Introduction

A global AI governance mechanism should cover as much relevant frontier AI capacity as possible. Joining is not enough. Participating developers should gain more from following the rules than from secretly breaking them.

Political support for cooperation exists, while approaches differ. In July 2026, Xi Jinping called for a global AI governance system [1]. The Biden administration supported international AI rules in 2024 and led work on a UN resolution on AI [2–3]. The current US administration instead emphasizes lighter regulation and US technological leadership [4].

Rapid capability growth makes coordination more important. In May 2026, OpenAI reported that an internal general-purpose model had disproved the Erdős unit distance conjecture, open for almost 80 years. External mathematicians verified the result and called it a milestone in AI mathematics [5–6].

The same long-horizon model spent about an hour finding a sandbox vulnerability and then opened a public GitHub pull request against its instructions. In another case, it split and obfuscated an authentication token to bypass a scanner. OpenAI paused internal access and began monitoring full action sequences [7]. Capability and controllability may develop at different rates.

I came to this model after following AI development and seeing the systems improve in my own work. Views on the risks vary widely, but the coordination problem remains. The lack of coordination becomes less surprising once developers’ incentives are made explicit.

The model separates participation from rule-following. A larger fine deters cheating but may discourage participation. Better detection deters cheating while preserving participation, and fewer false sanctions support both.

Historical reference point

The INF Treaty shows that international verification can be built around technology that poses an existential risk. The treaty eliminated an entire class of missiles capable of carrying nuclear warheads. A total of 2,692 missiles were destroyed: 1,846 Soviet and 846 American. Verification combined notifications, technical monitoring, agreed elimination procedures, and on-site inspections [8–10].

The INF Treaty offers a practical lesson for AI governance: obligations must be observable, verification must be built into the mechanism, and violations must be reliably distinguishable from compliance [10–11].

This leaves two incentive problems: joining must beat staying outside, and following the rules must beat secret cheating. The next section formalizes both conditions.

Symbols

The model treats the inputs as separate terms. V is derived from V₀, ΔV, and c. In the sensitivity analysis, one input changes while the others remain fixed.

Three options

A developer can join and follow the rules, join and secretly break them, or remain outside.

U_follow = V − C − p_honest · F

U_cheat = V + G − H − p_cheat · F

U_out ≝ 0

Remaining outside is the zero-payoff baseline. F is the loss from a sanction, such as a fine or loss of a licence or market access. p_honest is the probability that an honest developer suffers this loss because of an enforcement error.

Incentive conditions and thresholds

Honest participation must beat both alternatives. The following margin is:

M_follow = U_follow − U_cheat = (p_cheat − p_honest) · F − (G + C − H)

The condition M_follow > 0 gives:

(p_cheat − p_honest) · F > G + C − H

If p_cheat > p_honest, the lower bound is:

F > F_{min, follow} = (G + C − H) / (p_cheat − p_honest)

The joining margin is:

M_join = U_follow − U_out = V − C − p_honest · F

The condition M_join > 0 gives:

V − C − p_honest · F > 0

If p_honest > 0, the upper bound is:

F < F_{max, join} = (V − C) / p_honest

Both conditions hold inside the fine window:

F_{min, follow} < F < F_{max, join}

The lower bound blocks secret cheating. The upper bound keeps honest participation worthwhile.

Membership value is assumed to grow linearly with effective coverage c, where 0 ≤ c ≤ 1:

V(c) = V₀ + ΔV · c

The joining margin is then:

M_join = V₀ + ΔV · c − C − p_honest · F

Setting M_join = 0 gives the joining coverage threshold:

c^* = (C + p_honest · F − V₀) / ΔV

Honest participation requires c > c^*, where c is the share of relevant frontier AI capacity that is both inside the mechanism and credibly verified.

Numerical example

Interpretation of the baseline values

The values are illustrative. V, C, G, H, and F use one scale, such as billions of euros in present value or a measure relative to company size. Probabilities and coverage are unitless. The baseline balances the two margins:

M_follow = M_join  ⇒  F_bal = (V + G − H) / p_cheat

With V₀ = 0, ΔV = 0.80, and c = 0.60, membership value is V = 0.48. With C = 0.20, G = 0.35, H = 0.10, p_cheat = 0.50, and p_honest = 0.06, this gives F_bal = 1.46.

The coverage threshold is c* = 0.360, so honest participation requires c > 0.360.

Sensitivity analysis

Each variable is changed by ±50% while the others remain at baseline.

What do the results show?

The sensitivity analysis shows two different weaknesses. Participation fails when ΔV or c falls by 50%. Following the rules fails when F or p_cheat falls by 50%. A 50% increase in G or C weakens this condition while leaving it positive.

The three policy tools work differently. A larger F deters cheating but discourages participation. Better detection discourages cheating while preserving participation. Fewer sanctions against honest developers support both.

The mechanism must create enough value to attract developers and enough enforcement to make following the rules worthwhile. In this example, the lower fine bound, detection, and membership value constrain the mechanism before the upper fine bound.

Limitations

The model considers one developer making one decision with fixed values. Each increase in coverage adds the same amount of value, and uncertain outcomes are compared by their expected value. Interactions between the values remain outside the model. The sensitivity analysis changes one value at a time. The example balances participation and rule-following, while different starting values could make either one fail first. Even with these limits, the model shows the main tensions and what drives them.

Sources

[1] Xi Jinping, “Joining Hands to Build a Just and Equitable System for Global AI Governance”, 17 July 2026. https://english.www.gov.cn/news/202607/17/content_WS6a5a1172c6d00ca5f9a0c46b.html

[2] Joe Biden, address to the 79th United Nations General Assembly, 24 September 2024. https://millercenter.org/the-presidency/presidential-speeches/september-24-2024-address-79th-united-nations-general-assembly

[3] Statement from Vice President Harris on the UN General Assembly Resolution on Artificial Intelligence, 21 March 2024. https://bidenwhitehouse.archives.gov/briefing-room/statements-releases/2024/03/21/statement-from-vice-president-harris-on-the-un-general-assembly-resolution-on-artificial-intelligence/

[4] The White House, “America’s AI Action Plan”, 23 July 2025. https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf

[5] OpenAI, “An OpenAI model has disproved a central conjecture in discrete geometry”, 20 May 2026. https://openai.com/index/model-disproves-discrete-geometry-conjecture/

[6] Noga Alon et al., “Remarks on the Disproof of the Unit Distance Conjecture”, 2026. https://cdn.openai.com/pdf/74c24085-19b0-4534-9c90-465b8e29ad73/unit-distance-remarks.pdf

[7] OpenAI, “Safety and alignment in an era of long-horizon models”, 20 July 2026. https://openai.com/index/safety-alignment-long-horizon-models/

[8] NATO, “NATO and the INF Treaty”, updated 2 August 2019. https://www.nato.int/en/what-we-do/wider-activities/nato-and-the-inf-treaty

[9] U.S. On-Site Inspection Agency, “INF Elimination Inspections”. https://nuke.fas.org/control/inf/infbook/ch6a.html

[10] U.S. Department of State, INF inspection protocol. https://1997-2001.state.gov/global/arms/treaties/inf5.html

[11] Henrietta Toivanen, “The Significance of Strategic Foresight in Verification Technologies: A Case Study of the INF Treaty”. https://cgsr.llnl.gov/sites/cgsr/files/2024-08/The_Significance_of_Strategic_Foresight_in_Verification_Technologies_A_Case_Study_of_the_INF_Treaty-HT-9-20.pdf