I thought to myself, what a weird thing to thank someone for. Let’s unpack.

Merging vision and roadmap

So I asked her: out of what groove, exactly? ‘My vision’, she said.

I’d challenged her a little before that. She used the word vision five times in five minutes: ‘I know my vision is clear!’, over and over. Each time, I heard two different things bundled into one word.

A vision is an abstract endpoint. A world without plastic, that kind of thing. Hers was a world with safer ships, and she had a genuine insight into how to get there. That part was clear. The problem was what she’d attached to it.

The common reflex, once you’ve got a real insight, is to immediately start detailing it into a full product portfolio, and then call the whole bundle ‘vision.’ She admitted she does exactly that.

Her default mode is to design the whole product fully. As a result, she had quite an elaborate first ‘prototype’, and in her head, that prototype and her insight were the same thing.

Her cofounder had already told her not to develop it further, as twelve months of building with zero market validation is too risky. She knew this. Knowing it hadn’t unstuck her.

Most founders overcomplicate their roadmap

Many founders do this. They’ve got a five-year endpoint in their heads, but they mistake it for their roadmap.

It becomes a product portfolio with either six complicated products, or a piece of software with twenty-five ‘core’ features. Then, they plot these over a five-year roadmap. Most startups don’t work on multiple products in the initial years.

Even the companies famous for sprawling started narrow. Tesla’s only car was the Roadster for years five to nine. Amazon spent its first three years on just books, IPO’d on just that, and added CDs and DVDs the following year, electronics not long after. Expansion strategies are best kept simple. Yet most founders don’t do that. They think they’re Yamaha or Hitachi.

You cannot fully design a startup upfront

That temporally plotted vision is a horrible roadmap and doesn’t help with your GTM. A working waved GTM strategy isn’t something you can design upfront, like an architect designs a building. There’s a key difference.

An architect can design the whole thing upfront, because they can survey the ground first, and it turns out to be stable. Once you know the foundation holds, you design the full building and construct it exactly as drawn.

The market is a swamp

People apply that same thinking to a startup. But the ground you’re building on is the market, and the market is a swamp.

Every time you ship something small, people react, and the ground shifts under you. You don’t get to survey it once and build forever.

You drain the swamp by interacting with it, one step at a time, until you find a spot solid enough to lay a single brick. That comes long before you design the entire building. You only get to design your startup like a building once you’ve actually got a solid foundation under you. Which often is year three, not year one.

Back to the brick

Once she saw the problem, her grip on ‘vision’ loosened. At the start of the session she told me she wanted a roadmap, the whole plan mapped out. By the end, she was fine knowing just what to do for one week. She just needed the one brick.

She’d already built a platform that makes ship safety both safer and cheaper, which is rare, since most safety features just cost money and nobody wants to pay for them. She’d run three pilots with it. Corporate loved it. The people actually on the ships? Not so much, and that gap was the clue.

So that’s where we went looking. The pilots had one thing in common that worked with the crews, not just the buyers. We built a next step around that piece: a sales deck, pitched to new potential customers who’d hit the same friction. That’s the work for next month. The grand design can wait until we have better data.

Successful GTMs are built brick by brick

She walked in circling her vision like a broken record. She walked out with a next step small enough to actually take this week.

If you’re pre-revenue and you’ve fused your five-year vision with a fully designed roadmap, stop. Separate the two. Then go find the one brick you can lay this week instead.

More reading

• How to design your roadmap instead

• How to expand from your beachhead

• The steam engine’s slow take-off

• Need my personal help? Get in contact!

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“I have substantial recurring revenue, but our clients need something so different, it feels like I’m running an agency. I look at Lovable’s growth, and our numbers are nothing compared to that”

Startups include: automatic screening of job applicants, AI powered single source of truth for construction, LLM powered processing of complex regulation documents, AI agent that lives in your company’s Slack environment. These are all the somewhat low-hanging fruits of the AI craze.

It resembles the early 2000s when all companies realised they needed a website1. Therefore, many like me who had the least programming skills started selling websites. AI is a bit like that these days, as most of those web-developing agencies weren’t scalable. Company, yes, startup, no.

The recipe

A stark contrast between aspiration and result. How do they get there? The recipe is as follows. A smart founder with tech capabilities does outreach to industry enterprises or SMEs, pitches that they can solve a problem with AI, the company has a relevant problem, they build something, get MRR, and move to the next.

After five such projects, they become either a prototype factory or an agency building and maintaining custom AI software, with related maintenance contract revenue. The overlap between those projects? Too limited to scale.

The core of the technology they built overlaps between 30% and 60%, but a lot of the work is manual configuration of agents, data preparation, process setup, and pipeline implementation. To make matters riskier, they rely heavily on external models.

In the best situation, they do something unique with that model, something others have a hard time replicating because the founders are truly smart and have an insight others don’t and therefore have a competitive advantage. If that’s the case, you have a shot at winning. In my understanding, competitive advantage is crucial if you don’t have deep marketing pockets.

In other cases, the founders are glorified LLM-wrapper implementers. Nothing wrong with that; I believe you can grow to €10M ARR, but it doesn’t scale like turnkey SaaS or at least some simple onboarding SaaS. In that case, very often, your competition is not another startup, but customers using Claude themselves, or worse: the in-house dev team that can build what you build in 2 to 3 months. Guess what they often settle for.

In that latter situation, I meet the founder, almost panicking, asking if they have missed something. They stress that they don’t want to build an agency, and what they are running now doesn’t resemble the fast-growing AI startup they had in mind. What to do next?

The antidote

There’s a third option, and that requires some proper analysis and scrutinising your GTM strategy, and being willing to kill darlings and clients.

Even though that just 30% might overlap, that could be your core. A unique JTBD that you can serve so well that any in-house dev team would rather just implement your approach than build it themselves.

Likely, this is a smaller project than the megalomaniac projects the corporate clients are currently asking you for. They just see you as cheap developers that they can order around to kill 10 AI projects on their backlog with one stone. Likely, you gonna have to start to say no to particular customers, even though you can definitely build it; it’s too far separated from your core.

The problem was not that you got there because your technology sucks. The problem is that AI is such a general technology that it has a very broad application potential. That is unique; not all technologies are like that. But that also invites this scattered projects approach, which hinders building a diversifying core.

Here are some questions to move forward in identifying a core that might turn into a startup:

1. Who are your happiest customers? Cluster them into groups.

2. Who ripped your product out of your hand? Who had a sluggish sales process? Focus on the smooth sellers.

3. What JTBD did they hire you for?

4. What alternative option didn’t they hire?

5. What can other founders with some tech skills also build with off the shelf solutions in a month or two? Scrap those ideas.

6. Which of these remaining things is scalable? Select that one and start selling before building.

I suggest that, for each cluster, you complete a market fit canvas and find where you have the strongest problem-solution fit and the best competitive advantage. Link here:

If you need help with your analysis, first 30-minute call is always free. Book here.

Chapters

00:00 Introduction to Rob Snyder and His Book

04:00 Understanding the Core Premise of the Book

07:49 The Pull Framework Explained

11:49 The Importance of Unavoidable Projects

15:49 Demand vs. Supply in Startups

19:51 The Pull Hypothesis and Its Implications

23:57 Outreach Strategies and Customer Engagement

38:26 Rethinking Sales: The Pull Framework

40:58 The Importance of Articulating Needs

44:45 Understanding Customer Motivation

47:50 From First Customer to Multiple Customers

52:36 Identifying Hell Yes Customers

56:22 Building a Success Factory

01:10:07 The Myth of Market Readiness

01:12:04 Demand vs. Supply: The Startup Dilemma

01:13:31 The Complexity of Finding Demand

01:15:48 The Path to Startup Success

01:17:52 The Challenge of Creating Hell Yes Customers

01:20:24 Understanding Product-Market Fit

01:22:01 Critiquing Startup Methodologies

01:27:35 Sales vs. Startup Strategies

01:29:35 Exploring Consumer Behavior and Trends

01:37:52 Demand vs. Supply in Entrepreneurship

01:42:20 The Role of Designers in Identifying Demand

01:45:56 Critique of Business Schools

01:51:22 The Illusion of Knowledge in Entrepreneurship

01:55:33 Research Directions for Entrepreneurs

A designer I know got a job offer at a new cool AI startup and asked me: ‘Should I switch?’ ‘Well, I don’t know’, I replied. So I started asking some questions about his career priorities. Turned out it was to maximise learning and the potential to start something on the side. After finding his purpose, his conclusion came easily: asking for on-the-job learning in the new gig, with 32 hours per week for some potential of side hustling. The hiring party was okay with that!

An entrepreneur in Dubai wondered how to take her sole-trading to the next level by bringing somebody on board. She found one good candidate for this. ‘Which structure should this new partnership be in?’, she asked, and listed options: start a company together, a franchise, do revenue share, or adopt a cofounder? Again, I simply asked: what do you want with this new move? She doesn’t want to work alone anymore and wants to grow the business. Clear. So instead of obsessing over the legal structure, I suggested just starting with hiring that candidate freelance, ‘act like it’s a payroll’, and figuring out together what the right workform.

A B2B entrepreneur asked me how I run my Substack and my LinkedIn. If he should double down on either platform, ‘which platform is best?’ Again, after listing some cons and pros of each, it still didn’t seem I resolved his problem. ‘Why do you want to do this in the first place. Do you have too few leads?’ ‘No’, he replied, ‘not at all. But I’m worried it dries up in a few months.’ He then told me it never did. So this was an anxiety-fueled exploration. The true problem that we then surfaced is that he didn’t feel that all his clients were ICPs. He only relied on inbound. Doing Substack or LinkedIn posts will only increase inbound. If you want to be picky, do outbound, we concluded.

Three examples of focusing on the desired outcome over debating options moved people forward. And now, I’m staring at my own problem, where I was asking myself: what should be my next step? Instead, right now, I switched: what aspects of my life, i.e. dimensions, do I want to optimise for? Is it freedom, is it academic challenge, is it closeness to mountains? Something I have some time to ponder over.

The funny thing is, often such reframes are much easier achieved with an external party there. They don’t have the burden of the details of your life. Such a reflexive partner can be a lifesaver for making impactful decisions. And I felt so stupid for that realisation, because my entire freaking job is reframing other people’s problems. I just realised why sometimes an outside perspective is helpful. As a mentor.

The PMF Scale

• To figure out where you are in your PMF journey, you need to watch the signals

• They come twofold

  • The nature of the signal

  • The currency the customer is paying you with

• Using the scale below, you can identify where you are!

Pmf Scale A4 Clean

1.47MB ∙ PDF file

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Key pointers

• Attention = Vanity signal

• Time & Effort = Weak signal

• Money = The only signal that truly counts

Free Worksheets for workshops

Tally where you stand using this worksheet.

Pmf Scale A4 Worksheet

1.5MB ∙ PDF file

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Let me know: does it work?

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You likely weren’t.

When you used Spotify or Google earlier than your friends, you were among the first millions of users. Notion? Ditto.

Did you ever in your professional career purchase software after a talk with the CEO of a new startup? Or did you go with the industry standard? Or, like most of us, was software bought for us by your superior?

I believe only 1% of our yearly purchases, at best, are from startups.

You are, therefore, asking your potential customer to hop on a journey you have never—or rarely—experienced yourself.

We often compare ourselves to polished journeys

Because 99% of the time, we experience way more polished purchase journeys.

I remember a friend who excitedly told me about such a journey when he went to the gorgeous Apple Store at Leidseplein in Amsterdam. He was received by a host, who asked for his name and entered it into an iPad mini.

The glass spiral staircase of the neo-Gothic building took him to the next floor, where his customised MacBook Pro was sitting ready for pick-up. Within a few minutes, he exited the building happily, even after spending a few grand.

Are you winging your purchase journey?

Apple very deliberately designed this purchase journey. Did you design yours intentionally, or does it just happen? Next to bad segmentation and targeting, this is where founders lose customers.

This is not just about preventing the loss of potential customers, but also about having a slow journey in the first place. Anyone stuck buying you will take focus away from landing other clients.

Map the emotional and mental state to understand the design challenge

Originally trained as a designer, I learned that one of the key aspects is to understand the emotional and mental state of the user. Apply this to your purchase journey.

• Emotion: What does the customer feel when they first encounter you?

• Mental: What does the customer think when they first encounter you?

• Design challenge: What is the minimum thing required to move them forward?

Take Apple's host at the door.

• Emotion: A new visitor feels a little unsure, wondering where they should head

• Mental: They know they are there for a new laptop

• Design challenge: A smooth point of contact to point them in the right direction

The solution: a host asking for their name and pointing them to the right service desk. That is thinking in baby steps. Coolblue, a Dutch brand, has taken a similar host-based approach in many of their stores, sometimes with a digital variant, to guide people to the right place in the store.

Example #1: How I brought structure to selling

I had an issue with the mentoring service I sell.

I truly believe there’s no step-by-step process for startups; in the weekly mentoring calls, I don’t follow a recipe: the founder brings the problems and insights of last week, and we fix them together. I do bring in points of attention I find important, but that is situational.

How do I sell that? Because, at some point, I was basically saying: “Well, I show up and just wing it”. I experienced pushback because, yikes, who wants that? Point of friction. A clear sign of a purchase journey just happening instead of being intentionally designed.

Let’s apply the three points:

• Emotion: Founders feel uncertain about their startup journey to PMF. They come to me to feel more certain.

• Mental: They know I do things in a particular way; they are looking for a structure or a framework that captures that.

• Design Challenge: How can I explain the structure I bring?

• Solution:

  • I visualised (see below) my onboarding flow. It explains the structure and provides scaffolding on how to move forward. People generally seem to like this.

  • I grab my main framework in a sales call to show what topics I will help to address.

My gains:

• Sales calls feel smoother

• The customer has a much better picture of what they are buying

• Therefore, I get a much clearer Yes or No after a sales call

• I can’t prove it statistically, but I feel my conversion rate has gone up.

Example #2: Landing the leads

When a client wanted to work with me, I had a set of tasks to do to get them into my system. That improvised approach with required readings, exercises, and administrative tasks was scattered over email, LinkedIn DM, WhatsApp.

A clear sign of the purchase journey just happening. That’s normal, but clients would tell me they get lost in that chaos. Point of friction. They would show up at the kick-off session without having read the required readings, or the subscription was not set up.

Let’s apply the three customer perspectives again:

• Emotion: Customer feels overwhelmed because of different touchpoints

• Mental: They know they need to do stuff, and want a clear overview of what to do

• Design Challenge: Convey everything into a one-stop shop for onboarding

My first prototype was a simple onboarding email. After doing this manually for about a dozen clients, I created a simple Notion page called ‘The Onboarding Checklist’. It has administrative steps, required readings, and exercises.

Now I just need to send this one-pager to them, and presto. The results?

• 90% of clients now show up prepared

  • Required readings done

  • Exercises submitted ahead of time

  • Subscription started on Stripe

• Bonus: Clients that don’t manage to finish the onboarding checklist proactively inform me, because they have a clear overview

• Result for me: I have better kick-offs because we hit the ground running.

Aim for smooth enough

Because your buyer doesn’t often buy from startups, they will compare you to polished companies they buy from. Therefore, your journey needs to be smooth enough. Luckily, most of the fixes are simple and clear communication touchpoints.

Those three questions turn vague friction into something you can fix. And notice what actually fixed it in my case: not a spiral staircase, but a Notion checklist and a one-page flow. Low-tech, high-reward!

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The instructor opens with a keynote stipulating that most startups fail, and therefore jumps to the importance of validation. Your ideas are a set of hypotheses, he says. You need to isolate them, design an experiment, run it, and presto: you can validate your idea. The founders follow the instructions and begin jotting down their hypotheses on the sticky notes.

To most of you, the above might seem normal, even sensible. But this is exactly how people are cursed with the validation mindset. And that curse is more likely to kill momentum than enhance it, because it assumes hypothesis generating is the easy part. It isn’t, and here’s why.

Validation limits thinking

The hypothesis validation mindset, popularised in Eric Ries’ Lean Startup, echoes scientific principles. Formulate a hypothesis, design an experiment, run the experiment, and validate or reject the hypothesis. The problem, however, lies in the reasoning of hypothesis testing itself.

Validation suggests a yes-or-no question. That means you should be able to formulate your hypothesis in such a way that you can test it. However, based on my experience, asking a founder to formulate a hypothesis in the early stage nearly always leads to the formulation of an uninteresting or poor hypothesis: “Do people hate waiting for the bus?”. What people do is take their startup idea and transpose it to a yes/no question, for which we often have the answer ready.

This is because the validation mindset forces entrepreneurs to elicit inductive or deductive behaviour. Inductive means to go from available data to a rule: I’ve seen the street wet after it rained so many times, so I understand that rain causes wet streets.

Deductive reasoning means going from an available theory to new knowledge. I walk out my front door, see a wet street, and based on my generated theory, I know it has rained. I didn’t generate a new dataset; I could explain a singular observation through existing theories.

Startups need new thinking

But what if my neighbour just washed his car? Am I safe to refute that theory because I have something in place? Or have the neighbour’s kids held a water balloon fight? All of a sudden, there are multiple hypotheses for a single observation.

Notice what I just did? I reasoned backwards from one observation to generate several plausible explanations. That’s the third kind of reasoning: abduction (coined by Peirce). The big difference is that induction and deduction stay within the evidence available, whereas abduction generates new ideas.

This is exactly how startups work. A founder observes something inefficient and wonders why it can’t be improved by adding a new product (a hypothesis). But the true nature of that surprising observation isn’t fully available to the founder. He or she has seen it a few times and can say there’s something funky with waiting for the bus. Is it the wait time? Or is it the uncertainty of it potentially not showing up? Or is it the lack of shelter?

The muscle you haven’t built yet

The problem, however, is that generating abductive explanations for a weird situation is incredibly tricky. For the wet street, we can imagine dozens of explanations because we’ve roamed this planet for at least two or three decades. That builds a muscle to explain stuff, as we understand how rain and water work, for instance.

However, for your new startup idea, you very likely don’t have that track record yet. Therefore, the number of plausible hypotheses you can generate is either limited or of poor quality.

There are exceptions, though, and that is when I meet founders who start a business after working in the industry for ten years. They know what is up and can formulate those assumptions. However, they don’t need to. For them, it was never a hypothesis to begin with.

Why do we do this?

In most Western societies, we love to educate people in analytical thinking skills. Universities are the prime example. In training scientists, the validation aspect takes centre stage. You learn to write papers, and most attention goes to the justification of your findings through rigorous methodologies and data. So hordes of people have trained the justification muscle, rather than on how to generate the best ideas.

And these people then arrive at a startup, or give workshops on how to do startups. They use their best-trained muscle, read the Lean Startup, and love how it resonates. The validation mindset is just an echo of this scientific justification mindset. That explains the appeal: people feel at home in the seemingly safe environment of justification. But the feeling of ease is misleading, because the early stage of a startup is all but certain. By applying that mindset, people aim to tame uncertainty with rigorous induction and deduction; however, it is best treated through abduction. Nobody trained them in that.

That’s why I see so many (university) incubator and accelerator managers loving the validation approach. These people are not selling snake oil; they were just teaching with the muscle they had been trained in.

Startups are not scientific theories

That validation muscle is not equipped for startups. Scientific theories aim to describe the regularities of the world, whereas founders aim to change something about that very world. That transformative process doesn’t happen by itself if you have twelve hypotheses validated. You need to engage with the world.

I’m not advocating for ignoring evidence. However, signals in a startup journey are transient. The fact that you found four potential pilot users in three weeks might sound amazing. However, if that’s the only positive signal you generated after twenty weeks, I raise a red flag.

Evidence evaporates. Markets might change, and your momentum with a customer might drop. Your startup is in flux; it changes continuously. My to-be-published research finds that all your building blocks, including customer segment, value proposition, marketing channel, messaging, and positioning, continuously change form. Assuming you can validate something like a theory of gravity and then move on is a mismatch with venture reality.

What to do instead

Therefore, if I work with founders, I never obsess over the hypothesis. Sure, write down potential market segments; do that for fifteen minutes. But then spend 90% of your time selling to twenty of each segment.

By actively trying to transform the world, you will find which tiny section of the world allows you to. This trains your abductive muscle in two ways. It generates unexpected and surprising findings. And, instead of betting on one hypothesis, you learn about several at the same time. Therefore, the simple act of picking up the phone and selling beats the rigorous, time-consuming setup required for crafting experiments.

After a bunch of such experiences, your intuition will tell you where to go. The validation mindset’s deepest error is mistaking a generative activity for a confirmatory one.

Don’t do this

1. Joining startup/pitch competitions to pat your ego

2. Spending twenty hours on your landing page without having talked to a single potential customer, but now the shading of your font really is top notch

3. Trying PR campaigns to let the media write about you, which means spamming 100 journalists who don’t write about tech

4. Setting up a referral system for a startup with zero customers that in turn will—surprise—generate zero customers

5. Hosting a focus group with six friends instead of prospects: ‘It was really fun and insightful feedback’

6. Showing your website to your mom for feedback—shoutout to all moms though ❤️

7. Launching your ‘brand’ on social media, i.e. making profiles with your AI-generated logo and one long post talking about yourself and then getting mad there is no traction

8. Interviewing people for an hour who are the opposite of your ICP

9. Getting tons of free users for your product because you want to test it first before charging (just charge a little bit).

10. Having conversations with marketing agencies to consider a rebrand to improve the vision of the startup to get more traction

11. Having people sit on your waitlist for months because you are editing pilot plan version 38 in Google Docs

12. Convince people who say no to your pilot to join anyway, succeed, have unmotivated participants in your pilot and complain they don’t use it

13. Work on your pitch deck for a week, even though you are not fundraising, but outsource most of the strategic thinking to Claude

14. Doing market research using Claude, having a TL;DR of twenty competitors that sicophantically shows you are a winner

15. Having paid pilots running but not contacting your customers on how it’s going, but instead hiding behind your command line interface to optimise that one feature nobody is using

16. Having scattered unintentional team meetings throughout the week that kill your developers’ momentum

17. Making a moodboard of your brand identity even though aesthetics aren’t important in your industry

18. Setting up GTM automations in Zapier, Apollo, Clay, and HubSpot even though your CRM is completely empty

19. Asking Claude or OpenClaw to generate leads based on a poorly defined ICP, having 5000 leads is a needle-in-a-haystack situation.

20. Postponing sending that invoice for the services you provided because you don’t want to be rude

21. Reverting back to a strategic decision of last week because you are not ready to kill your darling, so you throw mud at a lake for three more weeks

22. Hiring a marketeer full time even though you could just spend 2k on a freelancer and have shit ready in a week or two.

23. Refining that one feature of your product that customers would hardly notice, which postpones shipping for another day

24. Going to trade fairs to scout competition for the fourth time this year

25. Hiring a salesperson to outsource a sales script that doesn’t work.

Still stuck? Then you might need a PMF & GTM check-up:

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Do this instead:

1. Aim to have 3 sales calls per day, a minimum of 5 per week

2. Invite your developers to a sales call every now and then so they understand who they are developing for

3. Go niche. Reflect regularly on which segments you can easily convert (feel pull, see Rob Snyder’s work) and drop sluggish ones.

4. If you are pre-launch, start selling today. You don’t need a fancy demo for that first call. Make a sales deck, see who wants to see the demo.

5. For B2G, pray.

6. You don’t want feedback on your product. You want people to use it actively. Don’t ask: can you check this out? Monitor if they use it. If not, ask why.

7. If you have fewer than 1000 customers, don’t use questionnaires to get feedback. You have a phone. Use it. Speak to your customers. They like it if they are super fans. If not, your PMF is off.

8. If your startup allows, try to visit your customers to observe them for a day using your product. You will see things you will NEVER expect.

9. More features don’t always convert more customers. Be sure you truly understand the main JTBD and the blocking obstacle (see my Market Fit Canvas, link in comments).

10. Obsess over how you're positioning. What are you? Aim for clarity: which product category? How do customers see you?

11. Make sure you don’t only have problem-solution fit; you should also have a competitive advantage. Startups’ raison d’être is having a product advantage; literature on successful product launches confirms this.

12. If you are the CEO, at least 50% of your time should be on sales. Don’t do 90% development and 10% sales. Sales is a full-time job.

13. Don’t outsource sales when you haven’t mastered the process yet. You can’t hand over what you don’t understand. And don’t even think about getting a customer development intern to do this for you. At most, as lead gen, nothing more.

14. Develop a cadence with your team to check in on goals. As a CEO, make sure you plot your strategy visually somewhere in the office. The fact that’s in your head doesn’t mean everyone understands it. Often, you need to repeat it 3 times before it sticks.

15. Make sure standups don’t become diary therapy sessions. Only discuss dependencies.

16. Marketing channels are like product features: you should iterate on them. Starting out, you may do spaghetti style; things that don’t work usually stand out as complete failures. Optimise what kinda works. Don’t expect home runs.

17. SEO & content marketing are often long-term strategies. If you are looking for sales now, it is a risky strategy

18. Referral strategies hardly work if your volume of customers is <1k. For b2b, a 100 bonus doesn’t mean much for a manager who makes €120k a year.

19. Always make sure you know your next steps in a sales call. Never let it rest at that: let me know when you are ready. Be in control: I will call you again in a week. If they push back, the lead is not hot.

20. Don’t build elaborate onboarding flows before onboarding customers yourself. Unscalable onboarding teaches you what is important and what you can scale, not the other way around. Sometimes, there’s a manual component that boosts retention by 50%. Keep it.

21. You don’t need to convince people to use your product. Your ad/landing page/DM/sales call/sales deck should act as a filter. It should be crystal clear what you do, so prospects can say: this is for me, this is not for me.

22. Start selling after 5 discovery calls. If you don’t know what you should sell after five calls, I’m not sure what you are doing. You had an idea, right? Just pitch it.

23. Revenue growth will initially always be slower than you think it will be. Any founder I meet feels that they should go faster. Onboarding clients takes longer than expected. That’s before systems are in place. Push through for a little bit, because the key processes will emerge as winners.

24. In some industries, partnerships with distributors or sales are key. Check out what channels other startups are using; if everyone is moving to D2C, perhaps you should do it too (if you don’t have a good reason for otherwise).

25. Finding your north star metric is important, but it’s not revenue. It’s about finding what type of behaviour predicts a 10/10 five-star customer. For online supermarkets, it has 5-8 orders completed to become loyal. On an online platform, it can take 3 responses to your inquiry. Figure out what is yours.

26. Always circle back to your core thesis. Why are you building the thing you are building? Does this new direction contribute to this, or distract from it? Identify things that are core, core adjacent, or new, and scrap the new for now to increase focus.

27. Celebrate achievements with your team. First client onboarding? Go out for dinner. It is something to celebrate. Funnel now converts at 20%? Celebrate. Often, startups move so fast that founders forget to celebrate.

28. Drop dead weight. If you have a co-founder or early employee who is not contributing to the mission and, after many conversations, doesn’t alter behaviour, this often doesn’t change. The fact that they were there before doesn’t mean they have a right to sit out the ride if they don’t add value.

29. If your new hires decrease your headspace, and this doesn’t evaporate after being onboarded, the new hire doesn’t fit the bill. You hire to outsource, not to babysit.

30. Fund-raising is a full-time job. Be careful what you wish for. When not raising, coffee connecting with VCs/Angels can help to be known about your progress: share goals and simple updates so they know you can deliver.

31. You gotta spend money to make money. Sometimes, founders are frugal. But if testing €500-5000 on a new channel is too much for you, you might be too risk-averse for entrepreneurship.

32. Advice is free; everyone gives it to you. Don’t outsource your decision-making. Don’t do something because somebody said so. Do something because you agree with the internal logic. This improves your gut feel as a founder.

33. Not everything needs to be supported with significant data or A/B tests. Sometimes a sample of 10 customers is enough, or even going with your gut. Postponing a decision is often more expensive than making the wrong decision for two weeks.

34. Most early-stage startup decisions are two-way doors: easy to reverse. Don’t sweat them too much. You can swap a failed segment out pretty easily.

35. Embrace uncertainty. The fact that you don’t know what will work proves that you are in a startup. If you have a recipe that will work, you are making a pie. If you can’t deal with the uncertainty, then this is not your journey. However, over time, it will get easier, as more things crystallise. The first phase is the hardest part.

36. Read books, podcasts and blog posts, but nobody has the secret recipe. Always include the context. Somebody launching B2C products, raving about SEO, doesn’t apply to selling navigational systems to one of the five global monster ship makers.

37. Respect the grind. Dumb stuff works. There are no clever workarounds to build a startup. Sometimes, you need to make 100 phone calls in two weeks. Sometimes, you need to hand out flyers at a conference, feeling like a bitch. That’s just part of the grind.

38. Have some fun along the way. Take the outcome seriously, but playfulness makes roller coasters way more fun!

🔥 Why I didn’t include pricing, PLG, unit economics, legal/IP, and founder mental health

Some extra spicy takes for the subscribers.

Read more

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

6. Spot the gaps through narrative prototyping ⬅️ Today

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 this chapter

So far, we’ve covered structure, framing, and how to extract jobs to be done. You’ve got most of the ingredients. But here’s the trap almost every founder falls into:

They finish an interview, look at their notes, and quietly assume the gaps aren’t there. Then they craft their GTM or evaluate their PMF and get stuck. Founders tell me: “Shit, I didn’t ask that.”

The gaps were there: they always are. The most important parts of your Market Fit Canvas are usually the ones your interviewee never mentioned: the “hidden ingredients” that don’t surface unless you go looking for them deliberately.

This chapter shows you a technique I call narrative prototyping: a way to construct short stories from your interview data, stress-test them against the canvas, and make the missing pieces jump out. I’ll walk through two narratives from the mock interview of the previous chapters, show you exactly where each one breaks, and give you the reasoning trick to fill a gap even when you forgot to ask.

6.1 Finding the logical missing pieces

Read more

The basics on GTM + PMFit

• The difference between GTM + PMFit

• Recognising early PMFit signals

• 9 building blocks of PMFit

• Why finding PMFit is hard

Beacheads & GTM Strategy

• Beachhead 101

• The 1-2-3 wave principle + subniches

• Thinking about wave 2 and 3

• Finding your beachhead customer (ICP)

• What segmentation actually is

Understanding competition

• Understanding (indirect) competition

• When people switch to new products (or don’t)

Finding problems and Jobs to be done

• JTBD 101

• Problem urgency and buying motivation

• Problems vs JTBD

• Understanding job hierarchies

Paywalled content

• Playbook to product-market fit: how to start out

• Ultimate guides to

  • Customer Interviews (10-chapter guide)

  • Brochure tests: How to test value props before making a landing page

  • Designing Revenue Models

1. You gotta propose yourself

Regardless of your reach, you need to put yourself out there. I have over 10,000 followers on LinkedIn and 5,500 on Substack. But the moment you don’t say, “Hey, this is how I can help you,” they will not respond. Ninety-nine per cent of people need an explicit offer to react to you, and you need to create that trigger.

In my early days of my LinkedIn and Substack journey, I didn’t explicitly include a call to action, saying, “Hey, if you are dealing with a problem around product-market fit, here’s where you can find me.” I forgot to add it in the first two years, which is dumb, but you need to do that.

Make it easy to buy you (or to say no)

2. Talk about yourself, not them

A mistake I made is that most outreach guides say you need to talk about them, saying, “Oh, you have this problem.” You don’t need to do that. If people have a problem, they know it. You don’t need to educate them on the problem that they have. If they are highly urgent buyers, you should talk about yourself, and this is counterintuitive.

Think about those infomercials where people mess up cooking an egg and say, “Don’t you hate it when an egg falls out of your skillet?” You feel disdain for this advertisement because it doesn’t treat you seriously as a buyer. “There must be a better way”. You know there must be a better way because you have been fed up with this problem for at least a couple of months.

Tell me what the better way is because I am already in pain. This is something most people do not realise. If you explain very well how you might be able to help them, people will filter themselves for you. They will filter: “This is for me,” or “No, this is not for me.” That is what you want to achieve. You want to be so crystal clear that people can tell, “This is for me.”

This is my current message that converts 28%.

Hi [name]!

I’m a mentor who has helped 350 startups in solving their market fit puzzle.

I recognise problems and potential solutions in startups quickly, and tell people what I think, no bullshit.

Founders check in with me weekly or biweekly. Here we do a sanity check on their priorities (are they missing any blind spots?) and critically question how they process market feedback.

It has helped founders to find their GTM focus, and feel more certain about the strategy taken.

Startups are fun puzzles which tickle my brain and solving them makes founders lives easier.

Let’s chat if you see a fit.

Cool right? If you actually want to chat, get in contact with me here.

3. Quality over volume is the wrong mindset

I have falsely preached that quality is more important than quantity. I thought that because this worked for me on a small scale, sometimes sending out a very carefully crafted message to some ICP converts pretty well, and it might do just that. But it doesn’t convert the best.

With Prateek’s help, I crafted that above message that I now copy, paste, and send to people. I just copy-paste the message. I never thought it would work, but right now I’m getting it in my first attempt, I’m getting a 13% meeting rate and 28% reply rate.

4. Your ICP might be right, but also limited

Based on my experience, I sell to early-stage founders. I know most people over three years in their startup are likely too close to or over product-market fit and they cannot really help them.

I used Prateek’s low-hanging fruit method, and simply, in a spreadsheet-style started to qualify who I think is my ICP. In an experiment, I reached out to a bunch of people whom I wasn’t sure were my ICP. Out of the twenty messages I sent, two of them were completely something I didn’t identify as ICP, and they said, “Yes, I want to book a call with you.”

And then I was baffled. I thought, “Huh? I would never think this ex-student of mine, because for some reason I don’t consider ex-students commercial, or I would never consider that this type of startup would consider my service.” And they say, “Yes, Jeroen, I want to talk about product-market fit”.

So, sure, you have an ICP, but also be open to the fact that you don’t fully understand who you should target.

If you want to read more about targeting and segmentation, here are some helpful reads:

• Beachhead 101

• The 1-2-3 wave principle + subniches

• Thinking about wave 2 and 3

• Finding your beachhead customer (ICP)

• What segmentation actually is

5. Automation is not evil. Bad targeting is.

Every outreach expert will tell you that automation sucks, and I agree. Because what most people do with automation is set their ICP or targeting to CEOs of mid-size SMEs in the Netherlands or CTOs in Europe. That is a recipe for disaster if you automate outreach messages to that target group.

Not because automation is inherently bad, just because you’re fishing with a way too wide net. AI filters can help a little bit, but they are not perfect. I am definitely not advising to spam everyone. I once mentored a founder who said I sent 5,000 DMs and only three people responded, and they all said no. That is a sign of bad targeting, not of bad automation.

• A good tool for LinkedIn automations is ReactIn, happy user myself

• LinkedIn's inbox is a horrible UI. I use Kondo to manage my LinkedIn DMs.

  

6. You have more leads in your rolodex than you think

I wrote about this before: you likely have way more leads in your rolodex than you think. In this guide, Prateek has shared how he makes sure that his clients maximise the connections they already have.

There is quite a lot of academic research on this, too. There is an idea between weak ties and strong ties. Entrepreneurs are seen, or effective entrepreneurs are seen, as people who are very capable of maximising their weak ties, and you have a lot of weak ties, my friends.

Just as I said, I had two people from a past relationship: an ex-student of mine who wanted to talk, and an ex-colleague who wanted to talk. I didn’t even know they were entrepreneurs, because my automation contacted them with a message I know now.

7. Accept that you are going to annoy some people

I’m not saying spamming is good, but with a broad outreach strategy, you are inevitably going to target people who cannot buy from you. And that is okay. Sometimes I get a reply from people that say, “Hey, thanks, I love your content, but currently I don’t need you.” So on paper, you have annoyed them with a message.

This is an inevitable aspect of doing sales. You’re going to get nos, you’re going to get objections, you’re going to get rejections, you’re going to annoy people. The main trick is not to annoy them the moment you realise they indicate you annoy them.

8. Fight ghosting with this follow-up message

Ghosting is annoying. You send a message out to people, but sometimes they don’t reply. This is fine. But you can send a reminder. How many times should you remind them? You are not going to send twenty messages, right?

I’m not sure who gave me this tip, but it’s a golden tip on email and DM marketing. Send up to three reminders maximum. The last reminder should say something very simple, as follows:

Hey, I see you didn’t reply to my emails. I can imagine you’re busy. If you’re still interested but cannot create a full response right now, I understand. Just let me know in a very brief response, and I’ll reach out to you in a month when dust has settled.

Founders report back that they get great conversions on this. It shoots up. Some people even respond with a 20% response rate to this message type, the third reminder.

9. Need help writing outreach copy? Ask Prateek for help.

Prateek has a highly unorthodox interview process where he asks funny questions like, “Do you think your future self, 10 years from now, will be proud of the work you are doing today?” or “Has anyone ever said thank you to you? Have you made someone’s life better? What does this mean to you? Why does this matter to you?”

With the help of your answers to these questions, he can craft these unusual outreach copies that aren’t even strictly about the problem and the solution or the product and service. They also mention the human element of the business, talking about the identity, mission, and purpose of the sender.

I found it nuts to talk about my psychology or motivation in a message to a complete stranger. But the strange thing is that it actually works.

Prateek is happy to help run this interview and writing process for free to anyone who asks. It is already a part of his paid LinkedIn outreach service, but he enjoys doing this little bit just to help people out.

Send a DM to Prateek

To get in contact with Prateek, send him a message on LinkedIn. It’s worth it. With Prateek’s help, I’ve gone from zero monthly outbound to closing 4 deals a month and a 13% meeting rate on LinkedIn.

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.