Through the lens of Silicon Valley and Wall Street, the path forward for artificial intelligence looks entirely pre-ordained. Four dominant tech hyperscalers are on track to spend an unprecedented $650 billion on data centers and infrastructure this year alone.
A narrow group of AI enablers now carries nearly half the total value of the S&P 500 — some measures now put the AI-linked share closer to 50%–57%, meaning the concentration is arguably even more extreme than the headline number suggests — and the buildout has become so massive that it is driving the lion’s share of U.S. GDP growth. The overarching consensus in Silicon Valley and on Wall Street is clear: The technology works, and the capability is unprecedented; ergo, mass adoption is an inevitability.
History teaches a different lesson, and the last few months have provided a series of flashing red lights regarding what could lie ahead. In April, a man motivated by anti-AI sentiment attacked OpenAI CEO Sam Altman’s home. In May, college graduates entering a workforce where the technology is the primary reason for job cuts met commencement speakers hailing the AI revolution—including former Google CEO Eric Schmidt at the University of Arizona—with boos.
June began with Anthropic co-founder Jack Clark publicly urging governments to build regulatory mechanisms to slow the technology down and President Donald Trump signing a cybersecurity executive order establishing a voluntary, 30-day national security review for frontier models. And we learned that frontline workers—particularly those from Gen Z—increasingly admit to quietly undermining and sabotaging the efficacy of their employers’ internal AI rollouts.
The tech sector and the broader corporate world rushing to deploy its tools are increasingly suffering from technological determinism: the fallacious assumption that society is a passive operating system that will automatically update to accommodate new capabilities and infrastructure.
Disruptive technologies do not scale based on their abstract, mathematical capability. They scale based on human incentive structures. Human nature doesn’t necessarily limit technological innovation, but it determines the terms of its adoption. Stakeholder alignment will determine how—and where—AI is successfully adopted. When you align the incentives correctly, adoption moves at warp speed. When you ignore them, the human system will build a firewall to freeze the technology in place.
When People Push Back #
What happens when an innovation is rolled out in a way that threatens stakeholders while concentrating 100% of the efficiency gains at the top? Look no further than what I call the Jersey Pump Principle.
As anyone who lives in or drives through New Jersey knows, it’s illegal for drivers to pump their own gasoline in the Garden State. In the late 1940s, the invention of the automatic shut-off gas nozzle enabled consumers to safely fill their own tanks without spilling gasoline or sparking a fire. This mechanical breakthrough fundamentally revolutionized the retail fueling model, shifting labor away from the pump and ultimately paving the way for the modern, high-volume convenience store layouts we have today. Yet several states, including New Jersey in 1949, banned drivers from pumping their own gas because independent station owners and labor advocates wanted to keep the status quo and protect jobs. Nearly 80 years later, New Jersey remains a lone holdout, legally locking in thousands of attendant jobs. A superior technology was frozen in time because the human terms of adoption were ignored.
We saw a contemporary, multibillion-dollar version of the Jersey Pump Principle on the streets of San Francisco and Austin with the rollout of autonomous robotaxis. Tech companies secured regulatory approval to expand driverless fleets 24/7. But because the rollout was completely divorced from the human element—marginalizing local drivers, drawing ire from fire departments whose emergency vehicles were repeatedly blocked and offering zero upside to residents—public retaliation followed. Activists discovered they could completely paralyze autonomous vehicles simply by placing cheap, orange traffic cones on their hoods, which tripped the cars’ sensors into permanent safety stalls.
Following highly publicized accidents and immense localized friction, California regulators suspended deployment licenses, with the DMV citing an “unreasonable risk to public safety” in ordering Cruise’s fleet off the road indefinitely. When you offload 100% of the risk onto a community and capture 100% of the reward, human nature will find a low-tech way to break your high-tech machine.
The Efficiency Dividend: When Incentives Align #
The inverse of the Jersey Pump Principle is equally true: The right incentive structure can hyperaccelerate adoption with virtually zero friction.
When Henry Ford rolled out the moving assembly line in 1913, it reduced Model T build times from 12.5 hours to 93 minutes each. But for workers, the extreme monotony turned the factory floor into a human disaster. Ford’s labor turnover rate skyrocketed to a staggering 370% in 1913, forcing him to hire more than 50,000 workers just to maintain a baseline workforce of 14,000. The technology was stalling because the human terms were broken. In 1914, Ford reengineered the incentive structure. He doubled the standard industry wage overnight to $5 a day and cut shift length by an hour. The trade-off was clear: Accept the monotony of the assembly line in exchange for immediate financial security. Attrition plummeted, productivity soared and Ford’s profits nearly doubled within two years.
Decades later, Toyota achieved a similar breakthrough with the Toyota Production System. While Western automakers used early robotics as a weapon to slash headcounts, Toyota took the threat of human obsolescence completely off the table. They gave frontline factory workers lifetime employment guarantees and installed the “Andon Cord”—a physical rope that gave any individual worker the power to completely halt the multibillion-dollar assembly line if they spotted an inefficiency. Because automation was tied to worker empowerment rather than displacement, factory workers actively partnered with engineers to optimize the robots, resulting in defect rates lower than those of their American competitors.
The C-Suite Mandate: Designing an Aligned Incentive Structure #
History contains a direct mandate for every CEO currently buying enterprise AI licenses. Right now, the overarching incentive structure driving corporate AI deployment is strictly extractive. Executives look to AI to slice headcounts, monitor employee outputs and squeeze margins out of fewer hours. The average employee looks at these expensive software deployments and asks a highly rational question: “Why should I help make this technology successful?” If the only reward for mastering an AI workflow is a higher performance quota or an impending pink slip, workers will quietly slow-roll, sandbag and sabotage the transition.
Technology is a tool, but adoption is a human choice. To navigate this shift successfully, leaders would be well served to conduct a rigorous examination of historical precedents, analyzing exactly how their specific organizations and industries have responded and adapted to past disruptions. The strategic insights needed to solve today’s cultural anxieties are already encoded in industrial history.
A powerful — and more honest — blueprint for this kind of analytical approach is unfolding right now within the automotive sector’s pivot to electric vehicles. Autoworkers initially viewed EVs as an existential threat, fearing that simpler electric drivetrains would mean far fewer assembly jobs. (That fear turned out to be overstated — later plant-level research found BEV assembly actually required comparable or more labor than combustion-engine lines — but the perception of threat was enough to trigger resistance.)
That resistance came to a head in September 2023, when the UAW launched its first-ever simultaneous “Stand-Up Strike” against Ford, GM, and Stellantis. The 46-day strike — not quiet negotiation — is what produced the “just transition” terms now shaping the EV rollout: priority transfer rights, an end to wage tiers, and explicit commitments to bring battery-plant jobs under national union agreements. By removing the threat of human obsolescence, the industry’s retooling is now moving forward with far less friction than it otherwise would have — but the lesson isn’t that incentive alignment avoided a shutdown. It’s that the shutdown is what forced the incentive alignment. For AI executives hoping to skip the messy leverage phase, that’s the less comfortable but more useful precedent.
The lesson for the AI era is clear: If you want your organization to genuinely embrace this transition, you need to move beyond short-term operational consolidation. The path forward requires structural incentive alignment. When employees realize that automated efficiency directly yields a tangible share of the productivity dividend—whether through relief from systemic burnout, clearer paths to high-value work or guaranteed career upskilling—they stop fearing the future and start actively helping you build it.
Trillions of dollars in capital and compute are waiting on the sidelines—but if your people don’t have a clear, personal incentive to build the future alongside you, they will use their collective inertia to ensure your expensive new technology stays underutilized. Just ask drivers in New Jersey.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
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