My Answer to Peter Thiel’s Question

Here is my answer to Peter Thiel’s now famous appeal to entrepreneurs to “tell me something that’s true that almost nobody agrees with.” Although it’s not explicitly stated in the question if you read Peter’s work it’s probably better if the assertion is forward-looking.

My Answer:
Most people think the future of business is about big data, but the future of business is about simply heuristics.

The answer is simple. At the highest level business has always been about simple heuristics. If you read any of the biographies about prominent business leaders of the past, say, 30 years – Jobs, Gates, Bezos, Musk – what stands out is that they follow their intuition. Often the intuition goes against all prevailing signals and their real talent is not seeing the future, but rather stubbornly creating it, often at great personal, and sometimes professional, expense.

The rule of heuristics often holds true for entrepreneurs as this quote below from the New Yorker’s profile of Mark Andreessen demonstrates (notice that both the old and new model of startup funding use heuristics):

When a startup is just an idea and a few employees, it looks for seed-round funding. When it has a product that early adopters like—or when it’s run through its seed-round money—it tries to raise an A round. Once the product catches on, it’s time for a B round, and on the rounds go. Most V.C.s contemplating an investment in one of these early rounds consider the same factors. “The bottom seventy per cent of V.C.s just go down a checklist,” Jordan Cooper, a New York entrepreneur and V.C., said. “Monthly recurring revenue? Founder with experience? Good sales pipeline? X per cent of month-over-month growth?” V.C.s also pattern-match. If the kids are into Snapchat, fund things like it: Yik Yak, Streetchat, ooVoo. Or, at a slightly deeper level, if two dropouts from Stanford’s computer-science Ph.D. program created Google, fund more Stanford C.S.P. dropouts, because they blend superior capacity with monetizable dissatisfaction.

Venture capitalists with a knack for the 1,000x know that true innovations don’t follow a pattern. The future is always stranger than we expect: mobile phones and the Internet, not flying cars. Doug Leone, one of the leaders of Sequoia Capital, by consensus Silicon Valley’s top firm, said, “The biggest outcomes come when you break your previous mental model. The black-swan events of the past forty years—the PC, the router, the Internet, the iPhone—nobody had theses around those. So what’s useful to us is having Dumbo ears.”* A great V.C. keeps his ears pricked for a disturbing story with the elements of a fairy tale. This tale begins in another age (which happens to be the future), and features a lowborn hero who knows a secret from his hardscrabble experience. The hero encounters royalty (the V.C.s) who test him, and he harnesses magic (technology) to prevail. The tale ends in heaping treasure chests for all, borne home on the unicorn’s back.

This doesn’t mean big data won’t have an impact on business, but that it’s not the future of business. When to fund big data projects and the collection of the massive amounts of data necessary to feed them, when to replace or augment existing business systems with big data systems, when big data is overkill and unnecessary, when data needs an injection of personal intuition, when business questions are formatted in a way that’s difficult for computer’s to understand, and many other questions hinge on the decisions of business leaders, which will inevitably are made using simple heuristics. Output from big data is just one more source of information that will be leveraged by business decision makers using simple heuristics.

You may get the impression that I’ve made the argument that almost no one believes that big data is the future of business. But alas any amount of magazine, blog, or newspaper reading, or even a large portion of listening to business leaders, will show you that my view is decidedly in the minority. Business leaders are using simple heuristics in their approach to big data even if they do not always realize it.


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