The startup formation process has traditionally depended on a founder's ability to synthesize market signals, craft a compelling narrative, and assemble resources under extreme uncertainty. Generative AI is now inserting itself into each of these stages — from ideation and business plan generation to financial modeling and investor pitch preparation. The question is not whether LLMs will play a role in venture creation, but how deeply they will reshape the entrepreneurial process and whether the ventures they help build will be substantively different from those created through purely human cognition.
From Brainstorming to Business Plan Generation
Cai et al. (2025) conduct one of the first systematic evaluations of large language models as tools for venture creation support. Their study, published as an arXiv preprint, examines how founders use LLMs across the venture development lifecycle — from initial opportunity identification through business model design, competitive analysis, and pitch deck creation. The findings suggest that LLMs are most useful in the early, divergent phases of entrepreneurship: generating alternative business models, identifying potential market segments, and drafting initial financial projections.
However, the research also identifies important limitations. LLMs tend to produce business plans that are internally coherent but insufficiently grounded in market-specific knowledge. A business plan generated by an LLM for a healthcare SaaS startup may read well but miss regulatory constraints that would be obvious to a domain expert. The models also exhibit what the authors call "optimism bias amplification" — they tend to generate projections and market analyses that are systematically more positive than warranted, potentially reinforcing the already-documented tendency of founders toward overconfidence.
The implication is that LLMs are better understood as co-founders with specific cognitive strengths and weaknesses rather than as general-purpose entrepreneurial assistants. They excel at pattern completion, analogy generation, and document production. They struggle with the kind of tacit, context-dependent judgment that experienced entrepreneurs bring to decisions about timing, team composition, and market entry strategy.
AI and Entrepreneurial Intention
Kostis et al. (2024), writing in IEEE Transactions on Engineering Management, examine a different aspect of the AI-entrepreneurship relationship: how awareness of and experience with AI technologies affects entrepreneurial intention — the psychological precursor to venture creation. Their study finds that exposure to AI capabilities does increase entrepreneurial intention, but through a specific mechanism: AI reduces the perceived difficulty of certain entrepreneurial tasks (market research, financial modeling, prototype development), which in turn increases self-efficacy, which drives intention.
This finding has significant implications for entrepreneurship education and policy. If AI lowers the perceived barriers to entrepreneurship, it could expand the pool of potential founders beyond the traditional demographics — younger founders who lack business experience but can leverage AI for expertise gaps, founders in developing economies who lack access to expensive consulting services, and technical founders who can now generate business strategy documents without an MBA co-founder.
But the self-efficacy mechanism also introduces a risk. If AI makes founders feel more capable than they actually are — if it reduces perceived difficulty without actually reducing actual difficulty — the result could be an increase in venture creation accompanied by a decrease in venture quality. More startups, but more failures. More pitches, but weaker businesses. The data to test this hypothesis does not yet exist in sufficient quantity, but the theoretical concern is well-grounded in the entrepreneurship literature on overconfidence and failure rates.
Due Diligence and Investment Decision Support
Mudapaka et al. (2025), presenting at the European Conference on Innovation and Entrepreneurship, examine the other side of the venture creation process: how AI is transforming due diligence and investment decision-making. Their research finds that venture capital firms are increasingly using AI tools to screen deal flow, analyze market opportunities, assess founder-team fit, and predict startup success probabilities.
The due diligence application is perhaps where generative AI's impact on entrepreneurship is most immediate and measurable. Traditional due diligence is labor-intensive: analysts spend weeks reviewing financial statements, interviewing references, analyzing competitive landscapes, and building financial models. AI can compress much of this work into hours, allowing VCs to evaluate more deals with the same team size and potentially improving the consistency of evaluation criteria across deals.
Yet the authors raise important concerns about algorithmic due diligence. VC investment decisions are fundamentally about predicting success under uncertainty, and the training data for such predictions is inherently biased toward certain types of founders, industries, and business models. AI due diligence tools trained on historical investment data will encode the biases of past investment decisions — favoring founders from elite universities, certain geographies, and established industry categories while potentially disadvantaging founders from non-traditional backgrounds.
The Emerging Hybrid Model
Taken together, these three lines of research point toward a hybrid model of AI-augmented entrepreneurship that is neither the techno-utopian vision of AI as a perfect co-founder nor the techno-skeptical dismissal of AI as irrelevant to the deeply human process of venture creation. The emerging reality is more nuanced: AI tools are becoming embedded in entrepreneurial processes in ways that change the economics, demographics, and cognitive dynamics of venture creation.
The economics change because AI reduces the cost of certain entrepreneurial activities — market research, document production, financial modeling — while leaving the cost of others unchanged — relationship building, team recruitment, product-market fit discovery through customer interaction. The demographics may change because lower costs and reduced perceived barriers could attract a wider range of founders. The cognitive dynamics change because founders who rely on AI for certain tasks may develop different patterns of attention and judgment than founders who perform those tasks manually.
The open questions are substantial. Will AI-augmented ventures perform better or worse than traditionally founded ventures? Will AI due diligence reduce or amplify investment biases? Will the expansion of the founder pool through AI-lowered barriers produce more successful ventures or more noise in an already-crowded startup ecosystem? These are empirical questions that the next decade of entrepreneurship research will need to address.