1. What is progress?
2. What are the most significant barriers holding back further progress?
3. If those challenges can be overcome, what does the world look like in 50 years?

I was honored to be asked by Clay Routledge to contribute answers to those questions alongside others, including: Steven Pinker (Harvard University), Jason Crawford (Roots of Progress), Matt Clancy (Institute for Progress), Marian Tupy (Human​Progress​.org), James Pethokoukis (AEI). I encourage you to jump over the roundtable and read all their excellent responses. I’ve included my answers down below:

What is progress?

Progress is the advancement of human health, happiness, and general well-being. Measures of well-being can be challenging, however, so we should consider a broad range of metrics, including: life expectancy, infant mortality, poverty measures, energy production/consumption, GDP, productivity, agricultural yields/nourishment, and access to various important goods, services, and conveniences. While each of these metrics may have limitations, taken together, they stand for something meaningful that represents a rough proxy for progress.

But we should always remember what progress means at a deeper level for every individual. Innovation and economic growth are important because they allow us to live lives of our own choosing and enjoy the fruits of a prosperous, pluralistic society.  Progress “is not just bigger piles of money,” as Hans Rosling once noted. “The ultimate goal is to have the freedom to do what we want.”  Accordingly, we should aim to broaden the range of opportunities available to all people to help them flourish.

What are the most significant barriers holding back further progress?

The most significant threat to continued progress is the risk of stagnation accompanying efforts to protect the status quo. As Virginia Postrel taught us in her wonderful book The Future & Its Enemies, we should reject stasis-minded thinking and instead shoot for a world of dynamism, which cherishes and protects the freedom to think and act differently.

Progress hinges upon the growth of knowledge. Knowledge comes from experience, and the most important experiences involve trial-and-error learning. Public attitudes and policies that restrict people and ideas from intermingling freely are a recipe for intellectual, social, and economic stagnation. Accordingly, when we consider public policies toward progress, we should first seek to identify and remove legal and regulatory impediments that limit risk-taking, entrepreneurialism, and technological innovation. As science writer Matt Ridley provocatively puts it, to unlock more growth and prosperity, we must first remove obstacles to “ideas having sex.”

The free movement of people and capital is essential to this process. Openness to immigration is the easiest way for a nation to expand its potential for innovation and growth. But domestic labor skills and mobility are equally important. For entrepreneurs and workers, we need to reframe the battle for progress as “the freedom to innovate” and “the right to earn a living.”

Unfortunately, many barriers exist to advancing those goals, like occupational licensing rules and permitting processes, cronyist industrial protectionist schemes, inefficient tax schemes, and many other layers of regulatory red tape. Reforming or eliminating such rules is crucial for broadening opportunities.

Finally, we need to address cultural barriers to progress. Technology and entrepreneurs often get a bad rap in the media and popular culture. Fear and pessimism dominate their narratives. We must do a better job communicating the benefits of openness to change and give people more reasons to be optimistic about a dynamic future.

If those challenges can be overcome, what does the world look like in 50 years?

I agree with Yogi Berra that “It’s tough to make predictions, especially about the future.” Nonetheless, history shows we can achieve remarkable things when we get the prerequisites for progress right and let people tap into their inherent inquisitiveness and inventiveness. Moving the needle on innovation and growth even just a little will yield compounding returns to future generations. But we should dare to dream bigger and think what progress means for each person today and in the future.

A pro-progress agenda will help us lead longer lives and significantly expand our capabilities because that is what people have always desired most. Accordingly, I believe the most significant advance of the next 50 years will be a radical increase in life expectancy and dramatic improvements in our physical and mental capabilities while we are alive.

Today’s tech critics often claim that technological innovation somehow undermines our humanity. They couldn’t be more wrong. There are few things more human than acts of invention. When we take steps to address practical human needs and wants, we enrich our lives and the lives of countless others. The future will be wonderful, so long as we are free to make it so.

[This is a draft of a section of a forthcoming study on “A Flexible Governance Framework for Artificial Intelligence,” which I hope to complete shortly. I welcome feedback. I have also cross-posted this essay at Medium.]

Debates about how to embed ethics and best practices into AI product design is where the question of public policy defaults becomes important. To the extent AI design becomes the subject of legal or regulatory decision-making, a choice must be made between two general approaches: the precautionary principle or the proactionary principle.[1] While there are many hybrid governance approaches in between these two poles, the crucial issue is whether the initial legal default for AI technologies will be set closer to the red light of the precautionary principle (i.e., permissioned innovation) or to the green light of the proactionary principle (i.e., (permissionless innovation). Each governance default will be discussed.

The Problem with the Precautionary Principle as the Policy Default for AI

The precautionary principle holds that innovations are to be curtailed or potentially even disallowed until the creators of those new technologies can prove that they will not cause any theoretical harms. The classic formulation of the precautionary principle can be found in the “Wingspan Statement,” which was formulated at an academic conference that took place at the Wingspread Conference Center in Wisconsin in 1998. It read: “Where an activity raises threats of harm to the environment or human health, precautionary measures should be taken even if some cause and effect relationships are not fully established scientifically.”[2] There have been many reformulations of the precautionary principle over time but, as legal scholar Cass Sunstein has noted, “in all of them, the animating idea is that regulators should take steps to protect against potential harms, even if causal chains are unclear and even if we do not know that those harms will come to fruition.”[3] Put simply, under almost all varieties of the precautionary principle, innovation is treated as “guilty until proven innocent.”^[4] We can also think of this as permissioned innovation.

The logic animating the precautionary principle reflects a well-intentioned desire to play it safe in the face of uncertainty. The problem lies in the way this instinct gets translated into law and regulation. Making the precautionary principle the public policy default for any given technology or sector has a strong bearing on how much innovation we can expect to flow from it. When trial-and-error experimentation is preemptively forbidden or discouraged by law, it can limit many of the positive outcomes that typically accompany efforts by people to be creative and entrepreneurial. This can, in turn, give rise to different risks for society in terms of forgone innovation, growth, and corresponding opportunities to improve human welfare in meaningful ways.

St. Thomas Aquinas once observed that if the sole goal of a captain were to preserve their ship, the captain would keep it in port forever. But that clearly is not the captain’s highest goal. Aquinas was making a simple but powerful point: There can be no reward without some effort and even some risk-taking. Ship captains brave the high seas because they are in search of a greater good, such as recognition, adventure, or income. Keeping ships in port forever would preserve their vessels, but at what cost?

Similarly, consider the wise words of Wilbur Wright, who pioneered human flight. Few people better understood the profound risks associated with entrepreneurial activities. After all, Wilbur and his brother were trying to figure out how to literally lift humans off the Earth. The dangers were real, but worth taking. “If you are looking for perfect safety,” Wright said, “you would do well to sit on a fence and watch the birds.” Humans would have never taken to the skies if the Wright brothers had not gotten off the fence and taken the risks they did. Risk-taking drives innovation and, over the long-haul, improves our well-being.^[5] Nothing ventured, nothing gained.

These lessons can be applied to public policy by considering what would happen if, in the name of safety, public officials told captains to never leave port or told aspiring pilots to never leave the ground. The opportunity cost of inaction can be hard to quantify, but it should be clear that if we organized our entire society around a rigid application of the precautionary principle, progress and prosperity would suffer.

Heavy-handed preemptive restraints on creative acts can have deleterious effects because they raise barriers to entry, increase compliance costs, and create more risk and uncertainty for entrepreneurs and investors. Thus, it is the unseen costs—primarily in the form of forgone innovation opportunities—that makes the precautionary principle so problematic as a policy default. This is why scientist Martin Rees speaks of “the hidden cost of saying no” that is associated with the precautionary principle.[6]

The precise way the precautionary principle leads to this result is that it derails the so-called learning curve by limiting opportunities to learn from trial-and-error experimentation with new and better ways of doing things.[7] The learning curve refers to the way that individuals, organizations, or industries are able to learn from their mistakes, improve their designs, enhance productivity, lower costs, and then offer superior products based on the resulting knowledge.[8] In his recent book, Where Is My Flying Car?, J. Storrs Hall documents how, over the last half century, “regulation clobbered the learning curve” for many important technologies in the U.S., especially nuclear, nanotech, and advanced aviation.[9] Hall shows how society was denied many important innovations due to endless foot-dragging or outright opposition to change from special interests, anti-innovation activists, and over-zealous bureaucrats.

In many cases, innovators don’t even know what they are up against because, as many scholars have noted, “the precautionary principle, in all of its forms, is fraught with vagueness and ambiguity.”[10] It creates confusion and fear about the wisdom of taking action in the face of uncertainty. Worst case thinking paralyzes regulators who aim to “play it safe” at all costs. The result is an endless snafu of red tape as layer upon layer of mandates build up and block progress. The result is what many scholars now decry as a culture of “vetocracy,” which describes the many veto points within modern political systems that hold back innovation, development and economic opportunity.[11] This endless accumulation of potential veto points in the policy process in the form of mandates and restrictions can greatly curtail innovation opportunities. “Like sediment in a harbor, law has steadily accumulated, mainly since the 1960s, until most productive activity requires slogging through a legal swamp,” says Philip K. Howard, chair of Common Good.[12] “Too much law,” he argues, “can have similar effects as too little law,” because:

People slow down, they become defensive, they don’t initiate projects because they are surrounded by legal risks and bureaucratic hurdles. They tiptoe through the day looking over their shoulders rather than driving forward on the power of their instincts. Instead of trial and error, they focus on avoiding error.[13]

This is exactly why it is important that policymakers not get too caught up in attempts to preemptively resolve every potential hypothetical worst case scenarios associated with AI technologies. The problem with that approach was succinctly summarized by the political scientist Aaron Wildavsky when he noted, “If you can do nothing without knowing first how it will turn out, you cannot do anything at all.”^[14] Or, as I have stated in a book on this topic, “living in constant fear of worst-case scenarios—and premising public policy on them—means that best-case scenarios will never come about.”[15]

This does not mean society should dismiss all concerns about the risks surrounding AI. Some technological risks do necessitate a degree of precautionary policy, but proportionality is crucial, notes Gabrielle Bauer, a Toronto-based medical writer. “Used too liberally,” she argues, “the precautionary principle can keep us stuck in a state of extreme risk-aversion, leading to cumbersome policies that weigh down our lives. To get to the good parts of life, we need to accept some risk.”[16] It is not enough to simply hypothesize that certain AI innovations might entail some risk. The critics need to prove it using risk analysis techniques that properly weigh both the potential costs and benefits.^[17] Moreover, when conducting such analyses, the full range of trade-offs associated with preemptive regulation must be evaluated. Again, where precautionary constraints might deny society life-enriching devices or services, those costs must be acknowledged.

Generally speaking, the most extreme precautionary controls should only be imposed when the potential harms in question are highly probable, tangible, immediate, irreversible, catastrophic, or directly threatening to life and limb in some fashion.^[18] In the context of AI and ML systems, it may be the case that such a test is satisfied already for law enforcement use of certain algorithmic profiling techniques. And that test is satisfied for so-called “killer robots,” or autonomous military technology.[19] These are often described as “existential risks.” The precautionary principle is the right default in these cases because it is abundantly clear how unrestricted use would have catastrophic consequences. For similar reasons, governments have long imposed comprehensive restrictions on certain types of weapons.[20] And although nuclear and chemical technologies have many important applications, their use must also be limited to some degree even outside of militaristic applications because they can pose grave danger if misused.

But the vast majority of AI-enabled technologies are not like this. Most innovations should not be treated the same a hand grenade or a ticking time bomb. In reality, most algorithmic failures will be more mundane and difficult to foresee in advance. By their very nature, algorithms are constantly evolving because programs and systems are being endlessly tweaked by designers to improve them. In his books on the evolution of engineering and systems design, Henry Petroski has noted that “the shortcomings of things are what drive their evolution.”[21] The normal state of things is “ubiquitous imperfection,” he notes, and it is precisely that reality that drives efforts to continuously innovate and iterate.[22]

Regulations rooted in the precautionary principle hope to preemptively find and address product imperfections before any harm comes from them. In reality, and as explained more below, it is only through ongoing experimentation that we find both the nature of failures and the knowledge to know how to correct them. As Petroski observes, “the history of engineering in general, may be told in its failures as well as in its triumphs. Success may be grand, but disappointment can often teach us more.”^[23] This is particularly true for complex algorithmic systems, where rapid-fire innovation and incessant iteration are the norm.

Importantly, the problem with precautionary regulation for AI is not just that it might be over-inclusive in seeking to regulate hypothetical problems that never develop. Precautionary regulation can also be under-inclusive by missing problematic behavior or harms that no one anticipated before the fact. Only experience and experimentation reveal certain problems.

In sum, we should not presume that there is a clear preemptive regulatory solution to every problem some people raise about AI, nor should we presume we can even accurately identify all such problems that might come about in the future. Moreover, some risks will never be eliminated entirely, meaning that risk mitigation is the wiser approach. This is why a more flexible bottom-up governance strategy focused on responsiveness and resiliency makes more sense than heavy-handed, top-down strategies that would only avoid risks by making future innovations extremely difficult if not impossible.

The “Proactionary Principle” is the Better Default for AI Policy

The previous section made it clear why the precautionary principle should generally not be used as our policy default if we hope to encourage the development of AI applications and services. What we need is a policy approach that:

• objectively evaluates the concerns raised about AI systems and applications;
• considers whether more flexible governance approaches might be available to address them; and,
• does so without resorting to the precautionary principle as a first-order response.

The proactionary principle is the better general policy default for AI because it satisfies these three objectives.[24] Philosopher Max More defines the proactionary principle as the idea that policymakers should, “[p]rotect the freedom to innovate and progress while thinking and planning intelligently for collateral effects.”^[25] There are different names for this same concept, including the innovation principle, which Daniel Castro and Michael McLaughlin of the Information Technology and Innovation Foundation say represents the belief that “the vast majority of new innovations are beneficial and pose little risk, so government should encourage them.”^[26] Permissionless innovation is another name for the same idea. Permissionless innovation refers to the idea that experimentation with new technologies and business models should generally be permitted by default.^[27]

What binds these concepts together is the belief that innovation should generally be treated as innocent until proven guilty. There will be risks and failures, of course, but the permissionless innovation mindset views them as important learning experiences. These experiences are chances for individuals, organizations, and all of society to make constant improvements through incessant experimentation with new and better ways of doing things.[28] As Virginia Postrel argued in her 1998 book, The Future and Its Enemies, progress demands “a decentralized, evolutionary process” and mindset in which mistakes are not viewed as permanent disasters but instead as “the correctable by-products of experimentation.”^[29] “No one wants to learn by mistakes,” Petroski once noted, “but we cannot learn enough from successes to go beyond the state of the art.”^[30] Instead we must realize, as other scholars have observed, that “[s]uccess is the culmination of many failures”^[31] and understand “failure as the natural consequence of risk and complexity.”^[32]

This is why the default for public policy for AI innovation should, whenever possible, be more green lights than red ones to allow for the maximum amount of trial-and-error experimentation, which encourages ongoing learning.[33] “Experimentation matters,” observes Stefan H. Thomke of the Harvard Business School, “because it fuels the discovery and creation of knowledge and thereby leads to the development and improvement of products, processes, systems, and organizations.”^[34]

Obviously, risks and mistakes are “the very things regulators inherently want to avoid,”^[35] but “if innovators fear they will be punished for every mistake,” Daniel Castro and Alan McQuinn argue, “then they will be much less assertive in trying to develop the next new thing.”^[36] And for all the reasons already stated, that would represent the end of progress because it would foreclose the learning process that allows society to discover new, better, and safer ways of doing things. Technology author Kevin Kelly puts it this way:

technologies must be evaluated in action, by action. We test them in labs, we try them out in prototypes, we use them in pilot programs, we adapt our expectations, we monitor their alterations, we redefine their aims as they are modified, we retest them given actual behavior, we re-direct them to new jobs when we are not happy with their outcomes.[37]

In other words, the proactionary principle appreciates the benefits that flow from learning by doing. The goal is to continuously assess and prioritize risks from natural and human-made systems alike, and then formulate and reformulate our toolkit of possible responses to those risks using the most practical and effective solutions available. This should make it clear that the proactionary approach is not synonymous with anarchy. Various laws, government bodies, and especially the courts play an important role in protecting rights, health, and order. But policies need to be formulated such that innovators and innovation are given the benefit of the doubt and risks are analyzed and addressed in a more flexible fashion.

Some of the most effective ways to address potential AI risks already exist in the form of “soft law” and decentralized governance solution. These will be discussed at greater length below. But existing legal remedies include various common law solutions (torts, class actions, contract law, etc), recall authority possessed by many regulatory agencies, and various consumer protection policies. Ex post remedies are generally superior to ex ante prior restraints if we hope to maximize innovation opportunities. Ex ante regulatory defaults are too often set closer to the red light of the precautionary principle and then enforced through volumes of convoluted red tape.

This is what the World Economic Forum has referred to as a “regulate-and-forget” system of governance,[38] or what others call a “build-and-freeze model” or regulation.[39] In such technological governance regimes, older rules are almost never revisited, even after new social, economic, and technical realities render them obsolete or ineffective.[40] A 2017 survey of U.S. Code of Regulations by Deloitte consultants revealed that 68 percent of federal regulations have never been updated and that 17 percent have only been updated once.^[41] Public policies for complex and fast-moving technologies like AI cannot be set in stone and forgotten like that if America hopes to remain on the cutting edge of this sector.

Advocates of the proactionary principle look to counter this problem not by eliminating all laws or agencies, but by bringing them in line with flexible governance principles rooted in more decentralized approaches to policy concerns.[42] As many regulatory advocates suggest, it is important to embed or “bake in” various ethical best practices into AI systems to ensure that they benefit humanity. But this, too, is a process of ongoing learning and there are many ways to accomplish such goals without derailing important technological advances. What is often referred to as “value alignment” or “ethically-aligned design” is challenged by the fact that humans regularly disagree profoundly about many moral issues.[43] “Before we can put our values into machines, we have to figure out how to make our values clear and consistent,” says Harvard University psychologist Joshua D. Greene.[44]

The “Three Laws of Robotics” famously formulated decades ago by Isaac Asimov in his science fiction stories continue to be widely discussed today as a guide to embedding ethics into machines.[45] They read:

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

What is usually forgotten about these principles, as AI expert Melanie Mitchell reminds us, is the way Asimov, “often focused on the unintended consequences of programming ethical rules into robots,” and how he made it clear that, if applied too literally, “such a set of rules would inevitably fail.”[46]

This is why flexibility and humility are essential virtues when thinking about AI policy. The optimal governance regime for AI can be shaped by responsible innovation practices and embed important ethical principles by design without immediately defaulting to a rigid application of the precautionary principle.[47] In other words, an innovation policy regime rooted in the proactionary principle can also be infused with the same values that animate a precautionary principle-based system.[48] The difference is that the proactionary principle-based approach will look to achieve these goals in a more flexible fashion using a variety of experimental governance approaches and ex post legal enforcement options, while also encouraging still more innovation to solve problems past innovations may have caused.

To reiterate, not every AI risk is foreseeable, and many risks and harms are more amorphous or uncertain. In this sense, the wisest governance approach for AI was recently outlined by the National Institute of Standards and Technology (NIST) in its initial draft AI Risk Management Framework, which is a multistakeholder effort “to describe how the risks from AI-based systems differ from other domains and to encourage and equip many different stakeholders in AI to address those risks purposefully.”[49] NIST notes that the goal of the Framework is:

to be responsive to new risks as they emerge rather than enumerating all known risks in advance. This flexibility is particularly important where impacts are not easily foreseeable, and applications are evolving rapidly. While AI benefits and some AI risks are well-known, the AI community is only beginning to understand and classify incidents and scenarios that result in harm.[50]

This is a sensible framework for how to address AI risks because it makes it clear that it will be difficult to preemptively identify and address all potential AI risks. At the same time, there will be a continuing need to advance AI innovation while addressing AI-related harms. The key to striking that balance will be decentralized governance approaches and soft law techniques described below.

[Note: The subsequent sections of the study will detail how decentralized governance approaches and soft law techniques already are helping to address concerns about AI risks.]

Endnotes:

[1]     Adam Thierer, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom, 2nd ed. (Arlington, VA: Mercatus Center at George Mason University, 2016): 1-6, 23-38; Adam Thierer, Evasive Entrepreneurs & the Future of Governance (Washington, DC: Cato Institute, 2020): 48-54.

[2]     “Wingspread Statement on the Precautionary Principle,” January 1998, https://www.gdrc.org/u-gov/precaution-3.html.

[3]     Cass R. Sunstein, Laws of Fear: Beyond the Precautionary Principle (Cambridge, UK: Cambridge University Press, 2005). (“The Precautionary Principle takes many forms. But in all of them, the animating idea is that regulators should take steps to protect against potential harms, even if causal chains are unclear and even if we do not know that those harms will come to fruition.”)

[4]     Henk van den Belt, “Debating the Precautionary Principle: ‘Guilty until Proven Innocent’ or ‘Innocent until Proven Guilty’?” Plant Physiology 132 (2003): 1124.

[5]     H.W. Lewis, Technological Risk (New York: WW. Norton & Co., 1990): x. (“The history of the human race would be dreary indeed if none of our forebears had ever been willing to accept risk in return for potential achievement.”)

[6]     Martin Rees, On the Future: Prospects for Humanity (Princeton, NJ: Princeton University Press, 2018): 136.

[7]     Adam Thierer, “Failing Better: What We Learn by Confronting Risk and Uncertainty,” in Sherzod Abdukadirov (ed.), Nudge Theory in Action: Behavioral Design in Policy and Markets (Palgrave Macmillan, 2016): 65-94.

[8]     Adam Thierer, “How to Get the Future We Were Promised,” Discourse, January 18, 2022, https://www.discoursemagazine.com/culture-and-society/2022/01/18/how-to-get-the-future-we-were-promised.

[9]     J. Storrs Hall, Where Is My Flying Car? (San Francisco: Stripe Press, 2021)

[10]    Derek Turner and Lauren Hartzell Nichols, “The Lack of Clarity in the Precautionary Principle,” Environmental Values, Vol 13, No. 4 (2004): 449.

[11]    William Rinehart, “Vetocracy, the Costs of Vetos and Inaction,” Center for Growth & Opportunity at Utah State University, March 24, 2022, https://www.thecgo.org/benchmark/vetocracy-the-costs-of-vetos-and-inaction; Adam Thierer, “Red Tape Reform is the Key to Building Again,” The Hill, April 28, 2022, https://thehill.com/opinion/finance/3470334-red-tape-reform-is-the-key-to-building-again.

[12]    Philip K. Howard, “Radically Simplify Law,” Cato Institute, Cato Online Forum, http://www.cato.org/publications/cato-online-forum/radically-simplify-law.

[13]    Ibid.

^[14]    Aaron Wildavsky, Searching for Safety (New Brunswick, NJ: Transaction Publishers, 1989): 38.

[15]    Thierer, Permissionless Innovation, at 2.

[16]    Gabrielle Bauer, “Danger: Caution Ahead,” The New Atlantis, February 4, 2022, https://www.thenewatlantis.com/publications/danger-caution-ahead.

[17]    Richard B. Belzer, “Risk Assessment, Safety Assessment, and the Estimation of Regulatory Benefits” (Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA, 2012), 5, http://mercatus.org/publication/risk-assessment-safety-assessment-and-estimation-regulatory-benefits; John D. Graham and Jonathan Baert Wiener, eds. Risk vs. Risk: Tradeoffs in Protecting Health and the Environment, (Cambridge, MA: Harvard University Press, 1995).

[18]    Thierer, Permissionless Innovation, at 33-8.

[19]    Adam Satariano, Nick Cumming-Bruce and Rick Gladstone, “Killer Robots Aren’t Science Fiction. A Push to Ban Them Is Growing,” New York Times, December 17, 2021, https://www.nytimes.com/2021/12/17/world/robot-drone-ban.html.

[20]    Adam Thierer, “Soft Law: The Reconciliation of Permissionless & Responsible Innovation,” in Adam Thierer, Evasive Entrepreneurs & the Future of Governance (Washington, DC: Cato Institute, 2020): 183-240, https://www.mercatus.org/publications/technology-and-innovation/soft-law-reconciliation-permissionless-responsible-innovation.

[21]    Henry Petroski, The Evolution of Useful Things (New York: Vintage Books, 1994): 34.

[22]    Ibid., 27,

[23]    Henry Petroski, To Engineer is Human: The Role of Failure in Successful Design (New York: Vintage, 1992): 9.

[24]    James Lawson, These Are the Droids You’re Looking For: An Optimistic Vision for Artificial Intelligence, Automation and the Future of Work (London: Adam Smith Institute, 2020): 86, https://www.adamsmith.org/research/these-are-the-droids-youre-looking-for.

[25]    Max More, “The Proactionary Principle (March 2008),” Max More’s Strategic Philosophy, March 28, 2008, http://strategicphilosophy.blogspot.com/2008/03/proactionary-principle-march-2008.html.

[26]    Daniel Castro & Michael McLaughlin, “Ten Ways the Precautionary Principle Undermines Progress in Artificial Intelligence,” Information Technology and Innovation Foundation, February 4, 2019, https://itif.org/publications/2019/02/04/ten-ways-precautionary-principle-undermines-progress-artificial-intelligence.

[27]    Thierer, Permissionless Innovation.

[28]    Thierer, “Failing Better.”

[29]    Virginia Postrel, The Future and Its Enemies (New York: The Free Press, 1998): xiv.

[30]    Henry Petroski, To Engineer is Human: The Role of Failure in Successful Design (New York: Vintage, 1992): 62.

[31]    Kevin Ashton, How to Fly a Horse: The Secret History of Creation, Invention, and Discovery (New York: Doubleday, 2015): 67.

[32]    Megan McArdle, The Up Side of Down: Why Failing Well is the Key to Success (New York: Viking, 2014), 214.

[33]    F. A. Hayek, The Constitution of Liberty (London: Routledge, 1960, 1990): 81. (“Humiliating to human pride as it may be, we must recognize that the advance and even preservation of civilization are dependent upon a maximum of opportunity for accidents to happen.”)

[34]    Stefan H. Thomke, Experimentation Matters: Unlocking the Potential of New Technologies for Innovation (Harvard Business Review Press, 2003), 1.

[35]    Daniel Castro and Alan McQuinn, “How and When Regulators Should Intervene,” Information Technology and Innovation Foundation Reports, (February 2015): 2 http://www.itif.org/publications/how-and-when-regulators-should-intervene.

[36]    Ibid.

[37]    Kevin Kelly, “The Pro-Actionary Principle,” The Technium, November 11, 2008, https://kk.org/thetechnium/the-pro-actiona.

[38]    World Economic Forum, Agile Regulation for the Fourth Industrial Revolution (Geneva: Switzerland: 2020): 4, https://www.weforum.org/projects/agile-regulation-for-the-fourth-industrial-revolution.

[39]    Jordan Reimschisel and Adam Thierer, “’Build & Freeze’ Regulation Versus Iterative Innovation,” Plain Text, November 1, 2017, https://readplaintext.com/build-freeze-regulation-versus-iterative-innovation-8d5a8802e5da.

[40]    Adam Thierer, “Spring Cleaning for the Regulatory State,” AIER, May 23, 2019, https://www.aier.org/article/spring-cleaning-for-the-regulatory-state.

[41]    Daniel Byler, Beth Flores & Jason Lewris, “Using Advanced Analytics to Drive Regulatory Reform: Understanding Presidential Orders on Regulation Reform,” Deloitte, 2017, https://www2.deloitte.com/us/en/pages/public-sector/articles/advanced-analytics-federal-regulatory-reform.html.

[42]    Adam Thierer, Governing Emerging Technology in an Age of Policy Fragmentation and Disequilibrium, American Enterprise Institute (April 2022), https://platforms.aei.org/can-the-knowledge-gap-between-regulators-and-innovators-be-narrowed.

[43]    Brian Christian, The Alignment Problem: Machine Learning and Human Values (New York: W.W. Norton & Company, 2020).

[44]    Joshua D. Greene, “Our Driverless Dilemma,” Science (June 2016): 1515.

[45]    Susan Leigh Anderson, “Asimov’s ‘Three Laws of Robotics’ and Machine Metaethics,” AI and Society, Vol. 22, No. 4, (2008): 477-493.

[46]    Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (New York: Farrar, Straus and Giroux, 2019): 126 [Kindle edition.]

[47]    Thomas A. Hemphill, “The Innovation Governance Dilemma: Alternatives to the Precautionary Principle,” Technology in Society, Vol. 63 (2020): 6, https://ideas.repec.org/a/eee/teinso/v63y2020ics0160791x2030751x.html.

[48]    Adam Thierer, “Are ‘Permissionless Innovation’ and ‘Responsible Innovation’ Compatible?” Technology Liberation Front, July 12, 2017, https://techliberation.com/2017/07/12/are-permissionless-innovation-and-responsible-innovation-compatible.

[49]    The National Institute of Standards and Technology, “AI Risk Management Framework: Initial Draft,” (March 17, 2022): 1, https://www.nist.gov/itl/ai-risk-management-framework.

[50]    Ibid., at 5.

This is a compendium of readings on “ progress studies ,” or essays and books which generally make the case for technological innovation, dynamism, economic growth, and abundance. I will update this list as additional material of relevance is brought to my attention.   

[Last update: 10/11/22]

Recent Essays

• Marc Andreessen, “The Techno-Optimist Manifesto,” a16z.com, October 16, 2023.
• Will Rinehart, “The Abundance Agenda,” The Exformation Newsletter, October 15, 2022.
• Sarah Constantin, “Unblocking Abundance: A Model for Activism,” Rough Diamonds, October 10, 2022.
• Derek Thompson, “The Forgotten Stage of Human Progress,” The Atlantic, May 11, 2022.
• Adam Thierer, “Where is ‘Progress Studies’ Going?” Progress Forum, April 23, 2022.
• Katherine Boyle, “The Case for American Seriousness,” Common Sense, April 18, 2022.
• William Rinehart, “Vetocracy, the Costs of Vetos and Inaction,” Center for Growth & Opportunity at Utah State University, March 24, 2022.
• James Pethokoukis, “Forget about Left Wing and Right Wing. How about an Up Wing America?” Faster Please, March 23, 2022.
• John W. Lettieri & Kenan Fikri, “The Case for Economic Dynamism and Why it Matters for the American Worker,” Economic Innovation Group, March 2022.
• Adam Kovacevich, “Saying YIMBY (Yes In My Back Yard) to “Civic Innovation,” Chamber of Progress, March 21, 2022.
• Eli Dourado, “Remove Barriers to Productivity,” City Journal, March 18, 2022.
• “A Case for Innovation and Optimism,” (A conversation with Jason Crawford), Discourse, March 2, 2022.
• Noah Smith, “A New Industrialist Roundup,” Noahpinion, February 3, 2022.
• James Pethokoukis, “When Will the Next Big Thing Arrive?” Faster Please, February 3, 2022.
• Alec Stapp & Caleb Watney,  “ Progress is a Policy Choice ,” Institute for Progress, January 20, 2022.
• Adam Thierer, “ How to Get the Future We Were Promised ,” Discourse, January 18, 2022.
• Katherine Boyle, “ Building American Dynamism ,” Future, January 14, 2022.  
• Derek Thompson, “ A Simple Plan to Solve All of America’s Problems ,” The Atlantic, January 12, 2022.
• Jason Crawford, “Progress, Humanism, Agency: An Intellectual Core for the Progress Movement,” Roots of Progress, January 11, 2022.
• Adam Thierer, “Defending Innovation Against Attacks from All Sides,” Discourse, November 9, 2021.
• Matthew Yglesias, “The Case for More Energy,” October 7, 2021.
• Jason Crawford, “ We need a new philosophy of progress ,” The Roots of Progress, August 23, 2021.
• Gale Pooley & Marian L. Tupy, “The Simon Abundance Index 2021,” Human Progress, April 22, 2021.
• Noah Smith, “ Techno-optimism for the 2020s ,” December 3, 2020. 
• Ezra Klein, “ Why We Can’t Build ,” Vox, April 22, 2020.
• Marc Andreesen, “ It’s Time to Build ,” Future, April 18, 2020.
• Eli Dourado, “ How do we move the needle on progress ?” September 26, 2019.
• José Luis Ricón, “ About the ‘Progress’ in Progress Studies ” September 6, 2019.
• Will Rinehart, “ Progress Studies: Some Initial Thoughts ,” August 30, 2019.
• Adam Thierer, “ Is There a Science of Progress ?” AIER, August 8, 2019.
• Patrick Collison and Tyler Cowen, “ We Need a New Science of Progress ,” The Atlantic, July 30, 2019.
• Tyler Cowen, “ The Case for the Longer Term ,” Cato Unbound, January 9, 2019.
• Vinod Khosla, “ We Need Large Innovations ,” Medium, January 1, 2018.
• Adam Thierer, “ How Technology Expands the Horizons of Our Humanity ,” Medium, November 19, 2018. 
• Chelsea Follett, “Utopianism: One of the Biggest Obstacles to Progress,” Human Progress, August15, 2018.
• Eli Dourado, “ How Technological Innovation Can Massively Reduce the Cost of Living ,” PlainText, January 29, 2016.

Books

• J. Storrs Hall, Where Is My Flying Car ? (Stripe Press, 2021).
• Matt Ridley, How Innovation Works: And Why It Flourishes in Freedom (New York: Harper, 2020).
• Andrew McAfee, More from Less (New York: Simon & Schuster, 2020).
• Ronald Bailey & Marian L. Tupy, Ten Global Trends Every Smart Person Should Know: And Many Others You Will Find Interesting (Cato Institute, 2020).
• Arthur M. Diamond Jr., Openness to Creative Destruction: Sustaining Innovative Dynamism (Oxford: Oxford University Press, 2019).
• Tyler Cowen, Stubborn Attachments: A Vision for a Society of Free, Prosperous, and Responsible Individuals (San Francisco, CA: Stripe Press, 2018).
• Steven Pinker, Enlightenment Now: The Case for Reason, Science, Humanism, and Progress (New York: Viking, 2018).
• Hans Rosling, Factfulness: Ten Reasons We’re Wrong about the World—and Why Things Are Better Than You Think (New York: Flatiron Books, 2018).
• Tim Harford, 50 Things That Made the Modern Economy (New York: Riverhead Books, 2017).
• Joel Mokyr, A Culture of Growth: The Origins of the Modern Economy (Princeton, NJ: Princeton University Press, 2017).
• Adam Thierer, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom , 2nd ed. (Arlington, VA: Mercatus Center at George Mason University, 2016).
• Calestous Juma, Innovation and Its Enemies: Why People Resist New Technologies (New York: Oxford University Press, 2016).
• Deirdre N. McCloskey, Bourgeois Equality: How Ideas, Not Capital or Institutions, Enriched the World (Chicago: University of Chicago Press, 2016).
• Joe Carlen, A Brief History of Entrepreneurship: The Pioneers, Profiteers, and Racketeers Who Shaped Our World (New York: Columbia University Press, 2016).
• Robert Bryce, Smaller Faster Lighter Denser Cheaper: How Innovation Keeps Proving the Catastrophists Wrong (New York: Public Affairs, 2014).
• Matt Ridley, The Rational Optimist: How Prosperity Evolves (New York: Harper Collins, 2010).
• Larry Downes, The Laws of Disruption: Harnessing the New Forces That Govern Life and Business in the Digital Age (New York: Basic Books, 2009).
• Amar Bhidé, The Venturesome Economy: How Innovation Sustains Prosperity in a More Connected World (Princeton, NJ: Princeton University Press, 2008).
• Robert Friedel, A Culture of Improvement: Technology and the Western Millennium (Cambridge, MA: The MIT Press, 2007).
• Benjamin M. Friedman, The Moral Consequences of Economic Growth , (New York: Vintage Books, 2006).
• Virginia Postrel, The Future and Its Enemies (New York: The Free Press, 1998).
• David Landes, The Wealth and Poverty of Nations: Why Some Are So Rich and Some are So Poor (New York: W.W. Norton & Co, 1998).
• Robert Nisbet, History of the Idea of Progress (New Brunswick, NJ: Transaction Publishers, 1994).
• Joel Mokyr, Lever of Riches: Technological Creativity and Economic Progress (New York: Oxford University Press, 1990).
• Nathan Rosenberg and L. E.. Birdzell, How the West Grew Rich: The Economic Transformation of the Industrial World (New York: Basic Books, 1986).
• Samuel C. Florman, The Existential Pleasures of Engineering (New York: St. Martin’s Griffin, 1976, 1994).

I was my pleasure to appear on the latest episode of the Dissed podcast to discuss economic liberty and the right to earn a living. The show was hosted by Anastasia Boden and Elizabeth Slattery of the Pacific Legal Foundation and it included legal scholars Hadley Arkes, Timothy Sandefur, and Clark Neily. I appear in the second half of the program.

I’ve spent many years writing about the relationship between innovation, entrepreneurialism, economic liberty, and the right to earn a living. My latest book (Evasive Entrepreneurs) and previous one (Permissionless Innovation) devoted considerable attention to this. But I tried to bring it all down to just a few hundred words in my 2018 essay, “The Right to Pursue Happiness, Earn a Living, and Innovate.”

I’ve reprinted that down below, but please make sure to click over to the  Dissed page and listen to that excellent podcast.

____

The Right to Pursue Happiness, Earn a Living, and Innovate by Adam Thierer

[originally appeared on The Bridge, September 20, 2018]

“ We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.”

That memorable line from America’s Declaration of Independence makes it clear that we are at liberty to pursue lives of our own choosing. Our path in this world is ours to make. It is not predestined by government.

It is time to think more expansively about the right to pursue happiness. Specifically, it is time we acknowledge that our freedom to pursue happiness is the basis of many other corresponding rights, including the right to innovate and the right to earn a living.

Our right to pursue happiness aligns with our corresponding rights to speak, learn, and move about the world. Our constitutional heritage secured these rights and made it clear that we possess them simply by nature of being human beings. So long as we do not bring harm to others, we are generally free to act as we wish. These rights also serve as the basis of more specific freedoms: the freedom to tinker and try, or to innovate more generally.

Knowledge isn’t a mere collection of words that have existed since the dawn of time, and growth isn’t merely a matter of luck or destiny. Knowledge comes from acts of trial-and-error experimentation, and growth comes from innovation.

Repressing innovation has profound consequences. When critics decry a particular innovation or propose limiting entrepreneurial acts, they are challenging our freedom to know and learn more about the world and pursue a better future. By challenging our freedom to experiment with new and better ways of doing things, critics are essentially condemning us to the status quo.

Worse yet, denying people the freedom to innovate deprives society of the wisdom and prosperity that accompanies innovation, which is the foundation of human flourishing.

In sum, if you are not free to innovate, you are not free to pursue happiness.

So, let us resolve to clearly establish that the freedom to pursue happiness and the freedom to innovate are, in reality, the exact same right. Our freedom to try, to tinker, to learn, and to know are all just the same as our “freedom to innovate” and our freedom to pursue happiness however we see fit to pursue it.

Fostering a social and political culture that protects entrepreneurialism, the freedom to innovate, and the right earn a living is a moral imperative because it has enormous consequences for the well-being of current and future generations. To the extent this freedom is denied, the burden of proof—and the consequences for this denial—lies with those critics who would wish it so.

Over at Discourse magazine, my Mercatus Center colleague Matt Mitchell and I have a new essay on, “Industrial Policy is a Very Old, New Idea.” We argue that, despite having a long history of disappointments and failures, that isn’t stopping many policymakers from proposing it industrial policies again. We compare national industrial policy efforts alongside state-based economic development policies, noting their many similarities. In both cases, the crucial issue comes down to targeting versus generality in terms of how policymakers go about encouraging innovation and economic growth. We note how:

The building blocks of the general approach—a mix of broadly applicable tax, spending, regulatory and legal rules—are often rejected because they seem less exciting than targeting specific companies or industries for help. Pundits and policymakers are fond of using machine-like metaphors to suggest they can “fine-tune” innovation or “dial-in” economic development according to a precise formula they believe they have concocted. They also savor the attention that goes along with ribbon-cutting ceremonies and the big headlines often generated by political targeting efforts.

We discuss the spectrum of economic development options (depicted in chart below) in more detail and explain the many pitfalls associated with some of the most highly targeted efforts. “The predicament for policymakers is that, while it is wiser to focus on the generalized approaches, the temptation will remain strong to jump to targeted gambles that may grab headlines but are far more risky and costly,” we argue. Head over to Discourse to read the entire thing.

[Updated: March 2022]

I was speaking at a conference recently and discussing my life’s work, which for 30 years has been focused on the importance of innovation and intellectual battles over what we mean progress. I put together up a short list of some things I have written over the last few years on this topic and thought I would just re-post them here. I will try to keep this regularly updated, at least for a few years.

UNDERSTANDING THE CHALLENGE WE FACE:

• Defending Innovation Against Attacks from All Sides (November 2021)
• How to Get the Future We Were Promised (January 2022)
• The Radicalization of Modern Tech Criticism (April 2019)
• Is It ‘Techno-Chauvinist’ & ‘Anti-Humanist’ to Believe in the Transformative Potential of Technology? (September 2018)

HOW WE MUST RESPOND = “Rational Optimism” / Right to Earn a Living / Permissionless Innovation

• A Case for Innovation and Optimism (A conversation with Jason Crawford, March 2022)
• How To Defend a Culture of Innovation During the Technopanic (April 2019)
• Is This the End of Permissionless Innovation? (January 2021)
• How Technology Expands the Horizons of Our Humanity (November 2018)
• Deep Technologies & Moonshots: Should We Dare to Dream? (September 2018)
• The Right to Pursue Happiness, Earn a Living, and Innovate (September 2018)
• Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom (2016)

ADDITIONAL READING:

• Is There a Science of Progress?  (August 2019)
• Technological Innovation and Economic Growth: A Brief Report on the Evidence (March 2019)
• 5 Books that Shaped My Thinking on Innovation (April 2020)
• Innovation and the Trouble with the Precautionary Principle (April 2020)

NEW BOOK (tying together all the essays and papers listed above):

• Evasive Entrepreneurs and the Future of Governance: How Innovation Improves Economies and Governments (2020)

Here’s a webinar video of a discussion I had recently with Kevin Gomez and his colleague at the Institute for Economic Inquiry at Creighton University’s School of Business.  We discussed my new book, Evasive Entrepreneurs and the Future of Governance: How Innovation Improves Economies and Governments and the future of “permissionless innovation” more generally. My thanks to Kevin and his team at Creighton for inviting me to join them for a fun discussion. Topics include:

• why evasive entrepreneurialism is expanding
• the growth of innovation arbitrage
• the difference between technologies that are “born free” versus “born in captivity”
• the nature of “the pacing problem” and what it means for policy
• the problem with “set-it-and-forget-it” & “build-and-freeze” regulations
• technological risk and the potential for “soft law” governance
• sensible legislative reforms to advance permissionless innovation (such as “the innovator’s presumption” and “the sunsetting imperative”)
• how the COVID crisis potentially opens the Overton Window to much-needed policy change

This week, the Trump Administration proposed a new policy framework for artificial intelligence (AI) technologies that attempts to balance the need for continued innovation with a set of principles to address concerns about new AI services and applications. This represents an important moment in the history of emerging technology governance as it creates a policy vision for AI that is generally consistent with earlier innovation governance frameworks established by previous administrations.

Generally speaking, the Trump governance vision for AI encourages regulatory humility and patience in the face of an uncertain technological future. However, the framework also endorses a combination of “hard” and “soft” law mechanisms to address policy concerns that have already been raised about developing or predicted AI innovations.

AI promises to revolutionize almost every sector of the economy and can potentially benefit our lives in numerous ways. But AI applications also raise a number of policy concerns, specifically regarding safety or fairness. On the safety front, for example, some are concerned about the AI systems that control drones, driverless cars, robots, and other autonomous systems. When it comes to fairness considerations, critics worry about “bias” in algorithmic systems that could deny people jobs, loans, or health care, among other things.

These concerns deserve serious consideration and some level of policy guidance or else the public may never come to trust AI systems, especially if the worst of those fears materialize as AI technologies spread. But how policy is formulated and imposed matters profoundly. A heavy-handed, top-down regulatory regime could undermine AI’s potential to improve lives and strengthen the economy. Accordingly, a flexible governance framework is needed and the administration’s new guidelines for AI regulation do a reasonably good job striking that balance.

Background

Last February, the White House issued Executive Order 13859, on “Maintaining American Leadership in Artificial Intelligence.” The Order announced the creation of the “American AI Initiative,” an effort to “focus the resources of the Federal government to develop AI.” It prioritized investments in AI-focused research and development (R&D), building a workforce ready for the AI era, international engagement on AI priorities, and the establishment governance standards for AI systems to “help Federal regulatory agencies develop and maintain approaches for the safe and trustworthy creation and adoption of new AI technologies.”

Regarding that last objective, Order 13589 required the Office of Management and Budget (OMB) and the Office of Science and Technology Policy (OSTP) to develop a framework and set of principles for federal agencies to follow when considering the development of regulatory and non‑regulatory approaches for AI. Importantly, the Order also specified that the framework should seek to “advance American innovation” and “reduce barriers to the use of AI technologies in order to promote their innovative application while protecting civil liberties, privacy, American values, and United States economic and national security.”

That resulted in the memorandum sent to heads of federal departments and agencies this week entitled, “Guidance for Regulation of Artificial Intelligence Applications” (hereinafter AI Guidance). The draft version of the AI Guidance specifies that “federal agencies must avoid regulatory or non-regulatory actions that needlessly hamper AI innovation and growth.” More specifically:

“Agencies must avoid a precautionary approach that holds AI systems to such an impossibly high standard that society cannot enjoy their benefits. Where AI entails risk, agencies should consider the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace.”

But the AI Guidance is certainly not a call for comprehensive deregulation or the abandonment of all AI federal oversight. The memorandum’s very title reflects an understanding that existing laws and agency rules will continue to play a role in guiding the development of AI, machine-learning, and autonomous systems.

Accordingly, and consistent with past executive orders and OMB regulatory guidance documents for federal agencies, the AI Guidance establishes a set of ten principles that agencies must take into consideration when considering AI policy:

1. Public trust in AI: Requiring that “the government’s regulatory and non-regulatory approaches to AI promote reliable, robust, and trustworthy AI applications, which will contribute to public trust in AI.”
2. Public participation: Agencies must provide “ample opportunities for the public to provide information and participate in all stages of the rulemaking process.”
3. Scientific integrity and information quality: Agencies should “leverage scientific and technical information and processes” to build trust and ensure data quality and transparency.
4. Risk assessment and management: Acknowledging that “all activities involve tradeoffs,” the AI Guidance requires that “a risk-based approach should be used to determine which risks are acceptable and which risks present the possibility of unacceptable harm, or harm that has expected costs greater than expected benefits.”
5. Benefits and costs: As part of those risk assessments, agencies must “carefully consider the full societal costs, benefits, and distributional effects before considering regulations related to the development and deployment of AI applications. Such consideration will include the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace, whether implementing AI will change the type of errors created by the system, as well as comparison to the degree of risk tolerated in other existing ones.”
6. Flexibility: OMB encourages agencies to “pursue performance-based and flexible approaches that can adapt to rapid changes and updates to AI applications.”
7. Fairness and non-discrimination: Acknowledging that “in some instances, introduce real-world bias that produces discriminatory outcomes or decisions that undermine public trust and confidence in AI,” the AI Guidance requires agencies to consider “issues of fairness and non-discrimination with respect to outcomes and decisions produced by the AI application at issue.”
8. Disclosure and transparency: Agencies are encouraged to consider how greater “transparency and disclosure can increase public trust and confidence in AI applications.”
9. Safety and security: Agencies are required to “promote the development of AI systems that are safe, secure, and operate as intended, and encourage the consideration of safety and security issues throughout the AI design, development, deployment, and operation process.”
10. Interagency coordination: The guidance makes it clear that a “coherent and whole-of-government approach to AI oversight requires interagency coordination.”

Soft Law Ascends

Importantly, the AI Guidance also encourages agencies to be open to “non-regulatory approaches to AI” governance and specifies three particular models:

• Sector-specific policy guidance or frameworks: OSTP writes that “agencies should consider using any existing statutory authority to issue non-regulatory policy statements, guidance, or testing and deployment frameworks, as a means of encouraging AI innovation in that sector.” The memorandum also notes that this can include “work done in collaboration with industry, such as development of playbooks and voluntary incentive frameworks.”
• Pilot programs and experiments: The document encourages the use of “pilot programs that provide safe harbors for specific AI applications” which “could produce useful data to inform future rulemaking and non-regulatory approaches.”
• Voluntary consensus standards: Before regulating, the AI Guidance encourages agencies to consider how voluntary consensus standards, assessment programs, and compliance programs might be used to address policy concerns.

These represent “soft law” approaches to technological governance and they are becoming all the rage in technology policy discussions today. Soft law mechanisms are informal, collaborative, and constantly evolving governance efforts. While not formerly binding like “hard law” rules and regulations, soft law efforts nonetheless create a set of expectations about sensible development and use of technologies. Soft law can include multistakeholder initiatives, best practices and standards, agency workshops and guidance documents, educational efforts, and much more.

Soft law has become the dominant governance approach for emerging technologies because it is often better able to address the “pacing problem,” which refers to the growing gap between the rate of technological innovation and policymakers’ ability to keep up with it. As I have previously noted, 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.”

Not only do traditional legislative and regulatory hard law systems struggle to keep up with fast-paced technological changes, but oftentimes those older mechanisms are just too rigid and unsuited for new sectors and developments. That is definitely the case for AI, which is multi-dimensional in nature and even defies easy definition. Soft law offers a more flexible, adaptive approach to learning on the fly and cobbling together principles and policies that can address new policy concerns as they develop in specific contexts, without derailing potentially important innovations.

Building on Past Governance Frameworks

In this sense, the Trump administration’s AI Guidance borrows from past policy frameworks by marrying up a desire to promote an exciting new set of emerging technologies alongside the need for reasonable but flexible oversight and governance mechanisms. At a high level, the AI Guidance builds on many of the same principles that motivated the Clinton administration’s Framework for Global Electronic Commerce, a statement of principles and policy objectives for the then-emerging Internet. The document, which was issued in July 1997, said that “governments should encourage industry self-regulation and private sector leadership where possible” and “avoid undue restrictions on electronic commerce.”

The Framework was a clean break from the top-down regulatory paradigm that had previously governed traditional communications and media technologies. Clinton’s Framework insisted that, to the extent government intervention was needed at all, “its aim should be to support and enforce a predictable, minimalist, consistent and simple legal environment for commerce.” The use of soft law and multistakeholder models was a key component of this vision, and those more flexible governance approaches were tapped by the subsequent administrations to address emerging tech policy concerns.

For example, the Obama administration considerably expanded the use of multistakeholder mechanisms and other soft law tools in response to the need of oversight of fast-moving technologies. The Obama administration had many different policy governance efforts underway for specific AI technologies and concerns, including workshops and multistakeholder efforts focused on the safety, security, and privacy-related issues surrounding “big data” systems, online advertising, connected cars, drones, and more.

Whereas the Obama administration was deeper in the weeds of the policy issues associated with specific AI and machine-learning applications, the Trump administration has sought to both build on those focused efforts while also stepping back to consider AI governance at the 30,000-foot level. In essence, the AI Guidance combines some of the aspirational elements found in the Clinton Framework alongside the Obama administration’s more targeted approach to consider specific policy concerns across many different sectors and technologies.

Trump’s AI Guidance adds an element of formality to this process regarding how federal agencies should address AI developments and formulate potential policy responses. It does so by counseling humility and even potential forbearance until all the facts are in. “Fostering innovation and growth through forbearing from new regulations may be appropriate,” the memorandum says. Agencies should consider new regulation only after they have reached the decision, in light of the foregoing section and other considerations, that Federal regulation is necessary.” Again, this is very much consistent with more general regulatory guidance issued by every administration since President Reagan was in office.

Flexible, Adaptive Governance is Key

The AI Guidance foreshadows the future of not only AI governance but the governance of many other emerging technologies. Hard law will continue to provide a backstop and have a role in guiding technological developments. Toward that end, efforts like the new AI Guidance are important because it represents an effort to “regulate the regulators” by placing some ground rules on how they go about applying old law to new developments.

But soft law governance is where the real action is at, both for AI and almost all emerging technologies today. The Trump AI Guidance reflects the extent to which soft law has become the dominant governance paradigm for modern tech sectors. As my colleagues Jennifer Huddleston and Trace Mitchell have noted, soft law is already effectively the law of the land for driverless cars, for example. After years of congressional wrangling over a federal autonomous vehicle regulatory framework—one that has widespread bipartisan support, no less—we still do not have a law on the books. Instead, the Department of Transportation has been cobbling together informal “rules of the road” through informal guidance documents that have been “versioned” as if they were computer software (i.e., Version 1.0, 2.0, 3.0). Version 4.0 of the DoT guidance for automated vehicles was just released this week.

That is the same approach that the National Institute of Standards and Technology (NIST) has taken with the privacy guidelines it developed. NIST’s Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management is also versioned like software. And many other federal agencies, especially the Federal Trade Commission, have tapped a wide variety of soft law tools—such as agency workshops and workshop reports that recommended privacy best practices for various technologies. Meanwhile, the National Telecommunications and Information Administration (NTIA) has used multistakeholder processes to address privacy concerns surrounding a wide range of technologies, including drones and facial recognition. NIST, FTC, and NTIA have undertaken these informal governance efforts because, despite over a decade of debate, Congress still has not advanced comprehensive federal privacy legislation. For better or worse, soft law has filled that governance gap.

Addressing Likely Objections from Left & Right

Many people of varying ideological dispositions will object to the growing role of soft law as the primary governance tool for emerging technology policy. Some conservatives will cringe at the sound of giving regulators greater leeway to address amorphous policy concerns, fearing that it will result in unconstrained exercises of unaccountable, extra-constitutional power.

Some of those concerns are valid, but they fail to account for the fact that the prospects for agency downsizing or deregulation they prefer are extremely limited. Practically speaking, the administrative state isn’t going anywhere. In some cases, agencies can actually do some real good by encouraging innovators to think about how to “bake-in” sensible best practices to preemptively address concerns about the privacy, safety, security, and fairness of various AI systems. Better those concerns be addressed in more flexible, adaptive fashion than by a heavy-handed, overly-rigid regulatory approach. Soft law offers that possibility, even if legitimate concerns remain about agency accountability and transparency.

Many to the left of center will be critical of this governance approach as well, but on very different grounds. As Associated Press reporter Matt O’Brien notes, “the vagueness of the principles announced by the White House is unlikely to satisfy AI watchdogs who have warned of a lack of accountability as computer systems are deployed to take on human roles in high-risk social settings, such as mortgage lending or job recruitment.”

These concerns actually are addressed in several of the OSTP’s ten principles, including those which stress the need for fairness and non-discrimination, information quality, public participation, disclosure and transparency, and safety and security. Yet many on the left will claim these principles merely pay lip service to these values and that what is really needed is a full-blown regulatory regime and some sort of corresponding new federal AI agency, which would preemptively determine which AI technologies would be allowed into the wild.

Already, an Algorithmic Accountability Act was introduced in Congress last year that would ask the FTC to take a more active role in policing “inaccurate, unfair, biased, or discriminatory decisions impacting consumers” that may have resulted from “automated decision systems.” Meanwhile, some academics have called for the creation of a Federal Robotics Commission or a National Algorithmic Technology Safety Administration to preemptively oversee new AI developments.

The problem with overly-precautionary regulation of that sort could potentially unduly limit AI innovation and the many benefits it entails. There may be some AI applications that pose serious and immediate risks to humanity and which require preemptive restraints on their development and use. Autonomous military and law enforcement applications are the most obvious examples. But most AI applications do not rise to that same level of regulatory concern, and other governance approaches are required to balance the use and misuse of them. This is why a more open and flexible governance approach is needed. Moreover, the old regulatory system just cannot keep up anymore, and it is ill-suited to address most policy concerns in a timely or efficient fashion.

Cristie Ford, and advocate of greater regulatory oversight for fintech, notes in her latest book that the problem with “old-style Welfare State regulation” is that it is “a clumsy, blunt instrument for achieving regulatory objectives” due to its reliance upon “one-size-fits-all mandates, prohibitions, and penalties.” Ford acknowledges what many other regulatory advocates are reluctant to admit:  public policies toward fast-paced technology sectors can no longer be governed effectively using the Analog Era’s top-down, command-and-control regulatory processes. Far too many federal agencies rely on a “build-and-freeze model” of regulation that puts rules in stone to deal with one sets of issues one day, but then either fails to eliminate them later when they become obsolete or to reform those rules to bring them in line with new social, economic, and technical realities.

If we hope to encourage continued innovation in sectors that could produce profoundly important, life-enriching technologies, America’s regulatory approach for AI and emerging technology needs to move away from “build-and-freeze” and toward “build-and-adapt.” Regulation is still needed, but the old regulatory toolkit is badly broken. For better or worse, soft law is going to fill the resulting governance gap, regardless of objections from some on the left or the right. Pragmatic policymaking is going to carry the day for emerging technology governance.

Conclusion

The Trump Administration AI Guidance represents a continuation and extension of this trend toward more flexible, adaptive governance approaches for emerging technologies. It offers a pragmatic vision that builds on the policies and paradigms of the past, while also encouraging fresh thinking about how best to balance the need for continued innovation alongside the various concerns about disruptive technological change.

There are many challenging issues that lie ahead and the new AI Guidance cannot provide bright-line answers to all the hypothetical questions that people want answered today. No one possesses a crystal ball that will allow them to forecast the technological future. Only ongoing trial-and-error experimentation and policy improvisation will allow us to find sensible solutions. A policy approach rooted in humility, flexibility, and forbearance will help ensure that America’s regulatory policies continue to promote both innovation and the public good.

Over at the Mercatus Center Bridge blog, Trace Mitchell and I just posted an essay entitled, “A Non-Partisan Way to Help Workers and Consumers,” which discusses the new Federal Trade Commission’s (FTC) Economic Liberty Task Force report on occupational licensing.

We applaud the FTC’s calls for greater occupational licensing uniformity and portability, but regret the missed opportunity to address root problem of excessive licensing more generally. But while FTC is right to push for greater occupational licensing uniformity and portability, policymakers need to confront the sheer absurdity of licensing so many jobs that pose zero risk to public health & safety. Licensing has become completely detached from risk realities and actual public needs.

As the FTC notes, excessive licensing limits employment opportunities, worker mobility, and competition while also “resulting in higher prices, reduced quality, and less convenience for consumers.” These are unambiguous facts that are widely accepted by experts of all stripes. Both the Obama and Trump Administrations, for example, have been completely in league on the need for comprehensive  licensing reforms.

Trace and I argue that we need serious occupational reforms built on the idea of the “right to earn a living” that must pass this test: “All occupational regulations shall be limited to those demonstrably necessary and carefully tailored to fulfill legitimate public health, safety, or welfare objectives.”  Also, all licensing authorities should be put on the clock and be required, within one year, to reassess the wisdom of all existing licenses to ensure they meet that test. If not, they are repealed or reformed.

In recent testimony in Texas, our Mercatus Center colleague Matthew Mitchell has also discussed other reform options, including the “Occupational Board Reform Act,” which recently passed in Nebraska. The goal of the law is to “protect the fundamental right of an individual to pursue a lawful occupation;.” They key provision of the Act demands that state actors:

use the least restrictive regulation which is necessary to protect consumers from undue risk of present, significant, and substantiated harms that clearly threaten or endanger the health, safety, or welfare of the public when competition alone is not sufficient and which is consistent with the public interest;

That’s an excellent approach to reform and when combined with the Right to Earn a Living Act, policymakers can begin to reverse the protectionist, anti-competitive licensing schemes that encumber entrepreneurs and workers across the land.

In forthcoming work, I hope to more fully develop the connection between the right to earn a living, the need for comprehensive licensing reform, and the freedom to innovate more generally. In the meantime, hop over to The Bridge to read our new essay on how the FTC report helps advance this cause..

FCC Chairman Ajit Pai recently delivered an excellent speech at the Resurgent Conference, Austin, TX. In it, he stressed the importance of adopting a permissionless innovation policy vision to ensure a bright future for technology, economic growth, and consumer welfare. The whole thing is worth your time, but the last two paragraphs make two essential points worth highlighting.

Pai correctly notes that we should reject the sort of knee-jerk hysteria or technopanic mentality that sometimes accompanies new technologies. Instead, we should have some patience and humility in the face of uncertainty and be open to new ideas and technologies creations.

“Here’s the bottom line,” Pai concludes:

Whenever a technological innovation creates uncertainty, some will always have the knee-jerk reaction to presume it’s bad. They’ll demand that we do whatever’s necessary to maintain the status quo. Strangle it with a study. Call for a commission. Bemoan those supposedly left behind. Stipulate absolute certainty. Regulate new services with the paradigms of old. But we should resist that temptation. “Guilty until proven innocent” is not a recipe for innovation, and it doesn’t make consumers better off. History tells us that it is not preemptive regulation, but permissionless innovation made possible by competitive free markets that best guarantees consumer welfare. A future enabled by the next generation of technology can be bright, if only we choose to let the light in.

Read the whole thing here. Good stuff. I also appreciate him citing my work on the topic, which you can find in my last book and other publications.

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.

This past week I posted two new essays related to my new book, “Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom.” Just thought I would post quick links here.

First, my old colleague Dan Rothschild was kind enough to ask me to contribute a post to the R Street Blog entitled, “Bucking the ‘Mother, May I?’ Mentality.” In it, I offered this definition and defense of permissionless innovation as a policy norm:

Permissionless innovation is about the creativity of the human mind to run wild in its inherent curiosity and inventiveness, even when it disrupts certain cultural norms or economic business models. It is that unhindered freedom to experiment that ushered in many of the remarkable technological advances of modern times. In particular, all the digital devices, systems and networks that we now take for granted came about because innovators were at liberty to let their minds run wild. Steve Jobs and Apple didn’t need a permit to produce the first iPhone. Jeff Bezos and Amazon didn’t need to ask anyone for the right to create a massive online marketplace. When Sergey Brin and Larry Page wanted to release Google’s innovative search engine into the wild, they didn’t need to get a license first. And Mark Zuckerberg never had to get anyone’s blessing to launch Facebook or let people freely create their own profile pages. All of these digital tools and services were creatively disruptive technologies that altered the fortunes of existing companies and challenged various social norms. Luckily, however, nothing preemptively stopped that innovation from happening. Today, the world is better off because of it, with more and better information choices than ever before.

I also posted an essay over on Medium entitled, ” Why Permissionless Innovation Matters.” It’s a longer essay that seeks to answer the question: Why does economic growth occur in some societies & not in others? I build on the recent comments of venture capitalist Fred Wilson of Union Square Ventures noted during recent testimony: “If you look at the countries around the world where the most innovation happens, you will see a very high, I would argue a direct, correlation between innovation and freedom. They are two sides of the same coin.” I continue on to argue in my essay:

that’s true in both a narrow and broad sense. It’s true in a narrow sense that innovation is tightly correlated with the general freedom to experiment, fail, and learn from it. More broadly, that general freedom to experiment and innovate is highly correlated with human freedom in the aggregate. Indeed, I argue in my book that we can link an embrace of dynamism and permissionless innovation to the expansion of cultural and economic freedom throughout history. In other words, there is a symbiotic relationship between freedom and progress. In his book, History of the Idea of Progress, Robert Nisbet wrote of those who adhere to “the belief that freedom is necessary to progress, and that the goal of progress, from most distant past to the remote future, is ever-ascending realization of freedom.” That’s generally the ethos that drives the dynamist vision and that also explains why getting the policy incentives right matters so much. Freedom — including the general freedom to engage in technological tinkering, endless experimentation, and acts of social and economic entrepreneurialism — is essential to achieving long-term progress and prosperity.

I also explain how the United States generally got policy right for the Internet and the digital economy in the 1990s by embracing this vision and enshrining it into law in various ways. I conclude by noting that:

If we hope to encourage the continued development of even more “technologies of freedom,” and enjoy the many benefits they provide, we must make sure that, to the maximum extent possible, the default position toward new forms of technological innovation remains “innovation allowed.” Permissionless innovation should, as a general rule, trump precautionary principle thinking. The burden of proof rests on those who favor precautionary policy prescriptions to explain why ongoing experimentation with new ways of doing things should be prevented preemptively.

Again, read the entire thing over at Medium. Also, over at Circle ID this week, Konstantinos Komaitis published a related essay, “Permissionless Innovation: Why It Matters,” in which he argued that “Permissionless innovation is key to the Internet’s continued development. We should preserve it and not question it.” He was kind enough to quote my book in that essay. I encourage you to check out his piece.

I am pleased to announce the release of my latest book, “Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom.” It’s a short manifesto (just under 100 pages) that condenses — and attempts to make more accessible — arguments that I have developed in various law review articles, working papers, and blog posts over the past few years. I have two goals with this book.

First, I attempt to show how the central fault line in almost all modern technology policy debates revolves around “the permission question,” which asks: Must the creators of new technologies seek the blessing of public officials before they develop and deploy their innovations? How that question is answered depends on the disposition one adopts toward new inventions. Two conflicting attitudes are evident.

One disposition is known as the “precautionary principle.” Generally speaking, it refers to the belief that new innovations should be curtailed or disallowed until their developers can prove that they will not cause any harms to individuals, groups, specific entities, cultural norms, or various existing laws, norms, or traditions.

The other vision can be labeled “permissionless innovation.” It refers to the notion that experimentation with new technologies and business models should generally be permitted by default. Unless a compelling case can be made that a new invention will bring serious harm to society, innovation should be allowed to continue unabated and problems, if they develop at all, can be addressed later.

I argue that we are witnessing a grand clash of visions between these two mindsets today in almost all major technology policy discussions today.

The second major objective of the book, as is made clear by the title, is to make a forceful case in favor of the latter disposition of “permissionless innovation.” I argue that policymakers should unapologetically embrace and defend the permissionless innovation ethos — not just for the Internet but also for all new classes of networked technologies and platforms. Some of the specific case studies discussed in the book include: the “Internet of Things” and wearable technologies, smart cars and autonomous vehicles, commercial drones, 3D printing, and various other new technologies that are just now emerging.

I explain how precautionary principle thinking is increasingly creeping into policy discussions about these technologies. The urge to regulate preemptively in these sectors is driven by a variety of safety, security, and privacy concerns, which are discussed throughout the book. Many of these concerns are valid and deserve serious consideration. However, I argue that if precautionary-minded regulatory solutions are adopted in a preemptive attempt to head-off these concerns, the consequences will be profoundly deleterious.

The central lesson of the booklet is this: Living in constant fear of hypothetical worst-case scenarios — and premising public policy upon them — means that best-case scenarios will never come about. When public policy is shaped by precautionary principle reasoning, it poses a serious threat to technological progress, economic entrepreneurialism, social adaptation, and long-run prosperity.

Again, that doesn’t mean we should ignore the various problems created by these highly disruptive technologies. But how we address these concerns matters greatly. If and when problems develop, there are many less burdensome ways to address them than through preemptive technological controls. The best solutions to complex social problems are almost always organic and “bottom-up” in nature. Luckily, there exists a wide variety of constructive approaches that can be tapped to address or alleviate concerns associated with new innovations. These include:

• education and empowerment efforts (including media literacy, digital citizenship efforts);
• social pressure from activists, academics, and the press and the public more generally.
• voluntary self-regulation and adoption of best practices (including privacy and security “by design” efforts); and,
• increased transparency and awareness-building efforts to enhance consumer knowledge about how new technologies work.

Such solutions are almost always superior to top-down, command-and-control regulatory edits and bureaucratic schemes of a “Mother, May I?” (i.e., permissioned) nature. The problem with “top-down” traditional regulatory systems is that they often tend to be overly-rigid, bureaucratic, inflexible, and slow to adapt to new realities. They focus on preemptive remedies that aim to predict the future, and future hypothetical problems that may not ever come about. Worse yet, administrative regulation generally preempts or prohibits the beneficial experiments that yield new and better ways of doing things. It raises the cost of starting or running a business or non-business venture, and generally discourages activities that benefit society.

To the extent that other public policies are needed to guide technological developments, simple legal principles are greatly preferable to technology-specific, micro-managed regulatory regimes. Again, ex ante (preemptive and precautionary) regulation is often highly inefficient, even dangerous. To the extent that any corrective legal action is needed to address harms, ex post measures, especially via the common law (torts, class actions, etc.), are typically superior. And the Federal Trade Commission will, of course, continue to play a backstop here by utilizing the broad consumer protection powers it possesses under Section 5 of the Federal Trade Commission Act, which prohibits “unfair or deceptive acts or practices in or affecting commerce.” In recent years, the FTC has already brought and settled many cases involving its Section 5 authority to address identity theft and data security matters. If still more is needed, enhanced disclosure and transparency requirements would certainly be superior to outright bans on new forms of experimentation or other forms of heavy-handed technological controls.

In the end, however, I argue that, to the maximum extent possible, our default position toward new forms of technological innovation must remain: “innovation allowed.” That is especially the case because, more often than not, citizens find ways to adapt to technological change by employing a variety of coping mechanisms, new norms, or other creative fixes. We should have a little more faith in the ability of humanity to adapt to the challenges new innovations create for our culture and economy. We have done it countless times before. We are creative, resilient creatures. That’s why I remain so optimistic about our collective ability to confront the challenges posed by these new technologies and prosper in the process.

If you’re interested in taking a look, you can find a free PDF of the book at the Mercatus Center website or you can find out how to order it from there as an eBook. Hardcopies are also available. I’ll be doing more blogging about the book in coming weeks and months. The debate between the “permissionless innovation” and “precautionary principle” worldviews is just getting started and it promises to touch every tech policy debate going forward.

Related Essays :

• “The Growing Conflict of Visions over the Internet of Things & Privacy,” Technology Liberation Front, January 14, 2014.
• “CES 2014 Report: The Internet of Things Arrives, but Will Washington Welcome It?” Technology Liberation Front, January 8, 2014.
• “Who Really Believes in ‘Permissionless Innovation’?” Technology Liberation Front, March 4, 2013.
• “What Does It Mean to ‘Have a Conversation’ about a New Technology?” Technology Liberation Front, May 23, 2013.
• “On the Line between Technology Ethics vs. Technology Policy,” Technology Liberation Front, August 1, 2013.
• “Planning for Hypothetical Horribles in Tech Policy Debates,” Technology Liberation Front, August 6, 2013.
• “Edith Ramirez’s ‘Big Data’ Speech: Privacy Concerns Prompt Precautionary Principle Thinking,” Technology Liberation Front, August 29, 2013.
• “When It Comes to Information Control, Everybody Has a Pet Issue & Everyone Will Be Disappointed,” Technology Liberation Front, April 29, 2011.
• “Copyright, Privacy, Property Rights & Information Control: Common Themes, Common Challenges,” Technology Liberation Front, April 10, 2012.
• “Can We Adapt to the Internet of Things?” IAPP Privacy Perspectives, June 19, 2013.
• “Why Do We Always Sell the Next Generation Short?” Forbes, January 8, 2012.
• “The Six Things That Drive ‘Technopanics,’” Forbes, March 4, 2012.

I’m pretty rough on all the Internet and info-tech policy books that I review. There are two reasons for that. First, the vast majority of tech policy books being written today should never have been books in the first place. Most of them would have worked just fine as long-form (magazine-length) essays. Too many authors stretch a promising thesis into a long-winded, highly repetitive narrative just to say they’ve written an entire book about a subject. Second, many info-tech policy books are poorly written or poorly argued. I’m not going to name names, but I am frequently unimpressed by the quality of many books being published today about digital technology and online policy issues.

The books of Harvard University cyberlaw scholars John Palfrey and Urs Gasser offer a welcome break from this mold. Their recent books, Born Digital: Understanding the First Generation of Digital Natives, and Interop: The Promise and Perils of Highly Interconnected Systems, are engaging and extremely well-written books that deserve to be books. There’s no wasted space or mindless filler. It’s all substantive and it’s all interesting. I encourage aspiring tech policy authors to examine their works for a model of how a book should be done.

In a 2008 review, I heaped praise on Born Digital and declared that this “fine early history of this generation serves as a starting point for any conversation about how to mentor the children of the Web.” I still recommend highly to others today. I’m going to be a bit more critical of their new book, Interop, but I assure you that it is a text you absolutely must have on your shelf if you follow digital policy debates. It’s a supremely balanced treatment of a complicated and sometimes quite contentious set of information policy issues.

In the end, however, I am concerned about the open-ended nature of the standard that Palfrey and Gasser develop to determine when government should intervene to manage or mandate interoperability between or among information systems. I’ll push back against their amorphous theory of “optimal interoperability” and offer an alternative framework that suggests patience, humility, and openness to ongoing marketplace experimentation as the primary public policy virtues that lawmakers should instead embrace.

Interop is Important, but Often Difficult & Filled with Trade-Offs

Palfrey and Gasser begin by noting that “there is no single, agreed-upon definition of interoperability” and that “there are even many views about what interop is and how it should be achieved” (p. 5). They set out to change that by developing “a normative theory identifying what we want out of all this interconnectivity” that the information age has brought us (p. 3).

Generally speaking, Palfrey and Gasser believe increased interoperability — especially among information networks and systems — is a good thing because it “provides consumers greater choice and autonomy” (p. 57), “is generally good for competition and innovation” (p. 90), and “can lead to systemic efficiencies” (p. 129).

But they wisely acknowledge that there are trade-offs, too, noting that “this growing level of interconnectedness comes at an increasingly high price” (p. 2). Whether we are talking about privacy, security, consumer choice, the state of competition, or anything else, Palfrey and Gasser argue that “the problems of too much interconnectivity present enormous challenges both for organizations and for society at large” (p. 2). Their chapter and privacy and security offers many examples, but one need only look around at their own digital existence to realize the truth of this paradox. The more interconnected our information systems become, and the more intertwined our social and economic lives become with those systems, the greater the possibility of spam, viruses, data breaches, and various types of privacy or reputational problems. Interoperability giveth and it taketh away.

When Does “the Public Interest” Demand Interoperability Regulation?

So, how do we know when increased interoperability is good for us or society? How do we strike a reasonable balance? And, most controversially, when should government intervene to tip the balance in one direction or another?

Palfrey and Gasser return to these questions repeatedly throughout the book but admit that their answers will be dissatisfying since “there is no single form or optimal amount of interoperability that will suit every circumstance” (p. 76). Thus, “most of the specifics of how to bring interop about [must] be determined on a case-by-case basis (p. 17). They elaborate:

That can feel unsatisfying. But it is an essential truth: the most interesting interop problems relate to society’s most complex and most fundamental systems. Their answers are never simple to come by, nor are they easy to implement. This characteristic of interop theory is a feature, not a bug. … The price to be paid for striving for a universal principle at the level of theory is that such a theory is full of nuances when it comes to application and practice (p. 17-18).

Fair enough. Yet, Palfrey and Gasser also make it clear they want government(s) to play an active role in ensuring optimal interoperability. They say they favor “blended approaches that draw upon the comparative advantages of the private and public sector” (p. 161), but they argue that government should feel free to tip or nudge interoperability determinations in superior directions. “If deployed with skill,” they argue, “the law can play a central role in ensuring that we get as close as possible to optimal levels of interoperability in complex systems” (p. 88).

That phrase — “optimal level of interoperability” — pops up repeatedly throughout the book. So, too, does the phrase “the public interest.” Palfrey and Gasser argue that governments must look out for “the public interest” and “optimal interoperability” since “market forces do not automatically lead to appropriate standards or to the adoption of the best available technology” (p. 167). Here they introduce two additional amorphous values that complicate the debate: “appropriate standards” and “best available technology.”

The fundamental problem this “public interest” approach to interoperability regulation is that it is no better than the “I-know-it-when-I-see-it” standard we sometimes at work in the realm of speech regulation. It’s an empty vessel, and if it is the lodestar by which policymakers make determinations about the optimal level of interoperability, then it leaves markets, innovators, and consumers subject to the arbitrary whims of what a handful of politicians or regulators think constitutes “optimal interoperability,” “appropriate standards,” and “best available technology.”

On the Limits of Knowledge

Palfrey and Gasser’s framework feels more than just “unsatisfying” in this regard; it feels downright insufficient. That’s because it is missing a major variable: the extent to which state actors are able to adequately define those terms or accurately forecast the future needs of markets or citizen-consumers.

Surprisingly, Palfrey and Gasser don’t really spend much time discussing the specific remedies the state might impose to achieve optimal interoperability. I would have liked to have seen them develop a matrix of interop options and then outline the strengths and weaknesses of each. But even absent a more detailed discussion of possible regulatory remedies, I would have settled for more concrete answers to the following questions: Why are we to assume that regulators possess the requisite knowledge needed to know when it makes sense to foreclose ongoing marketplace experimentation? And why should we trust that, by substituting their own will for that of countless other actors in the information technology marketplace, we will be left better off?

The closest Palfrey and Gasser get to defining a firm standard for when and why such state intervention is warranted comes on page 173 when they are discussing the need for the state to establish sound reasons for intervention. They argue:

The objective should not be interoperability per se but, rather, one or more public policy goal to which interoperability can lead. The goals that usually make sense are innovation and competition, but other objectives might include consumer choice, ease of use of a technology or system, diversity, and so forth (p. 173).

This is a bit better, but it still doesn’t fully grapple with the cost side of the cost-benefit calculus for intervention. Palfrey and Gasser are willing to at least acknowledge some of those problems when they remark that “the state is rarely in a position to call a winner among competing technologies” (p. 174). Moreover,

Lawmakers need to keep in view the limits of their own effectiveness when it comes to accomplishing optimal levels of interoperability. Case studies of government intervention, especially where complex information technologies are involved, show that states tend to be ill suited to determine on their own what specific technology will be the best option for the future (p. 175)

Quite right! Yet, that insight does not seem to influence their calls elsewhere in the book for regulatory activism. That’s a shame since the admonition about policymakers recognizing the “limits of their own effectiveness” should be able to help us devise some limiting principles regarding the state’s role.

Toward an Alternative Theory: Experimental, Evolutionary Interoperability

Allow me to offer a different theory of optimal interoperability that flows from these previous insights. It’s based on a more dynamic view of markets and the central importance of experimentation in the face of uncertainty. Let me just go ahead and articulate the core principles of what I will refer to as  “experimental, evolutionary interoperability theory.” Then I’ll explain it in more detail

• Experimental, evolutionary interoperability : The theory that 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.

Palfrey and Gasser would label this a “laissez-faire” theory of interoperability and oppose it since they believe “a pure laissez-faire approach to interop rarely works out well” (p. 160). But they are wrong, at least to the extent they include the sweeping modifier “rarely” to describe this model’s effectiveness. In reality, the vast majority of interoperability that occurs into today’s information economy happens in a completely natural, evolutionary fashion without any significant state intervention whatsoever. In countless small and big ways alike, interconnection and interoperability happens every day throughout society. Yes, it is true that interoperability often happens against the backdrop of a legal system that allows court action to enforce certain rights or address perceived harms, but I would not classify that as a significant direct state intervention to tip or nudge interconnection decisions in one direction or another. And when interoperability doesn’t happen naturally, there are often good reasons it doesn’t and, even if there aren’t, non-interop spawns beneficial marketplace reactions and innovations.

Experimental, evolutionary interoperability theory flows out of Schumpeterian competition theory and the related field of evolutionary economics, but it is also heavily influenced by public choice theory (which stresses the limitations of romanticized theories of politics, planning, and “public interest” regulation). This alternative theory begins by accepting the simple fact that, as Austrian economist F.A. Hayek taught us, “progress by its very nature cannot be planned.” The wiser man, Hayek noted, “is very much aware that we do not know all the answers and that he is not sure that the answers he has are certainly the right ones or even that we can find all the answers.”

Ongoing experimentation with varying business models and modalities of social and economic production allows us to see what consumer choice and trial and error experimentation yields naturally over time. Ongoing experiments with flexible, voluntary interop standards and negotiations also allows us to determine which technological standards seem to benefit consumers in the short-term while also encouraging innovators to leap-frog existing standards and platforms when they become locked-in for too long or seem sub-optimal.

In the short-term, it is entirely possible that such voluntary, evolutionary interop experiments “fail” in various ways. That is often a good thing. Failures are how individuals and a society learn to cope with change and devise systems and solutions to accommodate technological change. As Samuel Beckett once counseled: “Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.” Progress depends upon an embrace of this uncertainty and acceptance of a world of constant upheaval if we are to learn how to cope, adapt, and move forward.

In this model, technological innovation often springs from the quest for the prize of market power.  Palfrey and Gasser generally reject this Schumpeterian vision of dynamic competition, but they at least do a nice job of describing it:

firms may have a stronger incentive to be innovative when low levels of interoperability promise higher or even monopoly profits. This sort of competition… creates incentives for firms to come up with entirely new generations of technologies or business methods that are proprietary (p. 121).

They reject this approach based on (1) the mistaken notion that the quest of the prize of market power ends in the attainment and preservation of that market power; and (2) the belief that policymakers possess the ability to set us on a better course through wise interventions.

In a moment, I’ll prove why that is misguided by examining a few real-world cases studies. For now, however, let’s return to Palfrey & Gasser’s central operating principle and contrast it with the vision I’ve articulated here. Recall that they argue “it is important to maintain and facilitate diversity in the marketplace. We simply want systems to work together when we want them to and to not work together when we do not.” Again, there is no standard here if one is suggesting this as the principle by which to determine when state intervention is desirable . But if one is looking at that aspirational statement as a description of the natural order of things — namely, that we do indeed “want systems to work together when we want them to and to not work together when we do not” — then that is a perfectly sound principle for understanding why state intervention should be disfavored in all but the most extreme circumstances. To reiterate: We should not allow the state to foreclose interoperability experiments because (a) those experiments have value in and of themselves, and (b) state action is likely to have myriad unintended consequences and unforeseen costs that are not easily remedied or reversed.

There are moments in the book when Palfrey and Gasser appear somewhat sympathetic to the sort of alternative “evolutionary interop” theory I have articulated here. For example, they note that:

The web is a great equalizer for technology firms. As consumers, we have come to expect that everything will work together without incident or interruption. We think it bizarre when something in the digitally networked world does not mesh with something else, perceiving whatever it is to be broken, in need of repair. This high degree of expectation is a powerful driver of interoperability. Market players are increasingly responding to this consumer demand and making these invisible links work for their customers without any government intervention” (p. 28) [italics added]

You won’t be surprised to hear that I agree wholeheartedly! Moreover, what it really proves is that ongoing marketplace experimentation and the evolution of norms and standards generally solve interoperability problems as they develop. That doesn’t mean markets are perfectly competitive or always produce perfect interoperability. But, again, why should we believe state intervention will do a better job? And isn’t it possible that intervention could negatively counter those natural instincts that Palfrey and Gasser describe about how consumers and market actors interact to make those “invisible links” work out as nicely as they do today?

Interop, Competition & Innovation: Some Cases Studies of Evolutionary Interoperability in Action

To better explain experimental, evolutionary interop theory and how it plays out in the real-world, let’s examine the complex relationship between interoperability, competition, and innovation in the information economy through the prism of three case studies: AOL and instant messaging, video game consoles, and smartphones.

AOL

America Online’s (AOL) case study is probably the most profound example of Schumpeterian creative destruction rapidly eroding the market power of a once “dominant” digital giant. Not long ago, AOL was cast as the great villain of online openness and interoperability. In fact, when Lawrence Lessig penned his acclaimed book Code in the late 1990s, AOL was supposedly set to become the corporate enslaver of cyberspace.

For a time, it was easy to see why Lessig and others were worried. Twenty five million subscribers were willing to pay $20 per month to get a guided tour of AOL’s walled garden version of the Internet. Then AOL and media titan Time Warner announced a historic mega-merger that had some predicting the rise of “new totalitarianisms” and corporate “Big Brother.”

Fearing the worst, several conditions were placed on approval of the merger by both the Federal Trade Commission and the Federal Communication Commission. These included “open access” provisions that forced Time Warner to offer the competing ISP service from the second largest ISP at that time (Earthlink) before it made AOL’s service available across its largest cable divisions.  Another provision imposed by the FCC mandated interoperability of instant messaging systems based on the fear that AOL was poised to monopolize that emerging technology.

Palfrey and Gasser suggest this was a necessary and effective intervention. “The AOL IM case is another instance in which the role of government was key in establishing a more interoperable ecosystem” and they credit the FCC’s action with cutting AOL’s share of the IM (p. 68-9). That’s a huge stretch. The reality is that markets and technologies evolved around AOL’s walled garden and decimated whatever advantage the firm had in either the web portal business or instant messaging market.

First, despite all the hand-wringing and regulatory worry, AOL’s merger with Time Warner quickly went off the rails and AOL’s online “dominance” quickly evaporated. Looking back at the deal with TW, Fortune magazine senior editor Allan Sloan called it the “turkey of the decade” since it cost shareholders hundreds of billions. Second, AOL’s attempt to construct the largest walled garden ever also failed miserably as organic search and social networking flourished. Consumers showed they demanded more than the hand-held tour of cyberspace.

Finally, the hysteria about AOL’s threat to monopolize instant messaging and deny interoperability proved particularly unwarranted and also serves as a cautionary tale for those who argue regulation is needed to solve interoperability problems. At the time, well-heeled major competitors like Yahoo and Microsoft already had significant competing IM platforms, and others were rapidly developing. Interoperability among those systems was also spontaneously developing as consumers demanded greater flexibility among and within their communications systems. The development of Trillian, which allowed IM users to see all their various IM feeds at once, was an early precursor of what was to come. Today, anyone can download a free chat client like Digsby or Adium to manage multiple IM and email services from Yahoo!, Google, Facebook and just about anyone else, all within a single interface, essentially making it irrelevant which chat service friends use.

In a truly Schumpetrian sense, innovators came in and disrupted AOL’s plans to dominate instant messaging with innovative offerings that few critics or regulators would have believed possible just a decade ago. Progress happened, and nobody planned it from above. The FCC’s IM interoperability provision was quietly sunset less than three years after its inception since the evolution of technology and markets had rapidly eliminated the perceived problem. That mandate, as it turned out, wasn’t needed at all, and all it probably accomplished during its short life span was to hobble AOL’s ability to find a way to remain relevant in the increasingly competitive Web. 2.0 world.

Video game consoles

At first blush, the video game console wars might seem like the ideal case study for those who favor greater interoperability regulation. After all, in a static sense, why do we really need several competing video game platforms that prevent consumers from playing their games on more than one system? The lack of console interoperability also drives up development costs for game makers. Many of those developers would prefer to just code games for a single, universal gaming platform. Therefore, isn’t this the perfect excuse for state intervention to ensure “optimal interoperability”?

To the contrary, this is another example of why government should generally avoid intervening to try to achieve some sort of artificial optimal interoperability. This market has undergone continuous, turbulent change and witnessed remarkable pro-consumer innovation despite a lack of interoperability.

The video game console wars have raged since the late 1970s. The first generation of consoles was dominated Atari (2600), Mattel (Intellivision), and Coleco (ColecoVision). By the mid-1980s, the industry saw a new cast of characters displace the old players. Nintendo (NES), and Sega (Genesis) took the lead. Atari attempted a rebirth with its “Jaguar” console but failed miserably.

The demise of Atari’s 2600 console was particularly notable. When it debuted in 1977, the system revolutionized the home game market on its way to selling more than 30 million units.  For a few years, it utterly dominated the console market and the company “rushed out games, assuming that its customers would play whatever it released,” notes New York Times reporters Sam Grobart and Ian Austen. But demand rapidly dried up as other consoles and personal computers took the lead with more powerful, flexible platforms and games. In the end, “millions of unsold games and consoles were buried in a New Mexico landfill in 1983. Warner Communications, which bought Atari in 1976 for $28 million, sold it in 1984 for no cash.”

The next generation of machines was dominated by Nintendo and Sega. But by the turn of the century, more new faces appeared and disrupted the second generation of market leaders. Sony (PlayStation) and Microsoft (Xbox) introduced powerful new consoles that continue to evolve to this day. Both consoles have already cycled through three iterations, each increasingly powerful and more functional. Sega dropped out of the console business and refocused on game development. Nintendo managed to survive with its innovative “Wii” system, but has fallen from its perch as king of the console market. Many also forget Apple’s failed run at the console business with its “Pippin” system in the late 1990s. Steve Jobs killed off the console when he returned to once again lead Apple in 1997. Ironically, just a decade later, with the rise of the iPhone and the Apple App Store, the company would emerge as a major player in the gaming market as smartphone gaming exploded.

Of course, PC gaming existed across these generations and handheld gaming devices and now smartphones are also providing competition to traditional consoles. Arcade games also existed both then and now. Thus, the video game market has always been broader than just home gaming consoles.

Nonetheless, at no time during the turbulent history of this sector have major consoles interoperated. The result has been a constant effort by major console developers to leap-frog the competition with increasingly innovative and powerful consoles and peripherals. Would Microsoft have developed the Kinect motion-sensing device if Nintendo had not previously developed their game-changing Wii motion controllers? It’s impossible to know but it would seem that non-interoperability had something to do with that beneficial development. Microsoft needed a game-changing peripheral of its own to meet the Nintendo challenge since Nintendo was not about to share its innovations with the competition. Meanwhile, Sony has developed its own motion-based “Move” system to compete Microsoft and Nintendo.

This is a highly dynamic marketplace at work. Could policymakers have determined that 3 major non-interoperable home consoles would have produced so much innovation? Would they have judged that to be too much or too little competition?  Would they have been able to foresee or help bring about the disruptive competition from portable gaming devices or smartphones? What sort of interop regulation would have made that happen?

As Palfrey and Gasser suggest in their book, there really “is no single form or optimal amount of interoperability that will suit every circumstance.” The video game case study seems to prove that. Yet, their framework leaves the door open a bit wider for state meddling to determine “optimal interop.” I have little faith that state planners could have given us a more innovative video game marketplace through interop nudging. And I also worry that if the door had been open for regulators at the FCC or elsewhere to influence interoperability decisions, it might have also opened to the door to content regulation since many lawmakers have long had an appetite for video game censorship.

Smartphones

The mobile phone handset and operating system marketplace has undergone continuous change over the past 15 years and is still evolving rapidly. There are some interoperable elements, such as the ability to make connecting calls and send texts and IMs. But other parts of the smartphone ecosystem are not interoperable, such as underlying operating systems or apps and app stores.

In the midst of this mixed system of interoperable and non-interoperable elements, innovation and cut-throat competition have flourished.

When cellular telephone service first started taking off in the mid-1990s, handsets and mobile operating systems were essentially one in the same, and Nokia and Motorola dominated the sector with fairly rudimentary devices. The era of personal digital assistants (PDAs) dawned during this period, but mostly saw a series of overhyped devices, including Apple’s “Newton,” that failed to catch on. In the early 2000s, however, a host of new players and devices entered the market, many of which are still on the scene today, including LG, Sony, Samsung, Siemens, and HTC. Importantly, the sector began splitting into handsets versus operating systems (OS). Leading mobile OS makers have included: Microsoft, Palm, Symbian, BlackBerry (RIM), Apple, and Android (Google).

The sector continues to undergo rapid change and interoperability norms have evolved at the same time. Looking back, it’s hard to know whether increased interoperability would have helped or hurt the state of competition and innovation.

Consider Palm, Blackberry, and Microsoft which all limited interoperability with other systems in various ways. Palm smartphones were wildly popular for a brief time and brought many innovations to the marketplace, for example. Palm underwent many ownership and management changes, however, and rapidly faded from the scene.  After buying Palm in 2010, HP announced it would use its webOS platform in a variety of new products.  That effort failed, however, and HP instead announced it would transition webOS to an open source software development mode.

Similarly, RIM’s BlackBerry was thought to be the dominant smartphone device for a time, but it has recently been decimated. BlackBerry’s rollercoaster ride has left it “trying to avoid the hall of fallen giants” in the words of an early 2012 New York Times headline.  The company once commanded more than half of the American smartphone market but now has under 10 percent, and that number continues to fall.

Microsoft also had a huge lead in licensing its Windows Mobile OS to high-end smartphone handset makers until Apple and Android disrupted its business. It’s hard to believe now, but just a few years ago the idea of Apple or Google being serious contenders in the smartphone business was greeted with suspicion, even scorn by popular handset makers such as Nokia and Motorola. This serves as another classic example of those with a static snapshot mentality disregarding the potential for new entry and technological disruption. Just a few years later, Nokia’s profits and market share have plummeted and a struggling Motorola was purchased by Google. Meanwhile, again, Palm seems dead, BlackBerry is dying, and Microsoft is struggling to win back market share it has lost to Apple and Google in this arena.

It would seem logical to conclude that the ebbs and flows of interoperable and non-interoperable elements of the smartphone world have created a turbulent but vibrantly innovative sector. Has the lack of interoperable operating systems or apps and apps stores hurt smartphone consumers? It’s hard to see how. Mandating interoperability at either level could lead to an OS or app store monopoly, most likely for Apple if such a policy were pursued today.

While Apple has had great success and earned endless kudos for their slick, user-friendly innovations from consumers and tech wonks alike, some critics decry their proprietary business model and more “controlled” user experience. Apple tightly controls almost every level of production of its iPhone smartphone and iPad tablet. Interoperability with competing systems, standards, or technologies is limited in many ways. Is that bad? Some critics think so, suggesting that greater “openness” — presumably in the form of greater device or program interoperability — is needed. But so what? Consumers seem extremely happy with Apple devices. Moreover, well-heeled rivals like Google (Android) and Microsoft continue to innovate at a healthy clip and offer consumers a decidedly different user experience. As with video games consoles, non-interop has had some important dynamic effects and advantages for consumers. It’s hard to know what “optimal interoperability” would even look like in the modern smartphone marketplace and how it would be achieved, but it’s equally hard to believe that consumers would be significantly better off if regulators were trying to achieve it through top-down mandates on such a dynamic, fast-moving market.  [For more on this topic, see my 2011 book chapter, “The Case for Internet Optimism, Part 2 – Saving the Net From Its Supporters,” from the book, The Next Digital Decade.]

Case Study Summary & Analysis

These case studies suggest that defining “optimal interoperability” is a pipe dream. In some cases, consumers demanded a certain amount interoperability and they got it. But it seems equally obvious that they did not demand perfect interoperability in every case. Few consumers are tripping over their own feet in a mad rush to toss out their XBoxs or iPhones just because they are not perfectly interoperable. On the other hand, since the days of the old “walled garden” hell of AOL, CompuServe, Prodigy, and so on, it would seem that information technology markets are growing more “open” in other ways. You can’t completely lock-down a user’s online experience and expect to win their business over the long haul.

Palfrey and Gasser make that point quite nicely in the book:

Increasingly, though, businesses are seeing the merits of strategies based on openness. A growing number of businesses are pursuing models that incorporate interoperability as a core principle. More and more firms, especially in the information business, are shedding their proprietary approaches in favor of interoperability at multiple levels. The goal is not to be charitable to competitors or customers, of course, but to maximize returns over time by building an ecosystem with others that holds greater promise than the go-it-alone approach (p. 149).

Quite right, but let’s not pretend that any mass market information platforms or systems will ever be perfectly “open” or interoperable. There will always be some limitations on how such systems are used or shared. And that’s just fine once you embrace a more flexible theory of evolutionary interoperability.  Ongoing experiments will get us to a better place.

Conclusion: Let Interop Experiments Continue!

So, let me wrap up by restating my alternative theory of optimal interoperability as succinctly as possible: When in doubt, ongoing, bottom-up, dynamic experimentation will almost always yield better answers than arbitrary intervention and top-down planning. Again, that is not to say that all interoperability experiments will leave society better off in the short-term. Some interoperability experiments and resulting market norms or outcomes can create challenging dilemmas for individuals and institutions. There may be short-term spells of “market power,” for example, and some standards may get locked in longer than some of us think makes sense. If, however, we have faith in humans to solve problems with information and technology, then still more experimentation — not state intervention — is the answer. And that is especially true once you accept the fact that those seeking to intervene have very limited knowledge of all the relevant facts needed to even make wise decisions about the future course of technology markets or information systems.

Some will find my alternative theory of optimal interoperability no more satisfying than Palfrey and Gasser’s since they may find the experimental interop framework too inflexible when it comes to state action. Whereas the frustration with Palfrey and Gasser’s theory will likely flow from their failure to define a coherent standard for when intervention is warranted, my approach solves that problem by suggesting we should largely abandon the endeavor and instead let ongoing market experiments solve interop problems over time. For me, we would need to find ourselves in a veritable whole-world-is-about-to-go-to-hell sort of moment before I could go along with state intervention to tip the interop scales in one direction or another. And, generally speaking, this is exactly the sort of thing that antitrust laws are supposed to address after a clear showing of harm to consumer welfare. Stated differently, to the extent any state intervention to address interoperability can be justified, ex post antitrust remedies should almost always trump ex ante regulatory meddling.

This alternative vision of evolutionary, experimental interoperability will be rejected by those who believe the state has the ability to wisely intervene and nudge markets to achieve “optimal interoperability” through some sort of Goldilocks principle that can supposedly get it just right. For those of us who have doubts about the likelihood of such sagacious state action — especially for fast-paced information sectors — the benefits of ongoing marketplace experimentation far outweigh the costs of letting those experiments run their course.

Regardless, we should be thankful that John Palfrey and Urs Gasser have provided us with a book that so perfectly frames what should be a very interesting ongoing debate over these issues. I encourage everyone to pick up a copy of Interop so you can join us in this important discussion.

Additional Reading:

• the official book site
• buy the book on Amazon or Barnes & Noble
• Follow the authors on Twitter: @JPalfrey & @UGasser
• a video of their Harvard book launch follows:

Over at his always-informative Spectrum Blog, wireless guru Michael Marcus brings to my attention a new report that will definitely be of interest to everyone here about “The Economic Value Generated by Current and Future Allocations of Unlicensed Spectrum.”  It was written by Rich Thanki of Perspective Associates, a UK consulting firm. I haven’t had time to finish the whole thing yet, but it basically lays out the argument for opening up more spectrum, especially “white spaces,” to unlicensed use.

Anyway, Mike Marcus has an much better write-up of the report than I could ever do, so head over there to check out his discussion.  One important thing that Mike stresses is the importance of technical flexibility:

But the key issue here is not the presence or absence of a license, the key issue is deregulation. A major reason why unlicensed networks have been so innovative is that the descendants of the FCC Docket 81-413 rulemaking, e.g. Wi-Fi, Bluetooth, and Zigbee have been in spectrum bands with great technical flexibility… If you overregulate unlicensed systems, they can stagnate just as much as licensed one often do.

I think that is an important insight and essential lesson that we should always keep in mind when it comes to spectrum policy, regardless of whether we talking about licensed or unlicensed spectrum.  Although I’ve always been a bit torn about how much spectrum should be allocated on an unlicensed (or “commons”) basis versus auctioned (property rights model), as Marcus suggests, flexibility is crucial in either case.   In all the heated catfights over licensed and unlicensed spectrum, that point sometimes gets overlooked.

I’ve been quite depressed to witness Bruce Schneier’s ongoing conversion from opponent of government intervention in the high-tech economy (at least on encryption) to vociferous proponent (at least in terms of privacy regulation).  Anyway, his latest cheerleading piece for government privacy regulation in The Wall Street Journal includes lots of fear-mongering about private website data collection for, God forbid, purposes of trying to better target advertising and market us products we might actually want.

Schneier uses the term “deceptive” several times in the piece to refer to privacy policies that don’t make it explicitly clear that some of the information you leave on a site, or that is collected preemptively by them, will be used to craft more targeted marketing efforts.  Like many other would-be privacy regulators, Schneier seemingly wants companies to fly blimps over your desk as you surf the Net with big signs that basically say: ‘Hey stupid, your info may be used to market you stuff.’  It’s hard to be against more disclosure, of course — and most sites spell out what they do with data in their privacy policies — but it never seems to be good enough for most privacy advocates, who paint consumers out to be mindless sheep who cannot be trusted to make wise decisions for themselves.  Sorry, but I just don’t buy it.

Specifically, I think there’s a pretty easy solution to the concern Schneier articulates about cloud computing when he says:

Cloud computing services like Google Docs, and social networking sites like RealAge and Facebook, bring with them significant privacy and security risks over and above traditional computing models. Unlike data on my own computer, which I can protect to whatever level I believe prudent, I have no control over any of theses sites, nor any real knowledge of how these companies protect my privacy and security.  I have to trust them.

Huh?  Why do you just “have to trust them”?  How about just not using those services?!  Or, use privacy self-help solutions when possible to manage your privacy preferences.  And for God’s sake Bruce, you wrote the definitive textbook on cryptography!  How about using encryption if you’re so concerned about who might be collecting your data online??

Meanwhile, Schneier doesn’t bother telling us what economic engine is going to power the Internet economy going forward once the privacy regulations he favors get on the books and make targeted advertising and data collection a federal crime.  Should we expect all these free Internet sites and services to just fall like manna from heaven?  Again, while the supposed harms from private data collection are largely conjectural, the harm to the Internet economy from heavy-handed, top-down privacy regulations would be all too real.  As we always say here, there is no free lunch.

One of the reasons that so many of us here take issue with proposals to expand regulation of communications, broadband, and media markets is because we have studied the horrendous inefficiencies of economic regulation in practice. We oppose regulatory proposals not because of a “blind faith” in free markets, but because we understand that even when markets stumble they correct themselves quicker and more efficiently than regulatory systems do. One can profess the supposed theoretical benefits of enlightened “public interest” regulation all they want, but the facts are the facts. And the facts do not support the proposition that government regulation generally enhances consumer welfare.

In that regard, Tim Lee’s new Net neutrality report for Cato does a nice job of surveying some of the past unintended consequences of regulation. Also, even though it is now 10 years old, I highly recommend “Economic Deregulation and Customer Choice” by Jerry Ellig and Robert Crandall. It’s an outstanding overview of why economic regulation of various industries failed consumers so miserably in the past.

But if you want even more shocking proof of how horrendously inefficient communications regulation can be in practice, then you must read my PFF colleague Barbara Esbin’s two essays this week on the Universal Service Fund (USF): “The High Cost of USF Support,” and “More FCC Support Fund Follies.” In these two essays, Esbin walks the reader through various grim reports and statistics that have been released recently documenting the failures of the USF.

Her first essay notes how a recent FCC Inspector General report found that the USF “High Cost” fund is spiraling out of control. According to a FCC press release, that report found that “a program is at risk if the erroneous payment rate exceeds 2.5% and the amount of erroneous payments is greater than $10 million. The estimated erroneous payment rate for the High Cost Program (“HCP”) was 23.3%. The previous estimate was 16.6%. Total estimated erroneous payments were $ 971.2 million as compared with the previous estimate of erroneous payments of $617.8 million. Accordingly, the FCC-OIG concluded that the High Cost Fund program is “at risk” under applicable [..] criteria.”

Esbin puts these shocking results in perspective:

“At risk” is a surely a euphemism for a program that loses in “erroneous payments” nearly one out of every four dollars collected from telephone subscribers. In 2007, pursuant to FCC rules, telephone consumers were effectively taxed over $4 billion for the high-cost portion of the USF. Thus, nearly $1 billion dollars of subscriber money went out the door in “erroneous payments.” As the report makes clear, erroneous payments include both over- and underpayments, and also instances where the agency is unable to discern whether a payment was proper as a result of “lack of documentation.” The report’s conclusions state that the “rate of improper overpayments is 22.8%, and the proportion of improper overpayments out of total improper payments is 98.2%.” To be considered “erroneous,” an payment “need not be the result of fraudulent misrepresentation, or a corrupt administrative process.” “Nor does it necessarily exclude those factors as potential causes of erroneous payments.” Significantly, nor are “the erroneous payments . . . necessarily recoverable from recipients by process of law.” Fabulous. Not only has nearly $1 billion in erroneous overpayments gone missing, but even if final audits indicate where it has gone, it may not be recoverable! Among the interesting results of this preliminary report are the identified causes of erroneous payments. According to Table 2 of the report, 50% of the causes of erroneous payments can be attributed nearly equally to two factors: either “Inadequate Documentation” (25.3%) or “Inadequate Auditee Processes and/or Policies and Procedures” (24.6%). Another 10% “Disregarded FCC Rules” and 12% had “Applicant/Auditee Weak Internal Controls.” That is, roughly 75% of the erroneous overpayments can be attributed to poor bookkeeping, inadequate internal controls and “disregard” of FCC rules. This is stunning information. No wonder it made its appearance the day before Thanksgiving.

But wait, things get worse. So much worse. In Esbin’s second essay, she notes that:

On Monday, the OIG released its Semi-Annual Report to Congress, discussing the full range of audit activities conducted from April 1, 2008 to September 30, 2008. Thus we learn that in addition to the loss of nearly $1 billion in erroneous overpayments to the High Cost program, another fund the FCC is ultimately responsible for, the “Telecommunications Relay Service” (TRS) Fund, which provides funds for a variety of telephone transmission services for those with hearing and speech disabilities, also appears to be at risk for substantial overpayments due to the lack of adequate controls. Since 1993, according to the FCC’s website, the Commission’s rules have required that each common carrier providing voice transmission services provide TRS throughout its service area. All providers of interstate telecommunications services contribute to the TRS Fund, and TRS providers recover the costs of providing interstate services from the Fund on a minutes-of-use basis. Intrastate TRS funding is generally administered by the states, although some intrastate TRS offerings are supported by the interstate TRS Fund. The current TRS Fund Administrator is the National Exchange Carrier Association (NECA). Although NECA directly manages the Fund, the FCC sets the Fund size and carrier contribution factor annually and is ultimately responsible for Fund oversight. When the TRS Fund started, it disbursed about $31 million, growing to over $38 million by 1999. Since 1999, the OIG report states that the TRS Fund has increased approximately 50-80% each year, to reach $637 million for the Fund’s fiscal year from July, 2007 to June, 2008. The size of the fund for the current fiscal year is $850 million, a 26% increase over the previous fiscal year. That is, in roughly ten years the TRS Fund has ballooned from $38 million to $850 million! What, if any, other communications service has seen 50-80% growth in costs per year?

Indeed, that is a shocking degree of waste and inefficiency by just about any standard. And Esbin goes on to document specific examples of this waste and inefficiency in action within the TRS Fund. It’s shocking stuff and doesn’t make for pleasant reading if you care about good government.

Barbara is actually much more tempered and tolerant than me when it comes to what to do about all this. She recommends a lot more reform and oversight. If you ask me, however, then entire USF program should be dismantled immediately and any future support deemed necessary should be distributed directly to consumers at the state level in the form of a welfare payment. After all, at root, that’s what universal service is: a communications industry welfare program, but one in which most of the support flows to companies instead of individuals. And that makes it one of the most insanely misguided and inefficient regulatory / subsidization systems known to man. 13 years ago, in one of the very first things that PFF ever published ( The Telecom Revolution: An American Opportunity) I was advocating exactly this sort of a plan along with a dozen other think tank colleagues. (And we also set forth another, less radical reform plan than the “voucher-ize & devolve” plan I favored).

But no one listened. Business as usual continued. And so the endless waste and inefficiencies continue. Somebody will have to remind me how any of this benefits consumer welfare. I can’t see how anyone could make such a case, and I would hope the USF follies serve as a cautionary tale for how the best of intentions are meaningless when it comes to what regulation actually means in practice. Because it sure ain’t pretty.

But hey, it’ll all be different going forward right? We just need to have faith in the media reformistas and the Net neutralitistas.  If we click our heels together enough time and just wish hard enough, all our dreams can come true.

Sure.

As TLF readers may know, I took over in July as Chairman of the Board of the Space Frontier Foundation.  As I explained in my recent interview on The Space Show, SFF has been the leading citizens’ advocacy group for space commercialization since 1988.  Dedicated to promoting Princeton physicist Gerard O’Neill‘s vision of space settlement, as described in his 1976 masterpiece The High Frontier, the Foundation has always argued that “space is a place, not a program.”

We sent out the following press release on October 28, calling for a major transformation of the U.S. government’s space program by which the U.S. government would buy commercial transportation to the International Space Station.  We’ll have more to say about this in the coming weeks.

Space Frontier Foundation Finds Funding Source for COTS-D

The Space Frontier Foundation today called upon Presidential candidates Barack Obama and John McCain to invest the $2 billion in new funds they have promised to NASA for reducing the “Gap” in U.S. human spaceflight (after the Space Shuttle is retired in 2010) to spur innovation and competition in America.

Foundation Chairman Berin Szoka said “It’s time that our national leaders give American entrepreneurs a shot at closing this gap. Let’s take the two billion dollars in the candidates’ plans and fund up to five winners of COTS-D.”

The NASA Authorization Act of 2008, recently signed into law by the President, directs NASA to “issue a notice of intent [by mid-April 2009] … to enter into a funded, competitively awarded Space Act Agreement with two or more commercial entities’ for transporting humans to the ISS”-the “Capability D” of NASA’s Commercial Orbital Transportation Services program (or COTS-D for short). But that directive is not yet funded.

Szoka continued, “Let’s have an American competition in space – to create good jobs, fuel innovation, and close the gap more quickly. With private funds matching government’s investment, we can dramatically leverage the $2 billion to produce breakthroughs in a new American industry – commercial orbital human spaceflight.”

By investing in several different approaches, the government will win no matter who wins this new race, and also benefit from the resulting price competition.

Many American companies, including Boeing, PlanetSpace, SpaceDev, SpaceX, and t/Space have each previously submitted credible COTS-D proposals to NASA. Each of these firms has reached the semi-finals of one of the previous NASA COTS competitions. Increasing funding for COTS by $2 billion would allow NASA to fund all five of these promising companies’ proposals with COTS agreements, and in so doing, build redundancy into the human spaceflight capability available to NASA and other customers.

“It’s popular in Washington to use ‘The Gap’ to cynically justify continued funding of an expensive jobs program,” concluded the Foundation’s co-founder, Bob Werb. “We’re using ‘The Gap’ to advocate a policy that will bridge a gap that matters much more: the chasm between a dying government Human spaceflight monopoly and an emerging, free and competitive marketplace that can open the space frontier to everyone.”

The Federal Circuit significantly limited the patentability of software and business methods today.  Mike Masnick at TechDirt summarizes the holding of the case as follows:

the court has said that there’s a two-pronged test to determine whether a software of business method process patent is valid: (1) it is tied to a particular machine or apparatus, or (2) it transforms a particular article into a different state or thing. In other words, pure software or business method patents that are neither tied to a specific machine nor change something into a different state are not patentable.

I’m sure several of my TLF colleagues will have a great deal to say about this.   Tim Lee has already written about this on Ars Technica:

The Bilski decision, then, is a clear signal that the pendulum has begun to swing back toward tighter limits on software and business patents. However, it remains to be seen how far the court will go in this direction. Bilski was a relatively easy case. The applicant made little effort to hide the fact that he was seeking to patent a mental process, something the Supreme Court has clearly said is not allowed. Therefore, the Federal Circuit’s rejection of this patent doesn’t tell us how it will rule when confronted with software or business method patents that are tied more directly to a physical machine or a transformation of matter. And indeed, the Federal Circuit reiterated that some software and business method patents are valid, so we are unlikely to return to the near-prohibition on such patents that prevailed until the early 1980s.

Thoughts?

Debates about online privacy often seem to assume relatively homogeneous privacy preferences among Internet users.  But the reality is that users vary widely, with many people demonstrating that they just don’t care who sees what they do, post or say online.   Attitudes vary from application to application, of course, but that’s precisely the point:  While many reflexively talk about the “importance of privacy” as if a monolith of users held a single opinion, no clear consensus exists for all users, all applications and all situations.  

If a picture is worth a thousand words, this picture makes the point brilliantly—showing:

locations where [Flickr] users are more likely to post their photos as “public,” which is the default setting, in green. Places where Flickr users are more likely to put privacy controls on their photos show up in red.

Of course, geography is just one dimension across which users may vary in their attitudes about privacy, but the map makes the basic point about variation very well.  Seeing what users actually do in real life says a lot more about their preferences than merely polling them about what they think they care about in the abstract—as my colleagues Solveig Singleton and Jim Harper argued brilliantly in their 2001 paper With A Grain of Salt: What Consumer Privacy Surveys Don’t Tell Us (SSRN).

Google has just announced that it is now accepting applications from undergraduate, graduate and professional students for its summer 2009 Google Policy Fellowship.  Three think tanks employing TLFers are among the host organizations participating in the program: The Progress & Freedom Foundation, the Cato Institute and the Competitive Enterprise Institute. 

Applications are due by December 12, 2008.  The program will run for ten weeks during the summer of 2009 (June-August). Apply today!

The Progress & Freedom Foundation has just launched the new Center for Internet Freedom.  CIF offers an alternative to the proliferation of advocacy groups calling for government intervention online by offering timely analyses and critiques of proposals that diminish the vital role of free markets, free speech and property rights.  We aim to drive the Internet policy debate in new directions by emphasizing a layered approach of technological innovation, user education, user self-help, industry self-regulation, and the enforcement of existing laws consistent with the First Amendment.  Such an approach is a less restrictive—and generally more effective—alternative to increased regulation.  

Here are some of the issues I’ll be working on as CIF’s Director in conjunction with my esteemed colleagues Adam Thierer, Adam Marcus, and adjunct fellows: 

• Defending online advertising as the lifeblood of online content & services, especially in the “Long Tail”;
• Emphasizing market solutions to problems of privacy protection, especially regarding the use of cookies and packet inspection data;
• Protecting online speech and expression both in the U.S. and abroad;
• Defending Section 230 immunity for Internet intermediaries;
• Opposing online taxation and legal barriers to e-commerce and digital payments, especially at the state and local levels; and
• Ensuring that Internet governance remains transparent and accountable without hampering the evolution of the Internet.

Congress has very wisely cancelled the National Reconnaissance Office’s proposed Broad Area Space-Based Imagery Collection (BASIC) satellite system. The proposal to build two new imaging satellites at a cost to taxpayers of $1.7 billion would have represented a major break from what is possibly the U.S. government’s most successful effort to promote space commercialization to date: buying the imagery it needs from commercial providers, who can also sell imagery to other buyers.

Five years ago, the idea that Internet users could pull up a satellite image of just about any location on the planet at a whim would have seemed ludicrous. Yet that’s precisely what websites like Google Maps and Microsoft’s Live Search offer today—for free! Desktop applications like Microsoft’s Virtual Earth and Google Earth offer even more advanced geospatial tools—again, for free. But of course this library of incredibly rich imagery didn’t just “fall out of the sky,” as they say. It was collected by a handful of expensive commercial remote sensing satellites whose construction was made possible by the National Geospatial-Intelligence Agency‘s (Wikipedia) extraordinarily successful “Nextview” program implemented under the Commercial Remote Sensing Policy of 2003.  Rather than having the Federal government build its own satellites—and pay for the entire cost of the satatellites—the NGA very wisely chose to buy imagery from commercial providers in two ~$500 million, 4-year contracts with U.S. satellite imagery companies:  DigitalGlobe in 2003 and OrbImage (now GeoEye) in 2004.  

These long-term purchase agreements essentially made the U.S. Government the “anchor tenant” in a new class of remote sensing satellites, providing the initial funding for both companies to build and operate their satellites. But because the companies sell roughly half of imagery to foreign governments and commercial buyers like Google and Microsoft, these deals have saved U.S taxpayers money for the purchase of imagery for a wide variety of needs, ranging from agricultural monitoring to military intelligence. At the same time, the Nextview contracts have given birth to a vibrant geospatial industry whose immediate benefits should be obvious to anyone who’s ever pulled up a satellite map online and whose macroeconomic impact is potentially enormous. 

So why mess with success?  If the U.S. Government thinks it needs more satellite imagery, why not simply award another long-term purchase agreement to a commercial provider? Besides reducing the burden on the taxpayers, continuing the NextView approach would support the construction of a new generation of commercial satellites like GeoEye-1, which was launched just last month, and DigitalGlobe’s WorldView-1, launched last year.  Rather than rolling back NextView in favor of building its own systems, the U.S. Government should be looking for other space services it can buy on a commercial basis as a way of building industries rather than programs, ranging from sending crew & cargo to the International Space Station to communications and navigation services for NASA’s planned Return to the Moon.

Rather than giving up on the NextView approach in the area where it has already produced spectacular results, the U.S. government should be looking for other areas in which to apply the NextView model by buying space services from commercial providers.

Full disclosure: I was proud to handle FCC matters for GeoEye while practicing law at Latham & Watkins LLP. I currently have no greater personal interest in their success than should any American who wants to see the private sector succeed where the government has failed in opening up the space frontier to all mankind.

By Berin Szoka & Adam Thierer Progress Snapshot 4.19 (PDF)

Since the fall of 2008, a debate has raged in Washington over “targeted online advertising,” an ominous-sounding shorthand for the customization of Internet ads to match the interests of users.  Not only are these ads more relevant and therefore less annoying to Internet users than untargeted ads, they are more cost-effective to advertisers and more profitable to websites that sell ad space.  While such “smarter” online advertising scares some—prompting comparisons to a corporate “Big Brother” spying on Internet users—it is also expected to fuel the rapid growth of Internet advertising revenues from $21.7 billion in 2007 to $50.3 billion in 2011-an annual growth rate of more than 24%. Since this growing revenue stream ultimately funds the free content and services that Internet users increasingly take for granted, policymakers should think very carefully about what’s really best for consumers before rushing to regulate an industry that has thrived for over a decade under a layered approach that combines technological “self-help” by privacy-wary consumers, consumer education, industry self-regulation, existing state privacy tort laws, and Federal Trade Commission (FTC) enforcement of corporate privacy policies.

In an upcoming PFF Special Report, we will address the many technical, economic, and legal aspects of this complicated policy issue-especially the possibility that regulation may unintentionally thwart market responses to the growing phenomenon of users blocking online ads.

We will also issue a three-part challenge to those who call for regulation of online advertising practices:

1. Identify the harm or market failure that requires government intervention.
2. Prove that there is no less restrictive alternative to regulation.
3. Explain how the benefits of regulation outweigh its costs.

The Online Advertising Market

While there are other forms of targeted advertising based on who you are (“demographic”) or where you are (“locational”), the most important varieties are based on what you’re searching for, seeing or doing online at any particular moment (“contextual”) and the pattern of what you’re searching for, seeing or doing over time (“behavioral”). The bulk of Internet advertising falls into one or both of these last two categories, with behavioral advertising growing rapidly.

Search engines deliver contextual ads on search results pages based on the search keywords entered by a user, while third-party advertising networks (some of which also run search engines) deliver contextual ads on behalf of website operators who sell ad space to the network, with the ads displayed on each page chosen according to keywords on that page. Contextual advertising is far “smarter” than displaying the same “dumb” untargeted banner ads to every user, because the contextual ad uses keywords to “guess” what the user is interested in based on the context of each page. But the purely contextual ad network doesn’t “remember” what the user has looked at in the past, so its insights into what the user would find relevant are very limited, especially for some websites. Online behavioral advertising (OBA) solves this problem and increases the value of advertising space on all websites by targeting ads based on a “profile” of the user created by tracking websites the user has visited—as well as limiting the number of times a user is shown a particular ad.

The Perceived Harm Driving Calls for Regulation

For a decade, the basic technology behind OBA has changed little: When a user visits the typical webpage, they download not only the webpage contents but also a small piece of code that allows the website to distinguish that user’s browser from other browsers (a “cookie”)—without personally identifying the user. Some cookies are required to make sites work properly (“site cookies”) while others (“tracking cookies”) are used by the third party ad network in which that site participates to recognize that browser across multiple sites participating in the ad network, and thus create a “profile” of what the user might be interested in. Even though such profiles themselves are anonymous, many privacy advocates have pointed to four reasons why online profiling is becoming “too invasive:” (i) It is sometimes possible to infer the actual identity of the user; (ii) though all browsers allow users to opt-out of tracking by “cleaning out” their tracking cookies, a website may be able to restore deleted tracking cookies through the use of cookie alternatives such as “Flash cookies”; (iii) certain vulnerabilities in current browser design make it theoretically possible to “sniff” a user’s browsing history, cache or bookmarks; and (iv) the use of “packet inspection” by Internet Service Providers (ISPs) (instead of the use of cookies) to track online browsing amounts to illegal wiretapping.

The other concerns expressed by the advocates of regulation vary significantly. Some fear that browsing profiles could be captured by hackers, somehow associated with personally identifying information, and used for identity theft. These advocates demand limits on data retention as well as data security mandates. Others demand that users have access to their own profiles—a goal inherently in tension with data security. Most share a vague queasiness about “being tracked” and about advertising in general, while downplaying the effectiveness of self-regulation or user self-help.

Perhaps most legitimately, others fear that the real “Big Brother”—the government—will gain access to a “honeypot” of surveillance data that might be associated with individual users. A variety of other solutions have been proposed to what is, for the most part, a poorly defined problem, including a government-run “Do Not Track” registry to make it easier for users to block tracking cookies; mandating opt-in for some or all forms of profiling; and banning completely the collection of tracking data about sensitive subjects, cross-referencing of data sets, and use of packet inspection data for OBA.

The Less Restrictive Means: A Layered Approach

But how should policymakers decide which, if any, of these interventions are really necessary–or would even be effective? Ironically, those who demand immediate OBA regulation to protect user privacy are often the first to insist on less burdensome approaches whenever a policy “problem” involves purely non-commercial speech. For example, emphasizing personal and parental responsibility is often favored as the more sensible approach to dealing with free speech and child protection concerns. But, as Chapman University Law Professor Tom Bell has asked, why not apply the same standard across the board? Why not expect those especially privacy-sensitive users who object to OBA to do something about it? To the extent effective self-help privacy tools exist, they provide a means of solving policy problems that is not only “less restrictive” than government regulation but generally more effective and customizable as well. Why settle for one-size-fits-all solutions of incomplete effectiveness when users can quite easily and effectively manage their own privacy? Indeed, those who advocate personal responsibility and industry self-regulatory approaches to free speech and child protection issues should be advancing the same position with regards to privacy.

Fortunately, a wide variety of self-help tools and “technologies of evasion” are readily available to all users and can easily thwart traditional cookie-based tracking, as well as more sophisticated tracking technologies such as packet inspection. While cookie management tools that allow users to delete their cookies have been standard in browsers for some time, the latest generation of browsers incorporates far more advanced control over what kind of cookies browsers will accept from websites in the first place. Furthermore,  the extensible nature of modern browsers allows any freelance software developer who sees a way to improve a browser to do so by writing an add-on that “plugs in” to the browser using standard programming interfaces designed by each browser developer.  Many such add-ons are wildly popular, but even those users who never install a single one benefit from the acceleration of browser evolution made possible by add-ons.  We will be documenting examples of these tools in our upcoming Special Report and in an ongoing  series of blog essays.

The Benefits of Smarter Advertising

The “free” Internet economy is based on a simple value exchange: Users get access to an ever-expanding collection of content and services at no cost from websites that are able to generate revenue from “eyeballs” on their pages by selling space on their sites to advertisers, usually through ad networks. The smarter that advertising, the more free content and services it can support. This is the same value exchange that has supported free, over-the-air television and radio content for decades. The only difference is technological: Because websites can connect directly with the user, they need not rely on crude profiling tools such as Nielsen ratings.

There are larger economic benefits of smarter online advertising. First, it makes the overall economy more open and competitive by allowing small market entrants to reach consumers with messages about their products. Second, those who attack the use of packet inspection by ISPs for OBA fail to see that it is precisely the kind of “game-changer” that could disrupt Google’s currently dominant market position. Third, the involvement of ISPs in OBA could help defer broadband costs: Even if OBA revenue does not completely subsidize monthly service costs, smarter advertising could at least keep prices in check and potentially lower them significantly going forward.

But smarter advertising isn’t just about selling products or services. It is ultimately about making all kinds of speech more cost-effective. The ability to “target” listeners more narrowly also increases the ability of political and other not-for-profit speakers to communicate their messages. In short, smarter advertising means more voices, more choices, and more speech. The line between “advertising” and “content” is already blurring rapidly, as the technologies used to customize advertising are also used to customize webpages and ad networks themselves are used to deliver content.

The Larger Implications of Potential Regulation

As if reducing the advertising revenue generated by each web ad didn’t do enough to reduce the total amount of funding for free web content and services, government regulation of targeted online advertising could reduce advertising revenues even further by aggravating the problem of adblocking in two ways. First, the less relevant ads are, the more annoying users will find them, and the more likely users are to try to block them. Increased relevance is perhaps the most important remedy for adblocking and the best way to maintain the implicit value exchange that currently supports free Internet content and services

Second, regulation could short-circuit the eternal battle of technological one-upmanship between online advertisers and those users who rely on the technologies of evasion to “opt-out” of seeing ads or being tracked. Such privacy-conscious users are “free-riding” off of those users who don’t opt-out, since (at present) they generally don’t lose access to the free content and services supported by the targeted advertisements that other users do see. The user who blocks tracking, but not ads, is still free-riding off those users who don’t opt-out of tracking. On a large enough scale, such self-help has the potential to disrupt the value exchange of the Internet, just as automatic commercial-skipping has already disrupted the value exchange of television. As with all “Spy v. Spy” battles, this long-term trend is inevitable: As more sophisticated technologies of evasion are incorporated seamlessly into browsers and can be used without significantly degrading the browsing experience, their use will become increasingly mainstream. But ultimately, just as with television commercial-skipping, market forces can and will, if permitted, respond through technological means and the development of new business models. Today’s implicit quid pro quo may become, of necessity, explicit: Websites and ad networks will have to find increasingly creative ways to grant access to certain content and services for users who do not block ads or the tracking that makes ad space more valuable. Policymakers should take care not to ban such technologies or cripple such business models (e.g., through requiring opt-in), which may rely on more sophisticated forms of targeting such as the use of packet inspection data.

As users face an increasingly clear choice between (i) getting content and services for free supported by behavioral advertising and (ii) paying to receive those same services and content without tracking or even without ads altogether, policymakers will finally see whether users are really as bothered by profiling as the advocates of OBA regulation insist. Given the ongoing and widespread replacement of fee- or subscription-supported web business models with ad-supported models, it seems likely that the vast majority of consumers will continue to choose ad-supported models, including profiling.

Conclusion

The questions raised above—about the harm that supposedly requires intervention, the availability of less restrictive means, and the cost/benefit analysis of regulation—are vital considerations for the future of the Internet. Indeed, if smarter online advertising will not fund the Internet’s future, what will? As both the desire for “free” services and content and the need for bandwidth expand, OBA has the potential to offer important new revenue sources that can help support the entire ecosystem of online content creation and service innovation, while also providing a new source of funding for Internet infrastructure and making ads less annoying and more informative. That would certainly seem preferable to increased user fees or other “pay-per-view” pricing models for Internet content and services.

But looming legislative and regulatory action could stop all of that by replacing the current regime—in which the FTC merely enforces industry self-regulatory policies—with one in which the government preemptively dictates how data may be collected and used. The more enlightened approach is a “layered” approach to privacy protection that combines industry self-regulation, enforcement of industry-established privacy policies, consumer education, and user “self-help” solutions. These and other issues will be addressed in greater detail in our upcoming PFF Special Report.