AI Killed the MVP – Long Live the IUP

Posted on September 25, 2026 by steve blank

This post previously appeared in Poets and Quants

This is the second 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: This Post
Next Week
Part 3: The World Outside the Classroom Changed, The Class Didn’t
Part 4: Lean LaunchPad – The Next Generation

Leaving the After-Action Review of our class this spring, we thought we understood the problem AI had created. The Minimum Viable Product (MVP) was no longer a useful artifact for customer discovery and arriving at an evidence-based value proposition. (To make clear what we thought of these AI-generated day one products, we labeled them Initial Untested Products – IUPs.) We were confident that we could make some simple fixes to the syllabus to deal with this.

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

I thought it would be pretty simple to just make some minor tweaks to the class. But the more I thought about it, something troubled me. It felt like something much deeper was broken that I couldn’t yet articulate or put my finger on.

After a while I realized, you can’t know your future if you don’t understand your past. So I needed to go back to basics.

Who did I design the class for? How did I design the class? Why has it scaled and lasted almost two decades? What did we want students to learn 15 years ago? What do I want them to learn today?

It was time to rethink the pedagogy of the class – the why and how of what we were teaching. Then we could tackle the course design – the what, when, and in what order. The result would be the content of the class in an updated curriculum; and the syllabus for the students that describes all of it.

Only then could I make the changes to match the world as it is, not the world as it was.

What Was I Thinking?
I developed the Lean LaunchPad class in 2011 to break out of “how to write a business plan” which was then the capstone of entrepreneurial education. The old approach assumed that all founders of startups needed to do was to describe the future in a document, raise money and then execute the plan. The Lean approach started from the opposite premise: on day one, nearly every important fact in building a business is a hypothesis.

I realized that while existing organizations execute business models, startups search for them. And that a startup was a temporary organization designed to search for a repeatable and scalable business model. The class was designed to teach founders how to search for all parts of a business model, not just the product, and to create a classroom environment that closely resembled a startup – team-based, experiential, uncertain and relentless. (At the time this was heretical.)

In hindsight, the genius of the class design was to combine a universal framework, domain-specific field evidence, high-tempo accountability, and permission to change direction.

The original 10 Lean LaunchPad Course Design Elements included:

  1. The Business Model Canvas as a hypothesis map. The Business Model Canvas captured the team’s assumptions about customers, customer problems/needs, value propositions, channels, revenue, costs, partners and activities. The canvas created a common language across industries. It exposed the full set of assumptions that had to become facts. It also acted as connective tissue: when teams became lost in the details of a prototype or an interview, they could return to the map.
  2. Customer Development as the core activity. Customer Development pushed students outside the classroom and tested their assumptions directly with customers and stakeholders. Customer discovery could start before a finished product existed, making the method applicable in domains where building took days, months, or years (hardware, biotechnology, semiconductors, energy, defense, et al.)
  3. 100 customer and stakeholder interviews created Pattern Recognition. The demand for 100+ interviews added breadth, diversity and repetition. It allowed for testing the all the parts that make up a business model, not just the product. More than 100 interviews made it harder to rely on friendly contacts, anecdotes or confirmation bias. High volume exposed differences between users and buyers, early adopters and mainstream customers, and stated preferences and actual behavior.
  4. Hypotheses turned into experiments. Teams stated what they believed, identified the evidence needed, ran experiments, and reported whether each hypothesis survived. It was the scientific method applied to startups. The process remained consistent even though the experiments varied by industry.
  5. Minimum Viable Products (MVPs) took on two roles. First, the MVP was a sign of a team’s technical competence and reflected a team’s cumulative knowledge of customer problems and possible solutions. Agile engineering built Minimum Viable Products iteratively and incrementally. The iteration of incrementally refined MVPs allowed teams to find product/market fit between Stakeholders and a value proposition and a path to a repeatable and scalable business model.
  6. Minimum Viable Products (MVPs) were also used to test the entire business model – not just the product. Teams built the smallest artifact needed to validate any part of their business model. An MVP was whatever got you the most learning about your business model at a point in time, creating evidence and reducing uncertainty.  (E.g. a MVP could be a landing page or A/B test to validate a go-to-market strategy, a price list to test potential revenue, clinical endpoint, bill of materials to test costs, etc.)
  7. Pivoting or restarts were legitimate outcomes. Discovering that an opportunity was unattractive was a valid result. The class reduced pressure to manufacture positive findings and made it possible to allocate time and capital away from weak opportunities.
  8. Intensity and time pressure. Teams operated under demanding weekly deadlines, required fieldwork, weekly public presentations, and direct and unsparing instructor feedback, coupled with incomplete and uncertain information. The process forced teams to distinguish the evidence they were hearing from customers from their own opinions.This intensity exposed students to real-world startup conditions of rapid/good-enough decision-making. (As a side-effect it also exposed dysfunctional team behavior.) This cadence of operating in chaos and uncertainty compressed months of informal learning into a short, structured cycle. Pivots and restarts were recognized as legitimate outcomes rather than admissions of failure.
  9. Experienced practitioners as instructors. Class time was used primarily for critique and coaching rather than conventional lectures. Experienced instructors could recognize recurring startup patterns, while mentors supplied specialized domain knowledge. Instructors did not need to be the world’s leading experts in every technology.
  10. A standardized process with adaptable content. The course appeared open-ended to students, but its freedom sat inside a carefully designed structure. The cadence, canvas, interview process, MVP search for product-market fit, and evidence requirements were standardized, while the interview targets and types of experiments varied by domain. For example, in life sciences the canvas emphasized clinical research organizations and clinical end points, FDA regulations, insurer reimbursement, etc. while in B-to-B the emphasis was product/market fit.

The Result
The class proved surprisingly portable, durable (15 years and counting) and universal.

These design elements allowed the course to be flexible enough to be replicated across industries (enterprise software, consumer products, hardware, therapeutics, medical devices, energy, climate, oceans, semiconductors, and national-security) without becoming a generic entrepreneurship lecture or requiring a completely different curriculum for every technology.

The class was adopted by the U.S. research community; the National Science Foundation, National Institutes of Health, and ARPA-E, relabeled as “I-Corps” and in over 100 universities. All these agencies used the class to teach principal investigators how to commercialize their science (training ~10,000 scientists in ~3,500 teams and launching 1,400+ startups to date.)

Five years later, a spin-off of the class using the same syllabus would become Hacking for Defense, now in 60+ U.S. universities and 17 in the UK.

And yet…

AI Killed the MVP – Long Live the IUP
The class had been resilient and adaptable but now as I stared at its 10 design elements it became clear that AI had demolished a core pillar of the class. With teams capable of vibe coding a product, minimum viable products (MVPs) did not represent proof of technical competence. MVPs were also no longer reliable evidence of customer discovery, critical thinking, hypothesis testing, product/market fit or even commitment. This meant two of the 10 class design elements were obsolete. So much so that we will now label Minimum Viable Products (MVPs) as Initial Untested Products (IUPs.)

We didn’t want to kill AI in the class. Quite the opposite. That’s the world as it is. We want students to use AI to the max, just like in the real world. But AI has changed the world in which entrepreneurs will create new companies. The Lean LaunchPad class needed to change more than the MVP.

The World Changed, The Class Didn’t
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, in 2026 there were four important elements outside a classroom or a startup that would affect their success.

  1. Venture capital (how much startups could raise, when they could raise and who they could raise it from)
  2. How startups built their products (core tech platforms and development tools)
  3. The cost of building products (time to market, team size, capital requirements)
  4. 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.)

AI was also changing everything outside the classroom. Including what AI just did to Product/Market Fit.

I needed to understand those changes so I could have the class match the future, not the past.

More in Part 3: The World Outside the Classroom Changed, The Class Didn’t and in
Part 4: Lean LaunchPad – The Next Generation

Filed under: Lean LaunchPad, Teaching | 5 Comments »

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

Posted on September 23, 2026 by steve blank

This post previously appeared in Poets and Quants.

15 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.

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 3rd, 4th, 5th, 6th, 7th and 8th teams 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.

……………..Tired but accurate meme…………….

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 potential 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 their startup could 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 notetaking; 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 (an 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.

Filed under: Lean LaunchPad, Teaching | 10 Comments »

Lean Launch Pad 2026 @ Stanford – Lessons Learned Presentations

Posted on June 16, 2026 by steve blank

We just finished the 16th annual Lean LaunchPad class at Stanford.

In those 16 years, the class has gone from a radical idea – that the Lean method could provide a more productive framework for new startups – to something that everyone agrees is a way to build new startups.

The class had gotten so popular that in 2021 we started teaching it in both the winter and spring sessions.

During the 2026 spring quarter the eight teams spoke to 978 potential customers, beneficiaries and regulators. Most students spent 15-20 hours a week on the class, about double that of a normal class.

This Class Launched a Revolution in Teaching Entreprenurship – AI Is Changing It
This class was designed to break out of “how to write a business plan” as the capstone of entrepreneurial education. A business plan assumed that all startups needed to do was to write a plan, raise money and then execute the plan. We overturned that orthodoxy when we pointed out that while existing organizations execute business models, startups search for them. And that a startup was a temporary organization designed to search for a repeatable and scaleable business model. This class was designed to teach startups how to search for a business model. I’ll summarize some of the learnings about the use of AI at the end of this post.

Several government-funded programs have adopted this class at scale. The first was in 2011 when we turned this syllabus into the curriculum for the National Science Foundation I-Corps. Errol Arkilic, the then head of commercialization at the National Science Foundation, adopted the class saying, “You’ve developed the scientific method for startups, using the Business Model Canvas as the laboratory notebook.” Now in its second decade and in 100+ universities, I-Corps has become a standard for science commercialization at the NSF, National Institutes of Health and the Department of Energy –  training 3,251 teams and launching 1,400+ startups to date.

Team Office Hours

If you can’t see the Team Office Hours video click here

If you can’t see the Team Office hours slides click here

If you can’t see a demo of the Team Office Hours app click here

Design of This Class
While the Lean LaunchPad students are experiencing what appears to them to be a fully hands-on, experiential class, it’s a carefully designed illusion. In fact, it’s highly structured. The syllabus has been designed so that we are offering continual implicit guidance, structure, and repetition. This is a critical distinction between our class and an open-ended experiential class.

Guidance, Direction and Structure – For example, students start the class with their own initial guidance – they believe they have an idea for a product or service (Lean LaunchPad/I-Corps) or have been given a clear real-world problem (Hacking for Defense). Coming into the class, students believe their goal is to validate their commercialization or deployment hypotheses. (The teaching team knows that over the course of the class, students will discover that most of their initial hypotheses are incorrect.)

Team Izhaar

If you can’t see the Team Izhaar click here

If you can’t see the Team Izhaar presentation click here

Team Trained on Me

If you can’t see the Team Trained on Me video click here

If you can’t see the Team Trained on Me presentation click here

The Business Model Canvas
The business model / mission model canvas offers students guidance, explicit direction, and structure. First, the canvas offers a complete, visual roadmap of all the hypotheses they will need to test over the entire class. Second, the canvas helps the students goal-seek by visualizing what an optimal endpoint would look like – finding product/market fit. Finally, the canvas provides students with a map of what they learn week-to-week through their customer discovery work. I can’t overemphasize the important role of the canvas. Unlike an incubator or accelerator with no frame, the canvas acts as the connective tissue – the frame – that students can fall back on if they get lost or confused. It allows us to teach the theory of how to turn an idea, need, or problem into commercial practice, week by week a piece at a time.

Team Artemis

If you can’t see the Team Artemis video click here

If you can’t see the Team Artemis presentation click here

Lean LaunchPad Tools
The tools for customer discovery (videos, sample experiments, etc.) offer guidance and structure for students to work outside the classroom. The explicit goal of 10-15 customer interviews a week along with the requirement for building a continual series of minimal viable products provides metrics that track the team’s progress. The mandatory office hours with the instructors and support from mentors provide additional guidance and structure.

Team Remainder

If you can’t see the Team Remainder video click here

If you can’t see the Team Remainder slides click here

Team Microprint

If you can’t see the Team Microprint video click here

If you can’t see the Team Microprint slides click here

Team Vital Health


If you can’t see the team Vital Health video click here

If you can’t see the team Vital Health presentation click here

Team Nimbus

If you can’t see the Team Nimbus video click here

If you can’t see the Team Nimbus presentation click here

AI In the Classroom

AI Embedded in the Class
This was the first year where all teams used AI to help create their business model canvas, build working MVPs in hours, generate customer questions, analyze and summarizing interviews.

AI has had some obvious and not so obvious impacts on our class.
First, here’s a summary of how our students used AI in both classes I taught this quarter.

If you can’t see the AI Use In Class slide click here

AI Tools Used
Claude + Granola – were the AI tools used by everyone.
Large Language Models Used
– Claude, Claude Code, Claude Chrome extension, Claude Cowork, Claude Design
– ChatGPT
– Gemini
Note taking
– Granola
– Twinmind
Presentations
– Perplexity
Building prototypes
– Replit
– Lovable
Creating Synthetic Users
– Listen Labs
– Viewpoints AI
Summarizing Research
– Google NotebookLM
– Notion + G Suite (not strictly AI, but used as part of AI workflows)
Other
– Ultralytics YOLOv8 (used by the SwarmShield H4D team for drone detection/tracking MVP)

AI Classroom Usage
Three of our students did a tutorial of how they used AI in the classroom.

If you can’t see the AI Classroom Usage tutorial click here

Impact of AI in the Classroom
The obvious and positive changes of AI were that teams were able to do customer discovery more efficiently. The not so obvious change was that creating products rapidly allowed teams to make bad ideas go faster. In the past, MVPs were a sign of a teams technical competence, but now spinning up something in hours that previously took weeks, means that an MVP is no longer evidence of critical thinking and hypothesis testing.

This meant 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 need for Customer Validation. Team understanding was less nuanced. There was less depth uniformly across the teams about the problem they were solving and understanding customer needs. In this class it wasn’t the AI that was hallucinating –  it was teams. They pivoted late as they assumed that a polished product meant product/market fit.

Going forward we’ll have students come into class with a prototype but next time accompanied by the explicit hypotheses and experiments they’ll use to validate whether the prototype solved an actual problem.

On the other hand, students built some amazing Claude Skills and Gemini Gems. They were tons of untapped opportunities to build digital twins or test 10’s or 100’s of apps simultaneously.

More about this in a separate blog post.

It Takes A Village
While I authored this blog post, this class is a team project. The secret sauce of the success of Lean LaunchPad at Stanford is the extraordinary group of dedicated volunteers supporting our students in so many critical ways.

The teaching team consisted of myself and:

  • Steve Weinstein, partner at America’s Frontier Fund, 30-year veteran of Silicon Valley technology companies and Hollywood media companies. Steve was CEO of MovieLabs, the joint R&D lab of the major motion picture studios.
  • Lee Redden – CTO and co-founder of Blue River Technology (acquired by John Deere) who was a student in the first Lean LaunchPad class 14 years ago! I wrote a post about Lee’s journey here.
  • Jennifer Carolan, Co-Founder, Partner at Reach Capital the leading education VC and author of the Hacking for Education class.

Our teaching assistants this year were: Roya Meykadeh, Aditi Mahajan, Alina Hu.

The teams were assisted by mentors: David Kopp, Mitch Singer, Pradeep Jotwani, Dave Epstein, Anil Kamath, Bobby Mukherjee, Rekha Pai, Venkat Krisnamurthy and mentor team coordinator Todd Basche.

Filed under: Customer Development, Lean LaunchPad, Teaching | Leave a comment »

Hacking for Defense @ Stanford 2026 – Lessons Learned Presentations

Posted on June 8, 2026 by steve blank

We just wrapped up our Hacking for Defense class at Stanford.

This was the 11th year we’ve taught Hacking for Defense, and the impact of asymmetric warfare, (drones, off-the-shelf technologies, etc.,) disruptive technologies (AI, commercial access to space) and a startup friendly DoW acquisition system – make it feel like a much different class than the previous classes.
(I’ll summarize some of the learnings about the use of AI at the end of this post.)

Hacking for Defense is now in 70 universities, including 20+ in the UK – and this year in Poland and Germany – with teams of students working to understand and help solve national security problems.

This year’s problems came from the Navy, Air Force, Army Research Lab, Defense Innovation Unit, IQT, and NASA.

This quarter 9 teams of 42 students at Stanford collectively interviewed 1132 beneficiaries, stakeholders, requirements writers, program managers, industry partners, etc. – while simultaneously building a series of AI-driven minimal viable products and developing a path to deployment.

We opened this year’s final presentations session with a great talk about AI and defense – past, present and future – from (Ret) LTG Jack Shanahan. Jack was the Director of the DoD Joint Artificial Intelligence Center (JAIC). Watching his talk is a worthwhile use of your time.

If you can’t see Jack Shanahan’s video click here

During the quarter guest speakers in the class included Owen West – director of the Defense Innovation Unit, Mike Brown – partner at Shield Capital, (Ret) LTG Joseph McGee recent head of the Joint Staff J5 (strategy, plans, and policy,) and Hon Marise Payne Australia’s Minister for Foreign Affairs.

“Lessons Learned” Presentations
Each of the eight teams gave a final “Lessons Learned” presentation along with a 2-minute video to provide context about their problem. Unlike traditional demo days where teams show off, “Here’s how smart I am, and isn’t this a great product, please give me money,” the Lessons Learned presentations tell the story of each team’s 10-week journey and hard-won learning and discovery. It’s a roller coaster narrative describing what happens when they discover that everything they thought they knew on day one was wrong and how they eventually got it right.

While all the teams used the Mission Model Canvas, Customer Development and AI tools to build Minimal Viable Products, each of their journeys was unique.

This year we had the teams add two new slides at the end of their presentation: 1) tell us which AI tools they used, and 2) their estimate of progress on the Technology Readiness Level and Investment Readiness Level.

Here’s how they did it and what they delivered.

Team Noctua – Started with a problem that said, “Special operators can’t detect drones passively, without exposing their position.” They ended up understanding that a larger problem was, “Dismounted troops and base defenders lack a passive means to provide early warning detection of all types of drones, including those that are RF silent.

If you can’t see the Noctura video click here

If you can’t see the Noctura presentation click here

These are “Wicked” Problems
Wicked problems refer to really complex problems, ones with multiple moving parts, where the solution isn’t obvious and lacks a definitive formula. Most problems our Hacking For Defense students work on fall into this category. They are often ambiguous. They start with a problem from a sponsor, and not only is the solution unclear but figuring out how to acquire and deploy it is also complex. Most often students find that in hindsight the problem was a symptom of a more interesting and complex problem – and that Acquisition in the Dept of War is unlike anything in the commercial world.

Instead of admiring problems from inside a classroom our students get of the building and learn, discovery and iterate.

The figure shows the types of problems Hacking for Defense students encounter, with the most common ones shaded.

Team SwarmShield – The initial problem was framed as, the cost of using expensive interceptors to shoot down cheap drones. By the end of the class the Team realized the problem was building terminal guidance that lets a cheap, throwaway drone find and hit an attacker at night.

If you can’t see the SwarmShield summary video click here.

If you can’t see the SwarmShield presentation click here

Department of War Directory – This year the students had access to a Department of War Directory – essentially a phonebook of  ~5,700 names of “Who buys in the Dept of War?” The directory includes a tutorial on how the DoW buys and the various acquisition and funding processes and programs that exist for startups. It provides details on how to sell to the DoW and where the Program Acquistion Officers (PAEs) fit into that process.

 

Team Weapons Without Wait – The initial problem for this team was “Retool and scale defense manufacturing capacity to replenish critical munitions at the pace required by sustained, high-intensity conflicts.”  This is what I call a “boil the ocean” problem” – big and vast – and vague. By class end the team realized what was rapidly achievable (and needed) was affordable, certified munitions for small drones produced at the point-of-need.

If you can’t see the Weapons Without Wait video click here

If you can’t see the Weapons Without Wait presentation click here

It Started With An Idea
Hacking for Defense is built on the same methodology as Lean LaunchPad class I created at Stanford in 2011. It was adopted by the National Science Foundation (NSF) as the NSF I-Corps (Innovation Corps) to train Principal Investigators who wanted an SBIR grant. Now in its second decade and in 100+ universities, I-Corps has become a standard for science commercialization at the NSF, National Institutes of Health and the Department of Energy –  training 3,251 teams and launching 1,400+ startups to date.

Team IonX – IonX also started with a “boil the ocean” problem – The US needs a secure rare earth supply chain. They ended up with a problem more tangible and deliverable – Mineral processors across markets can’t identify and test better chemical reagent schemes.

If you can’t see the IonX video click here

If you can’t see the IonX presentation click here

Origins Of Hacking For Defense
In 2016, brainstorming with Pete Newell of BMNT and Joe Felter at Stanford, we observed that students in our research universities had little connection to the problems their government was trying to solve. We realized the same Lean LaunchPad/I-Corps class would provide a framework to do so. That year we launched both Hacking for Defense and Hacking for Diplomacy (with Professor Jeremy Weinstein and the State Department) at Stanford.

Team Cheese on the Moon – Started with a mandate to search for mineral deposits on the moon. By class end they realized that to do that lunar missions need to know what’s on and under the moon not only to mine, but to land.

If you can’t see the Cheese on the Moon video click here

If you can’t see the Cheese on the Moon presentation click here

Goals for Hacking for Defense
A decade ago, our goal for the class was to teach students Lean Innovation methods while they engaged in national public service. We wanted to familiarize students with the military as a profession and help them better understand its expertise, and its role in society. We also hoped the class would show our sponsors a methodology that builds problem understanding before writing requirements.

The class still does all this, but now that the DoW is buying from startups and defense venture capital is abundant, the class has turned into a national security incubator. Most of our teams form defense companies.

Team Fuel Forge started with the problem that combat units need to generate power and fuel locally. They ended with a more interesting observation that they could build networked, on-site hydrogen nodes to fuel drones in forward, contested environments where resupply is at risk,

If you can’t see the Fuel Forge video click here

If you can’t see the Fuel Forge presentation click here

Go-to-Market/Deployment Strategies
The initial goal of the teams is to ensure they understand the problem. The next step is to see if they can find mission/solution fit (the DoW equivalent of commercial product/market fit.) But most importantly, the class teaches the teams about the difficult and complex path of getting a solution in the hands of a warfighter/beneficiary. While the DoW has made tremendous strides in reforming how and who they buy from, students still need to know: Who writes the requirement? What’s an OTA? What’s color of money? What’s a Program Manager? Who owns the current contract?

Team Luminarch – Started with Tactical units lack the capability to visualize, manage, and adapt to the electromagnetic spectrum in real time. They ended with Tactical units lack low-cost, attritable RF sensors that can be deployed at scale, limiting their ability to detect threats, manage signatures, and communicate.

If you can’t see the Luminarch video click here

If you can’t see the Luminarch presentation click here

Team Tessellate– Started with the observation that drone missions don’t scale. And ended by realizing what’s missing is US multi-drone doctrine doesn’t exist and current drone warfare changes are happening faster than the software lifecycle.

If you can’t see the Tessellate video click here

If you can’t see the Tessellate presentation click here

AI In the Class Room
AI has had some obvious and not so obvious impacts on our class.
First, here’s a summary of how our students used AI in both classes I taught this quarter.

If you can’t see the AI Use In Class slide click here

If you can’t see the AI Rap Video click here

AI Tools Used
Claude + Granola – were the AI tools used by everyone.
Large Language Models Used
– Claude, Claude Code, Claude Chrome extension, Claude Cowork, Claude Design
– ChatGPT
– Gemini
Note taking
– Granola
– Twinmind
Presentations
– Perplexity
Building prototypes
– Replit
– Lovable
Creating Synthetic Users
– Listen Labs
– Viewpoints AI
Summarizing Research
– Google NotebookLM
– Notion + G Suite (not strictly AI, but used as part of AI workflows)
Other
– Ultralytics YOLOv8 (used by the SwarmShield H4D team for drone detection/tracking MVP)

The obvious and positive changes of AI were that teams were able to do customer discovery more efficiently. The not so obvious change was that creating products rapidly allowed teams to make bad ideas go faster.

In the past, MVPs were a sign of a teams technical competence, but now spinning up something in hours that previously took weeks, means that an MVP is no longer evidence of critical thinking and hypothesis testing.

This meant 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 need for Customer Validation. For defense startups that means understanding a path to a CRADA, or to a research or production OTA. We needed to slow the teams down. Going forward we’ll have students come into class with a prototype but next time accompanied by the explicit hypotheses and experiments they’ll use to validate whether the prototype solved an actual problem.

More about this in a separate blog post.

It Takes A Village
While I authored this blog post, this class is a team project. The secret sauce of the success of Hacking for Defense at Stanford is the extraordinary group of dedicated volunteers supporting our students in so many critical ways.

The teaching team consisted of myself and:

  • Pete Newell, retired Army Colonel and ex Director of the Army’s Rapid Equipping Force, now CEO of BMNT.
  • Joe Felter, retired Army Special Forces Colonel; and former deputy assistant secretary of defense for South and Southeast Asia, and Oceania; currently Director of the Gordian Knot Center for National Security Innovation at Stanford which we co-founded in 2021.
  • Steve Weinstein, partner at America’s Frontier Fund, 30-year veteran of Silicon Valley technology companies and Hollywood media companies. Steve was CEO of MovieLabs, the joint R&D lab of all the major motion picture studios.
  • Chris Moran, Executive Director and General Manager of Lockheed Martin Ventures; the venture capital investment arm of Lockheed Martin.
  • Jeff Decker, a Stanford researcher focusing on dual-use research. Jeff served in the U.S. Army as a special operations light infantry squad leader in Iraq and Afghanistan.
  • Jillian Manus, a venture partner at Shield Capital and Senior U.S Venture Advisor for the European Innovation Council

Our teaching assistants this year were: Evan John Twarog, Varsha Saravanan, Breno Casciello, and Luke Andrews.

34 Sponsors, Business and National Security Mentors
The teams were assisted by sponsors and mentors.

Sponsors were originators of the team problems. They gave us their toughest national security problems: Owen West, Will Ryan, Phillip “Donna” Smith, Joel Uzarski, Alexandra Bissey, Mark Breier, Jonathan Stock, Trent Emeneker,  Matthew Anderson, Ana Alvarez, Jonathan Boltersdorf.

National Security Mentors helped students who came into the class with no knowledge of the Department of War, understand the complexity, intricacies and nuances of those organizations: Katie Tobin, Kelly McGannon, Rachel Costello, Henning Heine, Josh Edwards, Marco Romani, Tom Schmitz, David Vernal, Rich Lawson, Dan Ruttenber, Ashley Perry, Sophia Vahanvaty, Rick Lu, Chris O’Connor

Business Mentors helped the teams understand if their solutions could be a commercially successful business: Doug Seiche, Jeremy Schoos, Adam Waters,, Matt Croce, Isobel Porteous, Eric Byler, Diane Schrader, Donnie Hasseltine, Mark McVay.

Sponsoring Organizations: Gordian Knot Center for National Security Innovation, Common Mission Project, Lockheed Martin, Boeing, BMNT, Defense Innovation Unit.

Thanks to all!


Filed under: Hacking For Defense, Teaching | Leave a comment »

Your Startup Is Probably Dead On Arrival

Posted on March 17, 2026 by steve blank

Your Startup Is Probably Dead On Arrival

If you started a company more than two years ago, it’s likely that many of your assumptions are no longer true.

You need to stop coding, building, recruiting, fund raising, etc., and take stock of what changed around you. Or your company will die.

I just had coffee with Chris, a startup founder I invested in six years ago. Since then he’s been heads-down focused working on 1) a complex autonomy problem, 2) in an existing market with 3) a unique business model.

Chris is now starting to raise his first large fundraising round. In looking at his investor deck I realized that while he’s been heads down, the world has changed around him – by a lot. The software moat he built with his 5-year investment in autonomy development is looking less unique every day. Autonomous drones and ground vehicles in Ukraine have spawned 10s, if not 100s, of companies with larger, better funded development teams working on the same problem. 

While Chris has been fighting for adoption for this niche market (one that is ripe for disruption, but the incumbents still control), the market for autonomy in an adjacent market – defense – has boomed. In the last five years VC Investment in defense startups has gone from zero to $20 billion/year. His product would be perfect for contested logistics and medical evacuation. But he had literally no clue these opportunities in the defense market had occurred. 

While there’s still a business to be had (Chris’s team has done amazing system integration with an existing airborne platform that makes his solution different from most), – it’s not the business he started. 

Catching up with Chris made me realize that most startups older than two years old have an obsolete business plan – and a technical stack and team that’s likely out of date.

Just as a reminder if you haven’t been paying attention.

What’s Changed
Venture capital has tilted hard toward AI. In 2025, AI deals represented two-thirds of all the dollars VCs invested. That means if you’re not building something AI-related, you’re competing for a smaller pool of dollars. Non-AI startups need to answer, “Why can’t a better-funded AI-native competitor eat your lunch?”

For software founders, AI has blown up the old math around cost, speed, and headcount. Vibe coding with tools like Claude Code or OpenAI Codex means you can build an MVP (minimal viable product) in days, sometimes hours, not months. (Which means an MVP is no longer proof of your team’s competency.)

These tools are changing the makeup of development teams (fewer engineers, and new types of engineers – outcome/business process engineers and deep technical types.) What used to require a team of developers can now be done by a handful of people – and sometimes just one. Data used to be a differentiator and a moat, but current foundation models (ChatGPT, Gemini, Claude) are commoditizing/embedding public data sources.

The notion of Agile development now needs rethinking.

The constraint used to be: Can we afford to build and ship this? Now the constraint is: Do we know what to test? And can we get in front of users fast enough to learn? Agile is no longer a serial process. AI Agents can run multiple things in parallel for the same or less cost. You can now test multiple versions of the same business at once (or simultaneously be testing different businesses). While you can be simultaneously testing five pricing models, ten messages or twenty UX flows, the “user interface” may no longer be a screen at all. Testing might be to find prompt(s) to AI Agent(s) deliver needed outcomes. The bottleneck is no longer engineering. It’s moving up the stack to judgment, customer insight for desired outcomes and distribution.

Agents
AI Agents will change every category of software – including yours. Today, software applications are built to give users information and then expect the users to do the work via a user interface of dashboards, alerts, workflow tools and reports. But customers buy software because they want to get a job done, not to look at more screens. Getting the job done is what AI Agents (orchestrated by tools like OpenClaw) will autonomously enable.

What that means is, if your current product tells a user what to do next, an AI Agent will eventually do that step for them. And if your competitor’s product does the task automatically while yours still waits for a human click, you no longer have a competitive product. The next generation of applications won’t just put information on a screen, they’ll act just like an employee.

They’ll resolve the support ticket, book the meeting, qualify the lead or reorder the inventory. And when products move from software-as-interface to software-as-outcome, pricing will move from seats to results; per resolved ticket, per booked meeting, per closed lead.

(The search for Product/Market fit will become the search for AI Agent/Customer Outcome fit. Minimum Viable Products (MVPs) will become Minimum Productive Outcomes (MPOs.) More on this in the next post.)

Hardware
For hardware founders, the shift is just as significant. Hardware is still constrained by physics, capital, supply chains, and manufacturing cycles. While you can’t fake your way past cutting metal, building prototypes or taping-out a chip, AI will let you kill bad ideas faster. Now, before you build a physical prototype, you can simulate more design variants, create digital twins, and stress-test assumptions earlier and much cheaper than before. The result is that you accelerate learning and discovery (at times getting to failure faster) and in startups, that’s a feature, not a bug.

And once AI is embedded as part of the system, the product itself changes. Adding AI as a backend of a camera means the camera can now become a surveillance system, a vibration sensor, a machine tool failure prediction system. A robot becomes a factory worker. The moat is no longer just the hardware. It’s the combination of what the hardware can sense and what the AI can do to use that data to decide and act.

The Sunk Cost Trap
Founders who started pre-2025 typically have built a technical stack optimized for a world where software development was bespoke and expensive. While Agile development and DevSecOps made us lean, they operate in a serial fashion, and startups hired a team sized for this structure. Companies that have spent years developing a “moat” of proprietary code and features are waking up to the fact that AI is commoditizing most of their tech stack. This leaves startups trying to raise money for a business model that may be partially (or wholly) obsolete.

None of this may be obvious to a founding team when you’re heads down trying to ship a product and searching for product/market fit.

Technical stack, product features, user interface, number of employees, all of these sunk costs become reasons not to pivot: How can we throw away years of work? Our VCs funded this specific idea. Customers still want a UI. The team believes in this roadmap. Our customers aren’t ready for this. (Chris is a perfect example. He built something genuinely impressive, and likely still competitive, but the business model around it needs to change.)

Some sunk costs continue to be assets; deep domain knowledge, customer relationships, proprietary data, hard-won regulatory approvals, physical integrations – those are worth keeping. In Chris’s startup – that’s his airframe integration.

The sunk costs that are liabilities are a large engineering team built for slow software cycles, a pricing model based on seats, a product roadmap built around features rather than outcomes. These are what is known as the “Dead Moose on the table” – something so obviously wrong but that no one wanted to challenge.

The founders who survive will be the ones who can look at what they’ve built and ask: if I were starting this company today, using today’s tools in today’s market, what would I actually build? 

That’s uncomfortable when you’ve raised money on a specific thesis. But it’s less uncomfortable than your investors telling you they’re not going to fund your next round, and going out of business defending an obsolete plan.

Lessons Learned

  • You don’t get to run a 2024 (or earlier) playbook in 2026Everything has changed – fund raising, tech, business models
    • Agile development is changing to parallel development
  • The search for Product/Market fit will become the search for AI Agent/Customer Outcome fit. Minimal Viable Products (MVPs) will become Minimal Productive Outcomes (MPOs.) More on this in the next post
  • The sunk cost mindset will put you out of business
  • Defensible moats may still be found in having proprietary data, deep understanding of customer outcomes, getting regulatory lock-in, or being a Program of Record
  • If you’re not losing sleep, you haven’t understood what’s happening
  • Founders who survive will get out of the building to take stock, pivot and course correct

Filed under: Customer Development, Teaching, Technology, Venture Capital | 5 Comments »

It only took 20 years, but the Strategic Management Society now Believes the Lean Startup is a Strategy

Posted on October 30, 2025 by steve blank

I’ve always thought of myself as a practitioner. In the startups I was part of, the only “strategy” were my marketing tactics on how to make the VP of Sales the richest person in the company. After I retired, I created Customer Development and co-created the Lean Startup as a simple methodology which codified founders best practices – in a language and process that was easy to understand and implement. All from a practitioner’s point of view.

So you can imagine my surprise when I received the annual “Strategy Leadership Impact” Award from the Strategic Management Society (SMS). The SMS is the strategy field’s main professional society with over 3,100 members. They publish three academic journals; the Strategic Management Journal, Strategic Entrepreneurship Journal, and Global Strategy Journal.

The award said, [Steve Blank] as the Father of Modern Entrepreneurship, changed how startups are built, how entrepreneurship is taught, how science is commercialized, and how companies and government innovate.

Here’s my acceptance speech.

Thank you for the Strategy Leadership Impact Award. As a practitioner standing in front of a room full of strategists, I’m humbled and honored.

George Bernard Shaw reminded us that Americans and British are “one people separated by a common language.” I’ve often felt the same way about the gap between practitioners and strategists.

The best analogy I can offer, is the time after a long plane flight to Sydney, I jumped into a taxi and as the taxi driver started talking I started panicking – wondering what language he was speaking, and how I was going to be able to communicate to him.

It took me almost till we got to the hotel to realize he was speaking in English.

That’s sometimes how it feels between those who do strategy and those who study it.

So today, I’d like to share with you how this practitioner accidently became a strategist and how that journey led to what we now call the Lean Startup.

It’s a story that begins, perhaps surprisingly with what I call the Secret History of Silicon Valley.

—-

Silicon Valley’s roots lie in solving urgent, high-uncertainty national-security problems during World War II and the Cold War with the Soviet Union.

During WW II, the United States mastered scale and exploitation—mass-producing ships, aircraft, and tanks through centralized coordination. Ford, GM, Dupont, GE and others became the “arsenals of democracy.” In less than 4 years the U.S. built 300,000 aircraft, 124,000 of all types of ships, 86,000 tanks.

But simultaneously we created something radically different, something no other nation did – we created the Office of Science and Research and Development – OSR&D. This was a decentralized network of university labs that worked on military problems that involved electronics, chemistry and physics. These labs solved problems where outcomes were unknown and time horizons uncertain—exactly the conditions that later came to define innovation under uncertainty.

These labs delivered radar, rockets, proximity fuses, penicillin, sulfa drugs, and for the first two years ran the U.S. nuclear weapons program.

In hindsight, way before we had the language, the U.S. was practicing dynamic capabilities: the capacity to sense, seize, and transform under extreme uncertainty. It was also an early case of organizational ambidexterity—balancing mass production with rapid exploration.

One branch of this Office of Science and Research and Development – focused on electronic warfare—became the true genesis of the Valley’s innovation model.

—

In 1943, U.S. bombers over Europe faced catastrophic losses—4–5% of planes were shot down every mission. The German’s had built a deadly effective radar-based air defense system. The U.S. responded by creating the Harvard Radio Research Lab, led by Stanford’s Fred Terman. The lab had nothing to do with Harvard, Radio or Research.

Its goal was to rapidly develop countermeasures: jammers, receivers, and radar intelligence.

In the span of three years, Terman’s lab created an entire electronic ecosystem to defeat the German air defense systems. By war’s end U.S. factories were running 24/7 mass producing tens of thousands of the most complicated electronics and microwave systems that went on every bomber over Europe and Japan.

These teams were interdisciplinary, field-connected, and operating in continuous learning cycles:

  • Scientists and engineers worked directly with pilots and operators—what we’d now call frontline customer immersion.
  • They built rapid prototypes—the Minimum Viable Products of their time.
  • They engaged in short feedback loops between lab and battlefield—what John Boyd would later formalize as the OODA loop.
  • They were, in essence, running a learning organization under fire—a live example of strategic adaptation and iterative sensemaking.

But what does this have to do with Silicon Valley?

When the war ended Terman came back to Stanford and became Dean of Engineering and institutionalized this model. He embedded government research into the university, recruited his wartime engineers as faculty, and redefined Stanford as an outward-facing institution.

While most universities pursued knowledge exploitation – publishing, teaching, and extending established disciplines, Terman at Stanford did something that few universities in the 1950’s, 60’s or 70’s were doing – he pursued knowledge exploration and recombination. Turning Stanford into an outward facing university – with a focus on commercializing their inventions.

  1. He reconfigured incentives — encouraging professors to consult and found companies, an unprecedented act of strategic boundary spanning
  2. He believed spinning out microwave and electronics companies from his engineering labs was good for the university and for the country.
  3. He embedded exploration in the curriculum — mixing physics, electronics, and systems engineering.
  4. Cultivating external linkages — he and his professors were on multiple advisory boards with the Department of Defense, intelligence agencies, and industry.

Terman’s policies as now Provost effectively turned Stanford into an early platform for innovation ecosystems—decades before the term existed.

The technology spinouts from Stanford and small business springing up nearby were by their very nature managing uncertainty, complexity, and unpredictability. These early Valley entrepreneurs weren’t “lone inventors”; they were learning organizations, long before that term existed. They were continuously testing, learning, and iterating based on real operational data and customer feedback rather than long static plans.

However, at the time there was no risk capital to guide them. They were undercapitalized small businesses chasing orders and trying to stay in business.

It wasn’t until the mid 1970’s when the “prudent man” rule was revised for pension funds, and Venture Capital began to be treated as an institutional asset class, that venture capital at scale became a business in Silicon Valley. This is the moment when finance replaced learning as the dominant logic.

For the next 25 years, Venture investors – most of them with MBAs or with backgrounds in finance, treated startups like smaller versions of large companies. None of them had worked on cold war projects nor were they familiar with the agile and customer centric models defense innovation organizations had built. No VC was thinking about whether lessons from corporate strategic management thinkers of the time could be used in startups. Instead, VCs imposed a waterfall mindset —business plans and execution of the strategy in the plan — the opposite of how the Valley first innovated. The earlier language of experimentation, iteration, and customer learning disappeared.

And now we come full circle – to the Lean Startup.

At the turn of the century after 21 years as a practitioner, and with a background working on cold war weapons systems, I retired from startups and had time to think.

The more I looked at the business I had been in, and the boards I was now sitting on, I realized a few things.

  1. No business plan survived first contact with customers.
  2. On day one all startups have is a series of untested hypotheses
    • Yet startups were executing rather than learning
  3. Our strategic language and tools—all designed for large firms—were useless in contexts of radical uncertainty.
  4. Startups that succeeded were the ones that learned from their customers and iterated on the plan. Those that didn’t, ended up selling off their furniture.
  5. Most importantly – as I started reading all the literature I found on innovation strategy, almost all of it was about corporate innovation.
    • We had almost a century of management tools and language to describe corporate strategy for both growth and innovation – yet there were no tools, language or methods for startups.
    • But it was worse. Because both practitioners and their investors weren’t strategists, we had been trapped in thinking that startups were smaller versions of large companies
    • When the reality was that at their core, large companies were executing known business models, but startups? Startups were searching for business models
    • This distinction between startup search and large company execution had never been clearly articulated.
  6. There was a mismatch between the reality and practice.
    • We needed to reframe entrepreneurship as a strategic process, not a financial one
  7. I realized that every startup believed their journey was unique, and thought they had to find their own path to profitability and scale.
  8. That was because we had no shared methodology, language or common tools. So I decided to build them.
    • The first was Customer Development – at its heart a very simple idea – there are no facts inside the building – so get outside.
    • Here we were reinventing what the best practices from the wartime military organizations, and from Lead User Research and Discovery Driven Planning – this time for startups
    • The goal is to test all the business model hypotheses – including the two most important – customer and value proposition – which we call product/ market fit.
  9. The next, Agile Engineering – a process to build products incrementally and iteratively – was a perfect match for customer development.
  10. And then finally, repurposing Alexander Osterwalder’s Business Model Canvas to map the hypotheses needed in commercialization of a technology

The sum of these tools – Customer Development, Agile Engineering and the Business Model Canvas – is the Lean Methodology.

What I had done is turn a craft into a discipline of strategic learning—a continuous loop of hypothesis testing, experimentation via minimum viable products, and adaptation via pivots.

Lean is a codified system for strategy formation under uncertainty.

Over the last two decades Lean has turned into the de facto standard for starting new ventures. The classes I created at Stanford were adopted by the National Science Foundation and the National Institutes of Health, to commercialize science in the U.S.

And while contemporary entrepreneurs didn’t know it they were adopting the continuous learning cycles that had fueled wartime innovation.

What comes next is going to be even more interesting.

We’re going to remember – for better or worse – 2025 as another inflection point.

AI in everything, synthetic biology, and capital at previously unimaginable scale, are collapsing the distance between exploration and exploitation.

The boundary between discovery, invention, and strategy is dissolving.

Given how fast things are changing I’m looking forward to seeing strategy itself become a dynamic capability—not a plan, but a process of learning faster than the environment changes.

I can’t wait to see what you all create next.

In closing, my work at Stanford was made possible by the unflinching support from Tom Byers, Kathy Eisenhardt and Riitta Katila in the Stanford Technology Ventures Program who let a practitioner into the building.

Thank you.

Filed under: Customer Development, Teaching | 8 Comments »

Lean Meets Wicked Problems

Posted on July 25, 2023 by steve blank

This post previously appeared in Poets & Quants.

I just spent a month and a half at Imperial College London co-teaching a “Wicked” Entrepreneurship class. In this case Wicked doesn’t mean morally evil, but refers to really complex problems, ones with multiple moving parts, where the solution isn’t obvious. (Understanding and solving homelessness, disinformation, climate change mitigation or an insurgency are examples of wicked problems. Companies also face Wicked problems. In contrast, designing AI-driven enterprise software or building dating apps are comparatively simple problems.)

I’ve known Professor Cristobal Garcia since 2010 when he hosted my first visit to Catholic University in Santiago of Chile and to southern Patagonia. Now at Imperial College Business School and Co-Founder of the Wicked Acceleration Labs, Cristobal and I wondered if we could combine the tenets of Lean (get out of the building, build MVPs, run experiments, move with speed and urgency) with the expanded toolset developed by researchers who work on Wicked problems and Systems’ Thinking.

Our goal was to see if we could get students to stop admiring problems and work rapidly on solving them. As Wicked and Lean seem to be mutually exclusive, this was a pretty audacious undertaking.

This five-week class was going to be our MVP.

Here’s what happened.

Finding The Problems
Professor Garcia scoured the world to find eight Wicked/complex problems for students to work on. He presented to organizations in the Netherlands, Chile, Spain, the UK (Ministry of Defense and the BBC), and aerospace companies. The end result was a truly ambitious, unique, and international set of curated Wicked problems.

  • Increasing security and prosperity amid the Mapuche conflict in Araucania region of Chile
  • Enabling and accelerating a Green Hydrogen economy
  • Turning the Basque Country in Spain into an AI hub
  • Solving Disinformation/Information Pollution for the BBC
  • Creating Blue Carbon projects for the UK Ministry of Defense
  • Improving patient outcomes for Ukrainian battlefield injuries
  • Imagining the future of a low-earth-orbit space economy
  • Creating a modular architecture for future UK defense ships

Recruiting the Students
With the problems in hand, we set about recruiting students from both Imperial College’s business school and the Royal College of Art’s design and engineering programs.

We held an info session explaining the problems and the unique parts of the class. We were going to share with them a “Swiss Army Knife” of traditional tools to understand Wicked/Complex problems, but they were not going to research these problems in the library. Instead, using the elements of Lean methodology, they were going to get out of the building and observe the problems first-hand. And instead of passively observing them, they were going to build and test MVPs.  All in six weeks.

50 students signed up to work on the eight problems with different degrees of “wickedness”.

Imperial Wicked Problems and Systems Thinking – 2023 Class

The Class
The pedagogy of the class (our teaching methods and the learning activities) were similar to all the Lean/I-Corps and Hacking for Defense classes we’ve previously taught. This meant the class was team-based, Lean-driven (hypothesis testing/business model/customer development/agile engineering) and experiential – where the students, rather than being presented with all of the essential information, must discover that information rapidly for themselves.

The teams were going to get out of the building and talk to 10 stakeholder a week. Then weekly each team will present 1) here’s what we thought, 2) here’s what we did, 3) here’s what we learned, 4) here’s what we’re going to do during this week.

More Tools
The key difference between this class and previous Lean/I-Corps and Hacking for Defense classes was that Wicked problems required more than just a business model or mission model to grasp the problem and map the solution. Here, to get a handle on the complexity of their problem the students needed a suite of tools –  Stakeholder Maps, Systems Maps, Assumptions Mapping, Experimentation Menus, Unintended Consequences Map, and finally Dr. Garcia’s derivative of the Alexander Osterwalder’s Business Model Canvas – the Wicked Canvas – which added the concept of unintended consequences and the “sub-problems” according to the different stakeholders’ perspectives to the traditional canvas.

During the class the teaching team offered explanations of each tool, but the teams got a firmer grasp on Wicked tools from a guest lecture by Professor Terry Irwin, Director of the Transition Design Institute at Carnegie Mellon (see her presentation here.) Throughout the class teams had the flexibility to select the tools they felt appropriate to rapidly gain an holistic understanding and yet to develop a minimum viable product to address and experiment with each of the wicked problems.

Class Flow
Week 1 

  • What is a simple idea? What are big ideas and Impact Hypotheses? 
    • Characteristics of each. Rewards, CEO, team, complexity, end point, etc. 
  • What is unique about Wicked Problems?
    • Beyond TAM and SAM (“back of the napkin”) for Wicked Problems
  • You need Big Ideas to tackle Wicked Problems: but who does it?
    •  Startups vs. Large Companies vs. Governments
    • Innovation at Speed for Horizon 1, 2 and 3 (Managing the Portfolio across Horizons)
  • What is Systems Thinking?
  • How to map stakeholders and systems’ dynamics?
  • Customer & Stakeholder Discovery: getting outside the building, city and country: why and how? 

Mapping the Problem(s), Stakeholders and Systems –  Wicked Tools

Week 2

  • Teams present for 6 min and receive 4 mins feedback
  • The Wicked Swiss Army Knife for the week: Mapping Assumptions Matrix, unintended consequences and how to run and design experiments
  • Prof Erkko Autio (ICBS and Wicked Labs) on AI Ecosystems and Prof Peter Palensky (TU Delft) on Smart Grids, Decarbornization and Green Hydrogen
  • Lecture on Minimal Viable Products (MVPs) and Experiments
  • Homework: getting outside the building & the country to run experiments

Assumption Mapping and Experimentation Type –  Wicked Tools

Week 3

  • Teams present in 6 min and receive 4 mins feedback
  • The Wicked Swiss Army Knife for the week: from problem to solution via “How Might We…” Builder and further initial solution experimentation
  • On Canvases: What, Why and How 
  • The Wicked Canvas 
  • Next Steps and Homework: continue running experiments with MVPs and start validating your business/mission/wicked canvas

The Wicked Canvas –  Wicked Tools

Experimentation Design and How We Might… –  Wicked Tools

Week 4

  • Teams present in 6 min and receive 5 mins feedback
  • Wicked Business Models – validating all building blocks
  • The Geography of Innovation – the milieu, creative cities & prosperous regions 
  • How World War II and the UK Started Silicon Valley
  • The Wicked Swiss Tool-  maps for acupuncture in the territory
  • Storytelling & Pitching 
  • Homework: Validated MVP & Lessons learned

Acupuncture Map for Regional System Intervention  – Wicked Tools


Week 5

  • Teams presented their Final Lessons Learned journey – Validated MVP, Insights & Hindsight (see the presentations at the end of the post.)
    • What did we understand about the problem on day 1?
    • What do we now understand?
    • How did we get here?
    • What solutions would we propose now?
    • What did we learn?
    • Reflections on the Wicked Tools

Results
To be honest, I wasn’t sure what to expect. We pushed the students way past what they have done in other classes. In spite of what we said in the info session and syllabus, many students were in shock when they realized that they couldn’t take the class by just showing up, and heard in no uncertain terms that no stakeholder/customer interviews in week 1 was unacceptable.

Yet, everyone got the message pretty quickly. The team working on the Mapuche conflict in the Araucania region of Chile, flew to Chile from London, interviewed multiple stakeholders and were back in time for next week’s class. The team working to turn the Basque Country in Spain into an AI hub did the same – they flew to Bilbao and interviewed several stakeholders. The team working on the Green Hydrogen got connected to the Rotterdam ecosystem and key stakeholders in the Port, energy incumbents, VCs and Tech Universities. The team working on Ukraine did not fly there for obvious reasons. The rest of the teams spread out across the UK – all of them furiously mapping stakeholders, assumptions, systems, etc., while proposing minimal viable solutions. By the end of the class it was a whirlwind of activity as students not only presented their progress but saw that of their peers. No one wanted to be left behind. They all moved with speed and alacrity.

Lessons Learned

  • Our conclusion? While this class is not a substitute for a years-long deep analysis of Wicked/complex problems it gave students:
    • a practical hands-on introduction to tools to map, sense, understand and potentially solve Wicked Problems
    • the confidence and tools to stop admiring problems and work on solving them
  • I think we’ll teach it again.

Team final presentations

The team’s final lessons learned presentations were pretty extraordinary, only matched by their post-class comments. Take a look below.

Team Wicked Araucania

Click here if you can’t see the Araucania presentation.

Team Accelerate Basque

Click here if you can’t see the Accelerate Basque presentation.

Team Green Hydrogen

Click here if you can’t see the Green Hydrogen presentation.

Team Into The Blue

Click here if you can’t see the Team Blue presentation.

Team Information Pollution

Click here if you can’t see the Team Information Pollution presentation.

Team Ukraine

Click here if you can’t see the Team Ukraine presentation.

Team Wicked Space

Click here if you can’t see the Team Wicked Space presentation.

Team Future Proof the Navy

Click here if you can’t see the Future Proof the Navy presentation.



Filed under: Customer Development, Lean LaunchPad, Teaching | 9 Comments »

The 6th Lean Innovation Educators Summit – Education and Innovation in the Age of Chaos and Disruption

Posted on November 14, 2022 by steve blank

Join Jerry Engel, Pete Newell, and Steve Weinstein for the sixth edition of the Lean Innovation Educators Summit December 14, 1-4 pm Eastern Time, 10 am-1 pm Pacific Time. Register here.

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This virtual gathering will bring together entrepreneurship educators from around the world who are putting Lean Innovation to work in their classrooms, accelerators, venture studios, and student-driven ventures.

The summit topic is “Education and Innovation in the Age of Chaos and Disruption.”

Our students will be facing the challenges of a world that’s rapidly changing, chaotic and uncertain. A world undergoing climate change, supply chain disruptions, political instability and continual technology innovation and disruption. It’s incumbent on us as educators to provide the next generation of innovators with the tools and mindset to meet these challenges.

Among the questions we’ll address in this short summit:

  • How do we as entrepreneurship and innovation educators best prepare the next generation?
  • What role should our institutions help us do this?
  • What are the other systems and partnerships that we need to take advantage of?

We will have concurrent breakout sessions so participants have the opportunity to choose their own path to explore. We’ll then going to pivot to hear from colleagues across three broad categories of innovation:

  • Curriculum – We’ll discuss how best to equip educators with the tools they need to cultivate and guide student teams around solving mission-driven problems.
  • Ecosystems – We’ll explore partnerships that engage and inform positive student engagement and outcomes and how to support diversity of thought, background.
  • Trends – The rate of technological disruption shows no sign of slowing down. Climate change was a hypothesis for our generation but will be the facts on the ground for our students. The struggle between great powers and a fluid global landscape will accelerate. All of these will shape what future curriculums our students need and educators must deliver.

Alexander Osterwalder, the creator of the business model canvas and Strategyzer co-founder will join the discussion about the intersection of education, innovation and entrepreneurship

During the breakout sessions, you will have the opportunity to contribute to the conversation via Chat, Q&A, and an online community bulletin board. We will close out the Summit with Alex Osterwalder’s fireside chat moderated by Dr. Jerry Engel.

How to register

When you register, you will receive a link to an online collaboration space where you can submit questions, challenges and feedback. This feedback will inform the content of the presentations, post-event white papers, and the curriculum delivered to our educator community.

This session is free but limited to Innovation educators. Register here and learn more on our website: We look forward to gathering as a community of educators to shape the future of Lean Innovation Education.

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Technology, Innovation, and Great Power Competition – Class 4- Semiconductors

Posted on November 2, 2021 by steve blank

This article first appeared in West Point’s Modern War Institute.  

We just completed the fourth week of our new national security class at Stanford – Technology, Innovation and Great Power Competition. Joe Felter, Raj Shah and I designed the class to cover how technology will shape all the elements of national power (America’s influence and footprint on the world stage).

In class 1, we learned that national power is the combination of a country’s diplomacy (soft power and alliances), information/intelligence, military power, economic strength, finance, intelligence, and law enforcement. This “whole of government approach” is known by the acronym DIME-FIL.  And after two decades focused on counter terrorism, the U.S. is now engaged in great power competition with both China and Russia.

In class 2 the class focused on China, the U.S.’s primary great power competitor. China is using all elements of national power: diplomacy (soft power, alliances, coercion), information/ intelligence (using its economic leverage over Hollywood, controlling the Covid narrative), its military might and economic strength (Belt and Road Initiative) as well as exploiting Western finance and technology. China’s goal is to challenge and overturn the U.S.-led liberal international order and replace it with a neo-totalitarian model.

The third class focused on Russia, which is asserting itself as a great power challenger. We learned how Russia pursues security and economic interests in parallel with its ideological aims. At times, these objectives complement each other. At other times they clash, Putin’s desire to restore Russia into a great power once again leads to a foreign policy that is opposite the interests of the Russia people. As Putin himself has said, “The collapse of the Soviet Union was a major geopolitical disaster of the century,” and that quote offers a window to his worldview as he tries to remake Russia into a great power once again.”

Having covered the elements of national power (DIME-FIL) and China and Russia, the class now shifts to the impact commercial technologies have on DIME-FIL. Today’s topic – Semiconductors.

Catch up with the class by reading our intro to the class, and summaries of Classes 1, 2 and 3.

Class 4 Required Readings:

Silicon Valley, the Military, and the Journey to the Fourth Industrial Revolution

Moore’s Law & the Global Semiconductor Industry

Semiconductor Case Study

Reading Assignment Questions:

Pick one of the below questions and answer in approximately 100 words, based on the required readings. Please note that this assignment will be graded and count towards course participation.

  1. Describe the roles of Fred Terman, William Shockley, and Fairchild Semiconductor in the genesis of Silicon Valley. Who had a greater role in creating Silicon Valley, Fred Terman or the Traitorous Eight?
  2. How would you characterize China’s attempt to catch-up in the semiconductor industry? Do you think China can credibly catch TSMC (without an invasion of Taiwan)? Why or why not?

Discussion Questions

  1. Put yourself in the shoes of Mark Liu, chairman of TSMC: Do you view China as more of a competitor or customer – and why?
  2. Now imagine you are the NSC Senior Director with responsibility for technology strategy. What’s the first thing the U.S. Gov’t should do regarding semiconductors?

Class 4: Guest Speaker
Our guest speaker for our fourth class was John Hurley, former Member of the President’s Intelligence Advisory Board, an expert on semiconductors and supply chains, and former Captain, U.S. Army.

Lecture 4

If you can’t see the slides, click here

Slide 4. The critical role of semiconductors in great power competition. Both our commercial and military systems are dependent on semiconductors. China spends more on semiconductor imports than it does on oil. We framed the advances in technology as part of the 4th industrial revolution. Slides 5-7. We reminded the students of the role the DoD and IC played at Stanford turning it into an outward-facing university, which kick-started technology entrepreneurship here in Silicon Valley.

Slides 9-11 Dual-use technology. For the first time in 75 years, federal labs and our prime contractors are no longer leading innovation in many critical technologies including AI, machine learning, autonomy, biotech, commercial access to space, etc. Rapid advances in these areas are now happening via commercial firms – many in China. This is a radical change in where advanced technology comes from. In the U.S., the government is painfully learning how to reorient its requirements and acquisition process to buy these commercial, off-the-shelf technologies. (Products that are sold commercially and to the DoD are called “dual-use.”)

Slide 15. Semiconductor industry. We began a deep dive into semiconductors by drawing the map of the semiconductor industry (Slides 3-15 from this required reading.) Five companies provide the majority of the wafer fab equipment needed to make chips. TSMC is the leading fab for manufacturing logic chips. (Slides 32-33 from this required reading.) Of the 29 new fabs starting construction in 2021-22, over half are in China and Taiwan.

Slide 16. TSMC Case. We took the class through the TSMC case study and mapped out the roles and interests of TSMC, China, Intel, and the U.S. Slides 17-18. We discussed China’s drive for semiconductor independence, U.S. export controls on Huawei (why and its consequences,) the various constituencies of a U.S. semiconductor policy (Commerce Department, DoD, U.S. chip makers, U.S. semi equipment suppliers, etc.), whether TSMC’s success makes Taiwan more or less secure, given China’s goals of reunification with Taiwan.

Slide 19-20. Policy.  How do decision makers formulate policy? Does it start by asking “What problem do we want to solve?” Using semiconductors as an example, is it China’s access to U.S. technology?  Or is it China embedding this advanced U.S.-designed technology into their military systems? Or what happens to TSMC and Western access to advanced technology if China quarantines or invades Taiwan?

How do policy makers select and narrow a problem? Is it based on the value the policy adds for identified stakeholders? Is it a personal passion/interest? Specifically for China and semiconductors, what are potential solution ideas? Export controls? Stronger CFIUS regulations? How do you take into account stakeholder feedback (DoD, Commerce Department, commercial firms)? And once you create a policy, how do you effectively implement it?

Slide 21 -23. Class midterm assignment: Assume you’re a policy maker. Write a 2,000-word policy memo that describes how a U.S. competitor is using a specific technology  (semiconductors, AI, autonomy, cyber, etc.) to counter U.S. interests. Propose how the U.S. should respond.

Slides 25- 32 Group Projects. We had several teams talk about their learnings from their out-of-the building interviews. Team ShortCircuit (Slide 29) is working on how the U.S. should improve its ability to design and produce semiconductors, and develop and retain relevant talent. They heard from a professor that the ratio of Stanford students taking software versus hardware courses was 10-to-1 software, a complete reversal from decades ago. We discussed whether  1) that was true or just anecdotal 2) if true, was it the same in other research universities, 3) why it happened (software startups are getting funded at obscene valuations)? 4) and what kind of incentives and policies would be needed to change that, and 5) where in the value chain those might be most effective (students, venture capitalists, government, etc.)

Next week: Artificial Intelligence / Machine Learning

Lessons Learned

  • Semiconductors are the oil of the 21st All economies run on them.
  • Semiconductors are China’s biggest imports
  • China’s roadmap for building an indigenous semiconductor industry and accelerating chip manufacturing is the National Integrated Circuit Plan
    • The goal is to meet its local chip demand by 2030
  • The U.S. is dependent on TSMC, located in Taiwan, for its most advanced logic chips
    • China claims Taiwan is a province of China
    • TSMC will build a fab in Arizona, but it will represent only 2% of its capacity
  • What are U.S. policy makers’ options?


11/02/2021 Technology, Innovation, and Great Power Competition – Class 4 – Semiconductors

Filed under: Teaching, Technology Innovation and Great Power Competition | Leave a comment »

Technology, Innovation, and Great Power Competition – Class 3 – Russia

Posted on October 26, 2021 by steve blank

This article first appeared in West Point’s Modern War Institute. 

We just had our third week of our new national security class at Stanford – Technology, Innovation and Great Power Competition. Joe Felter, Raj Shah and I designed the class to cover how technology will shape all the elements of national power (our influence and footprint on the world stage).

In class 1, we learned that national power is the combination of a country’s diplomacy (soft power and alliances), information/intelligence, its military, economic strength, finance, intelligence, and law enforcement. This “whole of government approach” is known by the acronym DIME-FIL. And after two decades focused on counter terrorism the U.S. is engaged in great power competition with both China and Russia.

In class 2, we learned how China is using all elements of national power: diplomacy (soft power, alliances, coercion), information/intelligence (using its economic leverage over Hollywood, controlling the Covid narrative), its military might and economic strength (Belt and Road Initiative,) to exploit Western finance and technology.  This has resulted in Western democracies prioritizing economic cooperation and trade with China above all else.  China’s goal is to challenge and overturn the U.S.-led liberal international order and replace it with a neo-totalitarian model.

Going forward, coexistence with China will involve competition but also cooperation. But it’s going to take the demonstrated resolve of the U.S. and its allies to continue to uphold a rules-based order where nations share a vision of a free and open Indo-Pacific where the sovereignty of all countries are respected.

Catch up with the class by reading our intro to the class, and summaries of Class 1 and Class 2.

All which leads to today’s topic, the other great power – Russia.

 

Class 3 Required Readings

Fall of the USSR

Russian Geopolitics & Foreign Policy

Putin & Putinism

Russia’s Pivot to Asia & the Chinese-Russian Relationship

Russian Technology Strategy

Reading Assignment Questions:

Pick one of the below questions and answer in approximately 100 words, based on the required readings. Please note that this assignment will be graded and count towards course participation.

  1. Compare and contrast the viewpoints of John Mearsheimer and Michael McFaul on drivers of Russian foreign policy. Where do they agree? Disagree? Which perspective do you agree with more and why?
  2. Evaluate the perspectives of Artyom Lukin and Chris Miller on Russia’s so-called pivot to Asia. Do you agree with one more than the other? Do you believe that the pivot is more a rhetorical or substantive strategic move on the part of Moscow?

Class Discussion Questions:

  1. What are Russia’s geopolitical interests, goals, and/or objectives? From Moscow’s perspective, what are the main obstacles standing in the way of achieving its national goals?
  2. To what degree is Vladimir Putin a unitary actor? How much is he the system of government versus the product of a system?
  3. How does Moscow view the existing, American-led rules-based international order?
  4. What role, if any, does ideology play in Moscow’s strategy?
  5. In what ways are Moscow’s goals compatible and/or incompatible with U.S. national interests?
  6. In what domains does the competition between the United States and the Russian Federation play out? How do these domains interact with one another? Is cooperation between the two possible and beneficial?
  7. How would you characterize the Sino-Russian relationship? In what dimensions is the relationship the strongest? Where are its fault lines? Is the relationship enduring or transient?

Class 3: Guest Speaker

Our guest speaker for our third class was Mike McFaul, the former U.S. Ambassador to the Russian Federation and former National Security Council Senior Director for Russian and Eurasian Affairs. Mike wrote about his experience as ambassador in From Cold War to Hot Peace: An American Ambassador in Putin’s Russia. At Stanford, Mike is the Director of the Freeman Spogli Institute for International Studies,Stanford’s research institute for international affairs, and the home for this class and the Gordian Knot Center for National Security Innovation.

Lecture 3

If you can’t see the slides, click here

Ambassador McFaul pointed out that at times Russia pursues security and economic interests in parallel to ideological aims. At times, these objectives complement each other. At other times, they clash. He posited it’s because Russian policy is run by Putin and his political institutions. Slide 7

We then reviewed highlights from the assigned readings. John Mearsheimer’s article took the contrarian position that the United States and its European allies share most of the responsibility for the crisis in Crimea. Slide 8.

Slides 10-12 led the conversation about the end of the Cold War & Collapse of the USSR. George Kennan was the author of the 1946 Long Telegram which set in motion the policy of “containment” of the Soviet Union. He lived to see its collapse a half-century later, and wrote, “I find it hard to think of any event more strange and startling, and at first glance more inexplicable, than the sudden and total disintegration and disappearance…of the great power known successively as the Russian Empire and then the Soviet Union.”  Stephen Kotkin maintains that if the Soviet elite had so chosen, they could have sustained the Soviet Union decades longer. Perhaps the most enduring quote is from Vladimir Putin himself, “the collapse of the Soviet Union was a major geopolitical disaster of the century,” as he tries to remake Russia into a great power once again.

Slide 13, Dmitri Trenin from the Carnegie Center points out that the 2014 Ukrainian crisis was the Rubicon. Russia broke a quarter century of cooperative relations among great powers pivoting away from the west, starting a new era of intense competition. Slide 14, Mike McFaul has a more nuanced view. “For a complete understanding of Russian foreign policy.., individuals, ideas, and institutions—President Vladimir Putin, Putinism, and autocracy—must be added to the analysis. (The).. three cases of recent Russian intervention (in Ukraine in 2014, Syria in 2015, and the United States election in 2016) illuminate the causal influence of these domestic determinants in the making of Russian foreign policy.

Slide 15, Russia’s pivot to China.

China-Russian relations are now at their highest point since the mid-1950s, being drawn to each other by the most elementary law of international politics: that of the balance of power. Slide 16 Russia has long struggled to overcome the constraints imposed by the country’s chronic inability to retain talent in support of homegrown innovation and R&D.

North Korea/Iran/Non-Nation States
We also covered the two regional threats to international security – North Korea and Iran – as well as the continued threats of terrorism from non-nation states (Al-Qaeda’s, ISIS).

Slides 20-22. North Korea has robust and expanding nuclear weapons program with 10-40 nuclear weapons. Their ballistic missile program not only threatens their neighbors, but their development of long-range ICBMs puts the entire continental United States in range of their nuclear weapons.

Slides 23-25 the Islamic Republic of Iran (IRI) has actively pursued nuclear weapons and long range ballistic missiles. Under the Iran Nuclear Deal (JCOPA) they had agreed to limit their uranium to 3.67% enrichment. They broke out of the deal in 2019. Today, their uranium enrichment has reached 60% enrichment (90% is weapons grade). Iran has been a major source of regional destabilization, hostage-taking, and sponsorship of terrorism: Ansar Allah (Houthis) in Yemen, Hezbollah in Lebanon, Hamas/PIJ in Palestine, numerous Shia militias in Iraq (Kataib Hezbollah, Asaib Ahl al Haq, Badr Organization). Iran’s long-running conflict with Israel is a perennial potential flashpoint for a broader conflict in the region. Iran has been actively using cyber attacks and has attacked and harassed commercial shipping and Freedom of Navigation Operations in the Persian Gulf and the Strait of Hormuz.

Slides 26-27 Non-nation states haven’t gone away. They are a persistent, survivable threat unconstrained by traditional geopolitical checks (irrational actor). They are capable of regional and international terror attacks. Some are actively pursuing acquisition of weapons of mass destruction (nuclear, chemical, biological). Addressing the problem through counterinsurgency/ counter terrorism operations, runs the risk of long-term engagements that damage other national objectives and, sometimes, the national interest. Yet, if left unaddressed, these insurgencies can spread globally and create second- and third-order challenges (al-Shabab, Boko Haram, Abu Sayyaf).

Slides 28-31 covered the Group Project. The class has formed into 7 teams – slides 32-38. We suggested they get out of the building to first deeply understand the problem they’ve selected.

We offered a series of questions they may want to ask:

Slide 33. Who has this problem? Why does the problem exist? Consequence of the problem? When do they need a solution? How does this get deployed/delivered? How are they solving it today? How do you know you solved the problem?

Slide 34. Next, after they validate the problem: What would a minimum viable product look like? Who would build and deliver the final product/service? How to you create an “Innovation Insurgency” around the idea? Who would have to get excited about the MVP to fund it? Who are the saboteurs?

Next week we start talking about the impact of commercial technology on Great Power Competition. First up – semiconductors.

Lessons Learned

  • After the collapse of the Soviet Union, the U.S. and the Russia Federation had a two-decade long cooperative relationship
  • In 2014 with the Russian-Ukrainian war and Russia’s annexation of Crimea, and in 2015 with Russian intervention in the Syrian civil war, Russia’s interests and the West’s have radically diverged
    • Mike McFaul makes the case that Putin, Putinism, and the Russian autocracy are key determinants of their foreign policy
  • This week, student teams will start getting out of the building to build reflexes and skills to deeply understand a problem
    • By gathering first-hand information to validate that the problem they are solving is the real problem, not a symptom of something else
    • Then, students will begin rapidly building minimal viable solutions as a way to test and validate their understanding of both the problem and what it would take to solve it


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