Do Lean Startup Methods Work for Deep Tech?

Over the last decade, a niche slice of the tech sector has delivered some of its most impressive breakthroughs. Deep-tech innovation — the practice of harnessing the most recent advancements in scientific understanding to create technologies that were previously inconceivable — has delivered groundbreaking companies like SpaceX and products such as mRNA vaccines.

Deep tech’s share of venture capital has doubled over the last decade, growing from approximately 10% to 20%. Deep tech-focused investment funds outperform traditional venture capital, delivering an average internal rate of return of 26% compared to 21%.

But deep-tech startups come with their own unique set of business challenges. Their products often involve prolonged R&D periods and substantial upfront costs, making it hard to iterate quickly and maintain cost efficiencies. The stringent regulatory landscapes and the technical complexities of deep tech necessitate a more sophisticated approach.

The lean startup methodology emphasizes quick iteration cycles, allowing startups to rapidly test and refine their products based on customer feedback. 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.

Deep-tech ventures face different kinds of risk. Their technologies do not yet exist and must navigate a labyrinth of technological uncertainty that goes beyond the scope of market feedback loops. De-risking a technology is fundamentally different from de-risking a market.

To reduce technological uncertainty, deep-tech founders can:

Demonstrate proof of concept, showing the technology can function under practical conditions.
Define milestones with compelling data to attract investor support.
Form strategic partnerships and navigate regulations early on.
Embrace “learning by thinking” — prioritizing thoughtful experiment design over rapid iteration.
Leverage AI and digital tools to test ideas via simulation.
Foster a culture of truth, where transparency and integrity are core values.

Deep-tech startups face greater complexity and resource demands than their low-tech counterparts. These tailored approaches mitigate the inherent uncertainty and pave the way for groundbreaking innovations that can transform industries and society.

_{The original content of the note was published on Hbr.org. To read the full note visit here}

Steve Blank: AI will revolutionize the ‘lean startup’

As you’ll have noted from our coverage, as far as startup land is concerned, AI is hot, hot, hot.
Meanwhile, the lean methodology — think of a hypothesis, test it, iterate on it — has been canon for entrepreneurs and founders the world over for the past decade. But AI will most likely play a role in building startups faster, cheaper and more efficiently. So I asked the man who invented the concept of the lean startup, Steve Blank, to see what he thinks.
AI might not have started with ChatGPT, but the ability for the general public to interact with generative AI on a wide scale did. But even still, Blank says we’re collectively underestimating the potential of generative AI.
Blank highlights how AI-assisted research has made leaps of progress in the form of AlphaFold, a project that is trying to translate proteins into their three-dimensional structures, which can help us understand processes in the body, including aging. AI, obviously, goes far beyond, and we’ve only just started to see the tremendous evolution that will span all sciences. Blank might be on to something: Just last week, researchers at University College, London and Moorfields Eye Hospital in the U.K. identified markers for Parkinson’s disease in eye scans using AI.

_{The original content of the note was published on TechCrunch.com. To read the full note visit here}