New research on how the competitive dynamics that created early tech giants may not repeat in the age of generative AI.
For the last two decades, businesses have operated under the shadow of the Web 2.0 era, where a handful of giants like Google built unassailable tech fortresses. Now, as generative AI transforms every industry, leaders face an urgent question: are we watching the same scenario again, or is this time genuinely different? In the new article “Contested Ground: Early Competition and Market Dynamics in Generative AI,” a team of researchers, including Chiara Farronato, co-Principal Investigator of the Platform Lab at the Digital Data Design Institute at Harvard (D^3) and Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School, explains how the GenAI market is more dynamic and “contested” than the headlines suggest. By analyzing the economic fundamentals of AI platforms, tracking hundreds of acquisitions across the AI value chain, and surveying more than 300 business leaders about their actual AI adoption patterns, their research reveals a market that remains remarkably open, provided you know where the real leverage points lie.
Key Insight: GenAI isn’t Repeating Web 2.0’s Playbook
“The debate on the successes and failures of antitrust in the Web 2.0 era has identified certain economic fundamentals that can contribute to market tipping.” [1]
While many observers fear that GenAI could follow the same path of consolidation as Web 2.0, the researchers found a different story. Whereas platforms like Facebook relied on network effects (where an increase in users created greater value for all users), GenAI tools currently function as individual productivity and knowledge aids without forming connections between users. Data feedback loops also appear weaker. While models do learn from interaction, the authors note that the complexity of GenAI interactions makes it harder to engineer the kind of self-improving loop that helped search and social products pull away from rivals. They even flag a downside risk, “model collapse,” where training on synthetic data can amplify errors and bias over time rather than improve quality. Finally, there’s pricing. Unlike many Web 2.0 services, GenAI consumes substantial compute and energy, which creates real costs and pushes providers toward tiered pricing. That matters competitively, because it reintroduces a familiar dynamic: entrants can attack on cost, quality, or both, rather than being boxed out by a dominant incumbent offering a free service.
Key Insight: Strategic Moves Across the AI Stack
“Some of the concerns stemming from the Web 2.0 experience are related to how large firms can leverage their position in one sector of the economy to increase control over adjacent segments.” [2]
The GenAI economy is best understood as a stack of five layers: chip manufacture, design, compute infrastructure, foundation models, and applications. While the application layer is exploding with roughly 1,600 active firms, the top of the stack remains highly concentrated. This has fueled a flurry of vertical integration. For instance, NVIDIA has expanded downstream from chip design into model orchestration through acquisitions like Run:AI, while cloud providers like Microsoft and Amazon are moving upstream into chip design and securing exclusive partnerships with model developers like OpenAI and Anthropic. However, this integration isn’t always a sign of impending monopoly. Cross-layer moves can actually increase competition by reducing dependencies. For example, NVIDIA partnering with emerging cloud providers like CoreWeave creates competition for AWS and Azure.
Key Insight: A Still-Open Market
“Most respondents reported multihoming, especially combinations involving ChatGPT, Microsoft Copilot, Claude, and Gemini.” [3]
The researchers surveyed 323 business leaders across industries and geographies in May 2025. Nearly 90 percent report some GenAI use within their organizations, but what’s striking is how they’re using it. The vast majority are multihoming—using multiple models such as ChatGPT, Claude, and Gemini, simultaneously. While this could be users taking advantage of greater capabilities for specific tasks within certain models, the authors also suggest a broader economic hypothesis: multihoming enables flexibility and thereby prevents potentially costly lock-in at an early stage of the GenAI transformation.
Bonus
Even if GenAI competition stays “contested,” you can still end up locked in through messy, unmanaged adoption. For a look at why AI strategy is organization design strategy, check out “The People, Processes, and Politics of AI ROI.”
Why This Matters
For business leaders and executives, this is a strategy and execution problem disguised as a technology trend. GenAI’s current economics suggest the market may stay contestable longer than Web 2.0 did, and the move is to treat the moment less like vendor selection and more like competition positioning. Design your organization to learn fast, build internal muscle, and avoid early lock-in while tools, pricing, and performance are still moving targets.
References
[1] Andrea Asoni et al., “Contested Ground: Early Competition and Market Dynamics in Generative AI,” Management and Business Review, 5(4) (2025): 55, https://doi.org/10.1177/2694104X251404174.
[2] Asoni et al., “Contested Ground”: 58.
[3] Asoni et al., “Contested Ground”: 61.
Meet the Authors
Andrea Asoni is an economist and Vice President of Charles River Associates.
Chiara Farronato is Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School and co-Principal Investigatory of the Platform Lab at the Digital Data Design Institute at Harvard (D^3).
Matteo Foschi is a Vice President in Charles River Associates’ European Competition Practice.
Oliver Latham is a Vice President in Charles River Associates’ European Competition Practice.