WAIC Dialogue with Turing Award Winner: How Ordinary People Can Preserve Irreplaceability

On July 19, 2026, the third day of WAIC 2026, a closed-door dialogue titled "Restructuring Social Relations in the AI Era: The Triple Fracture of Generations, Labor, and Industry" drew an unexpectedly large crowd at the Shanghai World Expo Center. The audience was not limited to AI professionals — factory workers, teachers, doctors, and small business owners were also present, united by a single question: as AI capabilities grow exponentially, what can "non-technical" people still do?

The panel was extraordinary: Yoshua Bengio, Turing Award laureate and professor at the University of Montreal; Tan Liang, Assistant Dean of HKU Business School; Xiao Mafeng, founder and CEO of TTC; and Yang Bolin, founder of SigmaZ AI Product Lab. The moderator was Zhou Yufang, CSO of Guancha.cn. Over two and a half hours, the discussion moved from the technological frontier to the dinner table of every working person, crystallizing into a central thesis: in the AI era, irreplaceability is not a gift — it is a capability that can be actively constructed.

Bengio's Warning: Don't Race Against AI

In his opening keynote at WAIC 2026, Bengio had already delivered a sobering warning: AI safety governance is falling dangerously behind the doubling of model capabilities. But in the evening dialogue, he used language far more accessible to ordinary people.

"If you try to compete with AI in pure computation, memorization, or pattern matching, you are guaranteed to lose," Bengio stated bluntly. "But I have observed an interesting phenomenon: those who fear AI replacement the most are often those who understand AI the least. Those who truly grasp the boundaries of AI capabilities are, paradoxically, more confident about where human value lies."

He shared a concrete case from his Montreal lab: researchers attempted to use state-of-the-art large models to design novel neural network architectures. The AI excelled at optimizing known solutions, but when it came to proposing entirely new, counterintuitive architectural ideas, it could not replace the "leap thinking" of human researchers. "AI excels at finding the best answer within historical data. Humans excel at asking questions no one has ever asked. The value of a question far exceeds the value of an answer."

From "Tool Users" to "Problem Definers"

Tan Liang approached the topic from an organizational management perspective, offering an observation that resonated deeply with the audience: AI is accelerating a new stratification within organizations — not between technical and non-technical roles, but between "problem definers" and "task executors."

"In the past, a product manager's job was to write PRDs, draw prototypes, and manage schedules — all of which are being rapidly taken over by AI," Tan said. "But defining why we should build this product, what users truly need, and whether a feature raises ethical concerns — these are things AI is not yet capable of doing independently. The ability to define problems is becoming the scarcest form of human capital."

Yang Bolin, as a Gen-Z entrepreneur, added a generational perspective: "Our generation's fear of AI is far lower than the previous generation's. Not because we are smarter, but because we grew up in an AI-native environment and naturally treat AI as a collaborative partner rather than a competitor. That mindset itself is a competitive advantage."

The Trinity Code: Creativity, Empathy, Judgment

The dialogue gradually converged on a practical question: if ordinary people want to actively build their irreplaceability, where should they start? The panelists reached a consensus on a "trinity code" — Creativity, Empathy, and Judgment.

Xiao Mafeng shared data insights from the TTC platform: "Over the past year, the most sought-after job title on our platform has shifted from 'AI Trainer' to 'AI Collaboration Designer.' Companies no longer just need people who can label data; they need people who can collaborate with AI and translate AI outputs into products and services that humans can understand. What this requires, fundamentally, is empathy and judgment."

Bengio endorsed this framework and elaborated: "Creativity is not the exclusive domain of artists. Every field requires creativity — how a nurse delivers bad news with warmth, how a teacher designs a classroom activity that genuinely engages students, how a social worker finds new solutions with limited resources. These seemingly 'ordinary' jobs are precisely the hardest for AI to replace. Because what they require is not data-driven optimization, but an understanding of the human condition."

A Real Case: From "Quality Inspector" to "Process Designer"

The most moving moment of the dialogue came from a frontline story. Tan Liang recounted his visit to an electronics manufacturer in Shenzhen: after the company introduced an AI-powered visual inspection system, a team of 200 quality inspectors faced transformation. Instead of simply laying people off, the company retrained 30 of them as "AI Quality Inspection Process Designers." They no longer stared at screens looking for product defects; instead, they designed inspection strategies, handled edge cases that AI could not judge, and continuously optimized inspection standards.

"Six months later, the average salary of these 30 people increased by 40 percent, because their work had shifted from repetitive labor to creative labor," Tan said. "This case tells us that irreplaceability is not static — it is dynamically constructed through human-AI collaboration."

Conclusion: The Weapon Is Not in the Toolbox, But in the Question

As the two-and-a-half-hour dialogue drew to a close, Zhou Yufang summarized: "AI has made answers cheap, but it has made the ability to ask good questions unprecedentedly expensive."

For every ordinary person worried about being replaced by AI, the message from this dialogue is both simple and powerful: don't try to become a better machine — strive to become a more complete human being. Your irreplaceability lies not in how many skills you possess, but in what new questions you can ask, what human needs you can understand, and what judgments you can make amid complexity and uncertainty.

This may be WAIC 2026's most precious gift — not from an algorithm or a model, but from those standing at the frontier of technology reaffirming the value of being human.