This essay is part of The Token Handbook.
IT WOULD BE NICE if business plans turned into businesses, if great ideas got funded, if most early-stage investors had decent returns, and if everyone reading this would send one ether to this address:
Back here in reality, I keep repeating the mantra: What gets built is what gets funded. And what gets funded is more arbitrary than you might think.
I’ll state the premise of this essay in four sentences:
1. VCs and angels spend a lot of time sifting the “bad” deals out and select only a handful to fund, and they have a 90+ percent failure rate. No more than ten percent of their deals fuel their returns, which in aggregate are poor. They are under a shared delusion that they are adding value. They aren’t.
2. The deals they don’t fund, taken in aggregate, have a far higher return than any but the top-decile of VCs.
3. We can say something similar for accelerators — there’s no evidence that accelerated companies outperform unaccelerated companies who get the same amount of funding.
4. World-scale liquidity is coming to private equity and all other fragmented markets. We’re at an inflection point. Over the next twenty years, we’ll creatively destroy the world we know and replace it with connected real-time digital markets that don’t rely on experts and bottlenecks. This is a one-time transition that will close many doors and open several new ones.
The Shared Delusion
I used to spend a lot of time on Quora, and then I read a post from an investor who wrote, “I give an equal weight to the team, the idea, and the market. When the score of those three things is over 90%, I get involved.” And this got voted up and up and up. That was the last thing I read on Quora. This is why I don’t meet with venture capitalists. This is why talking with early-stage investors is something I rarely do any more.
It’s not me; it’s them. I should know — I used to be one.
Recently, Vitalik Buterin wrote a short piece on token-funding models that explores token-design space. He correctly shows the difficulties (and impossibilities) of matching funds with projects and their promises. This is something professional investors (venture capitalists) think they can do using models, metrics, gut feel, and the infrequent application of expert advice. Almost everyone in angel investing and venture capital shares the same hallucination — that they can tell how things are going to play out and can obviously tell the losers ahead of time.
Obviously. Ten percent of the time. Maybe less.
And yet, we know that people, rules, and even neural networks fail this test — they simply cannot predict the future that far out, even though they think they can. Since tiny differences can make a huge difference (ask the original Twitter investors), and since 99 percent of startups either pivot or fail (ask the Fab, Quirky, and Gilt investors), starting new companies and projects is simply one of the least linear things humans do. I often remind my audiences that Google, after $25 million in funding, finally found their business model by talking with Bill Gross at a TED party. LEGO switched from wooden toys to plastic after a chance encounter with a man on a steamship, followed by a warehouse fire (plus, they copied an existing plastic-brick system almost exactly). Instagram started as a gaming-points site. Everybody pivots.
… starting new companies and projects is simply one of the least linear things humans do.
Launching a new project is much more like having a baby than building a house, yet most investors still think it’s like building a house (hence the talk of metrics and milestones). Most of today’s successful companies would never have made it through a linear milestones-for-money funding gauntlet.
The linearity is the shared delusion. There’s a bit of linear thinking in Vitalik’s post, which proposes funding according to milestones, letting the team deliver and then get more money as they go, or trying to bound the token price. This is now referred to as the “seed and feed” model. It’s not the only way to throw money at entrepreneurs. USA Today blew through a whopping $10 million in capital — a seemingly bottomless pit back in the 1980s, to emerge a decade later as the most profitable newspaper in history. After $50 million in that same decade, Pixar failed. It was refinanced as a film studio and went public several years later. Amazon, Google, and even Apple have had their share of expensive failures and near-death experiences.
The delusion comes from studying winners, not losers. The bestselling business book of all time profiled 18 cherrypicked “Built to Last” companies that went on to underperform the market in the ensuing decade (several didn’t last).
Business magazines and conferences feature the stories of big winners giving advice to others that doesn’t generalize. We categorically ignore failure (that’s an important one — don’t miss it). Stephan Tual and Christoph Jentzsch of Slock.it were flying high after their project, The DAO, raised big money in 2016, only to lay very low and consult lawyers for the following six months after the smart contracts were hacked. Failure is the rule — success is the exception. Luck is a critical component of success.
All angel-investing platforms are just beauty contests with whatever buzzword of the day happens to be popular with investors who think they can spot a good deal when they see one. Then they jump in unison, driving up the price. After all, if the price is that high, this must be the best deal, right? Several studies have shown that professional venture capitalists invest in people of the same sex, height, and ethnicity, and thinking as themselves. The stories they tell about their returns are heavily biased. Social validation and personal storytelling are strong drivers of investment — just ask Elizabeth Holmes and Bernie Madoff.
The Truth about Innovation
Contrary to what some people believe, innovation is rarely done by experts. I haven’t researched the data, but I expect that overfunded startups fare worse than underfunded ones. An overfunded startup tends to be full of stars — top experts who bring their brand to the team (who wouldn’t invest in people with the great track records?). And that does not a team make.
The truth is that innovation happens almost exclusively in the margins, where people are tinkering and doing experiments, usually with a market of one in mind. If you read Steven Johnson’s excellent books, Duncan Watts’ Everything is Obvious, Once you know the Answer and Nassim Taleb’s Antifragile, you’ll see that necessity is not the mother of invention. Rather, failure and randomness are the parents of success, and invention is happening continuously for assorted reasons. You’ve heard of the HyperLoop? A high-speed futuristic train from the mind of Elon Musk? The very first patent for a train running inside a tube with a vacuum was issued in 1834, years after working prototypes had been demonstrated.
The Truth about Token Sales
In today’s crypto-investing world, many projects are coming to market, hoping to score big by selling tokens ahead of their projects. I stand on a mountain of evidence when I say that almost every one of these projects will have to pivot to make those tokens valuable in the long run, and at least 50 percent will fail (I’m being generous). Ethereum itself has had its share of nonlinearity. We live in a black-swan world — the technical term is skewness. Unexpected things can and will happen. Tens of billions of dollars’ worth of cryptocurrency value hinges on one 23-year-old in Singapore not being in the wrong place at the wrong time.
I stand on a mountain of evidence when I say that almost every one of these projects will have to pivot to make those tokens valuable in the long run.
Expert predictions are useless in this world, and the wisdom of crowds is easily gamed. Nothing is as it seems. The market for tokens follows a Pareto distribution built on PR and cognitive biases. Status orchestrated a $270 million raise. Would this startup be better off if they had “only” raised $70 million? I think it’s hard to make a good argument for the extra $200m.
And yet, as I have pointed out, the game is being played for good reason on both sides. As I write this, more than one new ICO launches daily, even after the Chinese shut down all ICOs, taking the good projects down with the bad. It could potentially go on for many more months, with startups and new projects feeding the need for diversification and a quick flip. As more and more projects vie for this high-velocity money, we’re sure to see even crazier valuations (and some spectacular scams) before things finally a) settle down into a new equilibrium, b) crash with a loud bang, c) get regulated into some new artificial reality, or d) all three (I’m betting on d).
The Efficient-Market Hypothesis
And now a quick word from the efficient-market hypothesis, which doesn’t get enough credit and needs a better PR firm. Liquid markets are ruthlessly efficient, even if they don’t appear to be. Even the housing market, which you would think has plenty of biases and irrational participants, turns out to be more efficient than most people think. Not only is it harder to spot an undervalued asset, it’s also harder to call bubbles bubbles.
Does the EMH apply to the world of cryptocurrencies and tokens? Why are the prices of various coins so different on so many exchanges? Why do five tokens have more value than all the rest combined? Have Dunning and Krueger replaced Nash and Coase?
My answer is that the cryptocurrency market as a whole is actually quite efficient. Money flows from one currency to the next in response to various news reports and reasonable assumptions, making it almost impossible to spot any “deals.” You can have a “target price” in mind, but that’s really just technical trading, and technical trading is another shared delusion.
Overall, the results from statistical tests indicate that individual investors who use technical analysis to make investment decisions are disproportionately prone to speculate on short-term stock-market trends, hold more concentrated portfolios, turn over those securities at a higher rate than people who do not use charts, and earn lower returns. — Forbes.com
On an individual basis, however, we can see the beauty contests at work and many projects getting left behind for lack of the right famous connections. Any project that raises more than $30 million probably doesn’t need that much money and could probably benefit from having less, rather than more. In later chapters, we’ll see ways to fix many of these biases.
In complex adaptive systems, predictions are less useful than we think they are. The guy on stage is the one who predicted what would happen, but he’s one of thousands who predicted all kinds of things. These experiences don’t generalize. Luck plays a major role. And mean reversion comes to all but the very luckiest.
Fortunately, we can solve a lot of these problems with tokens, smart contracts, and efficient markets. That’s the context for everything that comes next.
Now you can go back to The Token Handbook.
David Siegel is a serial entrepreneur from the United States living in London. He is the CEO of 20|30 and the Pillar project, both of which have newsletters you may wish to subscribe to. He is the author of The Token Handbook and an essay on cryptocurrency bubbles. His full bio is at dsiegel.com. Connect to him on LinkedIn.