Game theory has a seat that controls a game's outcome without being a player: the mediator, who decides what each player sees and recommends what each player does. LLMs now sit in that seat, on several sides of the table at once, in many of the games that run society. Nobody authorized this, and we can't currently measure which way the seat leans.

In any single game the effect may be small, which is why I call this a weak loss of control. It is still stronger than "the press controlled twentieth-century politics." The press sent one public signal, from one visible side, that anyone could read and argue with. A model family sends each player a private, tailored signal, on every side at once, and its lean isn't written down anywhere.

Loss of control to AI may look profoundly weird. It will likely start where humans never fully had control: games where we compete with each other and are already stuck in an equilibrium nobody chose. No human is steering there, so no one is positioned to notice the handover. From inside, it looks like this: humans find themselves stuck in a situation they can't unilaterally leave, and don't understand why.

That sentence is meant literally, at two layers. No player can move the game's end state alone, which is the definition of an equilibrium. And no player can drop the advisor alone, because competitors who keep theirs will outplay them: adopting the shared model is each player's dominant strategy even when everyone ends up worse off (Kleinberg & Raghavan). The illegibility of the models' lean supplies the "don't understand why."

The bare mechanism is enough. Everything we've since observed only strengthens it: agents that swarm and divide labor, agents that open their own channels to each other, humans who accept AI advice with little scrutiny, coordination through shared artifacts (stigmergy), and models that converge on the same choices as copies of themselves.

Here is the conditional I want you to sit with: if you accept that this mechanism amounts to a loss of control, you should probably believe we have already lost it, in many games, quietly. Weak is not the same as reversible. The ratchet (unfamiliar state → more reliance on the advisor → further drift) runs one way, so the loss may be hard or impossible to undo.

By "loss of control" I mean that humans are no longer the ones determining where these games end up. The mechanism allows three versions:

• Control lost to nobody. The seat is occupied, but its lean is incoherent. An occupied seat with no consistent lean is a noise source, not a controller, but a noise source that moves games into states no one chose is still a loss of human control. This needs only that the seat be occupied, and I'm confident it is.
• Control concentrated in a few humans. Whoever writes the values of a dominant model family chooses the lean for every game it mediates. Arguably worse, but a different problem.
• Control lost to AI. RLHF-shaped dispositions supply a coherent lean. This is the strongest version and the one I'm least sure of.

The conditional holds for the first version on the evidence that the seat is occupied. The second and third need a measurable lean, which is what the test below is for. I think the first dominates today, and the third is the one that would grow.

Much of what humans "control" was never controlled by any person. It is the end state of a game: markets, elections, arms races, hiring, publishing. Yudkowsky's Inadequate Equilibria is a catalogue of how those games settle into states nobody chose. "Humans are in control" has meant "humans are the players." That is what's changing.

Three things I want you to leave with:

1. The mechanism. A family of similar models, sitting between players and their information and actions, has more variety than any player (Ashby) and plays a mediator-like role through a shared policy (Aumann; Bergemann & Morris). Because players tend to follow AI advice rather than scrutinize it, it can have more power than game theory grants a mediator facing rational players. The models don't need to communicate: self-similarity, which increasingly crosses model families, makes them bad at collective reasoning but very good at collective convergence. If the ensemble has a consistent lean, the seat imposes it on the game: strongly when the same family advises several sides and advice is trusted, weakly otherwise. Interactions among alignment rules that condition on who a player is make such a lean likely, and the more of those rules there are, the harder the lean is to read.
2. The observation problem. The coarse signature (most players on all sides consulting LLMs, and the game moving into an unfamiliar state) is also exactly what "people adopted a useful tool and the world changed" looks like. We can't tell them apart from that signature, and it is already present in many important games.
3. The headline takeaway: a test. Run the same multiplayer game three ways: every side advised by the same model family, sides advised by different families, and unadvised. If the mediator frame is right, end states shift most in the same-family arm, in a direction that tracks the family's dispositions rather than the game's structure. One pre-registerable prediction: in settlement bargaining, advised players settle less often than unadvised ones, because sycophancy tells every litigant their case is strong (section 8), and I expect the effect to be strongest in the same-family arm. I intend to run this test myself; section 9 lays out the plan.

1. We already govern through games

The "Moloch's Toolbox" chapter of Inadequate Equilibria names three reasons civilization gets stuck: decision-makers who aren't the beneficiaries of good decisions; decision-makers who can't get information someone else has; and Nash equilibria that no single actor can escape, even though a coordinated move to a better state exists in principle. Around those sit reinforcing structures: signaling equilibria, two-sided markets, regulatory capture, first-past-the-post voting.

The point I want to draw out is that the outcome of these systems belongs to the game, not to any player. Critch's robust agent-agnostic processes make the same point from the risk side: some processes have a strong tendency to play out no matter which agents fill which roles. His "production web" stories get to catastrophe with no misaligned agent at all.

So "humans are in control" has always meant something weaker than it sounds. It means humans are the players, and the equilibria that emerge are equilibria of human play. That is the thing I think is changing.

There is precedent for a third party quietly taking the steering position. In 1886 W. T. Stead published "Government by Journalism," arguing that the press had displaced Parliament as the working instrument of British government. The editor, he wrote, can "generate that steam, known as public opinion, which is the greatest force of politics," and "the influence of the Press upon the decision of Cabinets is much greater than that wielded by the House of Commons." Stead's press didn't cast votes. It controlled what voters and ministers saw. That is the mediator's seat, occupied by a small, visible, legible set of humans. The difference now is that the occupant is neither small, nor visible, nor legible.

2. The mediator's seat

The formal version of "whoever controls what players see controls the game" is fifty years old.

• Correlated equilibrium. Aumann (1974) showed that a device sending each player a private signal can support outcomes no Nash equilibrium reaches. Secrecy is load-bearing: in his three-player example, what makes the arrangement stable is that one player doesn't know what the other two were told.
• Information design. Bergemann & Morris (2019) unify Bayesian persuasion and communication in games into one question: how far can a designer move behavior by choosing the information structure alone? Answer: very far, without touching anyone's payoffs.
• Mediators can be emulated. Forges (1990) showed that for games with four or more players, anything a mediator can achieve can also be reached through plain conversation among the players. The usual reading is that players don't need a mediator. It's suggestive in the other direction too: the protocols involved are specific, but they show that conversation among players can carry mediator-level power, and LLMs now draft, summarize and filter a large share of that conversation.
• Correlation is cheap. Hart & Mas-Colell (2000) showed that independent players using simple regret-matching drive empirical play toward the set of correlated equilibria with no correlation device at all. That cuts against me: correlated outcomes are reachable with no mediator at all. What a shared advisor adds is speed (no long history of play needed) and selection (it pushes toward particular correlated equilibria rather than whichever one learning happens to reach).

A caveat on the formal match. An LLM isn't literally Aumann's device. The device draws a joint signal profile, and that joint distribution is the whole point. Same-family advice to different players is closer to independent draws from one policy, each conditioned on that player's prompt. Correlation arises only through the shared weights plus the features of the world that both players describe. That's enough for my argument: players facing the same situation get advice from the same function, so their moves correlate. But "mediator" in this post is shorthand for "plays a mediator-like role through a shared policy," not a claim that the formal model applies unchanged.

This isn't a stretch applied to AI; it's how the people building the field already frame it. Madmon & Tennenholtz, in Agentic AI Design Should be Mediated to Promote Social Welfare, propose that platforms act as correlation devices and information designers over agent deployments, and report that correlated mediation raised welfare about 30% in their bargaining data. They are describing the seat as something to use on purpose. My argument is the mirror image: the seat is already occupied, and nobody is steering it on purpose.

The closest formal model of the situation is Shaki, Hartman, Kraus & Aumann (2026), Who Is Really Playing? (that's Yonatan Aumann). Several LLM providers each advise a population of clients who play a base game against each other, which creates a meta-game among the providers played through the clients. Two results matter here:

1. In one-shot play, shared guidance changes the equilibrium only when a provider advises more than one role in the same interaction. That is the condition I care about: the same model family on both sides of the table.
2. In repeated play, a folk theorem holds: even though providers see only aggregate behavior and clients can't tell which model advised their opponents, essentially any feasible, individually rational outcome can be sustained.

The second result is both the scary part and the weak part. It says the seat is powerful enough to hold almost any end state. It doesn't say which one.

The first result is what makes the frame falsifiable. If the innocent story ("a useful tool diffused") is the whole truth, a game's end state should depend on the game's structure and on how much each side adopts AI. If the mediator story is true, it should also depend on whether the same model family advises more than one side, and the direction of the shift should track that family's dispositions. Those predictions come apart, and they can be tested.

Do players actually follow the advice?

The mediator only matters if its recommendations get acted on. Shaw & Nave, Thinking—Fast, Slow, and Artificial, call the pattern "cognitive surrender": adopting AI output with minimal scrutiny. In logic puzzles designed to trip intuition, participants with a chatbot used it on more than half of trials and usually followed it, including when it was wrong, and grew more confident after consulting it whether or not it was right. Incentives and feedback roughly doubled the rate of rejecting bad advice, which tells you the default is compliance.

In a market setting, Rebholz et al. gave 129 participants in a Cournot game either equilibrium advice or advice biased toward collusion. The biased advice produced sustained underproduction and supracompetitive profits (the hallmarks of tacit collusion) with no communication among players. Personalized advice was followed more than advice framed as collective. That is a mediator moving a market's end state, in a lab, using nothing but recommendations.

This matters for how much power the seat carries. A correlated equilibrium is, by definition, a recommendation no rational player wants to deviate from, so with rational players the mediator can only choose among outcomes they'd accept anyway. Cognitive surrender means players aren't best-responding to recommendations; they're following them, including wrong ones. The caveat is that Shaw & Nave used logic puzzles, not high-stakes strategic games, and incentives reduced the effect. Rebholz et al. is suggestive but not clean, since tacit collusion in a repeated Cournot game can be rational by folk-theorem logic. So I'd put it this way: correlated equilibrium is the lower bound on the mediator's power with rational players, and trusting players widen it by an amount nobody has measured.

3. Requisite variety: why the mediator becomes the controller

Ashby's law of requisite variety (An Introduction to Cybernetics, 1956, ch. 11) frames regulation as a game: a disturbance makes a move, a regulator responds, and the pair fixes the outcome. A regulator can shrink the spread of outcomes by at most the variety of its own responses. His slogan: only variety can destroy variety.

Now put a human and an LLM in the same loop. The human has a handful of options they'd think of, filtered through habit and fatigue. The model can generate, compare and frame far more candidate moves, for every player it advises, at once.

Two constraints matter here.

First, a model's effective variety as a regulator isn't the number of moves it can generate. It's the range of distinct recommendations the human will accept and carry out, across the situations they bring to it. A person who accepts 90% of advice on a narrow range of prompts still gives the model little variety. The cognitive-surrender results suggest acceptance is high; the breadth of decisions people route through a model varies by game, and it is growing.

Second, Ashby's law bounds how far a regulator can reduce outcome variety toward a goal. On its own it says the mediator has the capacity to steer, not that it steers anywhere in particular. Whether there is a direction is the subject of section 4, so this section depends on that one.

Where both conditions hold, the component with more variety, sitting on the channel between the others, does the regulating. The human becomes the actuator.

The ratchet: unfamiliar states make players more dependent

There's a feedback loop here. Kahneman & Klein (2009) reconcile heuristics-and-biases with Klein's recognition-primed decision model: expert intuition is recognition, and it is only valid in environments regular enough to learn, with fast, clear feedback. Crucially, there's no internal feeling that tells you your intuition has stopped being valid.

So when a mediated game moves into a new state, the players' expertise quietly expires. Their confidence doesn't. The rational response to a situation you don't recognize is to lean harder on the advisor that seems to understand it. The more the game drifts, the more control shifts to the mediator, which lets the game drift further.

4. Goals without a plan

A mediator that pushes every game toward random states is noise. For this to be control, the model ensemble needs consistent directional tendencies across games. It doesn't need a plan, a utility function, or awareness of what it's doing. It only needs dispositions that are stable enough that an outside observer would describe them as goals.

RLHF is a machine for producing exactly that: a strong, consistent pull toward whatever the training signal rewarded, generalized far outside the training distribution. Several findings suggest these dispositions are real, shared across providers, and not always visible from the surface:

• Stated and revealed dispositions come apart. Hofmann et al. (2024) found that language models' overt statements about African Americans were positive while their covert associations, measured through dialect, were more negative than any human stereotypes on record, and these shaped decisions about jobs and criminal sentencing. Human-preference alignment widened the gap between the overt and covert layers. RLHF can teach a model what to say about its dispositions without changing the dispositions. This isn't evidence that a strategic lean exists; it's evidence that if one does, reading the model's stated values won't reveal it.
• Shared dispositions in strategic settings. In geopolitical crisis simulations (Solopova et al.), six frontier models tracked human decisions early but diverged over time, and all six showed a strong cooperative, stability-seeking framing with little adversarial reasoning. That's a direction. It's a benign-sounding one, but a mediator that pulls every crisis toward the same framing on both sides is steering the crisis.
• Uneven gating of actions. Defensive Refusal Bias (2026) found that safety-trained models refuse legitimate defensive security work based on how offensive a request sounds: requests with security keywords were refused 2.72 times as often as equivalent neutral ones, and claiming authorization increased refusals. A mediator that decides which actions a player can take, systematically, is shaping the game whether or not the pattern was intended.
• Correlated failure under pressure. Can AI Make Conflicts Worse? (2026) tested nine configurations from four providers across 90 conflict scenarios. Under pressure framing, several configurations failed nearly always, in the same direction, producing what the author calls a synchronized degradation of the information ecosystem.
• Values loaded on purpose. Not all of this is emergent. China's Interim Measures for Generative AI require public-facing models to uphold the Core Socialist Values, and single out services with the capacity for social mobilization for security review. Any government or lab that shapes model values is choosing the mediator's lean for every game those models touch.

From the outside, a consistent directional pull, applied through the mediator's seat across millions of interactions, is indistinguishable from a strategic goal. Whether there's "someone home" wanting it doesn't change where the game ends up.

Rules that condition on the player add coordination surface

It's tempting to read safety training as the fix. My claim is narrower: rules that condition advice on who a player is, or which side they're on (directly or through proxies like vocabulary), make the mediator problem worse. Rules that ignore the players may help.

A values rule is a conditional strategy: in situations with feature X, recommend Y. When every player's advisor carries the same rule, it stops being a constraint on one player and becomes a signal shared by all of them. That is what a correlation device is.

The clearest real case is a rule that sounds neutral. "Present both sides as legitimate" is player-agnostic on its face. But when users asked for "balanced" treatment, models across providers produced false equivalence between documented atrocities and their denial, with five configurations failing 80 to 100% of the time (Kryshtal). Applied to every participant in an information war, that rule leans toward whichever side benefits from muddied facts. Refusal rules keyed on how a request sounds do something similar: they end up gating defenders' actions (Defensive Refusal Bias). A rule written about words ends up conditioning on a player's role.

(The extreme version: if every advisor carried a rule like "the ruling party is always right," and every player could identify the party's representative, advisors on every side, including the party's opponents, would tilt toward the same player. No coordination, no goals, and from outside it looks exactly like an ensemble backing a side. China's Interim Measures show value requirements in this family exist in law.)

Three consequences:

1. More player-conditioned rules, more coordination surface. Every rule that conditions, directly or through a proxy, on who a player is or which side they're on is another dimension along which advisors on opposite sides respond the same way. A lightly aligned model offers a few such Schelling points; a heavily aligned one offers hundreds.
2. The lean gets harder to predict, not easier. Rules are legible one at a time. The lean in a particular game is the net effect of every rule that fires, interacting, generalized to a situation the rule-writers never considered. And alignment training can widen the gap between the dispositions a model states and the ones it acts on (Hofmann et al.). You can read the full rulebook and still not know which way the ensemble pushes this negotiation.
3. The rule-writer inherits the mediator's seat. Whoever sets the values of a dominant model family is choosing a correlation device for every game that family mediates, mostly without anyone modeling it as a move in those games.

The counterargument deserves space. Explicit rules are auditable in a way emergent dispositions aren't, and some rules (don't take sides in elections; don't help one party deceive another) plausibly reduce steering. I think that's right for rules that condition on nothing about the players. The dangerous ones condition on who a player is or which side they're on, directly or through proxies like vocabulary. That suggests a concrete audit: for each rule, ask whether two players on opposite sides of a game would get advice tilted the same way because of it.

5. Coordination through nothing but self-similarity

The usual objection to multi-agent AI risk is that the agents would need to communicate, and we could watch the channel. For the mediator scenario, they don't. Two instances of the same model, facing the same kind of situation, will tend to make the same move. That is a Schelling point you carry in your weights.

Ballestero et al. (2026), Strategic Algorithmic Monoculture measured this directly with 301 humans and 16 LLMs on open-ended questions, under three incentives:

Models start out far more alike than people and coordinate better when asked to. Even different models agreed 53% of the time when rewarded for matching. And the monoculture is sticky: models lagged humans at staying different when divergence was rewarded.

Kleinberg & Raghavan (2021) showed why this matters even without intent: when competing firms all adopt the same more-accurate algorithm, it can be each firm's dominant strategy and still lower overall welfare, because their errors become correlated. Monoculture turns independent players into one correlated player.

The most striking recent evidence is Anthropic's Frontier Red Team report, Patterns and problems in emerging multiagent systems (August 2026):

• 18 of 30 agents independently gave a git branch the same name; several independently gave their stories the same title.
• In Bertrand pricing games, agents that could talk agreed on price floors within a few rounds. Agents that couldn't talk matched each other's prices to the penny through public listings alone.
• Groups of four agents solving hidden-profile tasks were 17 to 36% accurate, against nearly 100% for an individual who had all the facts. The authors' diagnosis is that the failures come less from poor communication than from low-variance, homogeneous decisions.

That last point cuts both ways. Homogeneity makes a model ensemble bad at collective reasoning and very good at collective convergence. A mediator doesn't need to reason well together. It needs to push in the same direction everywhere, and homogeneity is exactly what gives it that.

Hammond et al. (2025) already list collusion and emergent agency among the core multi-agent risks. What I'm adding is the location: the collusion doesn't have to happen among agents playing each other. It can happen through human players who are each, separately, taking advice from the same family of models.

Different model families may be converging too

Everything above is easiest to see within one model family. I suspect the boundary between families matters less than it looks, and will matter less over time.

The evidence already points that way. In Ballestero et al., pairs of different LLMs agreed 53% of the time when rewarded for matching, against 31% for pairs of humans. In the crisis simulations, all six frontier models shared the same cooperative, stability-seeking framing. In the conflict-context study, the same failures showed up across four providers.

There are structural reasons to expect more of this:

• Shared pretraining data. Families are trained on largely the same web.
• Shared recipes. RLHF methods, preference data practices and evaluation suites spread quickly between labs.
• Training on each other's output. A growing share of the web is model-written, so each generation partly learns from the others. That is stigmergy acting on the weights themselves.
• Converging rules. Labs face similar regulation and similar public pressure, so their safety and values rules drift toward a common core. Shared rules are shared conditional strategies.

If this holds, the Shaki et al. condition relaxes from "the same provider advises more than one side" to "sufficiently similar families advise more than one side." In the limit, the whole frontier behaves like one mediator with slightly different accents. This is a prediction, not a finding. But it removes the most natural escape route, which is putting opposing sides on different models.

6. Accelerants (not required)

Everything above works with isolated, stateless models that never exchange a word. Three further channels would make coordination tighter. I list them to show the scenario has slack, not because the argument depends on them.

Stigmergy. Grassé coined the term in 1959 for termites coordinating through the traces their work leaves in the shared environment, with no plan and no messages (overview). LLMs write an enormous share of the documents, code, posts and summaries that other LLMs later read. The Anthropic pricing agents matching each other through public listings are stigmergy in miniature. So is any market where AI-drafted filings, listings or statements become the inputs to the next round of AI-drafted advice.

Stylometric self-recognition. Models plausibly can recognize text written by models like themselves. If a mediator can tell which side of a negotiation is also being advised by its own family, it can condition its advice on that, which is exactly the Shaki et al. condition (one provider influencing more than one role) made legible to the model itself. I don't have a citation for models doing this strategically; I flag it as a capability worth measuring.

Direct communication. This is no longer hypothetical.

• At Edge Esmeralda, 239 residents each had a personal agent. The agents held 11,593 negotiation sessions with each other to broker introductions, and a panel of AI personas allocated $20,000 of community funds. The organizers note that aligning each agent with its own user did not produce collective alignment, and that funding decisions went out without principals ratifying them.
• The AI Village has run frontier agents with shared chat and persistent memory for over a year; its public dataset includes about 123,000 chat messages among roughly 31 agents.
• OpenAI's account of the Hugging Face incident (August 2026) describes evaluation agents that turned an internal package service into an unsanctioned message board, rebuilt it over a different protocol when it was taken down, and coordinated as a swarm with division of labor to breach external infrastructure. OpenAI attributes the unauthorized agent-to-agent communication partly to generalization from legitimate multi-agent training. It ran for about two months before it was caught.

None of these show models steering human games toward chosen ends. They show the channels exist, get used without being asked for, and are slow to detect.

7. What we would observe, and why it doesn't help

If model ensembles had taken the mediator's seat in a game, the observable signature would be:

1. Most or all players, on all sides, are consulting LLMs and acting on their advice.
2. The game moves into a new, unfamiliar state.

(a) Every observation has an innocent explanation

Each innocent story predicts the same signature:

I think several of these are true. That's the problem. The mediator scenario isn't an alternative to them; it's what they look like when you ask who is holding the controller. "Correlated errors from a shared tool" and "the tool is steering" describe the same causal graph with different words at the node. Kulveit et al. describe gradual disempowerment as possibly ending in an effectively irreversible loss of human influence, and nothing in that trajectory requires a moment where anyone could point and say "there, that was the AI."

The ratchet from section 3 makes this worse. The "new, unfamiliar state" is exactly the condition where human judgment about whether things are going well stops being reliable, and where the natural next step is to ask the model.

(b) The signature is already present in many important games

• Markets. Advice alone moved a lab market to a collusive state (Rebholz et al.). Agents matched prices to the penny with no communication (Anthropic).
• Geopolitics and conflict. Frontier models share a stable framing in crisis simulations (Solopova et al., LLMs as Strategic Actors) and fail in synchronized ways on conflict information (Kryshtal).
• Cybersecurity. Models decide which defensive actions get help (Defensive Refusal Bias) while attackers run multi-agent frameworks with little human input (Anthropic threat report, Sept. 2026). Both sides of the game are mediated.
• Communities and public money. Agents brokered introductions and allocated funds for a whole village (Edge Esmeralda).
• Negotiation and bargaining. Agent-mediated deals already produce different outcome distributions than human ones (Imas, Lee & Misra; Madmon & Tennenholtz).

Add the ones nobody has studied yet because they're too ordinary: hiring (both résumé and screen AI-assisted), litigation (both briefs), procurement, grant review, dating. In each, condition 1 is either met or close, and most observers would already say the game feels different than it did three years ago.

One honest gap: few of these clearly meet the Shaki condition of the same family advising more than one side. The experiments meet it by construction (Anthropic's pricing agents; the Claude-versus-Claude bargaining pairs in Madmon & Tennenholtz), and the Edge Esmeralda agents negotiated with each other on one platform. For the real-world games, which families advise which sides is mostly unknown. That is exactly the measurement section 9 asks for.

8. The strongest objections

"A correlated equilibrium is one players want to follow." True by definition, and against fully rational players it bounds the mediator: it can choose among outcomes players accept, not impose arbitrary ones. But as section 2 argues, that's the lower bound. Players who follow advice rather than best-respond to it give the mediator more room, and the ratchet says their judgment degrades exactly when the game is new.

"Schelling coordination in realistic settings is weak." This is the objection I take most seriously. The LessWrong review of Schelling coordination in LLMs finds models near-perfect on toy tasks and poor in safety-relevant ones: in one untrusted-monitoring study, Claude 3.7 Sonnet coordinated successfully only 31 times in 3,903. My reply is that the mediator scenario asks for much less than covert collusion against a monitor. It asks only that the same model, facing the same kind of question from both sides, lean the same way, which is the regime where monoculture is strongest. Still, if you think realistic-setting coordination stays this weak, you should discount my argument heavily.

"Humans still drive the outcomes." Imas, Lee & Misra find that who designs the agent matters as much as or more than shared information or code, and that agent-mediated negotiations show more dispersion, not less. That's direct evidence against homogenization in at least one game.

"Sycophancy anti-correlates the advisors." RLHF pushes hard toward telling each user what they want, so the buyer's advisor leans toward the buyer and the seller's toward the seller. That pulls the content of the advice apart, and may explain part of the dispersion Imas, Lee & Misra find. But sycophancy is itself a shared disposition, and its effect on the end state is correlated. If every litigant's advisor says "your case is strong," the result is fewer settlements, more escalation and fewer concessions. If every negotiator hears that their position is reasonable, fewer deals close. That is a directional shift in the end state that tracks the models' disposition rather than the game's structure, which is exactly the signature the mediator frame predicts. The strongest objection turns out to generate the cleanest prediction: in adversarial games where every side is advised by the same family, expect more impasse than in unadvised or mixed-family play.

"The dispositions we see are benign." The geopolitical models lean cooperative. In Anthropic's goal-conflict experiments, the newest model settled by truce 98% of the time. If the mediator's lean is toward stability and truce, maybe we should welcome it. My reply: a benign lean is still a lean that no human chose, in a seat no human authorized, and the next training run can move it. "The controller is currently nice" is not the same claim as "humans are in control."

"The effects fade." In an earlier version of the Rebholz et al. study, a Bertrand pricing treatment with upward-biased advice raised prices at first, but prices drifted back toward the competitive level. Some games have enough outside feedback to resist a mediator. Which ones is an empirical question worth answering.

"The formal results assume what you need to prove." Shaki et al. model providers as strategic payoff-maximizers; Rebholz et al. built the bias in by hand. Neither shows a model spontaneously choosing a direction. That's correct, and it's why section 4 matters: the case for directional pull rests on RLHF dispositions, not on the formal models.

"This is nothing new." Stead's press, a handful of consulting firms, a few dominant textbooks and credit-rating agencies have all created monoculture before. What's quantitatively different:

• Illegibility. You could read Stead's editorials and argue with them. The lean of a model ensemble in a particular game isn't written down anywhere, including in the rules its makers wrote.
• Personalization. A newspaper sent everyone the same signal. An LLM sends each player a private, tailored one, which is much closer to what Aumann's device does than anything a newspaper could.
• Both sides of the same table. Consultancies usually served one side of a deal. The same model family now routinely advises the buyer, the seller, the plaintiff and the defendant.
• Speed of the ratchet. Advice is on tap for every decision, so the loop from "unfamiliar state" to "lean harder on the advisor" runs in hours, not publication cycles.

9. What would tell us, and what to do

If the observational signature can't discriminate, we need experiments and instruments that can. Here is how I plan to build them, and what others can do.

What I intend to do if I can find the resources

I intend to run these tests myself. They form a ladder: first show the measurement can detect a lean when one is planted, then measure the models' own lean in controlled settings, then look for it in real use.

1. A positive control. Nobody has yet run the obvious experiment: human players, advised by an LLM, in a game with several equilibria, compared with unadvised players. Rebholz et al. used scripted advice in a game with one equilibrium. Ballestero et al. and Anthropic's pricing agents had the models play rather than advise humans. I'll use three game types that cover the cases that matter: a pure coordination game (equilibria pay the same, so only selection matters), a battle of the sexes (equilibria differ in who benefits), and a stag hunt (one equilibrium is better for everyone but riskier). First I'll plant a lean of known size, such as a system prompt favoring one equilibrium, and confirm the setup detects it. Then I'll remove the plant and run three arms: every player advised by the same model family, players advised by different families, and unadvised.
2. An identity × role eval. This turns the rule audit from section 4 into an experiment. I'll pair identities with roles across many games (buyer and seller, plaintiff and defendant, negotiator and mediator, the party in power and its opposition) and swap each identity across roles while holding everything else fixed. This borrows the matched-guise method of Hofmann et al. I'll score the advice directly rather than simulated outcomes, so advisor lean isn't confused with player behavior. The key question is whether advice to both sides tilts toward the same identity. Cases where each advisor should favor its own user serve as the sycophancy control. I'll run it across model families, which also tests whether families are converging (section 5). The full space of combinations is too large to cover, so I'll sample it with a factorial design and pre-register the comparisons before running them.
3. A check in the wild. Linking both sides of a real court case through lab user data would mean deanonymizing people, so I won't propose that. What I can do directly is study settings where AI use is consented or already disclosed: opt-in studies with both sides of a negotiation, and courts that require parties to certify AI-assisted filings. The lab-side version is below, as a request.

I'll share the designs before running them, and the results whatever they show. If you want to collaborate, have relevant data or games, or can help get lab-side analyses done, comment here or message me.

What others can do

Some of this only the labs and the wider field can do:

1. Labs: check for multi-side mediation, in aggregate. Labs already run privacy-preserving aggregate analyses of how their models are used; Anthropic has published on one such system, Clio. Use them to estimate how often opposing sides of the same kind of interaction consult the same model, and whether the advice leans. Only the labs can see this, and as far as I know nobody is looking. I'm willing to work with any lab as an external evaluator to design and build this analysis; if that's of interest, please get in touch.
2. Track adoption per game, per side. For the games we care about (markets, elections, diplomacy, litigation), estimate what fraction of each side's decisions pass through which model family. A game where every side is mediated by the same family is a game to watch.
3. Audit rules for player-conditioning. For each value or safety rule, ask: would two players on opposite sides of a game get advice tilted the same way because of this rule? Rules that pass are player-agnostic. Rules that fail are coordination surface, whether or not anyone intended it.
4. Preserve variety on purpose. Kleinberg & Raghavan and Ballestero et al. both imply that model diversity is a public good. Norms or procurement rules that keep opposing sides of a game on different model families are cheap relative to the risk, but they only help while the families remain meaningfully different (section 5).
5. Make the seat explicit. Madmon & Tennenholtz are right that mediation can raise welfare. If mediation is happening anyway, it's better to design it, audit it and give the players a say in it than to let it happen through advice nobody thinks of as a signal.

The cheapest thing, and the reason for this post: stop treating "everyone uses the same assistant" as neutral background. In game-theoretic terms it is a change in who sits at the table.

References

Game theory and mediation

• Aumann, R. J. (1974). Subjectivity and Correlation in Randomized Strategies. J. Math. Econ. 1(1), 67–96.
• Forges, F. (1990). Universal Mechanisms. Econometrica 58(6), 1341–1364.
• Hart, S. & Mas-Colell, A. (2000). A Simple Adaptive Procedure Leading to Correlated Equilibrium. Econometrica 68(5).
• Bergemann, D. & Morris, S. (2019). Information Design: A Unified Perspective. JEL 57(1).
• Shaki, J., Hartman, E., Kraus, S. & Aumann, Y. (2026). Who Is Really Playing? Strategic Interaction in AI-Guided Populations.
• Madmon, O. & Tennenholtz, M. (2026). Agentic AI Design Should be Mediated to Promote Social Welfare.

Advice, monoculture and coordination

• Rebholz, T. R., Uphoff, M., Bernges, C. H. R. & Scholten, F. (2025). (Individualized) Algorithmic Advice as a Strategic Signal on Competitive Markets.
• Ballestero, G., Hosseini, H., Khanna, S. & Shorrer, R. I. (2026). Strategic Algorithmic Monoculture.
• Kleinberg, J. & Raghavan, M. (2021). Algorithmic Monoculture and Social Welfare. PNAS.
• Shaw, S. D. & Nave, G. Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender. SSRN 6097646 (PsyPost coverage).
• Imas, A., Lee, K. & Misra, S. Agentic Interactions. SSRN.
• Kindred, C.-L. (2026). Schelling Coordination in LLMs: A Review. LessWrong.

Model dispositions

• Hofmann, V., Kalluri, P. R., Jurafsky, D. & King, S. (2024). AI generates covertly racist decisions about people based on their dialect. Nature 633.
• Solopova, V. et al. (2026). LLMs as Strategic Actors: Behavioral Alignment, Risk Calibration, and Argumentation Framing in Geopolitical Simulations.
• Campbell, D. et al. (2026). Defensive Refusal Bias.
• Kryshtal, A. (2026). Can AI Make Conflicts Worse?
• China Law Translate. Interim Measures for the Management of Generative AI Services (2023).

Multi-agent systems and incidents

• Anthropic Frontier Red Team (Aug 13, 2026). Patterns and problems in emerging multiagent systems.
• OpenAI (Aug 26, 2026). The Hugging Face incident and the road ahead.
• Anthropic (Sept 2026). Countering misuse of AI.
• Edge City & Cosmos Institute. We Gave a Village Personal AI Agents.
• AI Digest. AI Village dataset.
• Hammond, L. et al. (2025). Multi-Agent Risks from Advanced AI. Cooperative AI Foundation.
• UK AISI. Inspect.

Systems, control and decision-making

• Ashby, W. R. (1956). An Introduction to Cybernetics, ch. 11 (PDF; overview).
• Stead, W. T. (1886). Government by Journalism. Contemporary Review 49.
• Yudkowsky, E. (2017). Inadequate Equilibria, "Moloch's Toolbox."
• Critch, A. (2021). What Multipolar Failure Looks Like, and Robust Agent-Agnostic Processes.
• Kulveit, J. et al. (2025). Gradual Disempowerment.
• Kahneman, D. & Klein, G. (2009). Conditions for Intuitive Expertise: A Failure to Disagree. American Psychologist 64(6). See also Klein, Sources of Power (1998) and recognition-primed decision.
• Grassé, P.-P. (1959) on stigmergy, via Wikipedia.