Zero-sum thinking, the mentality that one person’s gain must come at the expense of another person’s loss, has dominated business, politics, and society for decades.
Tech disruption has repeatedly cracked the foundations of zero-sum thinking, and offered a glimpse into a better alternative. Now, advancements in Artificial Intelligence (AI) may finally shatter zero-sum thinking. The shift from scarcity to abundance isn’t just economic; it’s psychological, cultural, and moral. The real revolution will be in what we believe is possible, and how we pursue purposeful distribution.
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One reason why the AI revolution’s impact will be so profound is because value has always been constrained by scarcity
For most of history, value has been constrained by the scarcity of land, energy, labor, capital and intelligence. This has naturally entrenched the zero-sum logic that underpins our societal structures: If I win, you lose. There’s only so much to go around.
This mindset shaped how we designed markets, organizations, policies—even education and careers. Zero-sum thinking may never have been a universal truth and perhaps instead a reaction to limited conditions. But now, AI promises to give us the tools to democratize access to previously limited resources: time, human capability, and intelligence.
So, what happens when some of the core constraints behind zero-sum thinking disappear? What becomes the new definition of intelligence in the AI age?
Historical disruptions to scarcity
Industrial revolutions throughout history expanded abundance, but in every wave, scarcity held back progress. For instance, as energy scaled during the first Industrial Revolution thanks to steam engines, skilled labor remained scarce. And during the second Industrial Revolution, cheaper goods through mass production didn’t translate to scaled innovation. And while the internet opened access to knowledge, educational and income gaps have widened. Software improved productivity, but expertise and credentials remained locked in the top percentiles of the population.
Despite remarkable technological advancements across many sectors, productivity has remained stagnant over the last 20 years. In the advanced G7 economies, annual productivity growth plunged from an average of 2% during a growth surge (1995-2005) to 0.4% after the pandemic. In the U.S., the pace of growth is returning to pre-1995 levels as the repercussions of labor market polarization play out in which high-paying jobs are reserved for people with high levels of education, and those with fewer opportunities are siphoned to low-wage jobs.
Today, strong zero-sum narratives persist. CEOs are stating that AI will slash entry-level white-collar jobs in half, potentially pushing U.S. unemployment as high as 20% over the next five years. Protectionist economic policies assume that gains for foreign competitors must come at the expense of domestic industry. Institutional gatekeepers justify AI deregulation by saying U.S. dominance is the only way to protect American users and spur innovation.
But this justification is still based in zero-sum thinking. And there is opportunity for another path to broaden the human endeavor and capture possibilities we never imagined.
What makes AI different
AI brings expertise to your fingertips, reducing the cost of intelligence to near zero. Today’s AI agents can write business plans, code software, design products, conduct research, and support decision making by perceiving their environments and acting toward defined goals.
AI technology is advancing at an unprecedented pace. While Moore’s Law describes the steady doubling of compute power every 18 months, AI performance is scaling much faster. Its cost base is quickly approaching the cost of the computing power behind it. As models scale in compute, data, and size, they become significantly more capable with predictable gains in accuracy and generalization. AI is becoming cheaper, better, and more useful at a faster rate than previous technological curves.
This directly challenges the zero-sum systems that shape our society and economy, and we’re already feeling the friction in these legacy systems.
For example, our education system still gatekeeps opportunity through standardized tests and degree or credential-based validation—while students are leaping ahead using AI tools that curriculums struggle to integrate. And work is increasingly dynamic and interdisciplinary, but job roles, KPIs, and HR systems reward predictability and adherence to hierarchical norms. Plus, consumers today demand fluid and personalized interactions, but companies still offer static interfaces and siloed services.
This is a signal of systemic dissonance between old assumptions and new capabilities. Now, AI has the potential to expand our productivity. A 2023 study by Harvard Business School and Boston Consulting Group (BCG) shows that AI large language models (LLMs) increase productivity on knowledge tasks by 12-25% and improved work quality by up to 40%. At Cognizant, we are seeing productivity boosts up to 37% for our lower-level developers, equalizing the playing field with our higher-skilled developers who report a 17% gain using AI.
AI-driven abundance will continue to destabilize the zero-sum logic that underpins our legacy structures. Creation will no longer be limited by access to experts or capital. Individuals can gain the superpowers once held by institutions. Intelligence will become infinite leverage, collapsing the marginal cost of value creation. This means that AI has the opportunity to become the first technology that doesn’t just produce faster outputs, but increases what is possible—introducing new principles of exploration, enablement, and purposeful distribution.
With fewer constraints, what defines value?
In a world where AI brings capability to anyone with an idea, value will no longer be defined by what you do, but why you’re doing it.
Skills can be augmented. Execution can be automated. But imagination, intention, and purpose remain human. AI can complement your strengths and fills your blind spots, but it doesn’t tell you where to look.
Therefore, value will increasingly be defined not by an individual’s expertise or credentials, but rather by their creative integration of collective insight. And a product’s value will increasingly be grounded in originality, trust, and purpose. This foundational shift is a call for more fluid, cross-functional and adaptive ways of working and thinking.
Looking ahead
We need to redesigning our social structures to embrace an era of abundance— and we must evolve our assumptions about talent, work, and learning to design a future for inclusive growth.
As AI expands access to capability, it will reduce the amount of time it takes a worker to ramp up into their role. As a result, they will be able to make meaningful contributions much earlier in a career or new role. Positions that once required years of formal training will become more accessible through on-demand tools and faster learning cycles.
The old edge of information asymmetry shifted to expertise asymmetry in the internet age. Now the differentiation lies in how intelligence is applied to real-world, interdisciplinary problems where context matters most.
As AI gets better at solving defined problems, the value of human work will shift to our ability to identify problems—identifying unmet needs, asking better questions, and connecting disparate dots. Our opportunity lies within blending AI, design, domain expertise, and the human sciences. Interdisciplinary fluency will drive exploration of the new edges of innovation.
While some resource limitations will remain, AI and technological advances will arm us with the power to cure diseases, provide personalized education, find sources of sustainable energy, and create intelligent physical systems. But achieving that potential requires a shift toward exploration and preparing future generations to lead in this new paradigm. If we can design dynamic business and societal systems that enable people to surf the wave of exponential tech, rather than get washed out by it, we can create new kinds of value that shatter zero-sum systems.