Nick Beim

Thoughts on the Economics of Innovation

Collaborative Intelligence

Much of the early public discussion about AI focused on what AI could do better than humans and where it would replace human labor. The same thing happened with the emergence of computers in the 1960’s, personal computers in the 1970’s and the internet in the 1990’s. This line of thinking is perhaps a natural human response to new technologies perceived as a threat, but it missed the far more important point that the big breakthroughs in productivity and intelligence would come from human-AI, human-internet and human-computer teaming.

In this podcast discussion with Gautam Mukunda and Shawn Bice, we had a chance to dig into what the collaborative human-AI intelligence of the future would look like, how AI will change human decision-making and how it will impact employment, science and technology innovation. You can listen to the discussion here.


Technology’s Transformation of Geopolitics

Of all of the spheres of human activity that are being radically transformed by technology, one of the most consequential is geopolitics. Advances in technology are rapidly increasing national security vulnerabilities, changing the way countries compete and requiring a radical rethink of defense and intelligence strategies. Although the U.S. continues to lead the world in technology innovation, it has been surprisingly behind the curve in this transformation.

One organization that I believe can help the U.S. advance its thinking in this area is the Council on Foreign Relations, and I’m excited to join its Board of Directors. The CFR is one of the world’s leading foreign policy think tanks and perhaps the most prominent forum for debate about the role of the U.S. in the world. The organization has been ramping up its research activities in technology over the past several years and recently published reports on how to defend the country against digital election interference by foreign countries, how to share cyber threat information between the public and private sectors and how to ensure that the U.S. maintain its global leadership in science and technology.

The world is in a vulnerable state, and I believe the CFR has never been more needed. Beyond the current pandemic, the rules-based international order established by the U.S. and its allies after World War II is rapidly deteriorating. Democracy is in retreat, and autocracy, nationalism and instability are on the rise. Global problems beyond the reach of individual countries to solve on their own are also on the rise, including climate change, pandemics and cyber threats. It’s time for new ideas about how the U.S. should address these issues and how we can best harness technology to make the world a safer place. I believe the CFR can contribute meaningfully to these discussions and am looking forward getting more involved.

If you are interested in contributing to these discussions, I would encourage you to become a member.


Maintaining U.S. Leadership in Science and Technology

One of the most interesting things I’ve worked on this past year is a project at the Council on Foreign Relations examining the risk and consequences of the U.S. losing part of its science and technology leadership, principally to China.

We had a phenomenal group of people working on our Task Force from the technology, government and academic communities, including Admiral William McRaven, James Manyika, Reid Hoffman, DJ Patil, Eric Schmidt, Raj Shah, Doug Beck and Regina Dugan.

Today our final report was published – you can download it here. For a 2-minute overview, here’s an article I wrote with Congressman Jim Himes that highlights the report’s major ideas and recommendations.


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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