Why Lean Startup is not for Deep Tech ventures (and what this has to do with pudding)
Thoughts on DeepTech #2
Many of the startups that have received significant funding in the last decade in Europe and the US have been those leveraging existing technology platforms to address specific identified customer needs or problems (e.g., Airbnb, Uber, WeWork). Thus, these companies are primarily driven by user and customer insights to develop an ever-better understanding of the problem space, i.e., the specific customer needs that a product or service should fulfill. These insights are then translated into the solution space, i.e., the potential concrete offerings that companies can make to address these customer needs.
Following the principles of the Lean Startup methodology, these companies are creating product-market fit and business models through iterative cycles of build-measure-learn by leveraging rapid prototypes and small market experiments. Through systematic testing of hypotheses regarding customer preferences, they continuously improve and refine their offerings over time and on the go. Take Uber or Airbnb which still run hundreds of A/B tests each month to gather insights, test, and refine product and service features. As a partner of a Swiss Deep-Tech VC aptly put it in a recent conversation, for these kinds of startups “the proof of the pudding is in the eating.”
However, for reasons inherent in the characteristics of Deep Tech ventures, relying on such real-time proof of concept isn’t always feasible. Unlike conventional startups, the Lean Startup approach does not work, and in some cases, there might not even be a “pudding” to sample:
(1) Most obviously, building prototypes for highly hardware-driven Deep Tech startups, such as in quantum computing or robotics, comes with significant engineering risks and costs. This makes it impractical to develop rapid prototypes for iterative testing with potential customers.
(2) The development cycles of Deep Tech ventures are typically significantly longer compared to their non-Deep Tech counterparts. This poses another major obstacle to implementing the rapid prototyping philosophy.
(3) Most important, the customer, i.e., the problem space, is often unknown. Deep Tech Startups are typically tech-push, meaning they often originate from scientific breakthrough inventions. While founders possess a strong idea of the technology or solution space, they often lack insight into the practical problems the technology could solve. As a result, initially, they are often not aware of the diverse application possibilities and potential needs of customers that the technology could address.
For these reasons, Deep Tech startups require their own unique methods, processes, and tactics to identify application opportunities and achieve product-market fit. These should account for the unique characteristics of Deep Tech startups and balance complex technological exploration and engineering with diverse potential market applications.
Even at ETH Zurich, which regularly tops the European lists for tech transfer, the conversion of technology inventions into commercially viable applications remains at a relatively low level. In fact, the percentage of projects that make it to spin-offs is less than five percent - let alone the fact that not all of them can be considered Deep Tech. Thus, establishing a toolbox comprising methods, processes, and tactics for identifying business opportunities and product-market fit for Deep Tech startups would help ecosystem contributors such as universities, accelerators, VCs, or policymakers in constructing more tailored support systems. Consequently, this would enable ventures addressing the significant challenges of our time to reach the market more often more efficiently.
In the upcoming weeks, I will delve deeper into this topic. Specifically, I will further explore the issue of identifying commercialization pathways and product-market fit in Deep Tech. Through conversations with Deep Tech founders, such as those at MIT’s The Engine, my colleagues and I seek to uncover some of these unique processes, methods and tactics that have proven successful for them and pushed them toward achieving product-market fit. Some anecdotal insights will be shared here, so stay tuned.
By the way, if you know of any interesting stories around the commercialization of Deep Tech startups (I especially like the ones where companies have landed in markets they never expected), feel free to join the discussion. If you have other expertise in the domain of commercialization or related areas, get in touch.
