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
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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…


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

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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.


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


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.


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.

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