The GenAI Entrepreneur’s Dilemma: Overconfidence and Crumbling Moats

Balázs Kovács explores how generative AI, while democratizing the creation of new digital ventures, also fosters entrepreneurial overconfidence. He identifies the strategic pitfalls of this paradox and offers guidance for building defensible businesses when technical skill is no longer a competitive advantage.

Last semester, one of my MBA students, let’s call them Charlie, demonstrated a marketplace app in class. With a series of slides that could have described one of the established founders in the field, they presented an app with a polished interface and a seamless user flow. When I asked about their process, Charlie proudly said: “I built the prototype last weekend using ChatGPT and Cursor. Took about six hours.” Several of their classmates were impressed by how quickly they could build something new and started to consider their own ventures.

I asked the class to pause and think: “If you can build this in six hours, so can anyone else. What makes your company unique?” It’s a question that prompts students to think carefully about competitive advantage.

This scenario is not unique to my class. Students who once spent months or years learning to code can now run complex applications through the entire prototype process in a few hours. Founders who would once have needed a technically capable partner can build minimum viable products by themselves. The benefits of this democratization are clear, but it also carries the risk of dramatically increasing entrepreneurial overconfidence. Since competitors can also replicate innovations in days, this overconfidence is likely to be exacerbated by dwindling defensibility. These twin dynamics - psychological and strategic - reinforce each other, creating ventures that feel strong to their founders but prove vulnerable in practice. While generative AI’s (GenAI’s) power to encourage overconfidence depends on how entrepreneurs engage with it, observed patterns suggest that most people use these tools to reinforce rather than challenge their existing beliefs.

AI saves time and expense, especially in early product development, but turning its quick builds into viable and defensible businesses is a challenge. Throughout this analysis I have focused on using GenAI to develop high-growth digital ventures, in which the illusion of competitive advantage is most pronounced, and not on AI-first businesses in which artificial intelligence is the core value proposition.

Building is now simple, but creating lasting value is more difficult than ever, especially when enthusiasm for AI obscures the complexities of strategic decision making.

Generative AI influences most sectors, but its disruption of technology-driven entrepreneurship, where it influences various stages, is most immediate. GenAI can improve individual creativity and ideation, but shared training data and similar prompts can cause entrepreneurs to cluster around similar concepts or opportunities. During prototyping and implementation, the technology reduces development time sharply, but it also erodes the advantages of being first because competitors can replicate an innovation just as fast. The competitive advantage of easy and rapid prototyping may well be an illusion, encouraging people to launch a surge of similar startups that struggle to differentiate themselves. What all of this means is that the core challenge has changed: building is now simple, but creating lasting value is more difficult than ever, especially when enthusiasm for AI obscures the complexities of strategic decision making. ¹

The Great Acceleration

Entrepreneurial prototyping has accelerated, especially in the tech industries. It’s not just students like Charlie who now build functional prototypes in mere hours: prospective founders do it too, racing through a task that used to take months. This shift is changing how new ventures emerge. I have watched students with no coding experience quickly build sophisticated applications, including AI-powered software as a service (SaaS) tools, dating apps, and complex marketplaces, as an ordinary homework assignment.

Recent data reveals how far this trend has moved into the startup ecosystem, showing the impact of generative AI on early-stage ventures. Y Combinator’s CEO, Garry Tan, reported that AI wrote approximately 95 percent of the code for roughly a quarter of the startups in a recent cohort. ² In 2025, over 72 percent of new Y Combinator ventures were AI-powered. ³

These trends suggest an entrepreneurial renaissance in which more people can quickly build products. GenAI has substantially eroded the traditional barriers of technical skill, high development costs, and long time-to-market. While it has also lowered entry barriers, prior research reminds us that economic impact is driven largely by a small set of high-growth, innovative startups, not by broad surges of new firms. ⁴

This acceleration, then, introduces a paradox. While entrepreneurs are forming startups at ever increasing rates, the endeavor is fraught with new challenges. Success still depends on underlying entrepreneurial skill. As more ventures are launched, failure rates have also surged. Some analyses report that AI-driven startups are failing within three years at rates as high as 85 to 92 percent. ⁵ The primary bottleneck is no longer technical execution, but rather fitting products to a market. Anyone can build, so the problem is to determine what to build and whether customers will actually want it. The ease and rapidity of prototyping masks the complexity and risk of making a venture succeed.

Yet this democratization of entrepreneurship does have its benefits. Because experimentation is cheaper, entrepreneurs can afford to pursue ideas with a lower probability of success and the possibility of enormous value. ⁶ When photography went from film to digital, the average quality of individual photos may have declined, but photographers could suddenly take thousands of shots to get the perfect image. The inexpensive experimentation of GenAI has a similar effect; it may yield more failures, but it also enables breakthrough innovations that people would have been unlikely to attempt at a higher cost.

We must learn to distinguish between rational experimentation and perilous overconfidence.

Walk into any entrepreneurship classroom or startup accelerator today, and you’ll feel the electricity—a thrilling belief that we’re on the cusp of a new golden age for entrepreneurs. Founders describe building in days what once took months, investors speak of an unprecedented flow of deals, and educators marvel as their students launch a series of ventures each semester. The euphoria is understandable; a literature major can build a SaaS product over a weekend and a solo founder can realistically compete with a development team. The moment feels utopian: entrepreneurship democratized, innovation accelerated, human potential expanded. But the promise masks a trap: we must learn to distinguish between rational experimentation and perilous overconfidence.

The Overconfidence Trap

The term competition neglect describes the tendency of firm leaders to make decisions, like entering a new market, while focusing entirely on their own capabilities and ignoring those of their competitors. This phenomenon is not new; it is a fundamental error in strategic thinking. In their seminal experiments, Camerer and Lovallo asked subjects to decide whether to enter a market in which the payoffs depended on their skill, relative to that of other entrants. ⁷ They discovered that subjects were substantially more likely to enter unwisely when they knew it was a test of skill. Confident in their own abilities, they forgot that they were competing against a reference group of equally confident peers.

Generative AI is a catalyst for this kind of bias because our confidence tends to increase when tasks become easier. ⁸ By transforming traditionally difficult entrepreneurial tasks into easy ones, GenAI can make people overconfident. Finding development widely accessible, they fall into a self-reinforcing cycle of overconfidence and neglect their competition. This cycle includes three related but distinct psychological mechanisms: overestimation, overplacement, and overprecision.

The cycle begins when people overestimate, believing they have more skill than they do. ⁹ The ease of using GenAI amplifies this bias. When Charlie developed their app in a single weekend, the polished prototype gave them an exaggerated sense of achievement, masking the distinction between creating features and building a sustainable business. And they are not alone; many interpret the ease of AI-assisted tasks as evidence of their own entrepreneurial competence, reveling in a superficial sense of success that obscures their strategic weaknesses.

This inflated sense of personal capability feeds overplacement, the belief that one is superior to others, and competition neglect takes hold. Inflated by the speed and quality of their own execution, people mistakenly believe they are uniquely positioned at the head of the market. They fail to objectively evaluate their capabilities relative to those of competitors who have access to the same tools, neglecting their reference group. In my class, for instance, several teams independently proposed AI-powered tools for travel planning. Each group emphasized subtle differences in features as their advantage, but none of them acknowledged the central problem that rapidly creating a digital product is no longer, in itself, a unique competitive edge.

Next, people reinforce their flawed strategy with overprecision: an excessive and unwarranted certainty in one’s judgment. Their overconfidence, which is particularly harmful to the performance of a new venture, is fostered by the compressed feedback cycles of GenAI. ¹⁰ In the past, entrepreneurs could use the long delay before they received market feedback to reflect. GenAI removes this brake by accelerating the cycles of build, test, and improve, thereby creating the illusion of progress. It’s all too easy to mistake frequency of tests for depth of learning or to AI-generate what appears to be supportive information. Both can strengthen confirmation bias, reinforcing people’s erroneous confidence, even in the face of ambiguous external signals. This cycle helps explain why so many entrepreneurs launch ventures that face unfavorable odds, believing they have more than a 70 percent chance of success, when the actual survival rate is below 30 percent. ¹¹

While GenAI can amplify overconfidence, its effects depend on how users engage with it. With thoughtful prompting, GenAI can find opposing arguments, identify market blind spots, and test assumptions. The danger is that users who are predisposed to high self-confidence tend to compose prompts that cause these tools to confirm their existing beliefs, asking ‘How can I make this work?’ rather than ‘What could go wrong?’ GenAI’s potential to combat bias thus remains largely unrealized. And even when it does give sound advice, the results still depend on the user’s ability to interpret and act on that advice.

The Psychology of Deceptive Ease

So why does generative AI intensify overconfidence in would-be entrepreneurs? The key is what cognitive psychologists call the “illusion of explanatory depth. ¹² ” Briefly put, people overestimate their understanding of complex systems when they have only superficial interactions with them. Generative AI fuels this illusion, giving users surface access to complex tasks without requiring any deep understanding. ¹³

Consider software programming. Traditionally, software engineers had to confront complexity repeatedly as they learned to code. Every bug, every syntax error, every difficult architectural decision revealed the difficulty of building functional software. The unavoidable scale of the challenge and the struggle to meet it prevented overconfidence.

Generative AI eliminates this sobering struggle. Students can rapidly produce functional applications without ever grappling with their underlying complexity. Similarly, readers often can’t tell GPT-4

generated reviews from human ones, producing the sense that AI outputs, and by extension the entrepreneurs who prompt them, are easily producing content that seems authentic. ¹⁴ But this impression of mastery is largely illusory. Users mistake their proficiency with AI tools for entrepreneurial ability, and those with the least expertise are the most overconfident. This effect can be strongest among those who believe quick success with GenAI has allowed them to acquire new skills rather than recognizing it as a temporary shortcut that only postpones the need for technical expertise. And the illusion of mastery is particularly dangerous because it masks the underlying risks of generative AI, including data privacy issues, bias, and hallucinations, in which AI produces false or misleading results rooted in mistaken or nonexistent data patterns. ¹⁵

In short, people mistake the sophisticated products of AI tools for their own abilities. When AI generates complex code or elegant models, users see themselves as skilled programmers or gifted designers. The conceptual distinction between the tool’s abilities and the user’s fades, hindering people’s entrepreneurial judgment and obscuring the importance of human critical thinking.

Informal discussions with my entrepreneurship students over the past year confirm this pattern. They report that prototyping has become significantly easier and increasingly consider launching ventures. At the same time, few of them recognize the difference between easily creating a concept and building sustainable business value.

While AI tools do boost confidence by making difficult tasks easier, this extra confidence does not always reflect the user’s real abilities or produce better results.

A recent study offers concrete evidence of this effect. ¹⁶ In a field experiment with Kenyan entrepreneurs, researchers found that those whose startups had already performed well improved their business outcomes by 15 percent after using an AI coach. Those whose startups had previously performed badly, by contrast, saw an 8 percent decline. The authors concluded that, while AI tools do boost confidence by making difficult tasks easier, this extra confidence does not always reflect the user’s real abilities or produce better results.

This research also points to a deeper risk: entrepreneurs who rely on AI feedback may become not just overconfident but dependent on algorithmic suggestions. As my students sometimes demonstrate, the convenience of AI coaching can make it all too easy to substitute the tool’s advice for their own judgment. In the long term, this dependency can weaken their ability to spot problems and to adapt when the tool fails. Entrepreneurs must be alert to the danger that overreliance on AI may erode their ability to think independently and develop new skills.

Building Moats in Quicksand

The democratization of technical capabilities calls upon entrepreneurs to rethink their strategy. Traditional advantages like fast execution and desirable features disappear when everyone is using similar AI tools. Entrepreneurs must find ways to differentiate their product that cannot easily be replicated. Collaborations between human and machine and innovative business models driven by AI are good starting points. ¹⁷

Emerging research suggests five strategic moats that are still defensible. Maintaining these moats requires entrepreneurs to develop proprietary AI capabilities, such as data pipelines and algorithms, and apply them to innovative business models that focus on cocreation with customers and data-driven operations.

Proprietary data

Proprietary datasets improve the performance of AI models and enable unique functions. For instance, one team in my class proposed an AI that would recommend the best places for watching the sunset. They built a proprietary dataset that combined social media photos taken from many locations with weather and terrain data, producing a tool that could predict where the view would be most spectacular on any given day.

Example tactic: Get access to unique data and avoid sharing your own by making deals with smaller companies that need you more than you need them. Use their data to make your AI smarter.

Workflow integration

Integrating your product deeply into existing workflows encourages users to pay for it. Some ventures become indispensable by embedding their solutions into the daily routines of their users. By integrating an AI-powered note taker into the existing electronic health record systems used by medical professionals, for example, you could make it a vital part of their daily documentation process and generate significant revenue.

Example tactic: Start by building your tool as an add-on for a popular platform everyone already uses, like Salesforce or Google Chrome. Once your users rely on it, launch your full, independent product and encourage them to move to it.

Community and network effects

Human communities are difficult for AI to replicate. Ventures that build engaged user communities, cultivating the social capital they need at various stages of innovation, create a moat comprising collective investment and shared identity. ¹⁸ A fitness app that connects users with others who have similar training goals and encourages them to share their progress creates value that increases with each new member. The platform’s users find it ever more valuable and harder to abandon.

Example tactic: Start with an invitation-only version of your product. Give users free access to better features when they get their friends to sign up. Your platform will grow around tight-knit groups.

Regulatory advantages

Navigating complicated regulations and gaining certifications protects your product from rapid replication. One group of my students proposed establishing a novel government-backed payment system, providing a centralized, fee-free platform for seamless peer-to-peer transfers and retail payments, fully integrated with all banking institutions. This project would be defensible primarily because of the complexity of its regulatory position and licensing.

Example tactic: Coauthor a white paper with someone from a key industry body to establish your methods as the gold standard. You can then use their endorsement to cast your competitors as being a step behind on important industry practices.

Brand and trust

In a world of infinite options, brands are important. Users need heuristics - mental shortcuts or simple, efficient clues - to choose between functionally similar offerings. Building a trusted brand through quality, aligned values, and emotional connection still works. A mental health app, for example, might use transparent privacy practices, a consistent user experience, and authentic communication to build trust and command loyalty, even when competitors offer similar AI-powered therapeutic features.

Example tactic: Pay an outside firm to check the fairness and reliability of your AI and publish the results every quarter. When all products start looking the same, people will trust the one that is the most open and honest.

These moats require valuable abilities that GenAI cannot provide: patience, relationship building, and deep domain expertise. Unlike technical expertise, these abilities remain uniquely human.

While these strategic moats are not new in themselves, their importance has increased dramatically in the face of commoditized technical execution. Entrepreneurs should prioritize them over fast development. These moats require valuable abilities that GenAI cannot provide: patience, relationship building, and deep domain expertise. Unlike technical expertise, these abilities remain uniquely human.

Key Questions

I have used these observations to compose key questions for three groups of stakeholders.

Entrepreneurs

Before launching a new venture, entrepreneurs should consider the following:

Replication test: How quickly could competitors with the same AI tools replicate your core offering? If it’s days or weeks, you don’t have a sustainable advantage.

Skills audit: List your capabilities. Determine whether they belong to you or your tools. True advantages must be able to survive tool commoditization.

Moat map: Explicitly decide which of the five moat strategies you’re pursuing. It may be more than one. Ventures that do not have clear moat strategies rarely survive.

Overconfidence assessment: Regularly assess yourself for the three types of overconfidence: overestimation, overplacement, and overprecision. Warning signs include dismissing announcements from competitors, believing that your execution is unique and superior, and feeling certain about untested assumptions.

If we apply these tests to Charlie’s marketplace app, the results are clear: The replication test reveals that competitors could easily use similar AI tools to copy the core functions in days. The skills audit shows that Charlie has quick engineering but no original insight about the market. Charlie has not determined what will make their position defensible. And they are relying on untested assumptions. Without a stronger plan to stay ahead, Charlie’s venture is likely to become interchangeable with others almost immediately.

Investors

Before proceeding, investors should adapt their due diligence:

Build challenge: Ask entrepreneurs to explain their competitive advantage in light of their competitors’ equal access to AI. If they struggle to do so, they are probably overconfident.

Skills assessment: Probe beyond what entrepreneurs have built to explore their unique knowledge. Domain expertise, networks, and tacit knowledge of market dynamics matter more than technical execution.

Moat testing: Ask entrepreneurs to describe a scenario for how their venture will survive now that everyone can instantly build something similar for free. Those who don’t have compelling answers have probably bet on temporary advantages.

Educators

Educators must change how they teach entrepreneurs:

Teach AI literacy, not dependence: Teach students to use AI tools with a strong understanding of their limitations. Be sure they understand that AI dependence can reduce their ability to think critically. Plan assignments that require them to explain why their proposals will work.

Emphasize strategic thinking: Shift students’ focus from execution to strategy. Building is easy; original thinking differentiates.

Inoculate against overconfidence: Describe cases of failure and illustrate competitive dynamics. Students will build healthy skepticism by understanding how ventures with similarly excellent execution failed.

Require deep work: Assign projects that call for sustained effort and original thought. Deep work will force students to calibrate their confidence more accurately.

The Path Forward

GenAI has radically changed entrepreneurship, creating new opportunities and new perils. That transformation is irreversible; we cannot return to a world where the essential barriers prospective founders face are technical. Giving everyone the ability to build digital products is genuine progress, but it demands new wisdom. Tools that empower users can also give them the wrong impression about their competitive advantage. To succeed, entrepreneurs now have to navigate the paradox of embracing AI’s capabilities while maintaining the ability to clearly assess which of their advantages are sustainable. New entrepreneurs will need to use GenAI as a lever, not a crutch. Their strategic mindset will be crucial. ¹⁹ They must build faster but think deeper, focusing on innovative business models, not just product features. Most importantly, they must understand that when everyone can build anything, the question is not “Can I build this?” but “Should I build this and why will it matter?”

Future researchers should examine how investors are changing their evaluation criteria as technically proficient but strategically similar ventures proliferate. Early evidence suggests that some are combining algorithmic data with human insight to identify genuine differentiation. ²⁰ How the broader ecosystem will respond remains an open question.

As I prepare my next cohort of students, I am reminded of an age-old truth: entrepreneurship is about creating value that others cannot easily replicate. GenAI changes the how, not the why. Tools evolve, but the challenge remains: build something that matters, that lasts, and that others cannot replicate with a few simple prompts.

Author Bio

Balázs Kovács is a professor of management at the Yale School of Management and at the Yale Department of Sociology (courtesy). He studies organization theory, strategy, and entrepreneurship, examining social networks, authenticity, AI, learning, diffusion, identity, and status. His current work investigates the effects of category spanning and innovation in technological and cultural domains. [e-mail]