Why Founders Follow the Crowd—and How Ecosystems Can Change That

Herd behavior in startups is not just about incentives. It is also about how ecosystems signal which ventures deserve attention, capital, and recognition.

Spend time in any entrepreneurship ecosystem and a pattern emerges. Founders, investors, mentors, and support organizations begin to converge on the same ideas and markers of success. Academic research describes this as herding, isomorphism, or signaling dynamics, in which capital concentrates in a sector, founders move toward that sector, and additional capital follows. 

Over time, ecosystems converge on similar founder archetypes, investment theses, and venture types. This is intuitive in that incentives drive behavior. But the question becomes not whether incentives drive behavior, but why those incentives prove enticing and value-shaping in the first place. Entrepreneurship ecosystems shape entrepreneurial pursuits and outcomes by shaping what participants come to desire through socially mediated evaluation. When that process narrows what is seen as worth building, it limits the range of ventures pursued and encourages ecosystems to reproduce models that may not make sense for them.

Why convergence occurs

René Girard’s theory of mimetic desire offers insight into how desires form and why they converge on the same object. Girard was a cultural anthropologist at Stanford University who used literature to understand how desire forms and how that affects group dynamics. His theory has been applied to religion, politics, pop culture, and organizational behavior. His central claim is that an individual’s desires are not authentic or independently derived; individuals come to want objects, roles, and outcomes by observing what others desire or possess. 

To use a simple example, if babysitter puts five toys in a room with two toddlers, they’ll both converge on the same toy. In entrepreneurship ecosystems, we see the same behavior when founders and investors rush to “hot” sectors and ecosystems adopt the playbooks of other ecosystems. By applying mimetic desire to entrepreneurship ecosystems, researchers and practitioners can develop a deeper understanding of how entrepreneurship ecosystems operate and how founders' aspirations are formed. 

Researchers have explained convergence as the result of signaling, herding, and informational cascades. In environments characterized by high levels of uncertainty, people tend to look to others, or do what others do, to generate a higher level of confidence in their decision. For instance, when investing in an early-stage startup, funds will decide whether to invest based upon what other investors do. What has not been explained is why copying others seems to be the default response. 

Girard’s theory of mimetic desire helps us better understand why individuals begin to mimic one another in environments like entrepreneurship ecosystems, where they are in close proximity to one another; there are shared evaluative criteria, and exemplars of success are repeatedly amplified.

When we understand that desire is socially mediated, we have the opportunity to design, or redesign, systems that can mitigate some of the negative outcomes of mimetic desire. When convergence occurs within an entrepreneurship ecosystem, the types of ventures and founders deemed viable homogenizes. This can limit opportunity recognition and access. It can also lead to ecosystems using tactics that are less well suited to their particular environment and circumstance. Just because an ecosystem works a certain way in San Francisco doesn’t mean it should be replicated in Peoria, IL. 

The Consequences

Once desire is shaped through shared models and repeated evaluation, the effects become visible in how ecosystems allocate attention, capital, and effort. Convergence emerges through ordinary decisions as founders adjust what they build; investors adjust what they fund; and support organizations adjust what they promote. Over time, these adjustments accumulate and narrow the range of ventures, sectors, and outcomes that are taken seriously within the ecosystem. The consequences below illustrate how socially mediated desire produces convergence and other recurring patterns across entrepreneurship ecosystems.

Founders move toward ventures that align with dominant funding models.

When capital concentrates in specific categories, founders adjust accordingly. During the rise of fintech and, more recently with AI, founders without prior domain experience moved into these sectors to align with investor demand. This pattern reflects more than opportunity recognition; it is consistent with research on signaling and venture capital selection, which shows that entrepreneurs shape ventures to match what investors recognize as fundable, just as investors chase trends.

Capital and talent cluster around a narrow set of sectors.

Investment waves produce rapid entry into specific categories. The dot-com boom, clean tech, and crypto follow this pattern. Capital attracts talent, and talent attracts additional capital, reinforcing concentration. This dynamic is well documented in studies of herding and informational cascades. Within ecosystems, this clustering is visible in shorter cycles. A sector gains attention, events and programming follow, and founders reposition to participate. Over time, cohorts of startups emerge around a small number of themes.

Startups compete within increasingly similar spaces.

Within many ecosystems, multiple firms pursue adjacent ideas with limited differentiation. Food delivery, ridesharing, buy-now-pay-later, and vertical SaaS categories illustrate this pattern. Research on institutional isomorphism explains how organizations converge toward recognized models to gain legitimacy. Shared formats reinforce this convergence. In accelerator demo days, ventures are presented sequentially using similar narratives and metrics, making comparison immediate and unavoidable.

A single definition of success becomes dominant.

Across ecosystems, success is often reduced to valuation and exit. Alternative paths such as profitable firms, smaller-scale ventures, or regionally embedded businesses receive less recognition. The concentration of evaluation is visible in how outcomes are discussed. Funding announcements, valuation milestones, and exits are widely circulated and celebrated, while other outcomes remain less visible even when they are economically viable or socially valuable. The narrowing of recognition reflects not only market incentives, but also the absence of countervailing evaluative frameworks rooted in other associations. Ecosystem research shows how density, visibility, and interaction shape what is considered legitimate and worth pursuing. 

Designing Ecosystems that Shape Desire Differently

The preceding analysis shows that these patterns arise from how ecosystems organize evaluation and recognition. Changing how ventures are evaluated, compared, and recognized within entrepreneurship ecosystems changes what participants come to pursue. The four recommendations that follow identify specific ways ecosystem leaders can influence what participants come to value by changing how ventures are evaluated, compared, and recognized.

Expand what gets recognized.

How it typically operates
Recognition is concentrated around venture-backed growth. Investors prioritize scalable, high-growth firms; mentors reinforce those expectations; and ecosystem programming amplifies those signals. Y Combinator Demo Day illustrates this clearly: Companies present in rapid succession using standardized growth metrics (revenue, user growth, TAM), and post-demo discourse centers on funding outcomes. Similarly, platforms such as Crunchbase and TechCrunch systematically track and publicize funding rounds, valuations, and exits, while rarely covering profitable, non-venture-backed firms. Within firms like Sequoia Capital, internal memos and public essays (e.g., “RIP Good Times”) reinforce scale, growth, and market dominance as primary evaluative criteria.

How it can be revised
Recognition can be broadened to include multiple forms of entrepreneurial success, making alternative paths visible and legitimate.

Where this is happening
In Denmark, programs such as those in the Copenhagen School of Entrepreneurship and DTU Skylab explicitly support student and founder ventures that prioritize sustainability, social impact, and technical problem-solving without requiring venture-scale growth. These programs run incubators where success metrics include pilot deployments, partnerships, and societal impact rather than venture funding. Public initiatives such as Innovation Fund Denmark provide grants for early-stage ventures based on research translation and societal contribution rather than investor readiness, thereby expanding what is recognized as legitimate entrepreneurial success.

Reduce forced comparability.

How it typically operates
Standardized formats place ventures into direct comparison by forcing diverse firms to conform to the same template. This is evident in accelerator cohorts such as Techstars, where companies from different sectors present using identical pitch structures and timelines, and are evaluated on comparable KPIs regardless of domain differences. University-led pitch competitions, such as those run through NSF I-Corps–style programs, often apply uniform judging rubrics across hardware, software, and life sciences ventures, compressing fundamentally different development cycles into a single evaluative frame.

How it can be revised
Evaluation standards should vary across sectors, stages, and venture types, reducing pressure to conform to a single model.

Where this is happening
In Berlin, programs such as EIT Climate-KIC and APX operate with differentiated tracks. Climate-KIC, for example, evaluates ventures based on emissions reduction potential, policy alignment, and system-level impact, often over longer timelines than traditional venture programs. APX runs sector-agnostic cohorts but allows variation in milestones and evaluation pacing depending on venture type. Similarly, Creative Destruction Lab structures its programs by domain (AI, quantum, climate, health), with each stream using distinct technical milestones and expert evaluators rather than a single pitch-based comparison system. This reduces forced comparability and aligns evaluation with domain-specific realities.

Build through existing institutions.

How it typically operates: New ecosystem initiatives are frequently built as standalone entities, creating parallel systems of recognition that can displace existing institutions. This is visible in accelerator-centric ecosystems such as Austin, where organizations like Capital Factory function as central nodes of activity. Programming, mentorship, and investor access are concentrated within these hubs, and participation in them becomes a primary signal of legitimacy. Founders outside these networks often face reduced visibility regardless of venture quality.

How it can be revised: Ecosystems can develop through existing community institutions, maintaining multiple sources of recognition and evaluation.

Where this is happening: In Madison, entrepreneurship development is structured through coordinated institutional collaboration rather than a single dominant hub. Programs such as Discovery to Product (D2P) at the University of Wisconsin–Madison integrate research commercialization with industry partnerships and state economic development efforts. StartingBlock Madison operates as a convening space but works in coordination with university programs, local government, and private sector actors rather than replacing them. This preserves multiple channels of recognition tied to research, industry application, and community engagement.

Make trade-offs explicit.

How it typically operates: Ecosystems prioritize growth, scale, and visibility without articulating the consequences of those priorities, allowing convergence to emerge implicitly. Consider how venture capital allocates attention and resources. In Silicon Valley, firms such as Andreessen Horowitz and Sequoia Capital concentrate capital in sectors perceived as high-growth opportunities (e.g., social media, crypto, AI). Their investment theses are widely circulated through blogs, podcasts, and founder networks, shaping what founders perceive as viable opportunities. Media platforms such as TechCrunch reinforce this by disproportionately covering venture-backed firms, while databases like PitchBook track and rank ecosystems based on capital flows and exits, further institutionalizing these metrics.

How it can be revised: Trade-offs between concentrated growth and diversified participation can be made explicit and reflected in programming, evaluation criteria, and resource allocation.

Where this is happening: In Austin, while venture-backed growth is prominent through Capital Factory and SXSW startup programming, parallel institutional efforts such as PeopleFund and Economic Growth Business Incubator (EGBI) explicitly support small businesses, underrepresented founders, and non-venture pathways. These programs operate with different success metrics (revenue stability, job creation, community impact), making trade-offs between growth and inclusivity more visible. In Stockholm, organizations such as Vinnova fund both high-growth technology ventures and sustainability-oriented projects, while incubators like KTH Innovation support a range of ventures from deep tech startups to socially oriented enterprises. These programs operate alongside venture-backed success stories such as Spotify, reflecting a policy environment that makes explicit the balance between economic growth and broader societal outcomes.

Conclusion

Entrepreneurship ecosystems shape what actors come to want in addition to coordinating resources. They do so through evaluation, comparison, proximity, and concentrating recognition around particular outcomes. These dynamics operate through everyday practices rather than explicit direction, yet their effects are cumulative. Over time, they narrow the range of what’s considered a viable or legitimate path. The result is a limited set of ventures, sectors, and definitions of success.

This has implications beyond venture formation. When evaluation becomes concentrated, so too do the aspirations that guide entrepreneurial activity. Founders adjust what they build; investors adjust what they fund; and supporting institutions adjust what they promote. The result is not only economic clustering, but also a redefinition of what is considered worth pursuing within the ecosystem.

The patterns outlined here suggest that convergence is not an inevitable feature of entrepreneurship ecosystems. Expanding recognition, reducing forced comparability, building through existing institutions, and making trade-offs explicit alter the terms under which coordination occurs, thus diversifying sources of aspiration formation. Ecosystems will continue to shape aspiration. The relevant question is whether they do so narrowly, reinforcing a single trajectory, or more broadly, sustaining a wider range of viable and legitimate pursuits.

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