Nick Beim

Thoughts on the Economics of Innovation

Learning Effects, Network Effects and Runaway Leaders

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There’s a new economic force at work in the machine learning revolution that is capable of generating increasing returns to scale, much as network effects did in the internet revolution.

This force is automated learning, and its business impact comes in the form of learning effects: the more a product learns, the more valuable it becomes.

Learning effects have the potential to generate enormous economic value, as network effects do, if companies are able to close this loop and make it self-reinforcing: that is, if their products learn more because they have become more valuable.

This happens when more valuable products attract more users or customers, who provide more and richer data of the kind that enables machine learning models to make these products more valuable still, which attracts more users or customers still, and so on, creating a self-perpetuating cycle.

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Just as network effects determined who the biggest winners of the internet revolution were, learning effects will determine who the biggest winners of the machine learning revolution will be.

Because they enable increasing returns to scale, they will similarly give rise to a set of companies that become runaway leaders – that are capable of pulling away from their competitors and continuing to increase their leads over time.

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The Barbell Effect of Machine Learning

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If there is one technology that promises to change the world more than any other over the next several decades, it is arguably machine learning. By enabling computers to learn certain things more efficiently than humans and discover certain things that humans cannot, machine learning promises to bring increasing intelligence to software everywhere and enable computers to develop ever new capabilities – from driving cars to diagnosing disease – that were previously thought impossible.

While most of the core algorithms that drive machine learning have been around for decades, what has magnified its promise so dramatically in recent years is the extraordinary growth of the two fuels that power these algorithms – data and computing power. Both continue to grow at exponential rates, suggesting that machine learning is at the beginning of a very long and productive run.

As revolutionary as machine learning will be, its impact will be highly asymmetric. While most machine learning algorithms, libraries and tools are in the public domain and computing power is a widely available commodity, data ownership is highly concentrated.

This means that machine learning will likely have a profound barbell effect on the technology landscape. On one hand, it will democratize basic intelligence through the commoditization and diffusion of services such as image recognition and translation into software broadly. On the other, it will concentrate higher-order intelligence in the hands of a relatively small number of incumbents that control the lion’s share of their industry’s data.

For startups seeking to take advantage of the machine learning revolution, this barbell effect is a helpful lens to look for the biggest business opportunities. While there will be many new kinds of startups that machine learning will enable, the most promising will likely cluster around the incumbent end of the barbell.

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Dataminr and the Science of Real-Time Information Discovery

Today Dataminr announced a $130m round of financing from a group of leading financial institutions and prominent financial thought leaders including John Mack, Vikram Pandit, Tom Glocer and Noam Gottesman.  

A number of friends have asked me about the company and what I find most interesting about it. This seemed like a good opportunity to highlight a few thoughts. 

What I find most interesting about Dataminr is that in addition to building a business, it is pioneering a new science. The science is real-time information discovery, and it involves sifting through the ever-growing tidal wave of real-time public data to identify and determine the significance of breaking events by their nascent digital signatures, as they happen. Sometimes these events are well-wrapped, for example by someone witnessing an event and tweeting about it, with others providing corroboration. Sometimes they aren’t, with algorithms figuring out what is happening by seeing thousands of facets of something larger. The company has a deep strategic partnership with Twitter that makes this kind of discovery possible. 

This new science is, without a doubt, very cool. It enables one to discover news before it’s news and market-moving information before markets move. It provides a kind of X-ray vision into what is going on in the world in real-time with a filter for what is significant, and to whom. All on the basis of publicly available data.

In a period of five months, Dataminr has become the real-time wire service used almost universally by major news organizations, beating out the next best service by over an hour and discovering troves of unknown unknowns that would never have otherwise come to light. It has become adopted by the lion’s share of leading financial institutions to have access to the frontier of breaking information in real time.  

What’s also interesting is how Dataminr will change the world. In my view most industries that rely on real-time information — an ever-increasing number — will be influenced by it, and some will be transformed by it. The wave of change began in the fields of finance, news and public safety, and I think will move quickly to risk management, security and PR. And undoubtedly to other verticals in ways that are difficult to predict. I am particularly excited about what the company and its technology can do to help save lives in the fields of public safety and humanitarian assistance.  

Dataminr is in the early days of a long journey, but it is already impacting the world in significant ways, and it’s exciting to be a part of.


Are Venture Capitalists Biased Against Female Entrepreneurs?

In her article Taking a Hammer to the Silicon Ceiling, Amanda Bennett hits on a real problem in the venture industry where spoken and unspoken biases have a significant impact: it is harder for women to raise money than it is for men. However hopeful one’s outlook, this is an uncomfortable and inescapable truth that the industry should acknowledge.

What’s the reason for it? I’ve been in the venture business for 14 years, and rarely, but sometimes, I’ve seen it come from unabashed bias about women’s ability to do as good a job as men. Generally this relates to the subject of women already having or potentially having children. I’ve heard people remark: “Wouldn’t that be a big distraction for the company, and how could they possibly be as productive as men in those circumstances?” This particular kind of bias is rarely expressed in a public manner but certainly affects the thinking of some. The good news is that as younger generations of investors assume more prominent roles in the industry, I think it will substantially diminish.

More often, I’ve seen the challenges female entrepreneurs face in raising money result from a bias that is rooted in the primary way venture capitalists make decisions, which is through pattern recognition. In a private conversation, a successful west coast venture capitalist expressed the issue to a friend of mine in a backward-looking empirical fashion that was an attempt to be unbiased: “look at the numbers – most successful startups are started by men in their 20’s and 30’s; the number of successful startups founded by women is much smaller.” Yes, but most startups in any historical timeframe were started by men in their 20’s and 30’s. This doesn’t speak to the likelihood of women succeeding, particularly since a significantly larger number of women are starting companies today than in the past.

Social scientists call this logical flaw selecting on your dependent variable: determining that A is a principal cause of B by looking only at cases of B. Used as the primary lens for evaluating new investment opportunities in venture capital, it creates all sorts of intellectual distortions and inertia and is the principal reason most venture capitalists are late to promising new trends and only jump on board when there is a significant pattern of success. I think this is the cause of the biggest challenge that female entrepreneurs face in raising money. Most venture capitalists have not internalized the success of female entrepreneurs to a sufficient degree to have it influence their intuitive pattern recognition, partly due to what they perceive as a lack of a large enough n and partly no doubt due to the fact that they have not worked with female entrepreneurs directly. It was also the cause of challenges that entrepreneurs faced in raising money in a variety of pioneering new fields, from personal computers to the internet to digital animation. Success by entrepreneurs in these fields was not yet a large enough historical pattern to influence investors’ thinking.

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A Very Cool Thing I Learned About My Dad

Every so often a family member does something significant that makes you really proud. That happened to me this week when I learned about the full details of the role my Dad played in trying to prevent regulatory failure by the New York Federal Reserve in a pretty astonishing story that was uncovered by Pulitzer Prize-winning reporter Jake Bernstein at ProPublica and This American Life and covered subsequently by Michael Lewis on Bloomberg and by the Washington Post.

The story involves a former employee of the New York Federal Reserve named Carmen Segarra who was fired for refusing to back down from her conclusion that Goldman Sachs fell short of regulatory requirements for dealing with certain conflicts of interest. Sensing that she was working in an overly deferential regulatory system that would reject her conclusions, she secretly recorded meetings that supported her case.

The most notable smoking gun quotes were from a Goldman employee who said that “once clients are wealthy enough, certain consumer laws don’t apply to them” and from a fellow Fed regulator who responded to Segarra’s surprise at this statement by saying “you didn’t hear that.”

The background for this story is that in 2009, the head of the New York Federal Reserve asked my father, David Beim, a Professor at Columbia Business School, to write an internal report on how the Fed could have missed all the incredibly risky behavior at investment banks that helped cause the 2008 financial crisis.

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