Every once in a while a book comes along that doesn’t just change your tactical thinking, but makes you see the world in a different way. Reading this book is like taking the red pill in the Matrix.

Some will read this book, think it’s interesting and then get back to figuring out to how get their next big round of funding or how to deal with AI disruption in their large company.

But what they’ll miss is that this is the book that will rebuild the corporate and startup world after the next financial crash.

That’s exactly what happened when Eric’s work, Alexander Osterwald’s work and mine created the Lean Startup. Lean was a neat theory until the dot com bubble crashed and investors (those who still had jobs,) were hiding under their desks. Only then were startups and VCs amenable to a radically new idea about how to build new ventures.

The same will happen here.

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Part 1 is a great tutorial on how corporations morphed from serving the people to serving only its shareholders. Worth reading deeply.

Part 2 is the nuts and bolts about what to do about it. How to build companies with governance structures that endure.

Part 3 is about the network effect of building this class of companies. It also has a chapter that buries the lead. Eric had the core ideas for the concepts in the book in 2019 when he started the Long Term Stock Exchange (LTSE). Never heard of it? Welcome to the club. Its core diagnosis was absolutely right: public markets reward short-termism. LTSE tried to solve a governance problem with an exchange listing. In hindsight it took all the accumulated wisdom in this book to understand what it will take to make meaningful change.

Its time may come and this book may be the catalyst.

This book will possibly be more important than the Lean Startup ever was – for you, your company and society as a whole. Read it.

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.

To 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.

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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.

To see the Noctura video, click here

To 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.

To see the SwarmShield summary video, click here.

To 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.

To see the Weapons Without Wait video, click here

To 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.

To see the IonX video click here

To 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.

To see the Cheese on the Moon video, click here

To 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,

To see the Fuel Forge video click here

To 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.

To see the Luminarch video, click here

To 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.

To see the Tessellate video, click here

To 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.

To see the AI Use In Class slide, click here

To 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, LTC 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!

For a decade the cybersecurity community was predicting a cyber apocalypse tied to a single event – the day a Cryptographically Relevant Quantum Computer could run Shor’s algorithm and break the public-key cryptography systems most of the internet runs on.

We braced for a one-time shock we would absorb and adapt to. NIST (the National Institute for Standards and Technology) has already published standards for the first set of post-quantum cryptography codes.

It’s possible that the first cybersecurity apocalypse may have come early. Anthropic Mythos now tilts the odds in the cybersecurity arms race in favor of attackers – and the math of why it tilts, and how long it stays tilted, is different from anything our institutions were built to handle.

In 2013, Edward Snowden changed what people knew

In 2013 Edward Snowden changed what people understood about nation-state cyber capabilities. In the decade that followed disclosures and leaks of nation state cyber tools reduced uncertainty and accelerated the diffusion of cyber tradecraft.

The defensive playbook that followed – compartmentalization, need-to-know, leak-surface reduction, clearance reform, “worked” because the Snowden leaks and those that followed were one-time disclosures, absorbed over a decade, with the system returning to something like equilibrium.

We got good at responding to the shocks of disclosures. It became doctrine.

It was the right doctrine for the wrong future.

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Pandora’s Box

In 2026 Anthropic Mythos (and similar AI systems) changes what people can do. Mythos found Zero-day vulnerabilities and thousands of “bugs” that were not publicly known to exist (a must read article here.) Many of these were not just run-of-the-mill stack-smashing exploits but sophisticated attacks that required exploiting subtle race conditions, KASLR (Kernel Address Space Layout Randomization) bypasses, memory corruption vulnerabilities and logic flaws in cryptographic libraries in cryptography libraries, and bugs in TLS, AES-GCM, and SSH.

The reality is a number of these were not “bugs.” There were nation-state exploits built over decades.

What this means is that Anthropic Mythos, and the tools that will certainly follow, has exposed hacking tools previously only available to nation-states and transformed into tools that Script Kiddies will have within a few months (and certainly within a year.) No expertise will be required to apply that tradecraft, compressing both the learning curve and the execution barrier.

All Governments Will Scramble

When Mythos-class systems are used to analyze the code in critical infrastructure and systems, the hidden sophisticated zero-day exploits that are already in use, (including ones nation-states have been sitting on for years) will be found and patched. That means the sources intelligence agencies used to collect information will go dark as companies and governments patch these vulnerabilities.

Every intelligence service will scramble, likely with their own AI, to find new exploits and accesses to replace the ones that have been burned. This will build a cyber arms race with a new generation of AI-driven cyber exploits to replace the ones that have been discovered.

Whichever side sustains faster AI adoption – not just “procures” it, but ships it into operational systems, holds a widening advantage measured in powers of two every four months.

The constraint for intelligence agencies (and companies) wont be their budgets, or authorities or access to models. It will be their institutional capacity for change – the rate at which a defender organization can actually change what it deploys.

The Long Tail Will Not Be Patched

Anthropic has given companies early access to secure the world’s most critical software,.

That will help Fortune 100 companies. But the Fortune 100 is not just a small part of the software attack surface.

The attack surface includes the unpatched county water utility, the regional hospital, the third-tier defense supplier, the school district, the state Department of Motor Vehicles, the municipal 911 system, and the small-town electric co-op. It includes the tens of thousands of systems running software nobody has time to patch, maintained by teams that have never heard of KASLR.

Every one of those systems is now exposed to nation-state-grade tradecraft, wielded by attackers with no expertise required. Mythos-class hardening at the top of the pyramid does not trickle down. The long tail will stay unpatched for years.

Attackers Advantage – For Now

Under continuous exponential growth of AI designed cyber attacks, a cyber defender using traditional tools can’t just respond just once and stabilize their systems. They’ll need to keep investing at a rate that matches the offense’s growth rate. A one-time defensive shock like compartmentalization might work against a sudden attack, but it will fail against sustained exponential pressure of these AI attack tools because there’s no stable equilibrium to return to. A defender’s investment rate now has to track the offense’s exponential growth rate.

Ultimately/hopefully, the next generation of AI driven cyber-defense tools will create a new equilibrium.

What We Need to Do

Mythos and its follow-ons will change how we think about cyber-defense. We can’t just build a set of features to catch every exploit x or y. We need to build cyber systems that can maintain or exceed the capability rate of the attackers.

Here are the three tools governments and cyber defense companies need to build now:

1. Measure the Gap Between Attackers and Defenders. We need to know the gap between what the attackers can do and what we can defend against. We need to develop instrumented red/blue exercises (a simulation of a cyberattack, where two teams – the red team and the blue team – are pitted against each other) to estimate the number of new vulnerabilities vs cyber defense mitigation.

2. Measure the Defender Response Time. For each corporate or government mission system, measure how long it takes to implement a change from identification to production deployment. Then treat each organizational obstacle as equivalent to technical debt that needs to be fixed and obstacle to be removed..

3. Specify Speed, Not Features. Any new Cyber Defense tools and architecture – including the next-generation cloud-native systems sitting in review right now – should have explicit ‘rate’ requirements. Claims of “our product delivers X capability is now the wrong specification. “Closes detection gap at rate greater than or equal to the offense growth rate” is the right one.

Summary

Buckle up. It’s going to be a wild ride – for companies, for defense and for government agencies.

Mythos is a sea change. It requires a different response than what the current cyber security ecosystem was built for, and one the current system is not built to produce.

We are not behind yet. The gap between Mythos and what we can build to defend is small enough today that a serious response can still match it. A year from now, the same response will be eight times too slow. Two years, sixty-four.

By the way, the only thing left in Pandora’s Box was hope.

This is the 16th year we’ve been teaching the Stanford Lean LaunchPad class. This year, from the first hour of the first class, we realized we were seeing something extraordinary happen. It was both the end and beginning of a new era.

Teams showed up to the first day of class with MVPs (Minimal Viable Products) looking like finished products that previous classes had taken weeks or months to build. After the class, as the instructors sat processing what just happened, we realized there’s no going back.

I’ve been writing about how AI is going to change startups, but the shock of seeing 8 teams actually implementing it was mind blowing. And not a single team thought they were doing anything extraordinary.

Class Observations: Product Development Velocity is Off the Scale
The old sequence for our class was simple – we had teams replicate what they would do in a startup. Have an idea. Build a team. Get out of the building to talk to customers to understand their problems, do Agile development and DevSecOps to build Minimal Viable Products (MVPs) over 10 weeks to test the solutions. And if they were going to build a company, discover and develop a “moat” of proprietary code and features.

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This year, in the first week of the class our students used multiple AI tools to replace what previously would have taken a large development team. They used Perplexity and ChatGPT for research, Claude Code and Replit to build apps, Vercel/v0 for prototyping, Granola to auto-transcribe and summarize customer interviews. The whole flow was compressed.

Because it was so easy to have an idea and then build something in minutes/hours, our students showed up on the first day of the class with products. They no longer had to wait weeks or months before testing whether anyone cares.

What we realized we were watching was a massive acceleration of the Customer Discovery / Customer Validation timeline.

Learning 1. Impedance Mismatch Between Product Development and Learning

By the third week of the class we observed that the velocity of product development meant that teams could now generate more products than they could validate. The amount of product did not equal the amount of learning. Teams were so overwhelmed with so much information from the AI tools that they lost sight of the goal of customer development. They started to believe that the product itself was the truth.

Consequence 1. AI has made Customer Validation Harder

The abundance and ease of creating MVPs has become an accidental denial of service attack on the search for a repeatable and scalable business model. While this is an artifact of today, it means we need a different model for Customer Development as rapid coding isn’t going away.

Learning 2. Student Dependence On ChatGPT Decreased the Quality of Insights

After week two of the class, it was clear teams were delegating communication to an AI. This dumbed down communication turned into AI slop. ChatGPT and Claude are no substitute for thoughtful communication – whether it’s email, PowerPoint or weekly summaries of Lessons Learned. Luckily you can spot this quickly.

Learning 3. Customers are Feeling Disrupted

As the student teams got out of the building, they discovered that potential customers were already feeling disrupted by AI. Many of the companies the teams demo’d to realized that they were seeing not just incremental improvements, but in fact were being shown a “going out of business” scenario.

Learning 4. Customers realize their proprietary data might be their only moat

In some cases, potential customers who would have previously shared their data with students are now asking for NDAs to share information with the team. Customers are realizing that closely held and hard-won information might be one of the few barriers to AI.

Potential 1: Customer Co-Design

As AI tools are allowing our teams to build higher fidelity MVPs, a few are beginning to consider using the MVPs as digital twins (as a simulation of the final product.) When put in the cloud and shared with potential earlyvangelists, startups can now start co-designing the product with potential prospects.

Teams can monitor if the digital twin is being used, how it’s used, and the feedback of what features are needed can be shared instantly. Teams can update the digital twin as they add features.

Potential 2: Agent/Customer Outcome Fit

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 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. For some teams, future class sections may see the search for Product/Market fit become the search for AI Agent/Customer Outcome fit. Minimum Viable Products (MVPs) will become Minimum Productive Outcomes (MPOs.)

Lessons Learned

• MVPs are No Longer an Indication of Technical Competence

  • Vibe coding has transformed MVPs to the equivalent of PowerPoint slides

• Speed to MVPs Hasn’t Yet Meant Faster Learning About Building a Company

  • While we’re still early in the class, the blinding speed of the first week’s onslaught of MVPs hasn’t yet translated into faster learning about customer validation.

• Business Process and Business Models Still Matter

  • The bottleneck for our student teams has moved from needing the resources to build high-quality MVPs to judgment: how to choose the right problem, how to read user signals correctly, and deciding what to build next.

• Product/Market Fit and Agent/Outcome Fit Will Co-Exist (for a while.)

  • While some customers are ready to move to an Agentic workflow, for others delivering Product/Market Fit is still what users want to see.

• Startup Teams Will Be Smaller

  • Our class teams are 4-5. In the past, if they decided to pursue their idea and start a company they would need to hire a larger team to build the product, manage the product, find out whether they had product/market fit, create demand, etc. That’s mostly no longer true.

  • Most teams won’t need to raise money to find out if the problem is real or before they know if users care.

• Enterprise Pricing Models Will Change

  • Some teams are already testing pricing that will shift from per/seat to workflows, outcomes, results, resolutions, successful task

• Customer Development Will Change

  • Because the Customer Development cycle is faster and multiple MVPs now can be run simultaneously…

  • Effort shifts to the extra time needed on hypotheses testing because the velocity and volume of product development can overwhelm signals from potential customers

  • As MVPs rapidly change, they need to be instrumented to monitor customer usage/interactions

More Learning In the Weeks Ahead

When the targets are no longer just military assets but anything valuable on the surface, the long term math no longer favors the defender. To solve this problem the U.S. is spending $10s of billions of dollars on low-cost Counter-UAS systems – detection systems, inexpensive missiles, kamikaze drones, microwave and laser weapons.

But what we’re not spending $10s of billions on is learning how to cheaply and quickly put our high-value, hard-to-replace, and time-critical assets (munitions, fuel distribution, Command and Control continuity nodes, spares), etc., out of harm’s way – sheltered, underground (or in space).

The lessons from Gaza reinforce that underground systems can also preserve forces and enable maneuver. The lessons from Ukraine are that survivability while under constant drone observation/attack requires using underground facilities to provide overhead cover (while masking RF, infrared and other signatures). And the lessons from Iran’s attacks on infrastructure in the Gulf Cooperation Council countries is that anything on the surface is going to be a target.

We need to rethink the nature of force protection as well as military and civilian infrastructure protection.

Protecting Ground Forces

The problem of protecting troops with foxholes against artillery is hundreds of years old. In WWI, trenches connected foxholes into systems. Bunkers were hardened against direct hits. Each step was a response to increased lethality from above. Today, drones are the new artillery; a persistent, cheap and precise overhead threat but with the ability to maneuver laterally, enter openings, and loiter. And mass drone attacks put every high value military and civilian target on the surface at risk. Fielding more hardened shelters for soldiers like the Army’s Modular Protective System Overhead Cover shelters is a first step for FPV kamikaze drones defense, but drones can get inside buildings through any sufficiently sized openings.

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Drone Protection

Ukraine has installed ~500 miles of anti-drone net tunnels with a goal of 2,500 miles by the end of 2026. These are metal poles and fishing nets stretched over roads but they represent the same instinct: the surface is a kill zone, so cover it. Russia has done the same.

The logical response is to go underground (or out to space) but the technology to do it quickly, cheaply, and at scale is genuinely new. The gap in current thinking is between “put up nets” (cheap, fast, limited) and “build a Cold War concrete bunker” (expensive, slow, permanent). What’s missing is the middle layer – rapidly bored shallow tunnels that provide genuine overhead cover for movement corridors, equipment parking, and personnel protection.

What tunnels solve that nets and shelters don’t

A net stops an FPV drone’s propellers. A shelter stops shrapnel. But a tunnel 15-30 feet underground is invisible to ISR, immune most to top-attack munitions, can’t be entered by a drone through a door or window, and survives anything short of a bunker-buster. Gaza proved that even with total air superiority and ground control, Israel has destroyed only about 40 percent of Gaza’s tunnels after two and a half years of war.

That’s an asymmetric defender’s advantage the U.S. military should be thinking about for its own use, not just as a threat to overcome.

What’s changed to make this feasible is that we may not need boring tunnels per se, but instead modular, pre-fabricated tunnel segments that can be installed with cut-and-cover methods at expeditionary bases. Or autonomous boring machines sized for military logistics (smaller versions of the Boring Company TBMs) corridors rather than highway traffic.

The problem is a lack of urgency and imagination

The problem is real, the incumbents (Army Corps of Engineers) are slow, and the existing commercial tunneling industry isn’t thinking about expeditionary military applications.

The doctrinal gap is between “dig a foxhole with an entrenching tool” (individual soldier, hours) or deploy a few Army’s Modular Protective System Overhead Cover shelters or “build a Cold War hardened aircraft shelter” (major construction project, years, billions). There’s no doctrine for rapidly boring hardened underground movement corridors, dispersed equipment shelters, or protected command post positions using modern tunneling technology.

Army doctrine treats excavation as something done with organic engineer equipment — backhoes, bulldozers, troops with shovels — to create individual fighting positions and cut-and-cover bunkers. The Air Force doctrine barely addresses physical hardening at all, having spent 30 years assuming air superiority would substitute for it.

Nobody in the doctrinal community is asking: what if the Army could cut and cover 100 meters of precast tunnel segments in a day or if we could bore a 12-foot diameter tunnel 30 feet underground at a rate of a hundred of meters per week and use it as a protected logistics corridor, command post, or aircraft revetment?

Summary

Oceans on both sides and friendly nations on our borders have lulled America into a false sense of security. After all, the U.S. has not fought a foreign force on American soil since 1812.

Protection and survivability is no longer a problem for a single service nor is it a problem of a single solution or an incremental solution. Something fundamentally disruptive has changed in the nature of asymmetric warfare and there’s no going back. While we’re actively chasing immediate solutions (Golden Dome, JTAF-401, et al), we need to rethink the nature of force protection, and military and civilian infrastructure protection. Protection and survivability solutions are not as sexy as buying aircraft or weapons systems but they may be the key to winning a war.

The U.S. needs a coherent protection and survivability strategy across the DoW and all sectors of our economy. This conversation needs to be not only about how we do it, but how we organize to do it, how we budget and pay for it and how we rapidly deploy it.

Lessons Learned

• There is no coherent protection and survivability strategy that addresses drones across the DoW and the whole of nation

  • Just point solutions

• For troops near the front, tunnels could reduce visual, thermal, and RF signature while providing fragment protection with a network of small, concealed, overhead- covered positions, short connectors, buried command posts, protected aid stations, and revetted vehicle hides.

• We need to underground assets that cannot be quickly replaced

  • Command posts, comms nodes, ammunition, fuel distribution points, repair facilities, key power systems, maintenance spares, and high-value aircraft or drones.

  • Think protected taxiways, blast walls, covered trenches, buried cabling, alternate exits, redundant portals, and rapid runway repair. Sortie generation under attack depends on a whole system, not one bunker.

• We need to work with commercial companies to harden/defend their sites

  • Provide active defenses and incentives for under-grounding critical facilities

• The Army and Air Force need to rethink their doctrines and techniques for Protection and Survivability

  • Army Techniques Publications (ATP) 3-37.34 – Survivability Operations treat excavation as something done with backhoes, bulldozers, troops with shovels to create individual fighting positions and cut-and-cover bunkers. Update it.

  • The Air Force needs to do the same with AFDP 3-10, AFDP 3-0.1 (Force Protection and AFTTP 3-32.34v3, AFH 10-222, Volume 14 and UFC 3-340-02

• We need to think of force and infrastructure protection not piecemeal but holistically

  • Part of any weapons systems requirement and budget should now include protection and survivability

  • Protection and survivability should be deployed concurrently with weapons systems

• We need a Whole of Nation approach to protection and survivability for both the force and critical infrastructure

The Department of War is in the midst of the most ambitious acquisition reform in 60 years. It’s just in time as drone and missile warfare lessons (autonomy, Counter UAS, etc.) from Ukraine and the War in Iran are top of mind and reshaping what the DoW is buying. Reorganizing the DoW into Portfolio Acquisition Executives is reforming how the DoW is buying. The new Warfighting Acquisition System is working to reward speed to delivery.

These are real reforms, and they implement nearly every recommendation the defense innovation community has made for the last decade.

And yet many of the weapons and systems they are about to buy will be for yesterday’s problems.

Here’s why.

Do Something! Is Not the Same as Solving the Problem

Our combatant commands and allies desperately need an immediate solution to counter drones. We’re shipping what we have and we’re rapidly scaling up more of it. But that’s not the same as solving the Counter UAS problems.

Today, the Counter-UAS response has invested heavily in the develop, scale and deploy phases. JIATF-401 was stood up last August to proliferate counter-drone capabilities. The Army runs industry competitions. DIU scouts commercial technology. The PAE reform consolidates requirements, contracting, testing, and sustainment under a single portfolio leader. These are the middle phases of the innovation cycle, and they are getting real investment and real attention. But what’s missing is where the inputs to requirements will come from.

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If this isn’t fixed, we’ll end up solving yesterday’s problems. So, how do we ensure we’re working on the right problem with the right priority before locking in a requirement?

What’s needed is a rapid Portfolio Acquisition Executive requirements process to replace the rigid and unwieldy JCIDS – one that collapses the cycle time between problems, requirements and acquisition. One that can deliver a 70% solution fielded in weeks, assessed against operational reality, with findings distributed across the force and fed back into detection of the next problem.

The Innovation Targeting Cycle

Each Portfolio Acquisition Executives needs four things the current organization reforms don’t provide:

1. Forward-deployed Problem Discovery Teams in the Combatant Commands – embed small, cross-functional teams with operational units, sourcing and curating problems from direct observation. Not technology scouts. Problem scouts. These don’t need to be organic to the PAE.

2. Fusion Cells – collect data from the field, industry and labs and rapidly do due diligence to ensure we are working on the right problems at the right tempo with the right expected outcomes.

3. Rapid operational assessment is built into the cycle, not conducted as a post-mortem months after fielding. Every deployment of a capability should generate data: did it work? Did operators adopt it? Did the adversary adapt? That data feeds the next rotation.

4. Lateral distribution at operational speed – what one unit learns must reach every other unit facing the same threat before the next engagement, not the next rotation. Our institutional schoolhouses operate at institutional

The Innovation Targeting Cycle phases – Detect, Define, Develop, Deploy, Assess, Distribute – run continuously by a fusion cell, each rotation generating the input for the next. A solution fielded in weeks, assessed against reality in the field, with rapid dissemination of findings across the force.

Detect – Persistently monitor how a threat evolves at the tactical edge with forward-deployed problem discovery teams embedded with operational units, scanning for how the adversary adapted since last week. (Today’s case would be drones.)

Define – Scope the specific problem each unit faces with enough precision to drive useful solutions. A PAE leader at headquarters, no matter how empowered by the new reforms, cannot see the distinctions that matter without ground truth from the fight. Requirements still originate from within the institutional system – headquarters staffs, Service-level assessments – not from soldiers and Marines observing the problem in context.

Tying all of this together is a PAE Fusion Cell that collects the inputs from the operational force, industry and the labs and executes the discovery required to confirm we are working on actual problems (not symptoms) and the required speed to solve them.

Assess – Systematically measure whether fielded systems actually work against an adversary who adapts after every engagement. We tend to field systems and declare victory. Without assessment, there is no feedback loop. Without a feedback loop that anticipates adaptation, you cannot out-cycle the adversary. (Today this would be counter UAS systems.)

Distribute – Ensure that what one unit learns reaches every other unit facing the same threat at operational speed much less delivers that same assessment to industry.

Summary: The reforms to the Warfighter Acquisition System provide Portfolio Acquisition Executives (PAEs) with the organizational structures for Developing, Scaling and Deploying weapons.

An Innovation Targeting Cycle would provide the front end that connects the reality of the warfighter’s at the tip of the spear to the PAEs.

Several PAE organizations have already begun this journey. Others are just beginning. We need to develop and share best practices across PAEs and across the DoW.

For more from Steve on the Department of War acquisition reforms, read The Department of War Just Shot the Accountants and Opted for Speed.

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.

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

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 2026

  • Everything 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

For more from Steve on how successful CEOS manage disruptive technology, read Blind to Disruption – The CEOs Who Missed the Future.

This post previously appeared in Philanthropy.org

A while ago I wrote about what happens in a startup when a new event creates a wake-up call that makes founding engineers reevaluate their jobs. (It’s worth a read here.) Recently my wife and I had something happen that made us reevaluate a 25-year-old relationship.

These two bookends made me realize something larger: reevaluating all types of relationships – romantic, friendship, founders, business partnerships/ventures, and even countries – is a healthy and normal part of growing, getting older and, at times, wiser.

First World Problem

We had a close relationship with a local nonprofit for over a quarter of a century. By close I mean their first executive director lived rent free in a property we owned, we provided resources when they most needed it, I had sat on their board, and when I was a public official I listened carefully to their input and suggestions, and helped them where I could. When I couldn’t do something they requested I called them and let them know why. They did the same for me. When their next executive director took over (he had been the number 2 to the previous director), the relationship continued, but in hindsight was a bit more distant. About a year ago they hired their third executive director. He had none of the history with us. And here comes the wake-up call.

I called to ask for his support on an issue very important to us. The conversation ended with what I thought was “I’ll consider it.” I never heard back. So I was surprised (but shouldn’t have been) to discover a public letter from the nonprofit taking the opposite point of view. In the past when we disagreed I got a phone call or email that said, “We heard you, but here’s why we’re going to do X and Y.” This time, and the first time in 25 years, crickets – I heard nothing.

This wasn’t the end of the world and truly is a first world problem – but it was a wake-up call.

It took my wife and I about a week to take stock. We realized that the executive director didn’t do anything “wrong.” We weren’t “owed” a call. The new director was looking forward unencumbered by the past, while we were looking backwards at the 25-year relationship. Anything we did prior to his arrival obviously wasn’t on his radar. But it was a jarring change from how we interacted in the past.

We realized that our relationship had been on automatic pilot. Until then there was no reason to rethink it. Our original support was for work this nonprofit had been doing at the turn of this century. Now that was no longer their core mission. And as we thought deeper we applied the same lens to reevaluate other organizations we were supporting. And no surprise, many of their missions had also changed, or in many cases our own interests were now elsewhere.

Wake-up calls happen when you realize the contract you believed in isn’t shared anymore.

In the end, we are now supporting a new generation of non-profits.

But it reminded me about the bigger picture and the nature of relationships.

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Most Relationships Aren’t Forever

Almost every one of us will go through breakups, either initiating them or being on the receiving end. Rather than thinking that equals failure, consider it a type of a life pivot.

Most of us grow up with a belief that “real” relationships are permanent. That if something mattered once, it should always matter in the same way. That longevity of a relationship alone equals success. It doesn’t. Permanence is comforting, but it isn’t how humans, markets, or institutions actually work. People travel with us for a while then the convoy reconfigures as life roles and needs change.

People change. Leadership changes (in business and countries). Priorities change. Incentives change. Organizations change. Sometimes you change and the other side doesn’t. Sometimes it’s the opposite. Sometimes both change, just not in the same direction. None of that automatically means anyone failed. It usually means growth happened.

Why people move on

Moving on is often framed as disloyal or selfish. In practice, it’s usually neither. It’s reality finally catching up with a story you’ve been telling yourself. Common reasons:

1. The relationship was built for an earlier version of you. At different stages of life we value different things: exploration, stability, achievement, meaning, time. A relationship can be good and still no longer fit.

2. The relationship was built for an earlier version of them. This happens often to co-founders in startups. Skills needed in the early stages are no longer the ones needed to scale. One of you learns new skills while the other is heads down doing what they’ve always done.

3. The shared mission expires. Some relationships may be temporal or transactional. They exist to accomplish something specific: raise kids, start a company, survive a hard period, launch a project. When the mission ends, you discover what remains. (For founders it’s often done-and-gone and off to the next one.)

4. The implicit contract changes. Every relationship has unwritten rules: honesty, reciprocity, respect, no surprises, or, often fatal, a breach of trust. When those rules shift without discussion, friction appears. (Trust takes years to earn, but can be lost in a minute.)

5. Misalignment becomes chronic. Often there isn’t a single disagreement. It’s a pattern. You keep explaining away discomfort and keep lowering expectations. Eventually you realize you’re managing a declining relationship. You start calculating the lost opportunity cost of not moving on.

6. The cost of staying rises. As you get older, you become more aware that time is finite. You grow less willing to spend it on relationships that consistently drain more than they return.

7. People and institutions drift from your goals. Individuals move toward comfort, status, and security. Organizations move toward new goals, new donors, different metrics, and survival at all costs. Sometimes that drift still matches you. Sometimes it doesn’t.

Lessons Learned

• A wake-up call is an event that shatters your current view of a relationship and forces you to reevaluate

• You never know what will trigger a wake-up call

• As we get older, we perceive time as more limited. We invest more in meaningful relationships and prune the rest.

  • That doesn’t make us cynical, just more calibrated

• Time to reevaluate relationships when:

  • Values no longer align

  • You’re doing all the work

  • There’s a breakdown of trust

  • You would not be partnering with them if you met them today

The lessons still apply even though AI Agents will upend all of this and PR will end up being one of the many businesses that will no longer exist in its current form.

Here’s why.

We were a new public company about to launch our first product. I had given an exclusive story to a major publication whose name is now lost in the midst of time (either the Wall Street Journal or NY Times) to what I thought was an embargo. (An embargo is a fancy term for the publication agreeing not to release the story until a specific date and time.) To my surprise the newspaper published days before the date we had agreed on, screwing up what was supposed to be a perfectly synchronized press campaign.

Furious at my PR Agency, I naively told them to tell the publication that our company would never work with them again. While I don’t remember the exact conversation, today I can just imagine our PR agency’s CEO smiling with amusement on the other end of the phone line.

It took me years to fully understand who my service providers and consultants actually worked for and the long-term relationships they really cared about – hint it wasn’t me.

They Work For Me

I thought the business model between my PR agency and my company was a simple single-sided market – I was the PR agency’s customer and the PR agency worked for me.

Talking to the press was what I paid them to do for me. Their place in the universe orbited around me. Pretty straight forward.

The reality was not so simple.

Yes I was the PR agency’s customer, but their long-term relationships, and the core of their business, were the people in the media outlets – their most valuable assets. I was actually part of a multi-sided market.

Since that was the case, there was no possible way they were going to yell at an important news outlet, regardless of what I demanded as a client.

And I Was Just Another Customer

Not only that, but I was just one of many of the customers for my PR agency’s services. And as I learned later, I was one of their smaller accounts. The agency served multiple companies and while they were happy to take my money and (mostly) liked working with us, if they thought I was too unreasonable or asked them to achieve the impossible, they could just replace my relationship with another client. So the world actually looked like this.

In actuality, my place in their universe was pretty small.

This is true for almost all external vendors and consultants who supply services to companies and individuals. Think of lobbyists – they’re not going to get crosswise with government officials – those are their long term relationships. Or architects and building contractors who have essential long-term relationships with subcontractors, planning commissions, etc.

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One Last Thing – Learning What They Know

It wasn’t until much later in my career that I learned my most valuable lesson about service providers and consultants.

I had hired a consultant to set up distribution and manufacturing for our company in Japan. I knew nothing about those areas and got connected to a world class consultant. I made one trip to Japan with them but they did all the relationship building and knew their way around like a native. In our case, the relationship worked out– at least on the surface. But in hindsight, I realized I made an enormous strategic error. I had learned very little about how distribution and manufacturing worked in Japan.

I got the “service” I paid for, but didn’t realize the opportunity I lost. What I should’ve been doing to make me a better CEO was using them to teach me how Japanese distribution worked, not just doing it for me. That’s a trap I find many founders falling into. If you would have asked me then, my excuse would have been, “I don’t have enough time or bandwidth or even interest.” But the downside is that it not only makes you beholden to a service provider/consultant forever, but also you and your company can never get smarter than that single point of contact. Think of Apple’s dependence on Foxconn and China Inc..

While I was hiring someone to set up distribution and manufacturing, I should have also hired them to teach me how and why what works and doesn’t. This would have made me smarter and helped me to shape a better strategy of how to use them– and eventually replace them.

In the case of my PR agency, here’s what I didn’t understand: The most successful client-PR agency relationships aren’t transactional. They are partnerships marked by give and take — around messaging, communications strategies and tactics, etc.

Had I realized this early on, I could have asked my PR rep to explain how PR worked and what made for a great story. Understanding things from their perspective would have made me a better client – and a better source for the reporters we wanted to help tell our story. Instead, it would take me decades to learn.

Some service providers won’t share their knowledge claiming it’s their proprietary secret sauce. More want to sell you their services time and time again. But others are willing to do so if you’ll pay for their time. Find them and hire them to become a wiser manager.

The Future Will Be AI Agents

Here’s why this is relevant to you now, whether you’re fresh out of school or over 40 (or even 30!) Faster than we can imagine, most of these services are going to be done by AI agents. Several already do a competent job and the rate of improvement is staggering.

For our PR Agency example, while the message, media, messenger, audience loop will remain, it will be completely run by AI Agents.

Today, PR agencies already use AI to draft initial versions of press releases, blog posts, and social media updates (thank you ChatGPT.) There are now AI tools that can customize content to target and pitch journalists based on their coverage history and interests. The same AI agents generate personalized email subject lines and body copy. Meanwhile, to proactively manage and protect a company’s brand, PR AI agents can monitor and scan millions of sources to track brand mentions, sentiment, and competitor activity, as well detect emerging negative narratives, and forecast potential reputational threats – weeks before they reach the mainstream.

Meanwhile bloggers and journalists will be using their own AI tools to scan and filter these pitches. The question is whether they’ll trade off the personal connection with PR professionals or welcome making their jobs easier sorting through the noise?

Welcome to the machine-to-machine age of media communications.

PR Morphs or Disappears

Today, these AI tools are standalone, bespoke apps. It’s not long before they are complete workflows where the AI agents perform the majority of tasks autonomously, with humans supervising. However, it’s inevitable that these tools will move to just one more type of vibe coded apps that internal marketing departments and even small business will spin up themselves. Companies that lag in adopting these tools will be at a competitive disadvantage. PR agencies as they exist today will likely disappear and/or morph into providing higher level services.

This is just the impact of AI on one business that depends on high-touch humans. More will follow.

Most of how these agents will accomplish the tasks we send them out to do will be black boxes. We’ll get great results, but not know how they happened. Currently none of them have an “explain how and why you did this” mode.

I wonder if this will make us smarter or just create more AI slop.

Service providers who figure out how to use these tools in ways that let them lean further into the critical human aspects of their jobs will be the ones you’ll want to work with. To give your company an edge, find them, hire them and learn what they know — including about how using AI effectively.

Lessons Learned

• Your service providers/consultants have relationships that are more important than you

  • They will preserve and protect those over you as a client

  • For most service providers/consultants you’re one of many clients

  • You only think they work for you

• Using service providers/consultants as only a one-way street misses a strategic opportunity to get smarter

  • Pay them extra to teach you how they think

  • It will make you a better strategist

  • You’ll get better results in the long term

• Future service providers/consultants will be AI Agents

  • They will be black boxes – they’ll get the job done but we won’t know how and why

  • They’ll get smarter but we won’t

  • Welcome to our brave new world

Every once in a while you learn something new that makes you completely rethink how/why an event actually happened.

And then you consider how it affects the rest of our country and our lives.

This is one of those stories.

Over a decade ago, I was a public official and was at one of our commission meetings on the coast of California. A fellow commissioner and I decided to take a long lunchtime walk along the coast. As we chatted, we realized we had both worked on several of the same very classified programs. His involvement was in acquisition and finance, while mine was more deeply connected to the engineering development of the project and hands-on with the operators on site.

We Got Our Advanced Technology From Aliens

While we both were discreet about not talking about specifics, we recognized the projects we had worked on. So you can imagine my surprise when he turned to me and casually said, “You know this technology came from aliens.” I laughed, thinking that obviously he must be joking. But as we continued walking he continued on, claiming, “You know the equipment you worked on and stuff that followed came from our secret alien investigation site at Wright Patterson Air Force Base. All we did was reverse engineer Alien technology.” This time I stopped in my tracks and looked at him to see if he was smiling. I was puzzled as he looked dead serious. He explained that there was no possible way we could be doing what we were doing using existing technology. Before I changed the subject I asked him how he knew this, he replied with absolute sincerity, “I was head of acquisition on the program. I was briefed on the project. That’s what they told us and they swore us to secrecy.“

I really didn’t know how to process this. He was really a smart and level-headed guy. In fact he was the mayor at the time of Rancho Palos Verde. It took me a mile or two into our walk to rethink everything I knew about the project (even then it had been in decades past), including having sat with a few of the engineers (some strange, but not aliens) as they were designing the system (with me trying to keep up with the revised blueprints in document control), and then watching the system being built and assembled. While it had required incredibly creative engineering, and applying technology on a scale so massive no commercial company could afford it, this system was built by smart people with no aliens involved. But he was equally convinced they were. Over our time together on the commission we took more walks, had lots more to talk about, but we never broached the subject again.

Every once in a while, for the next few years, I puzzled on how he could have been so sure of something that I was sure was completely wrong.

We Did Tell Them It Was Aliens

Fast forward 15 years, and my world view of that conversation was upended when I read in the Wall Street Journal that the Department of Defense had been running a disinformation campaign, briefing finance and acquisition people that the technology for these classified programs was coming from aliens. (Take a minute and read the article.)

All of a sudden our coast-side conversation from a decade and a half ago made sense to me. Most of our most compartmentalized programs have different levels of what was called “need to know.” I never paid much attention as I was read all the way into the technical and operational details of these programs. I vaguely knew that others got fewer details, but as I was just discovering, others had received active disinformation. In a few cases, security officers were even using fake photos and documents to create the Alien cover-story for secret-weapons programs.

It turns out my fellow commissioner had been briefed by the U.S. government that it was Aliens, and he went to his grave believing it so.

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Are you going to believe me or your lying eyes?

What’s interesting is what happened after the news came out that the Alien story was government disinformation. A large percentage of people who were briefed, now “doubled down” and believed “we got the technology from Aliens” even more strongly – believing the new information itself was a coverup. Many dismissed the facts by prioritizing how they felt over reality, something we often see in political or religious contexts. (“Are you going to believe me or your lying eyes?”)

I wondered how my friend would have reacted.

Secrecy, Disinformation, and a Higher Power

While on its face this is an amusing story about secrecy, it’s really about the intersection of the secrecy’s impact on society and its role in misinformation, manipulation, the creation of cynicism and mistrust, and our need to believe in a higher power.

Manipulation
An example of secrecy used for manipulation in the 20th century was when the National Security Agency Venona project unmasked Soviet spies in the U.S. Even though this was one of the nation’s most secret programs, the FBI leaked its findings to Joe McCarthy and Richard Nixon. They used this classified knowledge to manipulate the American public, fueling McCarthyism and Richard Nixon’s career. 50 years later, when Venona was made public historians substantively revised the history of U.S. Cold War politics.

In the 21st century Social Media misinformation (e.g. Chinese and Russian influence campaigns, Qanon conspiracies) will look like toys next to the AI-driven manipulation that’s about to come.

Cynicism and mistrust
Secrecy created 75 years of cynicism and mistrust, when the U.S. began launching highly classified reconnaissance balloons (story here), and later the U-2 and SR-71 spy planes. These top secret projects gave rise to decades of UFO sightings. Instead of acknowledging these sightings were from classified military projects the Department of Defense issued cover stories (“you saw weather balloons”) that weren’t believable.

Governments and companies have always kept secrets and used misinformation and manipulation. However, things stay secret way too long – for many reasons – some reasonable (we’re still using the same methods – reconnaissance technology, tradecraft, or, it would harm people still alive – retired spies, etc) or not so reasonable (we broke U.S. or international laws – COINTELPRO, or it would embarrass us or our allies – Kennedy assassination, or the Epstein files).

Secrecy increases the odds of conspiracy beliefs. Because evidence can’t be checked, contradictions can’t be audited, a government “cover-up” becomes a plausible explanation. People don’t tolerate “I don’t know” for long when stakes are high (stolen elections, identity, national crises, the meaning of life, or what happens when we die). That vacuum gets filled by the most emotionally satisfying model: a hidden “higher power” concealing information and controlling events.

Summary

Just as social media replaced traditional news sources, AI-driven summaries of current events are likely to replace our understanding of the world around us. What happens to trust when AI manipulates human’s tendency to embrace conspiracy theories? Who will define the truth in the brave new world?

And by the way, I’m still pretty sure we didn’t get it from Aliens.

The Department of War (DoW) senior Acquisition leadership (the people who decide what and how the DoW buys equipment and services) now is headed by people from private capital (venture capital and private equity.)

• Deputy Secretary of War Steven Feinberg ran Cerebus Capital

• Secretary of the Army Daniel Driscoll was a former VC and investment banker

• Secretary of the Navy John Phelan ran MSD capital.

• Deputy Secretary of the Army Michael Obadal was a senior director at Anduril

The Department of War is in the midst of once-in-a-lifetime changes of how it acquires weapons, software and systems. The new Warfighting Acquisition System rewards speed and timely delivery of things that matter to the Warfighter. But this new system is at risk of making the wrong things go faster.

Here’s why and what they should do.

What Now?

Acquisition in the DoW is being reorganized how a Private Equity would reorganize a large company. They bring in (or empower) a new operating team, swap executives, change incentives, kill things not core to their mission, cut costs, invest for growth, and restructure to find additional financing.

That’s being played out at the Department of War right now. The announcement of the consolidation of individual weapons systems (each of which had their own silos of Requirements, Test/Evaluation, Budgeting, and Acquisition) into a unified Portfolio Acquisition Executive, is a classic Private Equity strategy. Instead of 100s of programs operating with separate budgets, across different Program Executive Offices, the intent of the Portfolio Acquisition Executives is to consolidate overlapping programs, eliminate the redundant ones, pick winners, kill losers, get rid of processes that kill speed, and focus on rapid deployment.

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What’s Missing?

Organizing by Portfolio Acquisition Executives is a great start, but simply consolidating the parts of the defense Acquisition system that were broken under one umbrella organization won’t make it better. Making bad ideas go faster should not be the goal. However, we’re at risk of doing just that. (Pete Newell at BMNT has been reminding me of this for years.)

For example, many of these new Portfolio executives are managing their programs by holding monthly reviews of proposed investments and current portfolio performance (just like private investors.) Here they’ll decide which programs get funded, which get more funding, and which should stop. (Actually having a regular process to kill programs early is sorely needed.) These are great ideas. However, if the meetings start by reviewing progress of prototypes to show that the technology works or that warfighters want it, and funds progress on those metrics, it misses the changes needed in an effective acquisition system.

The result will be building a faster version of a weapons requirements process that starts with a top-down list of features, or worse, shiny tech products (e.g. “I need drones.”) This “requirements first” process is what will drive the “bad ideas faster” problem.

A more productive approach – one that delivers truly decisive capabilities – would be to build a different process upfront – a rigorous problem identification and validation phase on the front-end of every acquisition program.

This process would start with a wide funnel of problems, ideas, technology, each with a 10-line problem summary that describes the specific challenge to address; why it can’t be solved currently; what it will take to solve it; and how a solution will be funded and deployed in the field.

The goal would be to 1) consolidate problems that may be different descriptions of the same core problem, and/or 2) discover if the problems are a symptom of something more complex.

Then each problem would go through an iterative process of problem and technical discovery. This will help define what a minimum deployable product and its minimum constraints (security, policy, reliability) should be, such as how long the solution would take to deploy, the source of funding for scale and who needs to buy-in.

This exercise will keep the focus where it needs to be — not on reforming a system but on delivering things to warfighters with speed and urgency so we can actually deter or win a war.

Want to Keep Up With the Changes in the DoW?

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Both a startup “go-to-market” guide and the first ever Directory of the Department of War. It’s an invaluable desk reference to figure out who, what and where.

Download the free DoW Directory here.

Keep current with updates here

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For more from Steve on the Department of War reforms, read The Department of War Just Shot the Accountants and Opted for Speed.

In November 2025 the Department of War (DoW) unveiled the biggest changes in 60 years of how they will buy weapons and services. This month Congress, with bipartisan support, rapidly made them into law in the National Defense Authorization Act (the NDAA) – 3,096 pages of legislative text and 636-page Joint Explanatory Statement.

This is a top-to-bottom transformation of how the DoW plans and buys weapons, moving from contracts that prioritized process and how much a weapon costs, to how fast it can be delivered. It’s the Lean Startup plan for the Department of War.

Instead of buying custom-designed weapons, the DoW will prioritize a “commercial first” strategy – buying off-the-shelf things that already exist and using fast-track acquisition processes, rather than the cumbersome existing Federal Acquisition Regulations. To manage all of this, they are reorganizing the entire Acquisition ecosystem across the Services.

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December 2025 Directory Update – Now Available Online and in Print

Our December 2025 update to the Directory (Online here, Print copy here) describes the New Warfighting Acquisition Organizations – The Portfolio Acquisition Executive and the Capability Program Managers.

If you’re a startup trying to sell to the DoW, until now the biggest barrier has been a lack of information. That changes with this 3rd edition of the 2025 DoW Directory.

Online here, Order a print copy here.

The DoW has pivoted from optimizing cost and performance to delivering advanced weapons at speed. Taking decades to deliver weapons is no longer an option. The DoW has joined the 21st century and adopted Lean Methodology.

Two organizations ought to be very concerned – China and the defense prime contractors.

Secretary of War Pete Hegseth unveiled the biggest changes in 60 years of how the Department of War (DoW) plans for and buys weapons and services. These changes aren’t a minor attempt at reform. It’s a top-to-bottom transformation of how the DoW plans and buys weapons, moving from contracts that prioritize how much a weapon costs to how fast it can be delivered.

Instead of buying custom-designed weapons, the DoW will prioritize buying off-the-shelf things that already exist, and using fast-track acquisition processes, rather than the cumbersome existing Federal Acquisition Regulations. To manage all of this, they are reorganizing the entire Acquisition ecosystem across the Services. These changes implement every piece of good advice the DoD had gotten in the last decade and had previously ignored.

The DoW is being redesigned to now operate at the speed of Silicon Valley, delivering more, better, and faster. Our warfighters will benefit from the innovation and lower cost of commercial technology, and the nation will once again get a military second to none.

It’s big, bold and brave and long overdue.

Background

In 1962 Robert McNamara, the then-Secretary of Defense (and ex CFO of Ford), discovered he had inherited a Defense Department whose spending was out of control. During the 1950s the Air Force built five different types of fighter planes, three generations of bombers, and three generations of ICBMs. The Navy had created a fleet of nuclear-powered attack and ballistic missile submarines and aircraft carriers. The Army bought three generations of its own nuclear-capable missile systems. Many of these systems duplicated capabilities of other services. But most importantly, the Services, in their rush to buy new technology, hadn’t adequately budgeted for the cost of operating, training, maintaining, and sustaining what they had bought.

In response, Secretary McNamara imposed the discipline of a Chief Financial Officer. He put in place a formal system of Planning (capability gaps, risks, scenarios, threats assumptions), Programming (5-year plans, affordability, quantities, phasing, unit fielding plans) and Budgeting that has lasted 60+ years. An entire defense university was created to train tens of thousands of contracting officers how to follow the detailed rules. Large contractors (the Primes) learned to work with this paperwork-heavy Defense acquisition system and lived with the very long time it took the DoD to buy.

The Problem

This unwieldy and lethargic acquisition system was adequate for over half a century when our adversary was the Soviet Union who had an equally complex acquisition system, or ISIS and Al Qaida who had none.

However, in the last decade it became painfully obvious that our acquisition system was broken and no longer worked for the world we lived in. Our existing defense industrial base suffers from schedule overruns and huge backlogs; cost increases have become the norm. We’ve been outpaced by adversaries. China, for example, implemented a much more agile system that delivered weapons in a fraction of the time it took us.

We needed a defense industrial base we could count on to scale in a crisis rather than one that will wait for money before taking action.

The war in Ukraine showed that even a small country could produce millions of drones a year while continually iterating on their design to match changes on the battlefield. (Something we couldn’t do.) Meanwhile, commercial technology from startups and scaleups (fueled by an immense pool of private capital) has created off-the-shelf products, many unmatched by our federal research development centers or primes, that can be delivered at a fraction of the cost/time. But the DoW acquisition system was impenetrable to startups.

Our Acquisition system was paralyzed by our own impossible risk thresholds, its focus on process not outcomes, and became risk averse and immoveable.

We needed an acquisition system that could deliver needed things faster.

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Reminder: What Did Our Acquisition System Look Like Until Last Week?

The Army, Navy, Air Force, Marines and Space Force train soldiers, sailors and airmen, and specify and buy the weapons for their Service. (It’s the Combatant Commands, e.g. INDOPACOM, CENTCOM, etc., who fight the wars.)

One of the confusing things about Acquisition in the DoW is that it is more than just the buyers of equipment. In the DoW Acquisition with capital “A”, includes the entire end-to-end process – from concept, requirements, prototyping, testing, buying it, to using it and maintaining it.

In each of the Services, the current Acquisition system started with a group that forecast what the Service would need in the future and wrote requirements for future weapons/services/software. This process could take a year or more. Next, Service laboratories developed the technology, tested prototypes and concepts. This could take 3 to 6 years. Next, a vendor was selected and began to prototype and refine the systems. This added another 3 to 4 years. Finally, the system was ready to be built and delivered. It could take 1 to 2 years to deliver weapons in low rate production, or 5 to 10 years for something complex (e.g. aircraft, ships, spacecraft). In the system we’re replacing the time from when a need was turned into a requirement to delivery of a weapon would take 8 to 16 years. As you can imagine, given the rate of change of current technology and new warfighting concepts our own Acquisition process was an obstacle to building a modern War Department.

As an example, the Army’s current Acquisition system has 32,000 civilians and military (program managers, contracting officers, etc.) If you include the long tail of sustainment that’s another 165,000+ people. The Acquisition system in the Army (representative of the other services) looks like this:

What Was Wrong With this Process?

• Responsibility in the Acquisition system was scattered across multiple, siloed organizations with no one individual responsible.

• The existing system was designed to acquire individual products (weapons, services, etc.) with a Program Executive Office to manage each effort that only indirectly solved warfighter problems.

• Requirements were written so that most everything the DoW bought was bespoke and required development from scratch.

• Acquisition was process-focused with rigid rules that emphasized compliance to contracting rules.

• Compliance to the rules and processes overrode speed of delivery

• Weapons and systems development used sequential “waterfall” development processes which precluded learning, pivots and iterative design. ​

• The result was that speed of delivery was on no one’s priority list.

Why Is The Warfighting Acquisition System A Big Deal?

While previous administrations tried to go around the process, this new system confronts it head on. It is a revolutionary transformation in the Department of War. It was clearly designed by people who have worked in industry and understand commercial Lean Processes. This transformation will solve the DoW critical Acquisition problems by:

• Prioritizing speed of delivery

• Moving the focus from process to outcomes

• Organizational redesign of the Acquisition process

• Changing what weapons we ask for and how we prioritize what we need to buy

• Changing the preferred vendors the DoW will buy from

• Changing the contracting methods the DoW will use

• Changing how we measure and reward success

• Changing how we educate Acquisition professionals

• Insisting that disparate systems/vendors interoperate

The New Warfighting Acquisition Organization – The Portfolio Acquisition Executive

To cut through the individual acquisition silos, the services are creating Portfolio Acquisition Executives (PAEs).

Each Portfolio Acquisition Executive (PAE) is responsible for the entire end-to-process of the different Acquisition functions: Capability Gaps/Requirements, System Centers, Programming, Acquisition, Testing, Contracting and Sustainment. PAEs are empowered to take calculated risks in pursuit of rapidly delivering innovative solutions.

PAE Offices Are Matrix Organizations
Portfolio Acquisition Executives (PAEs) are organized as a matrix organization – using people from existing organizations – requirements, PEOs, sustainment, contracting etc. The PAEs themselves will have a small staff for coordination.

Portfolios Around Common Problems
In the past, Acquisition was organized by weapon systems and managed by Program Executive Offices. Portfolios will organize instead around common Warfighting Concepts, technologies, or operational integration needs.

Multiple Portfolios In Each Service
Each of the services are consolidating and reorganizing the functions of what were their Program Executive Offices into Portfolios. Program Executive Offices/Officers (PEOs) will become Capability Program Executives (CPEs), and act as a Portfolios’ acquisition arm.

(The examples below are from the Army. Other Services will have equivalent organizational designs for their Portfolios.)

The acquisition chain of authority runs directly from Capability Program Manager to PAE to the Service Acquisition Executive (SAE), with no intermediate offices or approval layers. (The Service Acquisition Executive for the Army is the Assistant Secretary for Acquisition, Logistics & Technology. For the Navy/Marines, the Assistant Secretary for Research, Development & Acquisition. For the Air Force/Space Force the Assistant Secretary for Acquisition, Technology & Logistics.)

The Army Has 6 Portfolio Acquisition Executives
For example, the Army will likely reorganize its 12 existing PEO offices to become part of 6 portfolios aligned with Army Warfighting Concepts and functions. Each of the 6 portfolios headed by a PAEs will be commanded by a Major General.

The likely 6 Army Portfolios are: 1) Maneuver, 2) Maneuver Air, 3) Fires, 4) C2/CC2, 5) Agile Sustainment and Ammo, and 6) Layered Protection and CBRN. One additional portfolio, called the PIT, will likely include the Army’s Innovation at the Edge activities.

Army PAE Maneuver will likely combine elements of PEO Soldier, PEO Ground Combat Systems, Future Capabilities Division and Maneuver Divisions, Test and Evaluation Integrator, Strategic Contracting Office, and others. This portfolio will likely have the Abrams tank, XM30 Mechanized Infantry Combat Vehicle (replacing the M2 Bradley), the ISV (Infantry Squad Vehicle), Soldier Borne Mission Command program (SBMC), Next Generation Squad Weapon (NGSW), Soldier Borne Sensor (SBS) program, and Organization Clothing and Individual Equipment (OCIE).

Authority to Make Trade-offs
PAEs now have the authority to make trade-offs between cost, schedule and performance and apply flexible funding between weapons systems to rapidly deliver capabilities to the warfighter. This means focusing on fielding “good enough” technology instead of waiting for a product that meets every single requirement.

Army PAE Maneuver Air will likely combine elements of Program Executive Office Aviation, Aviation and Missile Command, Futures Command Future Vertical Lift team DEVCOM Aviation & Missile, and others. It will likely include the Long-Range Assault Aircraft (FLRAA) the Bell V-280 Valor (to replace the UH-60 Black Hawk), Uncrewed Aircraft Systems (UAS), Rotary and Fixed Wing, and Autonomy.

Program Executive Officers (PEOs) are Now Capability Program Executives (CPEs)
Inside each portfolio is a Capability Program Executive (CPE), typically a Brigadier General or a civilian SES. Capability Program Executives have similar roles and responsibilities as today’s PEOs. They are the Acquisition leader responsible for cradle-to-grave management of their programs within their portfolio.

Streamlined Layers of Bureaucracy
97 Army acquisition programs may be reassigned to align with the Army PAE reorganization. 46 organizations that were writing requirements likely will be consolidated into 9 Future Capability Directorates.

Army PAE Fires will likely combine elements from Program Executive Office Missiles and Space, Enterprise Information Systems, the Rapid Capabilities and Critical Technologies Office, Fires System Center, and others. It will likely include the Integrated Battle Command System (IBCS), Patriot/PAC-3, Precision Strike Missile (PrSM), Long-Range Hypersonic Weapon – Dark Eagle (LRHW), Common Autonomous Multi-Domain Launcher (CAML), Guam Defense and Golden Dome.

DoW Will Buy Commercial First
One of the biggest changes is the mandate for PAEs to buy Commercial Off the Shelf (COTS) products, modify them if necessary and only buy bespoke products as a last resort. This change by itself is going to send shockwaves through the existing Prime contractors.

It’s telling everyone that the playing field is now open to everyone. Forget who has more lobbyists on K-Street. Speed, mission impact, and innovation is what will be rewarded. What this means for startups is that if you can execute and deliver (not just PowerPoints) you can become a supplier to the DoW.

Incentive Compensation to PAEs and Program Managers
PAEs will be judged on whether they deliver systems to the warfighter on time and on schedule. PAEs and Program Managers will have “incentive compensation” tied to “capability delivery time, competition, and mission outcomes. (How they’ll pay that kind of compensation for a member of the military remains to be seen.)

Incentives and Scorecards for Contractors
They’ll be managing their contractors with “time-indexed incentives” to make sure contractors deliver on time and on budget, using “scorecards” to keep tabs on how each portfolio is doing.

Army PAE C2/CC2 (Command and Control/Counter Command and Control) will likely combine elements of PEO Command, Control, Communications and Network.. And include NGC2, TITAN, TENCAP, Next Generation Constructive, STE

Non-Traditional Entry Points
Companies selling to the DoW previously had to comply with the impenetrable DFAR and FAR – the Defense and Federal Acquisition Regulations – with over 5,000 pages of complex rules. It was designed for buying Aircraft Carriers, not startup technology.

Now the DoW is telling PAEs to toss those and use Non-FAR regulations like OTAs (Other Transaction Authorities). OTAs are not subject to the extensive, rigid rules and regulations of the DFAR. They allow for greater flexibility, speed, and allow the DoW to work with a broader range of innovative commercial companies. For startups this means massively reduced documentation, shorter timelines, and fewer barriers to working with the DoW.

PAEs Will Use Lean Methodology
Rather than fixed requirements and using waterfall development processes, the services are now insisting that vendors use Lean Methodology to set incremental and iterative delivery targets. That means they can field “good enough technology” that can be incrementally updated in the field and improved on a more frequent cadence.

The only requirement for each increment is that they need to target 1) an initial fielding date,
2) set a maximum cost of each unit and 3) meet the minimum standards for mission effectiveness. Other than that, PAEs have the authority that other attributes of the weapons/software can remain tradable throughout development to allow incremental enhancements and rapid delivery of subsequent increments. This includes the ability to waive technical standards and environmental and other compliance requirements, unless they are mandated by statute or safety.

One other interesting Lean mandate is that each PAE will set up lean technical advisory processes to inform accelerated decision-making, ensuring technical rigor without sacrificing speed.

Weapons Will Be Able to Talk to Each Other – By Design
The new PAEs are also tasked with insisting that all weapons across their programs use Modular Open System Architectures, including by asserting government purpose rights over critical software interfaces — a move that allows the Pentagon to retain the data rights needed to avoid “vendor lock” (weapon systems that can only be modified and/or repaired by the company that designed it).

Army PAE Agile Sustainment will likely combine elements of PEO Combat Support and Combat Service Support, PEO Solider and PEO Joint Program Office Armaments and Ammunition. It will likely include next generation Common Tactical Truck (CTT,) Family of Medium Tactical Vehicles (FMTV), 155mm, 6.8mm ammunition.

Two Vendors Through Initial Production
The DoW has painfully learned that having only one vendor selected leads to cost overruns and late projects. A new idea is that each critical acquisition program will have at least two qualified sources through initial production. While this will cost more upfront, it gives government leverage when it is strongest and enables them to re-compete modular components and find alternative suppliers if needed.

Design For Rapid Scale In a Crisis
PAEs have been told to establish acquisition strategies that decouple design from production to allow additional third-party suppliers to surge and rapidly scale manufacturing capacity in a crisis. They are to put in place guidelines for wartime consumption rates through manufacturing and supply chain partnerships and alternative sources.

Army PAE Layered Protection and CBRN (Chemical, Biological, Radiological, and Nuclear) will likely combine elements of PEO JPEO-CBRND. It will likely include Joint Chemical Agent Detector, UIPE, Decontamination Family of Systems, Biometrics

PAE Officers Now Have More Time To Learn On the Job
A complaint from past acquisition program managers is that they would only be there for two or three years, and then off to their next assignment. Two years was not enough time to see a program through. Now PAEs will have 4-year tours, extendible for another 2 years.

PAEs Top to Bottom
Every military service has 60 days to tell the Secretary of War a list of portfolios it is proposing to be initially stood up. A full implementation plan is due in 90 days. All major acquisition activities across all Services are going to be transitioned to PAE portfolios within two years.

Army PIT is the Army’s innovation initiatives at the edge. It’s the front door for startups wanting to partner with the Army.

• The PIT includes the Joint Innovation Outpost, the Global Tactical Edge Acquisition Directorate (G-TEAD) Marketplace, the FUZE program, and Disruptive Technologies.

• The G-TEAD Marketplace merges Prize Challenge events (e.g., Army xTech Program) and DEP submissions through open call announcements.

• FUZE brings together the Army SBIR/STTR seed funding, MANTECH (Army Manufacturing Technology program), TMI (Tech Maturation Initiative) and XTech the Army’s scouting program.

Reeducation Camp – Warfighting Acquisition University
To retrain/reeducate contracting and acquisition officers, the “Defense Acquisition University” will become the “Warfighting Acquisition University.” They have been ordered to stop compliance-focused training operations and in six months transform into a competency-based education institution.

The university will pivot to offer experiential team-based programs that work on real DoW challenges (does that ever sound like a description of Hacking for Defense.) And they’re going to have their students get out of the building and take part in industry-government exchanges. In the next six months they’re going to prioritize education and rotation programs to get their students exposure to commercial industry practices, manufacturing and operational expertise, and real-world problem-solving. All to develop Acquisition executives critical thinking and agile and rapid decision-making skills. (Note to DAU: we’ve been building these programs for a decade at the Stanford Gordian Knot Center for National Security Innovation. Our national security classes are in 60+ universities and we’re happy to help.)

The Joint Staff – Coordinating the Needs of All the Services

While each of the Services generated their own weapons requirements, plans and budgets, they all had to be approved by the Joint Staff (which reports to the Secretary of War) through a process called the JCIDS (Joint Capabilities Integration & Development System). In theory this was to coordinate each of the Service’s needs so they weren’t duplicating each other, to ensure that they were interoperable, and to give the Combatant Command a voice; and tie all the requirements to joint concepts – all of this needing to be done before Service weapons programs got funded and built.

The problem was that JCIDS moved at the speed of paperwork, not war, so the Secretary of War eliminated it earlier this year. (They kept part of it called the Joint Requirements Oversight Council but reoriented it from validating documents to identifying joint operational problems, which will drive the priorities for the entire department of War.)

In JCIDS’ place the Secretary of War created three new organizations:

• The Joint Acceleration Reserve, a pool of money set aside to quickly field promising capabilities.

• The Requirements and Resourcing Alignment Board (RRAB) that will tie money directly to the top warfighting priorities and how much money each will get from the new Joint Acceleration Reserve.

• The Mission Engineering and Integration Activity brings government, industry, and labs together early on to rapidly experiment, test, and prototype new tech.

It’s interesting to note that none of these changes at the Joint Staff have seemed to (at least publicly) filter down to the charter of the Services Portfolio Acquisition Executives (PAEs). The achilles heel of the Services Acquisition process appears that they are still planning to put the Requirements and Capability gap analysis up front. Here’s why that’s a problem and how to fix it.

Foreign military sales

One other tangential decision in this redesign was not in acquisition but in sales. The DoW wants a greater emphasis on selling our weapons to our Allies. They’ve moved two agencies responsible for those functions – the Defense Technology Security Administration DTSA and the Defense Security Cooperation Agency (DSCA) – from OSD Policy to OSD Acquisition and Sustainment.

This move is about selling more of our equipment, but makes no mention of buying any equipment from our allies.

Inferred But Not Mentioned
Pretty interesting that in this reorg no one has noticed that Elbridge Colby – Under Secretary for Policy – had three organizations taken away from him.

1. Defense Technology Security Administration DTSA

2. Defense Security Cooperation Agency (DSCA)

3. The Joint Production Accelerator Cell (JPAC) now renamed the Wartime Production Unit (WPU)

All three organizations were handed to Michael Duffey the Under Secretary for Acquisition & Sustainment. Regardless of the public statements the optics are not a vote of confidence.

Bigger and Better?

It appears that the Office of Strategic Capital may have been swallowed up by the Economic Defense Unit run by George Kolitdes. From all appearances the Economic Defense Unit is tasked to decouple our economy from China, using private and public capital. That means considering how to on-shore the critical components like minerals, chips, batteries, motors, PNT, etc.) The Acquisition announcement was how to buy things. This Economic Defense Unit is how do we ensure the things we buy are made with parts we know we can have an assured supply of?

Summary

• Startups and the DoW are now speaking the same language – Lean, feedback from the field, pivots, iterative and incremental product design, speed to delivery.

• The DoW mandate to first buy commercial-off-the-shelf products is a once-in-a-lifetime opportunity for every startup and scaleup.

  • But you have to deliver. Don’t hand wave with PowerPoints.

  • DoW will be ruthless in shutting down and freezing out non-performers.

• The use of Non-Federal Acquisition Regulations will eliminate huge amounts of paperwork.

  • It eliminates one of the reasons to subcontract with a prime or other company

• DoW needs to be ruthless in reforming the compliance culture

• Who to talk to in each service and how will they do business will be unclear for at least the next six months

  • Reorganizations will create uncertainty of who is the front door for startups, how the new rules apply, and who can commit to contracts.

  • The Army appears to be further along than the other services in putting a PAE organization in place.

• In theory this is a knife to the heart of the Primes’ business model.

  • They will flood Congress and the Executive Branch with infinite capital to change these rules.

  • It’s a race between private capital and public company lobbying money

• Let’s hope these changes stick

Thanks to Pete Newell of BMNT for the feedback and insight.

For more from Steve on the Department of War reforms, read Making the Wrong Things Go Faster at The Department of War.

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.

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—-

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.

The Department of War (DoW) is one of the world’s largest organizations. If you’re a startup trying to figure out who to call on and how to navigate the system, it can be – to put it politely – challenging.Those inside the DOW have little perspective of how hard it is to understand what to an outsider looks like in an impenetrable, incredibly complex system.

Insiders know who to call, and prime contractors have teams of people following broad area announcements and contracts, but if you’re startup, you have none of those relationships. (And with the advent of Social Media even our adversaries have better knowledge.)

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If we’re serious about building a next generation defense ecosystem (not just buying the next shiny object), then this is the directory the Department of War should be publishing.

Until then, here’s the second update to the Department of War PEO Directory.
500 new names/organizations in this DoW phonebook and startup Go-to-Market Strategy playbook.

(See Appendix H for a summary of the changes.)

Downloads of the Directory can be found here.

Sign up for timely updates here.

(Unfortunately, you won’t see answers to these questions in the general press – it’s not clickbait enough. Nor will you read about it in the science journals– it’s not technical enough. You won’t hear a succinct description from any of the universities under fire, either – they’ve long lost the ability to connect the value of their work to the day-to-day life of the general public.)

In this post I’m going to describe how science works, how science and engineering have worked together to build innovative startups and companies in the U.S.—and why you should care.

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(In a previous post I described how the U.S. built a science and technology ecosystem and why investment in science is directly correlated with a country’s national power. I suggest you read it first.)

How Science Works

I was older than I care to admit when I finally understood the difference between a scientist, an engineer, an entrepreneur and a venture capitalist; and the role that each played in the creation of advancements that made our economy thrive, our defense strong and America great.

Scientists

Scientists (sometimes called researchers) are the people who ask lots of questions about why and how things work. They don’t know the answers. Scientists are driven by curiosity, willing to make educated guesses (the fancy word is hypotheses) and run experiments to test their guesses. Most of the time their hypotheses are wrong. But every time they’re right they move the human race forward. We get new medicines, cures for diseases, new consumer goods, better and cheaper foods, etc.

Scientists tend to specialize in one area – biology, medical research, physics, agriculture, computer science, materials, math, etc. — although a few move between areas. The U.S. government has supported scientific research at scale (read billions of $s) since 1940.

Scientists tend to fall into two categories: Theorists and Experimentalists.

Theorists

Theorists develop mathematical models, abstract frameworks, and hypotheses for how the universe works. They don’t run experiments themselves—instead, they propose new ideas or principles, explain existing experimental results, predict phenomena that haven’t been observed yet. Theorists help define what reality might be.

Theorists can be found in different fields of science. For example:

Physics Quantum field theory, string theory, quantum mechanics
Biology Neuroscience and cognition, Systems Biology, gene regulation
Chemistry Molecular dynamics, Quantum chemistry
Computer Science Design algorithms, prove limits of computation
Economics Build models of markets or decision-making
Mathematics Causal inference, Bayesian networks, Deep Learning

The best-known 20th-century theorist was Albert Einstein. His tools were a chalkboard and his brain. in 1905 he wrote an equation E=MC² which told the world that a small amount of mass can be converted into a tremendous amount of energy. When he wrote it down, it was just theory. Other theorists in the 1930s and ’40s took Einstein’s theory and provided the impetus for building the atomic bomb. (Leo Szilard conceived neutron chain reaction idea, Hans Bethe led the Theoretical Division at Los Alamos, Edward Teller developed hydrogen bomb theory.) Einstein’s theory was demonstrably proved correct over Hiroshima and Nagasaki.

Experimentalists

In addition to theorists, other scientists – called experimentalists – design and run experiments in a lab. The pictures you see of scientists in lab coats in front of microscopes, test tubes, particle accelerators or NASA spacecraft are likely experimentalists. They test hypotheses by developing and performing experiments. An example of this would be NASA’s James Webb telescope or the LIGO Gravitational-Wave Observatory experiment. (As we’ll see later, often it’s engineers who build the devices the experimentalists use.)

Some of these experimentalists focus on Basic Science, working to get knowledge for its own sake and understand fundamental principles of nature with no immediate practical use in mind.

Other experimentalists work in Applied Science, which uses the findings and theories derived from Basic Science to design, innovate, and improve products and processes.

Applied scientists solve practical problems oriented toward real-world applications. (Scientists at Los Alamos weretrying to understand the critical mass of U-235 (the minimum amount that would explode.) Basic science lays the groundwork for breakthroughs in applied science. For instance: Quantum mechanics (basic science) led to semiconductors which led to computers (applied science). Germ theory (basic science) led to antibiotics and vaccines (applied science). In the 20th century Applied scientists did not start the companies that make end products. Engineers and entrepreneurs did this. (In the 21st century more Applied Scientists, particularly in life sciences, have also spun out companies from their labs.)

Scientists

Where is Science in the U.S. Done?

America’s unique insight that has allowed it to dominate Science and invention, is that after WWII we gave Research and Development money to universities, rather than only funding government laboratories. No other country did this at scale.

Why Universities Matter to Science

U.S. universities perform about 50% of all basic science research (physics, chemistry, biology, social sciences, etc.) because they are training grounds for graduate students and postdocs. Universities spend ~$109 billion a year on research. ~$60 billion of that $109 billion comes from the National Institutes for Health (NIH) for biomedical research, National Science Foundation (NSF) for basic science, Department of War (DoW), Department of Energy (DOE), for energy/physics/nuclear, DARPA, NASA. (Companies tend to invest in applied research and development, that leads directly to saleable products.)

Professors (especially in Science, Technology, Engineering and Math) run labs that function like mini startups. They ask research questions, then hire grad students, postdocs, and staff and write grant proposals to fund their work, often spending 30–50% of their time writing and managing grants. When they get a grant the lead researcher (typically a faculty member/head of the lab) is called the Principal Investigator (PI).

The Labs are both workplaces and classrooms. Graduate students and Postdocs do the day-to-day science work as part of their training (often for a Ph.D.). Postdocs are full-time researchers gaining further specialization. Undergraduates may also assist in research, especially at top-tier schools.

(Up until 2025, U.S. science was deeply international with ~40–50% of U.S. basic research done by foreign-born researchers (graduate students, postdocs, and faculty). Immigration and student visas were a critical part of American research capacity.)

The results of this research are shared with the agencies that funded it, published in journals, presented at conferences and often patented or spun off into startups via technology transfer offices. A lot of commercial tech—from Google search to CRISPR—started in university labs.

Universities support their science researchers with basic administrative staff (for compliance, purchasing, and safety) but uniquely in the U.S., by providing the best research facilities (labs, cleanrooms, telescopes), and core scientific services: DNA sequencing centers, electron microscopes, access to cloud, data analysis hubs, etc. These were the best in the world – until the sweeping cuts in 2025.

Engineers Build on the Work of Scientists

Engineers design and build things on top of the discoveries of scientists. For example, seven years after scientists split the atom, it took 10s of thousands of engineers to build an atomic bomb. From the outset, the engineers knew what they wanted to build because of the basic and applied scientific research that came before them.

Scientists Versus Engineers

Engineers create plans, use software to test their designs, then… cut sheet metal, build rocket engines, construct buildings and bridges, design chips, build equipment for experimentalists, design cars, etc.

As an example, at Nvidia their GPU chips are built in a chip factory (TSMC) using the Applied science done by companies like Applied Materials which in turn is based on Basic science of semiconductor researchers. And the massive data centers OpenAI, Microsoft, Google, et al that use Nvidia chips are being built by mechanical and other types of engineers.

My favorite example is that the reusable SpaceX rocket landings are made possible by the Applied Science research on Convex Optimization frameworks and algorithms by Steven Boyd of Stanford. And Boyd’s work was based on the Basic science mathematical field of convex analysis (SpaceX, NASA, JPL, Blue Origin, Rocket Lab all use variations of Convex Optimization for guidance, control, and landing.)

Startup Entrepreneurs Build Iteratively and Incrementally

Entrepreneurs build companies to bring new products to market. They hire engineers to build, test and refine products.

Engineers and entrepreneurs operate with very different mindsets, goals, and tolerances for risk and failure. (Many great entrepreneurs start as engineers e.g., Musk, Gates, Page/Brin). An engineer’s goal is to design and deliver a solution to a known problem with a given set of specifications.

In contrast, entrepreneurs start with a series of unknowns about who are the customers, what are the wanted product features, pricing, etc. They retire each of these risks by building an iterative series of minimum viable products to find product/market fit and customer adoption. They pivot their solution as needed when they discover their initial assumptions are incorrect. (Treating each business unknown as a hypothesis is the entrepreneurs’ version of the Scientific Method.)

Venture Capitalists Fund Entrepreneurs

Venture capitalists (VCs) are the people who fund entrepreneurs who work with engineers who build things that applied scientists have proven from basic researchers.

Unlike banks which will give out loans for projects that have known specifications and outcomes, VCs invest in a portfolio of much riskier investments. While banks make money on the interest they charge on each loan, VCs take part ownership (equity) in the companies they invest in. While most VC investments fail, the ones that succeed make up for that.

Most VCs are not scientists. Few are engineers, some have been entrepreneurs. The best VCs understand technical trends and their investments help shape the future. VCs do not invest in science/researchers. VCs want to minimize the risk of their investment, so they mostly want to take engineering and manufacturing risk, but less so on applied science risk and rarely on basic research risk. Hence the role of government and Universities.

VCs invest in projects that can take advantage of science and deliver products within the time horizon of their funds (3–7 years). Science often needs decades before a killer app is visible.

As the flow of science-based technologies dries up, the opportunities for U.S. venture capital based on deep tech will decline, with its future in countries that are investing in science – China or Europe.

Why Have Scientists? Why Not Just a Country of Engineers, Entrepreneurs and VCs (or AI)?

If you’ve read so far, you might be scratching your head and asking, “Why do we have scientists at all? Why pay for people to sit around and think? Why spend money on people who run experiments when most of those experiments fail? Can’t we replace them with AI?”

The output of this university-industry-government science partnership became the foundation of Silicon Valley, the aerospace sector, the biotechnology industry, Quantum and AI. These investments gave us rockets, cures for cancer, medical devices, the Internet, Chat GPT, AI and more.

Investment in science is directly correlated with national power. Weaken science, you weaken the long-term growth of the economy, and national defense.

Countries that neglect science become dependent on those that don’t. U.S. post-WWII dominance came from basic science investments (OSRD, NSF, NIH, DOE labs). After WWII ended, the UK slashed science investment which allowed the U.S. to commercialize the British inventions made during the war.

The Soviet Union’s collapse partly reflected failure to convert science into sustained innovation, during the same time that U.S. universities, startups and venture capital created Silicon Valley. Long-term military and economic advantage (nuclear weapons, GPS, AI) trace back to scientific research ecosystems.

Lessons Learned

• Scientists come in two categories

  • Theorists and experimentalists

  • Two types of experimentalists; Basic science (learn new things) or applied science (practical applications of the science)

  • Scientists train talent, create patentable inventions and solutions for national defense

• Engineers design and build things on top of the discoveries of scientists

• Entrepreneurs test and push the boundaries of what products could be built

• Venture Capital provides the money to startups

• Scientists, engineers, entrepreneurs – these roles are complementary

  • Remove one and the system degrades

• Science won’t stop

  • Cut U.S. funding, then science will happen in other countries that understand its relationship to making a nation great – like China.

  • National power is derived from investments in Science

  • Reducing investment in basic and applied science makes America weak

For more from Steve on how the U.S. built a science and technology ecosystem, and why investment in science is directly correlated with a country’s national power, read How the U.S. Became A Science Superpower.

Ordinary ones watch their companies burn down.

I just had coffee with two co-founders of an e-bike company who were mentoring one of our student teams. In short order I realized they were great founders – creative, agile and still having fun building their company. Unlike other e-bike rental companies, their business model was unique, offering riders free rental time in exchange for looking at ads. We had a great conversation, and they talked about everything – except the dead moose on the table.

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The Dead Moose

Before we met, I read they had just lost out to three other e-bike companies (including Uber) to operate in another major city. This meant they were now shut out of that market for the next four years. Being fourth in a group of three is painful, but good CEOs learn from failure and ensure that those lessons get baked in going forward so they never happen again. (And if not, their board hits them on the head until they do.) As we talked, I learned that wasn’t the case with these founders.

They casually mentioned they were again competing for the rights to operate in a major city, this time the one I was in.

I asked what I thought were obvious questions, starting with, “What did you learn from the loss? What did you change to ensure it won’t happen again?” And to me, most important, “What happens to your valuation and business if you lose this city?” The answers were vague, and if I had been on their board would have given me pause. (That’s a polite description of what I would have said.)

A Crisis – Ignored

While the founders were still talking about new product offerings, brand partnerships, and customer acquisition programs, they hadn’t processed what their past loss meant, and the potential consequences of losing this next city. Let alone that they were now in a life-and-death struggle for the survival of their company. If not for survival, at least in a fight for one- or two-orders of magnitude difference in their valuation.

The CEO just didn’t have the urgency of what would happen if they lost this next city selection. Having seen this movie before, I suggested that they needed to treat this competition as a four-alarm fire. This was a crisis, and they were treating it like any other day-to-day issue.

Recognize When It’s Not Business As Usual

Startups are inherently chaotic. Founders face a constant barrage of decisions, demands, and distractions. But they need to recognize when an event/outcome can have an order of magnitude/life or death impact on their company. When a crisis happens the CEO needs to marshal all resources and organize to deal with them differently than the multitude of other day-to-day “hair on fire” issues in a startup. Rather than making this “one more fire drill,” as a first step startup CEOs need to articulate why this is an existential threat to the survival of the company. I found the best way to do this is to draft a one-page memo laying out:

• What’s changed

• Why it matters

• Why our current “business as usual” organization/process/product is insufficient as a response

And unless the building is on fire, test the memo with some trusted advisors (not your exec staff or board.)

Then, the CEO needs to personally lead the response:

• With a team focused 100% on the problem

• The CEO and team need a “War Room” – with a wall covered by visual representation of how the problem is being worked and progress to date

• Move to the city/location to get the deal/fix the problem

• Identify and remove all obstacles

• Create a new strategy for sales, marketing, influence, roadmap, etc.

• Finally, as I suggested to the e-bike company, you need new people of a different caliber, experienced in whatever issue is on fire who have a track record of success.
  This was the hardest point to get across. Replacing or augmenting people who thought they were doing a good job but don’t see the need for change, is painful.

Lessons Learned

• A competent founder can recognize when it’s a crisis, not business as usual.

• A good founder knows how to build new skills and capacity to manage a crisis.

• A great founder already has a plan B in place.

• In a crisis if you can’t manage chaos and uncertainty, if you can’t bias yourself for action and if instead you wait around for someone else to tell you what to do, then your investors and competitors will make your decisions for you and/or you will run out of money and your company will die.

For more leadership lessons for startup founders from Steve, read Leadership is More Than a Memo.

Think of this PEO Directory as a “Who buys in the government?” phone book.

Finding a customer for your product in the Department of War is hard: Who should you talk to? How do you get their attention? What is the right Go-To-Market Strategy? What is a PEO and why should I care?

Ever since I co-founded Hacking for Defense, my students would ask, “Who should we call in the DoW to let them know what problem we solved? How can we show them the solution we built?” In the last few years that question kept coming, from new defense startups and their investors.

At the same time, I’d get questions from the new wave of Defense Investors asking, “What’s the best “Go-To-Market (GTM)” strategy for our startups?

PEOs, PMs, PIAs, PoRs, Consortia, SBIRs, OTAs, CSOs, FAR, CUI, SAM, CRADAs, Primes, Mid-tier Integrators, Tribal/ANC Firms, Direct-to-Operator, Direct-to-Field Units, Labs, DD-254… For a startup it’s an entirely new language, new buzzwords, new partners, new rules and it requires a new “Go-To-Market (GTM)” strategy.

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How to Work With the DoW

Below are simplified diagrams of two of the many paths for how a startup can get funding and revenue from the Department of War. The first example, the Patient Capital Path, illustrates a startup without a working product. They travel the traditional new company journey through the DoW processes.

The second example, the Impatient Capital Path, illustrates a startup with an MVP and/or working product. They ignore the traditional journey through the DoW process and go directly to the warfighter in the field. With the rise of Defense Venture Capital, this “swing-for-the fences” full-speed ahead approach is a Lean Startup approach to become a next generation Prime.

(Note that in 2025 selling to the DoW is likely to change – for the better.)

Selling to the DoW takes time, but a well-executed defense strategy can lead to billion-dollar contracts, sustained revenue, and technological impact at a national scale. Existing defense contractors know who these DoW organizations are and have teams of people tracking budgets and contracts. They know the path to getting an order from the Department of War. But startups?

Why Write the PEO Directory?

Most startups don’t have a clue where to start. And selling to the Department of War is unlike any enterprise or B-to-B sales process founders and their investors may be familiar with. Compared to the commercial world, the language is different, the organizations are different, the culture of risk taking (in acquisition) is different, and most importantly the go-to-market strategy is completely different.

Amazingly, until last year’s first edition of the PEO directory there wasn’t a DoW-wide phone book available to startups to identify who to call in the War Department. This lack of information made sense in a world where the DoW and its suppliers were a closely knit group who knew each other and technology innovation was happening at a sedate decades-long pace. (And assumed our adversaries didn’t have access to our DoW web pages, LinkedIn and ChatGPT.)

That’s no longer true. Given the rapid pace of innovation outside the DoW, and new vendors in UAS, counter UAS, autonomy, AI, quantum, biotech, et al, this lack of transparency is now an obstacle to a whole-of-nation approach to delivering innovation to the warfighter.

(This lack of information even extends internally to the DoW. I’ve started receiving requests from staff at multiple Combatant Commands for access to the PEO Directory. Why? Because “…it would be powerful to include a database of PEOs to link to our database of Requirements, Gaps, and Tracked Technologies to specific PEOs to call.”)

This is a classic case of information asymmetry, and it’s not healthy for either the increasingly urgent needs of the Department of War or the nascent startup defense ecosystem.

Our adversaries have had a whole-of-nation approach to delivering innovation to the warfighter in place for decades. This is our contribution to help the DoW compete.

2025 PEO Directory Edition Notes

The first edition of this document started solely as a PEO directory. Its emphasis was (and is) the value of a startup talking to PEOs early is to get signals on what warfighter problems to solve and whether the DoW will buy their product now or in the future. Those early conversations answer the questions of “Is there a need?” and “Is there a market?”

This 2025 edition of the PEO Directory attempts to capture the major changes that are occurring in the DoW – in organizations, in processes and in people. (For example, the PEO offices of the three largest new defense acquisition programs — Golden Dome, Sentinel and Columbia – will report directly to the Deputy Secretary of War, rather than to their respective Services. And the SecWar killed the cumbersome JCIDIS requirements process.)

What this means is that in 2025 the DoW will develop a new requirements and acquisition process that will identify the most urgent operational problems facing the U.S. military, work with industry earlier in the process, then rapidly turn those into fielded solutions. (That also means the Go-to-market description, people and organizations in this document will be out of date, and why we plan to update it regularly.)

What’s New?

This 2025 edition now includes as an introduction, a 30-page tutorial for startups 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 Executive Offices (PEOs) fit into that process.

The Directory now also includes information about the parts of the government and the regulations that influence how the DoW buys – the White House Office of Management and Budget (OMB), and the Federal Acquisition Regulations (FAR). It added new offices such as Golden Dome Direct Reporting Program, DIU, AFRL, DARPA, MDA, CDAO, OSC, IQT, Army Transformation and Training Command, SOCOM, and others.

To help startups understand the DoW, for each service we added links to the organization, structure, and language, as well as a list of each Service’s General Officers/Flag Officers.

Appendix B has a linked spreadsheet with the names in this document.

Appendix C has a list of Venture Capital firms, Corporate Investors, Private Equity firms and Government agencies who invest in Defense. In addition, the Appendix includes details about the various DoW SBIR programs, a list of OTA Consortia, Partnership Intermediary Agreement (PIA) Organizations, and Tribal/Alaska Native Corporation (ANC) Companies.

Appendix D now lists and links to the military and state FFRDC test centers where startups can conduct demos and test equipment.

Appendix E added a list and links of Defense Publications and Defense Trade Shows.

Appendix F has a list of all Army system contractors.

A few reminders:

• This is not an official publication of the U.S. government

• Do not depend on this document for accuracy, completeness or business advice.

• All data is from DoW websites and publicly available information.

Thanks to this year’s partners helping to maintain and host the Directory: Stanford Gordian Knot Center for National Security Innovation, America’s Frontier Fund and BMNT.

This edition of the PEO Directory is on-line so it can be updated as the latest changes become available.

Send updates and corrections to updates@americasfrontier.com

You can access and download the full document here.

Steve Blank writes about defense innovation at www.steveblank.com

How did you go bankrupt?”
Two ways. Gradually, then suddenly.”
— Ernest Hemingway, The Sun Also Rises

Every disruptive technology since the fire and the wheel have forced leaders to adapt or die. This post tells the story of what happened when 4,000 companies faced a disruptive technology and why only one survived.

In the early 20th century, the United States was home to more than 4,000 carriage and wagon manufacturers. They were the backbone of mobility and the precursors of automobiles, used for personal transportation, goods delivery, military logistics, public transit, and more. These companies employed tens of thousands of workers and formed the heart of an ecosystem of blacksmiths, wheelwrights, saddle makers, stables, and feed suppliers.

And within two decades, they were gone. Only 1 company out of 4,000 carriage and wagon makers pivoted to automobiles.

Today, this story feels uncannily familiar. Just as the carriage industry watched the automobile evolve from curiosity to dominance, modern companies in SaaS, media, software, logistics, defense and education are watching AI emerge from novelty into existential threat.

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A Comfortable Industry Misses the Turn

In 1900, the U.S. was the global leader in building carriages. South Bend, IN; Flint, MI; and Cincinnati, Ohio, were full of factories producing carriages, buggies, and wagons. On the high-end these companies made beautifully crafted vehicles, largely from wood and leather, hand-built by artisans. Others were more basic wagons for hauling goods.

When early automobiles began appearing in the 1890’s — first steam-powered, then electric, then gasoline –most carriage and wagon makers dismissed them. Why wouldn’t they? The first cars were:

• Loud and unreliable

• Expensive and hard to repair

• Starved for fuel in a world with no gas stations

• Unsuitable for the dirt roads of rural America

Early autos were worse on most key dimensions that mattered to customers. Clayton Christensen’s “Innovator’s Dilemma” described this perfectly – disruption begins with inferior products that incumbents don’t take seriously. But beneath that dismissiveness was something deeper: identity and hubris. Carriage manufacturers saw themselves not as transportation companies, but as craftsmen of elegant, horse-drawn vehicles. Cars weren’t an evolution—they were heresy. And so, they waited. And watched. And went out of business slowly and then all of a sudden.

Early Autos Were Niche and Experimental (1890s–1905) The first cars (steam, electric, and early gas) were expensive, unreliable, and slow. They were built by 19^th century mechanical nerds. And the few that were sold were considered toys for other nerds and the rich. (Carl Benz patented the first internal combustion engine in 1886. In 1893 Frank Duryea drove the first car in the U.S.)

These early cars coexisted with a massive horse-powered economy. Horses pulled wagons, delivered goods, powered streetcars, and people. The first automakers used the only design they knew: the carriage. Drivers sat up high like they did in a carriage when they had to see over the horses.

For the first 15 years carriage makers, teamsters, and stable owners saw no immediate threat. Like AI today: autos were powerful, new, buggy, unreliable and not yet mainstream.

Disruption Begins (1905–1910) 10 years after their first appearance, gasoline cars became more practical, they had better engines, rubber tires, and municipalities had begun to pave roads. From 1903 to 1908 Ford shipped 9 different models of cars as they experimented with what we would call today minimum viable products. Ford (and General Motors) broke away from their carriage legacies and began designing cars from first principles, optimized for speed, safety, mass production, and modern materials. That’s the moment the car became its own species. Until then, it was still mostly a carriage with a motor. Urban elites switched from carriages to autos for status and speed, and taxis, delivery fleets, and wealthy commuters adopted cars in major cities.

Even with evidence staring them in the face, carriage companies still did not pivot, assuming cars were a fad. For carriage companies this was the “denial and drift” phase of disruption.

The Tipping Point: Ford’s Model T and Mass Production (1908–1925) The Ford Model T introduced in 1908 was affordable ($825 to as little as $260 by the 1920s), durable and easy to repair, and made using assembly line mass production. Within 15 years tens of millions of Americans owned cars. Horse-related businesses — not only the carriage makers, but the entire ecosystem of blacksmiths, stables, and feed suppliers — began collapsing. Cities banned horses from downtown areas due to waste, disease, and congestion. This was like the arrival of Google, the iPhone or ChatGPT: a phase shift.

Collapse of the Old Ecosystem (1920s–1930s) Between 1900 and 1930 U.S. horse population fell from 21 million to 10 million and the carriage and buggy production plummeted. New infrastructure—roads, gas stations, driver licensing, traffic laws—was built around the car, not the horse.

Studebaker: From Horses to Horsepower

The one carriage maker who did not go out of business and became an automobile company was Studebaker. Founded in 1852 in South Bend, IN, Studebaker began by building wagons for farmers and pioneers heading west. They supplied wagons to the Union Army during the Civil War and became the largest wagon manufacturer in the world by the late 19th century. But unlike its peers, Studebaker made a series of early, strategic bets on the future.

In 1902, they began producing electric vehicles—a cautious but forward-thinking move. Two years later, in 1904, they entered the gasoline car business, at first by contracting out the engine and chassis. Eventually, they began making the entire car themselves.

Studebaker understood two things the other 4,000 carriage companies ignored:

1. The future wouldn’t be horse-drawn.

2. The company’s core capability wasn’t in carriages—it was in mobility.

Studebaker made the painful shift in manufacturing, retooled their factories, and retrained their workforce. By the 1910s, they were a full-fledged car company.

Studebaker survived long into the auto age—longer than most of the early automakers—and only stopped making cars in 1966.

Fisher Body: A Coach Builder for the Machine Age

While Studebaker made a direct pivot of their entire company from carriage to cars, a case can be made that Fisher Body was a spinoff. Founded in 1908 in Detroit by brothers Fred and Charles Fisher, the Fishers had worked at a carriage firm before starting their own auto-body business. They specialized in producing the car bodies, not an entire car. Their key innovation was making closed steel car bodies which was a major improvement over open carriages and wood frames. By 1919, Fisher was so successful that General Motors bought a controlling stake and in 1926, GM acquired them entirely. For decades, “Body by Fisher” was stamped into millions of GM cars.

Durant-Dort: The Origin of General Motors

While the Durant-Dort Carriage Company never made cars itself, its co-founder William C. (Billy) Durant saw what others didn’t. See the blog posts on Durant’s adventures here and here.

Durant used the fortune he made in carriages to invest in the burgeoning auto industry. He founded Buick in 1904 and in 1908 set up General Motors. Acting like one of Silicon Valley’s crazy entrepreneurs, he rapidly acquired Oldsmobile, Cadillac, and 11 other car companies and 10 parts/accessory companies, creating the first auto conglomerate. (In 1910 Durant would be fired by his board. Undeterred, Durant founded Chevrolet, took it public and in 1916 did a hostile takeover of GM and fired the board. He got thrown out again by his new board in 1920 and died penniless managing a bowling alley.)

While his financial overreach eventually cost him control of GM, his vision reshaped American manufacturing. General Motors became the largest car company in the 20^th century.

Why the Other 3,999 Carriage makers Didn’t Make It

Most carriage makers didn’t have a William Durant, a Fisher brother, or a Studebaker in the boardroom. Here’s why they failed:

• Technological Discontinuity

  • Carriages were made of wood, leather, and iron; cars required steel, engines, electrical systems. The skills didn’t transfer easily.

• Capital Requirements

  • Retooling for cars required huge investment. Most small and midsize carriage firms didn’t have the money—or couldn’t raise it in time.

• Business Model Inertia

  • Carriage makers sold low-volume, high-margin products. The car business, especially after Ford’s Model T, was about high-volume, low-margin scale.

• Cultural Identity

  • Carriage builders didn’t see themselves as engineers or industrialists. They were artisans. Cars were noisy, dirty machines—beneath them.

• Managers versus visionary founders

  • In each of the three companies that survived, it was the founders, not hired CEOs that drove the transition.

• Underestimating the adoption curve

  • Early cars were bad. But technological S-curves bend quickly. By the 1910s, cars were clearly better. And by the 1920s, the carriage was obsolete.

• How did you go bankrupt? “Two ways. Gradually, then suddenly.”

By 1925, out of the 4,000+ carriage companies in operation around 1900, nearly all were gone.

The tragedy of the carriage era and lessons for today

What does an early 20^th century disruption have to do with AI and today’s companies? Plenty. The lessons are timeless and relevant for today’s CEOs and boards.

It wasn’t just that carriage companies failed to pivot. It’s that they had time and customers—and still missed it. That same pattern happens at every disruptive transition; they were led by CEOs who simply couldn’t imagine a different world than the one they had mastered. (This happened when companies had to master the web, mobile and social media, and is repeating today with AI.)

Carriage company Presidents were tied to sales and increasing revenue. The threat to their business from cars seemed far in the future. That was true for two decades until the bottom dropped out of their market with the rapid adoption of autos, with the introduction of the Ford Model T. Today, CEO compensation is tied to quarterly earnings, not long-term reinvention. Most boards are packed with risk-averse fiduciaries, not builders or technologists. They reward share buybacks, not AI moonshots. The real problem isn’t that companies can’t see the future. It’s that they are structurally disincentivized to act on it. Meanwhile, disruption doesn’t wait for board approval.

If you’re a CEO, you’re not just managing a P&L. You are deciding whether your company will be the Studebaker—or one of the other 3,999.

I’ve been having coffee with lots of frustrated founders (my students and others) bemoaning most VCs won’t even meet with them unless they have AI in their fundraising pitch. And the AI startups they see are getting valuations that appear nonsensical. These conversations brought back a sense of Déjà vu from the Dot Com bubble (at the turn of this century), when if you didn’t have internet as part of your pitch you weren’t getting funded.

I realized that most of these founders were simply confused, thinking that a good business was of interest to VCs. When in fact VCs are looking for extraordinary businesses that can generate extraordinary returns.

In the U.S., startups raising money from venture capitalists are one of the engines that has driven multiple waves of innovation – from silicon, to life sciences, to the internet, and now to AI. However, one of the most frustrating things for founders who have companies with paying customers to see is other companies with no revenue or questionable technology raise enormous sums of cash from VCs.

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Why is that? The short answer is that the business model for most venture capital firms is not to build profitable companies, nor is it to build companies in the national interest. VCs’ business model and financial incentives are to invest in companies and markets that will make the most money for their investors. (If they happen to do the former that’s a byproduct, not the goal.) At times that has them investing in companies and sectors that won’t produce useful products or may cause harm but will generate awesome returns (e.g. Juul, and some can argue social media.)

Founders looking to approach VCs for investment need to understand the four forces that influence how and where VCs invest:

1) how VCs make money, 2) the Lemming Effect, 3) the current economic climate and 4) Secondaries.

How VCs Make Money

Just a reminder of some of the basics of venture capital. Venture is a just another financial asset class – with riskier investments that potentially offer much greater returns. A small number of a VC investments will generate 10x to 100x return to make up for the losses or smaller returns from other companies. The key idea is that most VCs are looking for potential homeruns, not small (successful?) businesses.

VCs seesaw between believing that a winning investment strategy is access to the hottest deals (think social media a decade ago, AI today), versus others believing in the skill of finding and investing in non-obvious winners (Amazon, Airbnb, SpaceX, Palantir.)

The ultimate goal of a VC investment is to achieve a successful “exit,” such as an Initial Public Offering (IPO) or acquisition, or today on a secondary, where they can sell their shares at a significant profit. Therefore, the metrics for their startups was to create the highest possible market cap(italization). A goal was to have a startup become a “unicorn” having a market cap of $1billion or more.

The Lemming Effect

VCs most often invest as a pack. Once a “brand-name” VC invests in a sector others tend to follow. Do they somehow all see a disruptive opportunity at the same time, or is it Fear Of Missing Out (FOMO)? (It was years after my company Rocket Science Games folded that my two investors admitted that they invested because they needed a multi-media game company in their portfolio.) Earlier in this century the VC play was fuel cells, climate, food delivery, scooters, social media, crypto, et al. Today, it’s defense and AI startups. Capital floods in when the sector is hot and dries up when the hype fades or a big failure occurs.

The current economic climate

In the 20^th century the primary path for liquidity for a VC investment in a startup (the way they turned their stock ownership in a startup into dollars) meant having the company “go public” via an initial public offering (IPO) on a U.S. stock exchange. Back then underwriters required that the company had a track record of increasing revenue and profit, and a foreseeable path to do so in the next year. Having your company bought just before the IPO was a tactic for a quick exit but was most often the last resort at a fire sale price if an IPO wasn’t possible.

Beginning with the Netscape IPO in 1995 and through 2000, the public markets began to have an appetite for Internet startups with no revenue or profits. These promised the next wave of disruption. The focus in this area became eyeballs and clicks versus revenue. Most of these companies crashed and burned in the dotcom crash and nuclear winter of 2001-2003, but VC who sold at the IPO or shortly after made money.

For the last two decades IPO windows have briefly opened (although intermittently) for startups with no hope for meaningful revenue, profit or even deliverable products (fusion, quantum, etc. heavy, infrastructure-scale moonshots that require decades to fruition). Yet with company and investor PR, hype and the public’s naivete about deep technology these companies raised money, their investors sold out and the public was left hanging with stock of decreasing value.

Today, the public markets are mostly closed for startup IPOs. That means that venture capital firms have money tied up in startups that are illiquid. They have to think about other ways to get their money from their startup investments.

Secondaries

Today with the Initial Public Offering path for liquidity for VCs mostly closed, secondaries have emerged as a new way for venture firms and their limited partners to make money.

Secondaries allow existing investors (and employees) to sell stock they already own – almost always at a higher price than their purchase price. These are not new shares and don’t dilute the existing investors. (Some VC funds can sell a stake in their entire fund if they want an early exit.) Secondaries offer VC funds a way to take money off the table and reduce their exposure.

The game here is that startups and their investors need to continually hype/promote their startup to increase the company’s perceived value. The new investors – later stage funds, growth equity firms, hedge funds or dedicated secondary funds, now have to do the same to make money on the secondary shares they’ve purchased.

What Do These Forces Mean For Founders?

• Most VCs care passionately about the industry they invest in. And if they invest in you they will do anything to help your company succeed.

  • However, you need to remember their firm is a business.

  • While they might like you, think you are extraordinarily talented, they are giving you money to make a lot more money for themselves and their investors (their limited partners.)

  • See my painful lesson here when I learned the difference between VC’s liking you, versus their fiduciary duty to make money.

• The minute you take money from someone their business model becomes yours.

  • If you don’t understand the financial engineering model a VC firm is operating under, you’re going to be an ex CEO.

  • You need to understand the time horizon, size, scale of the returns they are looking for.

• Some companies, while great businesses may not be venture fundable.

  • Can yours provide a 10 to 100x return? Is it in (or can it create) a large $1B market?

  • VC funds tend to look for a return in 7-10 years.

  • Is your team extraordinary and coachable?

• VCs tend to be either followers into hot deals and sectors or are looking for undiscovered big ideas.

  • Understand which type of investor you are talking to. Some firms have a consistent strategy; in others there may be different partners with contrary opinions.

• Storytelling matters. Not only does it matter, but it’s an integral part of the venture capital game.

  • If you cannot tell a great credible story that matches the criteria for a venture scale investment you’re not ready to be a venture funded CEO.

• If you’re lucky enough to have an AI background, grab the golden ring. It won’t be there forever.

For more from Steve on how and why startups raise money, read How to Raise Money – It’s a Journey Not An Event.