Communities of users, professionals, and small entrepreneurs are typically found toward the top of the stack, receiving services from lower layers. They flourish when uncertainty is high but the economies of mass are weak and where innovation comes through many small, seat-of-the-pants, trial-and-error bets. Community members often innovate for their own use or amusement, and sharing or selling to peers is an afterthought. Uncoordinated, autonomous agents compete and collaborate. They chase a million dead ends, then flood the occasional success with a million tweaks. They vary in just how “communitarian” they are: at one extreme, small developers competing to write apps for the iPhone without much, if any, shared social capital; at the other, Linux hackers or Wikipediasts collaborating to build a massive body of shared intellectual property.

Infrastructure organizations are typically found at the bottom of the stack, since they provide services to other layers without receiving services in return. They are most useful when uncertainty is low and economies of mass (specifically scale) are overwhelming. Thus, long-lived monopolies, utilities, or coops. Their core competence is in long-term, numbers-driven capacity management. Their mission is to be efficient and maximize access. It is not, in general, their job to innovate, though they may deploy successive generations of technology innovations generated by others.

Curatorial platforms, narrowly defined as organizations that exist solely as hosts for communities, are a hybrid. In the stack, they lie immediately below the community they curate. Often they start as an innovation by some community member. But they can grow into something resembling infrastructure, sometimes with stunning speed. Where social capital is negligible, the platform becomes a marketplace or a tech standard. Where social capital matters, the platform is rule setter and cultural arbiter, legitimate by grace of the community it supports.

Traditional oligopolists occupy the broad middle of the stack. They have the advantage when uncertainty is high but not incalculable, and economies of mass (scale, scope, and experience) are significant but not overwhelming. They exploit economies of scale and scope by placing big bets on technologies and facilities. They make incremental improvements in products and processes.

A company can participate in any of the four layers in a stack. Traditional oligopolists are companies by definition. Curatorial platforms may be nonprofits such as Wikipedia.org, but also corporations such as Facebook and InnoCentive. While some infrastructure organizations are owned by governments or municipalities, others are for-profit corporations, such as Amazon Web Services. Companies can even participate in communities as small ventures or venture capitalists, or indirectly by encouraging employees to contribute to projects such as Linux.

But what cannot be emphasized too much are the differences among these four types of activity. They require different skills and motives, present different financial profiles to investors, and need to be managed on different time horizons. A company can flourish in multiple layers—Amazon does it—but most organizations consistently underestimate the enormous challenges. Decades ago, in its evolution from mainframes to PCs, the computer industry moved from an oligopolistic to a stacked architecture. The Internet industry has had that architecture from the beginning, because the stacked architecture of the technologies served as a template for the stacked architecture of the institutions (corporate and noncorporate) that exploited them. The media industry is evolving painfully toward that structure. So is telecommunications. So is electrical power. So is transportation. So must health care. And every business that impinges on these sectors, as supplier or customer, has a profound stake in this evolution.

Much of what is broken in today’s economy stems from activities pursued with the wrong model.

Implications for Executives

IT IS FASHIONABLE (and correct) to assert that business leaders need to worry about disruption. But disruption takes very specific forms, and these forms are shifting. The disruptive impact of deconstruction—like that of low-cost technologies—is now widely understood, but the challenge of the very small, less so. And the challenge of the very large, hardly at all. Put them together and you pass from the familiar world of value chains to the world of platforms, ecosystems, and stacks. Extend that to the limits of ubiquity, and you enter the strange universe imagined by Borges.

So leaders need to focus on asymmetrical rivals and unlikely allies, on hackers and hobbyists, on rooftop solar panels and 3-D printers. They must also adapt their strategies to the possibility of shared infrastructure, to data that wants to be big, to the implacable embrace-and-extend bear hug of Google and Amazon and the National Security Agency. Conventional business models may be simultaneously too big and too small.

How should executives respond? Here are the four major drivers of the new industrial architecture and the key strategic imperatives for companies.

1. BIG DATA

Test your current analytics against the state of the art.

The field is moving so quickly that even well-versed companies can fall behind. There is currently a gold rush of new analytical methods: banks pricing mortgage collateral are adopting relational factor-graph techniques to predict the interdependence of adjacent property values; retailers focused on data-driven marketing are applying probabilistic graphical models to social-network data. Traditional spreadsheet methods are being applied at orders-of-magnitude larger scale, requiring new computer engineering even when the logic is unchanged. New data sources are becoming available. Families of problems trivial at small scale become noncomputable at large scale, so algorithms—successive guesses—substitute for closed-form solutions. Real-time computation replaces batch processing. Short cycles of experimentation and validation replace elaborate market tests. Organizations capable of all this will be ones in which business managers, programmers, and mathematicians talk each other's languages, where small teams iterate in fast cycles, where empirical validation counts for more than the judgments of hierarchies or senior executives.

Consolidate databases across the company.

Big data yields advantages from scope as well as scale, so siloed, business-unit-specific databases are quickly becoming antiquated. Data sets have value well beyond the silos within which they originate, but few companies can integrate their data across product lines or between online and offline channels. Tesco famously targeted promotions to members of its Clubcard loyalty program by developing an integrated understanding of buying patterns across households, time, and points of sale. Woolworths in Australia has used retail-shopping patterns to predict financial risk. It found that customers who drink lots of milk and eat lots of red meat are significantly better auto-insurance risks than customers who drink spirits, eat lots of pasta and rice, and fill their gas tanks at night.

Form partnerships to gain scale.

Given Tesco’s head start, its archrival, Sainsbury's, faced long odds in trying to catch up by playing the same game. So it outflanked Tesco on scope. It formed Nectar: a loyalty card shared with UK retailers such as BP, Homebase, and Argos—and operated by a third party called Loyalty Management Group. Consumers got the benefit of more points redeemable at more outlets, and retailers got the benefit of a wider set of behavioral data. The power of such aggregation lies in the million nonintuitive relationships between things like eating rice and driving safely. The value for sellers lies in more efficient promotion, and for buyers, in messaging that feels less like shrill coercion and more like helpful advice. Done with consideration for the consumer, this can be a win-win.

Manage data as a trustee.

Personal data collected by businesses cannot be treated as mere property, transferred once and irrevocably, like a used car, from data subject to data user. Data sharing will succeed only if the organizations involved earn the informed trust of their customers. Many such arrangements today are murky, furtive, undisclosed; many treat the data subject as a product to be resold, not a customer to be served. Those businesses risk a ferocious backlash, while their competitors are grabbing a competitive advantage by establishing trust and legitimacy with customers.

2. DECONSTRUCTION

Reorganize your business along its economic fault lines.

Define organizational units by their distinct competitive economics in their layer of the stack, and manage these units for standalone competitive advantage. Even if the strategy is to remain traditionally vertically integrated, this will give your managers a clear view of the threats they face and free them to compete as fiercely as any upstart. Never subordinate the competitiveness of one operation to the interests of another. Amazon functions at many different layers of a complex stack, but each part targets competitiveness on a standalone basis.

Look for opportunities to be the lateral aggressor.

Consider one example: the automotive and insurance industries are colliding. How and where a car is driven is the best predictor of the incidence and severity of accidents. For a few years now, innovative insurers such as Progressive have offered “black boxes” that track driving behavior and enable the company to undercut competitors in pricing policies for the best drivers. But cars are rapidly becoming computers on wheels for other reasons: the Ford Fusion contains 74 sensors, and each year’s model records and interprets more data: time and place, the identity and posture of the driver, seat belt usage, tire pressure, sharp braking, lane changes. All this data is uploaded to the mechanic and to services such as GM’s OnStar. That means the OEMs will own the most detailed underwriting data, across all drivers (not just the self-selecting best), at zero incremental cost. The separate black box will disappear as the OEMs realize they can suck up all that data—and so much more—and use it to take the insurers out of the game. The OEMs have the opportunity to think “outside the black box” and become a lateral aggressor.

Identify where your value chain is most susceptible to lateral attack.

With their actuarial tables and even their black boxes rendered obsolete, how can traditional car insurers survive? The first (and hardest) step is to recognize the problem five years before it hits. First movers that acquire the lower-risk drivers will be able to hold onto them. In many countries, regulators will mandate that consumers have access to their own data, so insurers will not be out of the game, but rather competing on a level playing field. To win, they need to build advantage in other layers of the stack: the analytics that interpret the data, claims adjustment, cross-selling customer service. Even in countries where OEMs own the data, there will be major elements of the business in which they will have little interest. That suggests an ecosystem with alliances among insurer, network provider, and OEM. Players should begin to position themselves today.

The same challenges and strategies—for both aggressor and incumbent—apply for many businesses.

3. POLARIZATION OF ECONOMIES OF MASS

"Up-source" activities to a community.

Digital communities are able to perform many tasks cheaper and faster than companies can. Customers provide free reviews for Amazon and perform crowd-sourced technical support for Cisco and several telecommunications companies. They do it out of a mixture of altruism, ego, and self-advertisement. Innovation contests with dollar prizes—such as GE's Ecomagination Challenge, Netflix's contest to improve its recommendation algorithm, or those posted on the InnoCentive platform—help companies accelerate the pace of innovation while decreasing the cost. The application programming interfaces provided by companies like Google and some telecommunications providers enable communities of entrepreneurs and programmers to create new applications quickly and cheaply by "mashing up" data streams. This drives users and metadata to the platform provider.

"Down-source" activities to shared infrastructure.

In mobile telecommunications, for example, there are significant scale economies at the bottom of the technology stack. In France, SFR and Bouygues Telecom have begun to share their infrastructure of towers and masts in lower-density service areas, allowing them to remove some 7,000 towers. Each company continues to compete with its own transponders. In the UK, carriers EE and Three share towers, masts, transponders, and backhaul, while larger rivals Vodafone and O2 have a passive sharing arrangement similar to the French plan. In Sweden, Telenor and Tele2 even share spectrum. In all these arrangements, competition is diminished in the lower layer of the stack, but the level playing field intensifies competition in the upper. There is additional complexity and some coordination costs in this kind of joint venture, but that is offset by the increased utilization of fixed assets. In the UK, these arrangements are forecast to save about £1 billion per year.

4. HOLISTIC, STACKED ARCHITECTURES

Curate a new industrial stack.

In light of evolving technologies, reevaluate your value added from first principles. To take just one example: imagine smart agriculture as a stack. Cheap, meshed sensors measure the temperature, humidity, and acidity of the soil; active repeaters embedded in agricultural machinery or in cell phone apps capture, aggregate, and relay the data; data services combine this local data with aggregate models of weather and crop prices; other services tap into their APIs to optimize planting, irrigation, fertilizing, and harvesting. Farmers collect the data, share in the aggregation and pattern recognition, and follow prescriptions that give them a better yield on their crops. Such an ecosystem creates social and private value in both developed and developing economies. For large agribusinesses, this is a major opportunity that poses no challenge to the business model. But where farming is fragmented, these technologies scale beyond the reach of individual farmers. The opportunity is therefore wide open—to governments, NGOs, processors of fertilizer, and builders of agricultural machinery—to orchestrate a new industrial stack.

Many industries could be reconceptualized along these lines by participants with the necessary resources, strategic insight, and imagination.

Where you can't curate your own stack, seek advantaged roles in stacks curated by others.

Every company wants to be the master of its own fate, but not all have the scale and scope to be orchestrators. The "smart" home, for example, is a vision of how thermostats, motion detectors, lighting, home theater, door locks, appliances, phones, and tablets will act and interact intelligently. There are immense benefits in convenience, safety, and cost savings there, but adoption has been stymied by balkanized, overpriced systems that use different control pads and interfaces, run on different wired and wireless networks, and cannot talk to each other. Google, with its recent acquisition of Nest, and Apple, with its launch of HomeKit, are building stacked architectures for granular integration of the various subsystems of the smart home that will allow homeowners customization and increased efficiency. It is not clear how this battle will play out, but the implications for other players are evident and imminent: they must hedge their bets and focus on defensible niches.

For power utilities, to take one example, this is bitter medicine. Although energy savings is one of the biggest benefits of smart homes, the logic for vertical integration by the utility is weak because the price of power is just a number. Utilities have been conspicuously unsuccessful in their attempts to play orchestrator of the smart home. Their biggest advantage is in the field, in installation, maintenance, and repair. They have a new opportunity to exploit their knowledge of grid behavior, neighborhood consumption patterns, and signals from smart-home devices to detect and anticipate mechanical failures in homes. This will broaden and deepen their relationship with customers, increase utilization of the field force, and ultimately reduce customer churn. They are better off fitting into a niche than trying to curate an ecosystem of their own.

Reshape regulation.

The logic of stacks has massive implications for the philosophy of regulation—and requires that both businesses and regulators think differently. Traditional metrics such as market power are insufficiently nuanced in an environment of polarizing economies of mass. Companies have a huge stake in how this thinking evolves, and they can and should influence policy in directions that favor efficiency at the bottom of the stack and open innovation at the top.

In 1945, John von Neumann, one of the greatest mathematicians of the twentieth century, wrote a paper describing a “Turing machine” that made no distinction between its data and its instructions to process that data. This so-called von Neumann architecture became the design of the digital computer: treating data and code as one.

Just a few months later, Argentinian writer Jorge Luis Borges penned the one-paragraph story at the top of this article, recounting how a lost empire became its own map. Borges imagined reality becoming a description of itself. His map and reality, like von Neumann’s data and code, are indistinguishable.

How exquisite that these two extraordinary visions, one from a supreme scientist and the other from a supreme fabulist, were formulated almost simultaneously. Now, through three waves of digital disruption, technology is finally catching up with both. Executives in the next decade must chart their course through the labyrinth of Borges’ map.

