From Scarcity to Abundance Economics

Geoffrey Moore’s 1991 concept of the technology “chasm” shaped 3 decades of software development and marketing strategy, expressed in his book Crossing the Chasm: Marking and Selling Disruptive Products to Mainstream Customers. His framework illuminated the gap between early adopters and mainstream markets, becoming a cornerstone of enterprise technology thinking. Today, as AI reshapes our landscape, we face a new kind of chasm—but it’s not what Moore described.

This column explores how enterprises can navigate this transformation. It examines practical strategies for building gift economies of knowledge, developing ethical AI frameworks, and measuring success beyond market metrics.

It draws on Indigenous wisdom—particularly that of Robin Wall Kimmerer, Potawatomi botanist and director of the Center for Native Peoples and the Environment at the State University of New York College of Environmental Science and Forestry. Her insights on gift economies, in The Serviceberry: Abundance and Reciprocity in the Natural World, spark an exploration into how knowledge workers can guide us toward a more abundant, ethical, and community-centered future.

The original chasm was rooted in scarcity economics: limited licenses, controlled distribution, and measured adoption.

Enterprise software development cycles stretched from 9 months to 2 years, with each release treated as precious intellectual property. We built walls around our innovations, believing scarcity created value.

The AI chasm before us today is fundamentally different. It demands a shift from scarcity thinking to abundance economics, market penetration to societal integration, and data extraction to knowledge stewardship. This transformation challenges everything we know about enterprise technology adoption.

Consider how our development cycles have evolved. We’ve steadily moved toward abundance from year-long waterfall releases to agile sprints to continuous deployment.

Now, AI capabilities double every 6 months, with open source models and APIs democratizing access. The challenge isn’t technical scarcity—it’s cultural integration.

Enterprise knowledge workers and software developers are at this crossroads. The traditional approach of gaining competitive advantage through controlled innovation no longer serves us. Instead, success requires building local gift economies of information while contributing to global understanding networks.

What does this mean in practice? Our librarians and knowledge management professionals become crucial bridges, curating AI capabilities for local context while ensuring ethical deployment. Software developers focus less on proprietary features and more on creating transparent, community-centered systems that amplify human wisdom rather than replace it.

This isn’t just theoretical. Leading organizations are already building these new models:

• Knowledge workers become AI capability curators, matching global tools to local needs
• Development teams prioritize explainability and community benefit over black-box optimization
• Enterprise systems evolve from data extractors to knowledge commons contributors

The parallels to Indigenous gift economies are instructive. Just as traditional communities manage abundant resources through cultural practices and local expertise, enterprises must develop new frameworks for stewarding AI capabilities.

Success means moving beyond the extractive social media model, in which users trade privacy for functionality.

We’re witnessing the emergence of regenerative knowledge networks, where value comes not from hoarding information but from ethically applying and sharing it. This requires new metrics beyond market penetration and user acquisition. We must measure success regarding community benefit, knowledge accessibility, and ethical deployment.

For enterprise leaders, this shift demands courage. It means questioning decades of market-driven development practices, investing in knowledge stewardship rather than technical capabilities, and building systems that serve community needs rather than merely extracting value.

The AI chasm isn’t about convincing mainstream markets to adopt new technology. It’s about transforming how we think about technology adoption itself. We must move from seeing AI as a competitive advantage to viewing it as a communal resource requiring careful stewardship.

The next wave of enterprise AI success stories will not come from those who best manipulate markets or extract data. Instead, they will come from organizations that successfully build bridges between local and global knowledge networks, prioritize transparency over black-box solutions, and understand the difference between information abundance and wisdom.