Over 200 experts jointly call for: The impact of the AI economy urgently needs a global policy framework.

Event Background

On July 13, 2026, over 200 economists, researchers, and technology leaders jointly signed an open statement urging governments and tech companies to take immediate action to address the structural impact of artificial intelligence (AI) on the global economy. Signatories include 15 Nobel laureates in Economics, as well as key figures from leading AI companies such as OpenAI, Anthropic, and Google DeepMind, including OpenAI CFO Sarah Friar, Google DeepMind Chief Scientist Jeff Dean, and Anthropic co-founder Jack Clark. The statement was initiated by Professor Anton Korinek of the University of Virginia (now part of the Anthropic economic research team), along with economists Erik Brynjolfsson, Ajay Agrawal, and Tom Cunningham.

The statement's core warning: AI may drive an economic transformation more dramatic than the Industrial Revolution, but the time window is "vastly shorter." Steam, electricity, and computers gave society decades to adapt, but AI may leave society only "a few years." The statement calls for more in-depth research on AI's economic impact and immediate efforts to build policies and institutions that ensure technological progress benefits society, especially to address risks such as mass unemployment.

Digital Economy Analysis

Speed of Transformation: Unprecedented Rhythm of Economic Change

The statement's judgment that "AI's economic transformation may be vastly shorter than the Industrial Revolution" reveals a key characteristic of the digital economy era: the speed of technology diffusion is growing exponentially. Historically, the普及 of steam power took about a century, electricity penetration took decades, and internet commercialization took twenty years. However, generative AI has taken less than three years from the advent of ChatGPT to permeate various industries. This acceleration means that enterprises and the labor force have almost no "buffer zone"—the skill retraining, social security system reconstruction, and business model iteration that traditional industrial transformation relied on must now be completed in a matter of "years."

From the perspective of the digital economy, AI is not just another new technology, but the third core driving force after the platform economy and the data economy. It is reshaping the basic unit of value creation: from "people + software" to "AI + data." This shift causes network effects to move from user scale to data quality and model capability, changing the logic of platform competition.

User Growth and Traffic ChangesThe widespread application of AI is transforming user behavior patterns. Search traffic is migrating to generative AI assistants, and according to Gartner, traditional search engine traffic is expected to drop by 30% by 2027. Content production on social platforms is being reshaped by AI-generated content (AIGC), with user interactions shifting from "people and content" to "people and AI conversations." This change directly impacts advertising efficiency and platform monetization capabilities. Platforms like Meta and TikTok, which rely on user-generated content, are facing challenges such as misinformation, copyright issues, and user trust brought about by AI-generated content.

Business Model Observations

The Rise of AI-Native Business Models

• The involvement of tech giants in the statement suggests that AI commercialization models have entered deeper waters. Current mainstream models include:
• Subscription-based AI services (such as ChatGPT Plus, Microsoft Copilot) are rapidly driving the enterprise SaaS market. IDC predicts that by 2028, the global AI subscription market will exceed $150 billion.
• Pay-as-you-go pricing (such as API calls) lowers the barrier for small and medium-sized enterprises to use AI, while also providing platforms with a steady revenue stream.
• AI agent economy: Platforms are transitioning from "tool providers" to "agent service providers." AI agents can independently complete complex tasks (such as booking, customer service, data analysis) and charge commissions. This model may disrupt traditional commission structures; for example, online travel platforms like Expedia and Booking have already begun integrating AI agents.

Data Flywheel and Platform Lock-in

The performance of AI models heavily depends on high-quality data feedback. Platforms with massive user interaction data (such as Google, Meta, ByteDance) have a natural advantage. They can consolidate their market position through a flywheel effect of "data input → model optimization → better services → more users → more data." This exacerbates the risk of data monopoly. Multiple economists in the statement emphasize that without institutional intervention, AI could further concentrate economic power.

Market Competition Analysis

Reshaping the Competitive Landscape of PlatformsThe signatories of the statement come from "competitor" companies—OpenAI, Anthropic, Google—implying a shared recognition of a more severe external challenge: the lack of AI governance will harm the entire industry. From a competitive perspective, the battle has three main fronts: - Foundation Model War: OpenAI (GPT-5), Anthropic (Claude 4), Google (Gemini Ultra), and China's DeepSeek, among others. The key to victory lies in computational cost and data acquisition. - Application Ecosystem War: Microsoft collaborates with OpenAI to embed AI into Office and Azure; Meta promotes its open-source model Llama to attract developers; ByteDance integrates AI into TikTok and Feishu. Ecosystem lock-in is the next phase of competitive focus. - AI Chip War: NVIDIA dominates the computing layer, but AMD, Intel, and custom chips (Google TPU, Amazon Trainium) are catching up.

The "urgent action" call in the statement may extend competition from pure commercial aspects to regulatory dimensions. For example, the EU AI Act has already prioritized systemic risks, and the establishment of the National AI Research Institute (NAIRI) in the U.S. may also accelerate.

Beneficiaries and Challengers

Short-term beneficiaries: Tech giants with mature data flywheels and computing reserves, as well as service providers offering AI transformation consulting to enterprises (e.g., Accenture). Challengers: Industries dependent on traditional human services (customer service, translation, basic programming) will face compression; small and medium-sized enterprises unable to access sufficient data and computing power may be further marginalized.

Data and Regulatory Implications

A New Data Governance Agenda

• The statement emphasizes the urgency of "policies and institutions," directly pointing to the inadequacies of current data governance frameworks. Regulations like GDPR and CCPA mainly focus on personal privacy but fail to effectively address the redistribution of data value brought by AI. For example, public data used in AI training generates benefits for model providers rather than data producers. The statement may promote:
• Data Dividend Mechanism: When user data is used to train models, users should receive compensation (e.g., a "data tax" or direct profit sharing).
• New Rules for Cross-Border Data Flow: AI models require global data, but the U.S., EU, and China are pushing for data localization. The statement may accelerate the establishment of international agreements on "trusted data flows."Statement signed by 15 Nobel laureates will put pressure on antitrust agencies worldwide. The U.S. Federal Trade Commission (FTC) and the European Commission have already launched investigations into AI market concentration. What we might see next:
• Prohibiting large platforms from eliminating AI startups through acquisitions (e.g., Microsoft's acquisition of Inflection AI has already drawn attention).
• Requiring open model interfaces or data ports to lower switching costs.
• Including "AI systems" in the category of "essential facilities" under antitrust review.

Global Trend Observations

AI Economy: From Tool to Infrastructure

The statement marks a turning point: AI is no longer seen merely as a technological tool but as infrastructure akin to electricity and railways. This perception will drive governments to incorporate AI into strategic investment plans. For example, the U.S. CHIPS and Science Act has allocated funding, and the EU's Digital Decade plan lists AI as a key capability. In the long term, the core issue of the AI economy is "how to distribute productivity gains driven by AI"—this will determine the social contract for the next decade.

Acceleration of Restructuring in Traditional Industries

The statement's "industrial revolution analogy" suggests that AI will systematically transform production functions. Manufacturing (intelligent quality inspection, predictive maintenance), finance (algorithmic trading, risk pricing), healthcare (drug discovery, imaging diagnostics), and others will undergo fundamental changes. However, the statement also warns that if the pace of transformation is too fast while social safety nets lag behind, it could lead to a short-term surge in unemployment.

Digital Sovereignty and Global Fragmentation

The statement's call for "policies and institutions" implies concerns about geopolitical competition in AI. The U.S., China, and the EU have divergent paths in AI governance: the U.S. and China focus on technological innovation and industrial leadership, while the EU emphasizes risk regulation. The statement may promote the establishment of multilateral mechanisms like an "International AI Economic Council" to coordinate standards and development.

DigitalEcoNews Insight

Accelerator or Disruptor? AI Economic Governance Must Outpace Technology

The significance of this statement, signed by over 200 top scholars and industry leaders, lies not in the novelty of its content—the impact of AI on employment and the economy has been discussed repeatedly—but in its timing and the lineup of signatories. In 2026, the global commercialization of AI has entered an explosive phase, but policies still remain in an "observe-and-react" mode. The statement transforms consensus into actionable directives. Notably, competitors like OpenAI, Anthropic, and Google have jointly endorsed it, indicating their recognition that a race without a governance framework will lead to a collapse of public trust, ultimately harming the entire industry.From a business model perspective, the statement reveals the core contradiction of the AI economy: the tension between the centralization of value creation and the inclusivity of value distribution. Currently, most of the value generated by AI flows to computing power providers (NVIDIA), basic model companies (OpenAI, etc.), and platforms that own data. Without proactive adjustments to the distribution mechanism, social inequality will intensify, leading to a backlash against technology.

For businesses and investors, this statement means that over the next two years, the regulatory environment will tighten sharply. Compliance costs may rise, but it also creates market opportunities for companies offering "responsible AI" solutions. Policymakers must consider how to maintain innovation vitality while ensuring social inclusiveness. The answer may lie in "data dividend sharing" and "AI-related unemployment protection systems"—these are no longer theoretical discussions but have entered the policy design agenda.

In summary, the economic impact of AI is not a future issue but an ongoing one. The statement reminds us that the next paradigm shift in the digital economy will be defined by governance frameworks rather than purely technological breakthroughs. Those enterprises and countries that first adapt to "policy-driven innovation" will gain an advantage in the next decade.