2025-10-15 21:00:11
The October 2025 PEO Directory – Update 2.
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.)
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.
2025-10-13 21:00:56
Tons of words have been written about the Trump Administrations war on Science in Universities. But few people have asked what, exactly, is science? How does it work? Who are the scientists? What do they do? And more importantly, why should anyone (outside of universities) care?
(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.
(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.)
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.
Corporate Research Centers
In the 20th century, U.S. companies put their excess profits into corporate research labs. Basic research in the U.S. was done in at Dupont, Bell Labs, IBM, AT&T, Xerox, Kodak, GE, et al.
This changed in 1982, when the Securities and Exchange Commission ruled that it was legal for companies to buy their own stock (reducing the number of shares available to the public and inflating their stock price.) Very quickly Basic Science in corporate research all but disappeared. Companies focused on Applied Research to maximize shareholder value. In its place, Theory and Basic research is now done in research universities.
Research Universities
From the outside (or if you’re an undergraduate) universities look like a place where students take classes and get a degree. However, in a research university there is something equally important going on. Science faculty in these schools not only teach, but they are expected to produce new knowledge—through experiments, publications, patents, or creative work. Professors get grants and contracts from federal agencies (e.g., NSF, NIH, DoD), foundations, and industry. And the university builds Labs, centers, libraries, and advanced computing facilities that support these activities.
In the U.S. there are 542 research universities, ranked by the Carnegie Classification into three categories.
R1: 187 Universities – Very High Research Activity
Conduct extensive research and award many doctoral degrees.
Examples: Stanford, UC Berkeley, Harvard, MIT, Michigan, Texas A&M …
R2: 139 Universities – High Research Activity
Substantial but smaller research scale.
Examples: James Madison, Wake Forest, Hunter College, …
R3: 216 Research Colleges/Universities
Limited research focus; more teaching-oriented doctoral programs.
Smaller state universities
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.
Tech firms’ investments of $100s of billions in AI data centers is greater than the federal government’s R&D expenditures. But these investments are in engineering not in science. The goal of making scientists redundant using artificial general intelligence misses the point that AI will (and is) making scientists more productive – not replacing them.
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
Appendix – How Does Science Work? – The Scientific Method
Whether you were a theorist or experimentalist, for the last 500 years the way to test science was by using the scientific method. This method starts by a scientist wondering and asking, “Here’s how I think this should work, let’s test the idea.”
The goal of the scientific method is to turn a guess (in science called a hypothesis) into actual evidence. Scientists do this by first designing an experiment to test their guess/hypothesis. They then run the experiment and collect and analyze the result and ask, “Did the result validate, invalidate the hypothesis? Or did it give us completely new ideas?” Scientists build instruments and run experiments not because of what they know, but because of what they don’t know.
These experiments can be simple ones costing thousands of dollars that can be run in a university biology lab while others may require billions of dollars to build a satellite, particle accelerator or telescope. (The U.S. took the lead in Science after WWII when the government realized that funding scientists was good for the American economy and defense.)
Good science is reproducible. Scientists just don’t publish their results, but they also publish the details of how they ran their experiment. That allows other scientists to run the same experiment and see if they get the same result for themselves. That makes the scientific method self-correcting (you or others can see mistakes).
One other benefit of the scientific method is that scientists (and the people who fund them) expect most of the experiments to fail, but the failures are part of learning and discovery. They teach us what works and what doesn’t. Failure in science testing unknowns means learning and discovery.
2025-09-17 21:00:43
Great founders shine in a crisis.
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.
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:
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:
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.
2025-09-10 21:00:06
Announcing the 2025 edition of the DoW PEO Directory. Online here.
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.
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:
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 [email protected]
You can access and download the full document here.
2025-07-08 21:00:00
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.
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:
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 19th 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.
Early automakers borrowed heavily from carriage design (1885–1910). Cars emerged in a world dominated by horse-drawn vehicles and they inherited the materials and mechanical designs from the coach builders.
– Leaf springs were the dominant suspension in 19th-century carriages. Early cars used the same.
– There were no shock absorbers in carriages, and early autos. They both relied on leaf spring damping, making them bouncy and unstable at speed. Why? Roads were terrible. Speeds were low. Coachbuilders understood how to make wagons survive cobblestones and dirt.
– Carriages used solid steel or wooden axles; early cars did the same.
Body Construction and Design Borrowed from Carriages
– Car bodies were wood framed with steel or aluminum sheathing, like a carriage.
– Upholstery, leatherwork, and ornamentation were also carried over.
– Terms like roadster, phaeton, landaulet, and brougham are directly inherited from carriage types.
– High seating and narrow track: Early cars had tall wheels and high ground clearance, like buggies and carriages, since early roads were rutted and muddy.
Result: Early automobiles looked like carriages without the horse, because they were, functionally and structurally, carriages with engines bolted on.
What Changed Over Time
As speeds increased and roads improved, wood carriage design couldn’t handle the torsional stress of faster, heavier cars. Leaf-spring suspensions were too crude for speed and handling. Car builders began using pressed steel bodies (Fisher Body’s breakthrough), independent front suspension (introduced in the 1930s), finally integrating the car body and chassis into a single, unified structure, rather than having a separate body and frame (in the 1930s–40s).
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:
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 20th 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:
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 20th 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.
2025-07-01 21:00:38
Founders with great businesses are often frustrated that they can’t raise money.
Here’s why.
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.
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.
Venture capital firms are run by general partners who raise money from limited partners (pension funds, endowments, sovereign wealth funds, high-net-worth individuals.) These limited partners expect a 3x net multiple on invested capital (MOIC) over 10 years, which translates to a 20–30% net internal rate of return (IRR). After 75 years of venture investing VC firms still can’t pick which individual company will succeed so they invest in a portfolio of startups.
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 20th 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.