Infinite Content: Chapter 14
Eleven Principles for Navigating Our Infinite Content Future
This is the draft fourteenth and final chapter of my book, Infinite Content: AI, The Next Great Disruption of Media, and How to Navigate What’s Coming, due to be published by The MIT Press in 2026. The introductory chapter is available for free here. Subsequent draft chapters were serialized for paid subscribers to The Mediator and can be found here.
Over the last couple of years, I’ve spoken to many companies about the ideas in this book. Before one of my first speaking engagements, I sent a draft presentation to my contact. “This is all good and thoughtful,” he said, the ‘but’ hanging in midair, “but I want people to leave feeling motivated and empowered to act. Reading through this, my concern is that they might feel overwhelmed by the amount of change and uncertainty and just shut down.” I have taken that comment to heart ever since.
This is the last chapter. The grand reveal! All the answers! As if. I wish I could provide a bunch of comfortingly definitive conclusions. I can’t.
One of the recurrent themes of this book is that there is still much we don’t and can’t know about our infinite content future. In Chapter 10, I wrote about the known unknowns of GenAI in media. There is a long list. But even that list overlooked the unknown unknowns. Is some new breakthrough technology around the corner? Regulation? Social movement? What happens if AI evolves enough that we have recursive, self-improving systems that train themselves?
Besides the uncertainty, the pace of change has also been startling. I started sketching out this book two years ago. So much has changed since then. Even if it is your job to stay on top of the latest models, applications, deals, and case law, it often feels impossible to keep up. If you have other things to do during the day, like going to meetings or answering email, forget it. Given the massive human and financial capital being thrown at AI, and the ethos in the AI researcher community to open-source breakthroughs, it won’t likely slow down.
So, what do you do? If you are a creator, creative, executive, vendor, agency, advertiser, investor, or policymaker, how do you not get overwhelmed and shut down?
The only option is to lean on what is enduring amidst all this change. In the opening chapter, I wrote that the future of the media business will be determined by what happens at the intersection of physics, economics, and human nature. Regardless of what happens with AI, none will be repealed. Those disciplines can serve as the ballast as we navigate what’s coming.
Below, I lay out 11 principles for plotting a course through our infinite content future, rooted in those three foundational disciplines. (I originally set out to write about 10, but, like Nigel Tufnel’s amp in This is Spinal Tap, this one goes to 11.) Speaking of 11, recall that in Chapter 11, I laid out scenarios around the two key known unknowns—technology development and consumer acceptance. The principles in this chapter are meant to hold across all of them, including the most disruptive (“Infinite Content”).
Before jumping into the principles, let’s ground ourselves in what I mean by physics, economics, and human nature. They all pivot around a central idea that I introduced in the prior chapter: technology, culture, society, and markets are all complex adaptive systems.
Physics
The word “complex” in complex adaptive systems is a little intimidating, but there is a science of complexity and much is understood about how these kinds of systems work. So, by “physics,” I mean the structural dynamics that govern how these systems behave. Understanding them makes it possible to both sketch out how the system might evolve and, in some cases, influence that evolution.
Complex (i.e., non-adaptive) systems are systems with a lot of moving parts, often including nested hierarchies of subsystems, with many interdependencies among them. They are not just unpredictable in the colloquial sense (as in “hard to guess”), they are inherently impossible to predict. There are too many interactions between the components and overlapping and competing feedback loops. They are also path dependent, as I wrote last chapter; outcomes depend on the sequence. Take the weather. We know a lot about how weather systems interact, but it is still impossible to predict weather with precision more than a few days out. If you slowly pour sand in a pile, it will pile ever higher until the top suddenly shears off in an unpredictable way. There is no way to know with certainty beforehand which will be the offending grain.
Even though complex systems are unpredictable, their behavior isn’t random. It is shaped by the constraints in the system (the scarcities and bottlenecks) and the interaction of those positive and negative feedback loops. These dynamics determine how resources flow across the system.
Complex adaptive systems are even harder to predict, because the components of the system—or “agents”—exchange and process information, learn, and continuously adapt. So, the behavior of the system is affected by another variable: the choices of the individual agents. As physicist Murray Gell-Mann put it, “Think how hard physics would be if particles could think.”
Economics
Markets are a specific kind of complex adaptive system in which the agents are humans. The behavior of markets is also shaped by constraints, feedback loops, and the decisions of the agents. Within those boundaries, the collective actions of the agents allocate resources—like time, attention, capital, knowledge, labor—toward the highest perceived return.
Because the agents are constantly learning and adapting, markets aren’t static. As I argued in Chapter 1, that’s why their default state is not equilibrium, but churn. When a shock alters the underlying constraints and changes what is relatively scarce and abundant—such as through new technology, regulation, or a supply shift—the system reorganizes around the new constraints.
Human Nature
Lastly, we come to the “agents,” people. If you know any people, or have met any by happenstance, you may have observed that they often seem complicated and inscrutable. That is probably true of individual people, but en masse, people are much more scrutable, to use a rarely used word.
It may seem an overreach to lay out a framework for understanding how people make decisions in a few sentences, but let’s try anyway. Human decision making is driven by the interplay of biology (what is hardwired), values (intrinsic motivations), and incentives (extrinsic motivations).
Biology is powerful and hard to override, but our fundamental biological needs—cognitive efficiency, novelty-seeking, social belonging, identity, risk aversion—often pull us in conflicting directions, as I wrote last chapter. We want ease and stimulation. We want personalization and shared experiences. We want to seize opportunities and avoid loss. Which needs prevail at any point in time depends on context, which is part of what makes human behavior so hard to predict.
Values—by which I mean our beliefs about what is right and wrong, admirable and reprehensible—are like geothermal pressure: a powerful, but latent force. Most people would claim they have a strong value system, but values are also murky and the benefits of adhering to them are often opaque, so they are easily rationalized away and often overridden. When values strengthen, however, they can become a very important driver of decisions. That’s especially true when they fuse with identity.
Incentives are important expressly because our internal hardwiring and value systems are conflicting and confusing. Since our biological needs are in tension and our values are often vague or dormant, people frequently let the strongest, clearest signal break the tie: external motivations, or incentives.
Last chapter, I focused entirely on the first of these, since media choices are dominated by biology. But since we’re thinking through the behavior of human systems more broadly, below we’ll touch on all three.
The 11 principles below all emerge from these foundational disciplines. They are organized as a progression. We start with the right mindset for confronting an uncertain future. We move through the dynamics of the systems we’re operating in. We examine the people who make up those systems and how they decide. We then look at the strategic consequences. And we end with a call to action.
