PayPal Co-Founder Max Levchin Hot Trends

Levchin described the evolution of PayPal — the online payment service whose 2002 initial public offering gave him the liquidity to get him started on close to 50 start-up investments.

In 2006, Levchin founded Slide — a social applications developer that Google (GOOG) bought in August 2010 for an estimated $182 million. Slide made social games but – in Levchin’s estimation its games were not its strength. Rather, what fascinated Levchin was the company’s ability to use data to find insights into consumer behavior. 

When Levchin started to invest in start-ups, he decided to apply three tests to decide where to invest. Over time, he has somewhat relaxed one of the constraints. The tests include the following:

• Does Levchin have a deep understanding of the company and its product?Initially, he would pass if he was not well-versed in a start-up’s industry. Now, he is more open to putting capital into start-ups where he does not have deep expertise.
• Does Levchin believe he can add value? By this, he means that if he does not think he can help the start-up with its strategy, its product development, or its team building; he will decline to invest.
• Does the start-up take advantage of what he thinks the world will look like in five years? Every year, Levchin likes to look at key trends and identify themes that help him predict how the world will be different five years into the future. Then he tries to invest in start-ups that have interesting ideas about how to take advantage of those themes.

Of the roughly 50 companies in which he has invested, one that best exemplifies Levchin’s approach to start-up investing is Yelp, the local business social review site of which he owns 13.2% with an estimated $100 million value. He first invested in Yelp in the summer of 2004 after meeting with two “really good technical guys he knew.”

What are Levchin’s themes for 2012? In addition to being interested in so-called big data — the notion of extracting useful insight from lots of data, that he discovered has become pretty popular among the technorati — here are three that strike me as unique:

• Data collectors and sensors. According to Levchin, “the proliferation of cheap data collectors and sensors (and rapidly falling costs of storing and processing such data) is exciting. Examples include accelerometers in phones and on wrists and hips; GPS receivers in every car, watch, and phone; cameras in practically everything, and indeed simple and ultra-cheap ID devices like RFIDs that helping us track things.” Levchin seeks applications that use all the data that these sensors collect in a way that’s profitable for start-ups and their customers.
• Valuing data. Levchin is also interested in businesses that can find data that has value to people. In thinking about this, he distinguishes between data narratives and data that companies, for example, would pay for. As an example, he mentioned that an insurance company might be able to buy a potential customer’s driving record for say, $1. If that potential customer ended up buying insurance from that company, then the $1 might be well spent since it would help the insurer set a profitable rate. If the driver went to another insurer, the money would be wasted. Levchin is seeking start-ups that can turn now “worthless” data into valuable information.
• Mis-allocated resources. Levchin sees all kinds of waste in the world and thanks to the ability to gather data inexpensively at the periphery and analyze it deeply at the center, he believes that markets can be created that slash this mis-allocation of resources. One example of this is in automobiles — some people have two cars in their garage that they never drive and others don’t have cars but need to travel 25 miles back and forth from their house to work every day. Levchin believes that a market could emerge that would match up the people without cars who need them with ones who have them but don’t drive too often. Similarly, Levchin sees an opportunity to create a service that brings trucks loaded with commonly purchased groceries to locations inferred from consumer location data, to maximize both convenience and sales.

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