From algorithmic hallucinations to alien minds: Addressing the ideator's dilemma through entrepreneurial work

Abstract

In recent years, the rapid adoption of Generative AI tools by entrepreneurs is transforming entrepreneurial ideation processes. Powered by increasingly sophisticated algorithms and massive computing facilities, Gen AI systems are capable of generating extraordinarily creative ideas that often surpass the abilities of human entrepreneurs. Yet, despite these benefits, Gen AI systems also create a series of important epistemic risks for entrepreneurs, most notably: algorithmic hallucinations and ‘alien minds.’ In the near term, the tendency of Gen AI systems to ‘hallucinate’ new ideas that appear plausible but lack a logical or factual basis amplifies the risks that entrepreneurs will invest valuable time, effort, and resources in pursuit of flawed ideas. As the capabilities and intelligence of these systems continue to grow, however, entrepreneurs also face an emerging risk of falsely rejecting breakthrough ideas recommended by ‘alien minds’ they do not understand. For entrepreneurs, the opaque processes through which Gen AI systems generate new ideas create an ideator's dilemma where entrepreneurs do not know if a Gen AI idea is a true, breakthrough innovation or simply a hallucinated mirage. In this study, we extend emerging theory on entrepreneurial work to examine the complementary roles of two distinct types of judgments – possibility and plausibility judgments – in enabling entrepreneurs to evaluate Gen AI ideas. Towards this end, we integrate these judgments into a comprehensive Popperian approach to entrepreneurial work, enabling entrepreneurs to more effectively address the inherent epistemic risks associated with using Gen AI in entrepreneurial ideation. In doing so, our study contributes important new insights regarding the fundamental role of entrepreneurial work in addressing the ideator's dilemma in entrepreneurial ideation processes.

Executive summary

Generative AI is fundamentally transforming how entrepreneurs identify and develop new venture opportunities. With 89 % of founders now using at least one AI model and 50 % employing four or more in daily ideation work, these tools have become integral to entrepreneurial innovation. However, this rapid adoption creates unprecedented challenges that demand new frameworks for evaluating AI-generated business concepts.

The democratization of AI tools creates fundamental challenge for entrepreneurs: if every entrepreneur can generate breakthrough ideas at scale, where lies competitive advantage? Our research reveals that judgment – not idea generation – becomes the scarce resource. As ventures pursue increasingly speculative concepts to maintain competitive edge (evidenced by Y Combinator's shift towards deep tech ventures), the ability to efficiently evaluate and actualize AI concepts becomes paramount.

At the same time, these fundamental challenges are amplified by the growing sophistication of the reasoning capabilities of frontier Gen AI systems. To illustrate these challenges, we posed a single question to the 7 leading Gen AI models, which all provided forecasts estimating the probability within five years that AI systems will generate venture concepts beyond expert entrepreneurs' full comprehension. Current systems already demonstrate this capability in specialized domains. The integration of multimodal models, real-time data, and agentic systems will accelerate this trend.

For entrepreneurs, this means reconceptualizing the entrepreneurial process. Rather than competing on idea generation – where AI increasingly dominates – successful ventures will differentiate through superior judgment, interpretation, and actualization capabilities. The entrepreneurs who thrive will be those who can effectively orchestrate human-AI collaboration, maintaining human agency while leveraging AI's superhuman pattern recognition.

The ideator's dilemma: Hallucinations and alien minds

Building on these arguments, one of the central contributions of our study examines what we refer to as the Ideator's Dilemma. Given the training methods – especially the growing use of deep reinforcement learning methods – leading AI systems are creating two critical, interrelated epistemic risks for entrepreneurs using these tools in ideation processes: First, algorithmic hallucinations occur when AI systems generate plausible-sounding ideas that lack factual or logical basis. These can lead entrepreneurs to waste valuable resources pursuing impossible ideas. Second, entrepreneurs are facing a growing set of challenges in addressing the “alien minds” problem – where AI systems generate genuinely breakthrough concepts through reasoning processes so advanced or unconventional that human entrepreneurs cannot fully comprehend them.

For entrepreneurs, these two epistemic risks combine to create what we term the “ideator's dilemma”: entrepreneurs cannot determine whether an AI-generated idea represents a revolutionary opportunity or a convincing mirage. As AI systems grow more sophisticated, particularly through deep reinforcement learning methods that operate without human oversight, this dilemma intensifies. Recent developments like DeepSeek's autonomous discovery of novel problem-solving strategies and AlphaDev's counterintuitive algorithmic breakthroughs demonstrate that AI systems are already generating solutions that initially appear incorrect but prove revolutionary.

A novel framework for AI-enabled entrepreneurship

To address Ideator's Dilemma, our study extends recent work in the field of entrepreneurship to build a Popperian approach to address these challenges through two complementary types of entrepreneurial judgment:

Possibility Judgments focus on falsification – rigorously testing whether necessary conditions exist for an idea to be actualizable. Entrepreneurs should ask “why won't this work?” before investing resources. This approach quickly eliminates algorithmic hallucinations by establishing clear boundaries between possible and impossible new venture concepts. The key is developing bold, testable conjectures that can be decisively falsified if the concept is fundamentally flawed.

Plausibility Judgments employ corroboration to identify sufficient conditions for actualization. Once an idea passes falsification tests, entrepreneurs assess how to configure enabling factors – market conditions, technical infrastructure, regulatory environment – to bring the concept to market. This stage addresses the alien minds problem by translating opaque AI reasoning into actionable business strategies.

The Popperian approach we outline provides a practical methodology for navigating this transition. To illustrate the benefits of such an approach, we provide an in-depth example of the Ideator's Dilemma through a Gen AI idea building on an extremely speculative, advanced integration of ‘new quantum physics.’ This idea illustrates the unique epistemic risks entrepreneurs already face in evaluating and vetting Gen AI ideas to identify algorithmic hallucinations while addressing the challenges of verifying an advanced concept that is well outside of our current capabilities.

Overall, our study illuminates an important set of fundamental shifts in how opportunities are identified, evaluated, and actualized. Startups that develop robust frameworks for human-AI collaboration in ideation will capture disproportionate value in the emerging economy, while those that fail to adapt risk being overwhelmed by the Ideator's Dilemma.