This time, I want to suggest a Forbes Online article that I really enjoyed reading and that ties well into my Thoughts on DeepTech #2, effectively illustrating their core aspects via a real-life case:
In the article titled “The Theranos Strategy Lesson,” Francis de Véricourt from ESMT Berlin revisits the Theranos case1, specifically discussing why the fast Silicon Valley type approach, which focuses on de-risking and achieving product-market fit through MVPs and iterative cycles of build-measure-learn, does not translate well to slow Deep Tech ventures.
In my previous blog article, I listed some of the core aspects of why this approach generally falls short in Deep Tech. Let’s briefly reiterate:
Building prototypes for highly hardware-driven Deep Tech startups comes with significant engineering risks and costs. This makes it impractical to develop rapid prototypes for iterative testing with potential customers.
The timelines of Deep Tech ventures are typically significantly longer compared to their non-Deep Tech counterparts. This is because Deep Tech startups need to separate the launch and development of their product, focusing on refining their technologies before real-world deployment. It’s been nearly 20 years since Boston Dynamics introduced its first robot, “BigDog,” in 2004, to its first commercial robot, Spot, in 2020.
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.
In his article, Francis de Véricourt addresses these aspects and further brings them to life against the backdrop of the Theranos case. You can find the article here - happy reading: The Theranos Strategy Lesson by Francis de Véricourt in Forbes Online, Nov 14, 2023.
Below: Boston Dynamic’s Spot conducting tests last week on the MIT campus. A great example of embracing slowness and thoroughly refining technology over years before commercial deployment.