Global Collaborative Governance in the Age of AI

As global artificial intelligence (AI) technology accelerates, the competition in technological research and development has never ceased. In February this year, UN Secretary-General António Guterres stated at the AI Impact Summit that while AI drives progress, it may also exacerbate inequality, amplify biases, and foster harm. This awareness of systemic risks has led to a growing consensus in the international community: the biggest challenge is not optimizing algorithm models or addressing GPU supply bottlenecks, but rather ensuring that governance keeps pace with code. The current pressing issue is not the speed of AI development, but the scale of AI governance. Questions about what to regulate and how to regulate AI have become a focal point for human society.

An Unavoidable Topic in Global Governance

The impact of AI technology and industry development on global economic structure, resource distribution, and public perception is becoming increasingly evident, making it an unavoidable topic in global governance.

Since February, with the publication of research reports like “The 2028 Global Intelligence Crisis,” the profound impact of AI on the global economic structure has drawn significant attention. In the United States, many tech companies have laid off employees not due to poor business, but because of excessive business success. The unexpected surge in orders and profits has led to layoffs.

This has plunged the macroeconomic structure into a rapidly unbalanced state: on one hand, productivity has surged, leading to exponential growth in product supply; on the other hand, a shrinking labor market has resulted in insufficient effective demand. This mismatch of “overproduction and depleted purchasing power” creates a new economic dilemma. In financial markets, many US tech companies have achieved high valuations due to productivity gains, yet weak consumer demand makes profit expectations hard to realize. The intense tug-of-war between these two forces may ultimately trigger economic and financial turmoil.

Like past technological revolutions, AI development relies on numerous resources from the real world. The seventh UN Environment Assembly held in Nairobi, Kenya in December 2025 issued a stern warning regarding the environmental justice issues of AI: developed countries are enjoying the efficiency dividends brought by AI, while the environmental costs—such as overloaded power grids, depleted water resources, and heavy metal pollution—are being borne by developing countries.

The energy consumption of AI is not only evident in electricity but also in water resource usage. AI servers generate significant heat during computation, and the current mainstream cooling methods primarily rely on water for evaporative cooling. Research indicates that responding to 10 to 50 inquiries with AI models can consume about 500 milliliters of water. Observing the future data center construction plans of major tech giants, there is a clear trend of relocating to Southern countries. This is not only due to lower local electricity and water costs but also because these countries have relatively weak discourse power in global environmental governance, making it easier to establish new high-energy and high-water-consuming data centers with less policy resistance. Besides the enormous consumption of energy and water resources, the heavy metal pollution caused by computing hardware manufacturing also requires urgent attention. The leap in computing power relies on massive investments in GPUs, and the rare metals and key minerals needed to manufacture these chips are predominantly mined in developing countries.

The ecological cost shift regarding natural resource consumption and environmental destruction is increasingly severe, and the imbalance in global investment distribution is also deteriorating rapidly. A report from the UN Conference on Trade and Development on May 6 indicated that AI is reshaping the global investment landscape, with capital flooding into a few technologically advanced countries, while other developing economies face increasing marginalization risks. This one-way siphoning of investment, coupled with rising barriers to computing power, not only leads to serious talent loss in developing countries but may also render them “discarded” in future economic competition. This technological gap will exponentially amplify the inequality in global resource distribution.

The erosion of public perception by AI cannot be overlooked either. The World Economic Forum’s “2026 Global Risks Report” published in January ranked “misinformation and disinformation” and “social polarization” as the second and third short-term risks. As AI technology is widely applied, the cost of generating and disseminating false information has significantly decreased, severely undermining public trust in scientific facts and media, and exacerbating social polarization.

In recent years, the focus of social governance has remained on the “information cocoon” formed by algorithmic recommendations. Now, however, the ability of AI to generate vast amounts of semi-true, emotionally charged, and misleading content at low cost has created an increasingly dense cognitive “fog” on the internet. The digital crisis humanity faces has escalated from the previous “information cocoon” to “cognitive ecological pollution.”

Research from misinformation monitoring organization NewsGuard shows that the proportion of false information disseminated by mainstream large language model-powered chatbots regarding controversial news surged from 18% in August 2024 to 35% in August 2025, nearly doubling in a year. The reasons include: on one hand, the AI’s previous “refusal to answer” mechanism for sensitive issues has been significantly weakened under the drive for commercial competition and “enhancing user experience,” with some AIs’ refusal rates dropping to nearly 0% by 2025; on the other hand, these models heavily rely on real-time internet searches, which are filled with false news generated by other AIs. AIs lack sufficient cross-validation and discernment capabilities when scraping content, leading to the dissemination of these materials as authoritative answers, effectively turning AI into a “transit station” and “amplifier” for false information.

Building a Global Collaborative Governance System for AI under the UN Framework

In the face of these unavoidable topics in global governance, countries must adhere to a win-win cooperation approach, relying on UN mechanisms to establish a multilateral collaborative governance framework that is universally representative and has strong binding force. This multilateral collaborative governance framework must accurately address the issues of “timing of governance” and “intensity of governance.” Overly early or strict regulation can stifle industry innovation and hinder industrial development, while overly late or lax regulation can amplify systemic risks and increase subsequent governance costs. This requires us to seek a scientific dynamic balance between “excessive intervention suppressing technological vitality” and “regulatory lag inducing systemic risks.” To translate this “dynamic balance” from a macro concept into effective governance, we must seek breakthroughs in the following three dimensions.

First, institutional transformation and distribution reconstruction. The productivity revolution brought by AI necessitates a shift in the focus of human social governance from “purely pursuing production” to “optimizing distribution mechanisms.” In response to the excess profits obtained by companies through AI replacing human labor, governments can explore mechanisms such as a “digital dividend tax” to return the dividends of technological development to society. Some countries have already begun discussions in this area. On May 12, the head of the South Korean presidential policy office, Kim Yong-bum, publicly stated that consideration should be given to using the excess tax revenue generated by the AI industry to establish a “citizen dividend,” returning it to all citizens through institutional arrangements. This approach not only secures people’s livelihoods but also links a portion of wealth creation, which is detached from labor, back to the purchasing power of human economic society, thereby resolving the contradiction between overproduction and shrinking consumption, supporting the minimum consumption power of society, and maintaining the basic circulation of the macroeconomic system.

Second, global resource and environmental auditing. With the exponential growth in the demand for large model training and inference, the AI industry is bringing unprecedented energy consumption and ecological burdens. Therefore, a globally unified and mandatory AI resource environmental auditing mechanism must be established, incorporating the electricity utilization efficiency, water resource consumption, and heavy metal pollution caused by hardware iteration into the compliance regulation and disclosure indicators of large tech companies. This will promote the visibility of hidden environmental costs and the green allocation of computing resources. At the same time, drawing on the mature experience of carbon emission trading markets, we should explore the establishment of a “global computing power and ecological quota trading mechanism.” Funds raised through this mechanism can respond to Guterres’ proposal at the AI Impact Summit in February to establish an “ecological and digital compensation fund,” specifically aiding Southern countries in building local green computing infrastructure, ensuring they are not marginalized in the AI era, and achieving dual fairness in technological dividends and ecological responsibilities.

Finally, cognitive ecology and ethical regulation. In the face of the cognitive “fog” brought by generative AI, humanity urgently needs to establish a set of “environmental standards” for the digital age to address the increasingly severe “cognitive pollution.” On one hand, all AI service providers facing the public must add immutable invisible watermarks and explicit labels to the generated texts, images, and videos, effectively safeguarding the public’s right to know, while strengthening ethical regulations for tech giants, requiring them to conduct regular algorithm safety assessments to prevent AI from being used to amplify biases or incite extreme emotions; on the other hand, countries may consider establishing a transnational joint monitoring and rapid response network for AI misinformation under the UN framework, providing early warnings and traceability for behaviors that use AI to mass-produce and transnationally disseminate false information. By establishing a globally unified ethical red line for AI, we can ensure that technological development always adheres to human ethical norms and value orientations.

China has always advocated for the inclusive, beneficial, and good development of AI to achieve win-win cooperation. In 2025, China clearly pointed out the gaps in the existing international mechanisms for governance in new domains such as AI in its global governance initiative. In 2025, at the World Artificial Intelligence Conference and the High-Level Meeting on Global AI Governance, China released the “Global AI Governance Action Plan,” providing a feasible “Chinese solution” for breaking down technical barriers and building a fair global digital order.

The future positioning of AI must and will be a new engine for enhancing the well-being of all humanity. In the face of this profound technological wave reshaping human civilization, only by abandoning the old zero-sum game mindset and strengthening collaboration under the UN framework can we ensure that the new round of technological dividends genuinely benefits all humanity.