I have a new R Street Institute policy study out this week doing a deep dive into the question: “Can We Predict the Jobs and Skills Needed for the AI Era?” There’s lots of hand-wringing going on today about AI and the future of employment, but that’s really nothing new. In fact, in light of past automation panics, we might want to step back and ask: Why isn’t everyone already unemployed due to technological innovation?

To get my answers, please read the paper! In the meantime, here’s the executive summary:

To better plan for the economy of the future, many academics and policymakers regularly attempt to forecast the jobs and worker skills that will be needed going forward. Driving these efforts are fears about how technological automation might disrupt workers, skills, professions, firms and entire industrial sectors. The continued growth of artificial intelligence (AI), robotics and other computational technologies exacerbate these anxieties. Yet the limits of both our collective knowledge and our individual imaginations constrain well-intentioned efforts to plan for the workforce of the future. Past attempts to assist workers or industries have often failed for various reasons. However, dystopian predictions about mass technological unemployment persist, as do retraining or reskilling programs that typically fail to produce much of value for workers or society. As public efforts to assist or train workers move from general to more specific, the potential for policy missteps grows greater. While transitional-support mechanisms can help alleviate some of the pain associated with fast-moving technological disruption, the most important thing policymakers can do is clear away barriers to economic dynamism and new opportunities for workers.

I do discuss some things that government can do to address automation fears at the end of the paper, but it’s important that policymakers first understand all the mistakes we’ve made with past retraining and reskilling efforts. The easiest thing to do to help in the short-term is clear away barriers to labor mobility and economic dynamism, I argue. Again, read the study for details.

For more info on other AI policy developments, check out my running list of research on AI, ML robotics policy.

We hear a lot today about the importance of “disruptive innovation,” “deep technologies,”  “moonshots,” and even “technological miracles.” What do these terms mean and how are they related? Are they just silly clichés used to hype techno-exuberant books, articles, and speeches? Or do these terms have real meaning and importance?

This article explores those questions and argues that, while these terms are confronted with definitional challenges and occasional overuse, they retain real importance to human flourishing, economic growth, and societal progress.

Basic Concepts

Don Boudreaux defines moonshots as, “radical but feasible solutions to important problems” and Mike Cushing has referred to them as “innovation that achieves the previously unthinkable.” “Deep technology” is another buzzword being used to describe such revolutionary and important innovations. Swati Chaturvedi of investment firm Propel[x] says deep technologies are innovations that are “built on tangible scientific discoveries or engineering innovations” and “are trying to solve big issues that really affect the world around them.”

“Disruptive technology” or “game-changing innovations” are other terms that are often used in reference to technologies and inventions with major societal impacts. “Transformative technologies” is another increasingly popular term, albeit one focused mostly on health and wellness-related innovations.

However one defines them and whatever one calls them, it is clear, as a 2015 report from the World Economic Forum (WEF) argued, that, “the list of potentially disruptive technologies keeps getting longer.” “Inventions previously seen only in science fiction,” the WEF report said, “will enable us to connect and invent in ways we never have before.”

More concretely, when people use these terms in reference to existing technologies, or ones currently on the drawing board, they often mention innovations like:

• Artificial intelligence / machine learning / robotics
• 3D printing / additive manufacturing
• Self-repairing / self-building objects
• Driverless cars / flying cars (VTOL), supersonic transport
• Private space travel / lunar mining
• Clean power / alternative energy production
• Genetic editing & life extension technologies
• Implantable tech / human augmentation
• Hyper-connected devices / wearable fitness / sensor tech / IoT
• Precision medicine
• Neural networks
• Quantum computing
• Nanotechnology / synthetic biology
• Immersive technology (AT & VR)

This is just a partial list of the type of technologies that experts mention when discussing “moonshots,” deep tech,” and other “disruptive” or “transformative innovations.” What unifies them more than anything else is the potential for major improvements in human well-being. Significant advancements in these areas could lead to substantial jumps in human welfare, health, and longevity.

Definitional Limitations

These terms have some problems and limitations, however. For example,“moonshots” conjures up thoughts of large, expensive government programs that are centrally-directed in a top-down fashion. Writing in The New Atlantis last year, Mark P. Mills argued that the notion of “ technological miracles ” can be taken to unrealistic extremes and he specifically cautioned against getting caught up in “moonshot fallacies” as well as “Moore’s Law fallacy.”

The “moonshot fallacy” is commonly heard in policy discussions whenever a policymaker or pundit insists that, “If we can put a man on the moon, then we can…” fill in the blank with your prefered aspirational goal du jour . But as Mills points out, this sort of talk often represents highly unrealistic, wishful thinking. “It is true that engineers have achieved amazing feats when tasked with particular, practical goals. But not all goals are equally achievable,” he correctly argues.  

“Moore’s Law fallacy” refers to the fact that innovation in the physical world of atoms is usually much harder and more costly than innovation in the digital world of bits. “If energy technology had followed a Moore’s Law trajectory, today’s car engine would have shrunk to the size of an ant while producing a thousandfold more horsepower,” Mills observes. The time horizons for big change are almost always going to be significantly longer in the physical world even with the increasing digitization in society and “ software eating the world .”

“Disruptive technology” is also a problematic term because its common use is quite different from Clayton M. Christensen’s original explanation of the term in his widely-cited Harvard Business Review articles from 1995 and then 2015 . “The original notion of disruption aimed to describe why great firms can fail,” Josh Gans explained in his recent book, The Disruption Dilemma . “Today, use of the term has gotten out of control,” he says. “As a concept, disruption has become so persuasive this it is at risk of becoming useless.”

Gans makes a good point. Not everything can be disruptive. Moreover, some techno-evangelists get carried away with such rhetoric regarding the “disruptive,” “transformative,” and “miracle”-working” potential of various technologies.  

But Sometimes Dreams Come True

Despite these definitional controversies or rhetorical excesses from some overly-exuberant tech boosters, these terms retain real meaning and significance.  

It is easy to ridicule dreamers, but quite a bit of life-changing innovation begins as a dream of some sort. Without a doubt, a great many “moonshots” will never get off the ground, and many “deep” technologies will end up sinking into the ocean of irrelevant or failed technologies. But that’s OK! It is in the process of risk-taking, experimentation, and failure that wisdom is generated and meaningful improvements in social and economic well-being come about.

It’s easy to talk about “trial-and-error” without thinking much about the “error” part of the process. It is only through constant experimentation and failure that we learn how to do things more efficiently and create or improve goods and services.

Perhaps the most straightforward definition of “technology” is Ian Barbour’s: “the application of organized knowledge to practical tasks by ordered systems of people and machines.” But organized knowledge requires lots of trials and lots of errors–by both people and machines–in order to find workable solutions to the tasks we hope to accomplish.

To the extent that we would like to see more technological improvements, we need more “dreamers” who hope to change the world. Entrepreneurs are the key to this process because, by their very nature, they refuse to settle for the status quo. They dream of a world that can work differently; one in which they can improve their own lot and (whether intentionally or not) improve the lot of humanity simultaneously. “What entrepreneurs do,” venture capitalist Vinod Khosla argues , “is they imagine what feels impossible to most people, and take it all the way from impossible, to improbable, to possible but unlikely, to plausible, to probable, to real!”  

That is why entrepreneurialism is so important , and it is also why shouldn’t roll our eyes when people dream about “moonshots” and the ways in which “deep technology” might “disrupt” and “transform” society for the better.  

While we should always keep both feet firmly rooted on the ground, there is nothing wrong with looking skyward and dreaming of a better future. Indeed, as a society, we should seek to foster a culture of innovation that rewards entrepreneurial dreaming and daring, because in seeking to make the world a better place, progress and prosperity become reality.  

Additional Reading

Donald J. Boudreaux, “What’s Your Moonshot?” Mercatus Center at George Mason University, Mercatus Original Video , November 16, 2017, https://www.mercatus.org/videos/whats-your-moonshot .

Joseph L. Bower & Clayton M. Christensen, “Disruptive Technologies: Catching the Wave,” Harvard Business Review , January-February 1995,   https://hbr.org/1995/01/disruptive-technologies-catching-the-wave .

Clayton M. Christensen, Michael E. Raynor & Rory McDonald, “What Is Disruptive Innovation?”  Harvard Business Review,December 2015, https://hbr.org/2015/12/what-is-disruptive-innovation.

Tyler Cowen, “Is Innovation Over? The Case against Pessimism,” Foreign Affairs , March/April 2016, https://www.foreignaffairs.com/reviews/review-essay/2016-02-15/innovation-over .

Swati Chaturvedi, “So What Exactly is ‘Deep Technology’?” LinkedIn , July 28, 2015, https://www.linkedin.com/pulse/so-what-exactly-deep-technology-swati-chaturvedi .

Mike Cushing, “Moonshot Projects – Innovation or Wishful Thinking?” Enterprise Innovation , http://www.enterpriseinnovation.com/articles/moonshot-projects-innovation-or-wishful-thinking .

Vinod Khosla, “We Need Large Innovations,” Medium , January 1, 2018, https://medium.com/@vkhosla/we-need-large-innovations-58e3eaaf8138 .

Josh Gans, The Disruption Dilemma (MIT Press, 2016), https://mitpress.mit.edu/books/disruption-dilemma .

Mark P. Mills, “Making Technological Miracles,” The New Atlantis , (Spring 2017): 37-55, http://www.thenewatlantis.com/publications/making-technological-miracles .

Albert H. Segars, “Seven Technologies Remaking the World,” MIT Sloan Management Review, March 9, 2018, https://sloanreview.mit.edu/projects/seven-technologies-remaking-the-world .  

Adam Thierer, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom , (Mercatus Center at George Mason University, 2016),   https://www.mercatus.org/publication/permissionless-innovation-continuing-case-comprehensive-technological-freedom

Adam Thierer and Trace Mitchell, “The Many Forms of Entrepreneurialism,” The Bridge , August 30, 2018, https://www.mercatus.org/bridge/commentary/many-forms-entrepreneurialism  

Adam Thierer, “Making the World Safe for More Moonshots,” The Bridge , February 5, 2018, https://www.mercatus.org/bridge/commentary/making-world-safe-more-moonshots

World Economic Forum , Deep Shift: Technology Tipping Points and Societal Impact (Geneva, Switzerland: September 2015), 3, http://www3.weforum.org/docs/WEF_GAC15_Technological_Tipping_Points_report_2015.pdf .

I recently posted an essay over at The Bridge about “The Pacing Problem and the Future of Technology Regulation.” In it, I explain why the pacing problem—the notion that technological innovation is increasingly outpacing the ability of laws and regulations to keep up—“is becoming the great equalizer in debates over technological governance because it forces governments to rethink their approach to the regulation of many sectors and technologies.”

In this follow-up article, I wanted to expand upon some of the themes developed in that essay and discuss how they relate to two other important concepts: the “Collingridge Dilemma” and technological determinism. In doing so, I will build on material that is included in a forthcoming law review article I have co-authored with Jennifer Skees, Ryan Hagemann (“Soft Law for Hard Problems: The Governance of Emerging Technologies in an Uncertain Future”) as well as a book I am finishing up on the growth of “evasive entrepreneurialism” and “technological civil disobedience.”

Recapping the Nature of the Pacing Problem

First, let us quickly recap that nature of “the pacing problem.” I believe Larry Downes did the best job explaining the “problem” in his 2009 book on The Laws of Disruption. Downes argued that “technology changes exponentially, but social, economic, and legal systems change incrementally” and that this “law” was becoming “a simple but unavoidable principle of modern life.”

Downes was generally a cheerleader for such developments. For him, the pacing problem is more like the pacing benefit. But Downes is in the minority among most tech policy scholars in this regard. In the field of Science and Technology Studies (STS), discussions about the pacing problem and what to do about it are omnipresent and full of foreboding gloominess.

STS is a broad field of interdisciplinary studies unified by a concern with “the impacts and control of science and technology, with particular focus on the risks, benefits and opportunities that S&T may pose” to a wide range of values. STS studies incorporates many disciplines: legal and philosophical studies, sociology, anthropology, engineering, and others. In countless essays, papers, journal articles, and books, STS scholars lament the pacing problem and often insist something must be done, often without ever getting around to explaining what that something is.

Regardless of their field of study, there is broad recognition among these scholars that new technological, social, and political realities make the pacing problem a phenomenon worth studying.  In my Bridge essay, I identified three primary drivers of the pacing problem:

• Technological driver: The power of “combinatorial innovation,” which is driven by “Moore’s Law,” fuels a constant expansion of technological capabilities.
• Social driver: As citizens quickly assimilate new tools into their daily lives and then expect that even more and better tools will be delivered tomorrow.
• Political driver: Government has grown increasingly dysfunctional and unable to adapt to those technological and social changes.

The “Collingridge Dilemma”

Although they do not always refer to it by name, STS scholars regularly stress the so-called “Collingridge dilemma” in their work. The Collingridge dilemma refers to the extreme difficulty of putting proverbial genies back in their bottles once a given technology has reached a certain inflection point in society. The concept is named after David Collingridge, who wrote about the challenges of governing emerging technologies in his 1980 book, The Social Control of Technology .

“The social consequences of a technology cannot be predicted early in the life of the technology,” Collingridge argued. “By the time undesirable consequences are discovered, however, the technology is often so much part of the whole economics and social fabric that its control is extremely difficult.” He called this the “dilemma of control,” and asserted that, “When change is easy, the need for it cannot be foreseen; when the need for change is apparent, change has become expensive, difficult and time-consuming.”

In a sense, the “Collingridge dilemma” is simply a restatement of the pacing problem but with (1) greater stress on the social drivers behind the pacing problem and, (2) an implicit solution to “the problem” in the form of preemptive control of new technologies while they are still young and more manageable.

Specifically, for many STS scholars, Collingridge’s “dilemma” is preferably solved through the application of the Precautionary Principle. The contours of the Precautionary Principle are notoriously murky and ill-defined. Nonetheless, as I discussed a great length in my last book on the subject, the Precautionary Principle generally refers to the belief that new innovations should be curtailed or disallowed until their developers can prove that they will not cause any harm to individuals, groups, specific entities, cultural norms, or various existing laws, norms, or traditions.

You can see the logic of the Collingridge dilemma and the Precautionary Principle at work everywhere in STS scholarship today. Few scholars want to admit they favor the Precautionary Principle, however, so they often use different terminology. “Anticipatory governance” or “upstream governance” are the preferred terms of art these days.

For example, in a recent law review article about “Regulating Disruptive Innovation,” Nathan Cortez argues that “new technologies can benefit from decisive, well-timed regulation” or even “early regulatory interventions.” Similarly, writing in Slate in 2014, John Frank Weaver insisted we should regulate emerging tech like artificial intelligence “early and often” to “get out ahead of” various social and economic concerns.

In his last book, A Dangerous Master: How to Keep Technology from Slipping beyond Our Control, bioethicist Wendell Wallach also argued for new forms of upstream governance and defined it as a system that allow for “more control over the way that potentially harmful technologies are developed or introduced into the larger society. Upstream management is certainly better than introducing regulations downstream, after a technology is deeply entrenched, or something major has already gone wrong,” he argued. Wallach is basically just restating the Collingridge dilemma in this regard.

The problem with all these calls for the anticipatory or upstream governance solutions to the pacing problem and the Collingridge dilemma is that, like the Precautionary Principle more generally, the specific solutions are very incoherent or sometimes completely lacking. STS scholars almost always leave the reader hanging without offering a conclusion to their gloomy, pessimistic narratives about whatever technology or technological process it is they are critiquing. Critics are quick to issue bold calls-to-action, but rarely provide a detailed blueprint.

There are some exceptions. Some STS scholars have advocated for Precautionary Principle-minded legislation or agencies, like an “Artificial Intelligence Development Act,” a “National Algorithmic Technology Safety Administration” or a federal AI agency, such as a “Federal Robotics Commission.” Meanwhile, over the past decade, many STS scholars have pushed for national privacy and cybersecurity legislation, or expansive new forms of liability for technology companies. The regulatory authority sought in these cases would be squarely precautionary in character, aimed at addressing a wide array of hypothetical harms through permissioned-based rulemaking before those problems even materialize.

Technological Determinism?

Discussions about the pacing problem and the Collingridge dilemma have an air of technological determinism to them. Technological determinism generally refers to the notion that technology almost has a mind of its own and that it will plow forward without much resistance from society or governments. Here is a more scholarly definition from Sally Wyatt, who has explained how technological determinism is generally defined in a two-part fashion:

The first part is that technological developments take place outside society, independently of social, economic, and political forces. New or improved products or ways of making things arise from the activities of inventors, engineers, and designers following an internal, technical logic that has nothing to do with social relationships. The more crucial second part is that technological change causes or determines social change.

The opposite of technological determinism is usually referred to as “social constructivism,” which as Thomas Hughes notes, “presumes that social and cultural forces determine technical change.”

Ironically, among STS scholars, technological determinist reasoning is both (a) regularly on display, and (b) generally reviled. That is, many STS scholars speaking in deterministic tones about the inevitability of certain technological developments, but then they effortlessly shift into social constructivist mode when commenting on what they hope to do about it.

One of the most well-known technology critics of the past century was French philosopher Jacques Ellul. It is impossible to read his tracts and not find deterministic reasoning flying off every other page. He argued, for example, that technology is “self-perpetuating, all-persuasive, and inescapable,” and that it represents “an autonomous and uncontrollable force that dehumanized all that it touches.” Moreover, within the field of Marxist studies, technological determinism is ubiquitous. Of course, that goes back to Marx himself and his many ideological descendants, who held strongly deterministic views about the role industrial technology played in sharping history and socio-political systems. Plenty of other STS scholars remain hard-core social constructivist, however, and insist that dealing with the pacing problem and the Collingridge dilemma really just comes down to a matter of sheer social and political willpower.

Techno-determinist thinking is usually on display in more vivid terms among technological optimists. Reading the writings of futurists like Ray Kurzweil and Kevin Kelly, one cannot help but get the sense that they are pining for the day when we are all just assimilated into The Matrix. There is an air of utter futility associated with humanity’s efforts to resist the spread of various technological systems and processes. Philosopher Michael Sacasas refers to this mentality as “the Borg Complex,” which, he says, is often “exhibited by writers and pundits who explicitly assert or implicitly assume that resistance to technology is futile.”

The point I am trying to make here is that technological determinism is at work in all sorts of scholarship and punditry. Regardless of whether one subscribes to what Ian Barbour has labelled the warring viewpoints of “Technology as Liberator” or “Technology as a Threat,” very different people can hold strongly deterministic viewpoints.

Soft Determinism

The problem with all this talk about determinism—technological, social, political, or whatever—is that the lines are never quite as bright as some suggest. “Hard” determinism of any of these varieties simply cannot be correct. We have too many historical examples that run counter to both narratives.

Personally, I’ve always subscribed to what some refer to as “ soft technological determinism.” Technological historian Merritt Roe Smith defines “soft determinism” as the view “which holds that technological change drives social change but at the same time responds discriminatingly to social pressures,” as compared to “hard determinism,” which “perceives technological development as an autonomous force, completely independent of social constraints.”

Konstantinos Stylianou has offered a variant of soft determinism that zeroes in on better understanding the unique attributes of specific technologies and political systems when considering how difficult they may be to control. He argues that “there are indeed technologies so disruptive that by their very nature they cause a certain change regardless of other factors,” such as the Internet. Stylianou concludes that:

It seems reasonable to infer that the thrust behind technological progress is so powerful that it is almost impossible for traditional legislation to catch up. While designing flexible rules may be of help, it also appears that technology has already advanced to the degree that is able to bypass or manipulate legislation. As a result, the cat-and-mouse chase game between the law and technology will probably always tip in favor of technology. It may thus be a wise choice for the law to stop underestimating the dynamics of technology, and instead adapt to embrace it.

That may sound like just more hard deterministic thinking, but it represents a softer variety that holds that the special characteristics of some technologies are indeed altering our capacity to govern many newer sectors using traditional regulatory mechanisms. In my new law review article with Jennifer Skees and Ryan Hagemann, we conclude that this is the key factor motivating the gradual move away from “hard law” and toward “soft law” governance tools for a great many emerging technologies.

To be clear, this does not mean we are going to soon reach the proverbial “end of politics” or the “death of the nation-state” due to technology, or anything like that. As I point out in my forthcoming book, that sort of talk is silly. Some technology enthusiasts or libertarians use techno-determinist talk as if they are preaching a gospel of liberation theology—liberation from the state through technology emancipation, that is.

In reality, technology giveth and technology taketh away. Technology can empower people and institutions and help them challenge laws, regulations, and entire political systems. My forthcoming book documents how many “evasive entrepreneurs” are doing just that today, and with increasing regularity. But technology empowers government actors, too. In an unpublished 2009 manuscript entitled, “Does Technology Drive the Growth of Government?” my Mercatus Center colleague Tyler Cowen noted how growth of big government in the 20th century was greatly facilitated by various modern technologies (advanced transportation and communications networks, in particular). “Future technologies may either increase or decrease the role of government in society,” he noted, “but if history shows one thing, it is that we should not neglect technology in understanding the shift from an old political equilibrium to a new one.”

Thus, those who think that the pacing problem is a one-way ratchet to emancipation from state control need to realize that technology can be used for good and bad ends, and it can be used (and abused) by governments to expand their powers and limit our liberties. Similarly, those tech critics and STS scholars who lament how the pacing problem will undermine governments, democracy, or other institutions or values without radical interventions also are going too far. They need to recognize that while it is true many new technologies will march forward at a steady clip, it does not mean that society is powerless to bring some order to technological processes. We shape our tools and then our tools shape us. And then we create still more tools to improve upon previous tools, and the process goes on and on.

John Seely Brown and Paul Duguid put it best in this 2001 essay responding to “doom-and-gloom technofuturists”:

[T]echnological and social systems shape each other. The same is true on a larger scale. . . . Technology and society are constantly forming and reforming new dynamic equilibriums with far-reaching implications. The challenge . . . is to see beyond the hype and past the over-simplifications to the full import of these new sociotechnical formations.

So yes, the pacing problem is real, and it will continue to raise problems for social and political systems. But as Brown and Paul Duguid suggest, we’ll constantly adapt, form and reform new dynamic equilibriums, and then “muddle through,” just as we have so many times before.

Related Reading

• “The Pacing Problem and the Future of Technology Regulation”
• “Muddling Through: How We Learn to Cope with Technological Change”
• “A Short Response to Michael Sacasas on Advice for Tech Writers”
• “Are “Permissionless Innovation” and “Responsible Innovation” Compatible?”
• “Wendell Wallach on the Challenge of Engineering Better Technology Ethics”
• “Book Review: Calestous Juma’s “Innovation and Its Enemies’”
• “Evasive Entrepreneurialism and Technological Civil Disobedience: Basic Definitions”

[first published at The Bridge on August 9, 2018]

What happens when technological innovation outpaces the ability of laws and regulations to keep up?

This phenomenon is known as “the pacing problem,” and it has profound ramifications for the governance of emerging technologies. Indeed, the pacing problem is becoming the great equalizer in debates over technological governance because it forces governments to rethink their approach to the regulation of many sectors and technologies.

The Innovation Cornucopia

Had Rip Van Winkle woken up his famous nap today, he’d be shocked by all the changes around him. At-home genetics tests, personal drones, driverless cars, lab-grown meats, and 3D-printed prosthetic limbs are just some of the amazing innovations that would boggle his mind. New devices and services are flying at us so rapidly that we sometimes forget that most did not even exist a short time ago. At this point, it feels like our smartphones have been in our lives forever, but even just a decade ago, very few of us had one. Likewise, plenty of people now regularly enjoy the benefits of the sharing economy, but ten years ago, Uber, Lyft, and Airbnb did not even exist. Most of the social networking platforms or online video and audio streaming services that we use today had not even been created 15 years ago. Back then, Netflix’s DVD mail subscription service seemed downright revolutionary.

With every innovation comes more questions about how the law should keep pace, or whether it even can. “There has always been a pacing problem,” observes Yale University bioethicist Wendell Wallach, author of  A Dangerous Master: How to Keep Technology from Slipping beyond Our Control. But what Wallach and many other scholars worry about today is that the pace of change has been kicked into overdrive, making it more difficult than ever for traditional legal schemes and regulatory mechanisms to stay relevant. Larry Downes refers to this as “The Law of Disruption.” In his 2009 book on this “law,” Downes showed how “technology changes exponentially, but social, economic, and legal systems change incrementally” and that this law was becoming “a simple but unavoidable principle of modern life.”

Moore’s Law Quickens the Pace

There are three primary reasons the pacing problem is such a force in our modern world. The root cause lies in the power of “combinatorial innovation,” which is driven by “Moore’s Law.”  The Information Revolution spawned a stunning array of new technological capabilities that build on top of one another in a symbiotic fashion. Think about the shared foundational elements of most modern inventions: microchips, sensors, digital code, big data, cloud computing, remote data storage, wireless networking and geolocation capabilities, machine-learning, cryptography, and more. Each of these underlying capabilities is becoming faster, cheaper, smaller, more powerful, and easier to find and use. Innovators are combining them as part of their ongoing search for new and better ways of doing things.

Moore’s Law powers these developments. Moore’s Law is the principle named after Intel co-founder Gordon E. Moore, who first observed in 1965 that “computing would dramatically increase in power, and decrease in relative cost, at an exponential pace” in coming years. Indeed, it has continued to do so for the past half century for many information technologies. A recent Technology Policy Institute white paper noted that “data transit prices fell from about $1200 per Mbps in 1998 to $0.02 per Mbps in 2017.”

These forces are now revolutionizing other sectors as “software eats the world” and innovators utilize these new technologies to address nearly every conceivable need and want. In the field of genetics, the biological equivalent of Moore’s Law is known as the “Carlson curve.” The past two decades have seen the cost of sequencing a human genome fall from over $100 million to under $1,000, a rate nearly three times faster than Moore’s Law.

What the Public Wants, the Public Gets

The second reason the pacing problem is accelerating is that the public wants it to! It is true that many people say they are uneasy with many emerging technologies. When new gadgets and services first gain attention, a “technopanic” attitude often ensues. That is unsurprising because, as others have noted, “fear has gone hand in hand with technological advancements throughout history.”

But societal attitudes toward technological change often shift rapidly. They do so even faster today as citizens quickly assimilate new tools into their daily lives and then expect that even more and better tools will be delivered tomorrow. As more people begin to realize how new technologies improve our lives in meaningful ways, it becomes extremely hard for policymakers to take those innovations away or even tell us not to expect better ones. This relationship between technological change and societal expectations acts as an extraordinarily powerful check on the ability of regulators to “roll back the clock” on innovative activities.

Broken Government Exacerbates the Problem

Finally, the pacing problem is becoming more acute because “demosclerosis” and “kludgeocracy” have taken hold within American government. Jonathan Rauch coined the term demosclerosis in his 1999 book Government’s End: Why Washington Stopped Working to describe “government’s progressive loss of the ability to adapt.” “[A]s layer is dropped upon layer,” he argued, “the accumulated mass becomes gradually less rational and less flexible.”

Instead of cleaning up old legalistic messes and adapting to the times, government solutions are more often clumsily cobbled together to patch past problems and create temporary solutions. Steven Teles refers to this as kludgeocracy. “The complexity and incoherence of our government often make it difficult for us to understand just what that government is doing,” Teles says. Kludgeocracy creates serious costs for individual citizens, governments themselves, and to our democratic systems more generally, he argues. Taken together, demosclerosis and kludgeocracy breed highly dysfunctional governments and make it even easier for the pacing problem to speed ahead.

Can Policymakers Adapt?

Regulators are not oblivious to the challenges posed by the pacing problem. “I have said more than once that innovation moves at the speed of imagination and that government has traditionally moved at, well, the speed of government,” remarked Michael Heurta, head of the Federal Aviation Administration, in a 2016 speech regarding drones. Shortly after Huerta made those comments, the Department of Transportation released a report on the regulation of driverless car technology which noted that “The speed with which [driverless cars] are advancing, combined with the complexity and novelty of these innovations, threatens to outpace the Agency’s conventional regulatory processes and capabilities.”

Food and Drug Administration (FDA) regulators have increasingly referenced the pacing problem when discussing the challenge of keeping up with new medical innovations.  The New York Times recently asked Dr. Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research, how the agency planned to deal with hundreds of “rogue” stem cell treatment clinics. “There are hundreds and hundreds of these clinics,” he said. “We simply don’t have the bandwidth to go after all of them at once.”

The pacing problem has even crept into antitrust enforcement. The US Department of Justice (DOJ) sought to break up Microsoft in the late 1990s, but as the legal proceedings dragged on through the early 2000’s, the market moved and made the DOJ’s case moot. Google Chrome and Mozilla Firefox emerged as legitimate competitors to Microsoft’s Internet Explorer without regulatory remedy. In the end, Microsoft reached a settlement with the DOJ that fell far short of the government’s original ambitions to bust up the firm, all because the market moved at a pace much faster than the regulator’s pace. More recent antitrust action in the US and EU also suffer from the pacing problem. Multi-year antitrust investigations reach conclusions that don’t reflect market trends in the intervening years and offer remedies that may be “too little, too late,” especially in the information technology sector.

Is the Pacing Problem Really the Pacing Benefit?

What should policymakers do in light of these new challenges? The extremes will not work. Lawmakers or regulators cannot simply double-down on the lethargic and unwieldy technocratic regulatory schemes of the past. Command-and-control tactics are not going to be effective in an age when technology evolves in a quicksilver fashion. In a world where “innovation arbitrage” is easier than ever, repressive crackdowns on new tech will often backfire. Evasive entrepreneurs will often move to those jurisdictions where their innovative acts are treated more hospitably. That, too, exacerbates the pacing problem.

From the perspective of many innovation advocates, this will make it seem like the pacing problem is more like the pacing  benefit. Generally speaking, that intuition is sound. Innovation is the fundamental driver of human betterment. We need more “moonshots”—“radical but feasible solutions to important problems”—to ensure that current and future generations enjoy more choices, greater mobility, increased wealth, better health, and longer lifespans. We don’t want archaic regulatory schemes and regimes holding that back.

Constructive Solutions

But policymakers will not abandon oversight of emerging technologies altogether, nor should we want them to. The potential harms associated with some new technologies could be significant enough that a certain degree of regulatory oversight will be required. But the pacing problem means the old, inflexible, top-down approaches will need to be discarded and that the administrative state itself must become more entrepreneurial.

In a forthcoming law review article entitled, “Soft Law for Hard Problems: The Governance of Emerging Technologies in an Uncertain Future,” Jennifer Skees, Ryan Hagemann, and I discuss how “soft law” mechanisms—multi-stakeholder processes, industry best practices and standards, workshops, agency guidance, and more—can help fill the governance gap as the pacing problem accelerates. Many agencies are already tapping soft law tools to help guide the development of new technologies such as driverless cars, drones, the Internet of Things, mobile medical applications, artificial intelligence, and others. In fact, we argue that soft law has already become the dominant form of technological governance for emerging tech in the US.

Critics might decry soft law as either being too lax (and open to private abuse) or too informal (and open to government abuse), but the pacing problem makes both arguments increasingly irrelevant. We need a new governance vision for the technological age. Our new governance systems must be more flexible and adaptive than the heavy-handed regulatory regimes that preceded them.

___________________

Related Reading

• Adam Thierer, “Evasive Entrepreneurialism and Technological Civil Disobedience: Basic Definitions,” The Bridge, July 20, 2018.
• Adam Thierer, “Making the World Safe for More Moonshots,” The Bridge, February 5, 2018.
• Jennifer Skees, “Do You Need a License to Innovate?” The Bridge, June 29, 2018,
• Andrea O’Sullivan & Adam Thierer, “3D Printers, Evasive Entrepreneurs and the Future of Tech Regulation,” The Bridge, August 1, 2018.
• Adam Thierer, “Does “Permissionless Innovation” Even Mean Anything?” Technology Liberation Front, May 18, 2017.
• Adam Thierer, “Wendell Wallach on the Challenge of Engineering Better Technology Ethics,” Technology Liberation Front,  April 20, 2016,

“The quickest way to find out who your enemies are is to try doing something new.” Thus begins Innovation and Its Enemies, an ambitious new book by Calestous Juma that will go down as one of the decade’s most important works on innovation policy.

Juma, who is affiliated with the Harvard Kennedy School’s Belfer Center for Science and International Affairs, has written a book that is rich in history and insights about the social and economic forces and factors that have, again and again, lead various groups and individuals to oppose technological change. Juma’s extensive research documents how “technological controversies often arise from tensions between the need to innovate and the pressure to maintain continuity, social order, and stability” (p. 5) and how this tension is “one of today’s biggest policy challenges.” (p. 8)

What Juma does better than any other technology policy scholar to date is that he identifies how these tensions develop out of deep-seated psychological biases that eventually come to affect attitudes about innovations among individuals, groups, corporations, and governments. “Public perceptions about the benefits and risks of new technologies cannot be fully understood without paying attention to intuitive aspects of human psychology,” he correctly observes. (p. 24)

Opposition to Change: It’s All in Your Head

Juma documents, for example, how “status quo bias,” loss aversion, and other psychological tendencies tend to encourage resistance to technological change. [Note: I discussed these and other “root-cause” explanations of opposition to technological change in Chapter 2 of my book, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom, as well as in my 2012 law review article on “Technopanics, Threat Inflation, and the Danger of an Information Technology Precautionary Principle.”]  Juma notes, for example, that “society is most likely to oppose a new technology if it perceives that the risks are likely to occur in the short run and the benefits will only accrue in the long run.” (p. 5) Moreover, “much of the concern is driven by perception of loss, not necessarily by concrete evidence of loss.” (p. 11)

Juma’s approach to innovation policy studies is strongly influenced by the path-breaking work of Austrian economist Joseph Schumpeter, who long ago documented how entrepreneurial activity and the “perennial gales of creative destruction” were the prime forces that spurred innovation and propelled society forward. But Schumpeter was also one of the first scholars to realize that psychological fears about such turbulent change was what ultimately lead to much of the short-term opposition to new technologies that, in due time, we eventually come to see as life-enriching or even life-essential innovations.  Juma uses Schumpeter’s insight as the launching point for his exploration and he successfully verifies it using meticulously-detailed case studies.

Case Study-Driven Analysis

My favorite case study in the book discusses how the dairy industry fought the creation and spread of margarine (excuse the pun!). I had no idea how ugly that situation got, but Juma provides all the gory details in what I consider one of the very best crony capitalist case studies ever penned.

In particular, in a subsection of that chapter entitled “The Laws against Margarine,” he provides a litany of examples of how effective the dairy industry was in convincing lawmakers to enact ridiculous anti-consumer regulations to stop margarine, even though the product offered the public a much-needed, and much more affordable, substitute for traditional butter. At one point, the daily industry successfully lobbied five states to adopt rules mandating that any imitation butter product had to be dyed pink! Other states enacted labelling laws that required butter substitutes to come in ominous-looking black packaging. Again, all this was done at the request of the incumbent dairy industry and the National Dairy Council, which would resort to almost any sort of deceptive tactic to keep a cheaper competing product out of the hands of consumers.

And so it goes in chapter after chapter of Juma’s book. The amount of detail in each of these unique case studies is absolutely stunning, but they nonetheless remain highly readable accounts of sectoral protectionism, special interest rent-seeking, and regulatory capture. In this way, Juma is plowing some familiar ground already covered by other economic historians and political scientists, such as Joel Mokyr and Mancur Olson, both of whom are mentioned in the book, as well as a long line of public choice scholars who are, somewhat surprisingly, not discussed in the text. Nonetheless, Juma’s approach is still fresh, unique, and highly informative. In fact, I don’t think I’ve ever seen so many distinct and highly detailed case studies assembled in one place by a single scholar.  What Juma has done here is truly impressive.

Related Innovation Policy Paradigms

Beyond Schumpeter’s clear influence, Juma’s approach to studying innovation policy also shares a great deal in common with two other unmentioned innovation policy scholars, Virginia Postrel and Robert D. Atkinson.

Postrel’s 1998 book, The Future and Its Enemies, contrasted the conflicting worldviews of “dynamism” and “stasis” and showed how the tensions between these two visions would affect the course of human affairs. She made the case for embracing dynamism — “a world of constant creation, discovery, and competition” — over the “regulated, engineered world” of the stasis mentality. Similarly, in his 2004 book, The Past and Future of America’s Economy, Atkinson documented how “American history is rife with resistance to change,” and in recounting some of the heated battles over previous technological revolutions he showed how two camps were always evident: “preservationists” and “modernizers.”

When Juma repeatedly recounts the fight between “innovation and incumbency” in his case studies, he is essentially describing the same paradigmatic divide that Postrel and Atkinson highlight in their works when they discuss “dynamist” vs. “stasis” tensions and the “modernizers” vs. “preservationists” battles that we have seen throughout history. [Note: In my 2014 essay on, “Thinking about Innovation Policy Debates: 4 Related Paradigms,” I discussed Postrel and Atkinson’s books and other approaches to understanding tech policy divisions and then related them to the paradigms I contrast in my work: the so-called “precautionary principle” vs. “permissionless Innovation” mindsets.]

Finally, Juma’s book could also be compared to another freshly released book, The Politics of Innovation, by Mark Zachary Taylor. Taylor’s book is also essential reading on this lamentable history of industrial protectionism and the resulting political opposition to change we have seen over time. [Note: Brent Skorup and provided many other high-tech cronyist case studies like these in our 2013 law review article, “A History of Cronyism and Capture in the Information Technology Sector.”]

To counter the prevalence of special interest influence and poor policymaking more generally, Juma stresses the need for evidence-based analysis and a corresponding rejection of fear-mongering and deceptive tactics by public officials and activist groups. He’s particularly concerned with “the use of demonization and false analogies to amplify the perception of risks associated with a new product.”

Accordingly, he would like to see improved educational and risk communication efforts aimed at better informing the public about risk trade-offs and the many potential future benefits of emerging technologies. “Learning how to communicate to the general public is an important aspect of reducing distrust [in new technologies],” Juma argues. (p. 312)

On the Pacing Problem

But Juma never really adequately squares that recommendation with another point he makes throughout the text about how “the pace of technological innovation is discernibly fast,” (p. 5) and how it is accelerating in an exponential fashion. “The implications of exponential growth will continue to elude political leaders if they persist in operating with linear worldviews.” (p. 14) But if it is indeed the case that things are moving that fast, then are we not potentially doomed to live in never-ending cycles of technopanics and misinformation campaigns about new technologies no matter how much education we try to do?

Regardless, Juma’s argument about the speed of modern technological change is quite valid and shared by many other scholars. He is essentially making the same case that Larry Downes did in his excellent 2009 book, The Laws of Disruption: Harnessing the New Forces That Govern Life and Business in the Digital Age. Downes argued that lawmaking in the information age is inexorably governed by the “law of disruption” or the fact that “technology changes exponentially, but social, economic, and legal systems change incrementally.”  This law, Downes said, is “a simple but unavoidable principle of modern life,” and it will have profound implications for the way businesses, government, and culture evolve going forward.  “As the gap between the old world and the new gets wider,” he argued, “conflicts between social, economic, political, and legal systems” will intensify and “nothing can stop the chaos that will follow.”

Again, Juma makes that same point repeatedly throughout the chapters of his book. This is also a restatement of the so-called “pacing problem,” as it is called in the field of the philosophy of technology. I discussed the pacing problem at length in my recent review of Wendell Wallach’s important new book, A Dangerous Master: How to Keep Technology from Slipping beyond Our Control. Wallach nicely defined the pacing problem as “the gap between the introduction of a new technology and the establishment of laws, regulations, and oversight mechanisms for shaping its safe development.” “There has always been a pacing problem,” he noted but, like Juma, Wallach believes that modern technological innovation is occurring at an unprecedented pace, making it harder than ever to “govern” using traditional legal and regulatory mechanisms.

New Approaches to Technological Governance Needed

Both Wallach in A Dangerous Master and Juma in Innovation and Its Enemies struggle with how to solve this problem. Wallach advocates “soft law” mechanisms or even informal “Governance Coordinating Committees,” which would oversee the development of new technology policies and advise existing governmental institutions. Juma is somewhat ambiguous regarding potential solutions, but he does stress the general need for a flexible approach to policy, as he notes on pg. 252:

It is important to make clear distinctions between hazards and risks. It is necessary to find a legal framework for addressing hazards. But such a framework should not take the form of rigid laws whose adoption needs to be guided by evidence of harm. More flexible standards that allow continuous assessment of emerging safety issues related to a new product are another way to address hazards. This approach would allow for evidence-based regulation.

Beyond that Juma wants to see “entrepreneurialism exercised in the public arena” (p. 282) and calls for “decisive leaders to champion the application of new technologies.” (p. 283) He argues such leadership is needed to ensure that life-enriching technologies are not derailed by opponents of change.

On the other hand, Juma sees a broader role for policymakers in helping to counter some of the potential side effects associated with many emerging technologies. He highlights three primary areas of concern. First, he suggests political leaders might need to find ways “to help balance the benefits and risks of automation” due to the rapid rise of robotics and artificial intelligence. Second, he notes that synthetic biology and gene-editing will give rise to many thorny issues that require policymakers to balance “potentially extraordinary benefits and the risk of catastrophic consequences.” (p. 284)  Finally, he points out that medicine and healthcare are set to be radically transformed by emerging technologies, but they are also threatened by archaic policies and practices in many countries.

In each case, Juma hopes that “decisive,” “adaptive” and “flexible” leaders will steer a sensible policy course with an eye toward limiting “the spread of political unrest and resentment toward technological innovation.” (p. 284)  That’s a noble goal, but Juma remains a bit vague on the steps needed to accomplish that balancing act without tipping public policy in favor a full-blown precautionary principle-based regime for new technologies. Juma clearly wants to avoid that result, but it remains unclear how or where he would draw clear lines in the sand to prevent it from occurring while at the same time achieving “decisive leadership” aimed at balancing potential risks and benefits.

Similarly, his repeated calls in the closing chapter for “inclusive innovation” efforts and strategies sounds sensible in theory, but Juma speaks in abstract generalities about what the term means and doesn’t provide a clear vision for how that would translate into concrete actions that would not end up giving vested interests a veto over new forms of technological innovation that they disfavor.

Nothing Ventured, Nothing Gained

Generally speaking, however, Juma wants this balance struck in favor of greater openness to change and an ongoing freedom to experiment with new technological capabilities. As he notes in his concluding chapter:

The biggest risk that society faces by adopting approaches that suppress innovation is that they amplify the activities of those who want to preserve the status quo by silencing those arguing for a more open future. […] Keeping the future open and experimenting in an inclusive and transparent way is more rewarding that imposing the dictum of old patterns. (pgs. 289, 316)

In that regard, the thing I liked most about Innovation and Its Enemies is the way throughout the text that Juma stressed the symbiotic relationship between risk-taking and progress. One of the ways he does so is by kicking off every chapter with a fun quote on that theme from some notable figure. He includes gems like these:

• “Nothing will ever be attempted if all possible objections must be first overcome.” – Samuel Johnson
• “Only those will risk going too far can possibly find out how far one can go.” – T.S. Eliot
• “If you risk nothing, then you risk everything.” – Geena Davis
• “Test fast, fail fast, adjust fast.” – Tom Peters

Of course, I was bound to enjoy his repeated discussion of this theme because that was the central thesis of my latest book, in which I made the argument that, “if we spend all our time living in constant fear of worst-case scenarios—and premising public policy upon such fears—then many best-case scenarios will never come about.” Or more simply, as the old saying goes: “nothing ventured, nothing gained.”

On Pastoral Myths

I also liked the way that Juma used his case studies to remind us how “the topics may have changed, but the tactics have not.” (p. 143) For example, much of the fear-mongering and deceptive tactics we have seen through the years are based on “pastoral ideals,” i.e., appeals to nature, farm life, old traditions, of just the proverbial “good old days,” whenever those supposedly were! “Demonizing innovation is often associated with campaigns to romanticize past products and practices,” Juma notes. “Opponents of innovation hark back to traditions as if traditions themselves were not inventions at some point in the past.” (p. 309)  So very true!

That was especially the case in battles over new farming methods and technologies, when opponents of change were frequently “championing a moral cause to preserve a way of life,” as Juma discusses in several chapters. (p. 129) New products or methods of production were repeatedly but wrongly characterized as dangerous simply because they were not supposedly “natural” or “traditional” enough in character.

Of course, if all farming and other work was to remain frozen in some past “natural” state, we’d all still be hunters and gathers struggling to find the next meal to put in our bellies. Or, if we were all still on the farms of the “good old days,” then we’d still be stuck using an ox and plow in the name of preserving the “traditional” ways of doing things.

Humanity has made amazing strides—including being able to feed more people more easily and cheaply than ever before—precisely because we broke with those old, “natural” traditions. Alas, many vested interests and even quite a few academics today still employ these same pastoral appeals and myths to oppose new forms of technological change. Juma’s case studies powerfully illustrate why that dynamic continues to be a driving force in innovation policy debates and how it has delayed the diffusion of many important new goods and services throughout history. When the opponents of change rest their case on pastoral myths and nostalgic arguments about the good old days we should remind them that the good old days weren’t really that great after all.

Conclusion

In closing, Innovation and Its Enemies earns my highest recommendation. Even though 2016 is only half done as I write this, Professor Juma’s book is probably already a shoo-in as my choice for best innovation policy book of the year. And I am certain that it will also go down as one of the decade’s most important innovation policy books. Buy the book now and read every word of it. It is well worth your time.

Additional material related to Juma’s book:

• Calestous Juma’s website at the Harvard Kennedy School
• Official book page for Innovation and Its Enemies
• Concise summary of the book from AAAS: “Why important innovations stall; understanding obstacles to change is key to future”
• Steven Overly, “Humans once opposed coffee and refrigeration. Here’s why we often hate new stuff,” Washington Post, July 21, 2016.
• Matt Ridley’s review of the book for The Times of London.

Other Related Books

In addition to the books that I already mentioned throughout this review, readers who find Juma’s book and the issues he discusses in it of interest should also consider reading these other books on innovation policy, technological governance, and regulatory capture.  Although many of them are more squarely focused on the information technology sector or other emerging technology fields, they all relate to the general subject matter and approach found throughout Juma’s book. [NOTE: Links, where provided, are to my reviews of these books.]

• The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, by Kevin Kelly (2016)
• Innovative Governance Models for Emerging Technologies, Gary E. Marchant, Kenneth W. Abbott & Braden Allenby (eds.), (2013)
• Consent of the Networked: The Worldwide Struggle for Internet Freedom ,by Rebecca MacKinnon (2012)
• Networks and States: The Global Politics of Internet Governance, by Milton Mueller (2010)
• The Master Switch: The Rise and Fall of Information Empires, by Tim Wu (2010)
• The Rational Optimist: How Prosperity Evolves, by Matt Ridley (2010)
• Blown to Bits: Your Life, Liberty, and Happiness After the Digital Explosion, by Hal Abelson, Ken Ledeen, and Harry Lewis (2008)
• Radical Evolution: The Promise and Peril of Enhancing Our Minds, Our Bodies — and What It Means to Be Human, by Joel Garreau (2005)
• The Creation of Media, by Paul Starr (2005)
• Ruling the Waves: Cycles of Discovery, Chaos, and Wealth from the Compass to the Internet, by Debora Spar (2004)
• Questioning Technology, by Andrew Feenberg (1999)
• Thinking through Technology: The Path between Engineering and Philosophy, by Carl Mitcham (1994)
• Technologies of Freedom: On Free Speech in an Electronic Age, by Ithiel de Sola Pool (1983)

I recently finished  Learning by Doing: The Real Connection between Innovation, Wages, and Wealth , by James Bessen of the Boston University Law School. It’s a good book to check out if you are worried about whether workers will be able to weather this latest wave of technological innovation.  One of the key insights of Bessen’s book is that, as with previous periods of turbulent technological change, today’s workers and businesses will obviously need find ways to adapt to rapidly-changing marketplace realities brought on by the Information Revolution, robotics, and automated systems.

That sort of adaptation takes time, but for technological revolutions to take hold and have meaningful impact on economic growth and worker conditions, it requires that large numbers of ordinary workers acquire new knowledge and skills, Bessen notes. But, “that is a slow and difficult process, and history suggests that it often requires social changes supported by accommodating institutions and culture.” (p 223) That is not a reason to resist disruptive forms of technological change, however. To the contrary, Bessen says, it is crucial to allow ongoing trial-and-error experimentation and innovation to continue precisely because it represents a learning process which helps people (and workers in particular) adapt to changing circumstances and acquire new skills to deal with them. That, in a nutshell, is “learning by doing.” As he elaborates elsewhere in the book:

Major new technologies become ‘revolutionary’ only after a long process of learning by doing and incremental improvement. Having the breakthrough idea is not enough. But learning through experience and experimentation is expensive and slow. Experimentation involves a search for productive techniques: testing and eliminating bad techniques in order to find good ones. This means that workers and equipment typically operate for extended periods at low levels of productivity using poor techniques and are able to eliminate those poor practices only when they find something better. (p. 50)

Luckily, however, history also suggests that, time and time again, that process has happened and the standard of living for workers and average citizens alike improved at the same time.

Of course, that won’t stop some from proclaiming that,  This time it’s different! Indeed, we’re hearing increasing concerns today about the “rise of the robots,” and the general negative impact of automation on the workforce.

But these concerns are really nothing new. “There have been periodic warnings in the last two centuries that automation and new technology were going to wipe out large numbers of middle class jobs,” notes MIT economist David H. Autor. Luckily, those dire predictions have not come to pass. The reason was because short-sighted skeptics failed to appreciate how as new technologies obliterated old businesses and jobs, it simultaneously opened up many more opportunities that were impossible to predict in advance. For every factory worker that lost a job due to technological innovation, new jobs opened up in entirely new sectors that usually offered workers better wages, a safer work environment, and more leisure time. And society clearly benefited in many other ways.

In a new essay for  The Journal of Economic Perspectives on “The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?” Joel Mokyr, Chris Vickers, and Nicolas L. Ziebarth, note that “Discussions of how technology may affect labor demand are often focused on existing jobs, which can offer insights about which occupations may suffer the greatest dislocation, but offer much less insight about the emergence of as-yet-nonexistent occupations of the future.” They continue on to note that:

In the end, the fears of the Luddites that machinery would impoverish workers were not realized, and the main reason is well understood. The mechanization of the early 19th century could only replace a limited number of human activities. At the same time, technological change increased the demand for other types of labor that were complementary to the capital goods embodied in the new technologies. This increased demand for labor included such obvious jobs as mechanics to fix the new machines, but it extended to jobs for supervisors to oversee the new factory system and accountants to manage enterprises operating on an unprecedented scale. More importantly, technological progress also took the form of product innovation, and thus created entirely new sectors for the economy, a development that was essentially missed in the discussions of economists of this time.

And despite a resurgence of automation anxiety in recent years, that historic trend still generally holds true. In late 2014, economists at Deloitte LLP published a sweeping survey of the impact of technology and jobs over the past 200 years and found that “Technology has transformed productivity and living standards, and, in the process, created new employment in new sectors.” This is because human needs and wants constantly change and, therefore, “The stock of work in the economy is not fixed; the last 200 years demonstrates that when a machine replaces a human, the result, paradoxically, is faster growth and, in time, rising employment.” And they conclude that: “Machines will take on more repetitive and laborious tasks, but seem no closer to eliminating the need for human labour than at any time in the last 150 years. It is not hard to think of pressing, unmet needs even in the rich world: the care of the elderly and the frail, lifetime education and retraining, health care, physical and mental well-being.”

While it is easy for critics to highlight disruptions in some notable sectors where machines replaced human labor, fewer news reports or panicky books discuss the many new sectors where people have found new opportunities. Again, the historical evidence suggests that there are good reasons to have faith that humans will once again muddle through and prevail in the face of turbulent, disruptive change. As venture capitalist Marc Andreessen has noted when addressing the fear that automation is running amuck and that robots will eat all our jobs,

We have no idea what the fields, industries, businesses, and jobs of the future will be. We just know we will create an enormous number of them. Because if robots and AI replace people for many of the things we do today, the new fields we create will be built on the huge number of people those robots and AI systems made available. To argue that huge numbers of people will be available but we will find nothing for them (us) to do is to dramatically short human creativity. And I am way long human creativity.

Some tech critics may reject Andreessen’s bullish optimism about human resiliency, but real-world evidence already supports that his conclusion that we’ll learn to adapt to a world full of robots and robotic systems. A 2015 economic analysis from Colin Lewis, a behavioral economist who runs Robotenomics, showed that “despite the headlines, companies that have installed industrial robots are actually increasingly employing more people whilst at the same time adding more robots.” His research revealed that 1.25 million new jobs had been added by companies that make extensive use of industrial robots over the previous 6 years. He also found that this trend held among more recent disruptive firms like Amazon and Tesla Motors, but also older and more established companies like Chrysler, Daimler, Philips Electronics and others.

So, it’s worth keeping these facts in mind next time you read an article or book that declares that the sky is falling and that technological innovation is going to destroy labor markets and living standards. The entirety of human history points in the opposite direction. We should be bullish about our ability to muddle through tough times of technological change and flourish in the long run.

On the whiteboard that hangs in my office, I have a giant matrix of technology policy issues and the various policy “threat vectors” that might end up driving regulation of particular technologies or sectors. Along with my colleagues at the Mercatus Center’s Technology Policy Program, we constantly revise this list of policy priorities and simultaneously make an (obviously quite subjective) attempt to put some weights on the potential policy severity associated with each threat of intervention. The matrix looks like this: [Sorry about the small fonts. You can click on the image to make it easier to see.]

I use 5 general policy concerns when considering the likelihood of regulatory intervention in any given area. Those policy concerns are:

1. privacy (reputation issues, fear of “profiling” & “discrimination,” amorphous psychological / cognitive harms);
2. safety (health & physical safety or, alternatively, child safety and speech / cultural concerns);
3. security (hacking, cybersecurity, law enforcement issues);
4. economic disruption (automation, job dislocation, sectoral disruptions); and,
5. intellectual property (copyright and patent issues).

I realize that some of these five categories could be sub-divided and refined. I also understand that these five groupings may not encapsulate the full range of potential policy issues out there, but I’ve tried to avoid having too many categories to keep this as conceptually tidy as is possible. However, I might need to add a separate category for civil rights and disabilities-related policy issues eventually. Likewise, “psychological considerations” might deserve its own category because they do not necessarily perfectly fit into either the privacy or safety buckets right now, even though that’s where I have them currently. For example, some privacy activists call for regulation of “big data” and large databases based on fears about how all that data collection makes people feel about themselves. I consider that a privacy-related concern now, but you could imagine that being in a separate category. Meanwhile, there’s long been calls to regulate various types of media content (music, movies, video games, online porn, etc) based on the psychological impact they have on children. Those “media effects” theories have always been considered a child safety issue, which is where I currently have them slotted, but they could probably be its own category that also included concerns about distraction and addiction (which could come to haunt VR technologies in the future).

Anyway, my colleagues and I use this current matrix to help us determine what we should be paying more attention to and what sort of scholarly outputs are needed to address regulatory threats on each front. Generally speaking, this is the portfolio of issues I try to stay on top of full-time at Mercatus as part of our ongoing “Permissionless Innovation” project.

Several people who have seen that matrix in my office tell me I should do something more with it, but I’m not really sure what that something would be. In any event, I thought it might make sense to post it here to give others a feel for the current set of emerging tech policy issues that interest us at Mercatus. I will try to upload new versions of the matrix as that giant whiteboard in my office morphs over time and the list of technologies and regulatory threats changes or grows.

Incidentally, I am often asked to explain the relative weights I’ve assigned to each potential regulatory threat, so I will try to justify some of those rankings here briefly. (Again, it’s all quite subjective and I’m always open to hearing the case for tweaking the rankings.)

• Big Data / Online Marketing / the Internet of Things (IoT): Privacy is the #1 policy threat for these sectors. From a public policy perspective, what unifies these technologies is a growing concern about how expanding private sector data collection efforts could affect our privacy or reputations. We’ve already seen a flurry of legislative and regulatory activity here in the U.S. aimed at placing restrictions on data collection or use. And it goes without saying that other countries, especially in Europe, already impose a wide variety of controls on data collection in the name of privacy protection. There also exists a variety of closely-related security concerns here. But the rise of IoT technologies have introduced safety concerns into the mix in a major way, too. That’s especially true because of the large number of Big Data services and IoT devices that are health and medical related.  Taken together, this is the issue set I spend the majority of my time covering because the privacy and security implications of a data-driven economy already occupies the attention of countless regulatory activists and public policymakers across the globe. I think that will continue to be the case for many years to come.
• Robotics: Safety concerns tend to be the biggest driver of calls for regulation of robotic and autonomous technology. For example, new laws and regulations are already being proposed for driverless cars based on fears about the hacking of connected vehicles. And commercial drones attract policy attention based on safety-related concerns such as whether a drone could strike an airplane, or even just fall on our heads. Proposals have been floated to mandate the equivalent of DRM for drones, which would force drone innovators to embed federally-approved technological controls into their systems designating where they are allowed to fly. Even if most of these concerns are overstated or are currently being dealt with, we can expect more safety-related policy proposals for robotic tech in coming years.  Economic concerns would be a close second here due to the increasing worry that robots will eat all our jobs. At least so far, however, that concern has tended to be more of an academic nature rather than a public policy consideration. And it remains unclear what the policy prescription would be in this regard without becoming a neo-Luddite, “smash-the-machines” sort of proposal. That could change in coming years, however. It all depends on the labor market situation over time. Meanwhile, academics are floating the idea of a Federal Robotics Commission to provide greater policy “expertise” in the form of yet another technocratic Beltway bureaucracy.
• Additive manufacturing / 3D printing: Safety is probably the #1 concern here, although depending on what type of 3D-printed object we are talking about, it could be the case that intellectual property concerns will be a bigger driver of calls for regulatory intervention. A lot of the policy-related concerns around 3D printing today are being driven by worries over things like 3D-printed guns. That’s mostly a safety concern, of course. But it we are talking about the replication of branded commercial objects (3D-printed toys or other things, for example), then IP tends to be the bigger concern. The question of product liability also looms large here and it remains unclear how claims might be sorted out when there are fewer large, deep-pocketed intermediaries to go after in a world of decentralized production. Hopefully, those liability norms will be left to the courts and common law to sort out over time, but I wouldn’t be surprised to see more calls for preemptive legislative interventions here in both directions: i.e., some will call legislators to impose greater liability on certain parties while others will push to immunize intermediaries from punishing forms of liability for the downstream actions of others (like a Sec. 230 norm for 3D printing).
• Medical tech innovation: It goes without saying that traditional safety concerns will drive policy for advanced medical technologies, just as they have for earlier drugs, devices, and treatments. As software continues to “eat the world” and invade the world of health and medicine, regulators are increasingly going to be trying to figure out how to pigeonhole new technologies into old regulatory constructs. That’s why I have been watching how the FDA continues to deal with 3D-printed prosthetics and mobile medical apps on our smartphones. Eventually, the continuing decentralized democratization of 3D printing (driven by rapidly falling costs) will collide with old medical device regulatory realities and a century’s worth of FDA command-and-control style regulation. Oh my, what a fight that will be! And then chemical printers will become more widespread and this issue will get even more intense. The policy fight here is even more interesting because of all the thorny ethical issues pertaining to the rise of embeddable technology, biohacking, and genome innovation. I have a feeling that my policy portfolio will shift rapidly in this direction in coming years as the modern info-tech revolution spreads to the world of medicine and health. I already have two new papers coming out on these issues in the next few weeks.
• Sharing economy: Economic disruption is clearly the big policy issue here. Specifically, many policymakers and incumbent industries aren’t very happy about new entrants coming into their sectors and offering consumers services without strictly complying with traditional regulations. But safety issues often pop up in these debates when regulators or advocates claim we can’t trust sharing economy operators. What’s particularly interesting about this space is how these policy battles are playing out at almost every level of government: federal, state, local, and international. At least thus far, sharing economy innovators tend to be winning most of those battles. But the fight continues.
• Crypto & Bitcoin: I think safety would probably be the biggest issue here, in the sense that policymakers fear a world of unregulated crypto and decentralized blockchain applications are a world in which the “bad guys” will be able to use those technologies to harm the public in some fashion. We’ve heard this all before, of course, but (going all the way back to the Clipper Chip wars) you can always bank on law enforcement officials resorting to Chicken Little claims about terrorists and child predators thriving in a world of unregulated crypto. In many ways, this is the most important of all these policy fights because if the government can regulate crypto and blockchain technologies, it severely undermines the fabric of almost all the other technologies and platforms discussed herein. This is why the current debate over government-mandated “backdoors” is so important; it has profound ramifications for every other tech regulation debate that follows.
• Immersive Tech (VR and augmented reality): This is an amorphous and evolving area that I am getting increasingly interested in, but the policy issues here have yet to come into clear focus. However, when Google Glass was launched, there was a brief technopanic of sorts over its privacy and security ramifications. Those concerns have subsided a bit as Google Glass has seemingly faded away (probably because of its high price point more than because of its privacy concerns), but I suspect that future iterations of augmented reality technologies will raise similar concerns. That will especially be true as more sophisticated biometric (and facial recognition) capabilities are integrated into them. Academics are already wondering how to enforce “notice and consent” privacy norms and rules in a world where everyone is wearing miniature body cams and heads-up displays in their sunglasses. I’m not sure it’s even possible, but that debate will continue and include all sorts of calls for technological controls. OK, that’s augmented reality, but what about virtual reality technologies? I think safety concerns could drive some policy proposals as critics grow concerned about the psychological implications of people (especially kids) spending more and more time in immersive virtual worlds. In that sense, we might see a replay of the earlier debate over violent video games and/or video game addition. But it remains to be seen.

Incidentally, I use this matrix and provide more context to it in my big presentation on “Permissionless Innovation & the Clash of Visions over Emerging Technologies.” [It’s embedded below.] And I discuss most of these issues in more detail in my book, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom. I am in the process of finishing up the second edition of that book and will be expanding the case studies about the issues discussed above. Finally, I discussed many of these policy threats during my recent appearance on the Andreessen Horowitz podcast.

Update 10/2/15: For another take on various new technology trends and the potential policy issues they raise, check out this report from the World Economic Forum, Deep Shift: Technology Tipping Points and Societal Impact. The WEF report identifies 21 technology “shifts” and then groups them into six “mega-trend” categories. Almost all these issues are on my matrix above, but the WEF report provides some nice additional context on why each technology trend will be so disruptive.

Ian Brown and Christopher T. Marsden’s new book, Regulating Code: Good Governance and Better Regulation in the Information Age, will go down as one of the most important Internet policy books of 2013 for two reasons. First, their book offers an excellent overview of how Internet regulation has unfolded on five different fronts: privacy and data protection; copyright; content censorship; social networks and user-generated content issues; and net neutrality regulation. They craft detailed case studies that incorporate important insights about how countries across the globe are dealing with these issues. Second, the authors endorse a specific normative approach to Net governance that they argue is taking hold across these policy arenas. They call their preferred policy paradigm “prosumer law” and it envisions an active role for governments, which they think should pursue “smarter regulation” of code.

In terms of organization, Brown and Marsden’s book follows the same format found in Milton Mueller’s important 2010 book Networks and States: The Global Politics of Internet Governance; both books feature meaty case studies in the middle bookended by chapters that endorse a specific approach to Internet policymaking. (Incidentally, both books were published by MIT Press.) And, also like Mueller’s book, Brown and Marsden’s Regulating Code does a somewhat better job using case studies to explore the forces shaping Internet policy across the globe than it does making the normative case for their preferred approach to these issues.

Thus, for most readers, the primary benefit of reading either book will be to see how the respective authors develop rich portraits of the institutional political economy surrounding various Internet policy issues over the past 10 to 15 years. In fact, of all the books I have read and reviewed in recent years, I cannot think of two titles that have done a better job developing detailed case studies for such a diverse set of issues. For that reason alone, both texts are important resources for those studying ongoing Internet policy developments.

That’s not to say that both books don’t also make a solid case for their preferred policy paradigms, it’s just that the normative elements of the texts are over-shadowed by the excellent case studies. As a result, readers are left wanting more detail about what their respective policy paradigms would (or should) mean in practice. Regardless, in the remainder of this review, I’ll discuss Brown and Marsden’s normative approach to digital policy and contrast it with Mueller’s since they stand in stark contrast and help frame the policy battles to come on this front.

Governing Cyberspace: Mueller vs. Brown & Marsden

Mueller’s normative goal in Networks and States was to breathe new life into the old cyber-libertarian philosophy that was more prevalent during the Net’s founding era but which has lost favor in recent years. He made the case for a “cyberliberty” movement rooted in what he described as a “denationalized liberalism” vision of Net governance. He argued that “we need to find ways to translate classical liberal rights and freedoms into a governance framework suitable for the global Internet. There can be no cyberliberty without a political movement to define, defend, and institutionalize individual rights and freedoms on a transnational scale.”

I wholeheartedly endorsed that vision in my review of Mueller’s book, even if he was a bit short on the details of how to bring it about. But it is useful to keep Mueller’s paradigm in mind because it provides a nice contrast with the approach Brown and Marsden advocate, which is quite different.

Generally speaking, Brown and Marsden reject most forms of “Internet exceptionalism” and certainly reject the sort of “cyberliberty” ethos that Mueller and I embrace. They instead endorse a fairly broad role for governments in ordering the affairs of cyberspace. In their self-described “prosumer” paradigm, the State is generally viewed as benevolent actor, well-positioned to guide the course of code development toward supposedly more enlightened ends.

Consistent with the strong focus on European policymaking found throughout the book, the authors are quite enamored with the “co-regulatory” models that have become increasing prevalent across the continent. Like many other scholars and policy advocates today, they occasionally call for “multi-stakeholderism” as a solution but they do not necessarily mean the sort of truly voluntary, bottom-up multi-stakeholderism of the Net’s early days. Rather, they are usually thinking of multi-stakeholderism as what is essentially pluralistic politics; it’s the government setting the table, inviting the stakeholders to it, and then guiding (or at least “nudging”) policy along the way. “We are convinced that fudging with nudges needs to be reinforced with the reality of regulation and coregulation, in order to enable prosumers to maximize their potential on the broadband Internet,” they say. (p. 187)

Meet the New Boss, Same as the Old Boss?

Thus, despite the new gloss, their “prosumer law” paradigm ends up sounding quite a bit like a rehash of traditional “public interest” law and common carrier regulation, albeit with a new appreciation of just how dynamics markets built on code can be. Indeed, Brown and Marsden repeatedly acknowledge how often law and regulation fails to keep pace with the rapid evolution of digital technology. “Code changes quickly, user adoption more slowly, legal contracting and judicial adaptation to new technologies slower yet, and regulation through legislation slowest of all,” they correctly note (p. xv). This reflects what Larry Downes refers to as the most fundamental “law of disruption” of the digital age: “technology changes exponentially, but social, economic, and legal systems change incrementally.”

At the end of the day, however, that insight doesn’t seem to inform Brown and Marsden’s policy prescriptions all that much. Theirs is a world in which policy tinkering errors will apparently be corrected promptly and efficiently by still more policy tinkering, or “smarter regulation.” Moreover, like many other Internet policy scholars today, they don’t mind regulatory interventions that come early and often since they believe that will help regulators get out ahead of the technological curve and steer markets in preferred directions. “If regulators fail to address regulatory objects at first, then the regulatory object can grow until its technique overwhelms the regulator,” they say (p. 31).

This is the same mentality that is often on display in Tim Wu’s work, which I have been quite critical of here and elsewhere. For example, Wu has advocated informal “agency threats” and the use of “threat regimes” to accomplish policy goals that prove difficult to steer though the formal democratic rulemaking process. As part of his “defense of regulatory threats in particular contexts,” Wu stresses the importance of regulators taking control of fast-moving tech markets early in their life cycles. “Threat regimes,” Wu argues, “are best justified when the industry is undergoing rapid change — under conditions of ‘high uncertainty.’ Highly informal regimes are most useful, that is, when the agency faces a problem in an environment in which facts are highly unclear and evolving. Examples include periods surrounding a newly invented technology or business model, or a practice about which little is known,” Wu concludes.

This is essentially where most of the “co-regulation” schemes that Brown and Marsden favor would take us: Code regulators would take an active role in shaping the evolution of digital technologies and markets early in its life cycle. What are the preferred regulatory mechanisms? Like Wu and many other cyberlaw professors today, Brown and Marsden favor robust interconnection and interoperability mandates bolstered by antitrust actions as well. And, again, they aren’t willing to wait around and let the courts adjudicate these issues in an ex post fashion. “Essential facilities law is a very poor substitute for the active role of prosumer law that we advocate, especially in its Chicago school minimalist phase” (p. 185). In other words, we shouldn’t wait for someone to bring a case and litigate it through the courts when preemptive, proactive regulatory interventions can sagaciously steer us to a superior end.

More specifically, they propose that “competition authorities should impose ex ante interoperability requirements upon dominant social utilities… to minimize network barriers” (p. 190) and they model this on traditional regulatory schemes such as must-carry obligations, API interface disclosure requirements, and other interconnection mandates (such as those imposed on AOL/Time Warner a decade ago to alleviate fears about instant messaging dominance). They also note that “Effective, scalable state regulation often depends on the recruitment of intermediaries as enforcers” to help achieve various policy objectives (p. 170).

The Problem with Interoperability Über Alles

So, in essence, the Brown-Marsden Internet policy paradigm might be thought of as interoperability über alles. Interoperability and interconnection in pursuit of more “open” and “neutral” systems is generally considered an unalloyed good and most everything else is subservient to this objective.

This is a serious policy error and one that I address in great detail in my absurdly long review of John Palfrey and Urs Gasser’s Interop: The Promise and Perils of Highly Interconnected Systems. I’m not going to repeat all 6,500 words of that critique here when you can just click back and read it, but here’s the high level summary: There is no such thing as “optimal interoperability” that can be determined in an a priori fashion. Ongoing marketplace experimentation with technical standards, modes of information production and dissemination, and interoperable information systems, is almost always preferable to the artificial foreclosure of this dynamic process through state action. The former allows for better learning and coping mechanisms to develop while also incentivizing the spontaneous, natural evolution of the market and market responses. The latter (regulatory foreclosure of experimentation) limits that potential.

More importantly, when interoperability is treated as sacrosanct and forcibly imposed through top-down regulatory schemes, it will often have many unintended consequences and costs. It can even lock in existing market power and market structures by encouraging users and companies to flock to a single platform instead of trying to innovate around it. (Go back and take a look at how the “Kingsbury Commitment” — the interconnection deal from the early days of the U.S. telecom system — actually allowed AT&T to gain greater control over the industry instead of assisting independent operators.)

Citing Palfrey and Gasser, Brown and Marsden do note that “mandated interoperability is neither necessary in all cases nor necessarily desirable” (p. 32), but they don’t spend as much time as Palfrey and Gasser itemizing these trade-offs and the potential downsides of some interoperability mandates. But what frustrates me about both books is the almost quasi-religious reverence accorded to interoperability and open standards when such faith is simply not warranted after historical experience is taken into consideration.

Plenty of the best forms of digital innovation today are due to a lack of interoperability and openness. Proprietary systems have produced some of the most exciting devices (iPhone) and content (video games) of modern times. Then again, voluntary interoperable and “open” services and devices thrive, too. The key point here — and one that I develop in far greater detail in my book chapter, “The Case for Internet Optimism, Part 2 – Saving the Net From Its Supporters” — is that the market for digital services is working marvelously and providing us with choices of many different flavors. Innovation continues to unfold rapidly in both directions along the “open” vs. “closed” continuum. (Here are 30 more essays I have written on this topic if you need more proof.)

Generally speaking, we should avoid mandatory interop and openness solutions. We should instead push those approaches and solutions in a truly voluntary, bottom-up fashion. And, more importantly, we should be pushing for outside-the-box solutions of the Schumpeterian (creative destruction / disruptive innovation) variety instead of surrendering so quickly on competition through forced sharing mandates.

The Case for Patience & Policy Restraint

But Brown and Marsden clearly do not subscribe to that sort of Schumpeterian thinking. They think most code markets tip and lock into monopoly in fairly short order and that only wise interventions can rectify that. For example, they claim that Facebook’s “monopoly is now durable,” which will certainly come as a big surprise to the millions of us who do not use it all. And the story of MySpace’s rapid rise and equally precipitous fall has little bearing on this story, they argue.

But, no matter how you define the “social networking market,” here are two facts about it: First, it is still very, very young. It’s only about a decade old. Second, in that short period of time, we have already witnessed the entire first generation of players fall by the wayside. While the second generation is currently dominated by Facebook, it is by no means alone. Again, millions like me don’t use it at all and get along just fine with other “social networking” technologies, including Twitter, LinkedIn, Google+, and even older tech like email, SMS, and yes, phone calls! Accusations of “monopoly” in this space strain credulity in the extreme. I invite you to read my Mercatus working paper, “The Perils of Classifying Social Media Platforms as Public Utilities,” for a more thorough debunking of this logic. (Note: The final version of that paper will be published in the CommLaw Conspectus shortly.)

Such facts should have a bearing on the debate about regulatory interventions. We continue to witness the power of Schumpeterian rivalry as new and existing players battle in a race for the prize of market power. Brown and Marsden fear that the race is already over in many sectors and that it is time to throw in the towel and get busy regulating. But when I look around at the information technology marketplace today, I am astonished just how radically different it looks from even just a few years ago, and not just in the social media market. I have written extensively about the smartphone marketplace, where innovation continues at a frantic pace. As I noted in my essay here on “Smartphones & Schumpeter,” it’s hard to remember now, but just 6 short years ago:

• The iPhone and Android had not yet landed.
• Most of the best-selling phones of 2007 were made by Nokia and Motorola.
• Feature phones still dominated the market; smartphones were still a luxury (and a clunky luxury at that).
• There were no app stores and what “apps” did exist were mostly proprietary and device or carrier-specific; and,
• There was no 4G service.

It’s also easy to forget just how many market analysts and policy wonks were making absurd predictions at the time about how the telecom operators at the time had so much market power that they would crush new innovation without regulation. Instead, in very short order, the market was completely upended in a way that mobile providers never saw coming. There was a huge shift in relative market power flowing from the core of these markets to the fringes, especially to Apple, which wasn’t even a player in that space before the launch of the iPhone.

As I noted in concluding that piece last year, these facts should lead us to believe that this is a healthy, dynamic marketplace in action. Not even Schumpeter could have imagined creative destruction on this scale. (Just look as BlackBerry). But much the same could be said of many other sectors of the information economy.  While it is certainly true that many large players exist, we continue to see a healthy amount of churn in these markets and an astonishing amount of technological innovation.

Public Choice Insights: What History Tells Us

One would hope these realities would have a greater bearing on the policy prescriptions suggested by analysts like Brown and Marsden, but they don’t seem to. Instead, the attitude on display here is that governments can, generally speaking, act wisely and nudge efficiently to correct short-term market hiccups and set us on a better course. But there are strong reasons to question that presumption.

Specifically, what I found most regrettable about Brown and Marsden’s book was the way — like all too many books in this field these days — the authors briefly introduce “public choice” insights and concerns only to summarily dismiss them as unfounded or overblown. (See my review of Brett Frischmann’s book, Infrastructure: The Social Value of Shared Resources for a more extended discussion of this problem as it pertains to discussions about not just infrastructure regulation by the regulation of all complex industries and technologies.)

Brown and Marsden make it clear that their intentions are pure and that their methods would incorporate the lessons of the past, but they aren’t very interested in dwelling on the long, lamentable history of regulatory failures and capture in the communications and media policy sectors. They do note the dangers of a growing “security-industrial complex” and argue that “commercial actors dominate technical actors in policy debates.” They also say that the “potential for capture by regulated interests, especially large corporate lobbies, is an essential insight” that informs their approach. The problem is that it really doesn’t. They largely ignore those insights and instead imply that, to the extent this is a problem at all, we can build a better breed of bureaucrats going forward who will craft “smarter regulation” that is immune from such pressures. Or, they claim that “multi-stakeholderism” — again, the new, more activist and government-influenced conception of it — can overcome these public choice problems.

A better understanding of power politics that is informed by the wisdom of the ages would instead counsel that minimizing the scope of politicization of technology markets is the better remedy. Capture and cronyism in communications and media markets has always grown in direct proportion to the overall scope of law governing those sectors. (I invite you to read all the troubling examples of this that Brent Skorup and I have documented in our new 72-page working paper, “A History of Cronyism and Capture in the Information Technology Sector.” Warning: It makes for miserable reading but proves beyond any doubt that there is something to public choice concerns.)

To be clear, it’s not that I believe that “market failures” or “code failures” never occur, rather, as I noted in this debate with Larry Lessig, it’s that such problems are typically “better addressed by voluntary, spontaneous, bottom-up, marketplace responses than by coerced, top-down, governmental solutions. Moreover, the decisive advantage of the market-driven approach to correcting code failure comes down to the rapidity and nimbleness of those response(s).” It’s not just that traditional regulatory remedies cannot keep pace with code markets, it’s that those attempting to craft the remedies do not possess the requisite knowledge needed to know how to steer us down a superior path. (See my essay, “Antitrust & Innovation in the New Economy: The Problem with the Static Equilibrium Mindset,” for more on that point.)

Regardless, at a minimum, I expect scholars to take seriously the very real public choice problems at work in this arena. You cannot talk about the history of these sectors without acknowledging the horrifically anti-consumer policies that were often put in place at the request of one industry or another to shield themselves from disruptive innovation. No amount of wishful thinking about “prosumer” policies will change these grim political realities. Only by minimizing chances to politicize technology markets and decisions can we overcome these problems.

Conclusion

For those of us who prefer to focus on freeing code, Brown and Marsden’s Regulating Code is another reminder that liberty is increasingly a loser in Internet policy circles these days. Milton Mueller’s dream of decentralized, denationalized liberalism seems more and more unlikely as armies of policymakers, regulators, special interests, regulatory advocates, academics, and others all line up and plead for their pet interest or cause to be satisfied through pure power politics. No matter what you call it — fudging, nudging, coregulation, smart regulation, multistakeholderism, prosumer law, or whatever else, — there is no escaping the fact that we are witnessing the complete politicization of almost every facet of code creation and digital decisionmaking today.

Despite my deep reservations about a more politicized cyberspace, Brown and Marsden’s book is an important text because it is one of the most sophisticated articulations and defenses of it to date. Their book also helps us better understand the rapidly developing institutional political economy of Internet regulation in both broad and narrow policy contexts. Thus, it is worth your time and attention even if, like me, you are disheartened to be reading yet another Net policy book that ultimately endorses mandates over of markets as the primary modus operandi of the information age.

Additional Resources about the book:

• the official MIT Press website for Regulating Code
• shorter Brown & Marsden paper on “Prosumer Law” (via SSRN)
• Little Atoms podcast featuring Brown & Marsden discussing Regulating Code
• Ian Brown edited this beefy Research Handbook On Governance Of The Internet, which makes a nice complement to Regulating Code. It offers even more detailed case studies on the major issues featured in the book. A terrific resource (if you can afford it!)
• a video of Brown & Marsden discussing the book at the Oxford Internet Institute (also embedded below):

Other books you should read alongside “Regulating Code” (links are for my reviews of each):

• Milton Mueller’s Networks and States: The Global Politics of Internet Governance
• John Palfrey & Urs Gasser’s Interop: The Promise and Perils of Highly Interconnected Systems
• Rebecca MacKinnon’s Consent of the Networked: The Worldwide Struggle for Internet Freedom
• David G. Post’s In Search of Jefferson’s Moose: Notes on the State of Cyberspace
• Christopher Yoo’s The Dynamic Internet: How Technology, Users, and Businesses are Transforming the Network
• Larry Lessig’s Code (my 2-part exchange with Lessig upon Code’s 10^th anniversary: Part 1, Part 2)

A reporter recently interviewed me for a story and asked a terrific question: Why is it that business model disruption and creative destruction seem to have sped up in recent times?  My guess — and excuse me if this seems too obvious — is that it must have something to do with the very nature of intangible, digital technologies of the new economy versus the tangible, analog technologies of the old economy. That is, in markets built largely upon binary code, the pace and nature of change becomes relentlessly hyper-Schumpeterian precisely because digital technologies and platforms are more easily disintermediated and leap-frogged than earlier tangible technologies and platforms were.  And so we get creative destruction on steroids.

Consider, for example, what constituted a “social networking site” in the old days versus today. Our old social networking sites and services in the past were town squares, parks, school parking lots, shopping malls, as well as media like newspapers, magazines, and even the mail. When we socially networked in those environments, we were creatures of our fixed, “real-space” environments as well as their many natural constraints. Disrupting, replacing, or even replicating those environments, technologies, or platforms was a monumental undertaking precisely because of the enormous costs associated with doing so.

Today, by contrast, our social networking spaces are increasingly intangible and digital. Disruption becomes much easier, and significantly cheaper, in a digital environment. This explains how the walled garden communities of the late 1990s (AOL, CompuServe, etc.) disappeared in less than a decade and gave way to sites like MySpace, which itself has already been disrupted by the likes of Facebook and Twitter, among others. And so the cycle continues, and it seems to be speeding up, probably because so much more of our modern economy is built on foundations of code.

This is not to say that every digital age giant will be easily displaced or disappear overnight. But the possibility of that happening has increased exponentially compared to the relative likelihood of the disruption of comparable platforms and technologies in the past.  The lesson here seems rather straightforward: tangibility matters.

[For further reading on this point, see “The Laws of Disruption” by Larry Downes and I also discussed some of these issues in my paper, “The Perils of Classifying Social Media Platforms as Public Utilities.”]

Jack Shafer, editor at large of Slate, is my favorite media pundit. Everything he does is worth reading, and his column this week is no different. It’s entitled “The Digital Slay-Ride: What’s killing newspapers is the same thing that killed the slide rule,” and in it he notes how “Hardly a day goes by, it seems, without some laid-off or bought-out journalist writing a letter of condolence to himself and his profession.” “The underlying cause of their grief,” Shafer argues, “can be traced to the same force that has destroyed other professions and industries: digital technology.” He recalls how people scoffed back in 1993 when Wired founder Louis Rossetto’s said that the “digital revolution is whipping through our lives like a Bengali typhoon” and destroying the old order. But no one is laughing anymore.  As I noted in my Media Metrics report, digital disruption and disintermediation has completely upended the media marketplace, as well as countless others. Toward that end, Shafer actually starts a list of professions or technologies that have been “typhooned” by the digital revolution. It’s a pretty amazing (and entertaining) list for those of us old enough to remember when all these things were dominate in our society and economy. Can you think of others?

• Bank tellers • Typewriters • Typesetting • Carburetors • Vacuum tubes • Slide rules • Disc jockeys • Stockbrokers • Telephone operators • Yellow pages • Repair guys • Bookbinders • Pimps (displaced by the cell phone and the Web) • Cassette and reel-to-reel recorders • VCRs • Turntables • Video stores • Record stores • Bookstores • Recording industry • Courier/messenger services • Travel agencies • Print and cinematic porn • Porn actors • Stenographers • Wired telcos • Drummers • Toll collectors (slayed by the E-ZPass) • Book publishing (especially reference works) • Conventional-watch makers • “Browse” shopping • U.S. Postal Service • Printing-press makers • Film cameras • Kodak (and other film-stock makers)