Momentum. The ultimate metric for pre-seed startups.

This is not a blog entry. Instead, it is a white paper I have produced in my line of work as General Partner at Accelerace Invest. But I post it here to log advances in my thinking.

Introduction 

Startups are defined by growth.  

Growth is critical because startups are founded, build, and invested in on the assumption of rapid growth. Few founders, founding employees, or investors would bet on a startup with poor prospects for growth. Nor would the same people bet on a startup with prospects for slow growth. 

To pre-seed investors, the potential for rapid growth is challenging to assess. Later stage investors enjoy the benefit of historical performance on actual growth. If a startup has grown rapidly over the past three years, it is reasonable to assume that the startup will continue its rapid growth. 

But if the startup is less than 12 months old, meaningful historical data is nonexistent. Growth has not yet set in. What can pre-seed investors do? 

Despite the lack of historical data, a startup should still be growing. However, instead of looking at the historical growth, pre-seed investors must look at the Momentum. Instead of asking: how fast has the startup grown? Pre-seed investors must ask: how fast is the startup growing? Or phrased differently: how strong is the Momentum of the startup?  

The answer would allow pre-seed investors to use Momentum as an indicator of future growth. Just like later-stage investor use past performance.  

Defining Momentum 

But to answer the question: how strong is the Momentum of the startup? we must first define Momentum.  

To pre-seed investors, Momentum is complex because in most cases financial metrics such as MRR, GMV, sales are absent. Instead, pre-seed investors must evaluate the accumulation of the resources that are foundational to financial growth. To use a race car analogy. Pre-seed investors must evaluate the making of the race car. Later stage investors can evaluate the lap times of the finished race car.  

The pre-seed investors must look at the bits and pieces of the car and evaluate their combined quality to assess the prospects of the car becoming a great race car. The better the different pieces, the better the faster the race car.  

Steve Blank argues that a startup is a temporary organization searching for a scalable business model. The search process is focused on obtaining insights and attract resources. Insights and resources are the bits and tools of the race car.  

It can be assumed that startups with great insight and strong resources have a higher likelihood of success in the future. Again, the better the bits and pieces, the better the car will perform. 

Or put simply, startups that have accumulated the most insight and resources are in a better position to generate growth in the future. Consequently, the accumulation of resources could be a good indicator of future performance.  

But what are the resources that define a pre-seed startup? 

A startup accumulates resources on four key dimensions. Those are TeamTechnologyCustomer insight, and Customer commitments. We will do brief reasoning to these four dimensions below: 

The team is the driving force behind the startup. Naturally, high quality teams outperform low-quality teams. Consequently, a key dimension of Momentum is improvements to the team. The critical part of the team consists of founders and founding employees. Founders participated in the founding of the startup, while founding employees joined later. Both are critical to the startup and own shares in the entity. Often founding employees are more senior than the founders and are defined by “paying” big opportunity costs when joining the startup. (Danish examples are Jesper Lindhardt in Trustpilot, Mette Lykke in Toogoodtogo, and Thor Angelo in Mymonii). Because of the critical nature of these founders and founding employees, the evaluation of Momentum should concentrate on the expansion of this group. Consequently, a startup that manages to attract the best people should increase the chance of success. 

The Technology is the basis of the value proposition. Most startups build their product on technology, and any advancement in the technology should improve the value proposition. Consequently, a key dimension of Momentum is technology. A startup that rapidly advances its technology should increase the chance of success. 

The Customer insight is another basis of the value proposition. Customer insight is the information founders use to turn their technology into a product. Obtaining customer insight is a key activity for startups, and deeper understanding improves the value proposition. Consequently, a key dimension of Momentum is customer insight. A startup that deepens their level of insight should increase the chance of success. 

The Customer commitments are de-risking the venture. If customers commit to pilot projects, payments, and contracts, the startup obtains proof of business points that can be leveraged when raising funding and attracting team members. Consequently, a key dimension of Momentum is customer commitments. A startup that amasses customer commitments should increase the chance of success. 

Now that we understand what defines Momentum for pre-seed startups, we can almost answer the question: how strong is the Momentum of the startup? 

However, we still need to define strong. Strength describes the efficiency of the progress. A startup might accumulate resources on the TeamTechnologyCustomer insight, and Customer commitments dimensions, but the price of this accumulation matters. The price is the constraint and consists of time and money

Momentum only makes sense if it is related to the time and money that has been available to the startup.  

If a startup has spent three years and 5 million to develop an app that has 10 pilot customers, one would evaluate the startup negatively, because the Momentum is unsatisfactory in relation to the time and money spent.  

Contrast the above scenarios to a startup that has developed the same app, but only have 2 pilot customers. If this has been achieved in two weeks and 10K, the Momentum would be relatively stronger. 

The examples above illustrate the power of evaluating progress relative to the time and moneyOnly by relating the progress to the constraints, we get a picture of the Momentum.  

To stay in our race car analogy, Momentum in relation to the constraint gives us a performance indicator equivalent to km/h1 for cars. Km/h enables us to compare the efficiency of various cars. 

Progress per Time or Progress per Money are the two most important Momentum metrics and they enable us to compare the efficiency of various startups. A metric that could be highly indicative of future growth.  

Standardizing progress to understand Momentum

To measure Momentum, we must standardize the progress a startup has made. To this end, we propose to use standardized levels for each of the dimensions of progress (TeamTechnologyCustomer insight, and Customer commitment)

The proposed levels can be seen below: 

The team can be classified depending on the completeness and experience of the team and its team members. We propose the following six levels: 

Level 0 team Single, first-time founder, no industry insight. The startup is the typical “Startup Weekend” project. One person who has recently conceived a vague business idea in an industry the person does not know from the inside.  
Level 1 team Incomplete, first-time team, no industry insight. The startup has a team. Often the lead founder has convinced a friend to join the project, but they lack real startup experience, and many critical skills are not possessed within the founder team. Also, they do not know the industry from the inside.  
Level 2 team Complete, first-time team, no industry insight. The startup has a complete team meaning that all critical skills are held in the founder team, but they lack real startup experience and industry insight. 
Level 3 team Complete founder team, one person with some startup experience, and related industry insight. The startup has a complete team meaning that all critical skills are held in the founder team. One of the persons has founded or been a founding employee in a startup before. Also, one of the persons has worked in a related but not the same industry. 
Level 4 team Complete founder team, one person with some startup experience, and same industry insight. The startup has a complete team meaning that all critical skills are held in the founder team. One of the persons has founded or been a founding employee in a startup before. Also, one of the persons has worked in the same industry.  
Level 5 team Complete founder team, all persons with significant startup experience, and same industry insight. The startup has a complete team meaning that all critical skills are held in the founder team. All team members have been founders or founding employees in successful startups before. Also, one of the persons has worked in the same industry.  

The team will advance as the startup develops. Often a single founder will bring in co-founders. Also, founding employees with significant startup experience tends to join in the early stages. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups. 

The technology can be classified according to commonly understood industry taxonomy. We propose the following six defined levels of technology

Level 0 technology Idea The technology is articulated in writing and verbally. Perhaps the founders have made a slide or document describing the idea. The idea is still rather general and lacks details and specifics.  
Level 1 technology Concept The technology has been sketched out and it can be described in specifics. There are drawings, models, and roadmaps that detail the idea. Typically, the founders have a full slide deck at this point. Often, they have a video using animations and renderings. It is also the stage that is typical for crowdfunding campaigns.  
Level 2 technology Prototype The technology has been created to a level where it can be tested for proof of technology. The key components of the product exist and can be interacted with.  This is often the stage for crowdfunding campaigns. Apps are often in TestFlight mode.  
Level 3 technology MVP The technology has been packaged into a minimal product that can be used by users. It includes the key feature(s) and is complete enough for the beachhead to start gaining value. This is often the stage that select pilot users and pilot customers are testing the product. 
Level 4 technology Version 1 The technology has been shipped as the first full-fledged product that the startup expects the customers to pay full price for.  It is complete enough for the beachhead to put into production and use daily. 
Level 5 technology Version 2 The technology has had its first major upgrade. The technology has stood the test of time and use, and the second generation of the product rebuilt to meet the requests of the customers of the first version and to add new features to start venturing outside the beachhead.  

The technology will advance as the startup develops. The startup overcomes technical hurdles and weeds out bugs. In the process, the technology matures and becomes a full product. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups.  

The Customer insight can be classified using the proprietary Original Insight tool developed by Accelerace2. It is a self-assessment tool provided to founders to help them clarify how well they understand their customers. The tool quantifies the level of customer insight. We propose the following six defined levels of customer insight

Level 0 Insight 10 – 30 points. The founders have no insight and only a vague and over-simplistic idea about their customers. 
Level 1 Insight 30 – 50 points. The founders have little insight and only a vague and over-simplistic idea about their customers. 
Level 2 Insight 50 – 70 points. The founders have some insight, and but still only general ideas about their customers. 
Level 3 Insight 70 – 90 points. The founders some insight and can describe their customers in detail.  
Level 4 Insight 90 – 110 points. The founders have the same level of insight as their customers. Perhaps the founders use to hold that job position themselves. 
Level 5 Insight 110 – 130 points. The founders have deep insight and know the customers better than they know themselves. The founders can be considered expert to a level that a scientist would be an expert in their respective field. 

The level of insight will advance as the startup develops. Typically, pre-seed startups are operating in the range between level 1 to level 3, to begin with. As the startup performs more customer interviews and get feedback from pilots, they advance their level of customer insight. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups. 

The Customer commitments can be classified according to commonly understood industry taxonomy. We propose the following six defined levels of commitment levels

Level 0 commitment Interest The startup has talked to customers and can anecdotally talk about customers who have expressed interest. 
Level 1 commitment LoI The startup has a signed letter of intent from a relevant customer. For consumer startups, people have signed up on a waitlist. 
Level 2 commitment PoC The startup has a signed agreement of doing a proof of concept with customers. For consumer startups, people have signed up on a waitlist. 
Level 3 commitment Pilot The startup has a signed agreement of doing a pilot to prove an articulated business outcome for the customer. For consumer startups, people are using the beta version. 
Level 4 commitment Customers The startup has paying customer that is using the product in “production”.   
Level 5 commitment Returning customer The startup has several customers that have renewed or in other ways shown that they are planning to remain customers for a significant time. 

The level of customer commitments will advance as the startup develops. As the startup begins to prove the value of their product, the commitments increase. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups. 

Now that we have defined standardized progress, we can measure progress along these four dimensions. In other words, turning progress into two Momentum metrics. Once progress has been converted to a number, we can divide this number with the constraints. Either time or money. This gives us the ultimate metrics for pre-seed investors: Progress per Time and Progress per Money

Calculating Progress per Time (PpT) 

How much progress does a startup produce per unit of time?  

Below we will lay out the mathematical model for calculating PpT. 

Conceptual equation detail level 1 

Conceptual equation detail level 2 

The PpT model in use 

Example: Imagine a startup that during a period of 10 months has progressed one level on the team dimension. This gives the startup 1 point in our equation. On the technology dimension they have progressed three levels giving them 3 points. On the customer insight dimension, they have progressed two levels giving them 2 points. Finally, they have progressed customer commitments with four levels giving them 4 points. Mathematically the equation will be populated as follows: 

Example level 1 

Example level 2 

Example level 3 

Example level 4 

Calculating Progress per Money (PpM)  

How much progress does a startup produce per unit of money?  

Below we will lay out the mathematical model for calculating PpM. 

Conceptual equation detail level 1 

Conceptual equation detail level 2 

Conceptual equation detail level 3 

The PpM model in use 

Example: Imagine a startup that has spent 1 million DKK and progressed one level on the team dimension. This gives the startup 1 point in our equation. On the technology dimension, they have progressed three levels giving them 3 points. On the customer insight dimension, they have progressed two levels giving them 2 points. Finally, they have progressed customer commitments with four levels giving them 4 points. Mathematically the equation will be populated as follows: 

Example level 1 

Example level 2 

Example level 3 

Example level 4 

Limitations of the model 

Naturally, there are limitations to the PpT and PpM model. The most important are: 

  1. Team progress does not take the quality of the individuals into account beyond requiring the team expansion to be of “critical” people. This means that two startups can score equally many points even though one startup has attracted a Nobel prize laureate and the other a merely skilled industry professional. 
  1. The technology dimension does not take the difficulty of the science into account. This means that two startups can score equally many points even though one startup has made a scientific breakthrough and the other had mere launched their app. 
  1. The customer commitments do not take the difficulty of customers into account. This means that two startups can score equally many points even though one startup sold to SMBs and one has sold multi-year recurring enterprise contracts. 

Some of the limitations can be dealt with by comparing startups within the same category. Thus comparing, enterprise software startups to other enterprise software startups. And consumer apps to other consumer apps. Few investors have big enough portfolios to enable a sub-segmentation. But if possible, it would be advisable.  

On a more generalized notion, the model does not account for all the factors that affect the likelihood of success. From experience, we know that team dynamics, the growth of the market, timing, competition, and other factors play a significant role in the life of a startup. The model only quantifies Momentum. To most, Momentum is just one of many elements investors assess when making investment decisions.  

Implications 

First, Momentum allows us to compare companies that have had different amounts of time and money available to them. In other words, the efficiency of which they create progress. This matters greatly because as pre-seed investors we are investing small tickets and the efficiency of the companies is critical. Also, in the absence of historical financial metrics, Momentum is perhaps the most objective metric for progress at the pre-seed stage and can arguably be a reliable indicator for the future. Having Momentum available, a pre-seed investor can use these metrics to aid them in the decision making when evaluating various investment opportunities.  

Second, the model provides input to the classic problem of making follow on investment decisions. Investors are often victims of the sunk cost fallacy. Often, the urge to support portfolio companies that are in urgent need of money to survive is strong. While this can be the right decision, often it is not. Momentum will provide data about how efficiently the portfolio company is spending the money and time provided with the investment. Startups with high Momentum scores suggest that the money are well spent, and that further investments are advisable.    

About the authors 

Peter Torstensen and David Ventzel are partners at Accelerace. Accelerace is a startup accelerator and pre-seed investor placed in Copenhagen Denmark. Accelerace was founded in 2009 and have accelerated more than 700 startups to date. 

The authors have been aided by their colleagues Claus Kristensen and Mads Løntoft in the conceptual development of the framework.  

Contact 

If you are interested in the model and collaborating further development of the framework, then contact the authors on David Ventzel: dav@accelerace.io or Peter Torstensen: pto@accelerace.io 

True startup founders have more activities than plans

On January 4, 2005, the BBC aired a strange program that would impact the dreams and aspirations of our generation.

The opening scene features four men and a woman sitting in an empty warehouse. They wear suits and serious looks. They are meant to intimidate. Not like gangsters. But like ruthless titans of industry ready to place judgment upon the business ideas of lesser men and women.

Dragons Den aired during the startup depression following the dot-com crash. But the timing proofed impeccably. Just six months earlier a little-known social network called Facebook had launched. Just two months later Y-combinator ran their first batch. The next year Spotify and Twitter were founded.

The second wave of internet startups did what dot-com could not. But more importantly, they created a new ideal. That of the tech-savvy startup founder.

The pervasive idealization of the startup founder has created a startup tsunami of un-imaginary proportions. As a startup accelerator, we are frontline to feel the effects.

We see more startups than ever. But do we also see more startup founders than ever?

In the early days of Accelerace, many of the founders that came to us needed help in describing what they did. They had gotten an idea and had started executing it. But they lacked the vocabulary and structure to communicate their business to other people. Namely investors. One such person was Peter Holten Mühlmann from Trustpilot. He had built a website where people wrote reviews of webshops. But he needed help to formulate the logic of his (what seemed to many at the time) questionable business.

Back then, industry terminology such as customer discovery, hypothesis testing, conversion rates, CAC to LTV ratio, virality, monetization, etc. was still in the making. Accelerators played a role in disseminating the latest theories and vocabulary to these people with activities they were unable to describe.

But something else was at play. Before the startup founder was idealized, the people who did startups were the people who could not help it. They had conceived of an idea that hunted them to the extent they absolutely had to pursue it. Regardless of warnings from friends and family.

So, they did. And at some point, they needed investors. But the investors asked them questions they struggled to answer. These were the people who came to Accelerace.

These people still come to us. But slowly, another type of people started showing up. And in increasing numbers. These people are the opposite. They have near perfect descriptions of what they want to do. But they are short in activities. And they come to us to get help realizing their plans.

The problem is that startup accelerators are not good at helping such people. Placing such a team in an accelerator is frustrating because these founders enjoy talking about their plans. And because the mentors are good at exactly that, the entire program is spent on enthusiastically making more plans, while nothing real happens.

I have come to the opinion that true startup founders can be spotted by having more activities than plans. And if you are one, you would benefit tremendously from being in a quality acceleration program.

Check out Accelerace and Overkill Ventures. We help pre-seed startups obtain product-market-fit.

Why Founding Employees are the most overlooked ingredient for startup success

In 2007, a young student from Aarhus Denmark embarked on a doomed mission. But a couple of years later, fate would change the odds against him. Today, he is among the most celebrated Danish founders of our generation.

Peter Holten Mühlmann had noticed a problem. The internet lowered the barrier for commerce. New webshops were constantly popping up. But fraud and bad service followed.

Peter envisioned a software that could help shoppers navigate the mushrooming e-commerce landscape. Like an antivirus program warning you against shops that could not be trusted. He called it Trustpilot.

However, the software needed data. And the most reliable data would be experiences from shoppers. In other words, reviews.

To collect data about webshops, Peter set up a website where people could report bad shopping experiences. Surprisingly, people did. The site found a beachhead among market mavens. The sort of people who find meaning in passing warnings and endorsements to other people. 

Peter started realizing that the reviews might be the product. Consequently, he abandoned the “antivirus” approach and turned Trustpilot into a destination for consumer reviews.

However, a problem arose. How would he make money on this website? Advertising seemed the logical approach, but Trustpilot was not a destination people hung out. Advertising would not be a good way to capitalize, he surmised.

And then it dawned on him. The businesses being reviewed were the customers. Good reviews were gold, and bad reviews were poison. Using good reviews in marketing was worth money. So was the ability to respond to bad reviews.

Trustpilot became a tool to manage reviews. However, this created a new problem for Peter. One that seemingly doomed his startup.

Peter had no experience in selling software to small businesses. Nor did his CTO. The task was daunting, and thousands of startups had shipwrecked on this challenge. 

At the time, software was seldomly sold to small businesses. Only enterprises could afford the high prices needed to cover expensive salespeople in suits doing presentations. 

The pivot meant that the founder team was utterly unqualified to execute the business model. In truth, this is quite common. Founders rarely limit their ideas to their current abilities.

Nonetheless, competence matters. Investors call it founder-market-fit. Investors evaluate how well the founder teams’ current competencies fit the challenges dictated by the market the startup is going after. Founder-market-fit defines whether the founders master the critical disciplines required to win. 

Critical Disciplines. How to win Tour de France

Cycling is a sport. And cyclists are athletes. Consequently, one would imagine that the best athlete would win the most prestigious races. Intriguingly, things are not that simple. 

The world championships in cycling is held every year. And the rainbow-striped winner jersey is a childhood dream for all cyclists. Still, the title of world champion is rivaled by another triumph. Winning Tour De France.

One would think that the same athlete would be a likely winner of both Tour de France and the World Championship. But as many know, that is seldom the case. In fact, this has not happened in more than 30 years.

The thing is that cycling includes different types of races. The World Championship is a one-day event. Tour de France lasts 23 days. Also, the route of the World Championship avoids the highest mountains. Tour de France includes snow-filled alpine peaks. Furthermore, Tour de France has a time trial.

The differences between the two races require riders to master different disciplines. In other words, the critical disciplines of winning the World Championships and Tour de France are different.

Throughout the history of Tour de France, the winners have been marked by mastering both time trials and mountain climbing. In contrast, world champions have been marked by break-away and sprinting abilities. 

The point is that cycling is a sport, but the different races require different critical disciplines for winning them. And trying to win Tour de France without mastering time trials and mountain climbing is a doomed mission. Just like trying to win the market for review management software without mastering small business sales. 

The importance of the Founding Employee

When Peter Holten Mühlmann pivoted to selling to small businesses, he needed someone with a rare competence. Then fate intervened. In 2010, Peter was introduced to someone who could give Trustpilot a winning chance.

Jesper Lindhardt was talented and successful. He had risen through the ranks of Navision, Realtime, SAP, Omniture, and Adobe. Most importantly, his focus was on small business sales. In other words, Jesper had spent 15 years honing the exact critical discipline needed by Trustpilot.

At this point, fate had played its part. Instead, true founder skill took center stage. Somehow, Peter convinced Jesper to abandon his meteoric career, leave Adobe, and join a completely unknown startup to become a founding employee.

Founding employees are probably the most underrated ingredient for startup success. A look at the most successful Danish startups founded by inexperienced founder teams illuminates the importance of this rare breed. 

The founders of Coinify, OrderYoYo, Templafy, TooGoodtoGo, and Planday all attracted one or more founding employees who brought their experience and competence to the startup during infancy. These people mastered the critical disciplines of the business model and gave the startup a winning chance.

After having spent almost ten years as a startup investor, I am continuously puzzled by startup teams that do not master the critical disciplines of their business model. Luckily, certain doom can be avoided. Because true founder skill is the ability to attract resources. And no resource is more important than the founding employee. If you are lucky to meet Peter Holten Mühlmann, he tells you the same.

The true reason startups fail, and how to avoid it

In 2006, two different startups set sail to change ticketing for events. One became a global leader. The other failed miserably. I know because I was the founder of one of them.

In the early years, the differences between the startups were marginal. Both saw an opportunity to provide an alternative to Ticketmaster. Ticketmaster had no self-service platform. The internet empowered events to manage their own ticket sales. Both startups recognized this opportunity.

However, there was a small difference. And it turned out to be the difference between a billion-dollar market cap and folding after two years.

My startup went after venues with lots of ticket sales. Like concert halls, arenas, and nightclubs. We got lots of meetings and even some pilots. Everyone expressed interest.

The other startup went after customers that did not sell many tickets. Those were meetup organizers. A new type of event enabled by social media.

We made fun of the other startup at our board meetings. They had no market. But the joke was on me. Because my startup failed. And the other startup was Eventbrite.

The difference between interest and desperation


We pitched venues. They were interested in seeing what we had to offer.

However, venues used Ticketmaster. Ticketmaster did all the work for them, and sometimes even guaranteed a minimum ticket sale. We did none of that.

Still, that did not prevent the venues from meeting with us. Even being polite and saying that they might try it.

The truth is that customers have a myriad of reasons that deter them from buying something. They have no need. They have a good alternative. They have existing relations with a competitor. They can only buy within certain periods. They have bad experiences with similar products. They only feel safe buying something proven. The list is endless.

Because there are so many reasons not to buy something, the customer must have a very strong need to buy. Something close to desperation. Clearly, venues were not desperate for another ticketing solution.

Eventbrite did not go after venues. Instead, they talked to meetups. None had cared to serve meetups before. Consequently, they had no alternative, no existing vendor relations. Nor high requirements. But they were desperate. At least enough to give Eventbrite a shot.

The true reason startups fail


When startups fail, it is simply because they spend their entire runway speaking to customers who will never buy the product. That is it.

Instead, failing startups chase after the interested people that always surrounds anything new. And try to convert ‘customers who will never buy’ by improving marketing, sales tactics, and pricing. But none of that helps because the customer will never buy.

Signs that customers are merely ‘interested’ is that they are already served by competition or alternatives. At meetings they ask about you, your vision, and your product roadmap.

Inversely, startups succeed when they identify customers who are desperate. Because only desperate people will buy a first version product from a new unknown company.

Signs that customers are desperate are if they have hacked together their own solutions, or using ill-fitted tools meant for a different purpose. At meetings, they ask how your product can be implemented, when they can get it, and how they can get support.

At Accelerace and Overkill Ventures we call this desperate group the Beachhead. It is a small group of customers who always serve as the earliest adopter and reference group for later customers.

Today, Eventbrite serves more than meetups. But in the beginning, this tiny desperate group was their Beachhead. My startups chased venues for two years that would never buy.

We confused interest with desperation. And so, do many startups founders. Consequently, most startups fail. There you have it. That is the true reason startups fail.

How to invest in startups at just the right time

In 1872, something strange took place in a small town in Pennsylvania. A seemingly mad man of Scottish origin was constructing a mysterious plant.

It turned out that the man was named Andrew, and that he was planning to produce steel in huge quantities.

The strange part was not the steel part. Steel had been around for thousands of years. The strange part was his ambition to make so much of it. Because at the time, steel was mostly used by artisans for jewelry. And they did not need much.

But Andrew was convinced that steel had superior properties. He imagined that steel could support buildings tall enough to scrape the sky, and bridges long enough to cross mighty rivers. To most people, it sounded like science fiction.

However, Andrew did not just make steel. He perfected it. Andrew refined the processes of steelmaking and broke new grounds in quality and cost.

Still, he produced more steel than was needed, and things looked bleak. In response, Andrew bet his future on a single audacious project. One that would either prove he was right or utterly humiliate him.

In 1874 Andrew revealed the world’s longest bridge (of its kind). The first bridge to cross the massive Mississippi River. Built entirely with his steel.

The immediate reaction was disbelief. Everyone knew that nothing sizable could be made with steel. And certainly not a bridge. But Andrew fetched an elephant he had borrowed and crossed the bridge with the enormous animal. This inspired confidence and hordes of people followed while newspaper photographers secured the frontpage.

Following the opening of Eads bridge, steel became a critical enabler of the Industrialization. At the time of Andrew Carnegie’s death in 1919, American cities had been utterly transformed. Iconic skyscrapers towered over Chicago and New York. Science fiction, indeed.

The real value of steel

Telling the story of magnificent steel skyscrapers paints an illustrious picture. However, it does not do steel justice. In fact, skyscrapers were one of the lesser impacts of steel.

The real benefit of steel was for machinery. The properties of steel made it uniquely suited for tools and machines that entrepreneurs could use to produce a wide variety of new innovative products.

In the years following the adoption of steel, a famous cohort of entrepreneurs used steel machines to make: Proctor and Gamble soaps, Levi’s jeans, Ford cars, Edison light bulbs and Heinz ketchup. Just to mention a few.

In other words, steel-based machinery served as infrastructure to produce new products. And as we will see, the distinction between infrastructure and product is key when timing investments.

Infrastructure before products

Entrepreneurs have limited resources. For this reason, their ideas need a mature infrastructure. A crafty entrepreneur prior to 1872 might have envisioned a skyscraper. But before Carnegie steel, eighty story buildings were not possible.

More than a century later, Reed Hastings of Netflix also had to wait for broadband internet to mature before he could realize his vision of streaming (until then he had to make do with enveloped DVDs).

Unfortunately, it is hard to know when infrastructure is mature. Those who invested in electric cars in the 1980s, webshops in the 1990s, and mobile applications in the first half of the 2000s learned exactly how hard.

In these cases, it turned out the investors were too early. But, why exactly do investors lose money when being too early? Because the products never get good enough before the companies run out of money. And why don’t the products get good enough? Because the technologies powering the products are neither powerful nor cheap enough to serve as effective infrastructure.

Electric cars in the 1980s did not have the lithium-ion batteries and AI that power a Tesla. Webshops from the 1990s did not have the payment processing and high-resolution imaging that power Shopify. Mobile apps before 2007 did not have touch navigation and GPS that power Pokemon Go.

The entrepreneurs behind electric cars, webshops, and apps need the infrastructure to reach maturity. Or more precisely, they need the many individual pieces of the infrastructure to converge and reach maturity in unison. This is an important detail because the infrastructure of most innovative applications are a mix of many individual innovations. A fact so important, it warrants the naming of a law.

The law of compound innovation

The story of Carnegie left out an important detail.

As you have already surmised, steel was not the only innovation required for Henry Ford to produce a car. Nor was it true for soap, ketchup, and jeans. In fact, the infrastructures were a mix of different innovations that converged and matured in unison. Besides steel, simultaneous advances in electricity, gasoline, and rubber were essential for car producers.

When products are built on infrastructure consisting of multiple innovations, two things happen. First, the timing of the maturity of the infrastructure becomes harder to predict. Two, the products that can be built, become harder to imagine. And as complexity theory teaches us, this effect is exponential. One might call it: the law of compound innovation.

But why does the law affect investors? Because investors cannot be too late either. In fact, it is the very nature of venture capital to invest before everyone else sees the value. That was how Light Speed Venture Partners made 2345x return as the first investor in Snapchat.

Talking about Snapchat, let us apply the law of compound innovation. It is fair to say that no internet expert in the late 1990s had foreseen the disappearing picture sharing app. Why? Because Snapchat required more than the internet. In fact, it required several innovations to compound.

To create Snapchat the infrastructure had to become mature enough for a couple of youngsters with no budget to build the first version. It required 4G connectivity, high-resolution mobile cameras, and app store distribution.

And when did these underlying innovations converge and form the necessary infrastructure?

The app store came in 2008. High-resolution mobile cameras started appearing in 2010. 4G was rolled out globally during 2010. The result: Snapchat launched in 2011. And so did all its cousins: Line, Viber, and WeChat.

Projecting compound innovation. Timing the future.

Venture Capitalists bet on the future. And for investors, the future is synonymous with timing.

Consequently, investors must construct a thesis about the future. And not just about what will happen (we all know that). But when it will happen.

In order to construct a valid thesis on timing, one must first understand the innovations that are forming new infrastructure. And the law of compound innovation hints that this becomes exponentially harder the more complex the infrastructure is.

Consider an emerging infrastructure like VR. In 2012, Oculus revived the forgotten dream of virtual reality. Almost eight years have passed, and very few people use VR. To understand why one must first understand the infrastructure for virtual 3D immersion.

In order to deliver a quality experience, one could theorize that the following infrastructure is needed: Wireless lightweight headset with long battery life and a screen resolution of 8K with 180 degrees field of view. 5G to stream the content. And controllers with individual finger and joint sensors. All within a price point of a mid-range smartphone.

In this light, it has clearly been too early for investors (and entrepreneurs) to bet on products like VR games, software, and films. Instead, investors should have been focusing on pieces of the infrastructure. Like controller and screen technology. (A topic for a later post).

But the thesis also hints that successful VR products could be close. The Oculus Quest headset from 2019 is not too far from the headset described. And 5G is being rolled out this year.

Consider a more complex infrastructure. The convergence of Blockchain and VR. An infrastructure that could be called “Virtual Society”. Blockchain is an infrastructure that allows to track and manage ownership. For Blockchain infrastructure to reach maturity, one could theorize that it needs: the ability to handle millions of transactions per minute, wallets to be pre-installed in browsers, and non-technical vocabulary for normal people understand it.

When VR and Blockchain mature and converge, the combined infrastructure will lay outside our experience. Much like the convergence of 4G, high-resolution cameras, and app store distribution was an unprecedented infrastructure that gave birth to the equally alien apps.

Perhaps we are already witnessing the first of such products. In February 2020, Decentraland launched. It is an immersive social network with blockchain-based ownership. An early example of products based on the convergence of VR and Blockchain infrastructure.

It is fair to say that Decentraland would be very hard to imagine a decade ago. But looking at the infrastructure powering it, you might be wondering if it is too early. And if you are, you are asking exactly the right question.

Perhaps the law of compound innovation can help you. See my first crude attempt at depicting it below:

Law of compund innovation

At Accelerace and Overkill we are pondering these things, and if you have views of your own, we would love to discuss them with you.

Why founders should never build the one-stop-shop

Are you okay? The founder was reacting to my painful facial expression. The pain I felt wasn’t physical, but it was real, nonetheless. The unpleasant sensation stemmed from a proclamation the founder had just made. One I have heard too many times before.

We are going to build a one-stop-shop platform! the founder had stated seconds earlier.

Right then, I knew I had a long and difficult conversation in front of me.

I began (again): Look. Many startups founders get the idea of building the one-stop-shop. The logic is obvious. Each individual element to a platform has value. If the platform has a lot of elements, it has a lot of value. And if the platform has ALL elements possible, then it has the most value. Thus, it will be superior to competing products.

The founder nodded.

Unfortunately, the logic is wrong, I said. The founder looked a bit confused.

I continued: Imagine we do a startup. We decide to build an online room planner for private homes. Millions of people need to design their home when moving or renovating, and we know the pain from having moved our self.

We launch, create some buzz, and a few hundred people start using it. However, the number of users is less than we have hoped for. And after a while, we realize that the hockey stick isn’t happening.

The scenario was exactly what the founder was currently experiencing. So, she was very attentive.

What do we do? I asked rhetorically.

Well, maybe there aren’t enough people who need a room planner for private homes, we might conclude. But if we add an office layout, then office managers could use it too. It is easy to add office furniture to the inventory library, so we do it.

What we hoped for happens. Offices are being designed on the platform. But it is still not a hockey stick. At the same time, we learn about multiple competitors that offer office planning. What to do? I asked again, rhetorically.

Well, we just learned that when we added the office elements, we broadened the scope of our product. This meant more people could use it. So, we decide to add more elements.

Factory planning! We add factory layouts and create 3D models of conveyor belts and machines. Now we have the only platform where users can plan homes, offices, and factories. We have no competition.

Still, the hockey stick eludes us. Worse still, we see a reduction in the number of private homes being planned. Maybe because we haven’t fixed all the bugs before we started focusing on the factory elements. Anyway, we have found a way to grow and escape competition, we think.

Next, we enable planning for yachts. Then restaurants. Then concert halls. With each element, we broaden the relevance of the product to even more people. Or so our logic goes.

Finally, we have built the ultimate room planner. The only place you can design any space regardless of function or size. We are the “standard”. The go-to-place. The one-stop-shop. Nobody else offers anything as complete as our software. And then God abandons us.

The founder looked a little confused. Maybe it was because I brought God into the picture. So, I elaborated:

What we would learn is what eBay learned when they added video conferencing (Skype) to its auction site. Or Burbn founders learned before they simplified their app and renamed it Instagram. Or Endomondo experienced when they added e-commerce and social networking to their running app. And these companies are among the surviving examples. The failed projects that have learned the same lesson are endless.

I looked directly at the founder and said: We would learn that the impressive breadth of use cases our software handles is utterly worthless to our customers. I continued:

The thing is that additional elements only provides value to the user if they are integral to the same problem. As an example, it’s valuable if a webshop also offers payment and delivery. The problem the user is trying to solve is to obtain a specific item. If users had to go to a third-party payment solution to transfer money. And to go to another service to arrange delivery, the friction would be unbearably high.

The question is: how integral must problems be to add value to the same platform? Well, more integral than you think, I said.

Take the running app Endomondo, I continued. Endomondo enables runners to track their runs. But runners also need running apparel. So, would Endomondo be a better product to runners if it also featured a shop with running apparel? I asked. The founder looked unsure. So, I continued: It turns out not. Endomono tried that, and it wasn’t a success. Why. Because tracking your run and buying apparel are very different problems. And no runners have these problems at the same time.

Our imagined room planner startup would suffer the same lesson. Nobody needs to design a home, an office and a factory at the same time. The section that supports office planning is worthless when you want to plan your bedroom. In fact, it’s confusing and adds clutter. The idea of the one-stop-shop is false.

Now, it was the founder who felt pain. And then came the inevitable question. But what about Amazon? The founder asked. I had expected it and said: Amazon is a perfect exampleAmazon sold only books for a long time. In fact, they decisively refrained from selling anything else before they had nailed the book problem. Amazon did not become successful because they sold everything. They became successful, exactly because they did not sell everything. Later, they added another element one by one. And so, should you, I said.

The founder was quiet, but slowly her painful facial expression lifted. Then she said: I guess Uber did the same thing. They started out offering expensive limousines, and not until later added cheap taxis and food delivery. Because….well none needs the same thing at the time. She let it sink in.

Then I could see her facial expression change into a very familiar one. Determination. Thank you, she said. I know what I must do. She got up and left. I looked out the window and thought about how often I have had this conversation. And then, I started writing this blog post.

How to spot a scalable startup and why I got it wrong in the past

There is a lie that permeates the startup industry. And venture capital especially.

The lie is this: startups are binary outcomes. They either become big or die trying.

After having logged my first decade as a VC, I know it’s not true. On the contrary, most startups become small businesses. They simply fail to scale.

This is an important fact. Because studying these non-scaling companies offers valuable lessons about the true nature of scalability.

What scalability is not

Economists teach us that scalability is about low marginal costs. Meaning it is cheap to serve an additional customer. In this view, services are never scalable because the cost of servicing one more customer isn’t falling.

In contrast, production can be scalable because a machine can produce one more widget cheaply. And SaaS is very scalable because letting one more customer access the software costs next to nothing.

The theory of low marginal costs makes investors love SaaS companies. And for good reason. There is just one problem. Most SaaS companies never scale.

Clearly, low marginal costs do not define scalability.

What Scalability is

After a decade of investing, I have come to understand scalability somewhat differently.

In venture capital, scalability is defined by a time constraint. Funds must exit the companies with 7-9 years. This means scalability is more about the speed of growth than marginal costs. Put differently, a scalable company is one that can grow fast. To this end, marginal costs matter very little because marginal costs define profitability, and not speed.

Growth can come from two sources. Beta and Alpha. Beta defines the growth rate of the market. Alpha defines how fast the company can grow (relative to its competitors) in the market.

The strength of Beta and Alpha can vary. As an example, the SUV market has long enjoyed moderate Beta. The SUV market grows more than other car categories. But it is a far cry from the strong Beta the electric car market enjoys.

Extreme Beta also exists. It happens when a market is “unlocked” and all the new actors rush to the marketplace at once. Like it happened for Airbnb when they “unlocked” a global latent market of private hotels. Or Uber did with ridesharing.

Strong Alpha occurs when the product enjoys a reinforcing value loop, and the loop spins faster than the competitor’s loop. A reinforcing value loop is one where the product becomes more valuable when the company wins more customers, which in turn makes the product more valuable, which will attract more customers, and so on. This self-reinforcing nature of such a dynamic means that the company will quickly become dominant in its market.

A company like Templafy (Accelerace alumni 2014) enjoys such s value loop. Each new customer creates new templates than can be added to the product for the next customer. This means Templafy has strong Alpha.

A perhaps even stronger example of Alpha is a company like Trustpilot (Accelerace alumni 2009). For Trustpilot, new users create reviews, that make the site more valuable to other users, who will create even more reviews that in turn increases the value of their product offering to the businesses who are reviewed. The businesses start using Trustpilot ratings in their marketing, which makes new users aware of Trustpilot, who then create more reviews. And so, the reinforcing value loop accelerates.

And as you will see, these forces greatly influence scalability.

What Scalability looks like

In our first fund (vintage 2011) with 49 investments, I have witnessed cohorts of very similar companies start around the same time. But over the ensuing years, they experienced unbelievable different trajectories.

A few have become bigger than even the founders imagined. And many never scaled, but still lives. For years the reason for this difference eluded me. Because it wasn’t marginal costs, market size, team, IP nor competition. In fact, one company is by far the strongest in all these parameters. But it still failed to scale.

In 2012 we invested in a SaaS company in a vertical with very little competition. We will call it WorkWeek (not the real name). The founders have industry insight. The product is great. The customers love it. The market is worth billions. The CLV is very high because customers never churn. The board is among the strongest I have seen.

We did the seed round, and the company projected to reach 10M ARR within three years. Today, eight years later they are at 3M ARR.

The problem is that WorkWeek enjoys no Beta. The market is stagnant. There are hundreds of thousands of customers in their vertical. But if the market is not growing, no new customers are appearing without a solution to their problems. Consequently, their Beta is zero.

In addition, WorkWeek enjoys no Alpha. There is no reinforcing value loop within their business. The product does not become more valuable to the next customers, regardless of how many customers they have.

The founder team estimated they would have “conquered” Germany within two years. It would take them five years to get the first German customer.

The problem was that the customers in Denmark didn’t make the product any better for the German prospects. On the contrary, each new sale gets harder because all the “low hanging fruits” have been sold to. What remains are customers who are hard to convince to change their ways.

WorkWeek is what you get when both Beta and Alpha are absent, but everything else is great. The company grows 50% per year and have done so since inception. Such growth rate means that if a company has 50.000 EUR in revenue year one, they will have less than 1 million EUR in year seven.

In contrast, Trustpilot and Templafy are what you get when strong Beta and strong Alpha are present simultaneously. Trustpilot rose during rapid growth in e-commerce which gave them strong Beta. And their Alpha is simply unique. Templafy enjoys strong Beta from the seismic shift to cloud-based office programs, and the user-generated templates create strong Alpha.

Today, I understand that to be truly “scalable”, companies must enjoy both Beta and Alpha simultaneously.

If both factors are in place, the growth from each source will compound, creating the famed hockey stick as a result. Witnessing a hockey stick unfold in real-time is quite remarkable. But low marginal costs and big markets are not enough if you want to see it for yourself.

The Startup Adoption Lifecycle

This article tells the story of how farmers in Iowa shaped the way startup founders think. Furthermore, it argues that we need a new way for startups to identify their early customer segments. In the end, founders will know how to obtain product-market fit, and why the article features a picture of an airline crew on heavy cases.

In 1927, scientists developed a new hybrid seed-corn. They knew their invention would give farmers 20% more yield. What they didn’t know was that the seed-corn would define how we came to understand innovation.

The new corn was offered to farmers in Iowa. Oddly, not everyone adopted it. The situation caught the attention of two sociologists at Iowa State University.

In 1941, the two researchers Bryce Ryan and Neal Gross went to interview the farmers. What they learned was puzzling.

Even though the new hybrid corn was objectively better, some farmers simply resisted using it. In fact, it would take about 10 years for all the farmers to adopt the new corn. And that was just Iowa. It then took another decade before it was fully adopted throughout the US.

Bryce Ryan and Neal Gross concluded that some people are just prone to try new things before others. Today, we know their theory as the Technology Adoption Lifecycle. For close to a century, the theory has defined how we understand the adoption of innovation.

The-technology-adoption-life-cycle-1024x336

Why the Technology adoption lifecycle is important and useless

The Technology Adoption Lifecycle basically explains that in the beginning, only a small group of people adopt a new product. Later, the majority follows suit. Finally, the last little group of resisting people gives in.

As trivial as it sounds, it was an immensely important realization. Because it provides a frame for innovators to view the world. I know this first-hand.

When I started my first startup in 2006, I was graduating from business school. And like any graduate, I knew the Technology Adoption Lifecycle. It helped me formulate our go-to-market strategy. First, we would go for the innovators and early adopters. Sounds right on paper.

But most startup founders learn that understanding who those innovators and early adopters are is much harder. In fact, the framework does not provide any guidance for this problem. At all.

The thing is that the Technology Adoption Lifecycle was never meant to help tech entrepreneurs. It was a retrospective view of a category over its entire lifecycle. That means it spans decades and each block of adopters represents years of slow gradual adoption. Although important, it is useless as a practical startup tool.

In reality, most startups face less than 12 months of quickly evaporating runway. And the next months are the only period of any importance because it’s all the time a startup is sure to have.

Unfortunately, the Technology Adoption Lifecycle is of little help. The model just says: innovators and early adopters. Whoever adopts the technology first, are the innovators and early adopters. That is called circular logic.

600 startups later a pattern emerges

Today, I am a partner at two accelerator-funds. And for the past seven years, I have met about a hundred startups per year, helping them obtain product-market fit. Or at least tried.

One consequence of specialization is a very granular understanding of a narrow field. In my case, I suspect my expertise has become the early phases of the Technology Adoption Lifecycle.

Having observed so many startups go from zero to their first hundred business customers (or first million users), I have witnessed a clear pattern.

The startup adoption lifecycle

All successful startups I have worked with have experienced adoption through the same sequence of micro-segments within the very first part of the adoption lifecycle. Let’s shrink into the micro-cosmos of the very first adopters. What I call the Startup Adoption Lifecycle. Here we go:

The first adopters are always friends, family, and colleagues. They sign up to support the founder(s) and cheer on. They rarely have a deep need for the product. This group constitutes the first 10 to 50 customers.

The second adopter group is always the “crazy” people. They don’t know the founder(s) personally, but for some reason, they are obsessed about the area the startup operates in. And I mean abnormally obsessed. This group often send something that looks like fan mail to the info@ or support@. This group varies in size but is probably the next 5 – 30 customers.

The third adopter group is by far the most important. This group is called the Beachhead. This group is also abnormal, but for a different reason. They are not “crazy”. Instead, they live under unique circumstances that impose extreme or unusual needs. Because this group is small, none has cared to serve their special needs. Consequently, they are somewhat “desperate” which makes them actively look for new solutions.

Examples were the first hardcore gamers on a live streaming website called Justin.tv. The founders realized the potential of this Beachhead and renamed Justin.tv to Twitch.

Another would be airline cabin crew. Few people fly every day, so why bother making wheeled suitcases for cabin crew who do. In 1987 someone finally did. Of course, cabin crew was the first adopters. Today we all have trolleys. (The crew members in the featured picture clearly needed them).

A third example would be victims of the Japanese tsunami in 2011 that starting using a chat app to communicate because the cell phone towers were gone. Today, that chat app has an estimated 500 million users and is known by the name Line.

Billede2

In truth, all successful startups eventually must find their Beachhead. It is the most important adopter group because they are the first people who adopt because of a true need. Their need might be unique, but that makes them willing to test a new product from an unknown startup.

Who is the Beachhead for any particular startup? It is the group that most founders overlook because it is far too small to fit the story of the billion-dollar market. It is the group that has an unusual job. Or live an unusual place. Or have an unusual interest. Or have been affected by an unusual event. Or perhaps a combination.

The Beachhead varies in size, but it is rarely bigger than 100 – 500 customers to begin with. Luckily, that is often the perfect size for a startup with an evaporating runway.

If startups can navigate the Startup Adoption Lifecycle, they will be well on their way. Because on the other side of the Beachhead is product-market fit. And with that, the beginning of twenty years of movement through the Technology Adoption Lifecycle. May your journey be smooth.

Tip: If you are a startup founder and want to get help finding your Beach Head, a qualify acceleration program might the right thing for you. At Accelerace and Overkill Ventures, we see this as our main job. Some other accelerators might do as well. At least check out my blog.

What porn and ridesharing can teach corporates, investors and startups

Most investors and corporates estimate the potential of a new technology. It’s a mistake. Real disruption comes from the convergence of different technologies. This post will explain how technology convergence creates parallel industries and Startup Tsunamis. Most importantly, it will tell the story of a young stripteasing college student that took down a billion-dollar empire.

In 1953, a 27-year-old entrepreneur raised angel investment from 45 investors. With the money, he pioneered one of the world’s biggest industries and built a true empire. Today, his product is an iconic piece of western culture.

The name of the entrepreneur was Hugh Marston Hefner. His product was a new type of magazine. He called it Playboy.

The world was changing its view on sexuality, and pornography was taking off. It became a billion-dollar industry with companies raking in huge profits.

The pornography studios appropriated most of the profit because they controlled the means of production. Print machines, studio light, cameras and retail distribution were expensive.

At the height of its glory, the industry launched its own award show rivaling the Oscars in glamour. And then one day, in late 1990’s, it was all over.

A young college student had set up a camera in her dorm room. She connected the camera to her computer and created a website she called JenniCam. She started broadcasting for the world to follow. She quickly learned that viewers increased when she did stripteases.

A few years later, Playboy delisted from the stock market. Its stock was plummeting.

JenniCam started a webcam revolution. Today, there are thousands of cam models. They connect directly with viewers thanks to cheap cameras, fast internet connection, chat and digital payment.

The webcam revolution is one of the clearest examples of disruption caused by the convergence of technological innovations. The old companies were built on an infrastructure of professional grade equipment, film studios, and physical distribution.

In contrast, the webcam industry is built on the availability of cheap consumer grade equipment, online distribution, and new communication and payment protocols.

What the pornography studios experienced is the phenomena of parallel industries. Few people understand it. You are about to become one of them.

The difference between competition and parallel industries

The runaway success of Playboy attracted many competitors. Among the biggest was Penthouse. The magazines competed on celebrity pictures and naughtiness. With the emergence of DVDs, the studios competed on distribution and licensing agreements.

But when JenniCam launched, few took notice. To the established players JenniCam wasn’t a competition, let alone a threat. Its young founder didn’t try to muscle them out of licensing deals. She didn’t invest in new studios or steal their models. In fact, she was utterly invisible to them.

The thing is that when certain technologic innovations converge, it creates entirely new industrial platforms. The startups that emerge on the new platform are not competing with the incumbents. Instead, they are building a parallel industry.

A parallel industry is an industry that has been rebuilt from the ground up on a new industrial platform. The new platform provides an infrastructure that is magnitudes faster, cheaper and more effective than the traditional industrial platform.

The new platform enables entrepreneurs to reimagine all components of their business model and apply new technology in every layer of its business. As a result, the startups that emerge are so different from the incumbents, that they go unnoticed by the old industry.

The next thing that happens is that the parallel industry matures. It develops its own suppliers, consultants, and networks. Suddenly, JenniCam had sparked an entire industry of innovative producers of webcam technology, video compression providers, and digital payment solutions.

At this point, the incumbents take notice. But it’s too late.

How parallel industries cause Startup Tsunamis

Parallel industries are supported by underlying technologies that serve as the infrastructure of the industry. Like: production technology, distribution technology, communication technology and financial technology.

When innovations in the different underlying technologies converge, a new industrial platform is born. That happened during the industrial revolution when factories (production), railroads (distribution), the telegraph (communication) and Wall Street (finance) converged and gave us a tsunami of new products.

Startup Tsunamis describe the phenomena of very large number of startups launched within a concentrated timespan, attempting similar business models. One of the latest examples of this is ride sharing.

Much like the pornography studios, taxi companies had enjoyed decades of steady business. But around 2010, a new industrial platform was emerging.

The smartphone converged with advances in payment infrastructure. At the same time, venture capital was reemerging as a source of capital for startups after being decimated by the global financial crisis.

The result was a true Startup Tsunami. Here are just some of the few startups that built their business on the new industrial platform: Uber (2009), Ola (2010), Wingz (2011), Sidecar (2011), Hailo (2011), Grab (2011), Lyft (2012), Didi Chuxing (2012), Careem (2012).

Today, few people doubt that ridesharing will change personal urban transportation for good. When electrified self-driving cars join the convergence to enhance the new industrial platform, taxi companies are history. But the story doesn’t end here.

Blockchains are creating new financial infrastructure. IoT is creating new communication infrastructure. 3D printing is creating new production infrastructure. Individually they might seem like toys. But so did webcams until they converged with high-speed internet, chat and digital payment. The cocktail enabled a young college student to initiate the fall of an empire.

At Accelerace we help both startups and corporates. Check us out at Accelerace.io.

Thank you to Jeremy Rifkin and his great book, The Third Industrial Revolution, to inspire me to write this post. I can recommend his book.

Why corporates are terrible at assessing startups and how to do right

Many corporates are billion dollar entities in stagnation or decline. So they run startup programs to look for the next big thing. But big corporates need big ideas to move the needle. That is why most corporate selection committees focus on market size. They think big market is a prerequisite for big opportunity. They are mistaken. This post will explain why market size is irrelevant and how corporates should be evaluating startups.

Today most corporates have some kind of startup engagement activity. Like outplaced innovation teams, hackathons, innovation garages, and accelerators. And they should. Startups create much of the innovation today and corporates would be foolish not to attempt to leverage it.

The problem is that it’s not working. The initiatives do bring the sense of innovative spirit and fun for the employees involved, but the billion-dollar success stories keep eluding them.

The problem is that many corporates are terrible at assessing startups. And this problem is magnified because the consultants and service providers helping the corporates design and manage these programs are too focused on getting the contract to question anything. Sucking up rarely produce truth.

The fallacy of big markets

Corporates need billion-dollar ideas and this fact makes them focus on startups with big (adjacent) markets. And it makes sense. Big markets are the prerequisite for big business. Unfortunately, this logic is flawed.

The thing is that startups aren’t really businesses. Instead, they are problem-solving entities. And this fact is immensely important when assessing the potential of startups.

Businesses have markets. Startups have niche products that target niche customers with a problem no one has cared to solve before. Per definition, most startups have tiny or even non-existing markets. Like a young startup called Unity in 2005 that made it easy to create games for Apple devices. There was no market because none played games on an Apple device. That was until Apple launched the iPhone. Today Unity Technologies is a unicorn.

Or Trustpilot (an Accelerace alumni company from 2008), that made it easy to review webshops. Their market was non-existing because they had no customers. Only free users. Today Trustpilot has thousands of business customers and raised $150 million in funding.

The thing is that market size is not relevant because the product evolves and the market sentiment changes.

Still, in most selection committees I’m in, the corporate representatives will regard the current product pitched as a fixed value proposition and estimate the potential from that snapshot. It’s a mistake.

How markets emerge over time

The truth is that most startups radically change their product. It happens because startups are founder driven, and founders can enact radical changes at will. To a corporate, sudden and radical product changes is unthinkable. Thus, corporates tend to gravely underestimate the plasticity of startups products and business models.

When the product change, the potential market changes as well. Like when the high-end limousine ordering app Uber added non-luxury cars to their app and became a taxi killer. The limousine market is small. The taxi market is not.

Just like the product can change, so can the market sentiment. It happens if the product has network effects or product consumption is highly observable.

When products have network effects, the product becomes more valuable over time. In the beginning, the product is only valuable to a small group of people. Like the first computers or an early version of the crowdfunding platform Kickstarter. But as more and more people use the products the relevant market increases. And Metcalf law teaches us it can happen very quickly.

In other cases, the market sentiment changes because of trends. If the product is highly observable, it can initiate a change of perception among potential customers that suddenly redefine the market.  Like electric vehicles, café latte and CrossFit.

For reasons above, market size is a terrible proxy for potential. And corporates need to unlearn the importance of it.

How to do it right

Instead, corporates must learn to construct a thesis about the future of their industry. The thesis must regard how technologies and trends will influence, reshape or even replace their industry. Once in place, corporate must target ideas and startups within the thesis.

They must learn to resist the temptation of attempting to foresee the potential of the individual startup but instead focus on executing their thesis. In all practicality, this means betting on a lot of teams doing similar things but from different angles.

The selection committee must still regard the potential, but the potential is already built into the thesis. So instead of questioning the market size, the committee members should question how closely the startup fits the thesis.

If telcos had done this in 2011, they might have caught either Line, Snapchat, Viper or WeChat. They all launched their chat apps that year, but telcos were missing a valid thesis on the future of communication. This should be a lesson for all.

Conclusion

  • Most corporates look for billion dollar ideas.
  • When assessing startups, corporates question market size.
  • Market size is a bad proxy for potential.
  • Corporates need to create a valid thesis about the future of their industry and start targeting a large number of startups within this thesis.