n Paris this year, two instincts about the future will sit side by side. At the G7 Summit, leaders will try to restore stability through governance—rules, safeguards, and coordination. Across the city at VivaTech, innovators will push acceleration—new models, new capabilities, new markets.
Both conversations are serious. But neither is organized around the question most people are actually asking: how does society broadly benefit from this future?
For much of the last century, the answer was embedded in the system. Productivity gains translated—imperfectly but predictably—into jobs, wages, and rising living standards. That linkage is under strain. Productivity and median wages have diverged for decades, and AI threatens to widen the gap by automating cognitive work that once anchored the middle class.
The evidence on AI's labor impact is still early and contested. But the direction of concern is consistent. In the Harvard Institute of Politics Fall 2025 Youth Poll, young Americans were three times more likely to say that AI will reduce opportunities than to say it will expand them. Similar skepticism appears in Pew and Eurobarometer data. One cohort does not settle the question, but the pattern is hard to dismiss.
What is at stake is public trust in democratic systems' ability to deliver tangible improvements in people's lives. When that trust erodes, policy becomes reactive, politics becomes zero–sum, and the space for long–horizon investment narrows.
The next durable consensus will be built around whether shared prosperity is visible, measurable, and real. That requires a shift from growth to abundance—the deliberate reduction in the cost of essentials: housing, energy, food, healthcare, learning, connectivity.
Three mechanisms deserve serious attention, each addressing a different failure mode.
The first is ownership. If AI concentrates value in fewer hands, participation has to bring structural benefits. The most promising model is a sovereign wealth approach—pooling returns from AI–relevant public assets (spectrum, data, compute, publicly funded research) and distributing dividends broadly, along the lines of the Alaska Permanent Fund.
The second is earning. Stable long–tenure jobs are unlikely to remain the dominant form of work. That need not mean precarity, but it does mean building the scaffolding—portable benefits, verified credentials, navigable project markets—that lets people move fluidly without falling through the gaps. Repeating the promise that upskilling will close the gap on its own is not a plan.
The third is affordability. AI's most direct benefit will come through lower prices for what people buy. But efficiency gains are often captured by incumbents. Closing that gap requires some combination of competition policy, public options in essentials, open models that commoditize AI itself, and procurement rules that force pass–through.
None of this is frictionless. Each mechanism means fighting with platforms, incumbents, and existing property and tax structures. An honest agenda names those fights rather than imagining them away.
The test of progress then becomes concrete: Do people have a stake? Do they have ways to earn? Are their lives becoming more affordable?
This is how legitimacy is rebuilt—not through promises, but through demonstrated, shared gains.
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The future will not be agreed upon, but it will be shared
Photo by Hudson Hintze on Unsplash
June 11, 2026
AI's legitimacy depends on shared prosperity—ownership, earning, and affordability—not governance or innovation alone, writes Dr. Alexander Nicholas.
I
n Paris this year, two instincts about the future will sit side by side. At the G7 Summit, leaders will try to restore stability through governance—rules, safeguards, and coordination. Across the city at VivaTech, innovators will push acceleration—new models, new capabilities, new markets.
Both conversations are serious. But neither is organized around the question most people are actually asking: how does society broadly benefit from this future?
For much of the last century, the answer was embedded in the system. Productivity gains translated—imperfectly but predictably—into jobs, wages, and rising living standards. That linkage is under strain. Productivity and median wages have diverged for decades, and AI threatens to widen the gap by automating cognitive work that once anchored the middle class.
The evidence on AI's labor impact is still early and contested. But the direction of concern is consistent. In the Harvard Institute of Politics Fall 2025 Youth Poll, young Americans were three times more likely to say that AI will reduce opportunities than to say it will expand them. Similar skepticism appears in Pew and Eurobarometer data. One cohort does not settle the question, but the pattern is hard to dismiss.
What is at stake is public trust in democratic systems' ability to deliver tangible improvements in people's lives. When that trust erodes, policy becomes reactive, politics becomes zero–sum, and the space for long–horizon investment narrows.
The next durable consensus will be built around whether shared prosperity is visible, measurable, and real. That requires a shift from growth to abundance—the deliberate reduction in the cost of essentials: housing, energy, food, healthcare, learning, connectivity.
Three mechanisms deserve serious attention, each addressing a different failure mode.
The first is ownership. If AI concentrates value in fewer hands, participation has to bring structural benefits. The most promising model is a sovereign wealth approach—pooling returns from AI–relevant public assets (spectrum, data, compute, publicly funded research) and distributing dividends broadly, along the lines of the Alaska Permanent Fund.
The second is earning. Stable long–tenure jobs are unlikely to remain the dominant form of work. That need not mean precarity, but it does mean building the scaffolding—portable benefits, verified credentials, navigable project markets—that lets people move fluidly without falling through the gaps. Repeating the promise that upskilling will close the gap on its own is not a plan.
The third is affordability. AI's most direct benefit will come through lower prices for what people buy. But efficiency gains are often captured by incumbents. Closing that gap requires some combination of competition policy, public options in essentials, open models that commoditize AI itself, and procurement rules that force pass–through.
None of this is frictionless. Each mechanism means fighting with platforms, incumbents, and existing property and tax structures. An honest agenda names those fights rather than imagining them away.
The test of progress then becomes concrete: Do people have a stake? Do they have ways to earn? Are their lives becoming more affordable?
This is how legitimacy is rebuilt—not through promises, but through demonstrated, shared gains.
