Title: Modeling the Economic Impacts of AI Openness Regulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: game theory

AI regulation

openness
Abstract: Regulatory frameworks, such as the EU AI Act, encourage openness of general-purpose AI models by offering legal exemptions for "open-source" models. Despite this legislative attention on openness, the definition of open-source foundation models remains ambiguous.  This paper presents a stylized model of the regulator's choice of an open-source definition  in order to evaluate which standards will establish appropriate economic incentives for developers. In particular, we model the strategic interactions among the creator of the general-purpose model (the generalist) and the entity that fine-tunes the general-purpose model to a specialized domain or task (the specialist), in response to the regulator. Our results characterize market equilibria -- specifically, upstream model release decisions and downstream fine-tuning efforts -- under various openness policies and present an optimal range of open-source thresholds as a function of model performance.  Overall, we identify a curve defined by initial model performance which determines whether increasing the regulatory penalty vs. increasing the open-source threshold will  meaningfully alter the generalist's model release strategy. Our model provides a theoretical foundation for AI governance decisions around openness and enables evaluation and refinement of practical open-source policies.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn an effort to understand and predict the impacts of AI governance policy in relation to open-source general-purpose AI foundation models, the authors introduce a game-theoretic model for assessing and predicting the effects of AI governance regulation thresholds and penalties on decisions made by the producers and refiners of open-source general-purpose AI models. The approach models the effects of strategic decisions made by AI developers to adjust levels of openness along a spectrum rather than under an open or closed binary construct. To reveal possible open-source value chain dynamics, the model allows for various levels of openness to be set by the producer of the general-purpose model, and weighs impacts of other criteria and forces such as revenue-related bargaining decisions between generalists and specialists, and regulatory open-source thresholds.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n•\tThe game is valuable as a way to understand how some regulatory approaches might affect AI developers, whether regulatory approaches could work, and areas where they might fail.\n•\tThe authors advance prior work in a way that somewhat reflects today’s regulatory and AI business landscapes, including by modeling for some of the nuances and complexities present in real-world scenarios.\n•\tThe authors are clear in their explanation of the game’s role players, forces and factors affecting predicted outcomes, and how the game works.\n\nWeaknesses\n•\tAn overarching and relatively obvious weakness of this work is the game’s existence as a theoretical exercise that -- despite its addition of nuances prior work does not consider (such as incorporating impacts of openness along a spectrum rather than an open/closed binary) – does not consider several other real world factors that could limit the model’s predictive capabilities. \n•\tFor instance, as just one example, the model does not consider the impacts of venture capital funding which could allow generalists or specialists to tolerate lower revenue goals. It also does not consider political forces, or other factors that might disrupt the model.\n•\tThe authors do not adequately define the regulatory goals their model addresses or considers. It is somewhat implied that the goals the model is designed to address are “innovation,” model performance as measured by industry-designed benchmarks, and possibly competition related goals, rather than other regulatory goals that are also facilitated through open-sourcing such as model transparency and explainability related goals. But it is important to better define these regulatory goals.\n•\tThe paper does not adequately define or explain reputational benefits, how they are measured, or how the size or market power of a company might affect reputational benefits/premiums.\n•\tA minor point, but the authors could more directly explain and cite the Nash bargaining solution.\n\nQUESTIONS:\n•\tDid the authors consider factoring-in the distinctions among how G, D, and regulators define model improvements or performance? As noted in the Weaknesses section, the regulatory goals addressed in the modeling should be better articulated.\n•\tDid the authors consider what group or groups are their target audience? It might be helpful for the reader to understand this to get a better sense of the ultimate goals for application of the model.\n\nLIMITATIONS:\nThe authors acknowledge some limitations and caveats to their proposed framework; for example, their stated limitations acknowledge some important areas for future research and model improvement, in particular inclusion of “social utility” related criteria in the modeling.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a two-stage game-theoretic model that links a foundation-model developer (generalist $G$) and a downstream fine-tuner (specialist $D$) who bargain over revenue, while a regulator sets an openness threshold and penalty. The game progresses in multiple stages. First, G chooses an openness level on a 0-1 continuum (Fig. 1) that governs whether the model clears the regulatory threshold and what costs it bears. Then, D decides whether (and how far) to fine-tune the model to a new performance level.\nThe authors derive closed-form expressions for D’s best response and for G’s sub-game-perfect openness choice. The authors demonstrate three policy regimes: (i) Pareto-improvement when $\\alpha_0$ is low and moderate, which push G to open the model, (ii) utility-loss zones where penalties only transfer surplus without added openness, and (iii) utility-transfer zones where tighter thresholds reroute surplus from G to D, raising $\\alpha_1$ but lowering $U_G$.\n\nThe authors claim this provides guidance for calibrating openness rules in the EU AI Act and similar regimes.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\nThe paper tackles a policy problem that is both immediate and under-specified: how openness rules in upcoming AI regulation will shape economic incentives along the foundation-model value chain. By allowing the \"generalist” developer, rather than the regulator, to choose an openness level endogenously, the authors capture a strategic knob that existing game-theoretic work leaves fixed. Their closed-form solutions make the comparative statics transparent, so readers can see precisely when higher openness lowers total welfare versus when it re-allocates surplus between the upstream model builder and downstream fine-tuner. Together, these features turn what could have been an abstract toy model into a practically useful lens for policymakers and practitioners who must weigh documentation burdens, revenue-sharing, and compliance costs.\n\n1. The model addresses a live regulatory gap rather than an abstract problem. The EU AI Act exemptions hinge on an ill-defined notion of \"open-source\", and industry \"open-washing” is widely discussed problem.\n2. This provides a realistic strategic lever for model developers. The relationship between producers of a general model and those building derived specialist models is under-studied. This extends the relevant game literature by letting G choose openness endogenously. Previous work treats openness as fixed.\n3. This work allows clear comparative-statics reasoning instead of pure simulation by obtaining closed-form best responses.\n\nOverall, this work can help readers reason about who bears which costs at different openness levels.\n\n# Weaknesses\nSeveral conceptual and modeling shortcuts undermine that practical promise. The paper treats \"foundation models” as uniquely troublesome for open-source definitions, yet earlier software and ML precedents raise identical dilemmas. This framing overstates novelty.\n\nMore overall, who decides what is high or low performance? These are vague notions the work depends on, but are never defined. The window of relevant performance changes over time? Once upon a time, GPT-3 was very high performing, but today we would call it low performing. The change in performance could trigger changes in openness. This analysis should minimally define performance, and to improve the modeling, introduce time variance.\n\n1. L24: \"Foundation models complicate the standard open-source definition\". The complication is not unique to foundation models. Binary-only drivers and source-available databases raised the same debates years ago. It's worth citing \"The Value of Open Source Software\" by Hoffman et. al. [0]\n2. L42-43: The discussion of specialists and fine-tuning appears could be foreshadowed earlier in the introduction. It's worth discussing fine-tuning and the game this implies before this statement.\n3. L113: \"dividing revenue from foundation model development\". The usage of the term \"foundation model\" does not add much to the discussion. Again, this phenomenon is not unique in the ML world to just foundation models. Look at Gupta et. al. for a discussion on the issues raised by the usage of \"foundation\" models in policy-related discussions [1]\n4. L125-126 and L130: \"$\\phi_G(\\omega, \\alpha_0|\\theta) = ... + c_{\\omega}\\alpha_1(1 - \\omega) + ...$\" Should this be $\\alpha_1$ or $\\alpha_0$? Currently this implies that operation costs hinge on the final performance reached after fine-tuning, not the baseline. Why does the generalist bear this cost?\n5. L125-126: Why are there not regulatory costs for the specialist? Fine-tuned models could be closed-source and/or regulated as well.\n6. L127: \"there are additional costs to documents and releasing the model publicly which scale with higher levels of openness and performance\". Sure, but this is not linearly so? There are already costs associated with documentation internally, as well as the cost of a proper internal model release process. Perhaps this is better modeled as log scaling rather than linear?\n7. L128: \"safety risk management for open models\". This is a regulatory cost, but you are trying to provide an example for production costs.\n8. L136: \"since incremental improvements are more expensive at higher performance levels...\" The entire point of fine-tuning a general model is because it was initially not performing well on the desired downstream task. Can you justify quadratic cost? Empirical fine-tuning curves often show super-linear returns for early steps. Look at literature like Xia et. al. [2]\n9. L139: \"fine-tune or improve\". There are plenty of ways to improve the performance of a closed-source model. In-context learning, RAG, tool use, etc. Maybe just say \"fine-tune\" instead?\n10. L148-149: \"model performance is the only feature in $G$'s strategy space\". This is a very limiting assumption. Generalists also worry about efficiency. Reference the debate around DeepSeek, o3, o3-mini, and new nano-style models.\n11. L157: \"higher openness increases production costs\". There is ample evidence showing this to be false. Projects like YOLO, vLLM, etc. have benefited drastically from open-souce contributions. Outside of ML, look at Docker, Redis, etc. Even within foundation models, improvements to Gemma have led to improvements in Gemini.\n\n# Checklist concerns\nFor question 5, the authors say \"no\" to open access to data and code. But, the authors reference a numerical simulation to produce their figures. Where is the code for that? The justification that \"the plots can be easily reproduced\" is hand-wavey. Ok, if it is easy, then just share the code?\n\nFor question 8, while the simulations can be trivially run on a CPU, a few extra words saying for just how long or how many CPUs would be useful.\n\n# Typos\nL221: \"effectsacross\" -> \"effects across\"\nL222: \"identifies conditions the\" -> \"identifies the conditions for\" ?\nL294: \"teh\" -> \"the\"\n\n[0] Hoffmann, Manuel, Frank Nagle, and Yanuo Zhou. \"The Value of Open Source Software.\" Harvard Business School Working Paper, No. 24-038, January 2024.\n\n[1] Gupta et al. \"Data-Centric AI Governance: Addressing the Limitations of Model-Focused Policies.\" arXiv preprint arXiv:2409.17216 (2024).\n\n[2] Y. Xia et al., \"Understanding the Performance and Estimating the Cost of LLM Fine-Tuning,\" 2024 IEEE International Symposium on Workload Characterization (IISWC), Vancouver, BC, Canada, 2024, pp. 210-223, doi: 10.1109/IISWC63097.2024.00027.\n\nQUESTIONS:\nGame theoretic analyses are a fantastic way to provide quantification and grounding for policy makers. This paper is commendable in doing exactly that. However, there are a few areas in which the authors can improve their paper.\n\nThe model presently assigns the entire regulatory burden to the generalist. Downstream fine-tuners, however, can face disclosure or safety-testing duties under both the EU Act (Article 28) and the US NIST RMF. I would like to see a specialist-side penalty term (or a brief formal argument for why it is negligible). If introducing that term alters the qualitative comparative statics, please highlight where; if it does not, you gain credibility by demonstrating robustness.\n\nThe paper assumes performance improvements exhibit diminishing returns as the base model approaches the frontier, and you treat performance as the only strategic dimension. Yet fine-tuning often delivers non-marginal gains on underperforming tasks, and many practitioners optimize for latency or parameter efficiency instead of raw benchmark scores. I ask for (a) at least one empirical learning-curve plot or citation supporting the concavity assumption, and (b) a short discussion of how a second axis such as inference cost would change the results. Demonstrated robustness to alternative tuning curves or an extended two-dimensional game would lift my novelty and insight scores; silence on the issue would pull them down.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents an economic model that tries to capture the economics of open sourcing foundational models for those who build them (the generalists) and those who finetunes these into specialized models (the domain specialists) given that the government enforces some form of regulation. \n\nThe results characterize market equilibria under various openness policies and present an optimal range of regulatory penalties and open-source thresholds as a function of the foundation model performance.\n\nThe model seeks to provide theoretical foundations for AI governance decisions around openness and enables evaluation and refinement of practical open-source policies, and the discussion concludes with three implications of the analysis.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n* The topic is novel, and the potential impact could be high given that it could help governments in their understanding of how to regulate foundational models. Such regulations are already in place in several countries, including EU, US, and Singapore, as explained in the introduction. \n* The model is simple but still captures the main drivers, and the analysis seems to me – at least – to be sound. I especially like the discussion where the central findings are discussed, although I have some suggestions for how to improve them, see below. \n* The paper is very well structured and generally well written. \n* I really like the explanations of the costs in the equations on line 125.\n* I like how you show how the model explains the real-world example in Figure 3.\n \n## Weaknesses\n* My main gripe with the paper is the writing but not all of it. I found especially the parts where the model was introduced in detail to be hard to read because variables were not properly introduced before using them. The introduction came at a later point, but this made me very confused when reading through it the first time. I do not think it will take a lot of effort to fix this. Quite the opposite, I think it is quite easy. Examples are $\\epsilon$, $p$ and $c_W$. Utility $U$ and cost ($\\phi$) are shown in Table 1 but not mentioned in the text. I would move Table 1 later in section 2.3 and introduced all variables in the text before using them, maybe even in section 2.2. \n* I would like a discussion on negative examples. Which scenarios will the model fail to capture? \n\n## Minor comments \n* Line 23: OSS describes publicly available source code with no restrictions on use … This is not fully correct. Many OSS licenses do not restrict use, but some do, especially those that have twin licenses where academic and non-commercial use is OK but commercial use is restricted. Such licenses are not uncommon even in academia, especially in the fields of AI and ML. \n* Fully open models, as described in lines 26 and 27, should also share the code used for training and inference/reasoning. If not, they are not reproducible (which should be the main goal of open-sourcing foundation models). See for example (Gundersen, 2021) for a definition of reproducibility that requires code and data.\n * Instead of using the term “industry developers” on line 29, which targets individuals and might therefore be much less coherent, I suggest using the term “software industry” or “industry” as this indicates companies, which is how I read the sentence anyhow. \n* The comment on $\\epsilon$ on line 117 confuses more than it helps, especially as the variable is not introduced.. \n* The caption of Figure 4 is very parameter description heavy. A higher level-explanation would be nice. If possible.\n\n## Reference\nGundersen, O. E. (2021). The fundamental principles of reproducibility. Philosophical Transactions of the Royal Society A, 379(2197), 20200210.\n\nQUESTIONS:\n* Regarding the implications in 5.1: Would it not be better to articulate all three of them as advises to the regulatory bodies, as is the case with the second one? For example, the first one could be formulated as something like “Regulators should adopt tailored approaches according to the maturity of the technology”? I think the results would be more accessible to regulatory bodies, who might not be as easily sold on pareto improvements. \n* Why is there not a constant term specifying the cost in the production cost term $\\alpha_0\\omega$? As I read the term, both $\\alpha_0$ and $\\omega$ are rates. When they are added together with operation and regulatory which both contains constants ($c_w$ and $p$), then this would not yield the correct result. Am I wrong? What is it that I do not understand?\n* Are there other parameters that could lead to the same behavior as described in Figure 3 than those that you give?\n* Regarding negative examples: Are there real-world examples, such as the one given in Figure 3, that are not described by the model. If yes, then why does it not capture this?\n* As far as I understand, the model only captures a single $G$ and a single $D$. How would many $G$ to many $D$ change the analysis. Would it be a worthwhile analysis? Is this future work?\n\nLIMITATIONS:\nI think this is sufficiently addressed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}