Ren Zhengfei’s remarks to ICPC participants underscore his conviction that the global shift toward artificial intelligence is irreversible, a judgment grounded in three mutually reinforcing foundations: the underlying logic of technological evolution, the extensive validation of AI through real-world applications, and the lessons drawn from profound historical analogies. This perspective offers a clear framework for understanding why AI’s advance is not merely a trend but a structural inevitability—one that demands careful analysis as societies navigate the opportunities and challenges ahead.
AI’s Tipping Point: How Chips and Compute Power Drive a New Era
The rapid evolution of foundational technologies—most notably high-performance chips and scalable computing power—has laid the groundwork for the emergence of the intelligent era. Much like the invention of trains, textile machinery, and ships signaled a historic turning point during Britain’s Industrial Revolution, today’s pivotal transformation is being driven by the application of artificial intelligence. What appears as an explosion of AI capability is, in reality, the result of sustained and synergistic progress across the entire underlying technology chain.
Continuous breakthroughs in GPUs, NPUs, and dedicated AI chips, coupled with the rise of distributed and cloud computing, have made large-scale access to computing power not only viable but increasingly efficient. At the same time, advancements in model-training frameworks and data infrastructure have accelerated the development of sophisticated AI systems. These elements reinforce one another, forming a positive feedback loop: stronger computing power enables larger models, which broaden applications, attract further investment, and in turn catalyze faster technological progress.
As these hardware and infrastructure systems mature, artificial intelligence has moved decisively from theory into large-scale, real-world deployment. Enhanced computing power now supports the real-time execution of complex algorithms, while the miniaturization and cost reduction of chips allow AI to permeate a wide range of devices and scenarios. This technological foundation has crossed a point of no return, establishing an irreversible trajectory toward deeper and broader integration of AI.
Just as the steam engine, steelmaking, and precision machining created the conditions for the Industrial Revolution—after which the adoption of trains and textile machinery became unstoppable—the maturation of chips and computing power has broken today’s technological tipping point. Artificial intelligence, propelled by these core capabilities, stands as the next major technological revolution after the Industrial and Information Revolutions, possessing a self-reinforcing momentum that will continue to accelerate its diffusion and impact.
AI’s Real-World Deployment Is Reshaping Key Industries, Driven by Its 98% Implementation Value
Artificial intelligence is no longer an emerging concept—it has already entered large-scale industrial deployment and is profoundly transforming key sectors of the global economy. Across countries at different stages of development, from Germany to Indonesia and from Latin America to Africa, AI is demonstrating strong universality and adaptability. Developed economies are integrating AI into advanced manufacturing and Industry 4.0 frameworks, while developing nations—guided by the principle that “98% of AI’s value lies in industrial implementation rather than invention”—are applying it directly to ports, logistics, agriculture, and public services. This divergence in approach reflects AI’s inclusiveness: large and wealthy nations focus on foundational research, whereas smaller and poorer nations prioritize high-impact applications that deliver immediate economic value.
Nowhere is this transformation more visible than in heavy industry and infrastructure. In ports such as Tianjin, operations once dependent on manual labor have been reorganized into fully automated systems powered by AI vision, high-precision positioning, digital twins, and unmanned electric trucks. A port covering more than 100 square kilometers now operates with minimal human presence. AI identifies containers, optimizes crane movements, schedules driverless vehicles, and predicts congestion through real-time simulation—raising operational efficiency by over 20%, reducing labor needs by more than 60%, and enabling round-the-clock safe operation. This shift marks the transition from “experience-driven” workflows to “data- and algorithm-driven” infrastructure, with the Tianjin model already spreading across China and to other regions along the Belt and Road.
In steelmaking, AI has redefined process control in an industry historically reliant on craftsmanship and physical risk. Workers no longer approach 1,600°C furnaces to manually extract samples. Instead, high-temperature vision systems and spectrometers analyze molten steel remotely, producing laboratory-grade assessments within seconds. AR smart glasses guide engineers with real-time data overlays, capturing decades of tacit expert knowledge and converting it into standardized digital instructions. AI-driven closed-loop control systems automatically adjust oxygen blowing, alloy ratios, and temperature, while predictive maintenance algorithms identify furnace wear days in advance. The result is safer operation, higher consistency, shorter smelting cycles, and meaningful reductions in energy consumption—demonstrating AI’s ability not only to enhance precision but to reshape entire industrial logics.
The energy-extraction sector presents another vivid example. Coal mining, long associated with high risk and low visibility, is undergoing a structural upgrade through AI, 5G, and industrial IoT. Traditional wired gas-sensing networks—prone to breakage and blind spots—are being replaced by wireless, battery-powered sensors linked through hybrid 5G/Wi-Fi 6 networks. Edge computing nodes analyze environmental data underground, triggering alarms and ventilation adjustments within seconds. AI models learn precursor patterns of gas outbursts and issue early warnings before abnormal conditions emerge. Digital twins allow engineers to inspect deep shafts virtually, while remote-control stations enable operators to manage mining equipment from the surface. These innovations redefine coal mining from a high-risk occupation into a data-driven, technology-intensive profession.
AI’s real-world impact extends beyond heavy industry into transportation, healthcare, and software engineering. Unmanned terminals in Tianjin and Peru’s Chancay Port, 450 km/h high-speed rail dispatch systems, AI-assisted pathology analysis, mobile-phone-based remote ophthalmic screening, and 5G-enabled ultrasound diagnostics in remote Tibetan regions illustrate how intelligent systems are already embedded in critical national and social infrastructure. Meanwhile, software development productivity has risen by roughly 30% due to AI coding assistants, a figure projected to reach 60–70% as models mature. These examples show that AI is not a theoretical or distant promise but a practical engine of measurable productivity gains.
Even the evolution of communications technology demonstrates how AI accelerates the conversion of theory into standardized, globally adopted technologies. Huawei’s contributions to 5G—such as engineering polar codes into workable industrial standards—required vast computational resources and AI-assisted verification. This illustrates a broader shift: the transformation of advanced mathematical theory into scalable global infrastructure increasingly depends on intelligent computing, simulation, and algorithmic optimization.
Collectively, these cases confirm that AI has already entered the core processes of high-value, high-complexity industries. Its economic impact is concrete, quantifiable, and irreversible. Once an industry proves that AI delivers orders-of-magnitude improvements in efficiency, cost, and safety, competitive pressure compels all others to adopt the same technologies. In this sense, AI has transitioned from an optional enhancement to an industrial necessity. The trajectory is unmistakable: AI’s global adoption is not a future projection but a present-day reality—and the momentum is unstoppable.
The Irreversible Logic of Major Tech Revolutions from Steam to AI
Technological revolutions have a historical logic of their own: once they emerge, they reshape society in ways that are ultimately irreversible. Ren Zhengfei often illustrates this by invoking the advent of trains, textile machinery, and steam-powered ships in Britain—innovations that fundamentally altered the trajectory of human development. These breakthroughs did not simply introduce new tools; they created turning points that reorganized economies, redefined labor, and expanded the boundaries of what societies could achieve.
Yet each of these technologies faced fierce resistance at the moment of their arrival. Early railways provoked outrage from coachmen and carriage operators, prompting Parliament to enact the restrictive Red Flag Act in an attempt to slow their spread. Mechanical looms encountered even more visceral opposition from the Luddites, skilled workers who destroyed machines not out of hostility toward progress, but out of fear for their livelihoods in an unfamiliar economic landscape. Despite such pushback, the immense gains in productivity, efficiency, and connectivity quickly outpaced attempts to halt or contain them. Over time, society reorganized itself around these innovations, generating new industries, new forms of work, and unprecedented economic growth.
Artificial intelligence stands today at a similar inflection point. It is not the strategic asset of a single company or nation, but a new class of general-purpose technology whose influence will permeate every sector, much like electricity or the internet. Although AI will continue to provoke skepticism, regulation, and anxiety—echoing the early stages of past technological upheavals—its capacity to multiply human capability and to open entirely new domains of value makes its long-term advance all but unavoidable.
We are, in effect, witnessing a new industrial revolution. The earlier machines transformed the physical world through engines of motion; AI is transforming the cognitive world through engines of thought. And as history has shown time and again, once such a revolution begins, it does not turn back.
Inevitable Technology and Governance Challenges in the Age of AI
Ren Zhengfei highlights a crucial distinction often blurred in public debate: technological progress and social adjustment must be viewed separately. Technological evolution, he argues, follows objective scientific and engineering laws; it does not depend on the will of individuals or nations. Artificial intelligence exemplifies this dynamic. Although it reduces the demand for certain types of labor, it simultaneously creates greater total wealth and raises overall social efficiency. This intrinsic, unstoppable momentum—not any particular policy choice—is what drives AI forward.
Ren stresses that the redistribution of benefits generated by technology is a governance problem, not an enterprise problem. When technological change displaces labor, social mechanisms—such as welfare systems or public services—can ensure basic security for those affected. In parallel, individuals engaged in high-value work experience income growth, further lifting productivity. He draws a comparison to Australia’s high-welfare model, where abundant resources, inclusive social policies, and a strong safety net allow people to enjoy a high quality of life. For China, he argues, long-term happiness depends on improving education quality and enabling citizens to adapt to an evolving economic landscape.
From a broader global perspective, Ren acknowledges that rapid AI development brings both benefits and pressure. Governments prioritize employment stability, while enterprises pursue innovation and competitiveness; this inherent tension is a social issue rather than a technological one. History shows that fears of new technologies—such as early British resistance to textile machinery—cannot halt progress. Instead, industries evolve, productivity climbs, and wealth grows. Thus, the role of technologists is to advance technology and create value, while the responsibility for social balance, employment policy, and equitable distribution rests with governments.
Ren’s deeper message is clear: although societies may shape the pace and manner of AI development, they cannot reverse or cancel its trajectory. The path of technological progress is inevitable; the task of ensuring its benefits are broadly shared is a matter of capable governance.
Final Thoughts
Ren Zhengfei argued that the rise of artificial intelligence is irreversible, noting that technological capabilities have reached a critical threshold and that widespread industrial application already confirms its practical momentum. He emphasized that throughout history, once a general-purpose technology reaches maturity, its diffusion becomes unstoppable. In today’s global competitive landscape, he added, sustainable progress depends on open collaboration, drawing lessons from the United States’ tradition of technological openness rather than pursuing insular approaches. As he concluded in the symposium, open innovation is the prevailing global trend—and AI now stands at its most powerful crest.
References
- Minutes of the meeting between Ren Zhengfei and the Chairman, coaches and award-winning athletes of the ICPC, November 2024
- Minutes of the meeting between Ren Zhengfei and the Chairman, coaches and award-winning athletes of the ICPC, November 2025