Nick Beim

Thoughts on the Economics of Innovation

Learning Effects, Network Effects and Runaway Leaders

September 22
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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

June 3
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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

March 17

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.