Verification was weak. Competitive pressure was constant.

The pattern repeated across domains: financial self-regulation before 2008, data privacy pledges before GDPR, labor standards in global supply chains. In each case, firms facing competitive pressure and unverifiable restraint converged on defection—not through conspiracy, but through the ordinary operation of incentives.

The executives making these decisions were not unusually cynical. They were responding to a structure that punished unilateral restraint and rewarded marginal defection. The ones who held firm lost market share. The ones who defected incrementally survived.

This is not a moral observation. It is an equilibrium observation.

LLM-Era Manifestation

Now apply this structure to large language models.

Firms face competitive pressure to deploy LLMs for behavioral targeting, workforce monitoring, persuasion systems, and decision acceleration. Each application offers measurable advantage. Each is difficult or impossible for competitors to verify. Each carries externalities—epistemic, social, political—that are diffuse and delayed.

A firm that restrains while competitors deploy loses immediately and concretely. A firm that deploys while competitors restrain gains immediately and concretely. The harms are collective; the gains are private.

This is the Prisoner's Dilemma in corporate form.

"Responsible AI" initiatives attempt to solve it through voluntary commitment. Ethics boards review deployment decisions. Principles are published. Executives speak at conferences about safety.

None of this changes the payoff matrix.

The firm that declines to optimize user engagement cedes market share to the firm that does. The firm that refuses to automate persuasion loses clients to the firm that offers it. The firm that maintains human review cycles operates slower than the firm that removes them.

The ethics pledges are not lies. They are non-binding signals in a game that punishes restraint.

Why Cooperation Remains Unstable

The Prisoner's Dilemma has known solutions—but they require conditions that do not obtain.

Repetition with reputation can stabilize cooperation. If actors expect to interact indefinitely and can punish defection, tit-for-tat strategies emerge. But LLM deployment is not a repeated game with clear rounds. Actions are continuous, attribution is ambiguous, and retaliation is slow. By the time defection is detected, the market has moved.

External enforcement can stabilize cooperation. If a third party imposes costs on defection, the payoff matrix shifts. But no such enforcer exists for LLM capability development. Governments compete with each other. Regulators lack technical capacity. International coordination faces its own Prisoner's Dilemma.

Payoff restructuring can stabilize cooperation. If defection is made more costly than cooperation regardless of others' choices, the dilemma dissolves. This is the logic of binding law, mandatory disclosure, and liability regimes. But such restructuring requires political will that is itself subject to competitive capture.

Without these stabilizers, defection remains the dominant strategy. Not because actors want bad outcomes, but because wanting good outcomes is insufficient to produce them.

The Accelerant Effect

LLMs do not create the Prisoner's Dilemma. They intensify it.

They reduce the cost of defection. Influence systems that once required human analysts can now be generated at scale. Surveillance that once required infrastructure can now be automated through API. Persuasion that once required creative labor can now be iterated continuously.

They reduce the visibility of defection. Model capabilities are opaque. Deployment is distributed. Attribution is deniable. "The system suggested it" becomes a structural shield.

They compress the time horizon. When decision cycles accelerate, long-term reputation effects matter less. The game becomes more one-shot, less repeated. Cooperation strategies that depend on future consequences lose their grip.

Each of these effects pushes the equilibrium toward defection. Not through any single actor's malice, but through the accumulated weight of rational response to structural pressure.

What This Section Establishes

The Prisoner's Dilemma is not a metaphor for LLM competition. It is a description of it.

Voluntary restraint fails when:

Cooperation requires verification that cannot be achieved
Defection offers private gain at collective cost
Competitive pressure is continuous
No external enforcer restructures payoffs

All four conditions obtain.

This does not mean catastrophe is inevitable. It means that hoping for restraint is not a strategy. The structure must change, or the outcome will not.

V. Security Dilemma — Defensive Moves That Escalate

The Security Dilemma describes a trap more insidious than the Prisoner's Dilemma: actors can produce mutual escalation while genuinely seeking only self-protection.

The structure is straightforward. Actor A improves its defensive position. Actor B cannot distinguish defense from preparation for offense. B responds with its own improvements. A interprets B's response as confirmation of threat. Both escalate. Neither intended aggression. Both end up less secure than before.

What makes this dilemma vicious is that no one needs to be wrong. A's defensive improvements may indeed be purely defensive. B's interpretation may be entirely reasonable given available information. The escalation emerges not from error but from the impossibility of verifying intent through observed capability.

Game theorists call this the problem of credible signaling under uncertainty. States and firms can declare their intentions. They cannot prove them. When capabilities speak louder than words, defensive investment and offensive preparation become indistinguishable from the outside.

This matters for LLMs because they systematically degrade the signaling channels that historically allowed actors to distinguish defense from offense, prudence from aggression.

The Verification Asymmetry

The Security Dilemma depends on a specific asymmetry: actors have privileged access to their own intentions but can only observe others' capabilities.

You know why you are arming. You do not know why they are arming. You can explain your reasoning, but explanation is cheap. Only costly signals—signals that would be irrational if your stated intent were false—carry credibility. And costly signals are, by definition, expensive to send.

Historically, this asymmetry was partially managed through transparency mechanisms, structural constraints, reputation accumulated over repeated interaction, and communication channels with established credibility. None of these mechanisms was perfect. All of them provided some friction between defensive action and offensive interpretation. That friction created space for de-escalation, clarification, and restraint.

LLMs erode each of these mechanisms.

Historical Precedent: The Dynamics of Convoy and Submarine

The naval war of 1939–1943 offers a clean example of Security Dilemma dynamics because it is purely operational, free of ideological confound.

Britain required maritime supply to survive. German submarine warfare threatened that supply. The initial British response was defensive: convoy systems to concentrate merchant vessels under escort protection.

This defensive measure had an unintended offensive consequence. Convoys required escort vessels. Escort vessels required extended range to cover Atlantic routes. Extended range required larger ships, more fuel capacity, better detection systems.

Germany observed capability expansion and responded rationally. If British escorts could range further into the Atlantic, submarines needed to operate further from shore. If detection improved, submarines needed to dive deeper and attack at night. If convoys concentrated, submarines needed to hunt in coordinated groups.

Each adaptation triggered counter-adaptation. British cryptanalysis enabled routing convoys around submarine concentrations. German response was more submarines, wider patrol areas, faster attack cycles. British response was escort carriers, air coverage, hunter-killer groups.

At no point did either side seek escalation for its own sake. Each sought survival and advantage within constraints. The escalation was structural, driven by the impossibility of distinguishing defensive improvement from capability expansion.

The lesson is not that escalation was wrong—both sides faced genuine threats. The lesson is that once the dynamic is triggered, intent becomes irrelevant to outcome. The spiral has its own logic.

The Cold War Intensification

The nuclear era amplified Security Dilemma dynamics without changing their fundamental structure.

Consider ballistic missile defense. From the perspective of the state deploying it, missile defense is purely defensive—it intercepts incoming weapons rather than delivering them. From the perspective of the opposing state, missile defense is profoundly threatening—it undermines the retaliatory capability that maintains deterrence.

If you can block my second strike, my deterrent loses credibility. If my deterrent loses credibility, your first strike becomes rational. Therefore I must expand my offensive capability to overwhelm your defense. You interpret my expansion as confirmation that defense was necessary. Both of us are now less secure and more heavily armed.

This is not hypothetical. The Anti-Ballistic Missile Treaty of 1972 was explicitly designed to prevent this spiral. The logic was counterintuitive but sound: limiting defense would limit offense by preserving mutual vulnerability.

The treaty held for thirty years. Its abandonment in 2002 was followed by precisely the capability expansion it was designed to prevent.

Again, the actors were not irrational. The structure was.

The Corporate Parallel

Remove existential stakes and the same dynamics appear in economic competition.

Consider competitive intelligence. A firm invests in understanding competitor strategy—market research, patent analysis, hiring patterns, supply chain mapping. This is defensive: knowledge reduces uncertainty, enables response, prevents surprise.

From the competitor's perspective, the same investment looks like preparation for predatory action. If you know my strategy before I execute it, you can preempt, undercut, or copy. My response is to invest in my own intelligence capability and to increase operational security.

Escalation follows. Intelligence operations expand. Security tightens. Both firms spend more to know more while revealing less. The information environment becomes adversarial. Trust that might enable cooperation—joint ventures, standard-setting, supply chain coordination—erodes.

Neither firm sought this outcome. Both would prefer a world of lower intelligence spending and higher operational transparency. But neither can unilaterally disarm without disadvantage.

LLM-Era Manifestation

Large language models intensify Security Dilemma dynamics through several mechanisms.

Capability opacity. LLM capabilities are discovered, not declared. A model's designers may not know what it can do in adversarial contexts. Its operators certainly do not know what competitors' models can do. This expands the uncertainty space in which defensive and offensive capabilities are indistinguishable.

When a competitor deploys an LLM system for "customer engagement," you cannot know whether that system is also being used for competitive intelligence, influence operations, or strategic planning against you specifically. You observe capability. You cannot observe intent.

Cycle compression. LLMs accelerate planning, analysis, and iteration. This compresses the time between action and response. When response cycles shorten, there is less time for clarification, less opportunity for signaling, less space for de-escalation.

Planning proliferation. LLMs reduce the cost of generating strategic analyses, scenario plans, and contingency preparations. This seems purely defensive—more planning, better preparation, reduced surprise.

From the competitor's perspective, proliferating plans are proliferating threats. If you have detailed plans for how to respond to every move I might make, the distinction between "prepared" and "targeting" collapses. Your preparation looks like my threat model.

Attribution diffusion. LLM-generated content and analysis can obscure authorship and intent. "The system suggested this strategy" is not a lie, but it is also not an accountable signal. When actions cannot be clearly attributed to actors, the signaling channels that enable trust and de-escalation degrade.

The State-Corporate Ouroboros

A distinctive feature of LLM-era Security Dilemma dynamics is the entanglement of state and corporate actors.

States outsource capability development to firms. Firms adopt intelligence and strategic methods from state practice. Each validates the other's escalation.

Government agencies procure LLM systems from private vendors. Those systems are also sold to competitors, adversaries, and actors across the economy. The capability is not contained within state control.

Simultaneously, corporations adopt methods—red-teaming, influence analysis, scenario planning, operational security—that were historically state intelligence functions. These methods become normalized as "business practice."

The result is a capability diffusion pattern where states justify LLM investment because adversary states are investing, corporations justify LLM investment because competitors are investing, state and corporate capability converge on shared infrastructure, and each sector's escalation provides cover for the other's.

This is the ouroboros: the system consuming its own tail, optimization feeding optimization, no exit within the game's logic.

What Distinguishes This From Prisoner's Dilemma

Both dilemmas produce escalation through rational response to structural incentives. The difference is in the mechanism.

In the Prisoner's Dilemma, actors defect because cooperation cannot be verified and defection is advantageous. The failure is trust.

In the Security Dilemma, actors escalate because defensive action cannot be distinguished from offensive preparation. The failure is signaling.

An actor in a Prisoner's Dilemma knows that defection harms the collective; they defect because individual incentives dominate. An actor in a Security Dilemma may genuinely believe their escalation is purely defensive; they escalate because they cannot credibly convey that belief.

This distinction matters because the remedies differ. Prisoner's Dilemma requires enforcement or payoff restructuring. Security Dilemma requires transparency, signaling mechanisms, and structural constraints on capability acquisition.

LLMs degrade the conditions for both sets of remedies. They make verification harder (worsening Prisoner's Dilemma) while simultaneously degrading signaling channels (worsening Security Dilemma). The failures compound.

What This Section Establishes

The Security Dilemma shows that escalation does not require aggressive intent.

Actors seeking only self-protection can produce mutual escalation when:

Defensive and offensive capabilities are observationally indistinguishable
Intent cannot be credibly signaled
Response cycles are short enough to prevent clarification
Capability improvements are continuous rather than discrete

LLMs intensify each condition. They obscure capability boundaries. They compress decision cycles. They proliferate planning and analysis. They diffuse attribution.

The result is not a single escalation event but a continuous escalation pressure that operates below the threshold of crisis. No actor experiences a decision point. All actors experience capability treadmill.

VI. Chicken — Commitment Without Verification

The game of Chicken captures a dynamic distinct from both the Prisoner's Dilemma and the Security Dilemma: the logic of brinkmanship, where victory belongs to whoever commits most credibly to not backing down.

The canonical image is two drivers racing toward each other. Each prefers that the other swerve. Neither wants to swerve first. The worst outcome is mutual destruction. The second-worst outcome is being the one who swerved while the other held firm.

The structure creates an incentive to appear committed—to signal, credibly, that you will not yield. The actor who successfully removes their own steering wheel, visibly, wins by default. The other must swerve or die.

This seems like a game that rewards recklessness. In fact, it rewards credible commitment. The distinction matters enormously for understanding how LLMs affect strategic dynamics.

Thomas Schelling's insight was that brinkmanship is not about wanting catastrophe. It is about manipulating the risk of catastrophe to coerce the other party. "The threat that leaves something to chance" works precisely because it is not fully controlled. The danger is the point.

What stabilized Cold War brinkmanship—what prevented Chicken from ending in collision—was not the rationality of actors but the legibility of commitment. Each side could observe the other's preparations, deployments, and political constraints. Commitment was visible. The signaling channel, however imperfect, functioned.

LLMs degrade this channel in specific ways that make Chicken dynamics more dangerous, not because they remove human hesitation, but because they make commitment unverifiable.

The Mechanics of Credible Commitment

Commitment in Chicken works only if the other side believes it.

A driver announcing "I will not swerve" changes nothing. A driver throwing their steering wheel out the window changes everything. The difference is not sincerity but observability. The second signal is costly, irreversible, and visible.

Game theorists formalize this as the distinction between cheap talk and credible signals. Cheap talk—statements of intent—can be made by anyone regardless of actual intent. Credible signals require actions that would be irrational if the stated intent were false.

In strategic competition, credible commitment historically took forms such as public statements that would be politically costly to reverse, force deployments that would be operationally difficult to recall, alliance commitments that would damage reputation if abandoned, and domestic political constraints that made backing down electorally impossible.

Each of these mechanisms worked because commitment was observable by the adversary. You could see the troops massing. You could read the parliamentary debate. You could assess whether the leader's domestic position allowed retreat.

The signaling channel was noisy, subject to misinterpretation, and occasionally failed catastrophically. But it existed. Brinkmanship was survivable because actors could, imperfectly, read each other's constraints.

Historical Precedent: The Cuban Missile Crisis as Legible Brinkmanship

October 1962 is the canonical case of Chicken dynamics in strategic competition. Its resolution depended entirely on signaling legibility.

The Soviet deployment of missiles to Cuba was observable. American reconnaissance confirmed it. The U.S. naval blockade was observable. Soviet ships could see it. Each side's political constraints were, to a remarkable degree, transparent.

Khrushchev understood that Kennedy faced domestic pressure that made acquiescence politically impossible. Kennedy understood that Khrushchev had invested prestige that made simple withdrawal humiliating. Both sides understood that the other understood.

The resolution—Soviet withdrawal in exchange for U.S. non-invasion pledge and quiet removal of Turkish missiles—was possible because each side could verify the other's constraints and commitments. The back-channel communications, the public statements, the observable military postures created a shared information environment.

This was not because leaders were wise or restrained. It was because the game was legible.

Contrast this with crises where signaling channels failed. The July Crisis of 1914 escalated in part because mobilization timetables created commitment that was visible but not understood—each side saw the other's mobilization as aggressive rather than defensive, and the signaling channel could not carry the necessary clarification before commitment became irreversible.

The difference between 1914 and 1962 was not the rationality of actors but the quality of signaling. When commitment is legible, brinkmanship can stabilize. When commitment is opaque, the game collapses toward collision.

The Cold War Automation Fear

It is worth noting that concern about signaling collapse is not new.

Throughout the Cold War, strategists worried about automation degrading the human judgment that made brinkmanship survivable. The fear was that automated systems—launch-on-warning protocols, dead-hand retaliation, compressed decision windows—would remove the human hesitation that allowed last-minute de-escalation.

These concerns were legitimate but somewhat misplaced. The deeper issue was not hesitation but verification. As long as each side could observe the other's automated systems, could assess their triggers and constraints, the signaling channel remained intact. Automation that was legible preserved the game's survivability, even if it accelerated it.

The danger was never automation per se but automation that obscured commitment. A visible automated response—known triggers, observable deployments, declared doctrine—could function as a credible signal. An invisible automated response could not.

This distinction clarifies what is actually new about LLMs.

LLM-Era Manifestation: Opacity, Not Speed

LLMs do accelerate decision cycles. But speed alone does not destabilize Chicken dynamics. What destabilizes them is the collapse of commitment legibility.

Consider how LLMs affect the signaling channel:

Strategic planning becomes invisible. LLM-assisted analysis generates scenarios, contingencies, and response options that remain internal to the actor. Historically, strategic planning required human staff, institutional processes, and documentary trails that leaked information. LLMs enable planning that leaves no observable trace.

An actor generating a thousand response scenarios has committed to none of them. An adversary observing the actor's behavior cannot distinguish preparation from commitment. The signaling channel degrades.

Commitment devices become ambiguous. Credible commitment historically required visible, costly action. LLMs enable the generation of signals—public statements, policy documents, strategic communications—without the underlying organizational commitment they once implied.

When a policy paper could only be produced by a staffed interagency process, its existence signaled institutional commitment. When a comparable document can be generated in hours by a model, it signals nothing about organizational intent.

Response generation outpaces verification. In Chicken, each side must assess whether the other's commitment is real. This assessment takes time—time to observe deployments, read political constraints, test resolve through probing actions.

LLMs compress the response cycle faster than the verification cycle. Actors can generate and execute strategic moves faster than adversaries can assess commitment. The gap between action and verification widens.

The steering wheel becomes virtual. The classic Chicken metaphor—throwing away the steering wheel—worked because the action was visible and irreversible. LLM-mediated strategy allows actors to simulate commitment without actually committing.

You can generate scenarios showing you will not back down. You can produce analyses justifying escalation. You can create the appearance of committed planning. But the commitment is virtual—it can be abandoned without observable reversal.

This is worse than having no steering wheel. It is having a steering wheel whose removal cannot be verified.

The Preemption Problem

When commitment becomes unverifiable, a specific pathology emerges: rational preemption.

In classic Chicken, the stable outcome is for one side to swerve at the last moment. The game is dangerous but survivable because both sides can observe the approach of disaster and calculate when to yield.

When commitment cannot be verified, this calculation breaks down. If I cannot tell whether you have committed, I cannot tell whether you will swerve. If I cannot tell whether you will swerve, I cannot safely wait to see.

The rational response to uncertainty about commitment is to act early—before the other side's commitment, real or simulated, becomes binding.

This transforms Chicken from a game of brinkmanship to a game of preemption. The winner is not who commits most credibly but who acts first. Speed becomes advantage. Hesitation becomes vulnerability.

The Cold War strategists' fear was that automation would remove hesitation. The actual danger is more subtle: automation that obscures commitment makes hesitation appear irrational, regardless of whether the human actor would choose to hesitate.

"We cannot wait—the model shows risk" becomes a structurally dominant strategy, not because actors want preemption, but because they cannot verify that restraint is safe.

The Corporate Parallel

Remove military stakes and the same dynamics appear in market competition.

Consider a price war. Two firms compete for market share. Each prefers that the other maintain prices. Neither wants to initiate a margin-destroying price cut. But if the other cuts and I do not respond, I lose market share catastrophically.

This is Chicken. The firm that credibly commits to matching any price cut—that removes its own steering wheel—forces the other to maintain prices or face mutual destruction.

Historically, commitment in price wars was legible through public pricing announcements, contractual commitments to customers, investment in capacity that would be stranded without volume, and executive statements that would be reputation-destroying if reversed.

LLMs affect this dynamic the same way they affect strategic brinkmanship. Pricing strategy becomes opaque. Analytical capacity to generate scenarios expands. The visible commitments that once signaled resolve can be generated without underlying organizational commitment.

The result is increased uncertainty about competitors' actual positions. Uncertainty favors preemption. Preemption triggers response. The price war happens not because either firm wanted it but because neither could verify the other's commitment to restraint.

What Distinguishes This From Security Dilemma

Both Chicken and the Security Dilemma involve signaling failures. The distinction is in what must be signaled.

In the Security Dilemma, the signaling failure concerns intent—whether capability expansion is defensive or offensive. Actors cannot credibly convey that their actions are not threatening.

In Chicken, the signaling failure concerns commitment—whether the actor will actually follow through on their stated position. Actors cannot credibly convey that they will not back down.

The remedies differ accordingly. Security Dilemma requires transparency about capability and intent. Chicken requires mechanisms for making commitment verifiable and reversibility observable.

LLMs degrade both kinds of signaling. They make capability opaque (worsening Security Dilemma) while simultaneously making commitment unverifiable (worsening Chicken). The failures compound.

What This Section Establishes

Chicken dynamics become more dangerous when commitment cannot be verified.

The historical survivability of brinkmanship depended on:

Visible commitment devices
Observable political and organizational constraints
Time to verify commitment before collision
Signaling channels that carried credible information

LLMs degrade each condition. They enable invisible planning, generate ambiguous signals, compress cycles faster than verification, and simulate commitment without binding force.

The result is not that actors become reckless but that actors cannot distinguish real commitment from performed commitment. Unable to verify restraint, they preempt. Unable to observe backing down, they collide.

VII. Stag Hunt — Why Good Faith Is Insufficient

The Stag Hunt describes a failure mode more subtle than defection, escalation, or brinkmanship. It shows how cooperation can collapse even when all parties genuinely want it to succeed.

The structure comes from Rousseau's parable. Hunters surround a stag. If all remain at their posts, the stag is caught and everyone eats well. But a rabbit runs past one hunter's position. If that hunter chases the rabbit, they eat—but the stag escapes and everyone else goes hungry.

The difference from the Prisoner's Dilemma is crucial. In the Prisoner's Dilemma, defection is the dominant strategy—each actor prefers to defect regardless of what others do. In the Stag Hunt, cooperation is preferred if and only if the actor believes others will also cooperate.

This is a coordination game, not a defection game. The problem is not that actors want to cheat. The problem is that actors cannot verify others' commitment, so they hedge by pursuing the smaller, safer payoff.

Everyone wants the stag. Everyone gets rabbits.

This matters for LLM governance because most policy discourse assumes the challenge is restraining bad actors. The Stag Hunt shows that restraining good actors from their own uncertainty may be the harder problem.

The Trust Dependency

The Prisoner's Dilemma is stable at mutual defection. No actor can improve their position by unilaterally changing strategy. The equilibrium is bad but robust.

The Stag Hunt has two equilibria. Mutual cooperation is stable: if everyone hunts stag, no one gains by switching to rabbit. But mutual defection is also stable: if everyone hunts rabbit, no one gains by unilaterally waiting for a stag that will never be caught.

Which equilibrium obtains depends entirely on what actors believe about each other.

If each hunter is confident that others will hold position, all hunt stag. If each hunter doubts that others will hold, all chase rabbits. The game is identical. The beliefs differ. The outcomes diverge completely.

This makes the Stag Hunt peculiarly sensitive to anything that degrades trust. Not trust in the sense of moral confidence—trust in the sense of justified expectation. Can I verify that you will do what you say? Can you verify the same about me?

In the Prisoner's Dilemma, the absence of trust is built into the payoff structure. Defection dominates regardless. In the Stag Hunt, trust is the variable that determines which equilibrium emerges. Degrade trust, and good-faith actors defect—not because they want to, but because they cannot afford to be the only one holding position.

Historical Precedent: Postwar Cooperation as Stag Hunt

The international order constructed after World War II provides a clear example of Stag Hunt dynamics—and the institutional effort required to reach the cooperative equilibrium.

The postwar planners faced a coordination problem. European recovery required investment. Investment required stability. Stability required coordination among former adversaries and current competitors. But each nation, acting alone, had reason to doubt whether others would contribute to shared stability.

If the United States invested in European recovery and Europe collapsed anyway, the investment was wasted. If European nations coordinated on recovery but the United States withdrew to isolation, coordination failed. Each party had incentive to pursue narrow national advantage—the rabbit—unless they could trust that others would hold for the stag.

The Marshall Plan, Bretton Woods, and the early multilateral institutions were not primarily about restraining bad actors. They were about making cooperation verifiable so that good-faith actors could trust each other to cooperate.

The mechanisms were specific: conditional aid that tied U.S. resources to European coordination, making American commitment observable; institutional architecture that created repeated interaction, allowing reputation to accumulate; mutual monitoring through shared institutions that made defection visible; binding commitments that raised the cost of abandonment.

These mechanisms worked not by changing preferences but by changing the information environment. They made it rational for good-faith actors to believe that other good-faith actors would hold position.

The stag was caught. For a generation, the cooperative equilibrium held.

The Decay of Institutional Trust

The postwar institutions were not permanent solutions. They were temporary equilibrium stabilizers that required continuous maintenance.

Over decades, the conditions that enabled Stag Hunt cooperation eroded. Conditional commitments became unconditional expectations, reducing the signal value of participation. Repeated interaction concentrated among elites who lost connection to domestic constituencies. Mutual monitoring became routine and formalistic, losing its verification function. Binding commitments were reinterpreted or circumvented when convenient.

The result was not a dramatic break but a gradual drift from the cooperative equilibrium. Each actor, observing reduced commitment from others, hedged by reducing their own exposure. The hedging was rational at each step. The cumulative effect was coordination decay.

By the early 21st century, the Stag Hunt had shifted toward the inferior equilibrium. Not because actors became malicious—many sincerely preferred cooperation—but because the verification mechanisms that made cooperation credible had degraded.

This is the background condition into which LLMs arrive. The institutional trust required for Stag Hunt cooperation was already weakened. LLMs intensify the degradation.

LLM-Era Mechanisms: Why Trust Erodes Faster

LLMs affect Stag Hunt dynamics through several specific mechanisms, each of which makes it harder for good-faith actors to verify each other's commitment.

Attribution collapse. Cooperation in a Stag Hunt requires knowing who is cooperating. When actions can be attributed to specific actors, cooperation builds reputation and defection carries cost.

LLMs obscure attribution. Content can be generated without clear authorship. Strategies can be executed through intermediaries. The "system suggested" construction diffuses responsibility across human-machine boundaries.

When attribution collapses, reputation mechanisms fail. If I cannot tell whether you cooperated or defected, I cannot update my beliefs about your future behavior. The information that enables trust accumulation disappears.

Signal pollution. Trust in a Stag Hunt depends on signals—statements, actions, commitments—that convey intent. Signals work when they are costly enough to be credible or scarce enough to be interpretable.

LLMs make signals cheap. Policy commitments, strategic communications, partnership announcements—all can be generated at scale without the organizational investment they once implied. When signals proliferate, their information value degrades.

A firm announcing an AI ethics commitment meant something when such announcements were rare and required board-level approval. When every firm can generate comparable announcements trivially, the signal carries no information about actual commitment.

Deniability expansion. Stag Hunt cooperation requires accountability. If you defect, there must be consequences—reputational, material, or relational—that make future cooperation with you less likely.

LLMs expand deniability. Decisions can be attributed to algorithmic recommendation. Outcomes can be blamed on system behavior rather than organizational choice. "The model produced this result" becomes a shield against accountability.

When deniability expands, the cost of defection falls. Actors can chase rabbits while claiming they were hunting stag. The verification problem intensifies.

Coordination cost asymmetry. Historically, coordination among many actors was expensive. It required communication, negotiation, and institutional infrastructure. This expense acted as a commitment device—investing in coordination signaled genuine intent.

LLMs reduce coordination costs asymmetrically. They make appearing to coordinate cheap while leaving actual coordination—the hard work of aligning interests, monitoring compliance, and enforcing agreements—expensive.

The result is coordination theater: proliferating agreements, partnerships, and commitments that lack the underlying alignment they advertise. Good-faith actors, observing this theater, cannot distinguish real coordination from performance. They hedge.

The Governance Implication

Most AI governance discourse assumes a bad-actor model.

The framing is: irresponsible developers, malicious users, and adversarial nations threaten AI safety; governance must restrain them; responsible actors will naturally cooperate once restraints are in place.

The Stag Hunt shows why this framing is insufficient.

Even if every actor in the AI ecosystem genuinely preferred cooperative outcomes—safety investment, capability restraint, transparency, coordination—cooperation would still fail if actors cannot verify each other's commitment.

The responsible developer who invests in safety cannot verify that competitors are doing the same. The firm that restrains capability deployment cannot verify that the market will reward rather than punish restraint. The nation that proposes governance frameworks cannot verify that other nations will implement rather than circumvent them.

Each actor, genuinely preferring the stag, chases rabbits.

This is not a moral failure. It is a coordination failure. The mechanisms that would enable good-faith actors to trust each other—attribution, credible signals, accountability, conditional cooperation—are precisely what LLMs degrade.

What Would Enable Stag Hunt Cooperation

Game theory does not prescribe hope. It prescribes mechanism design.

Stag Hunt cooperation requires verifiable commitment, attribution preservation, deniability reduction, and conditional cooperation.

Historically, verifiable commitment has been achieved through transparency mechanisms robust to signal pollution—auditable disclosure, third-party verification, institutional structures where cooperation is observable rather than announced.

Attribution preservation has required countering the diffusion of responsibility—provenance requirements, organizational accountability for algorithmic decisions, liability structures that prevent blame displacement.

Deniability reduction has required accountability mechanisms that hold organizations responsible for outcomes, not just intentions—outcome-based liability rather than process-based compliance, mandatory disclosure of AI-involved decisions, governance structures that pierce the "system suggested" shield.

Conditional cooperation has required explicitly contingent commitment rather than unconditional trust—staged commitment frameworks where each actor's contribution is contingent on others meeting milestones, verification protocols that gate deeper cooperation on demonstrated compliance.

None of these mechanisms currently exists at scale for AI governance. Building them is harder than restraining bad actors—but without them, restraining bad actors is insufficient.

The Relationship to Other Games

The Stag Hunt compounds with the previous failure modes.

The Prisoner's Dilemma shows that defection dominates when verification is absent. The Stag Hunt shows that even when cooperation would dominate if believed, uncertainty produces defection.

The Security Dilemma shows that defensive action triggers escalation when intent cannot be signaled. The Stag Hunt shows that cooperative intent, even when genuine, fails to produce cooperation when commitment cannot be verified.

Chicken shows that brinkmanship collapses into preemption when commitment becomes unobservable. The Stag Hunt shows that actors who genuinely prefer mutual restraint still defect when they cannot verify that restraint is mutual.

Each game describes a different failure mode. In practice, they interact. An actor facing Stag Hunt uncertainty may hedge in ways that look like Security Dilemma escalation. An actor unable to verify Stag Hunt cooperation may preempt in Chicken-like fashion.

The games are analytically distinct but operationally entangled. LLMs intensify all of them through the same basic mechanisms: opacity, signal degradation, attribution collapse, and verification failure.

What This Section Establishes

The Stag Hunt demonstrates that good faith is insufficient for cooperation.

Actors can genuinely prefer cooperative outcomes and still fail to achieve them when:

Commitment cannot be verified
Signals are cheap and abundant
Attribution is obscured
Deniability shields defection from consequence

These are not conditions created by bad actors. They are structural conditions that affect all actors equally. Good-faith participants suffer the same coordination failure as bad-faith participants—they simply suffer it reluctantly.

LLMs intensify Stag Hunt dynamics by degrading exactly the mechanisms that historically enabled good-faith actors to trust each other. Attribution, signal credibility, accountability, and conditional cooperation all become harder in an LLM-saturated environment.

The implication is that AI governance cannot rely on norms, values, or shared commitment. It must build verification infrastructure that makes cooperation observable. Without that infrastructure, the actors who most want cooperation will be unable to achieve it.

VIII. Tragedy of the Commons — The Exhaustion of Attention

The Tragedy of the Commons describes a failure mode distinct from the games examined so far. It concerns not bilateral strategic interaction but the cumulative degradation of a shared resource by individually rational actors.

Garrett Hardin's canonical formulation involves a pasture. Each herder benefits by adding another animal to graze. The cost of overgrazing is distributed across all herders. Each therefore adds animals until the pasture is destroyed. No herder intends destruction. Each acts rationally within their constraints. The outcome is collective ruin.

The structure requires three conditions: a shared resource, individual benefit from extraction, and extraction costs distributed across users rather than concentrated on the extractor. When all three obtain, the resource degrades regardless of actors' preferences.

This framework applies to commons that are not pastures. Fisheries. Aquifers. Atmospheric carbon capacity. And—relevant to LLM dynamics—human attention.

Attention is finite, rivalrous, and economically valuable. It can be extracted by actors who do not bear the full cost of its depletion. It is, in the precise sense, a commons. And it is being overgrazed.

Attention as Economic Resource

The attention economy is not a metaphor. Attention is the scarce input that advertisers purchase, platforms monetize, and content producers compete to capture.

Herbert Simon identified the dynamic in 1971: "A wealth of information creates a poverty of attention." As information becomes abundant, the binding constraint shifts from production to reception. The bottleneck is not what can be said but what can be heard.

This creates an extraction dynamic. Each actor seeking attention—a firm, a publisher, a platform, a political campaign—benefits from capturing more of it. The cost of capture is borne by the targets of attention extraction: cognitive load, reduced capacity for deliberation, degraded ability to allocate attention according to one's own priorities.

The individual extractor does not bear this cost. They benefit from engagement regardless of whether engagement serves the user's interests. The cost is distributed across the population of attention-providers.

This is the commons structure exactly. Individual benefit, distributed cost, shared resource degradation.

Historical Precedent: Advertising Escalation

The attention commons has been under extraction pressure since mass media emerged. Examining pre-LLM dynamics clarifies what the technology intensifies.

Early broadcast advertising operated in a relatively constrained environment. Airtime was scarce. Production costs were high. Regulation limited quantity and content. These constraints functioned as grazing limits—not through virtue but through friction.

Deregulation, cable proliferation, and digital transition removed constraints progressively. Each removal was individually rational: more advertising opportunities meant more revenue. The cumulative effect was attention environment degradation.

The pattern is visible in advertising load. Broadcast networks in 1960 carried approximately 9 minutes of advertising per hour. By 2020, cable networks approached 16 minutes. Digital environments removed even these temporal limits—ads could be inserted into any content stream, any scroll position, any pause.

No actor imposed this degradation intentionally. Each acted within competitive constraints. The advertiser who refused to escalate lost market share to competitors who did not refuse. The platform that limited advertising lost revenue to platforms that maximized it. The publisher that prioritized reader experience lost to publishers who prioritized engagement.

Escalation was structural. The commons degraded.

The LLM Intensification

LLMs intensify attention extraction through specific mechanisms that previous technologies could not achieve.

Personalization at scale. Pre-LLM advertising was mass-targeted or crudely segmented. LLMs enable extraction tailored to individual vulnerabilities, interests, and cognitive patterns.

This is not simply more effective advertising. It is extraction that adapts to resistance. When a user develops immunity to one approach, the system can generate alternatives. The historical defense against advertising saturation—habituation, filtering, avoidance—becomes less effective when extraction is individually optimized.

Content generation at marginal cost approaching zero. Pre-LLM content required human production. This created natural limits on extraction: there was only so much content that could be profitably created.

LLMs remove this constraint. Content can be generated at scale limited only by distribution, not production. The commons can be filled with extraction attempts at densities previously impossible.

Engagement optimization without content value. Historically, attention capture required offering something—information, entertainment, utility—that the target found valuable. The exchange, however exploitative, involved mutual benefit.

LLM-generated content can optimize for engagement independent of value. Systems can learn what captures attention without learning what serves attention-providers' interests. The gap between "engaging" and "valuable" widens.

Friction elimination. Pre-LLM extraction faced friction: production costs, distribution limits, regulatory constraints, social norms against aggressive solicitation. Each friction point functioned as a grazing limit.

LLMs reduce friction across the extraction process. Content production is cheaper. Targeting is more precise. Personalization is more responsive. The grazing limits relax.

The Degradation Pattern

Commons degradation follows a characteristic pattern. Initial extraction appears sustainable. Cumulative effects become visible only after significant damage. Recovery is slower than degradation and may be impossible past certain thresholds.

In the attention commons, degradation manifests as reduced deliberative capacity, raised extraction thresholds, trust erosion, and value capture displacement.

Reduced deliberative capacity. Attention extracted for engagement optimization is attention unavailable for reflection, analysis, or considered judgment. As extraction intensifies, the population's aggregate capacity for deliberation declines.

This is not a claim about individual intelligence. It is a resource allocation claim. Time and cognitive capacity spent on engagement-optimized content is time and capacity not spent on deliberative content. The shift is marginal but cumulative.

Raised extraction thresholds. As the commons becomes crowded with extraction attempts, each actor must extract more aggressively to achieve the same capture rate. The escalation is self-reinforcing.

Content that would have captured attention in a sparse information environment fails in a saturated environment. The response is not restraint but intensification. Louder signals, more aggressive personalization, more precisely targeted vulnerabilities.

Trust erosion. When extraction is ubiquitous, recipients become defensive. They discount signals, assume manipulation, and treat information environments as adversarial. This is rational adaptation—but it degrades the commons further by reducing the value of genuine communication.

The publisher offering legitimate information competes in the same attention market as the actor optimizing for extraction. When recipients cannot distinguish them, both suffer. The commons becomes adversarial.

Value capture displacement. Historically, attention was captured by offering value. As extraction intensifies, attention shifts toward actors who extract most effectively regardless of value provided.

The result is a selection effect: the entities that survive in the attention commons are those optimized for extraction, not those optimized for service. The commons becomes populated by extractors. Value-providers are outcompeted.

Why Individual Restraint Fails

The Tragedy of the Commons cannot be solved by individual actors choosing to extract less.

Consider an advertising firm that decides to reduce extraction pressure—fewer ads, less aggressive targeting, more respect for user attention. The immediate effect is lost revenue. The competitive effect is lost market share to less restrained competitors. The commons effect is negligible—one actor's restraint does not offset thousands of actors' extraction.

The restrained actor bears concentrated costs. The commons bears distributed extraction from all other actors. Restraint is punished. Extraction is rewarded. The incentive structure guarantees commons degradation regardless of individual preferences.

This is why appeals to "digital wellness" or "responsible engagement" fail at scale. They ask individual actors to bear costs without corresponding commons benefits. The structure makes virtue irrational.

Related Commons

Attention is not the only commons under LLM-intensified extraction pressure. Several related resources exhibit similar dynamics.

Epistemic commons. Shared capacity for truth-determination depends on the information environment being sparse enough for signal to exceed noise. When false or misleading content can be generated cheaply at scale, the epistemic commons degrades. Each actor generating content optimized for engagement rather than accuracy contributes to cumulative degradation.

Trust commons. Social coordination depends on justified expectations about others' behavior. When LLMs enable cheap impersonation, synthetic social interaction, and deniable manipulation, the trust commons degrades. Each actor exploiting trust contributes to cumulative erosion.

Democratic commons. Collective decision-making depends on shared information environments, deliberative capacity, and the ability to distinguish authentic from manufactured political expression. Each of these commons is under extraction pressure.

These related failures reinforce each other. Degraded attention reduces capacity to evaluate truth claims. Degraded epistemic environment erodes trust. Degraded trust undermines democratic coordination. The commons are not independent; they form an interconnected system under simultaneous pressure.

I note these related failures to acknowledge the scope of the problem without attempting to analyze each in depth. Each would require treatment as extensive as attention has received here. The pattern is consistent across commons; the mechanisms vary.

What Commons Preservation Requires

Tragedy of the Commons has known solutions—but they require conditions that the current environment does not provide.

Enclosure. Converting commons to private property gives owners incentive to preserve rather than exhaust. But attention cannot be enclosed. It is inalienably held by the individuals whose capacity it represents.

Regulation. External authority can impose extraction limits. Historically, broadcast advertising limits, content standards, and consumer protection regulation functioned as grazing restrictions. But regulatory capacity has not kept pace with extraction technology. LLMs operate faster than rule-making. Enforcement lags behind capability.

Collective management. Elinor Ostrom's work demonstrated that commons can be sustainably managed by communities of users without either privatization or external regulation. But such management requires shared norms, mutual monitoring, graduated sanctions, and mechanisms for collective decision-making. These conditions are difficult to satisfy in global digital environments with millions of actors.

Internalized costs. If extractors bore the full cost of their extraction, the incentive to overgraze would disappear. But attention costs are by nature distributed across the extracted-from. No mechanism currently exists to concentrate these costs on extractors.

The absence of effective preservation mechanisms does not mean preservation is impossible. It means that current extraction will continue until mechanisms are built. The trajectory is degradation by default.

What This Section Establishes

The Tragedy of the Commons shows that shared resources degrade under individually rational extraction.

The attention commons is under extraction pressure that LLMs intensify through personalization that adapts to resistance, content generation at near-zero marginal cost, engagement optimization independent of value, and friction elimination across the extraction process.

Degradation manifests as reduced deliberative capacity, raised extraction thresholds, trust erosion, and value capture displacement. The pattern is self-reinforcing: degradation creates conditions for further degradation.

Individual restraint cannot reverse commons degradation. The structure punishes restraint and rewards extraction. Only systemic intervention—regulation, collective management, or cost internalization—can stabilize the commons. Such intervention is not currently operative at the scale required.

IX. Red Queen Dynamics — Running to Stand Still

The Red Queen hypothesis takes its name from Lewis Carroll's Through the Looking-Glass: "It takes all the running you can do, to keep in the same place."

In evolutionary biology, the concept describes competitive co-evolution. Predators and prey, parasites and hosts, each must continuously adapt merely to maintain their relative position. Improvement is not progress toward a goal but response to others' improvement. The race has no finish line.

Game theorists recognize this pattern as a specific dynamic: continuous escalation in which restraint is punished, acceleration is required, and no stable equilibrium exists short of exhaustion or external intervention.

The Red Queen is not a sixth game. It is the meta-dynamic that emerges when the five games examined previously are played simultaneously and continuously. Each game's failure mode accelerates. The acceleration across games compounds. The system runs faster to stay in place—until it cannot.

The Mechanics of Continuous Escalation

Red Queen dynamics require specific conditions: relative payoffs, observable improvement, response capability, and compounding iteration.

Relative payoffs. Actors compete for position, not absolute outcomes. Gaining ground requires outpacing others. Holding ground requires matching others' pace. Falling behind is losing regardless of absolute performance.

Observable improvement. Actors can detect others' advancement. Each improvement is visible and demands response. The signal is clear; the pressure is immediate.

Response capability. Actors can respond to observed improvement. There is no natural limit on adaptation. Each response enables further response.

Compounding iteration. Responses trigger counter-responses without ceiling. The escalation is open-ended.

When all conditions obtain, the system enters continuous acceleration. No actor can afford to slow down. Each actor's acceleration forces others to accelerate. The race proceeds until external constraint intervenes or participants exhaust their capacity to continue.

Red Queen Effects Across the Five Games

Each game examined previously produces its own escalation pressure. The Red Queen dynamic emerges from their interaction.

Prisoner's Dilemma contribution. Defection pressure creates continuous capability investment. Each actor must assume others are building capabilities; therefore each must build capabilities. The verification failure that drives defection also obscures the pace of others' advancement, creating pressure to over-invest rather than risk falling behind.

Security Dilemma contribution. Defensive investment triggers offensive interpretation, which triggers further defensive investment. The cycle has no natural terminus. Each round of investment raises the baseline for the next round. What was adequate capability last year is inadequate this year because others have invested.

Chicken contribution. Commitment signaling requires ever-stronger signals as previous signals lose credibility. The commitment device that worked last cycle must be exceeded next cycle. Actors race to demonstrate resolve, each escalation requiring the next.

Stag Hunt contribution. Coordination failure forces actors toward individually safe strategies that are collectively inferior. As trust erodes, the gap between cooperative potential and actual outcomes widens. Actors run harder to achieve less.

Tragedy of the Commons contribution. Extraction pressure intensifies as the commons degrades. More aggressive extraction is required to capture the same value from a depleted resource. The race to extract accelerates as returns per unit of extraction decline.

Each game contributes acceleration pressure. The pressures interact. An actor responding to Prisoner's Dilemma dynamics (building capability to avoid being outcompeted) triggers Security Dilemma dynamics in observers (who interpret capability investment as threat). The Security Dilemma response (defensive escalation) worsens Stag Hunt dynamics (eroding the trust that would enable cooperation). The Stag Hunt failure forces Tragedy of the Commons extraction (since coordination on preservation is unavailable). The commons degradation intensifies Chicken dynamics (as actors compete for shrinking resources with higher stakes).

The games do not operate in isolation. They form a system. The system's emergent property is continuous acceleration.

Historical Precedent: The Military-Industrial Feedback

The Cold War arms race exemplifies Red Queen dynamics in their purest form.

The United States and Soviet Union did not seek war. Both would have preferred stable mutual security at lower cost. Neither could afford to fall behind. Each improvement by one required response by the other. Response required industrial capacity, which required economic investment, which required political commitment, which locked in further response.

The dynamic was not limited to weapons. It encompassed research and development investment, production capacity, delivery systems, and defensive systems. Each increment in one domain required matching increment.

At no point did either side seek escalation for its own sake. At every point, escalation was the rational response to the other's escalation. The race proceeded for four decades until one participant's economic system could no longer sustain the pace.

Critically, the outcome was not victory for the survivor but exhaustion. The "winner" emerged with an economy distorted by military investment, an industrial base oriented toward production it no longer needed, and an institutional structure adapted to a competition that had ended. Winning the Red Queen race does not produce flourishing. It produces survival in degraded condition.

The Corporate Parallel

Remove military stakes and the pattern persists.

Consider technology platform competition. Each platform must continuously improve to maintain market position. Improvement in one platform requires response from others. Response enables further improvement. The cycle accelerates.

The metrics are not weapons but engagement, capability, and market share: feature development, talent acquisition, data accumulation, and speed to deployment. If competitors add capability, users expect it everywhere. If competitors hire the best researchers, advantage compounds. If competitors ship faster, delay is disadvantage.

No platform seeks exhausting competition. Each would prefer stable market position at lower investment. None can afford to slow down. The race proceeds.

The winners do not flourish. They survive. The survival requires continuous investment that precludes other uses of resources. The organization becomes adapted to competition rather than to purpose.

LLM Intensification of Red Queen Dynamics

LLMs intensify Red Queen dynamics through mechanisms now familiar from previous sections.

Cycle compression. LLMs accelerate the response cycle. Analysis that required weeks can be completed in hours. Scenarios that required teams can be generated by individuals. The time between observing others' improvement and responding shortens. More iterations occur in the same elapsed time.

Threshold invisibility. LLMs obscure where others stand in the race. Capability development is internal until deployment. Deployment itself may be ambiguous. Actors cannot observe others' position clearly, creating pressure to assume the worst and over-invest.

Effort efficiency asymmetry. LLMs reduce the effort required for certain kinds of improvement (planning, analysis, content generation) while leaving other constraints unchanged (physical deployment, human judgment, institutional adaptation). This creates mismatched acceleration—some aspects of the race speed up while others do not, producing instability.

Requirement inflation. Each LLM-enabled improvement raises the baseline for competitors. What was advanced capability last quarter is expected capability next quarter. The goalposts move continuously. Standing still means falling behind.

The State-Corporate Ouroboros Revisited

The Red Queen dynamic is where state and corporate competition most visibly converge.

States observe corporate LLM development and conclude they must develop or acquire equivalent capability. Corporations observe state interest and conclude the market for LLM capability is expanding. Each validates the other's investment.

States justify LLM procurement because adversary states are investing. Corporations justify LLM development because government contracts are available. State investment subsidizes corporate capability development. Corporate capability development expands state appetite for investment.

The ouroboros: the system consuming its own tail. State and corporate actors are not separate competitors but entangled participants in a single acceleration dynamic. Each sector's logic reinforces the other's. The race has no external limit because the racers are also the racetrack.

Why the Red Queen Has No Natural Terminus

Red Queen dynamics end only through exhaustion, external constraint, voluntary coordination, or transformation.

Exhaustion. Participants can no longer sustain the pace. This is the Cold War outcome—one participant's system failed under the strain. Exhaustion is not victory; it is mutual degradation with asymmetric survival.

External constraint. An authority outside the race imposes limits. Arms control treaties, antitrust enforcement, binding international agreements. This requires governance capacity that can override competitive pressure. Such capacity is precisely what the five games erode.

Voluntary coordination. Participants agree to slow down together. This is the Stag Hunt: possible in principle, unstable without verification. The same dynamics that prevent Stag Hunt cooperation prevent Red Queen coordination.

Transformation. The race becomes irrelevant because the competitive domain shifts. New technologies, new actors, new games. This is not resolution but displacement. The Red Queen runs in a new arena.

None of these termination modes is currently operative for LLM competition. States cannot unilaterally slow down. Corporations cannot unilaterally slow down. Governance capacity is insufficient to impose limits. Coordination would require trust that the competition itself erodes.

The race continues by default.

What This Section Establishes

Red Queen dynamics describe the meta-pattern that emerges when the five games are played simultaneously and continuously.

Each game contributes escalation pressure. The pressures interact and compound. The system accelerates. No actor can afford to slow down. All actors would prefer to slow down.

LLMs intensify Red Queen dynamics through cycle compression, threshold invisibility, effort efficiency asymmetry, and requirement inflation. The race runs faster with less visibility into others' positions.

The state-corporate entanglement creates a closed loop: each sector's investment justifies the other's. The system has no natural exit.

Previous sections examined how each game produces failure. This section shows that the games together produce continuous failure—not a bad equilibrium but an accelerating trajectory toward exhaustion or external intervention.

X. World War II as Systems Failure, Not Moral Anomaly

The preceding sections have examined game-theoretic failure modes in abstraction, with historical examples serving to illustrate mechanisms. This section reverses the approach: it takes World War II as a unified case and shows how the games converged to produce catastrophe.

The purpose is not to add new analysis but to demonstrate that the framework is not academic invention. The dynamics described—verification failure, signaling collapse, commitment escalation, coordination breakdown, commons exhaustion, Red Queen acceleration—operated together in a specific historical context with documented outcomes.

World War II is the correct case because it is unambiguous. No serious interpreter disputes that the outcome was catastrophic. No ideological position defends the war as desirable. This allows analysis without the distortions that contemporary examples would introduce.

The claim is not that LLMs will produce World War II. The claim is that the same structural dynamics that produced that catastrophe are being intensified by the same technological pattern: optimization outrunning governance.

The Interwar System as Game-Theoretic Trap

The period between 1919 and 1939 was not a failure of morality. It was a failure of equilibrium.

The Versailles settlement attempted to restructure European incentives. Germany was disarmed. Borders were redrawn. A League of Nations was established to provide collective security. The architects understood that the previous system had failed catastrophically. They attempted to build a new one.

The attempt failed not because the architects were foolish but because the game-theoretic conditions for stability were not satisfied.

Prisoner's Dilemma dynamics: The disarmament regime required verification that could not be achieved. Germany's secret rearmament was rational given the payoff structure.

Security Dilemma dynamics: France's defensive investment appeared offensive to German observers. German rearmament appeared offensive to French observers. Each defensive measure triggered interpretive escalation.

Chicken dynamics: As the 1930s progressed, commitment signaling intensified. German territorial demands required increasingly visible commitment. Allied responses required visible commitment to avoid appearing appeasing. Each round raised stakes for the next.

Stag Hunt dynamics: Collective security through the League required coordination that could not be verified. Each nation, uncertain whether others would honor mutual defense commitments, hedged by pursuing bilateral arrangements.

Tragedy of the Commons dynamics: The commons of European stability was extracted by actors pursuing national advantage. Each extraction was individually rational. The cumulative effect was commons exhaustion.

Red Queen dynamics: Once rearmament began, each nation's investment required matching investment by others. The race accelerated through the 1930s with no natural terminus.

The games operated simultaneously and interactively. Prisoner's Dilemma defection triggered Security Dilemma escalation. Security Dilemma escalation eroded Stag Hunt trust. Stag Hunt failure intensified Tragedy of the Commons extraction. Commons exhaustion accelerated Red Queen dynamics. The system spiraled toward collapse.

Industrial Capacity as Optimization Engine

What made World War II distinctively catastrophic was not ideology but capacity.

Previous European wars had been constrained by production limits. Armies could only be so large. Weapons could only be produced so fast. Logistics could only support so much destruction. These constraints functioned as friction—they slowed the game-theoretic dynamics, creating space for negotiation, exhaustion, or stalemate.

By 1939, industrial capacity had removed these constraints without providing alternative friction.

Nations could produce weapons at scales previously impossible. They could mobilize populations with bureaucratic efficiency previously unavailable. They could project force across distances and sustain operations for durations that prior centuries could not have supported.

The optimization was genuine. Each nation became more capable of pursuing its strategic objectives. The cumulative effect was that strategic objectives could be pursued to their logical conclusions without the friction that had historically produced termination short of total outcomes.

This is the pattern LLMs recapitulate. Industrial capacity in 1939 was optimization technology. It did not create new motives. It reduced the friction that had previously constrained how far existing motives could be pursued.

Role Fidelity and Administrative Execution

The catastrophe was not executed by monsters. It was executed by administrators.

Hannah Arendt's analysis of Adolf Eichmann identified a pattern that extends beyond the specific horror she examined: individuals performing organizational roles without experiencing the aggregate effect of their actions.

The logistics officer ensuring trains ran efficiently. The factory manager meeting production quotas. The bureaucrat processing paperwork. Each performed their role competently. The system's output was mass death.

This is not an exculpation. It is an observation about how systems produce outcomes that no participant individually intends or perceives.

The same dynamic operated across all belligerents, though with different outputs: the British bomber crews executing strategic bombing doctrine, the American logistics officers enabling island-hopping campaigns, the Soviet commissars enforcing production quotas, the German engineers solving technical problems of industrial murder.

Role fidelity is how large systems function. It is also how they produce catastrophe. The individual actor sees their task. The system sees the aggregate. When the aggregate is destructive, role fidelity enables destruction at scale.

Arendt called this the banality of evil. A game-theoretic framing would call it optimization without constraint. The mechanism is identical: competent performance within roles, producing outcomes that role-performers do not evaluate and may not perceive.

Eisenhower's Warning as Systems Awareness

Dwight Eisenhower's 1961 farewell address is often quoted for its warning about the "military-industrial complex." Less noted is the systems reasoning underlying the warning.

Eisenhower did not warn against malice. He warned against momentum:

"In the councils of government, we must guard against the acquisition of unwarranted influence, whether sought or unsought, by the military-industrial complex. The potential for the disastrous rise of misplaced power exists and will persist."

The danger was not conspiracy but incentive structure. Defense contractors had incentive to sell weapons. Military services had incentive to acquire them. Congressional representatives had incentive to fund facilities in their districts. Each actor pursued rational objectives. The aggregate was continuous escalation.

Eisenhower—who had commanded the largest military operation in history—understood that catastrophe could emerge from competence. He had seen it. The warning was not about evil actors but about systems that produce outcomes no actor intends.

This is the Red Queen framed as political economy. The race continues not because anyone wants it but because no one can unilaterally stop. Eisenhower's warning was that the race was becoming institutionalized, embedded in structures that would persist beyond any individual's capacity to arrest.

What the Historical Case Demonstrates

World War II was not an aberration produced by uniquely evil actors. It was a systems outcome produced by game-theoretic dynamics operating simultaneously, industrial optimization removing friction, role fidelity enabling competent execution of systemically destructive outputs, and institutional structures that embedded escalation in normal operations.

The dynamics were understood at the time. Statesmen knew that arms races were dangerous. Economists knew that beggar-thy-neighbor policies were collectively destructive. Strategists knew that commitment escalation raised stakes. The knowledge was insufficient because understanding the game does not change the payoffs.

This is the lesson for the present. The failure modes examined in this essay are not obscure. They are well-documented in game theory, political science, and economic history. Understanding them does not dissolve them. The dynamics persist because the structural conditions persist.

LLMs do not change this pattern. They accelerate it. The optimization pressure that removed friction in 1939 is the same optimization pressure that LLMs intensify today. The domain differs. The structure is continuous.

XI. What This Essay Does Not Argue

An essay describing dangerous dynamics invites misreadings. This section clarifies what has and has not been claimed.

This Essay Does Not Argue Technological Determinism

The analysis does not claim that LLMs will inevitably produce catastrophe.

Game theory describes incentive structures and their stable outcomes. It does not describe fate. The dynamics examined here are tendencies, not necessities. They operate through actors making choices, not through mechanical inevitability.

The historical case of World War II demonstrates that these dynamics can produce catastrophe, not that they must. The postwar case demonstrates that game-theoretic traps can be navigated—for decades, the cooperative equilibrium held. Mechanisms were built. Frictions were introduced. The games continued, but their worst outcomes were avoided.

The claim is that LLMs intensify dynamics that require governance to remain survivable. The claim is not that governance will fail. Whether it succeeds is a question of institutional capacity and political choice, not technological destiny.

This Essay Does Not Argue Moral Equivalence

The analysis treats actors symmetrically because game theory treats actors symmetrically. This is analytical method, not moral judgment.

Stating that all actors face Prisoner's Dilemma incentives does not imply that all actors are equally culpable for outcomes. Stating that Security Dilemma dynamics affect all parties does not imply that defensive and aggressive intent are morally equivalent. Stating that Red Queen dynamics trap competitors does not imply that all competitors pursue equally legitimate objectives.

The essay examines structure. Moral evaluation of actors within structure is a separate enterprise. Readers may and should form such judgments. The essay does not provide them because it is not equipped to provide them. Game theory illuminates incentives. It does not adjudicate virtue.

This Essay Does Not Predict Imminent War

The WWII synthesis demonstrates historical continuity in structural dynamics. It does not predict that LLMs will produce war.

War is one possible outcome of game-theoretic failure. It is not the only outcome. Other possibilities include sustained suboptimal equilibria, institutional exhaustion without violent collapse, gradual commons degradation without acute crisis, and successful governance intervention that restructures payoffs.

The essay does not assign probabilities to these outcomes. It does not claim special knowledge of which will obtain. The analysis identifies trajectory, not destination.

Readers seeking predictions will not find them here. Readers seeking to understand the forces shaping possible futures may find the framework useful.

This Essay Does Not Advocate Specific Policies

The essay describes what governance would need to accomplish. It does not prescribe how governance should accomplish it.

Statements such as "cooperation requires verification" or "commons preservation requires cost internalization" are analytical observations about equilibrium conditions. They are not policy recommendations. The distance between identifying necessary conditions and specifying sufficient policies is vast. This essay does not attempt to cross it.

Readers across the political spectrum may draw different policy conclusions from the same structural analysis. A reader who favors market mechanisms may conclude that cost internalization requires pricing externalities. A reader who favors regulation may conclude that cost internalization requires prohibitions. A reader who favors institutional innovation may conclude that cost internalization requires new organizational forms.

The essay is compatible with all of these responses and advocates none of them. It provides orientation, not prescription.

This Essay Does Not Condemn Technology

The analysis does not argue that LLMs are bad, should not have been developed, or should be abandoned.

LLMs are optimization technology. Like previous optimization technologies—steam engines, electrical grids, computation, industrial manufacturing—they amplify human capacity. Amplification is neutral with respect to the ends it serves. The amplification of beneficial activity is beneficial. The amplification of harmful dynamics is harmful.

The essay examines how LLMs amplify game-theoretic dynamics that were already dangerous. This is not a condemnation of the technology. It is an observation about friction removal. The appropriate response is not to eliminate the technology but to provide alternative friction—governance, institutions, mechanisms that restructure payoffs.

Readers seeking anti-technology polemic will be disappointed. Readers seeking clear-eyed assessment of what optimization technologies require may find value.

What the Essay Does Argue

To clarify by contrast, the essay argues:

LLMs accelerate well-known game-theoretic failure modes. They do not create new dangers; they intensify existing ones by reducing cost, latency, and friction.
These failure modes interact and compound. Prisoner's Dilemma, Security Dilemma, Chicken, Stag Hunt, and Tragedy of the Commons are not isolated games but interconnected dynamics that reinforce each other.
Historical precedent demonstrates the pattern. World War II emerged from the same structural dynamics operating with the optimization technology of its era. The parallel is structural, not predictive.
Governance has historically navigated these dynamics. The postwar order demonstrates that game-theoretic traps can be managed through mechanism design, institutional friction, and payoff restructuring.
Current governance capacity is insufficient. The mechanisms that historically stabilized these dynamics are being outpaced by the acceleration LLMs provide. This is an observation about institutional lag, not a prediction of failure.
Understanding the dynamics is a prerequisite for addressing them. The essay provides orientation. What readers do with that orientation is their own affair.

XII. Conclusion — Recognizing the Pattern

This essay has described a pattern. The pattern is not new.

Actors pursuing rational objectives within competitive structures produce outcomes none of them intend. Cooperation fails when verification fails. Defensive measures trigger escalation. Commitment signaling collapses into preemption. Good-faith coordination dissolves under uncertainty. Shared resources degrade under individual extraction. The race continues until exhaustion or intervention.

These dynamics are documented in game theory, observed in history, and operative in the present. They are not obscure. They are not contested. They are simply difficult to escape from inside.

The Continuity of Optimization

Every generation builds systems that outrun its governance.

The railroad outran the regulatory frameworks designed for canals. The telegraph outran the communication norms designed for physical mail. Industrial manufacturing outran the labor protections designed for craft production. Nuclear weapons outran the strategic doctrines designed for conventional war.

In each case, the pattern was the same: optimization technology removed friction faster than institutions could supply alternative constraints. The systems ran ahead. Governance followed, slowly, after the consequences became undeniable.

LLMs are the current instance. They did not invent the pattern. They inherit it.

The friction they remove is the friction that made previous game-theoretic dynamics survivable. The cost of planning, the latency of response, the effort of persuasion, the visibility of strategic moves—each functioned as a constraint on how far and how fast the games could be played. Each is being reduced.

What remains is the underlying structure: actors competing under uncertainty, unable to verify cooperation, unable to signal intent, unable to coordinate on shared preservation, unable to stop running.

What History Demonstrates

The historical cases examined in this essay share a common feature: the dynamics were understood at the time.

Interwar statesmen knew that arms races were dangerous. They attempted treaties. The treaties failed not because the danger was unrecognized but because recognition did not change payoffs.

Cold War strategists understood the security dilemma. They built hotlines, negotiated limitations, established verification regimes. The regimes worked imperfectly and required continuous maintenance. When maintenance lapsed, the dynamics reasserted themselves.

Postwar planners understood that cooperation required institutional architecture. They built institutions. The institutions held for a generation. When the conditions that made them functional eroded, the cooperative equilibrium decayed.

Understanding is necessary. Understanding is not sufficient.

The game-theoretic traps described here are not secrets. They are taught in undergraduate courses. They are discussed in policy papers. They are invoked in strategic documents. The knowledge exists. The knowledge does not dissolve the traps. Only mechanism change dissolves the traps—restructured payoffs, imposed friction, binding constraints, verified cooperation.

Whether such mechanisms will be built for LLM-era dynamics is not a question this essay can answer. It is a question that institutional capacity and political choice will answer.

The Ouroboros

The essay's title invokes a symbol: the serpent consuming its own tail.

The image captures a specific dynamic. States and corporations optimize against each other using shared tools. Each sector's investment justifies the other's. Each acceleration triggers reciprocal acceleration. The system feeds on itself.

This is not conspiracy. It is not coordination. It is the emergent behavior of actors responding rationally to structures neither controls.

The ouroboros has no natural terminus. It continues until external constraint interrupts it or until the system can no longer sustain the pace. Neither outcome is assured. Both are possible.

What the image captures is the self-reinforcing quality of the dynamics described. Prisoner's Dilemma defection erodes the trust that Stag Hunt cooperation requires. Security Dilemma escalation intensifies Chicken commitment pressure. Tragedy of the Commons extraction accelerates Red Queen dynamics. Each failure mode feeds the others. The system tightens.

Orientation, Not Prediction

This essay has offered orientation.

It has described the games being played, the dynamics those games produce, the historical precedents that demonstrate those dynamics, and the ways LLMs intensify them. It has not predicted outcomes. It has not prescribed responses. It has not assigned blame.

The reader now possesses a framework. The framework may be useful for interpreting developments as they occur. It may illuminate why certain governance proposals fail while others might succeed. It may explain why voluntary restraint proves unstable, why good-faith coordination collapses, why the race continues despite no participant wanting it to continue.

What the reader does with the framework is not the essay's concern. Orientation is the essay's concern. The map has been provided.

A Closing Observation

The most dangerous systems failures are not the ones that announce themselves.

They are the ones that proceed through ordinary operations—competent administrators executing their roles, rational actors responding to incentives, institutions functioning as designed. The catastrophe emerges from the aggregate, invisible to participants focused on their tasks.

This is what the game-theoretic framework reveals. Not evil actors, but structural traps. Not moral failure, but coordination failure. Not ignorance, but the insufficiency of knowledge without mechanism.

The dynamics described in this essay are operating now. They operated before LLMs existed. They will operate after whatever comes next. The question is not whether optimization will occur—it will. The question is not whether competition will intensify—it will. The question is not whether friction will be removed—it is being removed.

The question is whether the remaining friction will be sufficient, whether new friction will be introduced, whether the games will be restructured before their logic runs to completion.

That question remains open.

Essay by Lathem Gibson
December 2025