Many founders struggle to identify which people actually have high problem urgency or high customer-problem fit.

🏁 This framework gives six levels.

1. Not your customer, take it with a grain of salt

2. Ditto

3. Risky, be sure if they recognise it as problematic

4. This is a money maker

5. This one too

6. Too late, identify what the window of opportunity looks like

📊 Which level did I talk to?

• If you are not speaking to 4s and 5s → Change your targeting who you talk to

• If 10%-20% is a 4 or 5 → Start proposing (discounted) pilots

• If 30%+ is a 4 or a 5 → RAMP UP OUTREACH to these ICPs

⚠️ Validating high problem urgency != validating PMF

Bitchin’ ain’t switchin’.

Always propose a sale (or paid pilot) to weed out the social talk See who really was urgent.

Rinse and repeat.

👍 Rule of thumb

Minimum: 5 sales calls per founder per week.

Amazing: 3 sales calls per founder per day

Your mileage may vary depending on industry and market size.

If you need help interpreting your market signals or are curious about your PMF, get a free 30-minute diagnostic call with me.

Before we judge them, look at the world now. In the past two decades, we have experienced several bubbles: At the turn of the millennium, the Dot-Com burst, the 2008 Crisis of Credit, more recently NFTs, and, currently, potentially, AI. To better understand why humans get caught up in such hysteria, the 400-year-old tulip mania offers a three-ingredient recipe.

The arrival of the flower

The roots of the tulip reach back to Central Asia. The flower departed from the valleys of the Pamirs and Tien Shan mountains, on the border of Afghanistan, Russia and China. By the 10th century, traders had pushed it into Persia. After a few regime changes and half a millennium later, the Ottomans in present-day Turkey appreciated its beauty and started to cultivate it. With some twists and turns, after a few decades, the Dutch noticed it too, and the flower was taken to the Netherlands in the 1590s.

Soon after arrival, the Dutch started to experiment with breeding, imitating the Ottomans. The challenge to make varieties even more beautiful than their predecessors was embraced by many. White tulips with red accents were very much sought after, and when such a crossing emerged, they would be traded for an increasingly higher value.

However, that initial trade was reserved for privileged tradesmen or nobles throughout Europe. Beyond displaying them in their gardens, elite Parisian femmes would wear them as corsages to galas, attracting eyes to their décolleté.

Such exquisite flowers continued to be traded, and the increasing prices seemed like the result of limited supply and demand with deep pockets. If so valuable, why wouldn’t they just breed more fancy coloured tulips, then?

Well, the fact that the parent bulb was of a desired colour didn’t always guarantee that the offspring would be too. Why? Because the amazing colours sought after were caused by a virus, rather than a genetic aspect, which was much less predictable. This botanical insight, nevertheless, was only discovered after the boom had busted. In short, the virus kept the fancy tulip supply limited, but the price didn’t fully skyrocket here.

The foundations of the bubble

The price stagnation was partly caused by some inherent properties of tulips. You can trade the bulbs for only three months per year; the rest of the year, they need to remain in the ground. That meant nine months of non-trading, limiting price growth.

To combat this, the innovative Dutch merchants—who invented stock market capitalism a few decades prior—started trading futures avant la lettre. Such a paper slip required you to buy bulbs when they were ready to be removed from the soil. This unlocked a dangerous new dynamic: you often didn’t need to pay the full price upfront. Only when the bulb was removed from its soil was the money due.

Therefore, such contracts were risky: you didn’t know if the bulb would be the one you were promised. That had two causes: one, people could just be fraudulent. But second, tulip’s offspring are not always guessed correctly, due to that unpredictable virus.

With these shifts, the foundations for a bubble were laid. The capital of entry was reduced, and the transactions were uncoupled from a bulb-reality.

The blowing of the bubble

To create the contracts, traders paid a third-party facilitator a small fee, called wijngeld (wine money). The name reveals a curious part of the story. These trades didn’t happen in the Amsterdam Stock Exchange, but in the smoky taverns of nearby cities, where Haarlem and Leiden played a main role. The wijngeld referred to what the money would immediately be used for.

The entry fee was quite low, roughly the price of ten to eighty pints of beer (a few stuivers or a gulden or two). This attracted a new species of traders: skilled artisans, weavers, and tradesmen of the Netherlands, who had a tradition of saving money and being opportunistic. With the tulip, they saw a chance to make a quick buck. Before, bulbs were mostly traded by botanists and the merchant elite who had spent at least some time understanding the varieties. Now, the low fees flooded the market with buyers without the relevant knowledge.

In this frenzy, a substantial portion of the trades were mere contracts. Some historians have shown trade-chains of dozens of transactions between traders, with each swap of the paper slip, the price goes up, and up.

This is how the boom accelerated onwards to one bulb being sold for close to 10.000 guilders. To put that in perspective, the average Dutch urban home at that time cost 1000 guilders. There are no records of actual Amsterdam canal houses being traded, but a few transactions did involve a house and a farmhouse. Therefore, while the trade for an actual canal house is likely an urban myth, 10000 guilders could buy you a prime one, maybe two.

The burst of the bubble

Because the people were trading in paper realities of uncertain bulbs, the transactions themselves were mostly based on the speculation that the tulip price would go up. At some point, after papers exchanged hands ten times, there was not so much factual grounding of the price beyond the fact that it would be worth more tomorrow. This worked as long as the market believed it. The only thing that could stop the bubble is when the self-fulfilling prophecy breaks. And one day, it did.

In a tavern auction in Haarlem, the auctioneer announced the price of a relatively normal bulb, a Switser (Swiss). The reaction: silence; no bids. Even when he went lower, nobody bought the tulp that would’ve been snatched in a heartbeat the day before. Slowly, people realised what was happening, and soon the news spread throughout Haarlem: the tulip trade was dead. You can imagine the staggering amount of outstanding debts.

The aftermath

Contrary to popular belief, the bust had very limited effects on the national economy. Most of the trades happened in unregulated taverns rather than on the stock markets. Instead of being backed by actual currency, the long paper chains had no money behind them. Yes, some individuals lost their life savings, but the consensus among historians today is that the macroeconomic impact was minimal.

To fix the problem of outstanding debts—long story short—most of the debts were annulled, or a law was proposed that people could buy off their debt for 3.5% of the price. However, the state of Holland ignored the proposal and suspended all outstanding deals; in Haarlem, they sometimes settled between 1%-5%.

Furthermore, the actual non-future tulip trade didn’t stop. The initial, elite trade in highly expensive bulbs continued. The layman wasn’t trading these to begin with. It’s almost as if two separate markets existed that hardly interfered with each other.

In summary, the tulip mania became a bubble because of three ingredients. One, the transactions got completely uncoupled from the actual product. Two, the influx of unregulated, unbacked capital in the form of paper slips. Three, the only basis for these transactions was the speculation that prices would rise. The moment that chain of belief breaks, the bubble bursts.

Fast forward to 2000 and onward

The tulip provides us with three ingredients which explain many bubbles we have seen in the past two decades, and the current AI one. For starters, the influx of uncertain capital. The future tulip trading echoes in the 2008 housing market crash. In this crisis, people with insufficient capital income were given mortgages they were too likely to default on, caused by poor regulations. Thus, insufficiently backed capital entered the market. Similarly, the tulip purchase contracts were an unregulated form of financing, where most laymen would not be able to pay the actual bulb price.

The next culprit, the uncoupling of transactions to realities, arises when comparing the bulbs to NFTs. The NFT bubble peaked when some ‘rights to a JPEG’ went for the record price of $69M. Today, that NFT is worth about $50k, according to some. NFTs, as many have pointed out, have no relationship to any reality. Those are purely speculative assets, and therefore more prone to bubbles.

The next ingredient is the cascading effect of the chain of belief breaking down. The Dot-Com bubble is a prime example. VCs pumped money into the new internet companies, but the substantial profits never materialised. When shareholders took notice, a selling frenzy occurred. On one day, the high-tech NASDAQ composite lost 10% of its value. Over the next two and a half years, this would end up losing 75% of its value, going from 5000 points to 1114 in 2002. Is AI bubble-proof?

Is AI a bubble?

There indeed is a lot of capital being pumped into that industry, mostly from VCs. Indeed, it’s better regulated than the taverns, but still, these investments hinge on the assumptions of great future results. How are these companies doing, economically?

Of course, Nvidia is doing great, but as many point out, that company is selling shovels. Turning to companies building on that infrastructure, right now, it looks like the unit economics of most AI companies are not sound.

Looking at OpenAI’s valuation, compared to its current profitability, shows a big gap between speculation and reality. The same can be said about Anthropic or Lovable, the latter of which ironically published an article this year on “How to Turn a Hobby Into a Profitable Business”.

VC money is massively funding tokens and, therefore, the adoption of AI, just like VC money gave us cheap Ubers or DoorDash meals. Obviously, there are profitable companies that build on that VC money, which have developed tooling that generates value for customers. I believe most of us have experienced the value of AI.

It’s mostly the business model, and more specifically the revenue model, and to be pedantic, the unit economics, that currently don’t make sense yet. However, prices may drop, and when they do sufficiently, the companies can rake in the profits and slowly pay back the investors, just like Uber did after 10 years or Spotify after 17 years.

To make all the bets worthwhile, and for costs to go down, some are advocating for more data centres and computing power. Economies of scale might fix the unit economics. While the lobby grows, people like Zuck are calling for it. Such lobbies are just another bet in the chain of investment, echoing the paper slips of the tulip, which we know don’t come without a risk.

For the Dot Com bubble, those bets laid much of the internet infrastructure we rely on today. For AI, it could be the same, or it could turn out that in two decades a new innovation hits (perhaps quantum computing, although that seems early), which renders all those investments obsolete.

The stark parallel is that out of hundreds of dot-com invested companies, eBay and Amazon are two of the big ones that survived—Yahoo isn’t doing so great. Which of the current AI companies will live on? Furthermore, the infrastructure was mostly laid out by the hegemonic telecom companies and public funds, not VC money. The question is not so much if AI is a bubble: it has all of the signs of a bubble. The ultimate question lies in the burst’s timing and the damage if it does.

If the AI chain of belief breaks, the main question is which stakeholders in that chain will take the biggest hit? With the tulip mania, most debts were annulled, and the effects were limited. The 2008 crisis had a more profound effect on society that still can be felt today, hurting not just companies but also ‘regular people’. These two extremes paint the spectrum of possible scenarios. Nobody knows for sure what will happen, but that massive funding can’t be sustained for more than a couple of years. The time for AI is ticking.

I owe a great debt for this story to Mike Dash’s book Tulipomania. It’s a great book that gives you more detail and very rich descriptions of what the history of the trade actually was like. Dash is an exquisite writer, and I can totally recommend this read.

What do you think will happen?

That's not how most people buy things.

Also, what's up with the "actual" science? Instead of the made-up science? If science is about ascertaining truth, actual science is a pleonasm. And ironic, because often there's no actual science behind such claims.

But Jeroen, what about medicine?

Do you really think Pfizer or Novo Nordisk say "Based on science" in their sales pitch? They have more advanced claims:

"It actually reduces symptoms by 70%. Oh yeah, btw, it's clinically trialled."

I've yet to find one GTM strategy where the secret ingredient in nailing their messaging was adding "Based on science!" 🥴

"Based on science" is similar to "Empowered by AI" or worse: "Based on NASA technology"

Dude, you made a hiking t-shirt, not a rocketship. Calm your tits.

I'm not saying scientifically backed products don't sell.

I'm saying that if throwing in "science" works for your product category, there's a 95% chance your customers are buying your snake oil.

I never understand it; the logic always feels very flawed.

If 10% convert, you spend 4 weeks getting 3 leads. You could've asked in 30 minutes. Some sources say 2.5% is the industry average 💥

If it's B2B, how many managers making $150k are going to celebrate getting a $150 dollar gift card?

They don't even know if their paying customers are 10/10 happy. Referrals often don't come in in week 3. It takes time for people to build up trust, let alone referrals.

But Jeroeeehoeeeeen, Gmail grew because they had such a referral system.

Well, no, not really. Gmail was invite-only. It launched a 1GB inbox when most were offering 25mb. It was unheard of, and they chose to make it scarce.

Scarcity marketing tactic doesn't easily apply to your AI that automates CRM entries from your sales agents' iPads.

These companies did it more successfully:

• Dropbox offered 500mb for every invite, still in a time when storage was expensive offline. It was 35% of their total sign-ups at its peak.

• PayPal offered a $20 referral bonus. It was up to 10% of their growth.

Can you imagine? One of the most successful referral programs accounted for just 10% - 35% of sign-ups. It's a great bonus, but it's nothing to rely on in full. Thread carefully!

I’m launching something new after a successful pilot:

Join the Dr. Market Fit Cohort

Apply here!

Deadline: May 15

First session: May 29

Any questions? DM me on LinkedIn or respond to this email.

• 450 cold DMs

• 71 replies

• 27 investor meetings

• €275,000 raised

‘Where do you get the leads?’ I asked him. He told me that usually, people have a minimum of 100 (up to 400) leads in their current network.

With some simple tips from him, I booked 6 meetings within 2 days. I’m not kidding. His approach centres on 1st degree connections.

Prateek’s Low-Hanging Fruit Method

The goal is to centralise every person you’ve ever digitally interacted with into one master sheet, and qualify these with his 4 criteria.

This is how you extract the data:

1. iPhone/Android: Use an app like “SA Contacts” to export your entire address book to CSV.

2. WhatsApp: Use a browser extension (like “WAWebPlus”) to scrape your chat list names and numbers.

3. LinkedIn: Go to Settings > Data Privacy > Get a copy of your data > Connections.

4. Email: Connect Attio or a similar CRM to your Gmail/Outlook. It will automatically pull every person you have ever emailed.

Now you make the master spreadsheet.

Create 4 columns next to each name and mark them 0 or 1:

1. Drama-Free: (1 = No personal baggage/history).

2. Truth-Teller: (1 = They give honest feedback, not “polite” lies).

3. Headspace: (1 = They are currently stable and can focus).

4. ICP Fit: (1 = They actually need the product).

Delete anyone who has a 0 in any column. What remains is your “low-hanging fruit” in cold outreach. Go get them!

My favourite tools for LinkedIn Outreach

• Kondo - The DM inbox interface LinkedIn forgot to build (think SuperHuman). Never forget to respond to a message. Already helped me to re-identify a dozen leads and book two meetings due to not forgetting to reply. Check it out!

  

• ReactIn - Simple, non-obtrusive automations for LinkedIn. Auto-invite post likers or profile visitors, with a decent AI filter on the leads. Check it out!

  

Need help or want to outsource outreach?

Prateek is your guy. Fixed fee per month, more info on his website. Want to get in contact? DM him on LinkedIn!

I’m Jeroen Coelen, a PhD candidate on early-stage startups, entrepreneurship and design. As an ex-founder and startup advisor, I’ve mentored over 350 startups towards product-market fit.

First and foremost, I completely agree with the conclusion of Neumann’s piece: these methods are not the silver bullets they claim to be. However, if we are going to properly diagnose the problem, we must arrive at that conclusion the right way. Neumann’s argument relies on three distinct evidentiary flaws.

First, in the viral essay, he shows a graph of the startup survival rate of the past 30 years: a daunting flat line. His conclusion: the methods don’t work. However, he knowingly cites data that includes all new businesses, not just startups. The critiqued methods, like Lean Startup or Mom-test, are not aimed at restaurants or hairdressers. That dataset is too broad to prove the point.

Second, in another graph that doesn’t show improvement in startup performance, he cites data only from venture-backed startups. This excludes the many startups that have never received funding, which some estimates place at 95% off all startups. Think of the bootstrapped startups, built in attics for 2 years and abandoned after only finding 5 customers. Summarising, Neumann’s selected evidence is either too narrow or too broad to prove his point. I’ve detailed a breakdown of the flaws in these number in Appendix A.

The patient skips the snake oil

Third, perhaps the simplest argument why the overall startup survival rate isn’t going up—a point Neumann seems to miss—is that most founders in the trenches actually don’t use these methods. Studies show that perhaps most startups just once used a business model canvas as part of an accelerator program, but the vast majority (90%) of startups don’t use risky assumptions, to name one. To make a dent across the line, the lion's share should deploy the method. The fact that a million books are sold doesn’t mean they are read, let alone applied well. Simply put, if the patients don’t take the medicine and remain ill, we can’t blame the medicine (see Appendix B for a breakdown with sources of this).

Still, even if adoption were perfect, I agree with Neumann that the effects are quite limited, or entirely non-existent for many methods. There are many scientific studies into entrepreneurial interventions, including methods, accelerators, and university courses, that either have no results or, at best, produce unreliable hits and misses. That’s a red flag for sure. Overall, the startup medicine is not working well (see Appendix C for specific studies).

If the medicine isn’t working, it’s helpful to see who the pharmacist is, as these methods originate from two distinct camps with very different goals.

Who is the pharmacist?

Neumann rightly shows that all of the pundit’s methods emerge in response to the strict managerial approaches aimed at larger enterprises. Startups indeed require different playbooks, which, if we zoom out, can be classified into two groups: those made by practitioners or by scientists.

Take Ries and Sarasvathy as archetypes. Eric Ries, author of Lean Startup, is a practitioner who has, in his work, developed a scientific method-inspired approach to startups. He writes a book about that method, and it becomes a hit. Many such practitioner-written books exist: Mom Test (Fitzpatrick), Build (Fadel), and Four Steps to Epiphany (Blank). They are valuable in their own right, but differ from people like Sarasvathy.

Saras Sarasvathy is a scientist interested in entrepreneurship. She is one of the biggest names in the academic field of entrepreneurship; I doubt I have ever met a fellow scholar who doesn’t know her work. The basic premise of Sarasvathy’s work is that entrepreneurs don’t reason backwards, i.e. to determine the means to acquire from a clearly defined outcome. Instead, entrepreneurs have a vague idea of what they want, and based on their available means, make a plan for the near future. Effectual logic, she dubbed it. Large studies have demonstrated that, indeed, effectuation can yield better results, in some cases.

Overarching, the origin of these two methods is distinctive. Effectuation stems from science, which describes how successful entrepreneurs launch ventures. Lean Startup comes from a practitioner who suggests a novel approach: n=1 versus n=many. This gives us two camps, summarised as the practitioners and the scientists.

1. Practitioners, who make frameworks and methods from their own experience

   1. Example: Lean Startup, Mom Test, Four Steps to Epiphany, for instance

2. Scientists who produce actual science on the phenomenon of entrepreneurship

   1. Example: Sarasvathy, Porter, Schumpeter

Still, neither of these methods is a silver bullet, and Neumann analogously invites medicine and botany, two academic disciplines which have had demonstrable success on society. Why don’t we have that for entrepreneurship? Simple answer, we haven’t been at it nearly long enough.

Entrepreneurship research is very young

Entrepreneurs have been around since about 10.000BC. For instance, with the Mesopotamians, who strategically farmed particular onions for selling, or ancient Greek sea merchants who would travel overseas on lucrative trades.

However, it is ‘only’ since the 18th century that entrepreneurs emerged as a subject of scientists. After the feudal system fell throughout Europe, the emerging capitalist economies prompted scholars to understand market behaviour. However, it took a little detour for what successful entrepreneurs do to become a dominant line of inquiry.

The economists wanted to understand where profits come from: some people can sell products above their costs. Those people were entrepreneurs: risk takers who speculated about buying something in the hopes their selling price would be higher. Others, like Schumpeter, explained that entrepreneurs are innovative and therefore can charge more. But, how the entrepreneur did exactly that was not in focus; the economists who described them were interested in market behaviour, not entrepreneurial behaviour.

The entrepreneurs themselves became a focal subject for psychologists after WWII, as policymakers were interested in identifying them among a general audience to rebuild a torn Europe. They found they were innovative, and some had a higher risk appetite. But what successful entrepreneurs actually did, actions which ought to be copied, remained largely out of focus until the 1980s and 1990s, when entrepreneurship became a scientific discipline of its own.

Today, it’s completely normal to have a ‘Professor of Entrepreneurship’, whereas in the 1920s, such a title would be very unlikely—some pinpoint the first ones to the 1980s. Increasingly, instead of explaining the market or the person, the actions of the entrepreneur appeared to be the most interesting bit to researchers; a realisation that swept through the field around the 1990s. Additionally, with the rise of entrepreneurship programs at universities, academia became increasingly concerned with more practical approaches to teaching entrepreneurship.

And thus, even though entrepreneurship has been a human practice for millennia, serious attention has been given to the development of better methods for only a few decades. Therefore, one could consider these books and methods perhaps the fledgling first results of decades of research and rising practitioner interest in the matter.

If one counters this by saying, “But computer science and engineering studies are relatively young too, and they have measurable results”, I need to acknowledge the effects. However, these disciplines have very strong roots in mathematics and physics, two disciplines which are millennia old. Entrepreneurship doesn’t have such a foundational theory. But even if they had one, scientists struggle with another aspect.

Getting your medicine to market

In this young and emerging field of entrepreneurial tools, many people have ideas of what good entrepreneurial approaches are. But not all ideas reach the masses. Whereas Lean Startup was popularised by Eric Ries since 2011, the idea of entrepreneurs as scientists has already persisted in the entrepreneurship field since at least 2009 (Felin & Zenger, proponents of the idea ever since). By 2012, that original paper had about 50 citations, whereas Ries’ book was nearing 100.000 copies sold.

While there are academic critiques of the scientific backing of Lean Startup (published in 2018), including the lack of empirical evidence for the method, the idea has found its way into many heads, and is perhaps one of the most well-known methods, getting global recognition in the previous decade.

In contrast, there are some ideas that remain quite esoteric: Schumpeter’s writings on creative destruction or Porter’s way of looking at markets have remained dominantly in the scholarly realm. An interesting middle form is the hybrid. Ideas that have an origin in science, but have been transposed to make them accessible.

The Business Model Canvas is a great example of this. The original research was a PhD by Alexander Osterwalder, but a Dutch company contracted Osterwalder to make a practitioner book about it, and sold millions. Similarly, Sarasvathy’s effectuation has been transposed into books that a founder actually may want to read.

Great ideas without an audience are useless. Yet, it may be striking, but many scientists in the field of entrepreneurship are not hugely concerned with the direct applicability of their findings. Sure, they see it ultimately might contribute, but I used the word direct here carefully.

This tendency, ironically, has been well described by scientists as resulting in the theory-practice gap. It boils down to two issues: what interests the academic might not be of interest to the entrepreneur. A theoretical discussion on the right definition of a startup might not be helpful to a founder who is trying to find product-market fit.

Furthermore, that gap is even broader because founders barely read papers, so even if the right topics were addressed, the medium is a problem too, which again, scholars realise. That is the reason I started this Substack at the start of my PhD: to have at least an outlet towards people in the trenches, which keeps me on my toes. But even if we make a superior theory, according to Neumann, it comes with a caveat.

Looking in the wrong place

If a method reaches the masses and when they would apply it effectively, everyone gets the same competitive advantage. Neumann calls this the Red Queen fallacy. If everyone gets the medicine, the virus will adapt: running faster just to keep the same place. Therefore, he is asking for a better meta-method:

• that can meet scientific standards,

• is capable of producing competitive advantages,

• is available to everyone,

• yet, won’t deplete these competitive advantages.

This seems paradoxical. If everyone has access to a competitive edge, it will surely eradicate itself. While he acknowledges that he’s not looking for another 26-step framework—Sorry, Bill Aulet—he’s looking for a meta-theory for generating startups or new methods for startups. Here, he draws heavily from the natural sciences, with evidence-based falsifiable theories, citing Popper. The good thing is, I believe there already is such a thing, yet Neumann is looking in completely the wrong direction, as in that section of his essay, he invokes a fatal flaw.

Indeed, Neumann rightfully assesses that there are many paradigms in entrepreneurship research. Much of entrepreneurship research is most closely related to a social science, as it describes people. Within such sciences, there are many ways to look at the world: we can explain people’s behaviour through a lens of group dynamics, cognition, and neurology, to name a few. Neither of these paradigms is right or wrong; it ultimately depends on the question you are after.

But what all these paradigms have in common is that they are trying to find the regularities in the way the world currently works. This is perhaps best described by Herbert Simon, Nobel Prize laureate. He made a case for that, describing how a tree forms or how a table comes about are two very different things. In one, a human acts to realise his mental state to create something that doesn’t exist in nature by itself; in another, a biological mechanism takes place. He contrasts the necessary, guided by natural laws, with the contingent, guided by human intention.

Simon says: there’s science about the natural, and there’s science of the artificial. The latter is called Design Science—which, contrary to what Neumann claims, is not to be seen as the precursor of Design Thinking, but that’s a different essay.

Design science can be viewed as an approach to researching how people change the world and derive usable patterns from it. Design science complements the scientific method, as it provides insight into how solutions are found. In that scientific domain of design research, advancements have been made since the 1950s that have reached a broader audience.

Entrepreneurship as a craft, founders as architects

Neumann says he’s looking for better theories of entrepreneurship, but focuses on the wrong paradigm that describes the world. Design science has answers, and an increasing number of entrepreneurship researchers are adopting a design perspective to understand how entrepreneurs actually do their work.

In this perspective, entrepreneurs are not scientists; they are architects. That analogy helps to explain why it’s so hard to make a silver bullet for startups. There’s designing a building, and proving the building. Is there a method that helps architects design beautiful buildings? Not really. Formalised numeric theories of beauty? Not really. Are there common rules of thumb that help young architects learn what works and doesn’t, aesthetically? Sure. Are they sometimes taught to break the rules? Very often.

Engineers can prove the building stays erect. There are engineering theories that help to calculate if the building will stay up. An architect has the luxury of being safely anchored by such quite objective physics. For startups, however, this is more than a bit trickier. Can you calculate if a startup idea will work?

We haven’t discovered such a full-fledged formalised physics of startups yet (We are waiting for it, Rob Snyder!). There is no underlying 'startup physics', no immutable natural laws like gravity or material strength, to mathematically prove a business model won't collapse. I wonder if it ever will be possible.

Crafting a better medicine

Neumann is right, the current medicine isn’t working. But that is because he expects startup methods to act like natural laws. In design science, there are no magic pills. Only better tools, and learning the craft to use them.

Everyone will appreciate that there’s an intuitive and subjective aspect to architecture. Such knowledge is hard to codify, and the education of architects has aligned with such a knowledge base. Architecture school is five years of studio work with designing many, many buildings to learn the craft. Often, many years of actual professional work are required to become great.

The same goes for doctors. They study for 5 to 10 years, and a big part of that education is being an apprentice doctor at a hospital. In the meantime, they get information about the latest theories and insights about novel diseases and treatments, but the medical profession recognises that learning to apply such insights is a skill in itself: a craft.

VCs asking for scalable, proven methods seem to ignore that entrepreneurship is also such a craft: a discipline requiring deep insight into the underlying mechanisms, which can mostly be obtained through experience.

But while we cannot codify a founder’s intuition, we can codify the essential principles they use. Design research allows for contingency and recognises regularities, without pretending there are full-blown natural laws. For example, Christopher Alexander, architect and researcher, has developed ‘A Pattern Language’, an accessible book of design patterns that work, a classic at architecture institutions.

Robust pattern finding

I imagine the future for startup methods is the development of such patterns. The patterns are building blocks that entrepreneurs can use in their own synthesis: making new competitive startups, but with a unique personal aspect.

One might argue that this sounds a lot like what the Business Model Canvas does. And indeed, the BMC was generated exactly using a design science approach to discover regularities. The fault was to treat this heuristic as a rigorous method. The BMC doesn’t tell you when you’ve filled it out correctly; anyone can give 9 answers, but only seasoned founders or workshoppers can evaluate it well. It is a sign of an early theory: getting abstract frameworks right, but not yet operationalised to the full details.

I foresee future research revealing very concrete patterns, such as: “This is what good product-market fit looks like in med-tech, with six distinct examples” or “These are the two dominant revenue models for aerospace polymers”. It would be a true catalogue, much like A Pattern Language. Right now, we rely on expert practitioners to intuitively spot these regularities in the wild. For instance, Rob Snyder has done excellent work uncovering the raw patterns of market demand and pull.

To advance these practice-rooted insights, I believe scholars must play a crucial role in making these principles testable and robust. In my PhD, I’m attempting such granular codification: translating advanced insights on product-market fit into simple, actionable tools for founders. When that is finished, I hope it proves the value of bridging rigorous theory and founder-led practice.

With entrepreneurship emerging as an academic Design Science where such principles are commonplace, the next three decades hold massive potential for making a real dent in the startup survival rate.

Appendices & References

Appendix A: Too narrow, too broad

In the essay, Neumann frames ‘the pundits’: the books, programs, methods, frameworks that everyone in startup land uses. The issue: no impact whatsoever.

The lack of effect is illustrated via two graphs. The first graph—which has gone viral on LinkedIn—shows the mostly constant multi-year survival rates. It is based on data from the US Bureau of Labour Statistics, and such numbers are trustworthy. Yet, there’s a caveat.

That dataset is about all new businesses in the US, not just startups. A startup is commonly defined as an emerging organisation in search of a repeatable, (highly) scalable, innovative business model.

Therefore, a new hairdresser or a freelance carpenter doesn’t fit that bill—About 80% of those businesses are sole proprietors, for that matter. Likely, most of these entrepreneurs don’t read Lean Startup, follow Design Thinking; that local carpenter probably doesn’t get into YCombinator (would be cool if they did, though).

Estimates are hard, but being generous, only ±3%1 of all new businesses fit the startup label. Even if startup methods were 100% effective, the impact would not be noticeable in that diagram, as fluctuations of the past have been higher than the share of startups. Thus, the above graph is not capable of proving Neumann’s thesis.

Additionally, in a different diagram, Neumann shows how the rate of ‘startups raising subsequent rounds’ is declining. Yet, to me, it’s not too clear why that can’t be explained by other factors than ineffective approaches (see caption). Furthermore, only about 1% (hard estimate to verify) of startups raise investment, leaving out 99% of the startup population. My conclusion: both graphs are insufficient to prove the methods aren’t working.

Appendix B: Entrepreneurs in the wild don’t actually use them

Actual entrepreneurs in the trenches don’t follow the methods. Yes, some accelerator programs will use Disciplined Entrepreneurship by Bill Aulet. But, for instance, a survey among hundreds of startups in Italy showed that only 11% use systematic hypothesis testing:

“Among the lean concepts, falsifiable hypotheses were the least used” - Ghezzi, 2019

The most popular tool was the Business Model Canvas, seemingly because the accelerator forced them to use it in a workshop. A graduate student of mine interviewed a set of founders, discovering a similar pattern: such methods were once provided during workshops, but in the day-to-day of their business, are not used.

I’ve worked with hundreds of startups, and very few work with such methods systematically, especially outside of accelerators. Some will do sprints or scrum for software development, but that’s about it.

What about the books?

Oh yeah, the books. Everyone knows the Mom-test, but do they read it in full? Well, a brief survey among my followers revealed that indeed some books are classics.

But, this sample surely is biased: who would admit they didn’t read a book? I didn’t read 0 to 1 cover-to-cover, I have to admit. So books might be popular, it doesn’t tell us anything about the application of it.

In general, there’s much research that says reading can improve your professional performance, but it comes with a caveat. Knowing and doing have a demonstrated gap, and being able to transfer learning from a book to practice is estimated at 10%-20%2. So if anything, regardless of the startup world, such books and education are not a silver bullet.

Appendix C: There is more science that says ‘meh’

Instead of looking at results in one entire nation, zooming in on contexts that ringfence the methods can reveal potential effects, or the absence of it. To find the impact of interventions in entrepreneurship, it is beneficial to categorise them into three groups that Neumann talks about: accelerators, university courses, and specific methods (such as the lean startup). Luckily, each of these has had ample attention in academia for the effects, and the results are mixed.

Entrepreneurship courses at universities don’t create ventures

For courses in entrepreneurship education, it’s debatable whether they create more ventures. There’s been ample research and several meta-reviews. Some papers say there’s no (or only a very weak) effect on venture creation or performance3. There is a small effect on the self-reported intention to start a business and a person’s belief in their entrepreneurial skill. Many studies suffer a self-selection bias of students who enrol—entrepreneurial students pick such courses, so what does it prove? A large study in Denmark says it can work when taught in secondary school, with 40% increase in venture creation. I know Denmark for its hands-on way of teaching entrepreneurship, so perhaps they are onto something.

Accelerators can work, but definitely not every program.

For accelerators, there are mixed results, but most meta-reviews are positive on performance4. The type of funding, participants and design of the program have a huge influence on the effect, of course. There are many ineffective programs; sometimes this is due to the startup-accelerator fit that accelerated startups, in fact, slow down5. But, overall, some meta-reviews show that about 10-15% of venture performance can be attributed to such programs6. So, they work to some extent, but it’s not a silver bullet.

Some methods do work

Then, the techniques and methods. The list of approaches is endless, here are some tactics:

• Hypotheses-based methods, such as Lean Startup: Neumann cites two studies by Camuffo et al (2020 and 2024) and states that this study only proves running your startup like a scientist influences failing fast. That is a misrepresentation of the results. The studies show effects of an increase in revenue, and startups are more likely to acquire or activate customers after a pivot: “The revenue of the treated firms grew faster than the control firms”. While significant, the difference was about 10%-20%, a big part stemming from killing 0 revenue ideas.

• Open information gathering: A study shows that being open to many aspects of your customer’s life, and not only the specific data point you're after, positively affects performance.

• Prioritising profitability over growth: A big study in Finland shows companies (so not just startups) that prioritise becoming profitable over growing fast have better performance (Read more)

Additional References & Footnotes

To increase momentum and gain headspace, they hire people for specific roles, such as head of product or customer success.

Then, several weeks later, they realise that the headspace didn’t free up. On the contrary, it shrank.

Why? Because they are outsourcing tasks instead of problems.

Outsourcing tasks takes time and headspace. You need to detail what needs to be done, which is exactly the thing that takes some time to formalise. And then, you check back if execution went accordingly, step by step.

As a result, some things don’t even get outsourced to your team. They get stuck inside your mind as you are stuck detailing everything. This kills momentum. I call this:

Delegation Stagnation

Founders may suffer from it for a variety of reasons

• They feel like they are burdening others when giving vague problems

• They feel like they should know everything before handing over things

• They want to be/feel in control of everything (neurotic tendencies)

• They have perfectionist tendencies, so all details should match their ideal vision

• They are insecure that if they don’t own the problem themselves, they might fail, and handing it over is a risk

All reasons are very human, yet you can’t let this stop your startup from growing. Luckily, there’s a way to approach this.

You should shift from handing out task/solution ownership to handing out problem ownership. And in doing so, you should onboard your team into your learning process. Really, they will get it.

Your team is a group of people that is eager to learn. If you listen to any podcast or read any book by a seasoned CEO, they often say that they needed to learn how to delegate.

You can just say this to your involved team members:

“Hey, I want to improve on giving you more ownership of problems, so in the coming weeks, I will be giving you problems instead of concrete tasks. I want to check in with you on how that is going, and evaluate and think of the best way for us to work together”

In this way, you create a feedback loop with the people you are learning with. Everyone knows they should talk to customers to learn what they need; your team members are no different.

Additionally, because people now own the problems they are solving, they will likely work with more drive towards the goals. Win-win!

So, do you suffer from delegation stagnation at times? Do you have any other tips for fellow founders to overcome it?

• PMFit: How well does your solution align with your target market?

• GTM (Go-To-Market): your strategy to get the show on the road

• GTMFit: How well does your GTM and target market align?

The building blocks are up for debate. This is my synthesis of going through dozens of frameworks on product, product-market fit, and GTM.

For nailing your product-market fit, check out this

• Free Market Fit Canvas + Feedback Tool

• Product-Market Fit Scale (infographic to ‘measure’ PMFit)

For nailing your GTM, check out these legends

• Alexander Estner’s GTM Foundation + Checklist

• Maja Voje’s ICP Sanity check + 14 GTM Miro Templates

• FletchPMM’s Robert and Anthony have great content on GTM.

To understand why product-market fit is elusive, thinking about completing a business model canvas is helpful.

Answering each of the nine building blocks is easy.

However, startups never have just one idea for any building block. They easily have four.

So, they end up with over thirty answers across the canvas.

Generating options is the easy part. Selecting the right ideas is the hard part, as my research

Common dilemmas

• Which segment is our beachhead, because each has certain benefits?

• Which JTBD is their main goal after talking to five different managers?

• What is the key problem, among the three you identified?

• What is the dominant competitor, among the five you’ve heard in interviews?

The solution to identifying the right answer is fit.

All these answers should fit together cohesively. You can imagine that among those nine building blocks of a successful business, there’s more than one fit.

In theory, for nine nodes, there are 36 connections possible. That would be very complex. I’ve made it simpler by identifying the key building blocks and linkages for product-market fit.

The 9 building blocks of product-market fit

When it comes to product-market fit, I’ve broken it down into nine building blocks.

Your task is to map out all your options for each segment and find the best set of 9 answers that are cohesive.

In the diagram below, you can see the 11 most important fits for your product-market fit. You can use these to identify the right set of answers.

Evaluating fit using your gut

Luckily, humans come with an intuitive fit assessment tool: our gut. Consider you are launching a taxi company, and are completing the canvas for the business segment.

• Customer: Bob, the business traveller

• JTBD: Needs to be at the airport at 05:00 AM

• Problem: However, he lives 100 km away from the airport

You get an idea of why that might be problematic. Bob needs to travel to the airport to catch the flight, so need to find a solution—hopefully your taxi company.

Do you have a blue car problem?

But now check out this one.

• Customer: Bob, the business traveller

• JTBD: Needs to be at the airport at 05:00 AM

• Problem: However, his car is blue.

You just feel it doesn’t make any sense. If you don’t, go see a doctor (me). You don’t want a blue car problem in your startup. Here’s another one, to test your gut feel.

• Customer: Bob, the business traveller

• JTBD: Needs to be at the airport at 05:00 AM

• Problem: However, he has skipped breakfast.

I still feel it doesn’t make a lot of sense, but a little more than his car being blue. You may feel that fit is a spectrum.

Evaluate your fit with this tool

For your startup, you should answer these nine building blocks to get a sense of your product-market fit. To help you with evaluating fit, I’ve made this tool, free to use for everyone.

The importance of fit is a fledgling insight of my scientific research on early-stage startups, part of my PhD that I’m finishing now.

Additional readings on fit

• The updated Market Fit Canvas (used with over 200 startups)

• A free AI tool to get feedback on your market fit

• Free Google Sheet + A4 Printable Template

• Instructions on how to use the framework

The rationale

Founders should be able to answer three questions:

1. Why is the customer in the market for a solution in the first place?

2. Why are the current alternatives on the market not good enough?

3. How is your solution better?

This gives three focal areas, which the above framework addresses.

• Customer

• Competitors

• Your solution

Market Fit Feedback Tool

To use the framework, I’ve made a web app that gives feedback and a 0/100 score. Some screenshots below.

Take me to the tool

Explanatory tooltips that help with your input

Get overarching score

Feedback per building block

Optimised for desktop, but works on mobile

Free Templates

Digital

If you want to save your work to share with yur team, you can use this template.

Get the free Google Sheet Template

Printable worksheet for workshops

A post-it optimised worksheet! Print on A4, regular post-its fit in the box. Grab those post-its and run a Market Fit Workshop yourself.

Market Fit Canvas Worksheet

3.17MB ∙ PDF file

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How to use

Likely, you will have multiple answers per building block. The goal is to make single narratives that fit together well. This might mean you can make 4 different stories for each customer segment.

For instance, for the ‘hasty family’, these could be the answers.

1. Customer Archetype: Hasty Family

2. Job to be Done: Get to the airport complete (with everyone and everything).

3. Root Problem: However, they have three young kids, six suitcases, and are leaving during the morning chaos.

4. Dominant Competitor: Therefore, they could drive their own large SUV and park it in long-term storage.

5. Competitor Flaws: However, loading the car is a “Tetris” nightmare; the parking shuttle is slow and hard to manage with strollers/heavy bags.

6. Consequence: As a result, they arrive at the gate exhausted, sweaty, and realise they forgot the toddler’s favourite toy in the trunk of their car 5 miles away.

7. Solution: Luckily, there’s a “Full-Service Family Van” transport.

8. Feature @ Problem: They provide an extra-large vehicle with pre-installed car kid seats; the driver assists with a “final check” list.

9. Feature @ Competitor: Curbside drop-off that handles luggage directly to a porter, skipping the parking shuttle entirely.

Customer (1-2-3)

In steps 1, 2, and 3 you need to lay out the situation of the customer.

1. Customer Segment: Who is this, specifically?

   1. Which beachhead customer segment are you talking about?

   2. Make it a real person, so ‘Head of HR’ instead of ‘HR Department’

2. Job to be done: What is this person trying to achieve?

   1. What is their Job to be Done?

   2. This is not your or your startup’s job to be done!

3. Problem: What obstacle stands in the way of achieving that Job to be Done?

   1. Understand that a problem and a job is not the same thing.

Competitors (4-5-6)

Here you identify that it’s not a blue ocean, and if your customer doesn’t buy you he does something else.

4. Dominant Competitor: What is the most dominant solution they use if they are not buying you?

   1. Base this on customer interviews

5. Competitor Flaw: What are the shortcomings of this solution?

   1. Why does it suck?

6. Consequence: What happens as a result of the current best option and not fixing the underlying problem?

   1. This must be something people really want to prevent from happening. It should be painful, such as ‘wasting €10k a day’ or ‘employees get fired’

Your solution (7-8-9)

Only now can you start yapping about yourself. But, if you crafted step 1-6 well, your solution should fit like a glove to the problem you just sketched.

7. Solution: Pitch your solution in 1 sentence.

   1. Explain the product category. What is it that you make

8. Benefit A: Explain how your solution fixes the problem

   1. Make sure your benefit actually tackles the problem of step 3

9. Benefit B: Explain how your solution addresses the shortcomings of alternatives

   1. Make sure that the problem you framed around the shortcomings of the alternatives (step 5) fits really well with this benefit

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Full Playbook

In my experience, founders spend time most time on competition and their solution, rather than understanding why they are buying in the first place.

1. Why is your potential customer on the market for a solution in the first place?

Understanding why people are on a market to buy is incredibly important. This reveals a hidden mechanism.

• AirBnB: Because most people don’t have a bed and home in cities they travel to

• Uber: Because they need to get from point A to point B

• Notion: Because our brains are too limited to capture everything, especially in teams

• The wired telephone: Because people are not in the same place

• The steam engine: Because mines were flooding with water

It might feel stupid, but formulating your problem and job to be done without talking about competition is incredibly hard. Can you do it for yours?

2. Why are the current solutions not good enough for the customer?

This is where founders spend some time. They look into the market and flag features of competition. But features in itself is not enough. People should complain and bitch about.

• AirBnB: Hotel rooms are often small and don’t have a nice experience of being at a home

• Uber: Waiting for a cab without knowing when it comes sucks

• Notion: 76 scattered Google Docs are hard to navigate

• The wired telephone: Because sending letters takes 2 weeks

• The steam engine: Because horses die and can’t work 24/7

Now, these complaints will differ per segment. The nature of the competition and shortcoming can be a way to segment your market.

Notion can target people that use Google Docs than teams that do everythig over Slack without proper documentation. This requires a different marketing message and perhaps channel.

3. How do you do it better?

This is what founders usually bite down on. They list how they are better, which features or benefits they have compared to competition. Applies to both hardware, medtech, and deep tech.

• AirBnB: Local homes with a bed that make you feel like home

• Uber: Application to order and track a cab to take you home

• Notion: Interconnected and dynamic wiki for your team to keep good overview of documentation

• The wired telephone: Hardware to speak to people across country

• The steam engine: Machine that converts coal into energy to pump water out of mines

So, these three questions explain a short story on why these innovations might have gotten traction.

If you write down great answers to these three questions, you are building a cohesive narrative of why your startup will succeed. At some point, it should feel logical that they will buy you. That is what product-market fit is all about: the arguments should fit each other like a glove.

Workshops for accelerators

Stress test your product-market fit

By building such a narrative, you can stress test your product-market fit easily. Based on many workshops I’ve done, this is harder than it seems. Try out this free template, the Product-Marekt Fit Logic.

Get free Google Sheet template

Get free Google Sheet template

🔥 Patron Content 🔥

Premium subscribers have access to a custom GPT that has been fed all my writings on product-market fit. It scores your input on all building blocks:

Get the GPT below:

Doctor Market Fit

Get your product-market fit score (0/100)

💡 Sorry Jesus, lord of AI, for I have sinned, but I’m tapping into the AI hype now. Yes, you read that correctly. I’ve made an AI tool that helps to get a sense of product-market fit…

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a year ago · 17 likes · 1 comment · Jeroen Coelen

1. Goal: Build a customer theory

2. Structuring and starting the interview: Keep your baby in your bag

3. Framing the interview

4. Don’t just ask for problems

5. Identifying the right JTBD ⬅️ Today

6. Understand the value in your value proposition

7. Using the film reel technique to get detailed explanations

8. Six techniques on how to wiggle out extra information

9. Getting your baby out of the bag

10. Crafting your customer theory

In constructing our theory of the customer (Chapter 1), we should understand why this customer is in the market for a solution in the first place.

The previous chapter laid out the basics of problems and jobs to be done. Consider this chapter an advanced deep dive into how to apply these ideas in your interviews.

The tactics will help you isolate the right Jobs to be Done for your startup. That’s quite important. Why?

Because if you know why customers hire your solution, the rest of your entrepreneurial journey becomes much easier, both on your problem-solution fit and GTM strategy.

To name a few, knowing your JTBD can give you:

• Easier solution scoping: you know what is essential and what is nice to have

• Precise segmentation: higher conversion rates when targeting

• Tailored messaging: potential customers respond more

• Improved positioning: customers understand you better

Sounds neat, right? Therefore, this chapter still focuses on building blocks 1-2-3 of the PMFit Logic (image below), which answers: why is this customer on the market for a solution to begin with? The JTBD is a key answer to nail.

5.1 Focus on emotional jobs to be done

JTBD-focused questions reveal the customer’s end goal. Such questions can get two types of answers: functional and emotional. Let’s see what the functional answer looks like (reiterating examples of the previous chapter).

Q (you): “What do you use Substack for?”

A (me): “To send a weekly newsletter”

That’s boring; it doesn’t provide a rich explanation of the ‘why’ behind my behaviour. Why do I do that? The other question might evoke something more.

Q (you): “If I may ask, what are you trying to achieve with your Substack?”

A (me): “To build a decent following of founders and build my personal brand”

It’s slightly more aspirational, but still, both answers are very functional jobs to be done. It’s fine to understand these, but this isn't always what primarily drives people.

It echoes what we did in the previous chapter, but we didn’t fully unpack it yet. For that, we need to focus on the emotional jobs to be done.

Read more

• Talk to customers

• Find your specific niche beachhead market

• Build something small (MVP) instead of a big vision

• Try to sell instead of fishing for compliments

• Move fast and iterate

But then I realise and get frustrated:

Subscribe now

The advice isn’t untrue. The general idea works.

The tips are not unknown. Most struggling founders have heard these ideas.

(This ‘Not X, But Y’ is not AI-generated, pinky promise)

The problem is elsewhere: the tips are bullshit because they exclude the nitpicky details of the execution.

As a result, the tips become empty mantras that founders repeat in front of the mirror, without giving them the tools to evaluate if they are doing it well.

Consequently, founders get lost. Here’s what to ask yourself instead:

1️⃣ How are you talking to customers?

Are you having fun chats with your demo on screenshare or digging deep to truly understand them? Are you proposing follow-up calls? Are you tracking who says yes and who says no to understand who is your ICP? More on this.

2️⃣ How have you identified and validated your beachhead market?

Have you defined ‘SMEs’ as your beachhead or have you sent 500 manually crafted LinkedIn DMs/emails over 8 weeks to find out which message resonates with which target group? 6 emails sent, you say? Wow, impressive. More on this.

3️⃣ Is your MVP really an MVP?

Do you have a prototype that works, or are customers actually shouting ‘OMG, YES THIS IS HELPING ME SO MUCH?’ Is it the minimum thing you could’ve built? Or did you just slap the label ‘minimum’ on your Notion Page? More on this.

4️⃣ Are you trying to sell or just fishing for compliments?

Listing all your features with a sales deck open is not a sales call. It is a push call. You want to learn how to organise your sales calls. Read books on this (e.g. Demand Side Sales, Gap Selling, or Rob Snyder)

5️⃣ Are you adding features as a proxy for progress?

Iterating fast is not about imagining and building as many features as possible. Iterations happen beyond your product. You should iterate on your marketing, channels, copy, messaging, and positioning. More on this.

Do you need help with applying the ideas correctly and getting your product to market? I’ve got room to take on two startups for mentoring the coming months. Link in comments to check out my way of work and pricing.

🔥Jeroen’s Hot Take🔥

Five additional reflections and frameworks. First one is on the house:

1. Not all evidence is equal

Many of their experiments referred only to interviews. For solution validation, that can be risky, because ‘bitchin’ ain’t switchin’. For instance, their pricing interviews were an airball. There are many frameworks to assess evidence, but they are probes, not truths. Take them as thinking tools to sharpen your mind.

Below is the non-linear confidence meter by Itamar Gilad. See how surveys are still ranked at 1-3, whereas actual results get to 7-10? That’s a huge gap. Thread lightly.

2. You can’t really validate a startup idea

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AI seems like a simple value unlock

However, when AI came along, attention for value unlock shifted towards AI. People were never really interested in PMFit; they were attracted to the potential money behind it.

That explains the superficial understanding of most people, and the lack of desire to go deeper, just like most AI evangelists don't understand neural networks or cognition, yet they rave on about AGI.

If it’s your first tech bubble, you can be easily persuaded

I guess this is the circle of news and hype, which for some reason keeps repeating itself. Still, my thesis is that it's mostly new gunslingers that jump on the hype train.

If you haven't been part of or invested in any tech hype, everything seems new and filtering out false promises can be challenging. After doing two or three, you get the drill.

It's like a 16-year-old girl falling for the wrong guy time after time, believing the promises of the next one that says 'he's really different'. After some breakups, she learns to avoid such guys.

We are really bad at predicting technology

Basically, tech bubbles, including AI, are like predatory bad boyfriends you learn to avoid in your teens and twenties. After big data, blockchain, NFTs, the metaverse, AR, AI is the true one that 'really matters'. Do you believe him?

We generally suck at predicting when technology will come about. See this graph with predictions of AGI. It’s always 10-20 years away or something. Sounds familiar?

“Fusion power has been one of those things that's been "only 20 years away" for about 50 years now.” - Source

In research reviewing tech forecasts, up to 80% of the predictions are off (Source + Source). Why, all of a sudden, did we get better at predicting AI’s future?

Technological innovation is non-linear, and a sudden jump in performance we’ve seen in the past years doesn’t mean anything for the future. It might plateau for 25 years, who knows? Nobody.

But Jeroehoeen, people do demand the iPhone.

Well, do they?

The masses demanded communication at a distance. The growing adoption of laptops and smartphones shows that.

Before that, we had telephones.

Before that, we had electrical telegraphs.

Before that, we had postcards.

Before that, we had smoke signals (kinda).

The iPhone, at launch, was an Mp3 player with phone functionality and a web browser. Much of the web wasn’t optimised for mobile yet, like it is today.

The App Store launched a full year after the first iPhone was released. Spotify was non-existent, iTunes was pay-per-song. The camera just had 2 Megapixels (Nokia N95 had 5MP).

All things considered, the first iPhones were relatively limited compared to the mini PCs we carry today.

Despite those ‘setbacks’, they sold over 6 million units in the first year.

So, yes, the introduction of the iPhone resulted in demand, but it didn’t create it. Why? It was always there.

Demand is like gravity working on a mug on a table

An interesting analogy I borrow from Ramoglou & Tsang (2016). They equate markets and demand to a mug on a table that doesn’t fall despite the gravity.

The force of gravity surely pulls the mug towards the centre of the Earth. However, the table blocks it. The moment the table is removed, or the mug slides off the table, the gravity starts moving the mug. The force is enacted.

Similarly, in markets, the market pull/need is the force. There are customer desires which can be met. However, sometimes, there’s not the right product for that, so no demand as a result. But the need remains.

When the right product gets introduced, i.e. the table is removed, all of a sudden, those forces turn into visible demand: people buying iPhones.

Why should you care?

That is why demand can only be fully demonstrated the moment actual sales are happening.

Everything before that is a proxy. All prototypes, problem interviews and other experiments that don’t involve transactions are insufficient to validate demand.

🔥 Jeroen’s Hot Take 🔥

There will be many people disagreeing with me on this post. I’ve held this argument many times, and there are always people who go against it. Here are the common complaints:

1. “But people demanded iPhones. They were camping outside”

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How to use

• The position on the x-axis says something about how strong a signal is related to product-market fit.

• 1000 followers on Instagram in itself are not a strong sign of product-market fit, whereas 10 customers happy in a full price pilot are.

• Indeed, those 1000 followers might convert to paid pilots, but we don’t know what the conversion rates are, and if they will be happy. Therefore, it’s a weaker proxy.

Free PDF version

Pmfit Scale By Dr Market Fit

1.17MB ∙ PDF file

Download

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🔥 Jeroen’s Hot Take 🔥

This is version 4 of my product-market fit scale. I feel this might not be the final version. Here’s why, and what options I considered.

Read more

How can I shorten my time to market?

That means, how can I get to sales or paid pilots as soon as possible? Often, people build an MVP for this. That is a great move in itself. However, the M in MVP often is not honoured: many MVPs have way too many features.

I see two reasons for this:

• Too many features are considered the core

• The beachhead segment is so broad that you need many features to satisfy all of them

I’ve written many posts (here, here, here, or this one) on defining your true beachhead segment. Still, a narrow beachhead doesn’t solve all problems. How do you go from a narrow beachhead to an MVP?

Identify true key features with MoSCoW framework

The MoSCoW framework, developed in 1994 by Dai Clegg, asks four questions:

1. What features MUST my first version have?

2. What features SHOULD my first version have?

3. What features COULD my first version have?

4. What features WON’T my first version have?

Are your founders’ beachhead markets too vague or broad? Are they building instead of talking to potential customers? I run two hands-on workshops for accelerator programs that fix these issues.

Check out my workshops

In answering these questions, you need to go through your mental or actual database of insights from sales calls and problem interviews.

1. Must Have:

   1. Features that 90% of your beachhead needs

   2. Essential to addressing the main Job to be Done

   3. Hair on fire problem stuff

   4. Won’t buy you without this

   5. Conclusion:

      1. Don’t skip this

      2. Make sure you have the data to support it—‘5 people said they were interested’ is not strong data

2. Should Have

   1. Features that generate value but don’t serve the main Job to be Done

   2. 60% of customers would seriously benefit from this, but can still generate enough value without it

   3. Conclusion:

      1. Include these in the later releases

      2. But don’t add before your Must-Haves work greatly

3. Could Have

   1. Cool ideas that do not add to the main job to be done

   2. About 25% of customers could seriously benefit from it, and future features built on this might unlock a new segment.

   3. Conclusion

      1. Postpone for now, unless you are ready to double down on that new segment.

4. Won’t Have

   1. Feature ideas that will never help more than 5% of your target market

   2. Features aimed at completely unrelated jobs to be done

   3. Often are the result of creative brainstorm sessions or strong opinions expressed by 1 potential customer

   4. Conclusion

      1. To hell with it

      2. Kill it with fire

Common challenges with this framework

• Problem: “Just 2 features feel too small to generate value”

  • Solution: That’s an assumption; make sure it’s true: pitch these two features before building anything with a brochure test. 10 pitches, and you know if you are on the right path.

• Problem: Some founders have limited market input; therefore, they are reasoning about which features might help their customers, rather than basing it on actual expressed interest/sales.

  • Solution: go out on the market, do 20-50 sales calls (even pre-launch) to see which aspects resonate the most.

• Problem: The beachhead market is too broad (all SMEs with $10M+ revenue). Your beachhead should be rooted in behaviour, not firmographics.

  • Solution: If you can’t answer the first 6 out of 9 questions (use this tool to verify), you need to talk to more people.

• Problem: I’ve got three features that 3 different groups of people like, not one that covers 90%

  • Solution: Your beachhead is too broad; you are targeting too many people. Unless you are launching in an inherently multi-stakeholder environment that needs all three from the start, you might want to pick one of the three to start out with.

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🔥 Extra Practical Examples 🔥

Whenever I run this with a startup, the above issues pop up. 95% of founders that can’t answer these questions with evidence just need to go to the market more often.

I mentored this construction startup that was very early stage. It was a new material technology that performed higher on sustainability aspects. However,

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