Archive for the ‘Innovation’ Category
As most of you know, RSCM is part of the group offering every TechStars company an additional $100K investment. When we first started talking to the TechStars folks, my immediate reaction was “kindred spirits”. This was also my reaction when we first spoke with Adeo Ressi of TheFunded and Founder Institute. Recently, I’ve been trying to put my finger on why I think of us all as similar. I think I’ve got it.
We’re all meta-startups—startups that working to improve the process of launching startups.
Up until a few years ago, founding a tech startup usually followed the same haphazard process it had for decades. Founders were pretty much on their own to thrash around and figure out how to test their innovations in the marketplace. It’s how I did my first startup in 1993. It’s how I did my last startup in 2004. Same with Dave Lambert, my partner at RSCM, with his first startup in 1993 and his last in 2003. It’s what we saw all our entrepreneurial friends do.
This “process” has a high bar for founders to clear and a low success rate, limiting innovation. Now, the Internet increased the speed at which the haphazard process can execute. So the situation has modestly improved over the last 15 years. But what we want is fundamental improvement. What we want is the equivalent of an Amazon, Google, or Facebook to change the rules of the startup game.
High-volume, high-speed incubators like Y Combinator (2005), TechStars (2006), and Founder Institute (2009) are a great leap forward in discovering a more systematic startup process. They’re paving the way toward more startups, higher success rates, and dramatically more innovation. Many of the improvements they pioneer should diffuse out into startup community. So they’ll enable startup in general, not just the ones in their programs, to launch more smoothly.
Our role in this revolution is eliminating the huge seed stage funding roadblock. Now, with all the press about angels and superangels funding startups in Silicon Valley and New York, you may think getting seed funding is easy. But I’ve run the numbers. Seed funding is actually down 40% from it’s peak in 2005. I’m pretty sure the cost of doing a startup hasn’t dropped 40%. And I’m quite sure that the number of quality founding teams hasn’t dropped 40%.
Startups succeed by challenging conventional wisdom. As a meta-startup, we are no different. Luckily, our job is easier than most tech startups’. They have to challenge the conventional wisdom about the future of technology. There just isn’t much data to go on. We have to challenge the conventional wisdom about funding startups. In our case, there’s a lot of data that existing investors are just ignoring:
- The seed stage generates very high returns. There’s a good dataset of angel investments from the Kauffman Foundation, mostly from 1998 and later. Depending on your selection criteria, returns are at least 30% and possibly over 40%. Calculations here and here.
- You can’t pick winners at the seed stage. This is precisely the kind of prediction tasks where “gut feel” or “expert judgement” performs poorly. Human predictions almost never beat even a simple checklist. Review of evidence here.
- Interviewing founding teams is a poor indicator of their ability. There’s been an incredible amount of research trying to figure out how to predict who will be good at which job tasks. Unstructured interviews are the worst predictor. Highly structured interviews or matching of past experience to current requirements does better. Review of evidence here.
- Grand slams aren’t necessary to achieve high returns. You don’t have to do anything special to make sure you get the “best” deals. First, nobody has shown they can identify such startups before they release a product. Second, even if all you get is base hits, you’ll still have returns of about 30%. Calculation here.
The conclusion from this evidence is pretty straightforward. But it implies a seed-stage funding approach very different what we see today. A classic opportunity for a startup (or meta-startup).
If the seed stage has high average returns but you can’t pick specific winners, basic financial theory says to use a portfolio approach. You want enough investments to have a reasonably high confidence of achieving the average return. I’ve done the calculation, and my conclusion is that you want to build a portfolio of hundreds of seed-stage investments.
That may sound like an unreasonable number. But if you don’t have to spend hours doing multiple interviews of every founding team, you can dramatically streamline the process. In fact, we have a demo version of our software that can produce a pre-money valuation from an online application in a few seconds [Edit 3/16/2013: we took down the Web app in Jan 2013 because it wasn’t getting enough hits anymore to justify maintaining it. We continue to use the algorithm internally as a spreadsheet app]. After that, it’s a matter of back-office automation. Luckily, previous generations of startups have made such automation pretty easy.
Just think about how these conclusions should lead to a much better environment for seed stage startups. Investors will actually decrease risk by making more investments. The same process that allows them to efficiently building a large portfolio means they can give a much faster response to entrepreneurs. Lower transaction costs mean startups will see more of the money investors put on the table. As always, innovation is a win-win. And in this case, we’re innovating in the process of funding innovation. A meta-innovation, if you will.
Even better, the average person on the street wins too. Because, as I’ve shown before here and here, increasing the rate of startup formation increases the rate of economic growth. So if meta-startups can permanently increase this rate, the returns to society as a whole will quickly compound.
Several people have pointed me to Dan Frommer’s post on Moneyball for Tech Startups, noting that “Moneyball” is actually a pretty good summary of our approach to seed-stage investing at RSCM. Steve Bennet, one of our advisors and investors, went so far as to kindly make this point publicly on his blog.
Regular readers already know that I’ve done a fair bit of Moneyball-type analysis using the available evidence for technology startups (see here, here, here, here, here, and here). But I thought I’d take this opportunity to make the analogy explicit.
I’d like to start by pointing out two specific elements of Moneyball, one that relates directly to technology startups and one that relates only indirectly:
- Don’t trust your gut feel, directly related. There’s a quote in the movie where Beane says, “Your gut makes mistakes and makes them all the time.” This is as true of tech startups as it is of baseball prospects. In fact, there’s been a lot of research on gut feel (known in academic circles as “expert clinical judgement”). I gave a fairly detailed account of the research in this post, but here’s the summary. Expert judgement never beats a statistical model built on a substantial data set. It rarely even beats a simple checklist, and then only in cases where the expert sees thousands of examples and gets feedback on most of the outcomes. Even when it comes to evaluating people, gut feel just doesn’t work. Unstructured interviews are the worst predictor of job performance.
- Use a “player” rating algorithm, indirectly related. In Moneyball, Beane advocates basing personnel decisions on statistical analyses of player performance. Of course, the typical baseball player has hundreds to thousands of plate appearances, each recorded in minute detail. A typical tech startup founder has 0-3 plate appearances, recorded at only the highest level. Moreover, with startups, the top 10% of the startups account for about 80% of the all the returns. I’m not a baseball stats guy, but I highly doubt the top 10% of players account for 80% of the offense in the Major Leagues. So you’ve got much less data and much more variance with startups. Any “player” rating system will therefore be much worse.
Despite the difficulty of constructing a founder rating algorithm, we can follow the general prescription of trying to find bargains. Don’t invest in “pedigreed” founders, with startups in hot sectors, that have lots of “social proof”, located in the Bay Area. Everyone wants to invest in those companies. So, as we saw in Angel Gate, valuations in these deals go way up. Instead, invest in a wide range of founders, in a wide range of sectors, before their startups have much social proof, across the entire US. Undoubtedly, these startups have a lower chance of succeeding. But the difference is more than made up for by lower valuations. Therefore, achieving better returns is simply a matter of adequate diversification, as I’ve demonstrated before.
Now, to balance out the disadvantage in rating “players”, startup investors have an advantage over baseball managers. The average return of pure seed stage angel deals is already plenty high, perhaps over 40% IRR in the US according to my calculation. You don’t need to beat the market. In fact, contrary to popular belief, you don’t even need to try and predict “homerun” startups. I’ve shown you’d still crush top quartile VC returns even if you don’t get anything approaching a homerun. Systematic base hits win the game.
But how do you pick seed stage startups? Well, the good news from the research on gut feel is that experts are actually pretty good at identifying important variables and predicting whether they positively or negatively affect the outcome. They just suck at combining lots of variables into an overall judgement. So we went out and talked to angels and VCs. Then, based on the the most commonly cited desirable characteristics, we built a simple checklist model for how to value seed-stage startups.
We’ve made the software that implements our model publicly available so anybody can try it out [Edit 3/16/2013: we took down the Web app in Jan 2013 because it wasn’t getting enough hits anymore to justify maintaining it. We continue to use the algorithm internally as a spreadsheet app]. We’ve calibrated it against a modest number of deals. I’ll be the first to admit that this model is currently fairly crude. But the great thing about an explicit model is that you can systematically measure results and refine it over time. The even better thing about an explicit model is you can automate it, so you can construct a big enough portfolio.
That’s how we’re doing Moneyball for tech startups.
- We have a, “…misplaced faith in the power of startups to create U.S. jobs.”
- “The scaling process is no longer happening in the U.S. And as long as that’s the case, plowing capital into young companies that build their factories elsewhere will continue to yield a bad return in terms of American jobs.”
- “Scaling isn’t easy. The investments required are much higher than in the invention phase.”
His basic argument is more sophisticated than the typical “America no longer makes things” rhetoric. It boils down to network effect. If the US doesn’t manufacture technology-intensive products, we incur two penalties because we lack the corresponding network of skills: (a) in technology areas that exist today, we will not be able to innovate as effectively in the future and (b) we will lack the knowledge it takes to scale tomorrow’s technology areas altogether. I don’t believe this argument for all the usual economic reasons, but let’s assume it’s true. Would the US be in trouble?
Grove makes a lot of anecdotal observations and examines some manufacturing employment numbers. However, I think it’s a mistake to generalize too much from any individual case or particular metric. The economy is diverse enough to provide examples of almost any condition and we expect a priori to find specialization within sectors. We should examine a variety of metrics to get the full picture of whether America is losing its ability to “scale up”. Bear with me, Grove’s essay is four pages long so it will take me a while to fully address it.
First, let’s look at the assertion that we aren’t creating jobs relevant to scaling up technologies. The real question is: what kinds of jobs build these skills? Does any old manufacturing job help? I don’t think Foxconn-suicide inducing manufacturing jobs are what we really want. Maybe what we want is “good” or “high-skill” manufacturing jobs? Well take a look at this graph from Carpe Diem of real manufacturing output per worker:
That’s right, the average manufacturing worker in the US produces almost $280K worth of stuff per year. More than 3x what his father or her mother produced 40 years ago. That should certainly support high quality jobs. But is the quality of manufacturing jobs actually increasing? Just check out these graphs of manufacturing employment by level of education from the Heritage Foundation:
This suggests to me that we’re replacing a bunch of low-skilled workers with fewer high-skilled workers. But that’s good. It means we’re creating jobs that require more knowledge (aka “human capital” that we can leverage). Look at the 44% increase in manufacturing workers with advanced degrees! Contrary to Grove, it seems like we’re accumulating a lot of high-powered know-how about how to scale up.
Now you might be thinking that we could still be losing ground if the productivity of our high-end manufacturing jobs isn’t enough to make up for job losses on the low-end. In Grove’s terms, our critical mass of scaling-up ability might be eroding. Not the case. Just consider the statistics on industrial production from the Federal Reserve. This table shows that overall industrial production in the US has increased 68% in real terms over the 25 years from 1986 to 2010, which is 2.1% per year.
“Aha!” you say, “But Grove is talking about the recent history of technology-related products.” Then how about semiconductors and related equipment from 2001-2010? That’s the sector Grove came from. There, we have a 334% increase in output, or 16% per year . Conveniently, the Fed also has a category covering all of hi-tech manufacturing output (HITEK2): 163% increase over the same period, or 10% per year. Now the US economy only grew at 1.5% per year from 2001 to 2010 (actually 4Q00 to 3Q10 because the 4Q10 GDP numbers aren’t out). So in the last ten years, our ability to manufacture high-technology products increased at almost 7x the rate of our overall economy. We’re actually getting much better at scaling up new technologies!
I can think of one other potential objection. Lack of investment in future production. There is a remote possibility that, despite the terrific productivity and output growth in US high-tech manufacturing today, we won’t be able to maintain this strength in the future. I think the best measure of expected future capability is foreign direct investment (FDI). These are dollars that companies in one country invest directly in business ventures of another country. They do NOT include investments in financial instruments. Because these dollars are coming from outside the country, they represent an objective assessment of which countries offer good opportunities. So let’s compare net inflows of FDI for China and the US using World Bank data from 2009. For China, we have $78B. For the US, we have $135B. This isn’t terribly surprising given the relative sizes of the economies, but there certainly doesn’t seem to be any market wisdom that the US is going to lose lots of important capabilities in the future.
Will China outcompete the US in some hi-tech industries? Absolutely. But that’s just what we expect from the theory of comparative advantage. They will specialize in the areas where they have advantages and we will specialize in other areas where we have advantages. Both economies will benefit from this specialization. An economist would be very surprised indeed if Grove couldn’t point to certain industries where China is “winning”. However, the data clearly shows that China is not poised to dominate hi-tech manufacturing across the board.
Startup Job Creation
So we’ve addressed Grove’s concerns about the US losing its ability to “scale up”. Let’s move on to the issue of startups. Remember, he said that startups, “…will continue to yield a bad return in terms of American jobs.” As I posted before, startups create a net of 3M jobs per year. Without startups, job growth would be negative. If Grove cares about jobs, he should care about startups. The data is clear.
The one plausible argument I’ve seen against this compelling data is that most of these jobs evaporate. It is true that many startups fail. The question is, what happens on average? Well, the Kauffman Foundation has recently done a study on that too, using Census Bureau Business Dynamic Statistics data. They make a key point about what happens as a cohort of startups matures:
The upper line represents the number of jobs on average at all startups, relative to their year of birth. The way to interpret the graph is that a lot of startups fail, but the ones that succeed grow enough to support about 2/3 of the initial job creation over the long term; 2/3 appears to be the asymptote of the top line. The number of firms continues declining, but job growth at survivors makes up the difference starting after about 15 years. For example, a bunch of startups founded in the late 90s imploded. But Google keeps growing and hiring. Same as in the mid 00s for Facebook. Bottom line: of the 3M jobs created by startups each year, about 2M of them are “permanent” in some sense. The other 1M get shifted to startups in later years. So startups are in fact a reliable source of employment.
I’d like to make one last point, not about employment per se, but about capturing the economic gains from startups. If we generalize Grove’s point, we might be worried that the US develops innovations, but other countries capture the economic gains. To dispel this concern, we need only refer back to my post on the economic gains attributable to startups, using data across states in the US. Recall that this study looked at differing rates of startup formation in states to conclude that a 5% increase in new firm births increases the GDP growth rate by 0.5 percentage points.
I would argue that it’s much more likely that a state next door could “siphon off” innovation gains from its neighbor than a distant country could siphon off innovation gains from the US: (a) the logistics make transactions more convenient, (b) there are no trade barriers between states, and (c) workers in New Mexico are a much closer substitute for workers in Texas than workers in China. But the study clearly shows that states are getting a good economic return from startups formed within their boundaries. Now, I’m certain there are positive “spillover” effects to neighboring states. But the states where the startups are located get a tremendous benefit even with the ease of trade among states.
I think it’s pretty clear that, even if you accept Grove’s logic, there’s no sign that the US is losing its ability to scale up. However, I would be remiss if I didn’t point out my disagreement with the logic. I’ve seen no evidence of a need to be near manufacturing to be able to innovate. In fact, every day I see evidence against it.
I live in Palo Alto. As far as I know, we don’t actually manufacture any technology products in significant quantities any more. Yet lots of people who live and work here make a great living focusing on technology innovation. As Don Boudreaux is fond of pointing out on his blog and in letters to the mainstream media, there is no difference in the trade between Palo Alto and San Jose and the trade between Palo Alto and Shanghai. In fact, I know lots of people in the technology industry who work on innovations here in the Bay Area and then fly to Singapore, Taipei, or Shanghai to work with people at the factories cranking out units.
Certainly, I acknowledge that a government can affect the ability of its citizens to compete in the global economy. But the best way to support its citizens is to reduce the barriers to creating new businesses and then enable those businesses to access markets, whether those markets are down the freeway or across the world. One of the worst thing a government could do is fight a trade war, which is what Grove advocates in the third-to-last paragraph of his essay.
The ingenuity of American engineers and entrepreneurs is doing just fine, as my data shows. We don’t need an industrial policy.
Dave Lambert pointed me to this new Kauffman Foundation paper by Tim Kane about job creation in the US. Then Will Ambrosini pointed to this Growthology post which reproduces the money diagram from page 5 :
Look carefully. Then think about this statement about US job creation:
The only firms that create jobs on average are brand new ones.
So yes, you can save the world with startups.
On a recent business trip trying to drum up support for RSCM, someone asked Dave and me why such obviously talented guys were starting a fund instead of a company. I’ve been thinking about that question for the last week and have a much better answer than the one I gave.
I want to make the world a better place. But it’s not clear precisely what interventions will lead to the best outcomes over the long term. I think I’m a really smart guy, but I’m quite sure I can’t evaluate all the potential interactions within a system as complex as the world society to figure out the optimal plan.
Luckily, I don’t have to be that smart. We just have to collectively be that smart. And economic markets are the best way I know to organize collective action. The more effectively we can all create value, the better off we’ll all be. Creating wealth won’t directly solve a lot of problems, but it enables the solution of an incredibly wide range of problems.
So here’s the math that leads to my conclusion that increasing the number of startups we can fund is the best thing I can do for the world. This study shows that a 5% improvement in startup creation leads to about a half a percentage point improvement in the economic growth rate. If we could increase the rate of startup creation by 10%, we could add a full percentage point to our economic growth rate.
From this dataset, I determined that the world GDP growth rate over the last 30 years has been about 4%. So we could probably achieve a 5% growth rate by increasing startup formation by 10%.
This seemingly small shift has dramatic results over the long term. In 50 years, world GDP would be 60% (1.6x) greater. In 100 years, GDP would be 160% (2.6x) greater. I think a world in which everyone were 2.6x richer would be pretty sweet. That’s a gift I want to give to my great-great grandchildren.
Seed-stage startups are the key because that’s where businesses are born. A larger pool of innovative seed stage companies will naturally attract a larger pool of investment in later stages. About $10B every year goes to professional investments in seed-stage startups in the US. So if we can add $1B, that’s 10%. Even better, if we develop a better process, this process can be copied all over the world. If it’s a lot better, I bet we can do significantly exceed a 10% improvement.
That’s why I’m focusing my time on revolutionizing the process for funding seed-stage startups.