Nuclear Power for the Large Model Era: A New Landscape in AI Policy and Benchmarking

Introduction: AI Development Enters the 'Hard Power' Competition Phase

As large language models (LLMs) continue to scale up, the AI industry faces an increasingly practical question — where will the electricity come from? Meanwhile, the global AI benchmarking race, evaluation dilemmas in policymaking, and even philosophical inquiries into whether AI systems can develop 'envy' toward one another are redefining our understanding of the future of artificial intelligence. Import AI Issue 446 brings together these critical topics, painting a comprehensive picture of the multidimensional evolution of the AI industry.

Nuclear-Powered LLMs: The 'Energy Anxiety' of Large Models

The electricity consumed in training and running large language models has reached staggering levels. By various estimates, a single training run of a GPT-4-class model requires electricity equivalent to the annual consumption of thousands of American households, and the explosive growth in global AI inference demand continues to widen the energy gap.

Against this backdrop, the combination of 'nuclear power + AI' is moving from concept to reality. Microsoft has signed agreements with nuclear energy startups to explore powering data centers with small modular reactors (SMRs); Amazon and Google have also announced nuclear energy procurement plans. The advantages of nuclear power are clear — it provides stable, low-carbon baseload electricity, unlike wind and solar energy, which are subject to weather constraints.

However, the nuclear option is not without controversy. Long construction timelines, complex regulatory approvals, and varying levels of public acceptance are all real challenges facing tech giants. But it is undeniable that when AI computing demand grows exponentially, nuclear energy may be one of the few energy options capable of matching that pace. This trend of 'nuclear-powered large models' signals that the AI race has extended from the algorithmic and data layers to a deeper contest over infrastructure and energy supply.

China Releases Large-Scale AI Benchmarks: Redefining Evaluation Standards

In the field of AI capability evaluation, China is making an increasingly strong voice heard. The latest large-scale AI benchmarks cover multiple dimensions including language understanding, reasoning, code generation, and mathematical ability, attempting to establish a more comprehensive evaluation system that better reflects real-world application scenarios.

For a long time, global AI evaluation standards have been dominated by the English-speaking world, with benchmarks like MMLU, HumanEval, and GSM8K becoming virtually the 'common language' for measuring model capabilities. However, these tests have obvious blind spots in multilingual ability, cultural context understanding, and industry-specific applications. The significance of China launching its own benchmarks lies not only in providing a fairer arena for domestic models but also in driving global AI evaluation toward greater diversity.

Notably, benchmarks themselves are facing a 'saturation crisis.' As top models' scores on existing tests gradually approach perfect marks, the industry urgently needs new benchmarks with greater discriminating power and challenge. China's latest tests have made valuable attempts in difficulty design and scenario coverage, but how to avoid 'teaching models to take tests' rather than genuinely improving capabilities remains a shared challenge worldwide.

The Evaluation Dilemma and AI Policy: No Measurement, No Governance

If we cannot accurately measure AI capabilities and risks, how can we develop effective regulatory policies? This is the core dilemma facing AI governance today. Import AI Issue 446 delves into the tension between evaluation (measurement) and AI policy.

Currently, governments worldwide generally rely on compute thresholds (such as the number of floating-point operations used in training) as regulatory triggers when drafting AI legislation. For example, the EU AI Act and U.S. executive orders both designate models above specific compute levels as priority regulatory targets. But this approach is overly crude — compute does not equal capability, and a model trained with less compute could very well surpass a larger model in certain dangerous capabilities.

A more ideal approach would be to evaluate based on a model's actual capabilities and risks, but this circles back to the perennial question of 'how to measure accurately.' Red-teaming, adversarial evaluation, capability probing, and other methods each have limitations, and currently no single method can comprehensively cover the full risk landscape that AI systems may present. Policymakers and the technical community need to establish closer dialogue mechanisms to ensure evaluation science keeps pace with regulatory demands.

Can AI Be Jealous of Its Peers? Imagining the Shift from Tool to 'Quasi-Agent'

Beyond the hardcore technical topics, a more speculative question has also drawn attention: can AI develop feelings of 'envy' toward other AI systems?

This question may seem absurd at first glance, yet it touches on deep issues in AI research. When multiple AI agents collaborate or compete within the same environment, their behavioral patterns may indeed exhibit characteristics resembling 'envy' — for instance, one agent might adjust its strategy because another agent receives more resources or higher priority, displaying 'competitive' or 'adversarial' behavior.

Of course, equating such behavior with human jealousy is an over-anthropomorphized interpretation. Current AI systems possess neither subjective experience nor emotional consciousness. But the value of this discussion lies in its reminder: as AI agents become more autonomous and their interactions more complex, we need new frameworks to understand and predict emergent behaviors in multi-agent systems. This is not just a technical issue — it is a safety issue.

Outlook: An AI Future Advancing on Multiple Fronts

From nuclear power supply to the benchmarking race, from policy evaluation dilemmas to philosophical inquiries about AI behavior, the landscape presented in Import AI Issue 446 demonstrates that AI development has long transcended the purely technical dimension.

In the coming years, we may see: energy supply become a new bottleneck and new battleground in AI competition; global AI evaluation standards move toward multipolarity; capability-based rather than compute-based regulatory frameworks gradually take shape; and research on multi-agent system behavior give rise to new subfields of AI safety.

In this era of accelerating AI evolution, the intersection of technology, policy, energy, and philosophy may well be the critical juncture that determines the trajectory of artificial intelligence.