In my post on Thriving in a Post Labor Economy of AI and Automation, I underscored the importance of not just knowing how to use AI yourself, but how to understand the systemic changes occurring around us to inform both your business strategy and personal career choices/decisions. Much of that post was informed by the expert opinions of the OODA Network. With this post I want to continue along that theme but highlight opinions from some authors and academics studying and articulating informed opinions on this topic.

The term “Post Labor Economy” is used to describe the point where human labor is no longer central to production and value creation. In this scenario, automation, artificial intelligence and robotics replace most forms of human work, making the traditional exchange of wages for labor obsolete or vastly diminished. Economic production in this system becomes highly automated. Researchers and authors working this topic frequently examine it from the perspective of how new mechanisms will be required to ensure widespread prosperity and meaningful economic participation.

The lessons that flow from this study are informative and can apply to decision-making long before we reach that age of a post labor economy. My favorite authors produce well researched and data driven assessments that can inform business and personal strategies for those seeking to thrive during today’s AI driven disruptions, even if a post labor economy is far away.

Here are some take-aways from three of my favorites in this field:

David Shapiro: The Economic Agency Paradox and the Ownership Solution

David Shapiro, a technologist and independent researcher, presents one of the most detailed and actionable frameworks for the post-labor economy. I like him for his great ability to communicate (he has a YouTube channel and is a frequent poster on X). His central thesis: as AI and automation become “better, faster, cheaper, and safer” at nearly every imaginable task, labor income will erode precipitously, threatening a collapse of consumer demand (the “economic agency paradox”). Shapiro warns that traditional solutions mentioned by many, such as UBI, provide only subsistence and do little to address people’s need for genuine agency and participation in the economy.

Shapiro’s solution is a fundamental overhaul of how value and economic participation work. He advocates for “universal asset tokenization” and a shift toward everyone being, in essence, an investor, owning shares in productive enterprise through digital tokens and AI-assisted investing. He supports these ideas with real data trends: the falling share of income from wages, the collapse in union membership, the boom in capital markets compared to wage growth, and the growing share of national income derived from property or transfer payments. Shapiro also references concrete tools like the Economic Agency Index and Inclusive Capital Income Ratio, which break down sources of household income and expose the vulnerability of regions overly reliant on wages. His work uses BEA and Federal Reserve data to show not just the theory of the coming change, but the current acceleration in automation-driven shifts.

Daniel Susskind: Marginalization of Human Labor

Daniel Susskind, economist and author, offers a macroeconomic perspective that both complements and grounds the discussion on these topics. He is not afraid to call out others who treat this discussion as something just for academics, and criticizes economists for underestimating the capabilities of AI, focusing too long on the belief that only routine tasks could be automated. Susskind identifies automation as a fundamental change agent, not simply destroying some jobs and creating others, but pushing humanity toward a point where human labor as a core input in production becomes increasingly peripheral. He frames this as a break from traditional economic “creative destruction,” where labor always ultimately found a new role, towards something more systemic, a future where capital and technology dominate entirely.

Susskind relies heavily on longitudinal workforce and productivity data. He references trends in productivity outpacing wage growth, a declining labor force participation rate, and evidence from advanced economies where jobless growth is increasingly the norm. Susskind also draws on case studies from labor-intensive and knowledge sectors, showing how automation, data platforms, and AI are steadily eroding once-secure professions. Rather than advocating for a single policy remedy, Susskind insists the urgency is in understanding the scale and speed of what’s happening, so policymakers, businesses, and individuals can grapple with new realities before they’re locked in.

Susskind argues that predicting which jobs will exist decades from now is “incredibly hubristic,” and that the challenge is preparing people for radical uncertainty rather than training them for specific roles. 

Andrew McAfee

Andrew McAfee, MIT principal research scientist, argues that the true revolution of generative AI is not just job automation, but the emergence of a “competitiveness chasm” between agile, tech-native organizations and legacy incumbents. He observes that, unlike the stable dominance of twentieth-century corporate giants, the most valuable companies today are young, driven by a culture of constant experimentation, and able to adapt at “geek speed.”

McAfee’s case studies, like Netflix overtaking Hollywood and SpaceX upending aerospace, illustrate how digitally proficient newcomers are rapidly surpassing their slower, tradition-bound rivals. He insists that generative AI will intensify these divides, rewarding organizations able to pivot and scale at the pace of digital change and exposing those that can’t to long-term obsolescence.

Rather than advocating a specific policy or training program, McAfee challenges leaders to fundamentally rethink their organizations’ cultures and operational rhythms. In his view, success in the AI age will belong to those who internalize rapid adaptation, not just to technology, but to the relentless, accelerating clock speed of digital competition.

What I take Away From This:

Preparing for uncertainty and fundamental change, not just incremental improvement, will be decisive for thriving amid the rise of AI and automation.

Business strategy and career choices must consider not just AI’s technical advances, but the systemic changes to economic participation, labor, and organizational competitiveness.

Adaptability, ownership, continuous reskilling, and cultural agility are key themes emerging from leading thinkers studying the disruption

Agility is key. Agility in your personal career, agility in your ability to learn, and agility in your business strategy. But one thing seems clear, aligning with businesses that leverage cutting edge tech to differentiate themselves seems like a winning strategy. .