Building Smarter Ways to Allocate Resources for Innovation in a Competitive World

Abstract

This report proposes a logic-driven framework for resource allocation in innovation-driven economies, emphasizing motivational consistency from Christian Schubert's behavioral political economy. It highlights how U.S. over-regulation, often rooted in empathetic rather than logical measures, risks an economic crisis in AI by allowing China to advance faster. Drawing on historical cases like thorium reactors and current AI dynamics, the framework integrates mission-oriented investment, regulatory agility, and diffusion-model simulations to ensure efficient, long-term funding while preserving safety and Western leadership.

1 Introduction and Motivation

Across history, advances in energy and computing have hinged on decisive investments by governments and firms. The United States once pioneered thorium-fuelled molten-salt reactors at Oak Ridge National Laboratory (ORNL) in the 1960s, but the Atomic Energy Commission shifted funding to other technologies and terminated the molten-salt program in 1973. ORNL engineers believed that technical problems could be solved if support continued, yet budget priorities and regulatory inertia shelved the technology. Over fifty years later, China built on ORNL's research and, in 2025, its TMSR-LF1 reactor achieved the first thorium-to-uranium fuel conversion, becoming the world's only operating liquid-fuelled thorium molten-salt reactor. This milestone allows China to "breed" uranium from thorium, advancing toward energy independence and potentially powering ships for a decade on a single charge.

The same pattern is visible in artificial intelligence (AI). Nvidia's chief executive Jensen Huang told the Financial Times in November 2025 that "China is going to win the AI race." He argued that China's lower energy prices and looser regulations give its firms cheaper compute, whereas the West faces high energy costs and heavy regulation. Huang later clarified that China is only "nanoseconds behind" the United States, yet his warnings reveal a larger issue: access to abundant energy and permissive regulations can tilt innovation races. Predictions from ai-2027.com and experts such as Yoshua Bengio warn that, if compute scales ten-fold by 2027, superhuman AI could appear within two years, shifting geopolitical power.

This proposal builds a logic-driven resource allocation framework that avoids the short-termism and regulatory stagnation that hindered past breakthroughs. It draws on behavioral political economy for motivational consistency, mission-oriented investment for bold public funding, and diffusion-model techniques to simulate and optimize investment decisions. The goal is to produce a transparent pipeline that aligns incentives, mobilises long-term R&D spending and ensures Western leadership in energy and AI while preserving safety and trust, in line with frameworks like the U.S. "America's AI Action Plan" from 2025.

1.1 The Role of Motivational Consistency: Insights from Christian Schubert

Christian Schubert's work in behavioral political economy emphasizes the importance of applying consistent motivational assumptions to both market and political behavior. In his survey, he argues that policymakers, like market agents, respond to incentives, and failing to recognize this leads to inefficient resource allocation.

Due to the United States' over-regulatory practices, often driven by empathetic measures rather than purely logical ones, the nation risks a future economic crisis in AI. These regulations, while well-intentioned, slow innovation and allow China to advance faster with lighter regulatory practices, potentially giving them an upper hand in the AI race. The United States could beat them, but companies like Nvidia appear willing to assist China, as Jensen Huang's comments suggest a close competition where energy subsidies play a key role. If China gains the technology first, they could use it against everyone, posing existential risks given differing values on safety and control.

2 Background: Evidence of Stagnation and Lessons from Past Cases

U.S. R&D Performance and Shifting Priorities

The United States remains the world's largest funder of research and development. In 2022 it performed $885.6 billion in R&Da nominal 12 % increase over 2021and its R&D intensity (R&D spending as a share of GDP) was 3.4 %, continuing a rise above 3 % since 2019. Business firms performed $692.7 billion (78 %) of this research, while higher education and the federal government contributed $91.4 billion and $73.3 billion respectively. Figure 1 illustrates the approximate upward trend in U.S. R&D spending, and Figure 2 shows the composition of performers in 2022.

US R&D expenditure growth 2015-2022 line chart
Figure 1: Approximate growth in total U.S. R&D expenditure between 2015 and 2022 (based on data from the National Science Foundation). Although the values are illustrative, they show the rising scale of R&D commitments.
US R&D performers composition 2022 pie chart
Figure 2: Composition of U.S. R&D performers in 2022. Business firms dominate R&D spending, while universities and the federal government each contribute less than 11 %. These shares mirror the data reported by the National Science Foundation.

Despite high absolute spending, the composition of investment and regulatory environment can undermine innovation. Tyler Cowen notes that the Great Stagnationa slowdown in productivity growth-coincided with a rising burden of regulation; he argues that increases in regulation, both good and bad, slow progress. Empirical research supports this view: a study summarised by MIT Sloan found that firms near regulatory thresholds (e.g., 50-employee threshold for additional labour rules) reduce innovation, and the resulting economic burden acts like a 2.5 % tax on profits that lowers aggregate innovation by roughly 5.4 %. Economist John Cochrane highlights a similar problem in the European Union, where burdensome carbon accounting rules and bureaucratic barriers create a drag of over regulation, leaving Europe's integrated market roughly 30 % poorer per capita than the United States.

The Thorium Reactor Experience

During the 1960s, ORNL's Molten Salt Reactor Experiment (MSRE) demonstrated that liquid fluoride salt could serve as a reactor fuel carrier. The broader molten-salt program was terminated in 1973 because the Atomic Energy Commission redirected resources to competing designs. The subsequent Molten Salt Breeder Reactor (MSBR) program was officially cancelled in 1976; a report from the Weinberg Foundation observes that the AEC cited budget constraints and chose to support liquid metal fast breeder reactors instead, despite ORNL's confidence that technical challenges could be overcome.

China's TMSR-LF1 is built on U.S. research. The 2-megawatt prototype uses low-enriched uranium and contains about 50 kilograms of thorium. In October 2025, it successfully converted thorium into fissile uranium-233, making it the only operating example of a thorium-fuelled molten-salt reactor in the world. This breakthrough allows for "breeding" uranium from thorium, advancing China's path to energy independence. The project plans to scale up to a 100-MW demonstration by 2035, underscoring how earlier under-investment and regulatory caution allowed other nations to capitalise on U.S. intellectual capital.

The AI Compute Race and Energy Dynamics

The AI boom depends on massive compute and energy. Jensen Huang explained that Chinese developers enjoy significantly lower electricity prices because their government subsidises energy, whereas Western data centres face higher energy costs and stricter regulations. He contended that such advantages could make China "win the AI race," though he later described the gap as mere nanoseconds. Meanwhile, AI-2027 forecasts suggest training compute could increase roughly ten-fold between 2023 and 2027, enabling models to surpass human coding ability by March 2027 and approach artificial general intelligence (AGI) soon after. Figure 3 visualises the projected growth in training compute.

Projected AI training compute growth 2023-2027 line chart
Figure 3: Projected growth in AI training compute relative to GPT-4. According to AI-2027 forecasts, compute could grow an order of magnitude by 2027, enabling superhuman performance.

Yoshua Bengio and other researchers have warned that the race for compute and unbridled deployment of AI systems carry enormous risks. Bengio argues that there is no known method to ensure that superhuman AI will behave as intended and calls for massive investment in AI safety, regulation and international treaties to prevent catastrophic misuse. Without such institutions, the rapid build-out of energy-hungry data centres could empower regimes that value control over openness. Recent 2025 frameworks like the U.S. "America's AI Action Plan" emphasize grid optimization and international cooperation to maintain leadership.

3 Theoretical Foundations

Motivational Consistency and Behavioral Political Economy

Christian Schubert's work in behavioral political economy emphasizes the importance of applying consistent motivational assumptions to both market and political behavior. In his survey, he argues that policy makers-like firms-seek to maximise their utility (e.g., electoral success, agency budgets), and they respond to incentives. Ignoring this leads to "money grabs" and inconsistent decision-making when allocating public funds. Designing resource allocation rules thus requires aligning incentives across market and political actors.

Mission-Oriented Investment and the State's Role

Economist Mariana Mazzucato emphasises that governments have historically driven radical innovation by taking mission-oriented risks. Her book "The Entrepreneurial State" notes that agencies such as DARPA and ARPA-E fund high-risk research, operate within the "risk space" and attract visionary talent. She argues that mission-oriented R&D is not about "picking winners" but about investing boldly in areas where private finance will not-defence, health, space, and green technology. Modern state investment banks, such as Brazil's BNDES and China's Development Bank, demonstrate how public finance can create markets when coupled with clear missions. A key lesson is that the state must remain patient, tolerating long horizons and failures while setting ambitious goals.

Regulation, Stagnation and Incentives

Evidence indicates that excessive regulation slows innovation. In addition to Cowen's remarks on rising regulation and stagnation, studies show that regulatory thresholds reduce firms' innovation rates and act like taxes on profits. Cochrane's analysis of Europe illustrates how bureaucratic complexity and burdensome rules can impose a drag on economic performance. Together, these insights urge policy designers to balance legitimate safety and environmental goals against the need to facilitate experimentation and adoption of new technologies.

Diffusion Models as a Metaphor and Tool

Generative diffusion models exemplify how structured mathematical logic can transform randomness into meaningful outputs. Chieh-Hsin Lai and co-authors explain that diffusion models gradually add Gaussian noise to data using a Markov process (forward diffusion) and then train a neural network-often a U-Net-to reverse the process and denoise step by step. At each timestep, the model performs:

x_t = \sqrt{1 - \beta_t} x_{t-1} + \sqrt{\beta_t} \epsilon, \epsilon \sim N(0, I),

where \beta_t controls the noise schedule. The reverse process learns to predict or remove the noise, effectively sampling from the data distribution. Diffusion models thus provide a metaphor for resource allocation: just as the forward process corrupts data into noise, a complex landscape of competing incentives and regulations can obscure the value of potential projects; the reverse denoising process is analogous to applying a structured, logic-driven rule to "extract" promising projects from the noise and allocate funds accordingly.

4 Proposed Innovation Allocation Pipeline

Building on the above, we propose a four-stage pipeline for smart resource allocation that ensures motivational consistency, encourages mission-oriented investment, and incorporates formal AI safety and diffusion-based simulations.

1. Structure: Define Logical Rules and Incentives

The first stage establishes transparent rules that determine eligibility and funding amounts. To achieve motivational consistency, political actors should be modeled as utility-maximising agents who respond to incentives. Rules should therefore:

2. Decision: Align Agents with Missions

The second stage implements decision mechanisms that rank projects based on mission alignment and expected social returns. Mazzucato's emphasis on mission-oriented investment suggests that governments should set clear goals-such as a carbon-neutral energy grid or safe AGI-and then solicit proposals to achieve them. Decision tools could include:

3. Policy: Build Institutions for Long-Term Funding and AI Safety

Sustained investment requires institutional capacity. The U.S. should expand agencies like ARPA-E and create a dedicated AI Safety Agency. This stage includes:

4. Computation: Simulate and Optimize with AI Tools

The final stage uses computational models to simulate outcomes and optimise funding portfolios. Diffusion models provide a flexible framework: starting from random initial allocations, one can simulate project returns with stochastic noise, then iteratively adjust allocations via a neural network to maximise an objective (e.g., social welfare). Such simulations could incorporate Monte-Carlo game theory, Bayesian networks, or reinforcement learning. The output helps policy makers explore "what-if" scenarios, identify robust strategies under uncertainty, and avoid over-reliance on human heuristics.

5 Case Studies

Thorium Reactor Revival

Applying the proposed pipeline to thorium reactors, we would set a mission of building safe, proliferation-resistant molten-salt reactors. Structure rules might require proposals to show high breeding ratios or low waste; decision scores would prioritise scalability and cost per megawatt. A long-term funding institution could partner with universities and firms, drawing lessons from the TMSR-LF1 project but ensuring domestic control and safety. Diffusion-based simulations could explore scenarios of resource allocation across thorium and other next-generation reactor designs to find robust portfolios.

The AI Compute Race

For AI, the mission could be developing safe, energy-efficient AI systems. Structure rules could cap energy use per training run and require adherence to safety standards. Decision mechanisms might auction compute quotas, awarding subsidies to models that demonstrate formal safety proofs. On the policy side, establishing an AI Safety Agency and negotiating international compute agreements would prevent unregulated accumulation of compute in regimes with lower oversight. Diffusion-model simulations could evaluate the impact of different compute allocations on innovation and safety outcomes, helping policy makers decide when to subsidise hardware research (e.g., neuromorphic chips) versus algorithms, in line with 2025 guides like "AI for Public Resource Allocation."

6 Expected Outcomes and Impact

The proposed system offers several benefits:

7 Implementation Roadmap and Future Work

To operationalise this framework, policy makers should:

  1. Establish a task force combining economists, technologists, ethicists, and behavioural scientists to refine the scoring functions and rules.
  2. Pilot the system in a targeted domain (e.g., nuclear innovation or AI safety research) to evaluate its effectiveness. Collect data on project outcomes and adjust rules accordingly.
  3. Invest in computational infrastructure for running diffusion-based simulations, leveraging partnerships with cloud providers while enforcing safety measures.
  4. Engage international partners to harmonise safety standards and compute allocations, preventing a global arms race.

Future research should expand on dynamic modelling of political incentives, integrate network effects (e.g., knowledge spillovers), and explore how the diffusion metaphor can be formalised for resource allocation algorithms. Additionally, researchers should investigate the interplay between energy policy and AI, exploring renewable energy sources (including thorium-based reactors) as enablers of AI compute that align with climate goals.

Engage with the Ideas: Discussion Prompts

8 Conclusion

History shows that under-investment and regulatory inertia can squander early technological leadership. By building a logic-driven, incentive-consistent resource allocation system grounded in missions and informed by AI-enabled simulations, the United States and its allies can revitalise innovation, reclaim leadership in emerging energy technologies, and ensure that AI advances serve the public interest. The lessons from thorium reactors, the AI compute race, and the Great Stagnation emphasise the need for bold, mission-oriented investment and careful governance-principles that this proposal seeks to put into practice.

References

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