In 2017, the equivalent of the arrival of an alien spaceship happened. A fund of a size none thought possible appeared from the edge of the world and descended on Silicon Valley. At the helm of the ship stood Masayoshi Son. An eccentric business daredevil with a risk appetite that would have made Genghis Khan wet his pants. The ship was loaded with 100 billion USD ready to be deployed through cash canons of doomsday-sized calibers. Once Son gave the command to fire, the cash cannons colored the skies green for the following years. When the cash rain ceased in 2019, the Soft Bank Vision Fund had deployed close to 100M USD per day, every day, since its arrival.
The enormity of the Vision Fund can best be illustrated by comparing it to the most legendary VC of all time. Sequoia Capital was founded in 1974 and has delivered above-market returns ever since. Consequently, investors from around the globe vie for receiving an allocation. Naturally, such pressure makes their funds grow. Still, their 2017 fund Sequoia Capital Global Growth Fund II, their biggest to date, was “just” 2 billion USD.
Despite Sequoia being dwarfed by the Vision Fund, Sequoia perhaps best depicts the development in VC fund sizes. The first Sequoia Capital fund I from 1974 was a mere 3 million USD. The second fund grew to 20M USD. The third amassed 44M USD. In 2011 and 12 funds in, Sequoia Capital crossed the 1 billion USD mark for the first time.
When VC funds grow, so does the number of people needed to manage them. The first Sequoia fund had one partner. Don Valentine. Later, more partners joined. Later still, the partners started employing investment professionals. Like Sequoia, most VCs start out as a group of partners investing together. These partners share the workload, and only have a couple of people employed. Perhaps a CFO and a secretary. But as VC funds grow, they employ more people. These investment professionals differ from the partners in one important aspect. They do not have their own money in the fund. Consequently, these people tend to regard the firm as an employer, and themselves as employees. To many, this fact may seem trivial. But in the world of investing, this is anything but. As we shall see.
The House Money Effect
When VCs assess deals, they must weigh risks and opportunities. But risk and opportunities are interpreted values. Interpreted by the people meeting the founders. Until a few years ago, startup founders would meet partners in the funds. And the partners would make the assessment of the risk and opportunities. But recently, founders mostly meet with investment professionals.
Because investment professionals are essentially employees, their frame of risk and opportunity is quite different from the partners. Partners can make or lose money. Investment professionals can be promoted or lose their job.
One might conclude, that losing money is worse than losing a job. And this should make Investment professionals more risk-tolerant. However, this conclusion would be mistaken because of something called the house-money effect.
The truth is that even though the partners risk losing their money. The money they invest typically stems from profits from previous investments. It is the equivalent of going to the slot machine, winning money, and only playing with the money you have just won while keeping the original amount safe. Money recently won feels like “free” money. And the behavior that stems from someone gambling recently gained money, is called the house-money-effect. The effect gives one much more risk appetite than if one was risking money diligently collected. Did Masayoshi Son have house-money? Son made close to 100 billion USD when betting on a young Jack Ma, founder of Alibaba.
Arguably, the importance of the house-money effect in venture capital is under-appreciated. One definition of startups is that they are: a temporary organization designed to search for a repeatable and scalable business model.” (Steve Blank). The definition oozes risk and uncertainty. A temporary organization that is searching does not seem like a sound investment. And it isn’t. Thus, some version of the house-money effect must excerpt influence to make otherwise smart people engage in such risky bets.
In fact, one could argue that the house-money effect underpins venture capital. But then what happens when investment professionals take over the risk and opportunity assessment? Will venture capital change? It already has. And a large group of founders suffers.
The tale of two founders
Many investment professionals come from finance and consulting. That means investment banking, PE funds, hedge funds, pension funds, McKinsey, Bain, etc. These people are used to hard data. However, startup investing seems opaque because there is little hard data available. This conundrum has two consequences. First, investment professionals gravitate towards companies that can present hard data. In practice, SaaS companies with three years’ operating history and 100K MRR. Second, and perhaps even more consequential, they gravitate towards founders with scaleup resumes. That means Founders, executives, and VPs from Zendesk, Klarna, Bolt, etc. Why would they do this? Well, imagine that the investment decision proves bad, and the firm loses money. To an investment professional, it seems much easier to defend having invested in proven people with impressive resumes, than a team of nobodies. The latter requires some explaining to do.
Does this mean that only founders with scaleup resumes get funding? Not entirely, because there are not enough scaleup founders for investment professionals to solely bet on them. But it does produce a tale of two founders. Founders with scaleup resumes who can raise exorbitant sums of money at fantasy valuations. And everyone else who struggles to merely solicit a term sheet. Besides being a frustrating experience for the latter group (which is the vast majority), it might also prove the beginning of the end.
The only way to make money investing
Much research finds that first-time funds perform best. One example comes from another legendary VC. In 2019, we got a rare view into the returns of Andreessen Horowitz (A16Z) when internal data slipped into public hands. The first A16Z fund from 2009 returned 44% net IRR. The second A16Z fund from 2010 returned 16% net IRR. The third fund from 2012 returned 15% net IRR. Although the final outcome could differ from these figures, there is a trend. And the trend is declining performance. But wait, there is another trend! The first fund was 300M UDS. The second was 656M USD. The third is 997M USD. I suspect you know where I am going.
When fund sizes increase, the firms get “institutionalized”. The risk tolerant house-money-effect is diluted and replaced by hard data seeking I-cannot-be-fired-from-investing-in-a-Klarna-product-manager conviction making. But if the house-money-effect is underpinning venture capital, what happens to venture capital when it is “institutionalized”? The answer might be comforting, or the opposite, depending on who you are.
Investing is a self-correcting game where non-viable strategies will disappear over the long run. And paying exorbitant premiums for founders with scaleup resumes is not viable. Why would I think so? Because there is a law in investing that not even all the money in the world and legions of Bain consultants can buck. The law is this: One can only make money investing if one is both right and non-consensus. Essentially the law says: if everyone agrees that something is a good investment, that something will become so expensive that no money can be made. When scaleup-resume founders obtain stratospheric valuations, it can only have one reason: Everyone agrees that it is a good investment. Consequently, the law ensures the firms engaging in this strategy will suffer in the long run.
However, time is relative and venture capital has painfully slow feedback loops. The self-correcting nature can take years, and perhaps even decades to exert itself. Until then, startup founders without scaleup resumes will suffer. And guess who are among these: yes, most women and minorities! But more importantly, a lot of entrepreneurs with the ideas and talent to create all the house-money needed to keep fueling the wonderful world of startups.
At Accelerace we insist on being indifferent to the resumes of founders. We do not care who you are. We care only about what you do. If you are a founder and our views resonate, then visit our website here.
This post will tell you a story from my youth that draws parallels to a feature of venture capital that has far-reaching consequences for startup founders. You will learn about my grades in school, how I dressed, and why all of it matters to pre-traction startups founders. Finally, you might see why VCs have not yet thrown terms sheets at you.
Two peculiar things happened to me when I turned sixteen. First, I got good at inline skating. Two, my grades in Danish started plummeting. At this point, you have made the connection. My grades started plummeting because I spent too much time at the skate park. But that is not what happened.
Until I turned sixteen, I was doing great in school. Danish especially. The library was my favorite place, and the Advanced Learner’s Dictionary was my favorite book. I got high on discovering unorthodox words to add to my growing vocabulary. My teacher acknowledged my love for Danish and it felt good.
One day, my friend got a pair of inline skates. They look like no skates I had seen before. They were called ‘Sabotage’ and looked like something special forces would wear if they rollerbladed into combat. I immediately asked my parents to buy me these skates.
From that day, I was inline skating. Stairs, rails, and ramps became my playground. However, my love for Danish persisted. In fact, it grew, and I started writing poetry, lyrics, and short stories. I had just exchanged my four days a week playing football with four days of inline skating.
At the same time, I enrolled in a new educational institution. What we call ‘gymnasium’ in Denmark (somewhat comparable to high school in the US). In contrast to my elementary school, the atmosphere at my new educational institution was traditional, academic, and conservative. And so was my new Danish teacher.
Unbeknownst to me, my new hobby would severely impact my new Danish teacher’s perception of my abilities. Obviously, the fact that I jumped large stair sets without protective gear had nothing to do with it. The problem was my appearance, although unaware of it at the time.
To gain inspiration for new tricks, I started watching American skate videos. These videos portrayed renegade youth with tattoos, baggy pants, and careless attitudes. Much of the footage includes youngsters fleeing from security guards and smoking weed. You get the vibe. Naturally, I started assimilating to what I saw.
Unfortunately, it turned out that my new attire of baggy pants, wife-beater, and a large tattoo did not match my teacher’s idea of an academically interested person. I am sure my application of gangster-rap-inspired hand gestures did not help.
Consequently, my new teacher gave me mediocre grades. I failed to understand why but I decided to prove that I was better than he gave me credit for. But no matter how much effort I exerted, I kept receiving mediocre grades. This was puzzling to me and to many of my fellow students, who knew me to be deeply intellectual.
At the final exam, an external evaluator would grade us. Before taking the exam, I told my teacher that he had been wrong about me for the past three years and that I would get a top grade from the evaluator. I did. My teacher acknowledged nothing. It felt strange.
Why some VCs are like my high school teacher
At Accelerace, we help startup founders. We provide the first cheque and help them raise follow-up funding. What I witness, sometimes eerily reminds me of my high school experience. If VC interest and valuations were grades, the best students do not always earn the highest grades.
During our acceleration programs, we get to know our teams intimately. We see who truly finds product-market-fit and who does not. We see what teams work well together and who do not. We see what teams obtain true momentum and who just obtain vanity trophies.
However, our intimate understanding of the quality of our startups rarely aligns with the interest shown by VCs. For a long time, this was puzzling to me. But I have started to see what is going on. And if you managed to endure the first part of this blog post (that you initially feared went nowhere), you see it too.
The fact is that some of our companies find it utterly difficult soliciting term sheets from VCs. That is despite these companies having all the elements required to make a venture-style return. On the other hand, we sometimes get surprised by the terms sheet offered to teams with much more dubious prospects.
When considering these cases, one thing becomes apparent. The founders that have a tough time obtaining term sheets from VCs simply do not appear VC investable. Much like a wannabe renegade skater at an academic institution.
But what does it mean to look VC investable?
VCs evaluate a spectrum of things when evaluating a startup. But regardless of the aspect, they are evaluating, it always happens during pitch-style conversations with the founder. The keyword is pitch-style because in this case the mode of data-transfer matters. When all the information is transferred through pitch-style conversation, this extremely specific type of conversational skill matters more than you can imagine. And the aspect of this skill set that matters the most is being convincing.
Most VCs work like this: An associate will initiate the first conversation with a founder. If the associate believes that she can convince an investment manager to meet the founder, the associate will pursue the deal. Then the investment manager will meet with the founder, and if the investment manager believes that she can convince the partners, the investment manager will pursue the deal.
If only one of these people does not believe they can convince the next person, this person will let it rest. You have no idea how often VCs have told me in private that they really like a startup, but do not think they can convince their colleagues.
When fundraising, the founder’s job is to convince someone that they can convince someone, that they can convince someone. That is a lot of convincing. And because some of the convincing are lost during this conviction funnel due to the many handovers, the amount of convincing must be handsome.
To my observation, the startups who get term sheets thrown at them are often the startups where the founders are extremely convincing. Not necessarily the startups who have the best product-market-fit. This might seem unfair. And it is. But that is the current reality.
Consequently, being convincing matters. And many founders are just not convincing enough. Often the lack of “convincingness” is because these founders are really smart. And really smart people know that nothing is certain. They know that everything rests on assumptions and that few things rarely go as planned. Consequently, these founders are mitigating when asked questions and avoid cherry-picking data to support their conclusions.
But being convincing might require founders to appear a little less smart.
Being convincing to a VC means educating them why strong and unstoppable forces in the world create the perfect storm for your startup. It is to preach that the problem you are solving is among humanity’s biggest and most pressing issues. It is to assure them that you have found a “magical” way to obtain customers in a rapid fashion that knows no limits besides the money at your disposal. It is to make them imagine a new product category that will carry your brand as synonymous naming. It is to paint a picture of your experience as having earned you world class-skills that make you the best team on the planet to execute this business. And all this convincing must survive the handover between several people over the course of several weeks and still maintain its “convincingness.”
The fact that “convincingness” plays such a determining role in who receives VC interest is a pity. Because it means that some incredibly smart people have a tough time getting VC funding. At the same time, it gives meaning to institutions such as Accelerace and others who help founders navigate a world where “convincingness” matters more than it should.
It has been the year my prediction from 2017 came true. Four years ago, I described how crypto would enable what I called ‘unique digital assets’. Today, they are known as ‘non-fungible tokens’ or NFTs.
Many people consider NFTs to be gimmicks. They are mistaken.
Humanity is engaged in a collective effort to create a digital version of reality. Why? Well, we have always tried to create alternate realities. And before the latest digital tooling became available to us, we relied on hallucinogens, theme parks, and roleplaying.
But since the birth of the internet, our brightest minds have focused their efforts on digital realities. Chatrooms, online games, and social media are all efforts in this direction.
With the latest tooling available, such as VR headsets, powerful graphics engines, streaming, machine learning etc. our collective project is accelerating. And with each new app, we are advancing towards a more digital reality.
Many people hope that the coming digital reality will be more equitable. I share this hope. However, some people want digital realities with no scarcities. The logic is simple. When code can be replicated endlessly, we can all have things in abundance. A sort of paradise, they surmise.
However, this thinking is flawed. A world without scarcity is not a paradise. It is something closer to hell.
Scarcity is important because scarcity makes us value things. If something is abundant, we assign zero value to it. And a world we assign zero value to sounds far from paradise. Instead, it sounds like an action game with all the cheats turned on. After 5 minutes of rampage, this ceases to be fun.
Even today, some of our most beloved apps have introduced artificial scarcity for the joy and benefits of their users. Pictures that disappear, a limited number of attendees for streams, time-limited access.
However, the current ways of imposing scarcity are crude because they are essentially just ways of “crippling” the apps. They are not true scarcities. In contrast, NFTs enable true scarcity. As true as digital scarcity gets anyway.
NFTs could turn hell into paradise. A digital version of reality. One that feels truly valuable. NFTs enable us to create, trade and collect. Actions that define us as a species and give us immense joy.
We are just at the beginning of NFTs. So early that we call them by name. Soon, we will no longer call them NFTs. They will just be things. Because we will take scarcity for granted. Just like we used to call things “online”. Today, we just assume things are online.
Startups have amble opportunities to speed up this development. All digital assets will be put on blockchains. Just like we have put everything digital on servers.
Many startups helped us get online. Just like many startups will help us get “on-chain”. In the coming year, I hope to bet on more startups doing this.
Folklore /ˈfəʊklɔː/: the traditional beliefs, customs, and stories of a community, passed through the generations by word of mouth.
Each competitive realm has folklores. Stories of fame, success, and paths to notoriety. In golf, we know the story of a young Tiger Woods demonstrating his putting skills on national television at the age of 3.
In acting, we tell stories of the crazy dedication by Matthew McConaughey that willingly embodies his characters to an extent that they fuse.
In science, we tell stories of the lifelong obsession of Jane Goodall, who lived in the jungle studying chimpanzees.
In the modern world, folklore is more influential than ever. Because when we have access to infinite information, stories echoed by communities stand out as authentic and real.
Folklore shapes our beliefs about the realm that it depicts. The story of Tiger Woods primes us to believe that becoming a professional golfer is hard. Unless one has demonstrated remarkable talent from an early age, becoming the next Tiger is impossible, we believe. Consequently, most parents would not support the idea of their kids dropping out of university to start a potential career in golf. Nor would the trainers, or even their friends.
The story of Matthew McConaughey means few people suffer from the delusion that becoming a movie star is easy. And few career advisors would recommend trying.
The story of Jane Goodall tells us that becoming a renowned scientist requires lifelong immersion. And the few who embark on this quest, understand the sacrifices.
Golf, Hollywood, and Science share the characteristic that making a living, let alone becoming a top performer, is hard. We understand the odds, the sacrifices, and the obsession. And most stay away.
Startups share the same characteristic of being hard. Making a living, let alone making it onto the unicorn list, is as difficult as becoming a Jane Goodall. In 2020, 120 startups became unicorns. It is estimated that about half a million startups are founded per year. That is a chance of 0.024%.
Even when we decrease the ambition from unicorn to just raising a series-A, the numbers illuminate the hardship. In Denmark in 2020 (where I live), we had about 12 series-A investments in Danish startups. It is estimated that 500-something startups are founded each year in Denmark. That is a chance of raising a series-A of about 2%
However, the facts do not shape the perception of the realm of startups. Startup folklore does. And unlike Golf, Hollywood, and Science; teachers, parents, peers, and career advisors seemingly support everyone to pursue a startup. For a long time, this puzzled me. But I have come to understand the phenomenon to be the power of folklore.
Startup folklore is heavy on stories of people materializing billion-dollar companies by conceiving of a good idea. These stories make us believe that the idea is what matters. Equal to talent in golf. Dedication in acting. Or obsession in science. If you have it, you can make it.
The forgiving thing about this belief is that everyone has ideas. Not everyone has talent, dedication, or obsession. But everyone has ideas. Thus, startups can be done by everyone, the logic goes.
Unfortunately, the facts tell a different story. But more importantly, those of us who have spent a lifetime working with startups know that ideas have very little to do with success. Instead, the foundation for success is ‘original insight’. And not everyone has it.
The misconception has the effect that many people are attempting startups without having the foundation to succeed. But that is not the problem. Because, through this experience, many people obtain the lessons for later success.
The real problem is that because everyone thinks they have a chance of startups, equally many people think they can mentor and advise startups.
Few people think they can mentor and advise golfers, actors, or scientists. We understand it requires intimate understanding, expertise, and experience.
Ultimately, the victims are startups. Because when true startup expertise is neglected, many programs and organizations created to help startups are useless.
In these places, startups meet mentors who are interested in startups. And sometimes passionately so. But interest does not equal expertise. Founders do not need cheering, idea jamming, and being retold the content of books they could otherwise buy. Well, sometimes founders need those things, but it won’t be enough.
Founders need insight into the unique challenges of their business model, their stage, and their team composition. They need experience from analogous startups and sparring from people with battle scars and costly paid learnings from years of doing what the founders are about to attempt.
Startups are one of the hardest realms of human activity. To truly help startups, we must see past the folklore, and organize real help to startups. And only by recognizing that startup is an area of expertise, it can be done.
And if you are a startup founder: Evaluate the help being offered. See past the self-proclaimed titles of accelerator, incubator, advisor, mentor, business angel. They mean nothing! Find out who is behind them and evaluate them as if your life depended on it. Because it does.
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 and Overkill Ventures. But I post it here to log advances in my thinking.
The theory, framework, and tools provided in this white paper are not attempts to create an exhaustive evaluation framework regarding investment decisions in startups. The focus of this paper is purely to assess the potential speed of growth. For most investors, the speed of growth is just one of many factors that investors must evaluate. Among these are: the quality of the team, the terms of the deal, and the momentum of startup. For the last part we recommend reading the white paper called Momentum from the same authors. The Momentum white paper can be found on here.
Since 2009, Accelerace has seen hundreds of startups unfold their full potential. Some have become unicorns and others have become lifestyle businesses. The ability to track these companies over a decade, has given us insight into different growth stories. Stories we extract lessons from and transform to processes at our accelerator programs and VC funds.
From some of our earliest cohorts, two companies stood out. Both were courted by big name VCs and received tickets to shoot for the ultimate outcome. The reasons were obvious. Both startups found early product-market-fit, had little competition, and were targeting massive market opportunities.
Twelve years later, the two companies have unbelievable different growth stories. One employs more than 1,000 people. The other counts about 30 people. The first has a valuation of about 1.5 billion USD. The other about 25 million USD.
Why did they grow at such different speeds?
At first glance this is puzzling. Both companies attracted a stellar team, board, and investors. In addition, they received equal portions of seed capital.
Upon a second look, the answer becomes obvious. But it requires the lens provided by two concepts to unfold. We call them: Beta and Alpha.
Venture capitalists and startup founders focus on growth. The reason is simple. Growth is synonymous with success in the world of startups. But to venture capitalists, growth is only half the story.
For venture capitalists, the speed of growth is even more important. The reason is that VCs measure their return in IRR (internal rate of return). Simply put, a 10X return is worth more than double if achieved in 5 years rather than in 10 years.
But how can one assess if a startup has the potential for rapid growth?
Today, we have come to understand the potential speed of growth of any company to be defined by two factors.
One, the growth of the market for the service/product: Beta
Two, the relative competitiveness against competitors in the same market: Alpha
Beta and Alpha will be illustrated below with a simple and fictional scenario of two startups. BlueApp and RedApp.
The effect of Beta
To illustrate Beta, we can imagine two startups: BlueApp and RedApp with identical value propositions. Let us imagine that these two startups target the same market of 10 potential customers interested in the product category that these startups offer. Next year, the number of customers grows to 20. And the following year, the number is 40. Put differently, the market grows 100% per year. This would be an attractive Beta for most companies.
In this example, both BlueApp and RedApp should start out with 5 customers each. The following year both startups have 10 customers. The following year, they both have 20 customers. The scenario is illustrated by the graph 1 below:
As we can see on the graph above, both startups grow 100% per year. The scenario could be called: “Attractive Beta, No Alpha.” That is because the market grows 100% per year, but there is no difference between the strength of their value propositions. In short, this means that each startup grows at the rate of the market. With no Alpha, the growth of a startup is simply defined by the market growth. Or if applying our terminology; by the Beta.
And here is the first hint at our mystery outlined in the Prelude. The startup valued at 1.5 billion USD operated in a market with incredibly attractive Beta. The other startup was in a market with zero Beta. The first startup entered a market just about to take off and the market would keep growing rapidly for the next decade. The other startup entered a market with no growth. Consequently, every customer had existing routines, alternative solutions and existing vendor relationships. Importantly, even though the startup valued at 1.5 billion USD had a much smaller market to begin with, this would soon change.
The effect of Alpha
Rarely do companies differ in name only. To make the example more realistic, let us imagine that only RedApp has an algorithm based on user data that improves the app. This would be a rather attractive Alpha for RedApp.
The first year, the startups share the market with 5 customers each. But the following year, the 5 customers of RedApp has improved the dataset behind RedApp’s algorithm. Now, RedApp’s value proposition is superior. Consequently, most new customers on the market prefer BlueApp. Now RedApp has 15 customers that help improve the app. In year three, most new customers also choose RedApp. And so, it continues. In other words, RedApp has a Reinforcing Value Loop that spins at an increasing pace, meaning that BlueApp cannot keep up.
The scenario is illustrated by the graph 2 below:
As illustrated on the graph above, Beta and Alpha both influence the growth of BlueApp and RedApp. The scenario could be called: “Attractive Beta, Attractive Alpha for RedApp.” That is because that even though the market grows with 100%, RedApp grows significantly more than 100% a year, whereas BlueApp grows less than 100% a year.
And here is the second and definitive answer to our mystery outlined in the Prelude. The startup valued at 1.5 billion USD did not only have attractive Beta. It also had an incredibly attractive Alpha. Every new user improved their app, and past a certain point, competition was irrelevant. In contrast, the other company enjoyed neither Beta nor Alpha.
Sources of Beta
In the previous section we made Beta synonymous with the growth of the market. However, this was a simplification.
In fact, Beta comes from a combination of two sources. First, the rate of new customers to the market. Second, shifting preferences among the customers in the market.
We define the market as the buyers and sellers engaging in transactions of a product category.
We find that the rate of new customers to the market is the clearest source of Beta. If considerable amounts of new customers enter the market every year, there are a vast number of willing buyers with no existing vendor relationships to sell to. And even though a startup faces competition, the number of new customers can be so large that competitors are preoccupied with servicing their own part of the growing market.
On rare occasion, extremely high rates of new customers appear. Often, this is due to radical innovations that provide a leap in value or/and lower costs for customers. In these cases, markets are “unlocked” and floods of new customers appear seemingly overnight. That was true for short term apartment rentals that were “unlocked” by Airbnb. Consequently, the rate of people wanting to rent out their apartment exploded.
Other examples of strong rates of new customers include: The rise of mobile developers during the late 2000s (caused by the iPhone) and explosion of delivery focused restaurants in the early 2020s (caused by Covid). Companies that benefitted from those examples include Unity Technologies and Wolt.
Another source of Beta is shifting preferences among customers. Regardless of the rate of new customers, the preferences among the existing customers can shift. If a startup is positioned to benefit from this shift, it can win customers as a result. A recent example is the preference for recruitment tools that ensure non-biased hiring. The number of corporate HR managers is stagnant. However, their preference is shifting. Consequently, startups that offer recruitment tools with candidate anonymization, could experience rapid growth driven by this source of Beta.
Real life examples of shifting preferences among customers include: The shifting consumer preference towards craft beer in the late 2000s. Corporate preference towards consumer style communication tools in the mid 2010s. Companies that benefitted from those examples include Mikkeller and Slack.
Obviously, the most powerful form of Beta comes from the combination new customers and shifting preferences. If the rate of new customers to a market is rapid and the preferences among customers in the market is shifting to the benefit of the startup, one has a cocktail for explosive growth. An example of such a cocktail was review management for online shops in the late 2010s. This period was marked with an explosion of new webshops. At the same time, these webshops increasingly started using reviews in their marketing. A company that enjoyed this “Beta cocktail” was the review site Trustpilot (Accelerace alumni 2009).
The five levels of Beta
As outlined above, Beta comes from two sources. And various combinations of strengths of these two sources lead to varying strengths of Beta.
Accelerace has developed a classification system for different strengths of Beta. The levels are easily identifiable by their combination of the rate of new customers and the shifting preference among customers. The right-hand side provides further details to aid with accurate classification.
When classifying a startup, it is important to note that the classification should not be based on historic dynamics. Instead, the classification must forward oriented. That is because startups benefit from the growth to come, whereas previous market growth is mostly irrelevant.
Naturally, the future is impossible to predict. Consequently, investors must classify the startups according to a qualified estimations of the rate of new customers and the shifting preference among customers.
The levels can be seen below. The scale goes from level 1 (weakest) to level 5 (strongest).
Level 1 Beta
The startup faces: Slow rate of new customers. & Slow shift in preferences among customers.
In this scenario the number of customers in the market is almost stagnant. Furthermore, the customers are not increasing their budgets or buying activity for the type of product category the startup sells. This is the lowest level of beta and makes it difficult for a startup to grow. An example could be cash handling POS systems for canteens. The number of canteens is not growing. And few canteens are looking to buy POS systems that can handle cash.
Level 2 Beta
The startup faces: Slow rate of new customers. & Steady shift in preferences among customers.
In this scenario the number of customers in the market is stagnant. However, the customers are increasingly interested in buying the product category that the startup sells. This is still a low level of beta because the customers often have existing vendor relations and will ask their vendor to supply the new product. Examples would be digital security camara systems for public parking. The number of public parking spaces is almost stagnant, but many are looking to upgrade their existing security systems. But because they have bought security systems for years, the existing vendors will fill most of this demand and leave little room for startups.
Level 3 Beta
The startup faces: Steady rate of new customers. & Steady shift in preferences among customers.
In this scenario the number of customers in the market is growing steadily. Furthermore, the customers are increasingly interested in buying the product category the startup sells. This is an attractive Beta because the new customers will have no existing vendor relations and startups have a more “level playing field”. Examples would be mental health apps. The number of people who engage in mental health is growing steadily. In addition, these people are increasingly using digital tools rather than just attending physical classes and treatment.
Level 4 Beta
The startup faces: Steady rate of new customers. & Rapid shift in preferences among customers.
In this scenario the number of customers in the market is growing steadily. However, the customers are rapidly adopting the product category the startup sells. This is an attractive Beta because the market has new customers with no existing vendor relations which gives startups a more “equal playing field”. Furthermore, the strong shift in preferences among customers mean that many of the existing vendors cannot innovate fast enough, and startups can swoop in. Examples would be many Fintech products. The number of people who seeks to administer their finances is growing steadily, and people are no longer seeking financial advisors and banks to scratch this itch. Instead, they rapidly seek digital tools.
Level 5 Beta
The startup faces: Rapid rate of new customers. & Rapid shift in preferences among customers.
In this scenario the number of customers in the market is growing rapidly. In addition, the customers are rapidly adopting the kind of product the startup sells. This is an extremely attractive Beta because the market has many new customers with no existing vendor relations which gives startups a “blue ocean”. Furthermore, the strong shift in preferences for the benefit of the startups means that many of the existing vendors cannot innovate fast enough, and startups can swoop in. Examples would digital collectibles. The number of people who seeks to collect digital items are exploding. In addition, the blockchainbased solutions are becoming the technology platform of choice rather than paper certificates.
Sources of Alpha
Alpha describes outperformance by a startup relative to the market growth rate. However, as investors we need to estimate the future growth rate. This means that we must understand the sources of Alpha.
At Accelerace we find that the only lasting source of Alpha originates from Reinforcing Value Loops (RVLs). A RVL is in place when the product increases in value with each new customer. Thereby, the loop is “reinforced” with each “spin”1.
The most obvious form of RVL comes from network effects. Marketplaces tend to enjoy RVLs because each new user adds goods/services that increase the value of the marketplace to new users.
However, we find that network effects are just one of various sources of RVLs. RVLs can also stem from economies of scale, where higher volume increases the value of the product. A classic example is deal sites. If they get more users, they can negotiate better deals, which in turn attracts more users.
Another potential source of Alpha is data. The more users, the more data is generated, which can be used to create a better product experience for new users.
The authors are not blind to other sources of Alpha than those that stems from RVL. Such as unique access to key people, superior know how, special rights, etc. However, we find that these are short lived and that RVLs are the only sustainable source of Alpha.
The five levels of Alpha
As outlined above, the only sustainable Alpha comes from RVLs. And various types of RVLs lead to varying strengths of Alpha.
Accelerace have developed a classification system for different strengths of Alpha. The levels are easily identifiable by their combination title. The third column provides further details to aid with accurate classification.
The levels can be seen below. The scale goes from level 1 (weakest) to level 5 (strongest).
Level 1 Alpha
The product is one where: Customers legitimize the product.
The startup has a product that introduces a new way of doing things. Most customers are waiting for other customers to use the product before they take the jump. Consequently, it becomes easier to sell as more customers buy because potential customers increasingly regard it as a legitimate solution. This is a low level of Alpha because legitimacy can take a long time to build due to the laws of the technology adoption lifecycle. Examples would include Bitcoin. Many people want to be sure that Bitcoins are valid assets before engaging themselves.
Level 2 Alpha
The product is one where: Customers enable the product.
The customers make it possible to offer the product because a certain scale is required. Thus, the more customers the startup gets, the better product experience they can offer. This is a decent level of Alpha because it has elements of network effects. Examples would be deal sites and collaboration tools. Unless a certain number of people use the deal site, the deal site cannot make good deals with shops. And unless a collaboration tool has enough users, the tool has no value. However, at a certain scale this effect has diminishing returns.
Level 3 Alpha
The product is one where: Customers contribute to the product.
The customers create part of the content that is offered to new customers. That could be valuable data, templates, and creations. This is a strong level of Alpha because the customers are directly affecting the value of the product. Examples would be template-based design tools (like Canva). Here the users are creating designs and templates that can be added for new users.
Level 4 Alpha
The product is one where: Customers create the product.
The customers create the key content that is offered to new customers. That could be valuable data, templates, and creations. This is a strong level of Alpha because the customers are directly creating the value of the product. Examples would be review apps (like Vivino). Here the users are creating the key content that other people are seeking.
Level 5 Alpha
The product is one where: Customers are the product.
The customers are the product. This is the most extreme form of Alpha because it is pure network effects. Examples are dating apps and social media. Once a startup gains a head start in accumulating users, their Reinforcing Value Loop will spin so fast everyone else will be left in the dust.
Beta and Alpha combinations
Both Beta and Alpha define the growth potential of a startup. However, startups do not need high levels of Beta and Alpha simultaneously to grow fast. In our experience, a high level on one is enough.
In cases where a startup enjoys high Beta but limited Alpha, the startup can still monopolize the rapidly growing market through sheer execution and operational excellence. In cases where the startup enjoys high Alpha but limited Beta, the startup can win most of the customers in the market by providing a much better product than competitors.
In the table below, we have illustrated the various combinations of Beta and Alpha possible. The darkness of the color indicates the attractiveness of the combination.
As illustrated in the table above, the highest potential for rapid growth is found in the upper right corner. Here startups are defined by high levels of Beta and Alpha simultaneously.
One would be forgiven to think this means that investors should focus on social media and dating apps because these products have level 5 Alpha. However, the authors stress that this would be a faulty interpretation. The table merely shows that such startups have the potential to grow faster than startups with low levels of Alpha. Whether this potential is realized is another story.
Beta and Alpha in use
We suggest classifying all investment candidates with Beta and Alpha assessments. This can be done by whomever scouts the startup because the classification system should enable anyone to accurately assesses Beta and Alpha levels of investment prospects and enrich the pipeline tool with this data.
In addition, Investment Managers and Partners can use the tool to enrich their investment proposals with these assessments. This will enable the Partners and/or the Investment Committee to use a shared taxonomy when discussing the growth potential of the companies in question.
Another use of Beta and Alpha classifications is for acceleration or “value-add” purposes. Investment Managers, Board Members, and Advisors can use the classification to aid portfolio companies with strategic decisions. Taking Beta into consideration is useful when doing segmentation and contemplating go-to-market strategies. Taking Alpha into consideration is useful when discussing strategic product directions because some features could add elements of Alpha.
At Accelerace we use the Beta and Alpha classification in our Investment Proposal document template. Investment Managers assess the Beta and Alpha levels of an investment candidate and present this assessment to the Investment Committee (IC). Because the IC members understand the classifications, we find that the discussions about the potential speed of growth of companies become radically more effective.
As can be seen on the screenshot of our Investment Proposal document template below, Beta and Alpha scores are requested in the section: Investment Conviction and Forecast.
In addition, we teach Beta and Alpha as topics in our acceleration programs. We include courses on Beta and Alpha and have design tools to help the startups design RVLs. We find that teaching these concepts and addressing potential scaling issues becomes much easier and more effective using this classification.
Limitations of the model
Naturally, there are limitations to the Beta and Alpha model. The most important are:
Beta and Alpha levels have little predictive power for the outcome of an investment. Investment outcomes are influenced by a myriad of factors including macroeconomic circumstances, trends, legislation, competition, team etc. For this reason, it is easy to identify examples of companies with high Beta and Alpha that fared worse than certain companies with low Beta and Alpha. That said, Beta and Alpha are defining of the potential outcome at the time of the investment.
The model does not define the potential size a startup. A company can continue to grow over generations, as have been the case with companies such as Disney and Coca Cola. The model only addresses the potential speed of growth.
With just five levels of Beta, there are combinations of Beta scenarios that are not included. E.g., Stagnant rate of new customers & Strong shift in preference among customers. The scenarios that are not included are rarer and the authors have chosen to prioritize simplicity over exhaustiveness. That said, the model could be expanded to include all scenarios.
There are sources of Alpha that are not included in this model. Many of these are what investors would call “edges” or “unfair advantages.” That could be know-how, personal relations, unique access to resources etc. All of these would make a startup grow faster than the market rate. However, these types of Alpha are often unique and unsustainable. Thus, we have not (yet) been able to classify these. Consequently, we suggest evaluating these on a case-by-case basis.
First, Beta and Alpha allow for quantitative evaluation of the growth potential of investment candidates. This is useful when communicating and discussing this aspect during partner meetings and IC meetings. Furthermore, they allow for clear communication between decision makers and the scouts that often perform the initial assessment of startups. With this tool, one could easily imagine scouts being given the brief to look for startups with “Beta levels above 3” and “Alpha levels above 2”.
Second, the model enables quantitative analyses of past investment decisions. If the Beta and Alpha levels are recorded, it is easy compare these scores with actual perform and use this to optimize the investment decision tools.
About the authors
Peter Torstensen and David Ventzel are partners at Accelerace. Accelerace is a startup accelerator and VC 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 Head of Acceleration, Mads Løntoft and Peter Marculans, Managing Partner at Overkill Ventures in the development of this paper.
If you are interested in the model and collaborating further development of the framework, then contact David Ventzel: firstname.lastname@example.org.
All founders have an idea. Better founders have a vision. The best founders have a thesis. This post will teach you about the concept of ‘Original Thesis’. It will show how the three most valuable companies from Accelerace benefitted from having one. Finally, it will be apparent why the post features a chimpanzee.
There are no holes in the market.
In 1759, Adam Smith coined a term that became a pillar of economic understanding. Smith understood the following: In a free market economy, people will find ways to serve the needs of others. He called it the invisible hand.
An economy has network effects, meaning that the strength of an economy is related to the number of participants. Today, infinitely more people are participating than in the days of Adam Smith. Thus, the invisible hand is equally more forceful.
Now, the invisible hand is sweeping with unprecedented force. If people taste bubble tea in Kuala Lumpur, a few weeks later, bubble tea pops up in Copenhagen. If people get medicine delivered by bike in Berlin, next month, the same is offered in Buenos Aires.
Today, the needs of people are satisfied before you can write a business plan. I suspect this makes you slightly uncomfortable. It means; there are “no holes in the market”.
Startup founders who think they have spotted a large unmet need are simply deluding themselves. And if there is an unmet need, it is extremely short-lived because hordes of hopeful entrepreneurs are already on it. Thus, the “opportunity” is not compelling. Not to a VC anyway.
After having accelerated 700 startups, we see the most successful companies did not have a large market. In fact, many did not have a market at all. Instead, these founders had something else. Something much more potent. An Original Thesis.
The difference between Vision and Thesis.
Arguably, the three most successful companies from Accelerace are Trustpilot, Templafy and Labster. None of these founders thought they had spotted a “hole in the market”.
Instead, Peter Holten Mühlmann, founder of Trustpilot, had an Original Thesis. In 2007, he surmised that in the (near) future, the internet would enable anyone could set up a webshop. Consequently, consumers would be flooded with the availability of new online shops. Peter further speculated that because web shops would be easy to set up, lots of fraudulent shops would appear. Peter believed that consumers would find it hard to navigate between good and bad shops. Consequently, they would need Trustpilot. A tool that would warn people about bad shops.
When Peter pitched Trustpilot, he did not claim a big market. The facts were clear. Less than 2% of commerce was done online. In fact, the market was non-existing. Today, Trustpilot is valued at 1,3 billion dollars and traded on the London stock exchange.
If you had tried to identify the “hole in the market” for Trustpilot, you would come up short. No amount of market research, customer interview, focus groups, or Garter Reports would have identified the market for Trustpilot. Why? Because at the time, there was no problem.
Peter Holten Mühlmann understood this. His claim was that this problem would arise in the (near) future. But more importantly, he could articulate why. And as we will learn later, this defines an Original Thesis.
When Christian and Henrik co-founded Templafy during our acceleration program in 2013, they too were seeing into the future. Below is a slide from their first pitch deck.
(slide from the Templafy 2013 seed round pitch deck above)
The slide paints a picture of a future when corporate employees are working in cloud-based programs and on mobile devices. This was not yet a reality, but the founders surmised that this future was nigh. They knew that some of the most innovative corporates were already planning to migrate to cloud versions of office programs. They also speculated that Microsoft and Google would not offer advanced template control when that happened. How did they know this? Because the founders were leading consultants within the field of template management.
In other words, Christian and Henrik had an Original Thesis. Their thesis was that in the (near) future corporates would migrate to cloud-based programs and mobile devices. Furthermore, the leading providers would not offer advanced template management, and that would create a need for a separate tool. In addition, they surmised that Microsoft and Google would endorse Templafy because Templafy would be an important enabler for corporates to migrate to the cloud.
Christian and Henrik turned out to be correct. But again, if you had attempted to validate the market for Templafy in 2013, you would have failed. There was no market. Nor did the founders claim so. Instead, they argued the validity of their thesis. And because it was based on Original Insight, it was an Original Thesis.
The third example is Labster. In 2012, the Founder Mads Tvillinggaard Bonde surmised that in the (near) future, STEM degrees would increase in popularity due to scientific and technology-driven innovation. This would course the problem that the current university campuses would have little laboratory space for the growing number of STEM students.
Simultaneously, advances in computing power, game engines, GPUs would make virtual labs good enough to replace physical labs. It would take many years before his thesis would be proven correct. But it ultimately did. And in early 2021, almost ten years after his Original Thesis was conceived, Andreessen Horowitz invested in Labster.
The truth is none of these founders claimed to have spotted a “hole in the market”, or a “business opportunity”. Instead, they had an Original Thesis. And so must you. But at this point you might wonder: how do you qualify a thesis?
Describing the future in detail
Anyone can make guesses about the future. A lot of people say that China will replace the US as the global leader. But few people can tell you why they think so. And even fewer people can state original arguments.
An Original Thesis has two requirements. It is must Original. And it must be a Thesis.
For something to qualify as a Thesis, it must include a time perspective. Most people can agree that one day, we will live in Virtual Reality. But that is not a Thesis. That is a vision.
A Thesis about Virtual Reality must include when it will happen. Furthermore, it must identify the drivers behind the development. A Thesis would sound more like 5G will enable high enough bandwidth to stream 8K content to VR headsets. 8K resolutions will remove the grainy effect in VR, and streaming will make the headsets light enough to be comfortable for long periods of time. Tactile suits will develop due to advances in smart materials and will make the VR experience fully immersive. At the same time, the largest gaming studios will focus on VR releases due to premium price points on VR versions. This cocktail will take VR from a fun experience, into an alternate reality. 8K resolutions will be coming within three years, 5G and smart suits within two years.
Put differently, something is a Thesis when you can describe your vision in detail and understand the drivers that will make your vision come true.
However, if you took my (mock) thesis from above and put it into a pitch deck, it would lack Originality. It is a thesis, but it would not be Original. Why does originality matter? Because ideally, you are the only person with this thesis. Because if you are non-consensus, then you will be free of competition.
If Charles Darwin had been one out of thousand people who had the thesis about natural evolution, the Origin of Species would be a lot less special.
More importantly, originality gives you conviction. The brutality of startup journeys tests your conviction to the fullest. During hard times, conviction makes you persevere. And conviction makes you speak with enough passion to rally your troops.
It took more than ten years before the Original Thesis of Charles Darwin was accepted by the scientific community. Ten years of mockery. But never doubt.
Ten years is also the time it takes most startups to reach maturity. Perhaps not with mockery. But certainly, with doubt. That unless you have an Original Thesis. I know that our most successful founders would agree.
If you want to learn more from the best startups we have worked with, apply to Accelerace and Overkill Ventures. We invest in startups.
There is a fear that has no name. But most startup founders experience it.
Perhaps, it is the fear that kills most startups. And no, it is not the fear of failure. It is a fear much more visceral. I call it Beachhead Phobia.
In our acceleration program, we teach all founders the concept of the Beachhead. It is the most important tool for finding product-market fit.
We teach our startups to focus all their resources on a single homogeneous segment that has a desperate need for their product. The desperation usually arises from the fact the segment is new and fast-growing. Consequently, the Beachhead has not yet found a solution that adequately solves their problem. This makes the Beachhead willing to test an early product from an unknown startup.
The beachhead is borrowed from military strategy. Here, invading forces must focus all their resources on a single spot on the beach to conquer enemy territory.
All successful startups find a Beachhead. But before they do, startups typically begin with a very broad customer definition. Then they learn that customers are different and want different things. This eventually leads startups to focus on a Beachhead. Once, startups dominate the Beachhead, they slowly broaden their focus again.
The puzzling thing is that even though all successful startups go through this process, all founders fight it. And after having accelerated startups for a decade, I see what is going on.
The fog of startup
When launching a startup, founders feel the intoxicating promise of infinite opportunity. The sense arises from the “fog-of-startup”. We want to be the next big startup success. But we are not completely sure how to get there. The space between the current situation and the future aspiration is the fog-of-startup.
In the fog-of-startup, we expect advantageous things will happen. Perhaps, a famous VC will flood us with cash. Or a big company will start distributing our product. Or a celebrity will endorse us. But our biggest hope is that we will immediately get flooded by customers from around the globe.
To keep this dream alive, we communicate in the biggest and broadest terms possible. We call our product the one-stop shop. Or the platform. Or the go-to software. We claim to be born global and be blitz scaling.
Accordingly, we launch and prepare champagne bottles. But instead of servers crashing due to insane customer demand. Things get murky. Some people sign up. But not nearly the numbers we hoped. The “fog of startup” has been lifted and it hid no miracles.
At this point, many founders make a fatal mistake. We surmise that we did not communicate to enough people. Not enough people understood the brilliance of our product. So, we respond by painting an even broader picture. We might state that our product is relevant for all industries or all consumers. Surely, this will make us seem bigger and relevant to more people.
But it does not have the intended effect. The response turns even murkier.
At this point, we get worried. Maybe we did something wrong. So, we seek advice (and funding). At some point, we encounter people who know about startups. That could be investors, other founders, and advisors. These people will tell us to “focus”. But at first, this advice seems strange.
Because we already focus all of our time on our startup. So, the advice seems patronizing and unnecessary. Sometimes, those providing the advice manage to convey that the focus is related to customers. But since launch, we have done little else than answering requests for features and bug reports from customers.
At some point, lucky founders encounter the concept of the Beachhead. The logic is clear. We must focus on a single homogenous segment to whom we can offer a perfect product. Once, we have conquered this Beachhead, we can focus on the next adjacent segment.
In other words, we must abandon the one-stop shop for all companies. Instead, we must offer a unique product for a specific person, in a specific type of company, with a specific problem, to be used in a specific use case.
We get it. But then we feel it. The fear that has no name. So, I dubbed it Beachhead Phobia .
Successful founders realize they must focus on a Beachhead. Still, most founders hesitate. The reason is the unpleasant sensation when contemplating the change. That sensation is Beachhead Phobia.
The sensation stems from the fact that the advice seemingly conflicts with several common beliefs.
The first belief is that VCs only invest in billion-dollar markets. Consequently, many founders articulate their market in the widest possible terms. Unfortunately, these founders confuse different time perspectives. When VCs talk about billion-dollar markets, they mean markets 10 years from now. But when we advise founders to focus on a Beachhead, we mean for the next six months.
The second belief is that “thinking small” means lowering our ambition and impact. Many founders are avid readers of books with titles like: The magic of thinking big. In addition, our personalities compel us to make a “dent in the universe”.
Going from declaring that you serve all companies everywhere! to serving a small group of specific people in specific companies, simply feels unambitious. But again, we confuse time perspectives. Anyone who succeeds in anything big, first succeeds in something small. The Beachhead is just the first step.
The third belief is not a belief. It is a feeling. And for this reason, it is the strongest cause for Beachhead Phobia. It is the psychological truth that it feels much worse to be rejected by someone specific than to be ignored by a crowd.
During our program, we ask founders to name and list the Beachhead. If a startup claims their Beachhead is HR managers in SMEs. Then we ask the founders to make a list with names of the exact HR managers they plan to sell to. And then create a “perfect” value proposition for these people.
Creating a specific value proposition to a specific person infinitely increases the chance of a positive response. Any woman using dating apps can attest to this. And so can you (even if you are not a women using dating apps).
The problem is that contacting a specific person with a tailored message feels wildly uncomfortable. Why? Because suddenly our actions are measurable, and rejection becomes impossible to ignore.
In a nightclub, it feels much worse to approach a specific person and be rejected, than to be ignored on the dancefloor.
On the dancefloor, we can convince ourselves that someone attractive will soon appear. But approaching a specific person with a personalized compliment and be rejected, ruins the night.
But the best founders overcome Beachhead Phobia. They target the Beachhead, get rejected, learn from it, adjust their value proposition, and do it again. They feel visceral pain with every invalidation of their assumptions, but they never succumb to the fear. And neither will you.
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.
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.
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 Team, Technology, Customer 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.
TheTechnology 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 Team, Technology, Customer insight, and Customer commitmentsdimensions, but the price of this accumulation matters. The price is the constraint and consists of time and money.
Momentum only makessenseif 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 money. Only 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(Team, Technology, Customer 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:
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.
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.
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.
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.
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.
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.
Thetechnology can be classified according to commonly understood industry taxonomy. We propose the following six defined levels of technology.
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.
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.
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.
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
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.
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 technologywill 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.
10 – 30 points.
The founders have no insight and only a vague and over-simplistic idea about their customers.
30 – 50 points.
The founders have little insight and only a vague and over-simplistic idea about their customers.
50 – 70 points.
The founders have some insight, and but still only general ideas about their customers.
70 – 90 points.
The founders some insight and can describe their customers in detail.
90 – 110 points.
The founders have the same level of insight as their customers. Perhaps the founders use to hold that job position themselves.
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.
TheCustomer commitments can be classified according to commonly understood industry taxonomy. We propose the following six defined levels of commitment levels.
The startup has talked to customers and can anecdotally talk about customers who have expressed interest.
The startup has a signed letter of intent from a relevant customer. For consumer startups, people have signed up on a waitlist.
The startup has a signed agreement of doing a proof of concept with customers. For consumer startups, people have signed up on a waitlist.
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.
The startup has paying customer that is using the product in “production”.
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 customercommitments 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:
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.
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.
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.
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.
If you are interested in the model and collaborating further development of the framework, then contact the authors on David Ventzel: email@example.com or Peter Torstensen: firstname.lastname@example.org
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.
The obvious focus for investing in the coming year is anything that supports living and working at home. I never enjoyed investing in the obvious.
I view the world through the lenses of technology accessibility. Once the technology becomes accessible enough for founders to take advantage of, startups are created.
Luckily, running startup accelerators means you get a firsthand glimpse into exactly that. What are the nascent technologies founders are playing around with? And what can they do with it?
Reflecting on the past year, I have seen two sparks that are worth watching in 2021.
Blockchain pitches that are not about coins.
Blockchain has long been a theme for me. However, every year disappoints. The people who insisted on paying with bitcoins have vanished. And the first widespread consumer adopted blockchain app remains elusive.
Still, this year was the first time the pitch decks did not centralize around a token and used complicated blockchain language. Instead, the focus was on the customer’s needs. This development has been long awaited. I see it as a strong indicator that blockchain is maturing as an infrastructure. Consequently, we should see a lot more startups leveraging blockchain technology in 2021.
Protein extraction from low impact plants.
In 2019, researchers from DTU in Copenhagen extracted protein from plain grass. Protein extraction from plants is nothing new. However, the plant matter. The most common plant-based protein is soya. The problem with soya is its relatively large environmental impact.
But grass is not growing where protein is needed the most. But the extraction technology pioneered by the researchers at DTU could be applied for other types of plants. Among them the cheap and plentiful casava. This year, we saw just such a startup in our program. This could be a sign of a tsunami of protein extraction from local plants. The benefits are potentially both in terms of costs and the environment. And cheap and nutritious protein could underpin a new generation of functional foods. Perhaps already in 2021.