The AI Era and the Challenge for Venture Capitalists

Artificial intelligence has revolutionized the product development landscape, making it easier than ever to create visually impressive demos and prototypes. What once took months of work can now be achieved in days, with sleek interfaces and convincing functionalities. While this acceleration democratizes innovation, it poses a significant challenge for Seed-stage investors: how to distinguish a product with real value, defensible technology, and a team capable of building something lasting, beyond the superficial shine?

To address this question, we spoke with Alison Imbert, Partner at Partech, one of the leading global venture capital firms. Founded in 1982 and headquartered in Paris, Partech manages approximately €2.5-3 billion in assets and supports a portfolio of over 200 companies across 40 countries. Its “full-stack” structure allows it to invest in startups from Seed stage to growth, maintaining continuous involvement in company development. Imbert, with a background in engineering and data science, focuses on investing in top engineering talent, an area where her experience and network prove crucial.

Beyond the Demo: The Search for Intrinsic Value

For Imbert, Seed-stage investing goes far beyond a flawless pitch deck or a brilliant demo. "The demo is worth nothing. It’s so easy to build," she asserts, highlighting how the reduction in development costs has changed company evaluation metrics. The focus thus shifts to a deep understanding of the market and customers, an insight that AI alone cannot provide.

"AI won’t give you deep market insight — only direct conversations with customers will," Imbert explains. She looks for founders obsessed with understanding their customers' "pain" and willing to spend the next decade working on it. A recurring question is: "How many clients or prospects did you interview in the last two months?" It's not polished materials that matter, but the depth of insight, often hidden in "messy notes" from fieldwork. This dedication to customer understanding is a crucial signal of long-term commitment and a solid foundation for product development. For companies operating with LLMs or complex AI solutions, this in-depth research is even more critical, as understanding specific end-user needs and deployment constraints (such as data sovereignty or on-premise performance) can determine the success or failure of an implementation.

The Founder at the Core: Credibility, Vision, and Humility

The quality of the founding team is a fundamental pillar in Partech's evaluation. Imbert looks for not only intellectual intelligence (IQ) but also emotional intelligence (EQ), and founders who are true "talent magnets," capable of attracting and inspiring people. Credibility is vital, especially for younger founders interacting with established organizations. Partech tests this credibility by introducing startups to CTOs or CISOs from companies in their network, observing how they perform under pressure and if they handle difficult questions with clarity and confidence.

Equally important is humility. In a rapidly evolving market, founders must be able to adapt quickly, admit their mistakes, and, if necessary, pivot. "We’re not just looking for smart people with a plan; we want people who can learn," Imbert states. Finally, alignment among co-founders on the company's long-term vision is non-negotiable. Imbert interviews them separately, asking questions like their willingness to take a €20 million exit tomorrow. Misaligned answers often end the conversation, as cohesion and shared vision are essential for overcoming growth challenges.

Beyond the Product: Proprietary Data and Distribution Strategies

In a context where AI facilitates product creation, competitive advantage shifts. Proprietary data becomes a crucial asset. "We want to understand how founders will access and build unique datasets that improve their product over time," Imbert explains. This ability to generate and leverage exclusive data is a form of "moat" (defensible advantage) that makes the product harder to replicate. For companies developing LLMs or AI solutions, the quality and exclusivity of the data used for fine-tuning or to power models can be a distinguishing factor, especially in scenarios requiring high security and data sovereignty standards, often managed through self-hosted or air-gapped deployments.

Distribution is another key element. While some founders rely on capital to dominate distribution, Imbert seeks more defensible strategies, based on network effects, brand, or critical user access. "Differentiation is no longer just the product," she emphasizes. For AI-embedded products, there's an inherent tension between decision-makers (who push for adoption) and end-users (often resistant to changing workflows). Onboarding becomes critical: change cannot be forced, but guided. Success depends on how well this transition is managed, as strong top-down demand does not guarantee adoption. This is particularly true for AI solutions requiring deep integration with existing infrastructure, where ease of deployment and user acceptance are critical factors for TCO and overall success.