AI Drives a Rethink of Work in the Fourth Industrial Revolution
As artificial intelligence reshapes labour markets, a leading global prize redefines computing as a foundational science
Artificial intelligence (AI) is automating a growing share of routine work and changing how human effort is allocated across the economy. Rather than eliminating work altogether, the current wave of AI should be understood as an industrial revolution that reallocates human effort and reshapes how value is created and which skills societies reward.
“Every industrial revolution replaces some jobs, but it also creates new ones,” Tony Chan, former president of King Abdullah University of Science and Technology, told TechJournal.uk in an interview in Hong Kong.
He added that the question is not whether people will have things to do, but how people will adapt and find new things to spend their time on.”
That view contrasts with arguments from some technology leaders, including Elon Musk, who has repeatedly warned that advances in AI and robotics could displace human labour at such scale that traditional employment becomes optional.
Musk has argued that a form of “universal basic income” (UBI) or “universal high income” may become necessary to ensure people can meet basic needs in a future of extreme productivity and abundance driven by machines.
“I hear the argument that machines will take over, so we should have universal basic income or something like that. But I think that’s a dream. I don’t think we’re there yet,” Chan said.
He rejected the idea that technological change automatically leaves society worse off. Drawing on past industrial revolutions, he said productivity gains have historically opened up new kinds of work, even if those opportunities are not obvious at the outset. In that sense, he said, AI should be seen as reallocating human effort rather than simply replacing it.
“You can do more things. Maybe you will have more time for recreation and creativity,” he said.
He acknowledged that the adjustment would be uneven in the short term, particularly for professions once assumed to be insulated from automation, including programmers and computer science graduates — an outcome that would have sounded implausible only a few years ago.
The scale of that transition has led many observers to frame AI as a fourth industrial revolution, following three earlier shifts that fundamentally changed how societies organise work and production.
The first industrial revolution was mechanisation in the late 18th and early 19th centuries, when water- and steam-powered machines displaced large amounts of manual labour. The second followed from the late 19th century into the early 20th century with electrification and mass production, reshaping factories, cities, and supply chains. The third gathered pace from the late 20th century, driven by computers and the internet, transforming offices, services, and the flow of information across the global economy.
Why computer science matters
That broader view of AI as a societal transformation helps explain why leading scientific institutions are re‑examining the foundations of computing itself.
Against that backdrop, the Shaw Prize in January added Computer Science as its fourth award category, placing the discipline alongside Astronomy, Life Science and Medicine, and Mathematical Sciences. The move expands a framework that for decades recognised only three core scientific fields.
By elevating computer science in this way, the Shaw Prize is signalling that computation should no longer be seen merely as an enabling technology or industrial tool. Instead, it is being recognised as a foundational science, defined by deep theoretical questions, long intellectual traditions, and standards of breakthrough that often take years — or decades — to become visible.
Chan said computer science is not just about building products or writing code, but about fundamental questions of computation, learning, systems, and abstraction, many of which may take years or even decades to show their full impact. Chan is the Chair of the Planning Committee and a member of the Selection Committee for The Shaw Prize in Computer Science.
The addition of the new category comes at a moment when AI has brought unprecedented public attention to computing, while also blurring the boundary between scientific advance and commercial deployment.
“I would imagine the selection committee, or any prize committee, always asks this question: if we award this work, will people in the community accept that it represents a fundamental advance that the work has made in the field? That is the key question,” he said.
Judgement over rules
Chan said that defining rigid criteria for excellence would risk constraining a fast‑moving discipline whose most important contributions are often unexpected.
“That’s why you need to convene a committee. If you write down all the criteria, you may find that two years from now those criteria no longer apply. You don’t know what the future is, and you don’t want to limit yourself or limit the ambition of the field,” he said.
Instead, the Shaw Prize relies on the collective judgement of experienced scientists who can assess work in context and over time.
“The credibility and experience of the committee is very important. When you take on the responsibility of something as momentous as the Shaw Prize, you need people who have decades of experience and judgement,” Chan said.
That emphasis on judgement reflects the nature of computer science itself, a comparatively young field in which dominant approaches can change rapidly and breakthroughs often emerge from unexpected directions.
Independence from geopolitics
Chan also stressed that the new prize category is designed to remain insulated from geopolitical rivalry, even as AI becomes an increasingly strategic domain for governments.
“The Shaw Prize should be independent of politics. Especially in this day and age, when people talk about geopolitical rivalry, we should show by example that prizes are given for what people have done, not because of nationality or all this other stuff,” he said.
Scientific merit, he said, cannot be meaningfully assessed through national quotas or regional balance.
“You don’t look at a quota. You don’t ask how many are from the US or how many are from China, because the quality will speak for itself,” he said.
That principle mirrors how science operates in practice, with researchers across borders scrutinising, replicating, and building on each other’s work.
Beyond today’s AI models
While AI dominates headlines, Chan cautioned against equating computer science with large language models (LLMs), massive datasets, and GPU‑intensive training alone.
“The current approach using big data, neural networks and GPU chips is only one way,” he said. “As neuroscientists don’t know how human brains work, someone may look at the brain and say: maybe there’s a new way of doing it. And if that proves to work, I think it’s worth it.”
Historically, Chan said, constraints have often forced deeper innovation. Limited resources, such as a lack of high-end GPUs, can push researchers to rethink assumptions and develop more elegant or efficient solutions that reshape entire fields.
For Chan, the significance of adding computer science to the Shaw Prize extends beyond recognising past achievements. It also sends a signal about the kind of scientific ambition society should value as AI reshapes economies and labour markets.
As machines take on more routine tasks, he said, future researchers will be needed to ask harder questions about limits, consequences, and new directions for computation.
He said the new category is intended to inspire young scientists to pursue foundational work that may not deliver immediate products, but could shape science and society for decades to come.