Co-authored by Stewart Noyce and John-Michael Scott

Executive Summary

The promises of exponential abundance runs directly through issues of scarcity. On the one hand, Generative AI creates an abundance of cognitive labor. It’s like adding staff to your information project on-demand, at a fraction of the cost. But it also creates new scarcity of attention, trust and human connection.

As very real threats to the social contract emerge, we can expect companies and governing bodies to take action that keeps the peace. Many will act without permission, followed by regulators who impose rules of engagement. With this article, we advocate for individuals to educate themselves and participate in the decision making that assures abundance.

I. Scarcity and Abundance

Scarcity emerges where unlimited human wants meet limited resources. It forces choices: every decision to allocate resources toward one goal means abandoning alternatives. This dynamic shapes every human society, driving innovation while creating conflict over who gets what, when, and how.

The core conflict comes from Natural Scarcity, driven by physical limitations imposed by finite resources, energy, materials, and time.  Such conflicts come from common sources like these:

  • Access – Who is allowed to draw from the well, walk the land, or connect to the network? Restrictions often emerge when a resource becomes more valuable than plentiful.
  • Control – Who sets the rules? From kings and priests to corporations and governments, power consolidates around chokepoints of scarcity.
  • Allocations – Who receives how much? Systems of taxation, rationing, or property rights attempt to settle distribution, often favoring some groups while excluding others.
  • Timing - When are resources available, and for how long?

As conflicts threaten the tenuous balance of profit motive and social stability, societies respond by imposing Artificial Scarcity to maintain peaceful coexistence.  Human institutions create these constraints - from government regulations to industry coordination to the product choices of individual companies.

Over centuries, technological cycles have repeatedly transformed scarcity patterns. Each transformation followed a similar pattern: new technology eliminated old scarcities while creating new ones, forcing social adaptation. Those who adapted their governance and social contracts thrived; those who clung to old models faced disruption or collapse.

For example - social media promised to eliminate the scarcity of nationwide conversation, connecting diverse voices across geographic and social boundaries. Instead, algorithmic curation and engagement optimization created filter bubbles—artificial scarcity of diverse perspectives. We gained infinite connection capacity but lost the ability to hear viewpoints that challenge our existing beliefs.

And so, each adaptation carried the seeds of its own obsolescence. As newer technologies emerged to challenge the status quo they would inevitably need to transform. We now face just such a transformation, but with unprecedented scope and speed.

II. Generative AI Changes the Rules

Generative AI fundamentally alters the scarcity equation by making cognitive labor abundant. With skilled assistants available on-demand, AI can reduce bottlenecks in:

  • Information processing: Analysis, synthesis, and communication that previously required teams of knowledge workers
  • Creative production: Content generation, design iteration, and problem-solving at unprecedented speed and scale
  • Decision support: Pattern recognition, scenario modeling, and recommendation systems that augment human judgment
  • Skill democratization: Expertise previously limited to specialists becomes accessible to anyone with AI tools

This increased abundance of cognitive assistance is already driving measurable productivity gains - studies show 25-40% improvements in task performance when AI is used appropriately. However, rather than eliminating cognitive work entirely, AI is reshaping how knowledge workers spend their time and what skills become most valuable.

As an example, software developers using AI coding assistants like GitHub Copilot report 55% faster task completion while spending more time on system design and problem-solving rather than syntax debugging. Legal teams use AI for document review and contract analysis, freeing lawyers for strategic counsel and client relationships—higher-value work requiring human judgment.

The impact is profound. Teams that once struggled under the weight of information processing now find themselves with the capacity to do more—faster, and often with higher quality. Creative production expands, decision support deepens, and experimentation accelerates. Entire organizations discover they can generate insight at a pace that was impossible before. Productivity, in certain areas, is no longer gated by human bandwidth.

But the social contract is strained when technology suddenly multiplies output. If capital simply absorbs the gain—if corporations harness abundant cognitive labor to maximize efficiency without regard for human purpose—the result may be destabilizing. Teams powered by unfettered AI productivity risk producing not only more value, but also more harm. The negative impacts are many:

  • Erosion of trust – A flood of AI-generated content blurs the line between authentic and synthetic, making it harder for people to know what, or who, to believe.
  • Attention collapse – Overproduction of content overwhelms human capacity to filter, leading to distraction and disengagement.
  • Job displacement – Junior roles, once the entry point to professions, may disappear, leaving fewer paths for developing expertise.
  • Exploitation of labor – Capital may concentrate the gains, widening inequality and weakening the middle class.
  • Devaluation of creativity – As AI-generated output becomes abundant, the perceived worth of human originality may decline.
  • Cultural fragmentation – Personalized AI experiences risk isolating communities into narrow informational bubbles.
  • Commons depletion – Knowledge extracted to train AI may be repackaged behind proprietary walls, reducing the shared intellectual pool.

Each of these outcomes strikes at the foundations of community, eroding the stability on which abundance must rest. History suggests what follows. When asymmetric control threatens social stability, companies and governing organizations will take action, but positive outcomes are not guaranteed.

III. Breaking the Cycle

Generative AI gives us a glimpse of exponential abundance in thought, creativity, and decision-making. However, unless we carefully navigate the social contract and guard against government overreach, the cycle will repeat—abundance created, artificial scarcity imposed, and the promise of transformation reduced to the privilege of the few.

How do we break the cycle?  First, let’s look at how artificial scarcity will likely unfold from the actions of industry players. Then we can understand the need to level up civilian capabilities to participate on both the profit side and the governance side.

The Permissionless Innovation Response

When critical needs are not met, private enterprise often responds quicker than incumbent institutions. Fewer mandates or policies restrict their actions. They can create and launch a new product within months, or almost immediately release a new feature.

As an example, in recent years YouTube introduced age-restricted content controls and parental supervision features to address the scarcity of safe digital spaces for children. It wasn’t perfect though. While it sent the right message and reduced some harm, it often blocked access to certain educational content while still allowing a flood of harmful nonsense for others.

Groups of companies working together in coalition can be more effective. For example, major platforms collaborated through the Global Internet Forum to Counter Terrorism (GIFCT) to share terrorist content databases and detection algorithms. Despite being fierce competitors, Facebook, Twitter, YouTube, and others recognized that coordinated response was more effective than individual efforts.

Similar dynamics are beginning to emerge around AI-era constraints:

Trust Infrastructure can fight deepfakes through content verification services, reputation systems, and synthetic media detection tools, though trust gaps remain significant across demographics and income levels.

Attention Economy Solutions can introduce AI-powered curation systems, micro-subscription models, and community-moderated spaces as creators seek sustainable revenue models.

Access and Infrastructure Development may accelerate deployment of decentralized computing networks, edge computing networks, community-owned connectivity, open-source AI development, cooperative data centers, and distributed manufacturing.

Public Sector Innovation is emerging through municipal AI utilities, community data trusts, digital public squares, regulatory sandboxes, interoperability standards, and public-private partnerships.

The pattern is clear: permissionless innovation fills gaps that established institutions cannot address. The question isn't whether these solutions will emerge—they already are. The question is whether governing entities will participate in shaping them or find themselves bypassed by more agile alternatives.

Everyone Must Level Up

The path from scarcity to abundance runs through understanding. If we want AI's benefits to spread rather than concentrate, everyone must develop fluency with both the technology's capabilities and its implications for society.

This isn't just technical training—it's civic education for the AI era. Citizens need to understand:

  • How AI systems work - People need to learn that generative AI can be helpful or harmful. It is a technology. AI doesn't "think" or "know" things - it predicts the most statistically likely next word/pixel/sound based on patterns in training data. It's like an extraordinarily sophisticated autocomplete that learned from billions of examples. This fundamental understanding prevents both over-trust ("AI said it so it must be true") and under-utilization ("it's just making things up").
  • How to participate - AI changes the rules for abundance and scarcity as it makes cognitive labor cheap. People will need to know how to adapt. They will both use the technology and advocate for constraints on its use.  They're not just tweaking existing systems but potentially redesigning fundamental agreements about work, ownership, and value creation.
  • Solving problems through innovation - Permissionless creation of value that solves problems will be normal. Coordination challenges occur when everyone would benefit from collective action, but individual incentives prevent it from happening. Citizens need to recognize these as solvable technical and social challenges, not inevitable features of human nature.
  • Why new governance will emerge - Current democratic and regulatory systems operate on human timescales - months or years for policy development and implementation. But AI capabilities evolve weekly, creating a fundamental mismatch. Citizens need to understand that governance must become more adaptive and experimental rather than just more restrictive.

With this foundation, informed citizens can take action that encourages best use of generative AI for the public good. With education they will understand both the technology's transformative potential and the social dynamics that could limit its benefits.

The choice isn’t whether AI will transform our world; it’s whether that transformation concentrates value or compounds it for everyone. Exponential abundance is within reach. Scarcity, where it persists, is a design problem—one we solve by aligning tools, institutions, and norms so capacity flows outward rather than up. The task before us is not to slow the future, but to architect it: to turn abundance from a headline into a habit.