Solution Oriented Analysis: Labor Market Restructuring and AI

Part of a four part series to combat fear mongering and provide a measured antidote to existential dread by analyzing historical precedent, introducing a range of scholarly sources, and looking at potential social, political and economic solutions to the challenges we face in a shifting world.

I do not believe in fear mongering. The public, specifically younger generations, have suffered to the point of paralysis due to politicians and media presenting them doomsday scenario after doomsday scenario. Psychological phenomena like climate anxiety, have led millennials and gen-z to make major life decisions with nihilistic attitudes and a sense of disenfranchisement.  Fears surrounding AI have been irresponsibly stoked without offering any policy solutions and as such, violence and unrest have broken out among workers.  This is an obvious and historically founded response which is due directly to the actions of media and politicians who at best, carelessly and at worst, maliciously have fearmongered the public into hatred of a new existential threat.  I will never introduce a problem without empowering my audience with potential policy solutions in order to make systems level changes towards improvement.  Only focusing on the AI positive bubble, dismissing fears, or telling people to essentially save themselves and let their fellow man suffer is not the answer.  Understanding historical parallels, listening to field-specific scholars, and introduction to policy initiatives can go a long way towards assuaging the paralysing fear being peddled through the media ecosystem. 

I hear a lot of pushback against AI based on the fears surrounding labor displacement and workforce restructuring.  This is a valid concern if left unchecked and I have publicly taken an approach in favor of taxation reform and advocated for the transition from taxing income to taxing value generation. This is an approach informed by exposure to automation due to my work in physical AI and my early support of Andrew Yang’s 2020 Political Campaign where he introduced his Freedom Dividend concept to the US, but as we know, software AI will come for knowledge work first. In this work I’ll focus on corporate software AI use and draw historical parallels to the British Industrial Revolution, introduce scholarly perspectives from social, political and economic perspectives on the challenges we face from contemporary analysts John Danaher, Daniel Susskind, and Aaron Benanav, and introduce actionable political and economic solutions.  Armed with these tools, you as the reader will see why we haven’t yet seen the positive impact we were promised and the path by which we might get there. 

First, as always, we can look back to move forward. The British Industrial Revolution was characterized by the transition from hand production to machines, the rise of the factories, and the use of steam power and coal. In the early years from 1790-1840, productivity and GDP surged while real wages for the working class barely budged. Author Friedrich Engels documented the grim living and working conditions of this period in his 1845 work, The Condition of the Working Class in England.  At the time, productivity and wages were completely decoupled and the working class saw owners of capital like factories and machines reaping the benefits of heightened productivity, rather than the laborers themselves.  This phase was later coined as "Engels' Pause" by economic historian Robert C. Allen.  The technology was working but the social and political systems had not caught up.  The anxiety and unrest in the working class show us that we are in a "Digital Engels' Pause." AI has permeated every sector and we see its productivity everywhere from our emails to our creativity and yet global GDP growth remains relatively stagnant and the middle class feels under more pressure than ever.  Part of this is down to the “productivity illusion” which refers to the increase in metrics like emails and data but doesn’t translate to profit.  Another part is that shareholder pressure led to AI layoffs which had to be backtracked because non human-in-the-loop processes weren’t working. The only way out of the Engles’ Pause is to make active policy choices.  The initial Engels' Pause didn't end because the machines got more efficient, it ended because of corrective social and political decisions. The weekend, the 40-hour work week, and child labor laws were political choices made to ensure the technology served the people and that the labor market was regulated in order to embrace the new technology while sharing the benefits of increased productivity. Now is the time to ascend from the mindset of using AI to cut costs and to start using it to build a new social contract with new labor laws. The exploration of works by three scholars on modern labor politics can create a foundational understanding of the social, economic and political challenges we face in doing so. 

AI and labor have a fraught relationship dynamic. Daniel Susskind tackles this dynamic in his 2020 work, A World Without Work. He argues that AI doesn't replace entire jobs so much as it replaces tasks. He argues that economically, this is a subtle but dangerous differentiation. For example, if a lawyer spends the majority of their time reviewing documents and AI takes that task over, you don't necessarily need fewer lawyers, you need a totally different way to pay them. Susskind’s main concern is economic distribution.  He asks, if the work disappears task-by-task, how do we make sure the wealth doesn't just aggregate at the top? Aaron Benanav wrote differently in his work from the same year, Automation and the Future of Work. He believes that the AI revolution and the ensuing layoffs are actually a myth used to cover up a stagnating global economy. We aren't losing jobs because AI is so much better than workers, we are losing them because industrial growth has slowed to a crawl. In his book he asserts that the gig economy isn't a product of technological innovation but simply what happens when there aren’t enough real jobs for the current labor market. Lastly, we see the social and psychological angle through John Danaher’s 2019 work, Automation and Utopia.  In it, he argues that we are facing what he terms a "Great Reversal." For centuries, work was the source of our identity and social status. As AI takes over increasingly complex tasks, Danaher cautions that we may face a "meaning gap." He posits the question, if we aren't working to survive, what are we doing? He suggests we move toward a "Virtual Utopia," where we find fulfillment in interconnectedness and hobbies rather than a career. Now, in order to focus on the social or psychological aspects of responding to this moment in history and move towards a virtual utopia, as suggested by Danaher, we need to deal with the economic and political aspects first. There have been a number of trends that can give us clues as to how that might pan out.

Reading headlines in 2024, you saw a smattering of Fortune 500 companies laying off workers and leaning into AI first strategies. By 2026, those boardrooms have their tails between their legs and are hiring back workers away from public view.  The initial stock price bump that rewarded decreasing payroll and "modernizing" the labor of the company was really only benefitting shareholders for a brief moment in time.  The operational collapses, cybersecurity disasters and public backlash were enormous compared to the payroll costs saved. The best analysis I’ve seen on this was from the AI Daily Brief.  They dissected the differentiation between productivity and profitability. Productivity is a trap, profitability is the goal. This is backed by a recent PwC 2026 AI Business Survey, which shows that the top firms aren't using AI to replace workers. They are using it for “High-Margin Optimization,” or simply put, tasks like that were simply impossible for humans alone like supply chain predictive modeling or the creation of personalized medicine.  To fund a better society, we don't need more emails or lines of code, we don’t need higher productivity, we need the massive profit margins that come from AI actually solving hard problems. Only high profits create the tax base necessary for the social and political engineering of solutions that can lift us out of the Engels’ Pause. 

So how might that happen? We have a few prospective avenues for emerging from the Engels’ Pause which ideally, we may have some combination of in order to have the best outcome.  We have an economic path, with proposed financial reform through taxation reform which completely eschews labor in favor of land and capital, we have the political path, with a focus on reforming labor standards where we would see improved working conditions, and we have the social path, which would embrace what makes us human and focus on human empathy and the care economy. For the economic path, we actually have suggestions directly from Sam Altman, the CEO of OpenAI, who recommends shifting the tax burden in order to fund our future.  Altman proposes rebalancing the tax base away from human labor and toward capital-based revenues. This includes higher taxes on corporate income, capital gains for high earners, and a new category of taxes on automated labor, colloquially the robot tax. He proposes a national fund that captures growth from AI companies and firms adopting AI and performing a redistribution to citizens. This would answer the question, if robots take our jobs, who will buy the products they’re making? This type of payment restructuring echoes the work of Daniel Susskind who called for a new method of payment to ensure capital doesn’t aggregate at the top.  If we take Aaron Benanav’s thoughts on a stagnating global economy and not having enough labor to justify a 40-hour week for the entire population, we get to our second approach which is political and centers around labor standards reform. Perhaps not having enough labor for a 40 hour work week isn’t a negative, but a guide. A legislated move towards a 32 hour or four day work week could redistribute remaining tasks amongst more people and avoid a “winner take all” labor market of layoffs. This would give people the "leisure capital" that our third scholar, John Danaher, argues is necessary to find meaning through play and community rather than just survival. And this brings us to our final path, the social approach. There will always be jobs which humans are better suited to perform like those that require high-stakes empathy and physical care.  While AI handles logic and logistics work, we can increasingly pay humans higher wages to handle the care economy, including roles like elderly care, environmental restoration, and mentorship. We have a number of avenues to explore in order to avoid the AI apocalypse being so flagrantly and irresponsibly pitched to the public as their latest existential threat. First, we need to shift the way in which we view a number of foundational aspects of our society in order to travel these paths together, like meaning, identity and more.  We can either be paralyzed by fear and stay in our Engels’ pause, or collectively move forward into a new era.

Technology always outpaces laws and social understanding. The efficiency layoffs and quiet rehiring initial shockwave was a perfect example of a corporate world trying to run 21st-century AI on 19th-century economic models.  The Engels' Pause only ended when we decided that the impact of the industrial revolution needed to be distributed socially, economically and politically across all of society. Our era will be no different. We are not victims of an inevitable AI apocalypse. We are the architects of our future. By shifting our focus from productivity to equity, we can shift our mindsets to operating our society in a way that complements the technology we have at our fingertips and create fair labor practices, more social safety nets and keep humanity at the core of our world.