Heat Death: Venture Capital in the 1980s

The history repeats itself crowd thinks that that there must be a bubble sooner or later. “Now?” they constantly ask, “Is it a bubble now?” as if history has to repeat whatever was most memorable about the last time. History may repeat itself, but there’s an awful lot of history that this particular venture capital cycle could repeat. Below is a short history of venture capital in the 1980s, my interpretation and comparison to the ’90s and today, and some thoughts about what that means. It’s long. If you’re attention-deprived, skip to ‘1980s v. 1990s’, about four-fifths of the way down.

To 1980

Baby, baby drove up in a Cadillac
I said, “Jesus Christ, where’d you get that Cadillac?”

- The Clash, 1979.

The Carter years were tough.

They started out well. The recovery from the 1973-1975 recession brought unemployment down and incomes up. But all this was undone by the return of inflation. By 1980, when inflation reached its peak, unemployment was rising, interest rates were at their highest levels since World War II, productivity growth had slowed, and business investment was falling. Fear ruled the markets, a “crisis of confidence” ruled the people.

Venture capital had a different trajectory in the 1970s: until 1978 there was almost nothing, then suddenly, it took off.

One of the reasons for venture capital’s current heady successes is the good judgment men like Burr [Craig Burr of Burr, Egan, Deleage] and Cronin [Dan Cronin of Ampersand Associates] learned while slugging their way through the near-dormant mid-’70s. The period between 1972 and 1978 may someday be remembered as venture capital’s years in the desert. After a heady adolescence in the late ’60s, the business almost disappeared from public view after the bull market of 1968-69 went into eclipse, taking with it the new-issues market that had buoyed the venture business. (Inc. Magazine, “The Billion Dollar Gamble“, 9/1/1981)

The pioneers of the 1960s and 1970s had figured out a winning formula: build a great network to source opportunities, spend months getting to know the management team and doing due diligence, invest at the earliest possible stage, work hard to help founders get the right team in place and put together partnerships, and take the company public only when it was ready to be a public company. The result was that, despite an IPO market that had virtually disappeared, iconic VC-backed companies made it out into the market. Cray and Tandem in 1976, Evans & Sutherland and Federal Express in 1978, and Apple and Genentech in 1980. The Reagan years looked promising.

The Accelerating Universe, 1980-1983

Never for money, always for love…
I guess this must be the place.

-

DataHero Number of Funds, by Vintage Year
DataHero IPOs
DataHero Tech IPOs
Screen Shot 2014-11-10 at 11.46.44 AM
avg irr vintage yr
DataHero New Capital Committed to VC (in millions)
DataHero VC Disbursements by Industry
DataHero High-tech Companies Formed in the US
Source: Gompers, Paul, and Josh Lerner. “The Venture Capital Revolution.” Journal of Economic Perspectives 2001 : 145-168
Source: National Science Foundation, “Science and Engineering Indicators–2002″
Source: Thomson Reuters, 2008 Investment Benchmarks Report: Venture Capital
Continue reading "Heat Death: Venture Capital in the 1980s"

Best of Reaction Wheel

The other night someone asked me “Have you ever thought about ____?” I can’t believe you’re asking me that, I thought, I wrote a 20 page post on that four years ago.

I’ve written 270 posts over the last seven years. Most of them suck, especially those prior to 2010. A few of them I think are pretty good and have stood up over a couple of years. This is a list of those.

On entrepreneurship

Who profits from innovation? Startups or incumbents? (September 2014)
How you capitalize on being early to market.

How to kiss your elbow (September 2012)
How to see Product Market Fit when it happens.

You Can’t Learn from Failure, You Can Only Learn from Success (April 2013)
Why you should focus on how to win, not on how not to lose.

On being an asshole (March 2013)
The difference between criticism and critique; an oblique attack on lean launchpadesque pedagogy.

On adtech

The Immediate Future for Adtech Startups (November 2013)
Why it’s too late to start a programmatic company.

Advertising, the Fallacy of Perfectibility, and the Best Minds of My Generation (April 2011)
How the matching problem (ie. adtech) is as economically important as the price problem.

Open Source the Ad Exchange (January 2010),
Everybody’s an ad exchange (The Thin Exchange, 1) (April 2010) and
The End to End Principle in Ad Exchange Design (The Thin Exchange, 2) (April 2010)
I predict that the ad exchange is doomed but argue for saving it.

The last days of the ad exchange (June 2010)
I accept that the ad exchange is doomed and predict client direct.

Disruptive innovation, buy vs. build, the most pernicious lie in business, and how to know if you’re fooling yourself (October 2011)
What Christensen meant by Disruptive and why adtech isn’t it.

The long siege of Corbenic (February 2010)
The problem adtech is solving and a prediction for a move to Marketing Tech.

Fiddling while Rome burns (June 2010)
I make several predictions about the Google/Invite deal’s aftermath, all of which have turned out to be correct.

Your personal data is not worth anywhere near what you think it’s worth (June 2012)
Why companies helping you monetize your own personal data will not work.

On angel investing

Betting on the Ponies: non-Unicorn Investing (July 2014)
This is half a summary of my thinking on angel investing and half an extended rant.

Angel Investing Series:
This was my attempt at breaking up my extended rants on angel investing into readable pieces. It was meant to be a much longer series.

  1. Intro: Why I’m Not an Angel (March 2013)
  2. How to spend your time: The Work-Work Balance  (March 2013)
  3. Positioning: How to be Continue reading "Best of Reaction Wheel"

Who profits from innovation? Startups or incumbents?

The idea that only startups can innovate and that incumbents can’t respond is wrong. Apple responds to innovation and, though I hate to tell you this, they are not a startup. They were founded some 40 years ago and have more than 80,000 employees. I think the startup community feels proprietary about them because they still profiting from innovation. Just yesterday they put a score of startups out of business with innovative products. Not products where they came up with the idea first and for themselves, but innovative products all the same.

And not just Apple: Google, Amazon. These are non-startups that appreciate innovation and make money from it. Not that this in itself–big companies profiting from innovation–is new. It’s not something companies started doing after the CEO read the The Innovator’s Dilemma or Ries or Blank. It’s not because those companies have some inbuilt innovation DNA from being started in Silicon Valley or venture backed. Big, established, well-managed businesses have always profited from innovation, even when they are not really innovators themselves.

Here’s Andrall Pearson, President of Pepsi from 1970 to 1985, then a professor at Harvard Business School, in an article called Tough Minded Ways to Get Innovative1:

Looking hard at what’s already working in the marketplace is the tactic likely to produce the quickest results. I call this robbing a few gas stations so that you don’t starve to death while you’re planning the perfect crime.

Lots of companies think that the only good innovations are the ones they develop themselves, not the ideas they get from smaller competitors–the familiar not-invented-here syndrome. In my experience, the opposite is usually true. Normally, outside ideas are useful simply because your competitors are already doing your market research for you. They’re proving what customers want in the marketplace, where it counts.

I’ve found that good ideas come from all over–conventional competitors, regionals, small companies, even international competitors in Europe and Japan. So it may not surprise you to learn that most of PepsiCo’s major strategic successes are ideas we borrowed from the marketplace–often from small regional or local competitors.

For example, Doritos, Tostitos, and Sabritos (whose combined sales total roughly $1 billion) were products developed by three small chippers on the West Coast. The idea for pan pizza (a $500 million business for Kansas-based Pizza Hut) originated with several local pizzerias in Chicago. And the pattern for Wilson 1200 golf clubs (the most successful new club line ever) came from a small, golf clubber in Arizona.

In each case, PepsiCo spotted a promising new idea, improved on it, and then out-executed the competition. To some people this sounds like copycatting. To me it amounts to finding out what’s

teece flow 3.001
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Betting on the Ponies: non-Unicorn Investing

Twenty fucking five to one
My gambling days are done
I bet on a horse called the Bottle of Smoke
And my horse won

- The Pogues

I have decided to angel invest. Any advice?

I spent some time decades ago in the horse-racing world, as a guest of someone who was actually in the horse-racing world. Two things: (1) it’s not as glamorous as it sounds, and (2) everyone has a system. Everyone. And, as you might expect if you thought about it for a minute, you can always tell the people who know what they’re doing: they’re the ones that don’t tell you about their system.

Everybody else likes to talk non-stop about their system and the sophisticated statistical modeling they’ve done on it on their laptop using Excel. Most of them have never ridden a horse. Most of them have not looked–really looked–at enough horses to know what a great horse looks like. In fact, they’ve probably only ever seen one or two great horses, because truly great horses are exceedingly rare.

You’re rambling, old man, I don’t care about horses.

Well, me neither. But I care about betting systems. And horse-racing is the ecosystem with the worst betting systems in the world. Ridiculous statistical models built by people who don’t understand statistics or models used as psuedo-rationalizations for rules of thumb and rabbit’s feet. In the end, the bets of 95% of bettors are a frantic attempt to avoid betting on any horse that resembles in any way any nag they’ve ever lost on before. But they need a win, they have to go home with at least one win if they ever want to be allowed to come back. So they spend all day making small, stupid bets, waiting to make the big bet on the sure thing, once it’s obvious it’s a sure thing. Yeah, it’s 1:10 odds because every other bettor at the track also knows it’s a sure thing, but that sure thing isn’t a horse, it’s a…

Unicorn.

Yes, that’s right. A freakin unicorn.

As far as I can tell Aileen Lee popularized this term in a Techcrunch article last year. Some days I wonder why she hates us, and some days I thank the gods that she didn’t decide to call them Princesses, or worse. But there was in her analysis the rationale for The System as well as the reason for its absurdity.

Aileen looked at venture exits over the previous ten years, and found 39 companies that had been valued at $1 billion or more in the public or private markets (that number seems to have gone up a bit in the eight months since she wrote the

Continue reading "Betting on the Ponies: non-Unicorn Investing"

The Lewis and Clark Business Plan Competition

Jefferson sat in his office, looking out the window towards the Potomac. In the distance he imagined he could see Alexandria, though the heavy swamp air on a hot day (they were all hot in this swamp!) certainly made that impossible. It was a pleasant house, though it sat forlornly in a vast empty field. He imagined that someday the government might grow a bit and some of the space, though surely not all, would be used for other buildings.

The view from the window was beautiful. “A wild and romantic view,” he heard Abigail had called it, “albeit, in a wilderness.” But this small wilderness did not concern him. He was thinking of the other one.

There had been some debate about his purchase of the Louisiana Territory, and he had some misgivings himself. But he knew that allowing France to think it controlled the land that Americans would inevitably expand into would eventually lead to conflict. So he bought it. For whom and under what authority he did not really know, despite what he said in public. But it was done and those questions were no longer worth thinking about. Now he wondered what owning it meant for the country.

Many men had explored the lands west of the States. He had read of expeditions up the Missouri, and up the western coast of the continent. He knew about the mighty Columbia River and about the natives that lived along its banks, and the banks of the Missouri. But these tales of exploration were stories and anecdotes, they were not science. And Jefferson knew himself to be a man of science. He needed to know the land he had purchased, the people who lived there, the resources it contained. And he badly needed to know if there was a water route through the territory to the Pacific. If there were, the destiny of the country, to inhabit the lands from the Atlantic Ocean to the Pacific, could be fulfilled.

Meriwether Lewis, his personal aide, came in from the adjoining office. Jefferson had known Lewis’s father back home in Albemarle County before he passed, and the young man had proved himself an able officer and a patriot in the intervening years. Jefferson knew Lewis would be the right person for the job he had in mind.

“Merry,” he said, “the Louisiana Territory is of inestimable value. We need to determine how best to exploit its potential. It could be the key to the future of our nation.” Jefferson paused and looked at Lewis. Lewis waited patiently; the President’s pronouncements were never so short.

“We are a young nation and must avoid conflict if we are to Continue reading "The Lewis and Clark Business Plan Competition"

Automated Ad Buying is Already Mainstream, Whether Most Marketers Understand it or Not

The Wall Street Journal has the startling news that “Most Marketers Don’t Understand Automated Ad Buying.” Ten years into the programmatic revolution and most marketers don’t understand it! ANA Chief Executive Bob Liodice is quoted, saying “confusion about how the technology works might be slowing its adoption.” Are we failing?

It’s almost certainly true, as Liodice says, that confusion about programmatic is slowing its adoption. I believe this because it has also been true of every non-lethal technology in history1. It takes time for new technologies to win the market, and different customer sets adopt them at different rates. Everett Rogers wrote about this back in 1962. In an idea made famous by Geoffrey Moore in Crossing the Chasm (required reading), he argued that it takes a certain amount of time for innovations to spread. Every business person has seen the pictorial representation of this idea, distilled into the chart on the cover of Moore’s book. It shows how innovations spread: from Innovators to Early Adopters to Early Majority, etc.

DiffusionOfInnovation

Back to the WSJ article. The WSJ based its argument on a poll.

programmaticpoll

The poll shows that only 23% of CMOs are using programmatic and that only 33% understand it well enough to apply it. In contrast, 44% don’t understand it or are not even aware of it. I know that sounds bad, but before we worry, let’s compare the self-reported to the theoretical–let’s see where on the diffusion of innovations curve we are.

Here are the poll results superimposed on the Innovation Adoption Lifecycle curve.

annotateddiffusion

In the adoption of programmatic ad buying, we’re past the innovation stage, we’re past the early adoption phase, and we’re well into the early majority phase. That we’re in the early majority phase is important, because these are the customers that are using the technology not because they like the new new thing and not because they are willing to take big risks to get big rewards, but because they are pragmatists: they make business decisions based purely on what is best for their business. As such, they are trusted by everyone else in the market, and what they do carries enormous weight as others make their decisions about whether to use the technology or not.

Getting to the point where the early majority is using your product is the “Crossing the Chasm” that Moore wrote his book about: going from meeting the expectations of the early adopters to meeting the very different ones of the early majority. Moore says that the late majority will eventually, automatically–albeit somewhat begrudgingly–follow the early majority into using an innovation2. Getting from the early adopters to the early majority is the place where Continue reading "Automated Ad Buying is Already Mainstream, Whether Most Marketers Understand it or Not"

Midas List Feeder Angels

Like the feeder firms, but with angels. I used AngelList’s API to figure out which angels invested in the companies that the Midas List firms invested in. The list is ranked by what percentage of the firms these angels reported to AngelList are also Midas List investments.

Some caveats on this list. The data was a bit noisy. Most of the people the algorithm returned are actually venture capitalists. It is unclear how many of the companies they report as investments are actually their own angel investments and how many are someone else’s money. So I took out all people who work for or have recently worked for a venture fund. Except I left in the people whose venture fund is, I’m pretty sure, actually a vehicle for their own money. That may not be fair in some cases and I may also be wrong. I also tried to leave in people who run accelerators but where it seems like the companies they’ve invested in are with their own money. Also a bit apples-to-oranges perhaps.

To avoid the law of small numbers, I only counted angels who have at least ten companies that overlap with the Midas firms. I picked ten because it is a nice round number. It’s arbitrary.

I cross-checked the algorithm’s results against Crunchbase. There were a couple of changes. And I took out Carolyn White, whose existence I can’t seem to verify. Let me know if that was a mistake and I’ll put her back in.

There are probably also people reporting some of the companies they advise as investments. And all sorts of other problems. Because of that, I left off the percentages and just rank-ordered everyone.

I used the first location the angel used on AngelList as their location. Except for one or two that I changed because I happen to know where they live. That the Bay Area dominates is no surprise. How much it dominates was a little bit surprising.

Last, this should not be taken as a list of either who are the best angel investors or who are the most helpful. Some of both of these things probably play into this, but so does a whole lot of other stuff. In NYC alone, some of the most helpful angels I know (Joanne Wilson, say, or Mark Kingdon, who are both notably helpful investors in NYC, according to what entrepreneurs tell me) did not come up on this list for whatever reason.

And finally, you get what you pay for; happy to make changes to egregious errors, but don’t take this list as any more authoritative than it is: something I hacked up at 4am because I couldn’t sleep.

1 Continue reading "Midas List Feeder Angels"

Midas List Feeder Firms, 2014 Edition

It’s that time of year again. Midas List time of year. I bet you’re out right now, buying drinks for your nine favorite Sequoia partners (and the two who got away.) Cheers!

Wait, what? You don’t know any of the nine Sequoia partners that made the list? You don’t know any of the Midas List people? Well, you’re in luck, because the fine data science team at Neu Venture Capital has scoured the Crunchbases looking for other firms that invested in the companies that the Midas List firms invested in, either before them or with them.

The list is below. They’re ranked by deals they did before or with a Midas List firm as a percent of all their deals. At least, according to Crunchbase. I know there are some mistakes here (mainly because my CB page is missing at least one company that a Midas List firm invested in.) But because the data science team has to go to bed soon, I just used the CB data as is. If you don’t like it, fix your CB data. Or go get the code and run it your own way.

1 Harrison Metal Capital 69%
2 Red Swan 69%
3 Worldview Technology Partners 68%
4 Lerer Ventures 68%
5 Founder Collective 66%
6 Freestyle Capital 66%
7 Collaborative Fund 64%
8 Draper Richards 62%
9 Accelerator Ventures 62%
10 Crunchfund 57%
11 Lux Capital 57%
12 Integral Capital Partners 56%
13 Nextview Ventures 56%
14 O’Reilly Alphatech Ventures 54%
15 Sherpalo Ventures 54%
16 Softtech VC 54%
17 Star Ventures 54%
18 BoxGroup 54%
19 Data Collective 53%
20 Aol Ventures 52%
21 XG Ventures 50%
21 Morado Ventures Partners 50%
23 Flagship Ventures 48%
24 Promus Ventures 48%
25 Thrive Capital 48%
26 Kapor Capital 47%
27 Google Ventures 46%
28 Learncapital 46%
29 Grandbanks Capital 45%
30 Advanced Technology Ventures 45%
31 Neu Venture Capital 45%
31 GSR Ventures Management 45%
33 Webb Investment Network 44%
34 Rothenberg Ventures 44%
35 Labrador Ventures 43%
36 Firstmark Capital 43%
37 Signia Venture Partners 43%
38 Bay Partners 43%
39 Gemini Israel Funds 43%
39 Highland Capital Partners 43%
39 Walden International 43%
39 Allegis Capital 43%
43 Morgenthaler Ventures 43%
44 Harris Harris Group 42%
45 New Atlantic Ventures 42%
46 Quest Venture Partners 42%
47 Frazier Healthcare Ventures 41%
47 Zelkova Ventures 41%
47 Vulcan Capital 41%
50 Innovation Endeavors 41%
50 Boldstart Ventures 41%
52 Foundation Capital 41%
53 RRE Ventures 41%
54 Mobius Venture Capital 40%
54 Sutter Hill Ventures 40%
54 IA Ventures 40%
54 RPM Ventures 40%
54 Eniac Ventures 40%

The Immediate Future for Adtech Startups

I spoke at the AppNexus Summit a couple of weeks ago. Brian O’Kelley read my post on adtech investing trends and asked if I could expand during the session. The video is online (my part at 1:48), but I thought I’d write it up also.

I figured in the AppNexus audience of 600 that there was quite a bit of overlap with the few thousand people who read the post. Because, really, who else would read it? So I didn’t want to just talk about the post. I started from a couple of the slides and went into what it means for us, adtech entrepreneurs and investors.

These, to me, are the two key charts in the previous post[1]. The first shows that VC investment in adtech companies is down. The second shows that, even more alarmingly, the number of companies founded is way down.

DataHero Investments by Round, Companies founded 2006 and afterDataHero Companies Founded

Now these numbers are from Crunchbase, so they are skewed, incomplete, and there is an evident selection bias[2]. And, of course, I chose which companies were considered adtech and which were not, so there’s that. But everyone I showed the second chart to disputed it. They were all certain that more companies than Crunchbase was showing had been founded in 2013.

And, if you think about it, it’s pretty odd. The programmatic industry has finally attained widespread acceptance. It is now an accepted part of the media-buying landscape. Nobody asks why you’re buying programmatic anymore, now they ask why you’re not. And you better have a pretty well thought-out reason. We’re finally at Step 3: Profit and company formation seems to have died. Common sense would suggest there would be a lot of startups, where are they?

I was puzzled, so I did what any self-respecting data hack would do and scraped AngelList for adtech startups. The story here was different than what I found on Crunchbase. There are scores of companies I deemed adtech listed on AngelList that were launched in 2013. [Edit: Steven from Crunchbase pointed out that some of the companies that AngelList has as ‘launched’ in 2013 are noted in Crunchbase as ‘founded’ prior to 2013; I stopped counting the AngelList companies at one hundred, so I can’t stand by my previous assertion that ‘more than 100 companies’ were launched in 2013. It does seem, however, that scores were. My point: that founders are more optimistic than VCs by a long shot still stands, I believe.]

So, on the one hand, you have a ton of companies being started in adtech, according to AngelList. And on the other, you have only a handful, according to Crunchbase[3].

slide 4What is going on?

I think the answer is actually in the Crunchbase bias:

slide 5
slide 6
slide 7
Screen Shot 2013-11-23 at 3.15.42 PM
slide 12
slide 13
slide 14
slide 9
slide 10
slide 11
slide 15
slide 16
DataHero Investments by Round, Companies founded 2006 and after(3)
slide 18
DataHero Ad Network Size circa 2007(1)
slide 20
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slide comments
Continue reading "The Immediate Future for Adtech Startups"

The aim of marketing is to make selling superfluous

I was preparing to teach my entrepreneurship class about marketing last week and ran across an old blog post of Fred Wilson’s*.

A very experienced and successful entrepreneur came into our office a week ago to pitch his latest company. At the end of his pitch he showed us some numbers. Normally for a raw startup we see almost all product and engineering expenses (headcount). But his plan had a monthly budget for customer acquisition. After he left, we talked about his plan and my partners focused on the customer acquisition number. It bugged us. It felt wrong.

The first comment on the post was from ex-entrepreneur Seth Godin, who said

Marketing ≠ Advertising.

If you redo the post with Advertising throughout, I won’t argue much.

Marketing is the name we use to describe the promises a company makes, the story it tells, the authentic way it delivers on that promise.

Fred didn’t defend his assertion, but he should have. He made an important point that shouldn’t be swept aside: customer acquisition in an early-stage startup needs to cost minimal dollars. And that’s true whether customer acquisition is done through advertising or any other marketing method. Seth Godin is right, but Fred is not wrong. And how could Fred be wrong? He is interacting with companies in the real world, and a good number of them: his assertion is data-driven. The distinction here is primarily semantic.

But the semantic difference is important. Nobody wants entrepreneurs to assume they shouldn’t pay attention to marketing, because they should. But what is marketing? In my class I try to expand the students’ minds beyond thinking marketing is no more than advertising. I teach them the classic Four Ps and add customer relationship management to bring them up to date**. But Peter Drucker, in his Management: Tasks, Responsibilities, Practices, explains it better:

There will always, one can assume, be need for some selling. But the aim of marketing is to make selling superfluous. The aim of marketing is to know and understand the customer so well that the product or service fits him and sells itself. Ideally, marketing should result in a customer who is ready to buy. All that should be needed then is to make the product or service available.

Selling here means the company reaching out to persuade. This might be a salesman making a sales call, or it might be any form of marketing. Drucker is distinguishing the marketing that listens to the customer and makes a product they want from the marketing that tells the customer they want the product that is already on offer. If you think about it this way, Fred is absolutely right: the Continue reading "The aim of marketing is to make selling superfluous"