On Tokens Value

How traditional equity valuation methods come short in crypto.

I’ve been observing the development of the crypto space on and off since 2012, though more actively participating in it only for the last 12–18 months. Like most, I haven’t been immune to the jaw-dropping numbers that are currently floating around. Both critics and avid fans point to a developing bubble, citing the ‘valuations’ that these crypto projects achieve raising multi-million funding rounds through Initial Coin Offerings often before even having a working product.

This below shows the market capitalisation of the top 10 largest tokens in circulation as of this writing:

Source: coinmarketcap.com (14/6/2017)

I’ll spare you the math, it’s just over $100 billion (while the total for all crypto tokens is $115 billion). But what does that number actually mean? I’ve made some reflections on this below.

Taking a step back, equity is a security that gives its legal owner certain rights, above all the right to receive some form of cashflow from the underlying company. VCs generally own ‘preferred’ equity, a different class of it that allows them to veto certain decisions, get preferential treatment in an eventual liquidation, amongst other things; listed market retail investors typically own shares that may allow them to cast a vote while earning a share of the company’s profits through dividends. Theoretically, any valuation of an equity-like security can be arrived at via a discounted cashflow model by making some assumptions about its future cashflows and how risky the equity holder perceives they are.

What about token valuations?

By owning a token one has the right and the incentive to participate in an economy (existing or future).

As long as participating in such economy is expected to yield some sort of utility value to the holder of the tokens, the tokens will have a market clearing price that is a monetary reflection of such utility. To state the obvious, it does not reflect an equity ownership in a company. For example, the Siacoin token gives you the right to use some virtual storage space from a decentralised storage network. That’s valuable, in the same way as Dropbox users think 1TB of space is worth $100/yr (with the difference that there is no Dropbox Inc. behind it, but other users that earn tokens by giving up some of their disk space). If Dropbox’s equity is worth say $10b, that is the price some shareholders are willing to pay today to own the rights to its future cashflows. At the moment of this writing, the total circulating supply of Siacoin tokens is valued at $400 million. But what does that actually mean?

The example of the funfair comes handy here, so I will dwell on it at the risk of being very predictable. Imagine a funfair issues a limited number of tokens which customers have to buy in order to get into the various attractions. I’ll call them Funcoins, the default currency of the “funfair economy” (imagine it’s a country where everything is priced in Funcoins). Most intend to use the tokens to actually have fun, but inevitably there will be some speculators who just buy Funcoins to then resell them to other suitors in the expectation they can make a profit.
The funfair turns out to be super fun, so much so that at one point if you multiply the total number of Funcoins issued by the going price it totals a stupidly high number, say $1 billion. Does that mean that the funfair is ‘worth’ $1b? Most definitely not. So what’s worth $1 billion tangibly, and to whom?

It’s the present value of the expected utility (i.e. the fun) that all Funcoins will deliver to their holders.

So, does that mean that if I show up with $1 billion in cash at the door, I can have all the fun myself? This is where the funfair example comes short, as the funfair is actually a centralised economy owned by its equity holders who at the end of it end up with all of the Funcoins (minus the ones that got lost) and the fiat they collected by selling them. A ‘decentralised funfair’ would actually be one where anyone can participate as a ‘fun provider’ (earning Funcoins for that service) and/or ‘fun beneficiary’ (giving out Funcoins to receive it), with the economics flowing directly between the two parties. The decentralised funfair would have no employees nor middlemen, only utilitarian participants; there would actually be no end to it, as long as the economic incentives on both sides remain attractive enough. If I wanted to own all the Funcoins myself with my $1b in cash, I would remove all the incentives from the system for participants to provide and buy ‘fun’. The funfair economy would dry up and ultimately die and its Funcoins would be worthless. I would be left all alone, entertaining myself. This is fundamentally different from the equity world, where control actually warrants a valuation premium, not a discount.

In a decentralised economy, democratising (rather than concentrating) ownership via incentives creates value.

Tokens should not be valued in the same way a company’s equity gets valued, they should be valued as (potential) self-sustaining economies that generate utility exclusively for their participants. Because such economies are decentralised, i.e. they have no central authority taking a toll, the most liquid tokens in any economy should in theory ultimately prevail and that would constitute the best outcome for its participants. It gets a bit surreal, but, continuing along the decentralised funfair parallel, let’s imagine there are multiple funfairs where a fun provider or a fun beneficiary can participate: inevitably they would choose the one that provides the most attractive incentives (i.e. more fun, higher rewards). So ultimately there will only be one decentralised funfair left and its “fun token”, the most liquid one that delivers the most value to its participants. There are some arguments in favour of token interoperability and co-existence, but I will ignore them for the purposes of this post. This is another major difference from centralised economies where, thanks to their profit moats, incumbents manage to consolidate monopolies and deter new entrants, extracting economic value out of the system and ultimately creating a suboptimal economy for its participants (think eBay sellers upping their prices to make up for eBay fees, think Facebook showing you what an algorithm thinks will be more profitable, think about Western Union turning over $5+ billion a year…).

So when our jaws drop at some token’s market cap, we are immediately drawn to think that a pre-product company gets valued sky-highly and to call it a bubble. What’s actually happening is that the market is anticipating that the utility value of that economy / use case will one day be $x and that there may not be any other currency to participate in it, then applies some sort of discount rate reflective of risk to NPV it to today. Since most crypto tokens are coded to be scarce, a thriving underlying decentralised economy will result in the appreciation of its only currency.

It’s not simple to immediately grasp this idea for anyone used to valuing equity based on future earnings potential because it’s a whole new way of looking at value creation that requires a different level of abstraction.

If you are interested in this stuff I strongly recommend following Chris Burniske from Ark Invest on Twitter, he constantly publishes fascinating materials on crypto valuation methodologies.

It also helps to draw some comparisons: comparing crypto market cap to the equity value of all public and private companies (maybe $200 trillion in total?), or comparing it to global GDP (c. $80 trillion) would be apples to oranges. Some think that the M1 and (some parts) of the M2 money supply is the most meaningful comparable in the long run as it represents the total amount of cash and liquid assets sitting in savers pockets or bank accounts, which could theoretically be converted into tokens if all goods and services ended up being delivered through token-based decentralised economies. Globally, that’s anywhere between $40100 trillion.

Assuming the M1 money supply is a decent comparable, $100b prices in for example that:

  • A. [20]% of it gets ‘tokenized’
  • B. by year [2025]
  • C. at a [90]% deflation rate (rough, but Sia claims to be 10x cheaper than Amazon S3, Google Cloud etc.)
  • D. discounted to today at [30%] per annum to account for risk.

Depending on ones view about A, B, C and D, $100 billion can either be a dirt cheap option on a future decentralised Economy, or a tulip.

PS: I’m going to be thinking about assumption A next, as that’s clearly a major part of the value equation. If you have any thoughts on decentralisation potential of various economies please get in touch.

Thanks to Stefano Bernardi & Alex Shelkovnikov for sharing thoughts on the first draft.


Graduation Rates for Accelerated Startups

Relative odds of making it through funding rounds for European accelerator alumni.

I recently wrote about Accelerators as Series A Engines, and got a number of follow-up questions from that post. In particular, a number of people showed interest in whether going through an accelerator increases the odds of ‘making it’.

One possible attempt to address that question is to look at round by round graduation rates for cohorts of accelerated and non-accelerated startups separately. Yoram Wijngaarde and the team at Dealroom came to the rescue providing the data, as this is a topic they have covered in the past, comparing Europe with the US over time.

Europe only as of March 2017. Source: Dealroom.co 

What the bar chart above shows is the percentage of European seed-funded companies that progress through to subsequent rounds of funding (capped at Series D), for both cohorts of accelerated and non-accelerated startups.

A few observations worth making:

  • As a caveat, these percentages are likely to be slightly inflated compared to reality since a non-insignificant number of seed rounds do not get publicly disclosed relative to larger more late stage rounds that are more likely to be announced. However, it is still relevant to look at them on a relative basis;
  • At first glance, it’s apparent that a larger number of accelerated startups make it through to Series A and Series B compared to non-accelerated. Cumulatively, c. 40% more startups manage to raise a Series A round (37% vs 27%) and almost 2x as many go on to raise a Series B round (27% vs 14%);
  • In particular, 71% of accelerated startups that have raised a Series A, go on to raise a Series B (vs. ‘only’ 53% for non-accelerated), which is comparable to, if not higher than, the A-to-B graduation rate of the very top tier US VC portfolio companies:
  • However, post Series B the two cohorts seem to re-calibrate. It’s hard though to attribute statistical significance to the deltas in percentages given the much smaller sample sizes at those stages; the drop-off from B to C for accelerated startups looks particularly pronounced, with 67% of Series B funded companies not proceeding to raise a Series C round;
  • Overall across the two cohorts, 4–7% end up going all the way to Series D and (if you believed in the statistical significance of numbers at this stages) 58% more accelerated startups end up raising a Series D round (7.0% vs 4.4%);
  • What these numbers do not tell us though is what happens to startups that do not progress to the next funding round. In fact there are four possible outcomes for a company after each funding round and the numbers in the chart only represent one of them (the 1st):
  1. Raises a further round of funding;
  2. Does not raise / has not yet raised a further round of funding (i.e. becomes self-sufficient or has yet to go to market)
  3. Fails;
  4. Gets acquired.
  • Without actual data on each of these four possible outcomes at each funding round, one can only speculate on the causes of leakage. So one could, for example, speculate that accelerated startups get to cash-flow profitability faster than non-accelerated ones and therefore do not need to continue raising dilutive capital post Series B; or that accelerated startups become attractive M&A targets much earlier in their life than their counterparts; or quite simply that there isn’t enough data on accelerated startups post Series B because the cohort is just too young and most have not gone to market yet (thanks Jon Bradford for pointing that out!);
  • A more skeptical one could speculate that accelerated startup live off ‘demo-day hype’ and momentum up until Series B and then gravity brings them back down to earth after that, either failing or transforming into “cockroaches” (Matt H. Lerner’s favourite nomenclature for startups that keep chugging along between life and death); or that accelerated startups tend to turn into great acqu-hires, failing to generate the type of exits VCs strive for.

I am hoping to get more data on these potential outcomes, so hold tight for a follow-up post!


European Accelerators as Series A Engines

A relative analysis on the shape & size of Series A rounds for ‘accelerated’ startups

I was intrigued by the numbers recently released by Mattermark on accelerators share of the US Series A market (tl;dr c. 10% of all Series A are raised by graduates of the top 3 accelerators, Y Combinator, Techstars and 500 Startups), so I did some digging to see what the situation is like over here in Europe.

The headline is that in 2016 18% of all European Series A rounds were raised by startups that at one point went through an accelerator or incubator programme.

This table has many more interesting numbers in it, which I will dissect below.

Source: Crunchbase

A few takeouts from the data:
  • 10x more rounds. The number of Series A rounds raised by accelerator graduates has increased more than 10-fold since 2012, from 6 to 61, while the rest of the A market ‘only’ grew by 2.8x over the same period;
  • Bigger share of the volume pie. Series A rounds raised by accelerator graduates have taken a larger and larger share, trebling from 6% of all A rounds in 2012 to 18% in 2016. This year we can be pretty confident that at least 1 out of 5 Series A are of this type. Series A investors better show up at those demo days!;
  • Bigger share of the dollar pie. Accelerator graduates have raised $456m worth of Series A rounds over the past 5 years, representing c. 8% of the total Series A capital raised over the same period, although that was 14% in 2016 and only 3% in 2012. So the pace of capital deployment is accelerating, literally;
  • Smaller A rounds. The average Series A round size raised by accelerator graduates is consistently lower than that of non-graduates, with the average discount being c. 28% over the last 5 years ($3.6m vs $5.0m). It would be fascinating to have the data on how pre-money valuations (and thus dilution) compare between graduates and non-graduates. I would speculate that this discount can be attributed to either: a) adverse selection (i.e. the more confident, experienced and networked founders do not need an accelerator and/or are able to raise more capital); or b) once ‘accelerated’ a startups has less funding needs, having invested previous capital more wisely and achieved more with it;
  • Less pre-A capital. The average amount of pre-Series A capital raised by accelerator graduates is 22% lower than that of their counterparts ($1.1m vs $1.4m). In other words, graduates get to a Series A more capital efficiently, ‘wasting’ less capital, or perhaps it is adverse selection at work again;
  • More pre-A rounds. The average number of pre-Series A rounds for ‘accelerated’ startups is more than double the number of rounds than their counterparts require to get to a Series A event (1.5 vs 0.7). i.e. graduates needs an extra round to get to a Series A (which I guess is glaringly obvious if the acceleration is considered as a round per se);
  • The A-crunch is real. Across the board it is very clear that the Series A bar has consistently risen, with the average amount of capital needed before getting to the A having increased 7x from $0.4m over 0.34 rounds in 2012, to $2.6m over 1.03 round in 2016.

While one can easily get stuck in it, it is always interesting to draw some comparisons to what is happening in the US, where in 2016 21% of all Series A rounds were accelerator graduates (so not too far from the 18% in Europe).

Source: Crunchbase

Other then the most obvious observation, such as that Series A rounds are generally larger in the US (just shy of 50% larger, whether or not the startup has been through an accelerator), it is interesting to note that:

  • It appears hard to unlock a US Series A with less than $3m in pre-Series A funding, regardless of accelerators involvement;
  • In Europe a Series A can happen with a lot less pre-Series A funding, particularly going though an accelerator which gets you there on about half the capital ($1.5m vs $3.1m) and fewer rounds (1.8 vs 2.3);
  • The accelerator Series A ‘discount’ applies in similar fashion across the pond, with average size of Series A rounds 37% higher for non-accelerated startups in both US and Europe.

So overall, with various caveats, it seems clear that accelerators and incubators in Europe can be a very strong engine of Series A creation, just like they have been in the US.


2016: The Year European Venture Capital Changed Gear

68 tech VC funds raised €8.4B in a record year for Europe.

Despite the unprecedented macro and political uncertainty that has characterised 2016, this has undoubtedly been a very prolific year for European tech VC funding.

I compiled a list of new tech-focused funds publicly announced this year and, with a couple of weeks to go, the headlines are hot: close to €8.4B of capital was raised by 68 European funds, at an average fund size of €127m (I’ve ignored clean/bio/med-tech funds for simplicity).

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Note: numbers in this table exclude two funds that were announced, but whose size was not disclosed.

 

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Amount raised and number of venture funds announced in European tech (by quarter)

A few take-outs from the data:

1. Growth! Since I’ve only been tracking new funds this year, I haven’t got a direct comparable for 2015. Luckily Atomico came to the rescue with their freshly released State of European Tech 2016 report, with data on new tech funds provided by Invest Europe (EVCA) up to 2015. After applying some adjustments to my data to make it as like-for-like as possible (e.g. they exclude Russia and Israel, they do not include all “growth” funds nor all corporate venture funds), in the most conservative scenario where I completely ignore corporate venture funds and growth funds, it looks like 2016 will be up at least 33% in terms of capital raised (well on pace with last year’s growth) and 10% in number of funds (reverting a trend that saw number of funds closed drop by 20% in ‘15), making ‘16 a record year as far as recent memory is concerned. And that is by excluding some funds that undoubtedly do ‘venture’ such as Idinvest Growth (backers of Onfido, Happn, Zenly), Nokia Growth (Clue, Drivy) and Santander InnoVentures (PayKey, Elliptic) which Invest Europe might actually have included. So the actual growth could be much higher.

Within the year, H2 appears markedly down compared to H1. One could infer that the Brexit referendum and US election had an impact on fundraising activity in the second part of the year. In reality Q1 & Q2 appear to be two exceptional quarters with the mega funds (>€250m) like Index Ventures, Nokia Growth Partners, Rocket, Accel London, EQT Ventures and Partech Ventures Growth together raising €2.6b in capital in the first half of the year. Excluding mega funds, H2 was only about 17% down in value compared to H1, and interestingly was up 46% in the sub-€100m category.

Fresh new funds raised by VCs is typically a strong leading indicator of the direction of the industry, as funds have to deploy capital within a specified period of time (3–4 years for first cheques). So, in a year in that will see capital invested at levels roughly on par with 2015, this is a great sign of things to come for European startups.

2. More late stage capital. Capital was raised at all levels and for all venture stages, not just at the small early end as it’s often conventional wisdom with European VC. The mega funds in particular raised €3.5b combined. These are the funds that can write €20m+ cheques at Series B-C-D and support companies until larger exit, a segment of the market in which Europe has historically lagged the US.

It’s great to see more capital becoming available at that level, to help European champions scale, with the welcome appearance of EQT Ventures as a 1st time fund in that category (and the largest raised in ’16 at €566m).

3. New breed of GPs. While the mega funds continue to raise larger funds, a new generation of 1st time funds is clearly emerging. 44% of the new funds announced were 1st time funds (30), representing 30% of the capital (€2.5b), with the average first time fund being €65m (excluding EQT Ventures mega fund, which would skew the numbers). This is a very important category as 1st time funds tend to come to market with innovative strategies and models (e.g. Entrepreneur First) , often tailored to their local market (e.g. Kibo, Daphni) or to the previous experiences of the GPs (e.g. BlueYard, EQT Ventures), new ideas and perspectives, filling gaps overlooked by established funds. Shai Goldman’s spreadsheet on sub-$200m US funds reports ‘only’ 25 1st time funds raised in the US this year. In that size category Europe has 27 already and, if one assumes that most if not all 1st time funds are in the sub-$200m category (maybe an incorrect assumption for the US), then Europe is likely to have produced more 1st time funds than the US this year, which would be remarkable.

I’ve written at length about 1st time funds and what they entail here, it’s great to finally see LPs embracing this category in Europe too. It’s also great to see successful entrepreneurs, executives and investors recycling their wealth and experience back into the ecosystem.

4. More specialisation. More funds are being raised to go after a specific opportunity or strategy: from Seraphim and OHB space-tech funds, to Entrepreneur First follow-on fund for deep-tech startups, to Anterra’s agri-tech fund, to Partech Growth fund, to Kibo and K Fund Spain-focused funds, to Barcamper in Italy, Daphni in France and Karma in Estonia. Stage, sector and country specialists are emerging, led by specialist managers with relevant experience.

This is fantastic news for European founders who can finally find the best-fit capital for their business or sector, while increasing their chances of success with the right support.

5. Democratisation. It’s clearly not just about the UK and Germany anymore. While this math is bit simplistic, as most funds can also invest outside of their home country, non-UK and non-German HQ’d funds accounted for c. 60% of all capital raised, with Sweden and France crossing the billion euro mark, Netherland, Finland and Israel each solidly in the €400–500m range and Southern-European countries like Italy, Spain and Portugal emerging with €200–250m in capital raised in each. Even Estonia and Bulgaria are coming up with their local VC funds.

This was the main theme that emerged from Atomico’s recent report published at Slush ‘16: capital is following companies from an increasingly diverse range of geographies and hubs across Europe. Great news for founders outside of the traditional tech hotbeds.

6. Government support. To the best of my knowledge, at least 26 out of 68 funds (38% by number and 45% by value) announced in 2016 had a national or European governmental institution as a LP, with the European Investment Fund (EIF) alone present in 20 funds (I believe in line with 2015). The recent announcements of a €1.6B pan-European EU-sponsored fund-of-fund programme and of a £400m top-up to the British Business Bank suggest there is more of that to come in the near future. This continues to be the reality of the fragmented European venture capital market. The bet remains that over time, as the industry continues to grow and mature, generating large and larger exits, attractive returns will eventually flow and attract private institutional capital from the likes of pension funds and insurance companies to the asset class.

This is already starting to happen at an accelerating pace, which is a great sign, with the recent news of Legal & General committing to Accelerated Digital Ventures, AP4 (Swedish national pension fund) investing in EQT Ventures, and Italian insurance group Generali in Earlybird Venture Capital most recent fund. It’s also worth noting that, out of my list, 40 funds raising a total of €4.6B do not have any government agency as LP, including 20 1st time funds, showing signs of European funds being able to stand on their own two feet.

Now onto more and bigger exits, please! If every fund manager is targeting to return 3x to their LPs, this vintage will need to return >€25b. That’s easily somewhere in the €100–200b worth of exits from the portfolio companies of these 68 funds over the next 5–10 years. While it certainly feels like a big endeavour, considering the pace of exits is now in the order of magnitude of €10b/year, the European tech industry has never been stronger and better positioned.

Onto an even greater 2017!


On First-Time VC Fund Performance

I recently tweeted two charts from a Preqin report on Venture Capital. Since that tweet got a lot of interest (relative to the amount of interest my tweets generally get) ranging from celebratory to cynic, I thought I would dissect it further.

The top chart (Fig. 1) shows the performance of first-time funds and non-first time funds compared to the venture industry as a whole, by vintage year (2006 through to 2013), measured by the median net IRR as of September 2016. The bottom chart (Fig. 2) plots the performance (again measured by net IRR) of first-time funds vs non-first time funds from all vintages from 2003 to 2013 relative to the standard deviation of that performance (standard deviation in statistics is a measure of the dispersion of a data set around its mean, and applied to the financial world is a common proxy for risk). I can infer the size of the bubble represents the size of the population for each bucket.
Since the measure of performance is net IRR, this is all to be read with a LP hat on.

The point I was trying to convey in the tweet is that while first-time funds do outperform non-first time funds on an absolute basis (over that period), the outperformance is (in part or entirely?) a function of first-time funds being riskier than non-first-time funds, as measured by the standard deviation of their returns. In crude terms and as a way of example, a first-time fund is more likely to return <1x than a non-first time fund, but equally more likely to return >5x, while the returns from a non-first time funds are less disperse and more concentrated around the mean of the distribution. That is, the distribution of returns from first-time funds are more “fat-tailed“.

The relationship between risk and expected return should ring bells to anyone who has taken portfolio management class at school and recalls what the efficient frontier is: investors only accept higher risk if it is compensated by higher returns, or in other words there is no ‘free lunch’ in an efficient capital market i.e. the only way to earn an extra unit of return is by taking an extra unit of risk, and therefore assets are priced accordingly by the market.

So why are first-time funds riskier?

  1. Size. To start with, first-time funds are likely to be smaller in size, just like a Seed round is smaller than a Series A round. Preqin data shows how first-time fund size targets have ranged between $100-170m for the past 10 years, compared to a range of $200-370m for non-first-time funds. Smaller funds tend to play more earlier stage where the attrition rate is higher, as is the dilution risk from not having enough funds to follow on in the winners, but also where the potential return from hitting the outliers is much higher (this is also referred to as convexity of returns, in that the downside is capped at 0x but the upside is, theoretically, limitless);
  2. Focus. As a first corollary to the smaller size, first-time funds tend to focus on a single region or on a single sector, thus not benefiting from a more diversified portfolio that would reduce the volatility of returns;
  3. Incentives. A second corollary to fund size is GP incentives: raising larger subsequent funds allows GPs to earn larger, and cumulative, fixed cash compensation regardless of fund performance. Management fees on first-time funds tend not to be substantial in absolute terms, particularly when netted off of GP commitments. So one could argue the attitude towards risk taking is higher in managers of first-time funds;
  4. Impatience. As sub-corollary to incentives, first-time funds main goal is to demonstrate enough proof points to allow them to raise a second fund; in doing so, they may be more tempted to accept quick early exits as validation, which might generate higher IRR (though lower multiple and less cash creation) compared to more patient non-first time fund managers;
  5. Experience.
    • First-time funds can be managed by first-time managers who don’t benefit from previous investment experience or come with a completely different background from existing funds. That can either make them more susceptible to making mistakes, but equally more receptive to ground breaking, yet dismissed, ideas in the absence of preconceived notions or powered by unique perspectives;
    • First-time funds can also be managed by experienced GPs who may have spun-off from more established funds; they may come with proprietary experience and networks, taking them away from non-first time funds, which coupled with a clean sheet may yield very different returns;
  6. Innovation. First-time funds are more likely to be exploring innovative strategies and funds models, which are yet unproved and could yield very different (read more dispersed) outcomes from those on non-first-time funds; such strategies can also be developed in response to markets, conditions and opportunities that non-first-time funds cannot adapt to (quickly enough), thus missing out on them;
  7. Brand. Lastly, a first-time fund, by its very nature, has no institutional heritage nor brand to leverage (though as mentioned above “spinoff GPs” in first-time funds do), which could potentially lead to a different quality of dealflow from non-first-time funds.

It would indeed be very interesting to have the same top chart, but showing returns adjusted for risk, to see if first-time funds do indeed generate true alpha (i.e. that portion of excess return that isn’t explained by extra risk) compared to the industry as a whole. It would equally be very interesting to split performance for first-time funds into first-time GPs vs first-time vehicles managed by spinoff GPs, and also first time fund performance by fund size or strategy.

While I don’t have that data at hand, and the preconceived notion in the industry is that success breeds even more success, I like to believe that first time funds have an increasingly good chance at capturing alpha in the industry. A recent Cambridge Associate report demonstrates how venture capital itself is undergoing disruption: returns are getting more democratised and are no longer exclusive property of the top 10 firms on Sand Hill road.

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As innovation and company creation happen literally everywhere, the next unicorn can be hiding where non-first-time funds aren’t closing looking. There is also a softer argument made by an investor who commented to my tweet, that first time fund managers work harder to to prove themselves. There may be some truth in there too!

While entirely anecdotal, the delta between performance of first-time and non-first-time funds in Fig. 1 of the Preqin report appears to be increasing over the period under consideration: is that because first-time funds are getting riskier or could there be some alpha in there?

The good news is that LPs are taking notes. According to Preqin, half of the c. 4,200 investors tracked by the platform and confirmed as actively investing in venture capital have either committed to first-time funds or are open to the idea. Also, according to Shai Goldman‘s spreadsheet that tracks <$200m venture funds closed since 2011, first-time funds continue to attract strong LP interest, with average fund size having increased from $54m in ’13 to $81m so far in ’16, showing more conviction. And notably 2016 is already higher than 2015 in both number and amount raised for first-time funds (though likely to end up down from the 2014 peak by 30-40% in numbers and 20-25% in dollars).

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Specials thanks to Scott Sage, Simon Menashy, Stefano Bernardi and Julian Carter for reviewing early drafts of this post.


What I Obsess About as an Early Stage Investor

Last night I was invited as a guest to a networking event, hosted by Tablecrowd and attended by about 20 people. The format is more intimate than other networking dues I get to attend, consisting of drinks, followed by a proper dinner, closing off with a quick speech by the guest and Q&A. Given most of the audience was made of entrepreneurs, I talked about what I look for in entrepreneurs.

Here is a quick summary of what I talked about.

When investing at the early stages of a company there are two factors that matter more than anything else in my view: people and market. So in evaluating investment opportunities, I end up spending the large majority of my time thinking about them.

The reason I am obsessed with people and market is that if you get one of them wrong, or worst you get both wrong, you have limited scope for manoeuvring. While a team may be able to fix a product, or improve unit economics, it’s incredibly disruptive to replace founders when an early stage business has 12–18 months of runway, and it’s obviously inconceivable to change a market. Of course a company can pivot to address a different audience, or to a new business model or pricing strategy, we often expect them to do so. It’s rare that a company successfully pivots to a different market. It can happen, but as an investor I’d rather not take that risk.

People

So, what do I look for in founders? The answer is very subjective, a different investor will have a very different answer to that question. I found the answers to the following three questions to be highly correlated to ultimate outcomes:

  1. Would I work for this founder(s)? I find that it’s almost always a great sign when I am tempted to drop everything and join them in their journey, regardless of what they are working on. It’s a way to set the bar very high. Founders I would have worked for all had similar traits:
    • ability to inspire smart people to join by selling them a dream, a vision, and making it look achievable
    • ability to lead employees towards realising that vision, with all that leadership entails (e.g. long term strategic thinking coupled with attention to details, delegation skills, hustle, relentlessness, honesty, trustworthiness, ethics etc )
    • ability to raise capital, a necessary ingredient until the business is cashflow positive
  2. Does the founder posses proprietary knowledge? Does the founder know something that others don’t or does she understand something better than anyone else?
    • The answer to this question often revolves around the founder’s personal history and what brought her to start that business in the first place. What I am looking for is an obsessive passion for solving a specific problem. Passion often derives from a deep, visceral understanding of the problem, the market, the customers. The stronger the passion, the more proprietary the knowledge.
    • Passion is critical because when the going gets tough entrepreneurs only keep hustling through it if they are deeply passionate about what they are working on. That’s why I am typically less keen on what I would call a management-consultant approach to startup: a numerical exercise to picking an opportunity. Again, this is just a personal framework, there are plenty of successful founders who used that very approach.
  3. Are the founders working on a problem I understand? I need believe I can play a role in helping the founders achieve their vision, beyond just providing the capital. Have I got other investments in the space? Do I know people in my network who can help? Do I know potential customers?

Market

What do I look for in a market? I have narrowed it down to three tests:

  1. Is the addressable market large enough to sustain at least a £50m revenue business in a capital efficient way and in a sensible time frame, without having to make absurd assumptions on long term market share? By absurd market share I generally mean > 10% and by sensible timeframe I mean 5-10 years. Again this is completely subjective and highly dependent on the size of the fund under management and stage of investing.
  2. Is the market addressable right now? Is the timing right? One can be too early, and with limited runway the market can “remain irrational longer than you can remain solvent” (J. M. Keynes); or too late, in which case it will take much more capital to catch up with the market leaders.
  3. Has the company got a good shot at becoming the market leader? The rationale behind this is that value tends to accrue disproportionately to the #1 in a market, so as a VC you really want to back the leader, rather than #2 or#3.