‘Fail fast, fail cheap’ frequently appears in the business model design literature suggesting that startups should make fast and low-cost experiments while searching for their business models. This has left underexplored instances in which experimentation is inherently high-cost and product development requires an extended timeline. Such is the case of designing business models when technical uncertainty is high. Many examples can be seen in industries including biotechnology, healthcare, and mega-engineering. This is important because not all startups can make low-cost experiments, and not all experiments can be done quickly. The design of effective business models can have an impact on the profitability and long-term survival of startups, especially when they must navigate expensive costs of experimentation and product development that takes years. To investigate this phenomenon, we conducted a longitudinal case study in a biotechnology startup. We contribute in three ways. First, rather than testing multiple activity systems, startups can leverage a single system to explore multiple commercial opportunities, thereby focusing on reducing uncertainty about the products they can or cannot build. Second, we extend validated learning beyond a market-focused approach to also address technical uncertainty, showing that scientific experimentation can guide product development when rapid prototyping is not possible. Finally, we demonstrate that entrepreneurial resource mobilization involves not only seeking financial, human, and social capital but also ‘resource-holder-partners’ who provide access to resources and collaborate in product creation. We advance the understanding of the process of designing business models, especially when ‘fail fast, fail cheap’ may not be applicable.
