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There’s a popular meme among the hedge fund set: that college education, with it’s steadily increasing cost and questionably useful skills in the workplace, is a bad financial decision. Instead, they argue, the best path is to directly join the work force and take advantage of all the great opportunities right at 18. These lucky souls can save on the expensive tuition and get right to the joy the workforce brings.

As an important side note, none of these hedge funders skipped college themselves. They typically went to Ivies. They don’t know anyone that skipped college, except anecdotal outliers like Mark Zuckerberg and Bill Gates. And they’re rarely talking about their own kids – often it’s just a course for “other” folks.

The jobless statistics paint a fairly clear picture as to why this is a joke. The country is a stark tale of two very different classes of worker.

“The Haves”: Those with a college education have a very low unemployment rate of 4.4%, with 77% of the workforce participating (non-participating includes retirees and those who are so despondent about their prospects that they don’t even try). Basically, the economy is booming!

“The Have-Nots”: Those without a college education have a 10% unemployment rate. Even worse, only 60% of the workforce is participating. This means there is an incremental 17% of the workforce that has given up.

Bottom line: Even without accounting for the higher wages paid to those with a college education, the picture for those without a degree is bleak. While the costs of college are high, and often saddle students with lots of debt, it’s the cost of entry into the good life.

Stats from the Bureau of Labor Statistics:

http://www.bls.gov/news.release/empsit.t04.htm

A little personal press!

We announced today that we are being acquired by MediaBank, the up and coming competitor to Donovan Data Systems one-time monopoly on agency processing software. We’re going to become the foundation for their marketplaces businesses, which will allow agencies to interact more seamlessly with their publisher partners.

It’s a great opportunity, a great team, and a really exciting category. I’m excited to get AdBuyer’s platform to a broader audience of buyers and continue the cross media march!

Is there anything more inspiring than the rebirth of spring after a long winter? The world was dead and bright color and life is pushing back!

Tulips, daffodils, and cherry blossoms are among my favorite things.

Slide 46 of Mary Meeker’s excellent mobile state-of-the-union presentation is a zinger. It shows the relative conversion rates from free to paid in Apple’s iOS versus all others. Apple holds steady at roughly 12%, whereas other (which presumably includes Android) hover between 2% and 3%.

Put a different way: Android needs 5-6X the number of users to deliver the same economics to App developers. That’s a very steep mountain to climb given Apple’s recent execution. And, as we saw with PCs, app availability will dictate the OS winner.

Android has done a great job getting carriers to ship Android phones, but that growth has come at the expense of standardization. I expect this may act as a wakeup call to figure out how to streamline and standardize the app experience.

The presentation is below.

While New York’s tech elite make lots of noise about progress in the tech and media markets, there’s another bubbling ecosystem that’s making incredible strides and could be even more transformative for New York: synthetic biology.

See the recent NY Times article about Genspace, the first hacker space for work synthetic biology. I think this will prove incredibly interesting. We have made great strides decoding the myriad functions of genes, but it’s incredibly difficult to use that information to encode new functionality.

The pioneers in synthetic biology are building the tools to hack together new forms of life. I’m convinced that when those building blocks are in place, we’ll witness a revolution that will transform everything about our world.

So how do we grow the ecosystem? I think it’s three things:

1. Hacking spaces: There are a small number (<100?) of labs with the required expertise and they are typically confined to top universities and big pharma/biotech companies.  Hacking spaces like Genspace will dramatically increase the number of potential “engineers” that can program using genes.
2. Education: In line with point #1, we’re going to find ourselves with a shortage of programmers who can handle all the applications we can dream up. Genspace is getting started with some great educational programs, but
3. “Programming” tools. The current process of creating new forms of life is extremely time consuming, requires graduate level course work in genetics, and only works about 50% of the time. We need the equivalent of visual studio for synthetic biology, allowing programmers to design their work in a safe environment with testing/validation tools. The current tools really suck, from what I can see.

Know anybody working on any of these? I’d love to connect with them and learn what they’re up to, whether they’re in NY or not.

There is lots of recent debate regarding the rise of “super-angels” to fund early stage companies and the coming death of the traditional VCs. This is much ado about nothing. It’s simply a reflection of the relative value investors are placing on the key risks of a company’s formation and growth.  Specifically, I’d argue that it’s a developing market between traditional VCs who are “buyers” of consumer traction, and the new crop of superangels who are manufacturing and “selling” traction.

VCs and angels make money in the same way: by “de-risking” a company to the point where a new investor is willing to pay a premium for their ownership stake. For consumer internet businesses, those risks can be over-simplified into four biggies: management team, consumer traction, revenue model, and execution/scaling. VCs buy businesses, work with the management team to remove one or more of the key risks, and sell those businesses to acquirers.

During the first internet bubble VCs made large bets on companies that worked in excel and powerpoint, but not in reality. Many of these flameouts were due to a massive underestimation of the costs of acquiring users. i.e. They couldn’t get consumer traction. So they apply a very large discount to compensate them for the large risk that the company won’t be able to get consumer traction.

Apply this mentality across the entire asset class and you get today’s marketplace, where VCs will largely only invest in business with either proven teams or proven traction. But since there’s a lot of money at work through these funds, the premium paid for that traction is extremely high.  So VCs become buyers of traction, as a necessary component of the very large companies they are looking to build.

The new crop of angels/superangels thinks that risk premium creates a compelling investment opportunity. They systematically seek out pre-traction companies with (typically) young management teams and try to engineer traction. Hence Dave McClure’s startup metrics for pirates, increased awareness around Steve Blanks Customer development, and the incubator’s focus around customer acquisition. These are ways to systematically de-risk their investments by delivering companies that have the right types of risk for venture investors. They become, in effect, sellers of traction.

There’s a healthy tension between buyers and sellers in every market, particularly when they are on opposite sides of a trade. But their differences seem to come down to an opinion on whether traction is the ineffable stuff of great companies, or something that can be engineered through a systematic process. Entrepreneurs who want to maximize their fundraising appeal would do well to match their company-building process to the investors types at each stage.

I’ve been thinking recently about the Prisoner’s dilemma, a classic game theory problem, and how it relates to search marketing. If you’re not familiar with it, the description from Wikipedia follows:

Two suspects are arrested by the police. The police have insufficient evidence for a conviction, and, having separated both prisoners, visit each of them to offer the same deal. If one testifies (defects from the other) for the prosecution against the other and the other remains silent (cooperates with the other), the betrayer goes free and the silent accomplice receives the full 10-year sentence. If both remain silent, both prisoners are sentenced to only six months in jail for a minor charge. If each betrays the other, each receives a five-year sentence. Each prisoner must choose to betray the other or to remain silent. Each one is assured that the other would not know about the betrayal before the end of the investigation. How should the prisoners act?

The rational response is to betray the other prisoner because, no matter what the other player does, it leads to the best outcome. This has always been a little frustrating for me as the 6-month sentence seems so easily achievable, but it’s impossible to coordinate your response and to have any method for punishing your fellow criminal for betraying you.

When the situation is repeated over and over, it gets more interesting. In these cases, each prisoner gets to  observe how his fellow criminal acts, and react accordingly in the future.  Put a different way: each prisoner uses the information gained from the previous round to improve his future strategy. This tends to lead to cooperation as the desired strategy.

Search marketers are engaged in an iterated multi-prisoner dilemma on a daily basis. There are a relatively small number of bidders in each auction with well-defined payoffs (aka expected revenue) associated with each paid click. Increasing my bid on an individual keyword or media unit will my lower unit profit, in return for potentially increased volume. My increase will also increase the costs of the same media unit to my competitors, decreasing their profit without any increase in volume. These auctions are iterated millions of times daily. Theory suggests that this should lead to cooperation, whereas I’m not familiar with attempts to leverage this data.

Note that I’m not suggesting that advertisers (who are often competitors) collude to coordinate auction bidding. Instead, they should be using the auction results available in the marketplace to develop a more optimal bidding strategy.  In subsequent posts I will explore more detail regarding:

1. The size of the opportunity
2. How advertisers can incorporate more rational strategies
3. How to identify and punish irrational bidders

I’d love any feedback or commentary. I’m not a game theory academic, but an active practicioner looking to improve results for AdBuyer’s advertisers in both search and display.