The Same Frame, Built Bigger: How AI Redefines Work Instead of Ending It

By Swara Mishra —

 In 1812, a workman named George Mellor led his fellow croppers into an attack on a mill in Yorkshire. Together, they smashed cropping frames that threatened their trade. Mellor wasn’t simply afraid of machines; he watched one frame take the work of several skilled men and understood what that meant about his livelihood. Weeks later, he, along with two others, ambushed and killed the mill owner who had installed them. Mellor was hanged for the crime in January 1813. Two centuries later, the shape of the machine has changed, but the fear it produces in workers still remains similar to what Mellor felt. These similarities can be applied to several situations; for instance, when the steam engine transformed manufacturing, and the internet transformed communication, society was conflicted between fearing the changes or appreciating the potential developments. These feelings are now amplified since AI won't just transform one industry, but almost all of them. Similar to prior industrializations, AI will also fundamentally change labor markets. Artificial Intelligence won’t eliminate work, but redefine it, creating new opportunities while forcing society to reconsider what work actually means.

Historically, every major technological revolution has evoked the same cycle. People fear job losses, but instead, the economy restructures and grows. During the Industrial Revolution, farmers lost work but gained factory jobs. The computer revolution automated clerical work but created the entire digital economy. Austrian economist Joseph Schumpeter calls this pattern “creative destruction.” Innovation threatens old industries while generating new ones to replace them. The same cycle is happening now as McKinsey Global Institute predicts generative AI will add approximately $2.6 to $4.4 trillion to the global economy annually. That level of economic expansion doesn’t happen without generating significant employment opportunities.

The integration of AI into a variety of industries has created new roles that didn’t exist a decade ago. The World Economic Forum predicts 170 million jobs created by 2030, 92 million displaced, and a net gain of 78 million. However, the biggest impact isn’t creating new jobs but changing existing ones. Doctors, for instance, use AI to assist with scans and diagnoses, but assessing patients and final decisions still require a human. Teachers also use AI to generate lesson plans, but are still needed for mentorship and human connection, which is something AI could never replace. In reference to the labor market, 52% of demand shifts come from new roles being created, and 40% come from existing roles taking on different responsibilities. An example of a carpenter helps visualize this; AI doesn't replace the carpenter, but the toolbox instead. AI changes processes rather than replacing humans. Studies analyzing millions of job postings found that AI adoption changes skill requirements of existing roles, with firms dropping some tasks entirely while introducing new ones.

However, discussing AI purely from a historical or technological standpoint isn’t enough to understand the impacts. Common philosophical experiments can challenge the fundamental understanding of the limits of AI. Take, for example, the Chinese room thought experiment conducted by philosopher John Searle. Imagine a man locked in a room who receives slips of paper with Chinese symbols. The man in the room has a rulebook that tells him exactly what symbols to write back to respond correctly, and anyone outside the room would believe the man fully understands Chinese. However, the man doesn't have any comprehension of the language itself. This experiment represents how performing any task correctly isn’t the same thing as understanding it.

Another example is Mary's room, which is a thought experiment conducted by philosopher Frank Jackson. Mary has only lived in a black-and-white room. She has studied color and obtained all possible knowledge about it. One day, she leaves the room and sees red for the first time. Jackson claimed that she learned something new because facts can’t substitute actual experience. Nagel's bat also represents this. Even if someone studies all the biological or scientific facts about bats, they would never know what it is like to be one. He argues that being a conscious creature provides certain knowledge that no amount of research can reveal to the outside.

How does any of this relate to AI’s impact on work? All three of these thought experiments reveal that processing information can never be the same as understanding it. No matter how advanced AI gets, it will always operate like the Chinese room. It can produce correct outputs, but lacks comprehension. Any task that requires genuine understanding or judgement remains human, and experience will continue to be invaluable. AI doesn't possess knowledge or genuine understanding, only an illusion of it, which is why it can't fully replace humans.
Despite the fact that AI can’t directly replace demand for human labor in terms of employment, the transition isn’t going to be painless. Jobs like data entry, bookkeeping, and administrative support are most at risk because the work is repetitive and follows set rules. The IMF puts the global exposure at around 40%, and closer to 60% in countries like the United States. Furthermore, the benefits of AI won’t be distributed equally and would initially only be available to established companies or highly skilled workers, which worsens inequality. Adapting is necessary, and the real challenge is whether institutions keep up with how fast skill requirements are changing.

Two centuries ago, Mellor smashed the frames and killed the man who owned them because he only saw what the machine would take. He couldn't see what it would make possible two centuries later, work he never could have imagined. AI is the same frame, built bigger. In order to determine the impact of AI on opportunities or the meaning of work itself, it’s important to consider what work means in context of other activities. Philosopher Hannah Arendt compared the three types of human activity in her book The Human Condition. Labor includes repetitive cyclical tasks such as cooking, cleaning, and eating. Work is creating durable things that last beyond human lifespans. Action is participating publicly, like speaking or forming connections, and is necessary for most jobs. Based on this definition, AI can automate parts of labor and work, but never automate action. This is because action requires human judgement and experience, which AI can't possess. Similar to Nagel's bat, there’s something about being human that’s a subjective experience, which no AI could replicate. That "something" is where most meaningful work comes from. Even if AI takes over parts of labor and work, humans aren’t replaced, but are pushed towards action, which, according to Arendt, is the highest type of contribution. AI doesn't threaten work, but makes society reconsider the purpose of work itself. Overall, the future of work isn’t defined by what AI can do to human work opportunities, but by what AI can do for humans and how they choose to use it.

Works Cited

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Chui, Michael, et al. "The Economic Potential of Generative AI: The Next Productivity Frontier." McKinsey Global Institute, June 2023. www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier.

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