Steve Blank

This post previously appeared in Poets and Quants

This is the third of four posts on how AI has impacted our Lean LaunchPad class and what we did about it.
Part 1: The Year AI Came For Us
Part 2: AI Killed the MVP – Long Live the IUP
Part 3: This Post
Part 4: Lean LaunchPad – The Next Generation

The Lean LaunchPad has successfully accommodated 15 years of evolution of technology and markets – until now. AI is not only changing our classroom; it is changing everything outside the classroom.

I needed to understand those external changes so the class could change with it.

The class I designed in 2011 focused on teaching founders how to understand a company’s entire business model, not just the product features and customers. However, there were four important elements outside a classroom or a startup that would affect its success.

• Venture capital (how much startups could raise, when they could raise and who they could raise it from)
• How startups built their products (core tech platforms and development tools)
• The cost of building products (time to market, team size, capital requirements)
• Customer Adoption (how did they evaluate, buy, deploy and use products. And the speed in which they did that. And customer build versus buy criteria.)

The Lean LaunchPad class was designed to teach founders to use Lean Methods to derisk their new ventures — all while emulating the speed and tempo of 2011 startups.

The sidebar below summarizes what each of those types of companies looked like in 2011 compared to 2026. Skip this if you have a good memory.

The bottom line is that the world is a very different place and operates at a different pace from when I first designed the class, changes that AI has dramatically accelerated.

Looking at this comparison several things jump out:

1. Bottlenecks for adoption and scale still exist, they’ve just moved elsewhere.
2. Product/Market fit needs market-specific targeted end points
3. Customers are willing to be design partners
4. Finding defensible moats is critical

Product/Market Fit Needs Specific Targets
When I looked at this table it struck me that for 15 years, we used the term “product/market fit” as a one-size-fits-all phrase to describe the intersection between Stakeholders and the product features they needed/wanted. It managed to cover end users, influencers, recommenders, requirements writers, regulators, etc. across deep tech, life science, defense. Up until now it worked well enough.

Today, product/market fit is evolving. In enterprise software it is becoming Agent/Outcome fit; in hardware, it’s digital twins and physical-world models; in life sciences, computationally tested endpoints. When we next teach the class, we will replace generic product/market fit with category-specific evidence: outcome/agent fit for enterprise, mass creation and testing for consumer, digital-twin-to-physical fit for hardware, endpoint-clinical-evidence fit for life sciences, and testing with standardized or validated external assessments for education.

AI Enables Faster Time to Market
While we need to teach founders to “think deeply” in the first half of the class, we also need to teach them “to act quickly.”

To do that, the second half of the class will ask teams to acquire one or more Design Partners as evidence of Customer Validation. A Design Partner is a co-developer who first provides feedback, data, workflow access, and real-world testing in exchange for early access, preferential terms, or influence over the product. The partner commits scarce resources: staff time, data, integration effort, permissions, test environments, changes to workflow and the ability to influence the product.

In Part 4, how all the pieces fit together to create a new Lean LaunchPad Course design.

Filed under: Lean LaunchPad, Teaching |