Future of Work: AI, Talent & Upward Mobility
Typically, my writing focuses on the challenges business leaders face and on improving work from an operational perspective. Today, I want to zoom out and tackle a bigger, more personal question my partner and I keep coming back to:
What do we actually want the future of Work to look like?
We’re not the only couple navigating a stagnant job market. We’ve been through the Great Recession and COVID-19, like many others. Each crisis brought anxiety and disruption, but there was still a shared understanding of how work created value and which skills mattered.
Today feels different.
The demand for talent, especially white-collar talent, feels frozen. On one side, we have economic whiplash driven by government economic policy. On the other hand, we have a structural disruptor: AI. Together, they can paralyze decision-making in the boardroom and on the shop floor.
I want to focus on the AI component because its impact will be more profound, longer-lasting, and more complex to unwind.
The AI drumbeat and what’s missing from the conversation
Weekly, we see headlines quoting CEOs predicting the end of white-collar work or dramatic reductions in headcount due to AI. Last month it was one tech giant; the month before, a global manufacturer; next month, it will be another household name.
Yes, some of this is headline theater. Yes, AI is having its moment.
But my concern extends beyond the headlines. It’s twofold:
What do we, as a society, actually want Work to look like in an AI-driven economy?
Are we doing anything meaningful to prepare for it beyond chasing efficiency and margin?
AI has the potential to widen the gap between the haves and the have-nots and to shrink the number of high-quality, upwardly mobile jobs. Yet that part of the conversation is often an afterthought in the rush to automate operations, protect margins, and justify massive AI investments.
We’re talking a lot about what AI can do.
We’re talking very little about what kind of Work we want to exist on the other side.
Scenario 1: AI as a cost-cutting machine
In the first scenario, business leaders primarily use AI to reduce overhead and headcount. Companies become more profitable, but fewer people share in the upside.
In this world:
AI replaces a meaningful share of white-collar roles.
New industries and markets don’t absorb displaced workers at similar pay or skill levels.
The jobs that are created demand less specialized skills or provide less value, putting downward pressure on wages.
We end up with an oversupply of skilled labor and not enough desirable jobs.
This isn’t just a labor-market problem; it’s a societal one. Much of America’s strength has come from ambition and the promise of upward mobility. If we quietly remove or compress the kinds of roles that enable that mobility, we shouldn’t be surprised by the downstream effects: social tension, political volatility, and weaker communities.
And from a purely economic perspective, there’s a simple question:
What happens when purchasing power declines in an economy built on consumer spending?
If AI-driven efficiency erodes the middle of the labor market, we don’t just shift profit pools, but we risk undercutting the very consumers businesses depend on.
That leads to a bigger question for leaders:
What is the role of government?
What is the role of business?
And where do they need to work together rather than at odds?
Scenario 2: AI as a productivity multiplier
In the second scenario, AI is a productivity amplifier rather than a pure cost-cutting tool.
Here, AI:
Augments people instead of simply replacing them.
Creates new markets, business models, and job categories.
Enables individuals to retool into higher-value work where human strengths matter: judgment, creativity, relationships, and leadership.
In this version of the future, people who adapt and reskill are rewarded, much like those who leaned into the internet and digital transformation. The mechanisms of capitalism operate as intended, and the economy transitions to a new era without requiring massive emergency intervention.
This scenario is possible. But it will not happen automatically.
Capitalism, incentives, and unintended consequences
I’m not a clairvoyant, but I’ve seen how businesses really work and what motivates leaders when decisions get hard.
We place significant faith in capitalism, and, in many ways, it has delivered for a large portion of Americans with all its imperfections. But it’s important to remember:
Capitalism is designed to optimize for business outcomes, not automatically for societal ones.
When success is measured almost exclusively by profitability, we increase the risk of unintended consequences, especially when a powerful tool like AI shows up.
That’s why I believe we need to expand how we define success for businesses, workers, and society. Not to blunt innovation, but to make sure AI becomes a transformative tool that broadens opportunity rather than deepens divides.
Retooling is necessary but not sufficient.
Recently, I read an article in Scott Galloway’s newsletter in which he argues that individuals will need to retool around distinctly human strengths: curation, curiosity, and connection.
I agree with him, and for the record, those will be critical differentiators in an AI-heavy world.
But it doesn’t fully address the elephant in the room:
We’re facing a potentially massive shift in job composition and wage structures, particularly in white-collar work.
That shift determines where wealth flows, who gets access to opportunity, and how people enter and progress through the workforce.
If a large segment of knowledge workers sees their income compressed, we will feel it in consumer demand, savings, and long-term economic resilience.
Retraining, upskilling, and lifelong learning are necessary. They are not sufficient on their own.
We also need to ask: retool into what, at what pay, and with what support during the transition?
What leadership needs to look like now
Several EEA countries are already mapping out the future state of work in an AI-driven economy. In the U.S., we need to move from reacting to headlines to proactively designing that future.
That means:
Businesses are bringing more than a cost lens — including talent mobility, community impact, and long-term demand into the equation.
The government is creating frameworks and incentives that encourage innovation and protect pathways to upward mobility.
Academia and training providers aligning learning with where work is actually going, not where it’s been.
Workers and labor groups should have a voice in shaping transitions, not just reacting after the fact.
This can’t be done in silos. When one interest group dominates the conversation, whether it’s tech, capital, or government, we end up with solutions that are efficient on paper and brittle in practice.
We need a multidisciplinary approach that brings business, academia, labor, and government to the same table, aligned around a shared question:
How do we make AI a force multiplier for human potential, not just a lever for short-term margin?
A moment of promise and responsibility
I can’t remember another time when so much promise and so much uncertainty have shared the same stage.
AI can unlock incredible productivity and creativity. It can also quietly strip away the rungs of mobility that made the American story possible.
The future of Work will not be decided by fate or technology alone. It will be shaped by the choices we make now — in boardrooms, in government, and in how we define “success” for our organizations.
Let’s not stumble into that future.
Let’s design it deliberately, thoughtfully, and together.
