The lean startup methodology is widely taught, and for a good reason. It helps entrepreneurs to think about ways to reduce market uncertainty and put the customers in the front seat, which makes a lot of sense given that most start$-$up failures are due to poor product$-$market fit. The methodology emphasizes quick iteration cycles, allowing startups to rapidly test and refine their products based on customer feedback, which helps to minimize risk and improve product$-$market fit. Additionally, its focus on cost efficiency, by minimizing upfront expenses and avoiding unnecessary waste, makes it appealing for many startups. However, for deep$-$tech ventures $$ which often face prolonged R&D periods, high upfront costs, and complex technologies $$ this approach can sometimes be challenging to implement.

Lean startup methodology$$may only partially apply to deep$-$tech ventures, however, because these ventures face different kinds of risk.

The products of most startups rely on proven technologies and rarely $$ if ever $$ cite technical issues as a reason for failure. Deep$-$tech ventures, on the other hand, are built on technologies that do not yet exist and must navigate a labyrinth of technological uncertainty that goes beyond the scope of market feedback loops. Over $90\%$ of new drugs fail the clinical trials required for approval $$ and this percentage increases substantially when considering drugs that do not even progress to trials. In contrast, only $10\%$ of newly approved drugs fail due to a lack of market need or poor strategic planning.

De$-$risking a technology is fundamentally different from de$-$risking a market. First, deep$-$tech projects often necessitate prolonged research and development periods beyond the quick iteration cycles advocated by lean principles. The significant upfront costs required for initial experimentation can also be at odds with the lean startup$$s emphasis on cost efficiency. The inherent complexity of deep$-$tech products often requires a level of technical understanding that may not be accessible to generalist investors or consumer markets, complicating the feedback and funding aspects of the lean approach. It can take years to know whether the technology works. This limits the implementation of a core tenet of the lean startup approach: quickly placing a real product into the hands of actual customers.

There are other stumbling blocks, too. The stringent regulatory landscape governing many deep$-$tech innovations $$ such as biotechnology, regulated by the Food and Drug Administration in the U$.$S$.,$ or space tech, regulated by the Federal Aviation Administration and the Federal Communications Commission in the U$.$S$.$ can hinder the rapid iterative process, as compliance and certification can be lengthy and complex. The assumption of swift market feedback and adoption may not hold in the deep$-$tech sector, where customer education and acceptance typically take longer. Finally, the traditional approach of developing a $$minimum viable product$($MVP$)$ to test market waters must be recalibrated for deep tech.

For all these caveats, deep tech faces a similar issue of uncertainty. It begs the question of what can be done to reduce technological uncertainty for those willing to venture into the deep. While the lean methodology doesn$$t directly translate to deep$-$tech ventures, it does have useful features that can be adapted. Entrepreneurs and investors need to know what tenets of lean can be helpful $$ and which to leave behind.

Review the innovation concept$($s$)$ highlighted by the article.

Describe the companies and$/$or industries affected by the innovation.

Evaluate the likely impact of the innovation on the firm and$/$or society and the extent to which the case supports $($or contradicts$)$ what we have discussed in class.

Forecast the future of the innovation.