Is This Time Different?
Beyond the Hype: Confronting the Real Challenges of AI and Job Displacement
Whenever we dare suggest that AI and robotics will displace human workers, we are labeled as doomsayers and Luddites. We are swiftly reminded that historical advancements in machinery and automation have not had a detrimental impact on employment, in fact, they have often coincided with job growth. To a large extent, this is true. However, it begs the question. Are these job gains always equitable?
Fifteen years ago, I firmly believed that this time would be no different. I maintained that automation would ultimately boost productivity and lead to a net increase in jobs. I conducted extensive research and wrote numerous articles and papers on the topic, even critiquing alarmist headlines proclaiming that 47% of jobs were at risk of being computerized. One of the authors of that study later admitted, 'I think a lot of the risk to professions has been overhyped.'
Then I encountered Asimo, Honda's advanced humanoid robot, and began to reconsider my position. Perhaps this time was different. In April 2014, I wrote an article highlighting prescient research that garnered over 200,000 views and attracted mainstream media attention. The study, by Sturat Elliot, indicated that automation and robots could potentially replace up to 80% of existing jobs. This prompted me to re-examine the historical narrative surrounding technological advancements. Was the Industrial Revolution truly a period of unmitigated prosperity for all?
Inequality
Digging through the economic history books I was reminded of Engels' Pause, a period in economic history where productivity and capital accumulation increased, but real wages stagnated, leading to a surge in inequality. This phenomenon, first observed by Friedrich Engels during the 19th-century Industrial Revolution, highlights a critical phase where technological progress disproportionately benefited capitalists while leaving laborers behind.
Engels' Pause unfolded during the early stages of the Industrial Revolution, characterized by the rise of mechanized textile production and other groundbreaking technologies. While these innovations undoubtedly boosted productivity and profits, they also exacerbated income inequality, resulting in decades of stagnant real wages. The enduring relevance of Engels' Pause lies in its ability to illustrate how institutional and societal responses can shape the impacts of technological change, a dynamic we face once again amidst the disruptions brought about by AI, robotics, and automation.
What is the Pause?
Friedrich Engels’ seminal work, The Condition of the Working Class in England (1845), provides the foundation for this concept. Engels documented the impact of transformative technologies such as mechanized textile looms, the steam engine, and improved iron production methods, which significantly increased productivity but disproportionately benefited the owners. Real wages stagnated, and the laboring classes endured severe socioeconomic dislocation amidst burgeoning industrial wealth for a few.
‘‘Since the Reform Act of 1832 the most important social issue in England has been the condition of the working classes, who form the vast majority of the English people. . . What is to become of these propertyless millions who own nothing and consume today what they earned yesterday?. . . The English middle classes prefer to ignore the distress of the workers and this is particularly true of the industrialists, who grow rich on the misery of the mass of wage earners.”
~ Friedrich Engels, The Condition of the Working Class in England
Measuring real wages during this period reveals the complexities of assessing living standards, as stagnant wages often masked deteriorating working conditions and the concentration of wealth among industrialists. Scholars such as Robert C. Allen later quantified this “pause,” illustrating how the first half of the 19th century saw significant gains in output per worker (productivity) while real wages lagged far behind.
Engels’ Pause was not perpetual. By the mid-19th century (after 30 to 40 years), wage growth began to align with productivity gains, largely driven by capital accumulation reaching a steady state and labor’s growing bargaining power. This transition offers a critical lesson. Structural adjustments, whether institutional or economic, are painful for the vast majority of workers.
Parallels Today
Fast forward to the 21st century, and the specter of Engels’ Pause re-emerges, this time with AI, robotics and automation as the catalysts. For example, Meta has announced it will part with 5% of it's staff, Mark Zuckerburg made it clear that mediocre employees will be the first out the door and mid level software engineers will likely follow closely behind. What is mid-level? It is those encapsulated in Tyler's book Average is Over. Businesses will cull those exhibiting mediocrity and, as Zuckerberg said, sometime in 2025 AI will do the jobs of mid level software engineers.
These technologies drive transformative changes across industries, with AI-powered tools reshaping logistics, healthcare, and customer service, while automation continues to replace roles in manufacturing and retail. For instance, automated warehouses employ fewer workers, and self-checkout systems have reduced cashier positions. Algorithm-driven tools have automated tasks in fields ranging from legal research to journalism. While some workers transition to roles requiring advanced technical skills, many will find themselves relegated to lower-paying, precarious gig work, underscoring the uneven impacts of these shifts on the labor market.
Moreover, the “pause” is exacerbated by policy inertia and inadequate adaptation of educational systems to prepare the workforce for emerging industries. As Didem Ozkiziltan’s research highlights, governments’ failure to govern this technological transition effectively, through robust labor market policies or universal skills training, risks deepening economic inequality and fostering societal unrest.
Lessons from History
Engels’ Pause underscores a pivotal truth, technology’s economic benefits are neither inherently equitable nor inevitable. Institutions play a decisive role in shaping outcomes, beyond just trade unions and political enfranchisement. Government policies, educational reforms, and robust social safety nets are crucial in mitigating the challenges of technological change. For example, targeted education programs, as in the case of Singapore, can equip workers with skills for emerging industries, while universal healthcare and income protections provide a buffer against job displacement. Additionally, addressing globalization and international competition, which exacerbate wage stagnation and job insecurity, requires coordinated policy efforts to ensure that economic gains are shared equitably. Mark Carney, former Governor of the Bank of England, analysis highlights how aligning institutional frameworks with technological progress can mitigate transitional costs and foster inclusion. Carney warned:
“If it is similar to previous industrial revolutions, it seems likely there will be a period of technological unemployment, dislocation and rising inequality.”
The lessons of Engels’ Pause resonate urgently today. The concentration of economic gains in the hands of capital owners and the stagnation of real wages for the majority threaten not only economic stability but also social cohesion and may lead to civil unrest. The rise of platform economies, gig work, and AI-driven labor displacement intensifies these challenges. For instance, ride-hailing services and food delivery platforms have redefined employment as temporary and task-based, often stripping workers of traditional benefits like healthcare and job security. This follows historical patterns of labor displacement during the Industrial Revolution, where workers faced precarious conditions in newly industrialized settings. However, the gig economy also introduces unique regulatory challenges, such as ensuring fair wages and rights for independent contractors. Yet, as Carl Benedikt Frey argues in his book The Technology Trap, this trajectory is not deterministic.
Policies fostering skills development, targeted regulation to protect workers, redistributive taxation, and social safety nets can bridge the gap between productivity growth and equitable income distribution.
We Must Prepare
Engels’ Pause serves as both a cautionary tale and a guiding framework for navigating the complexities of modern technological upheaval. To address the challenges of today’s technological transformations, policymakers must prioritize investments in education and reskilling, enforce labor protections in emerging industries, and design redistributive measures such as progressive taxation to reduce inequality. Additionally, fostering international cooperation to regulate AI and Robotics and manage globalization’s effects will be critical.
A new industrial revolution has started. This leads to a broader, societal question: How do we prepare people for a world where adaptability and empathy are the only true security? The answer lies partly in education, but not in the narrow sense of standardized curriculums and rote memorization. It also lies in business and governments. We need to foster an environment of intellectual agility, where learning is seen as a continuous, iterative process rather than a finite achievement. And we all need to take Tyler Cowen’s advice and move far beyond mediocrity.
Stay curious
Colin
Image created with Google Imagen
I had a discussion on the same topic of AI taking jobs away with one of the most intelligent people I know and an original thinker earlier today, and here is our conclusion:
1. Implementation is where the rubber meets the road.
There’s always a gap between theoretical capabilities and practical implementation. While AI may seem powerful in controlled environments, the real world is full of edge cases—unpredictable scenarios that systems weren't designed to handle. These edge cases often reveal the limitations of even the most advanced technologies.
For example:
- Autonomous vehicles: Despite their impressive capabilities in ideal conditions, challenges like lousy weather, unpredictable pedestrians, and local traffic laws have slowed their rollout.
- Healthcare AI: Models can diagnose diseases with incredible accuracy, but implementing them in hospitals requires regulatory approval, integration with existing systems, and trust from doctors and patients.
Ultimately, implementation is where the actual test of AI’s impact will occur. It’s not just about what AI can do but whether it can deliver consistent, reliable results in the messy realities of the world.
2. Capable by 2034, but not necessarily sufficient.
The distinction between capability and utility is critical. By 2034, AI will almost certainly be capable of automating many tasks. However, just because it can do something doesn’t mean it will fully replace human roles or meet all needs.
For instance:
- AI might automate 95% of a job, but the remaining 5%—often involving creativity, ethical judgment, or empathy—could still require human involvement.
Broader systemic issues, like regulatory frameworks, public trust, and ethical alignment, might not be ready for AI to fully take over.
Even with advanced capabilities, we’ll likely see a hybrid model where AI handles repetitive or technical tasks while humans oversee and address the gaps it can’t fill.
3. Will it reach everyone? Probably not.
This reflects the reality that technological progress is rarely, if ever, evenly distributed. William Gibson’s famous quote—“The future is already here; it’s just not evenly distributed”—is a perfect fit.
We’ve seen this pattern with countless technologies:
- The internet revolutionized communication, but millions still lack reliable access.
- Advanced healthcare tools exist but often remain out of reach for low-income or rural populations.
Similarly, AI adoption will vary widely. Wealthier nations, industries, and companies will likely benefit first, while less developed regions or sectors may lag. Cultural, economic, and political factors will also shape how widely AI spreads. For many, the question may not be whether AI is capable but whether they’ll have access to it at all.
4. Human behavior and incentives drive innovation.
Humans are remarkably adaptable, and when faced with constraints, they often innovate unexpectedly. Incentives play a huge role in shaping outcomes. As Charlie Munger said, “Show me the incentive, and I’ll show you the outcome.”
AI will likely follow a similar pattern. While it will undoubtedly disrupt many roles, it will also create opportunities—especially if the right incentives encourage humans to collaborate with AI instead of competing against it.
I have some more thoughts. I will write them later.
Interesting post. I will have to think on how to be less mediocre, how to transcend being merely average.
With that said, I question whether any educational system could be designed that equips most (or even a large minority) of people to adapt to AI. The last few decades have seen repeated refrains for better education as a solution to labour market dislocation. Do you remember when newspapers were talking about teaching miners or truckers to code? At least for older workers, I’ve seen very little in the way of encouraging evidence that would suggest reskilling is possible. And even for younger workers the prospects seem somewhat gloomy. Sure, if one is within say the top 5-10% of intellectual ability and conscientiousness then education might work (though I think even this might is too generous a threshold). But for everybody else? I don’t know. I worry for myself in this regard because I doubt I’d cross the minimum threshold.