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From Governance Consensus to Application Deployment: WAIC Releases New Signals of AI Evolution

Keywords: World AI Conference, AI Governance, Qiming Venture Partners, Top 10 AI Outlooks, Foundation Models, Embodied Intelligence, AI Infrastructure

Introduction: AI Enters the Deep Water of Transformation

After eight years of deep cultivation, the World AI Conference (WAIC) has not only become the most influential industry event in the global AI field, but also plays a key role as a "technology bellwether, application showcase, industry accelerator, and governance forum." As AI technology permeates core areas such as information dissemination, healthcare, finance, and even national security at an unprecedented speed, we stand at a crossroads where opportunities and risks coexist. At the "Qiming Venture Partners · Entrepreneurship and Investment Forum" hosted by Qiming Venture Partners yesterday, a major report and ten future outlooks were released, providing a clear "navigation map" for this global transformation. This is not only an objective review of current technological bottlenecks, but also a forward-looking prediction of the industry evolution path in the next 12-24 months.

Part 1: Global Consensus on Building AI Guardrails

At the forum, the report "International Landscape of AI Governance," completed by the Institute for Global Cooperation and Understanding at Peking University with support from Qiming Venture Partners, was officially released. As the first-phase result of the project "Global Consensus on Building AI Guardrails," this report systematically sorts out the complex landscape of current global AI governance, providing solid theoretical support for bridging governance differences among countries and building multilateral dialogue bridges.

The report first categorizes the core risks of current AI into three major types: systemic risk, malicious use risk, and failure risk. In response to these risks, the report proposes three types of governance tools: ethical guardrails, technical guardrails, and regulatory guardrails. By deeply comparing the governance frameworks of representative regions, countries, and international organizations such as China, the United States, the European Union, ASEAN, and the OECD, the report reveals a crucial feature of current global AI governance: "principle convergence, path divergence."

All parties generally agree on basic principles such as safety, transparency, accountability, and human-centricity, but there are significant differences in development priorities, regulatory intensity, and institutional paths. Based on this, the report delineates governance red lines that must be strictly adhered to, covering aspects such as model reliability, fairness assurance, and prevention of high-risk uses; at the same time, it identifies governance yellow lines that need to be improved in areas such as runaway risk prevention and regulatory sandbox construction.

This finding has far-reaching implications. It indicates that global AI governance does not seek a one-size-fits-all single standard, but rather aims to establish a dynamic governance system that can adapt to rapid technological iterations. The future direction lies in promoting rule harmonization and result mutual recognition among countries in specific areas such as risk assessment, content labeling, safety testing, and incident reporting, ultimately forming a collaborative governance pattern that stimulates innovation vitality while ensuring safety and controllability.

Part 2: Qiming Venture Partners Top 10 Outlooks: A "Roadmap" for AI Evolution

As another highlight of the forum, Zhou Zhifeng, Managing Partner of Qiming Venture Partners, released the "2026 Qiming Venture Partners AI Top 10 Outlooks." This is the fourth consecutive year that Qiming Venture Partners has released this series of outlooks, and the content accurately captures the evolutionary path from computing power origin to application deployment.

I. Internalization and Evolution of Foundation Models

Outlooks 1 and 2 indicate that in the next 12-24 months, top-tier models will internalize "external" capabilities such as task planning and tool calling, and multimodal models will evolve toward interactive world modeling. This means that model capabilities will leap from "understanding the world" to "interacting with the world," becoming a key technical path for large-scale deployment of AI in the physical world. This requires models not only to "read" text and images, but also to possess comprehensive capabilities for planning, executing, and sensing the environment.

II. "Data Leap" in Embodied Intelligence

Outlooks 3 and 4 focus on embodied intelligence. The effective data of leading robotics companies will leap from the "tens of thousands of hours" level to the "millions of hours" level, with human first-person perspective data dominating. At the same time, dexterous hands with tactile sensing will accelerate development, forming a high-low combination with two-finger gripper solutions, gradually penetrating complex operation scenarios. This indicates that robots will move from a single mode of "executing preset actions" to a closed loop of "perception-planning-execution," and truly intelligent robots are accelerating their birth.

III. AI Infrastructure: Structural Shortage and System-Level Competition

Outlooks 5 and 6 reveal profound changes at the AI hardware level. As inference demand becomes the center of computing power consumption, storage, advanced process, and packaging capacity will remain tight, and computing power asset reserves will become a core strategy for AI enterprises. In the next two years, AI infrastructure will face a structural shortage. Meanwhile, AI infrastructure will enter a system-level competition phase, covering chips, interconnects, cooling, and power supply, and is expected to give rise to new architecture-based computing chips and supernode large clusters to achieve low-cost, high-efficiency token production. This is not only a competition of technology, but also a contest of large-scale engineering and system integration capabilities.

IV. Commercialization Inflection Point of AI Applications

Outlooks 7 to 10 constitute the "poetry and distance" of application deployment. Security and trustworthiness will upgrade from "optional" to "mandatory," becoming one of the three key variables for large-scale AI deployment in enterprises, alongside product effectiveness and token cost. In terms of business models, AI applications will accelerate away from the traditional internet's freemium logic, shifting to result- and value-based pricing. The core metric for measuring a company will shift from user scale to commercial value created by unit intelligence cost.

On the commercialization path, the efficiency side (Save Time) will break out ahead of the consumption side (Kill Time), focusing on vertical scenarios and high-paying users. Although phenomenal AI consumer applications are still some time away, this pattern is gradually being broken as token costs decline and interaction paradigms innovate. Ultimately, in the next 12-24 months, AI-native organizations will move toward empirical proof, achieving several times the per capita output of traditional organizations. This means that enterprises that can embrace AI in organizational structure, product thinking, and business models will gain enormous dividends of the era.

Conclusion: Act in Consensus, Move Forward in Divergence

From the "principle convergence, path divergence" revealed by the "International Landscape of AI Governance" report to the Top 10 Technology and Application Outlooks released by Qiming Venture Partners, the signals sent by WAIC are very clear: the evolution of AI is shifting from the "force awakening" of technological exploration to the "meticulous cultivation" of global engineering deployment.

Whether it is building consensus on global governance guardrails or precisely betting on computing power, models, robots, and applications, we need to find certainty amid uncertainty. In this process, we must both see the cooperation opportunities brought by "principle convergence" and face the competitive challenges brought by "path divergence." Only by balancing innovation vitality with safety and controllability, and global collaboration with local practice, can we truly steer the giant ship of AI toward a smarter, more efficient, and fairer future. The insights released at this forum are the most powerful compass for all practitioners heading toward this future.