RESEARCH / DOMAIN 1
Technology changes environments
Core question: How have technologies historically reshaped human and organizational environments?
Thesis
Technology's most significant impact is not the tasks it automates but the environments it creates. Throughout history, technological revolutions have reshaped the conditions under which people communicate, learn, decide, collaborate, and create value. Artificial intelligence continues this pattern by changing the accessibility of intelligence itself, creating new environments to which individuals and organizations must adapt.
Every major technological advancement in human history has done more than improve efficiency or automate tasks. Technology changes the environments in which people live, communicate, learn, collaborate, and create value.
The printing press expanded access to knowledge and transformed literacy. Electricity reshaped industrial production and daily life. The internet fundamentally changed how information is created, distributed, and consumed. In each case, the most significant impact was not the technology itself, but the environmental changes it produced.
Organizations often focus on technology adoption while underestimating the broader consequences of environmental change. New technologies alter information flows, decision-making processes, communication patterns, expectations, incentives, and social structures. As these conditions evolve, individuals and organizations adapt their behaviors in response.
Understanding how technology changes environments is foundational to understanding organizational evolution in the age of artificial intelligence. Before exploring culture, leadership, human potential, or intelligent agents, it is important to recognize a simple principle:
Technology changes environments. People adapt to environments. Organizations evolve through adaptation.
What science knows
Technology reshapes the conditions in which people operate
Throughout history, technological revolutions have consistently altered the environments surrounding human activity.
Media theorist Marshall McLuhan argued that the most important effects of technology emerge not from the content it carries, but from the environmental changes it creates. His famous observation that "the medium is the message" suggests that technologies reshape patterns of thought, communication, and behavior regardless of their intended purpose.¹
McLuhan's work challenged the common assumption that technologies are neutral tools. Instead, he proposed that technologies become extensions of human capability that ultimately reshape the environments in which people think, communicate, and interact. The printing press changed how knowledge was distributed. Television changed how information was consumed. The internet changed how information was accessed. In each case, the technology altered the environment, and people adapted accordingly.
The pattern is remarkably consistent. Technology changes the conditions under which people operate. Those new conditions influence how individuals and groups behave.
Technological revolutions reshape societies and institutions
Economic historian Carlota Perez studied multiple technological revolutions, including steam power, railroads, electricity, automobiles, and information technology.²
Her research suggests that technological breakthroughs are followed by long periods of institutional and social adaptation. The technology arrives first. New infrastructure, business models, organizational structures, and cultural norms emerge later.
Perez argues that societies do not simply adopt technologies. They reorganize around them. Entire industries, institutions, and economic systems evolve to take advantage of newly available capabilities.
The greatest impact of a technology is often not the tool itself, but the systems that develop around it.
This perspective is particularly relevant to artificial intelligence. While many organizations focus on AI tools and applications, history suggests that the more significant transformation may come from how organizations restructure themselves around newly available capabilities.
This view is echoed by contemporary AI leaders. Sam Altman has argued that AI may be "more like the transistor than anything else"—a foundational technology whose impact comes from being embedded throughout society rather than from any single application.³ Similarly, Dario Amodei has suggested that increasingly capable AI systems could transform knowledge work across entire industries, creating changes that extend far beyond productivity gains from individual tools.⁴
These perspectives reinforce the idea that the most important effects of AI may emerge from the organizational and societal adaptations that follow widespread adoption.
Organizations succeed or fail based on their ability to adapt
Clayton Christensen's work on disruptive innovation demonstrated that organizations often struggle when new technologies change the conditions under which they compete.⁵
His observation that successful companies can fail by doing everything right remains one of the most important insights in management theory. Organizations often recognize technological change. The challenge is rarely awareness.
The difficulty arises because existing structures, incentives, processes, and assumptions were designed for a previous environment.
When environments change, organizational adaptation becomes essential.
This insight shifts the conversation away from technology adoption and toward organizational evolution. The question is not whether new technologies will emerge. The question is whether organizations can adapt quickly enough to take advantage of them.
This principle becomes increasingly important in the age of AI. Competitive advantage may depend less on access to technology and more on the speed at which organizations learn, adapt, and evolve in response to changing conditions.
Information abundance creates new constraints
Nobel Prize-winning economist Herbert Simon observed in 1971 that:
"A wealth of information creates a poverty of attention."⁶
This insight has become increasingly relevant in the digital era.
Historically, organizations struggled to access information. Today, information is abundant. The challenge has shifted from acquisition to management, memory, interpretation, and application.
As information environments expand, attention becomes the limiting factor. Technologies that increase access to information also increase complexity, noise, and competition for human attention.
Artificial intelligence may represent the next stage of this evolution. Rather than increasing information abundance alone, AI dramatically expands access to synthesized information, recommendations, generated intelligence, and reasoning support.
If the Information Age was defined by information abundance, the AI Age may increasingly be defined by intelligence abundance.
The implications of this shift are still emerging.
What science is exploring
How AI influences human cognition
Researchers in cognitive science, education, human-computer interaction, and organizational psychology—including Ethan Mollick, Fabrizio Dell'Acqua, and colleagues at Wharton, Harvard Business School, MIT, and Stanford—are actively exploring how artificial intelligence affects human thinking, learning, memory, creativity, and decision-making.⁷
Studies from institutions such as MIT, Stanford, Harvard Business School, and the National Bureau of Economic Research suggest that AI can improve problem-solving, increase productivity, and enhance learning outcomes. Research by Ethan Mollick and colleagues has shown performance gains among knowledge workers using generative AI, while studies by Erik Brynjolfsson and collaborators have documented significant productivity improvements in customer support and other professional settings.⁸
Other researchers have raised important questions regarding overreliance, cognitive offloading, automation bias, and the potential erosion of certain skills when individuals become overly dependent on intelligent systems.
The long-term effects remain unclear.
What is becoming increasingly evident is that AI changes the cognitive environment in which people operate. Access to synthesized knowledge, expert-level assistance, and real-time reasoning support introduces conditions that have never previously existed at scale.
Human-AI collaboration
Research increasingly suggests that the most effective outcomes often emerge when humans and intelligent systems work together.⁹
A large-scale field experiment by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that generative AI significantly improved the productivity and quality of work performed by customer support agents, with the greatest gains occurring among less experienced workers.¹⁰
Similarly, research conducted by Harvard Business School and Boston Consulting Group found that consultants using GPT-4 completed tasks faster and produced higher-quality outputs than those working without AI assistance, particularly when operating within the model's areas of competence.¹¹
Across multiple studies, AI appears particularly effective at augmenting human capability rather than replacing it outright. In many domains, human judgment combined with machine intelligence outperforms either acting independently.
This has led researchers to explore questions around trust, autonomy, delegation, collaboration, and decision-making within human-AI systems.
Organizational cognition
A growing body of research on organizational learning, knowledge management, and collective intelligence is exploring how knowledge is created, shared, and applied across organizations.¹²
Building on the work of Ikujiro Nonaka, Hirotaka Takeuchi, Thomas Davenport, Etienne Wenger, Anita Woolley, and others, researchers have examined how tacit knowledge becomes explicit, how communities of practice accelerate learning, and how collective intelligence emerges within groups.¹³
Their findings suggest that organizations learn most effectively when knowledge is continuously exchanged, refined, and embedded into everyday work.
This emerging field increasingly points toward a concept that can be described as collective cognition: the shared process through which knowledge is created, refined, remembered, and applied across a system.
Historically, organizational knowledge has been fragmented across documents, systems, and individuals. AI introduces the possibility of more dynamic knowledge environments capable of supporting learning, memory, and decision-making at scale.
While the field remains early, questions about collective intelligence, organizational cognition, and AI-enabled knowledge systems are becoming increasingly important.
Competing viewpoints
Technology is only a tool
A common perspective argues that technology itself changes nothing.
According to this view, people determine how technologies are used. Outcomes depend on leadership, incentives, culture, governance, and human choice rather than the technology itself.
There is considerable truth in this position.
The same technology can produce dramatically different outcomes across organizations. Leadership decisions matter. Cultural factors matter. Human agency matters.
Technology does not determine outcomes.
However, focusing exclusively on human agency overlooks an important reality.
While technologies do not determine behavior, they do alter environmental conditions. When environmental conditions change, behavior adapts. Technology may not dictate outcomes, but it influences the environments in which outcomes emerge.
The relationship is not deterministic.
It is adaptive.
A modern perspective
Technology creates possibility.
Humans adopt.
Adoption creates environmental change.
Environmental change creates adaptation.
Throughout history, organizations have focused on adopting technologies. The organizations that achieved lasting advantage learned how to adapt to the environments those technologies created.
Artificial intelligence represents more than another software category. Previous technological revolutions expanded access to land, energy, transportation, manufacturing, and information.
AI expands access to intelligence itself.
For the first time, organizations can provide broad access to reasoning support, synthesized knowledge, content generation, pattern recognition, and increasingly autonomous digital capabilities.
This changes the environment.
As intelligence becomes more abundant, individuals gain the ability to operate at higher levels of abstraction. More time can be spent on exploration, judgment, creativity, innovation, and problem framing. New forms of organizational learning become possible. New forms of intellectual property emerge. New capabilities become available to people who previously lacked access to specialized expertise.
This process can be described as cognitive elevation: the ability for humans to operate at higher levels of judgment, creativity, and contribution by delegating lower-level cognitive tasks to intelligent systems.
The greatest opportunity may not be automation.
It may be Humanᴬᴵ.
As intelligence becomes embedded throughout organizations, competitive advantage will increasingly emerge from how effectively people and intelligent systems learn, adapt, and create value together.
The organizations that benefit most will not be those that deploy the most AI.
They will be those that adapt most effectively to the environments AI creates.
Organizations do not compete on AI adoption.
They compete on adaptation velocity.
Devonport concepts
Adaptation velocity
The rate at which individuals, teams, or organizations learn, accept, integrate, and operationalize environmental change.
Organizations do not gain advantage from technology alone. Advantage emerges from the speed and effectiveness with which people adapt to new conditions.
Cognitive elevation
The process through which humans operate at higher levels of abstraction, judgment, creativity, and problem framing by delegating lower-level cognitive tasks to intelligent systems.
As routine cognitive tasks become increasingly supported by intelligent systems, human attention can shift toward more complex and meaningful work.
Collective cognition
The shared process through which knowledge is created, refined, remembered, and applied across a system.
Organizational intelligence emerges not from what individuals know, but from how knowledge moves, evolves, and compounds throughout a network of people and intelligent systems.
Implications for leaders
Focus on adaptation, not simply adoption.
Design environments that encourage learning and experimentation.
Measure behavioral change alongside technology utilization.
Increase autonomy as capability increases.
View AI as a capability amplifier rather than a replacement strategy.
Invest in systems that capture and compound organizational knowledge.
Treat collective cognition as a strategic asset.
Build organizational structures that can evolve as environments change.
Recognize that competitive advantage increasingly emerges from adaptation velocity.
Prepare for a future in which intelligence is abundant but understanding remains scarce.
References
Foundational works on technology and environmental change
[1] McLuhan, Marshall. Understanding Media: The Extensions of Man (1964)
[2] Perez, Carlota. Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages (2002)
[3] Christensen, Clayton M. The Innovator's Dilemma (1997)
[4] Simon, Herbert A. "Designing Organizations for an Information-Rich World" (1971)
[5] Postman, Neil. Technopoly: The Surrender of Culture to Technology (1992)
Organizational learning and collective cognition
[6] Nonaka, Ikujiro & Takeuchi, Hirotaka. The Knowledge-Creating Company (1995)
[7] Wenger, Etienne. Communities of Practice (1998)
[8] Davenport, Thomas H. & Prusak, Laurence. Working Knowledge (1998)
[9] Woolley, Anita W. et al. "Evidence for a Collective Intelligence Factor in the Performance of Human Groups." Science (2010)
Human-AI collaboration and augmentation
[10] Brynjolfsson, Erik, Li, Danielle & Raymond, Lindsey. Generative AI at Work (NBER, 2023)
[11] Dell'Acqua, Fabrizio et al. Navigating the Jagged Technological Frontier (Harvard Business School, 2023)
[12] Mollick, Ethan & Mollick, Lilach. Research on generative AI and knowledge work (Wharton School)
[13] Stanford Human-Centered Artificial Intelligence (HAI) research publications
[14] Massachusetts Institute of Technology (MIT) research on AI, productivity, and organizational change
[15] Microsoft Work Trend Index Reports (2023–Present)
[16] National Bureau of Economic Research (NBER) working papers on AI adoption, productivity, and labor market impacts
Contemporary AI perspectives
[17] Altman, Sam. Essays, interviews, and public statements regarding AI as foundational infrastructure
[18] Amodei, Dario. Essays and public statements on transformative AI, knowledge work, and economic change