The Year AI Came For Us: Teaching Entrepreneurship Will Never Be The Same  

Editor’s note: First of a four-part series. Part 2 will be published Sept. 24. For more about Steve Blank and Lean LaunchPad, read The Man Who Disrupted Entrepreneurship Education Says AI Just Disrupted Him.

Fifteen years ago, my Lean LaunchPad class changed how entrepreneurship is taught. The class is now taught in hundreds of universities worldwide and helped launch thousands of startups. But this past summer, I got thinking about whether AI killed our Lean LaunchPad class, and with it the Lean Startup and Customer Development.

I realized that if we wanted students to learn how to build businesses rather than AI slop, we had to rethink the class. 

Here’s what happened, how we diagnosed the changes needed, and what we are planning to do going forward.

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For the last 15 years the cadence of the Lean LaunchPad class has been the same. Students arrived with hypotheses of their product idea and target customers. Each week student teams got out of the building and talked to 10-15 stakeholders. The following week, they presented “Here’s what we thought, here’s what we did, here’s what we learned, and here’s what we’re going to do next week.” Over the 10-week quarter, teams would talk to 100+ stakeholders and use this feedback to validate, modify or invalidate their hypotheses about their business and refine and iterate their Minimal Viable Product in search for product/market fit.  

The experience was meant to mirror the real-world journey of a startup founder, and regardless of the technical fad of the moment (social media, mobile apps, energy, life science, defense, et al). This pedagogy has worked like clockwork for 15 years. Coming into class in Spring 2026, I had no idea that this year would be its last, and how teaching entrepreneurship would never be the same. 

The first week of class is always exciting. 

Several months before the Spring 2026 class was to start, we interviewed the teams to hear what problems they wanted to work on and select which would join the class. In the first official class, it was always interesting to see how much they’d dug into the problem before the first day of the class.

In the first class, teams typically presented a PowerPoint or wireframe of their product concept. This year, however, when the first team presented, there were no PowerPoint or wireframe prototypes. Instead they demoed a complete product (for Pediatric Sleep Apnea, Freight Forwarding, 3D-printed cooling for GPUs, music attribution …)  Wow. We were impressed – this was the first time we had ever seen a team develop something this fast and feature-rich on day one. As I was still processing what a great job this team had done, the next team got up and also demoed a finished product. This time I was taken aback. Two in a row!? By the time the third, fourth, fifth, sixth, seventh, and eighth teams had presented their complete products, all of us on the teaching team were stunned. 

We kept looking at each other trying to confirm, “Did you see what I saw?”

To be honest, at first we instructors were giddy. All the teams had used AI to build apps, digital twins or clinical endpoints that in previous years we would have hoped to see at the end of week 10. We left class thinking these teams were on a great trajectory and thought for sure this start would lead to amazing outcomes for all of them.

We were wrong, wrong, wrong. 

As a teaching team, we were so enamored with this phantom progress that we didn’t stop the presses and refocus the students fast enough. We didn’t realize that AI had set our class on fire and would burn it to the ground.

WHAT AI CHANGED INSIDE THE CLASSROOM

In past years, students would get of the building and spend time trying to deeply understand customers’ problems. They used the business model canvas to capture what they learned as they tested all their hypotheses (go-to-market strategy, pricing, product/market fit, revenue, costs, etc.) – all the essential elements needed to turn an idea into business. 

AI made that process fail. 

Students used AI instead of customers for insights and validation. And because this information came from AI, they assumed it was correct.

AI made it easier and faster for students to translate ideas into products – but they had no idea if this AI product met a customer need or solved a customer’s problem.

As the weeks went by, the teams that had looked so promising were learning less. Minimal Viable Products (MVPs) became sales pitches instead of experiments; teams collected compliments instead of disconfirming evidence; interviewees reacted to the product rather than explaining their problems/needs.

Meanwhile, teams were surprised to discover that many customers were already using the same AI tools to create their own alternatives just as fast as they could. (This bit of discovery was a signal to the team of what the floor was for features a startup was going to sell.)

In the end, many students couldn’t let go of the initial ideas that AI had helped them build. Pivots become more expensive psychologically, and those initial ideas became frozen regardless of evidence they heard from customers. This was ironic, given pivoting the product was now technically cheap. The teaching team had to intervene to pry these Initial Untested Products (what we had started calling MVPs) out of students’ hands.

THE AFTER-ACTION REVIEW (AAR)

A few days after the class, while our memories were still fresh, we gathered the teaching team and Stanford faculty to share notes about what happened, why it happened, and how to improve. 

As we went around the room describing what we had seen and what we thought it meant, a few things became clear.  

The impact on learning far outweighed the benefits AI provided. To be sure there were positive parts of using AI in the class. Students had built these amazing products using Claude Skills and Gemini Gems. There were tons of untapped opportunities to build digital twins or test 10s or 100s of apps simultaneously. The impact on customer discovery was equally impressive. Assisted by AI, teams were able to surface the right questions to ask of the right people to get better answers to test their hypotheses faster. Teams used ChatGPT for market research and Replit to build websites,  Granola and Twinmind for note-taking; created synthetic users with Listen Labs and Viewpoints AI to test against real customer data; summarized their research in Google NotebookLM or Notion, then used Perplexity to create their weekly presentations.

THE MVP IS DEAD

What was immediately obvious was AI’s impact on the Minimal Viable Product (MVP). In the past, an MVP was painfully developed, reflecting the week-to-week cumulative knowledge gathered by talking to stakeholders. An MVP also was evidence of a team’s technical competence. 

It struck us that having a product on day one meant an MVP was no longer evidence of anything: not customer discovery, critical thinking, hypothesis testing, product/market fit, customer validation or even commitment.

Creating products rapidly at almost no cost had allowed teams to make bad ideas go faster. 

AI had created evidence theater. These Initial Untested Products felt like evidence but had been built with minimal or no contact with customers. They looked like progress but dramatically raised confirmation bias and delayed pivots.

Student learning was unbalanced. A finished-looking product felt like success. Students confused a polished deliverable with the need to deeply understand the needs of all the stakeholders, as well as the search for Customer Validation. Team understanding was less nuanced; there was less depth uniformly across the teams about the problem they were solving and how well they understood customer needs. 

It wasn’t that AI was hallucinating – the teams were. If they pivoted at all, they pivoted late as they assumed that a polished product meant product/market fit. (Pre-AI teams pivoted 3-4 times.) One team did go “IUP crazy” and created new IUPs weekly while never letting their learned evidence mount up.

All this added up to learning debt. These Initial Untested Products (IUPs) let teams skip the struggle which in the past had led to customer insight and understanding. The code worked, the deck was polished, and the analysis was coherent, but by using AI to summarize their interviews, teams missed the customer insights. As a result, they could not defend the assumptions or explain the edge cases.

As the teaching team discussed what we had seen and what we thought it meant, a few things became clear.

1. The MVP as an artifact of learning about a value proposition was dead.
2. The bottleneck in startups has moved from the time and cost of building a product to judgment about what to build and who to build it for. This means founders still need to know which problem matters, who will pay, how to distribute, and how to move faster than the other teams who can also build something in a weekend. 
3. When everyone can build quickly, what you choose to build and for whom becomes the whole game.
4. This means the competitive landscape is much more important. Previously a team could spend a semester largely ignoring competitors because the time and cost of building a product became a moat. That moat no longer exists. Teams now need a deep understanding of the current competitive landscape and the rapid competitive trends.
5. AI has made customers more sophisticated – now they can use AI to build solutions as fast as startups can. This means the discovery process now also needs to find moats and paths to scale.
6. There are low barriers to cloning. That same ease of creation means startups need to learn how to build defensible moats – and to treat a moat as a discovery problem, not a slide in the fundraising deck. IP used to be defensible. Now what is defensible IP?

Leaving the After-Action Review, we thought we understood the problem. The Minimum Viable Product was no longer a useful artifact for learning and discovery about the value proposition and product/market fit. I felt confident that we could make some simple fixes to the syllabus to deal with this. 

[Insert laughter here.]

Much like our students, we had just confused the symptoms (the MVP is dead) with much, much larger real problems. After lots of iterations we discovered the root causes and how to keep the class fresh for the AI age. 

Part 2 describes what we learned.

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Entrepreneur-turned-educator Steve Blank is an adjunct professor at Stanford University and co-founder of the Gordian Knot Center for National Security Innovation. He has been described as the Father of Modern Entrepreneurship. Credited with launching the Lean Startup movement and the curriculums for the National Science Foundation Innovation Corps and Hacking for Defense and Diplomacy, he’s changed how startups are built; how entrepreneurship is taught; how science is commercialized; and how companies and the government innovate. Read his blog here.

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