From a certain perspective, Uber’s surge-pricing, which was again in the headlines this past New Year’s Eve, is easy to defend: the only way to balance supply and demand is to adjust the price. In fact, there is a lot of fundamental economic theory behind this simple explanation, specifically the idea of a “price mechanism.” A price mechanism (i.e. the surge pricing) has three functions:

• Signaling: A higher price tells suppliers to increase production, while a lower price tells suppliers to do the opposite; in the case of Uber surge prices signal drivers who might prefer to not work to that it is worth the inconvenience to get on the road
• Rationing: If supply is insufficient, a higher price reduces demand and ensures those who most want the good in question can obtain it; in the case of Uber surge pricing ought to compel many riders to take alternative modes of transportation or to wait until there is more supply and/or less demand, while those who need a ride right now can be sure they get one
• Transmission of preference: Signaling and rationing are all well and good, but what makes the price mechanism so amazing is that it ties the two together: disparate consumers inform disparate suppliers about how much supply is needed without the need for any coordination

Key to the price mechanism is money; while barter works, it carries a huge information burden: who can quickly and easily compare the value of a cow to the value of a bushel of grain to the value of a piece of pottery to the value of an Uber ride? It is much easier to have an intermediary that easily transmits relative value, which is why Uber rides are priced in dollars and cents and not in ounces of meat.

The end result is a system that ensures that those who need a ride are guaranteed to get one; those who really could do without self-select out of the system, at least until more drivers are compelled to increase the supply. It is much better than the alternative, where someone who could just as easily walk a couple of blocks might by pure chance grab the taxi needed by, say, a woman in labor. That’s an extreme example, but I use it to make the point: pricing ensures those who truly need a good can get it, and, on a holiday defined by champagne, we should all be grateful.

The Problem with Money

In the context of the price mechanism, money serves the role of a medium of exchange. The problem, though, is that money serves other functions as well: specifically, money is a unit of account and a store of value. It is the latter that is the rub when it comes to Uber and the idea of allocating rides based on price. To return to the extreme example above, what if the woman in labor is poor, and the person who only needs to travel a few blocks is rich? It very well may be that the latter’s ability-to-pay will trump the former’s willingness-to-pay; this is, to my mind anyways, the most valid reason to oppose surge pricing.

What, though, are the alternatives? As I noted, the current taxi system basically reduces rides to a lottery: you either get an empty taxi or you don’t.¹ That in itself is frustrating enough, but the bigger cost is the uncertainty of it all: if you are not sure whether or not you will be able to get a ride, you are less likely to depend on the ride service in question at all. This is especially problematic on occasions like New Year’s Eve: when those needing a ride are drunk, the last thing we as a society should hope for is that folks default to a ride that is guaranteed, i.e. their own car. And, frankly, those needing a ride should, in the grand scheme of things, be similarly grateful that surge pricing guarantees that rides are available: sure, an unexpected $200 fare is annoying, but a DUI with all its attendant cost is far worse (and that’s not even close to the worst-case scenario when it comes to driving drunk).

It is also not realistic to expect traditional taxi companies to have sufficient supply for high demand times like New Year’s Eve: the problem is that all of the supply necessary to fulfill peak demand would sit idle the vast majority of the time. That idleness has a very real cost — specifically, opportunity cost. Any resource, whether it be vehicular or human, that is devoted to one activity is by definition not devoted to another. That may not seem like much when it comes to a few taxis, but in aggregate this makes the “pie”, which is the total pool of economic resources available, smaller for everyone.

This gets at why Uber is a much bigger deal than any one New Year’s Eve: the way in which the service much more efficiently utilizes resources, both vehicular and human, actually grows the pie: indeed, the only possible way to grow gross domestic product is through increased efficiency, which frees up resources for new value-generating activities.

Still, what of the poor woman in labor?

In fact, the relative wealth of the woman in labor and the lazy rich person ought to have nothing to do with ride allocation at all: however, due to the fact that money works as both a medium of exchange and a store of value they are easily intermingled. The answer is to disentangle them; instead of ruining the brilliant mechanism by which rides are both distributed to those who signal the greatest need and through which resources are most efficiently allocated, It would be far better to focus on ensuring that everyone has the same opportunity to signal their preference. To contort Uber into a welfare provider is to ruin both.

A New Politics For a New World

At the heart of the Uber conundrum and its potential solution is a new political philosophy for technology. Mobile and ubiquitous connectivity have the potential to unlock efficiencies that were never before possible. Take taxis, to stick with the Uber theme: the justification for most taxi regulations were important ones like safety, dependability, and consumer protection. Given the fact that taxis would be out on the street unsupervised it made sense to tightly control entrance to the market. However, were it possible to address all those same concerns far more effectively, through, say, precise tracking and full histories of both drivers and passengers, as well as knowledge about pick-up and intended drop-off points, would not the regulations look significantly different?

Similarly, in a world where the key to building a sustainable business was controlling distribution, the greatest gains naturally accrued to the biggest companies. And, by extension, it was reasonable to ask those companies to not only pay their workers well, but to also provide for needs beyond salary, like health insurance and disability insurance. But do those same assumptions hold in a world where distribution is free, and where preferences and needs can be distilled to an individual or gig basis?

The money problem — the fact it is both a means of exchange and a store of value — is an allegory for the dysfunctional nature of what passes for a social safety net, particularly in the United States: things like health insurance and disability are intermingled with a salary or fee. This is problematic on both sides: new efficiencies that are unlocked through mobile and ubiquitous connectivity are not fully realized thanks to regulations from an era that operated on fundamentally different assumptions. This, ultimately, hurts everyone because it limits the growth of the economic pie,

On the other side are the people actually doing these new jobs, or those who would like to. Given the fact many social safety nets are built by traditional companies, those not in those companies are left completely exposed. This is unacceptable both morally and economically: morally because to deny healthcare or basic insurance is to deny the humanity of those in need; economically both because of higher costs incurred because of treatments not received, but especially because of the cost of opportunities not pursued for fear of having no net.

It would be far better — and a far better match for the reality of today’s labor market — to disentangle once-and-for-all employment from the social safety net. This should be the central political focus of technologists in particular. Outdated regulations forged under fundamentally different assumptions are one of the chief obstacles to the opportunities afforded by mobile and the Internet, particularly when it comes to the aggregation of consumers in markets that weren’t even imaginable 10 years ago.

What Technology Owes

Of course, to argue for less regulation is hardly controversial in Silicon Valley: what is missing is the necessary trade-off. Specifically, as the opportunities for technologists and their investors continue to grow, so should the willingness to pay: that pregnant woman still needs a ride.

This is where articles like Paul Graham’s weekend piece Economic Inequality ring hollow. Graham’s defense of the broad-based gains that accrue from new technology is absolutely correct: increased efficiency, which technology is uniquely suited to deliver, is the only way to grow the pie for everyone’s benefit. But given that much of those efficiency gains also contribute to winner-take-all dynamics, it is reasonable to expect that those winners — and their investors — pay commensurately more. Imagine if Graham had written his article accompanied with a call to close the carried interest tax loophole, which allows venture capitalists to be taxed at the (significantly lower) capital gains rate on money they themselves did not invest: his defense of getting rich — which wasn’t necessarily wrong! — would have had much more gravitas.²

Still, I’m glad Graham opened the debate. Technology is changing the world, and it is naive to not expect the world to begin to push back. Rather than always be reactionary, it is past time for the technology industry broadly and Silicon Valley in particular to get serious about what that world will look like in the future, especially given the fact there is actually a way forward that is a win for not just technology companies and their investors, but for those who are impacted — i.e. everyone. Just as we should separate the means by which Uber allocates drivers from the ability to pay for a ride, it makes sense to separate work from the provision of a social safety net, and those most able to capitalize on this new world order should be the most willing to pay.

1. And the driver has far greater discretion to discriminate based on appearance [↩]
2. Not to say this would be sufficient, but it’s a place to start that would mean more coming from someone like Graham [↩]

2015 was Stratechery’s third year, and the first one I spent completely devoted to it full-time. This year I wrote 47 free Weekly Articles and 180 subscriber-only Daily Updates. Given that most were between 1800 and 2000 words, that’s the equivalent of about 6.5 books!

Here are the highlights (here are the 2014 and 2013 editions):

The Five Most-Viewed Articles:

1. Why Web Pages Suck — Everyone complains about web pages that suck, but the reality is that it is advertisers who call the shots. This should, at a minimum, put Facebook’s Instant Articles and Apple’s News app in a new light
2. Why BuzzFeed is the Most Important News Organization in the World — The key to sustainable, ethical journalism is aligning the business and editorial sides of a publication. No company has done a better job of doing that on the Internet then BuzzFeed
3. Apple’s New Market — Apple is on the verge of leaving the narrowly-defined smartphone market behind entirely, instead making a play to be involved in every aspect of its consumers’ lives. And, if the importance of an integrated experience matter more with your phone than your PC, because you use it more, how much more important is an integrated experience that touches every detail of your life?
4. Twitter’s Moment — Twitter has had a rough stretch, and most are pessimistic about its chances. I was previously, but I think the upside is looking much brighter than it did before this week
5. Apple Watch and Continuous Computing — The Apple Watch’s success depends on three things: the physical design, the interaction model, and how it interacts with its environment. It’s on the right track

Five Big Ideas

• Aggregation Theory — The disruption caused by the Internet in industry after industry has a common theoretical basis described by Aggregation Theory
• Netflix and the Conservation of Attractive Profits — Netflix has a lot more in common with Uber and Airbnb than you might think: it all comes back to the Law of Conservation of Attractive Profits, a core principle of disruption
• Airbnb and the Internet Revolution — Airbnb gets less press than Uber, but in some respects its even more radical: understanding how it works leads one to question many of the premises of modern society from hotels to regulations. It’s an important marker in the Internet Revolution
• Beyond Disruption — Clayton Christensen claims that Uber is not disruptive, and he’s exactly right. In fact, disruption theory often doesn’t make sense when it comes to understanding how companies succeed in the age of the Internet
• The End of Trickle-Down Technology — Reaching developing markets depends on understanding that consumers with a small budget are very different from consumers who aren’t interested in spending much

Five Company-Specific Posts

• The Facebook Epoch — First came the PC, and on top of the PC the Internet. Then, mobile, but what will rule mobile?
• From Products to Platforms — Apple was at its best in its most recent keynote: unveiling the sorts of products the company is uniquely capable of creating. The question, though, is whether the company has the vision and capability of making those products into platforms
• The AWS IPO — AWS has long been a question mark when it comes to Amazon: it’s a good idea, and it makes money, but like it’s parent company, will it ever be profitable? The revelation that AWS is already very profitable indeed is a really big deal both for AWS but also for Amazon itself. (Related: Venture Capital and the Internet’s Impact)
• Old-Fashioned Snapchat — How Snapchat is positioning itself to win an outsized share of television’s brand advertising
• Slack and the State of Technology at the End of 2015 — Slack has announced the Slack Platform. It’s an obvious move, but it’s the obviousness that indicates what a huge opportunity it is

Five Posts About the Media Business

• Grantland and the (Surprising) Future of Publishing — ESPN’s decision to close Grantland seems to be more evidence that there is no future outside of massive scale or one-man operations. Bill Simmons’ recent successes, though, suggest that the answer could be the exact opposite
• Popping the Publishing Bubble — For years publishers haven’t had to worry about business models: they just captured attention and watched the money come in. Those days, though, are over: the publications that survive will start with business models and build journalism around it
• Verizon-AOL, Facebook Instant Articles, and the Future of Digital Advertising — Two seemingly unrelated stores — Verizon buying AOL. and Facebook offering publishers the option of Instant Articles — are actually very much connected: advertising online is undergoing a fundamental shift, and while there will be a few big winners, there will be a lot more losers. (Related: The Facebook Reckoning)
• The Changing — and Unchanging — Structure of TV — The way we get TV may be changing, but the importance and defensibility of great content will persist (Related: Why Disney and ESPN Will Be OK)
• Tidal and the Future of Music — Predicting success and failure is about understanding who has leverage. In the case of music, the winners will be the labels, not the artist. But perhaps there is hope for tomorrow. (Related: Apple Music and Apple’s Focus)

Five Daily Updates

(Please note that these are subscriber-only links; you can sign-up here)

• June 5 — Tim Cook’s Unfair and Unrealistic Privacy Speech, Strategy Credits, The Privacy Priority Problem
• August 17 — The New York Times on Amazon, Jeff Bezos’ Email, Why Work for Amazon
• August 31 — Ballmer’s Bad Bundle Economics, Netflix Loses Epix Movie Deal
• September 21 — Malware Hits iOS, The Importance of the App Store, XcodeGhost: What Happened and What Now?
• October 12 — AWS Re:invent, Pure Storage IPOs, Dell to Buy EMC; Enterprise Disruption; Dell’s Logic

Plus five more:

• October 27 — Chase Pay and the Payments Stack, Apple Pay and Opportunity Cost, Applying Aggregation Theory
• October 28 — Stop Doubting the iPhone, The Macintosh Company
• Novenber 17 — Marriott Acquires Starwood, Online Travel Agents and Aggregation, Surviving as an Incumbent
• November 20 — Adele Won’t Stream 25, Windowing Versus Piracy
• December 22 — SpaceX Makes History, SpaceX and Unicorns, Disney in the Age of Abundance

Happy New Year. I’m looking forward to a great 2016!

Last December I wrote an article entitled The State of Consumer Technology at the End of 2014. That article was more than a year-in-review though: in it I both defined the different epochs of computing — PC, Internet, and mobile — as well as the distinct arenas of competition within each epoch: the operating system and killer applications for productivity and communications.

The question I raised is what comes next?

The Facebook Epoch

The answer, at least when it comes to the consumer space (and excluding China) is Facebook. I laid out why in an article entitled, appropriately enough, The Facebook Epoch:

Mobile is a great market. It is the greatest market the tech industry, or any industry for that matter, has ever seen, and the reason why is best seen by contrasting mobile with the PC: first, while PCs were on every desk and in every home, mobile is in every pocket of a huge percentage of the world’s population. The sheer numbers triple or quadruple the size, and the separation is increasing. Secondly, though, while using a PC required intent, the use of mobile devices occupies all of the available time around intent. It is only when we’re doing something specific that we aren’t using our phones, and the empty spaces of our lives are far greater than anyone imagined.

Into this void — this massive market, both in terms of numbers and available time — came the perfect product: a means of following, communicating, and interacting with our friends and family. And, while we use a PC with intent, what we humans most want to do with our free time is connect with other humans: as Aristotle long ago observed, “Man is by nature a social animal.” It turned out Facebook was most people’s natural habitat, and by most people I mean those billions using mobile.

Note that in that piece I rearranged the epochs slightly: specifically, I defined the “Internet Epoch” — which was dominated by Google — as sitting on top of PCs, which meant the relative size of this epoch was constrained by the fact that PCs were, relatively speaking, not mobile; rather, both PC usage and Google uses was defined by intent. Mobile, and by extension Facebook, were different: their usage was defined not only by increased availability, but also by the “empty spaces” in our lives.

What, though, about enterprise computing? And what is the killer app when it comes to work and productivity on mobile?

The Reorganization of the Enterprise Stack

Microsoft has arguably dominated enterprise computing even more than they dominated the PC epoch generally, in part because they delivered an integrated solution that, to put it simply, made life easier for Chief Information Officer’s in particular. I wrote in Redmond and Reality:

Consider your typical Chief Information Officer in the pre-Cloud era: for various reasons she has bought in to some aspect of the Microsoft stack (likely Exchange). So, in order to support Exchange, the CIO must obviously buy Windows Server. And Windows Server includes Active Directory, so obviously that will be the identity service. However, now that the CIO has parts of the Microsoft stack in place, she is likely to be much more inclined to go with other Microsoft products as well, whether that be SQL Server, Dynamics CRM, SharePoint, etc. True, the Microsoft product may not always be the best in a vacuum, but no CIO operates in a vacuum: maintenance and service costs are a huge concern, and there is a lot to be gained by buying from fewer vendors rather than more. In fact, much of Microsoft’s growth over the last 15 years can be traced to Ballmer’s cleverness in exploiting this advantage through both new products and also new pricing and licensing agreements that heavily incentivized Microsoft customers to buy ever more from the company.

Microsoft’s dominance, though, has been chipped away via the one-two punch of the cloud and mobile. Cloud-based applications not only offered a payment model that was more attractive to many businesses, but they also removed the need for troublesome upkeep. This, then, allowed other aspects of the product to rise in relative importance when it came to the purchase decision, whether that be specific features or just the general user experience.

It’s here that mobile mattered: for years Microsoft products were well behind the competition when it came to the mobile experience on non-Microsoft platforms like iOS and Android. This is the one-two punch I was referring to: the cloud removed one of Microsoft’s biggest lock-ins in the enterprise, while mobile gave enterprises a reason to try something different.

The Cloud Epoch

Indeed, the way in which cloud and mobile worked hand-in-hand to uproot Microsoft is no accident. When it comes to the enterprise side of computing, I would place the cloud as the fourth epoch, and just as the Internet (or in the case of enterprise, on-premise applications) rested on PCs, the cloud very much rests on a mobile foundation: not only do all workers, blue collar or white, have a phone, but they also have that phone in more and more places, and the fact you always have your phone with you means you are, effectively, always available to work.¹

To Satya Nadella‘s credit, he has over the past two years strongly pushed Microsoft to be competitive on all those other platforms, and in fact the article I just quoted was written in the context of Office adding file picker support for Dropbox and Box, despite the fact both were direct competitors. The reality is that leveraging one piece of software to sell another is a strategy that simply doesn’t make sense in the cloud: there is no implementation advantage, so you have to simply compete on a feature and user experience basis.

Still, that doesn’t mean integration isn’t desirable: what will tie all of these cloud services together? Back in 2014 I theorized that the key player — the “OS” of the cloud epoch — might be whoever owns a company’s data, like, for example, Box:

Pure storage isn’t a great business. The cost is trending towards zero…Data, though, is priceless; it can’t be replaced, and it’s the essence of what makes a particular organization unique. For this reason, and for regulatory ones, there are all kinds of specialized controls that IT departments need for data. This is where Box has worked diligently to differentiate themselves from consumer-focused competitors like Dropbox…

Just because the operating system is no longer the platform does not mean that the need — and opportunity — for a platform does not exist. Something needs to tie together all those computing devices, and data, which needs to be everywhere, is the logical place to start.

I think, in retrospect, I outsmarted myself: companies aren’t made of data, they’re made of people, just like every other single institution on earth. And, as I noted in the context of Facebook, what people love to do, more than anything else in the world, is communicate. Why wouldn’t you start there?

Enter messaging broadly, and Slack (and it’s competitors like HipChat) specifically.

Messaging: The Cloud’s OS

I have been writing about the importance of messaging ever since this blog started, most notably in Messaging: Mobile‘s Killer App.² Messaging, in conjunction with mobile, is one of the most powerful platforms this industry has ever seen:

Still, it’s only recently that the killer app for this era, when the nodes of communication are smartphones, has become apparent, and it is messaging. While the home telephone enabled real-time communication, and the web passive communication, messaging enables constant communication. Conversations are never ending, and friends come and go at a pace dictated not by physicality, but rather by attention. And, given that we are all humans and crave human interaction and affection, we are more than happy to give massive amounts of attention to messaging, to those who matter most to us, and who are always there in our pockets and purses.

Those words are an awfully close match to the words I used to describe the cloud epoch just a couple of paragraphs ago: everyone, everywhere, always available. Indeed, combined with the human desire to connect and communicate, how could the operating system of the cloud be anything but messaging? This is what makes Slacks announcement yesterday of the Slack Platform so compelling — obvious, even.³ From the company’s blog:

We live in an exciting time for work. Instead of three or four big vendors providing end-to-end software suites, we have a variety of top notch products at our fingertips ready to make us more powerful and productive in our jobs. But all of these great products come with a small cost: the tools we use every day don’t always play well together. Progress and productivity can end up in silos instead of being reviewed and tracked by the whole team.

The Slack Platform aims to make your experience with apps even better. We know that just a fraction of improvement in everyday interactions between the business services you use makes a world of difference. And so today we’re taking a few bigs steps forward in bringing them all together.

Right now, the Slack Platform consist of a new Slack App Directory, already populated with over 160 apps, a Slack Fund, to invest in new apps, and Botkit, a new framework to easily build new apps. Just as important, though, is Slack’s business model of paid licensing: I’ve noted previously in the context of Facebook that advertising-based businesses don’t make good platform providers:

It’s better for an advertising business to not be a platform. There are certain roles and responsibilities a platform must bear with regards to the user experience, and many of these work against effective advertising. That’s why, for example, you don’t see any advertising in Android, despite the fact it’s built by the top advertising company in the world. [On the other hand,] a Facebook app owns the entire screen, and can use all of that screen for what benefits Facebook, and Facebook alone.

That’s actually ideal for a consumer-focused company: after all, consumers don’t pay for software. Enterprises, though, are a different story: they don’t tolerate advertising, and are eager to pay for a service that provides a real return on investment. Indeed, going forward, outside of Apple (which makes money through selling hardware), I suspect the vast majority of profitable platforms will be primarily enterprise-focused.

Slack’s Opportunity

That said, it’s hard to see anyone — including Microsoft — having a bigger opportunity than Slack.⁴ The trend in every aspect of computing is higher and higher levels of abstraction, and that doesn’t apply just to things like programming languages. In the case of platforms, the operating system of the PC used to really matter, and then the Internet came along and it didn’t. Similarly, in mobile, the operating system, whether that be iOS or Android, used to really matter, but now it doesn’t. In the consumer space, Facebook or WeChat runs on both, and that is far more important to the day-to-day experience of the vast majority of people.

It turns out that “mobile” is not about devices, but rather, at a fundamental level, about computing anywhere; to differentiate between PCs or phones is an ultimately meaningless exercise. They are simply different form factors of effectively identical devices, the purpose of which is to connect us to the cloud (consumer or enterprise). And, by extension, if the device is simply an implementation detail, then the operating system that runs on that device is a detail of a detail.

What matters — what always matters! — is what actual users want to do, and what jobs they want to accomplish. And, whatever they want to do almost certainly involves communicating, which means Slack and its competitors are the best-placed to be the foundational platform of the cloud epoch. More broadly, humans are social creatures: why should we be surprised that social networks are primed to be the most important businesseses of all?

1. Whether or not this is a good thing is the subject for another article [↩]
2. Rather fortuitously, I posted that article exactly one day before Facebook shocked the world by buying WhatsApp [↩]
3. HipChat, Slack’s biggest competitor, actually beat Slack to the punch, having announced their development platform last month [↩]
4. Note that I said “opportunity”; opportunity means it’s possible, not that it’s necessarily going to happen [↩]

I share Professor Clayton Christensen’s consternation about the overuse of the term “disruption.” In this month’s issue of the Harvard Business Review, Christensen and his co-authors Michael Raynor and Rory McDonald write:

Disruption theory is in danger of becoming a victim of its own success. Despite broad dissemination, the theory’s core concepts have been widely misunderstood and its basic tenets frequently misapplied…

As the article notes, disruption is a bottom-up process:

“Disruption” describes a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses. Specifically, as incumbents focus on improving their products and services for their most demanding (and usually most profitable) customers, they exceed the needs of some segments and ignore the needs of others. Entrants that prove disruptive begin by successfully targeting those overlooked segments, gaining a foothold by delivering more-suitable functionality—frequently at a lower price. Incumbents, chasing higher profitability in more-demanding segments, tend not to respond vigorously. Entrants then move upmarket, delivering the performance that incumbents’ mainstream customers require, while preserving the advantages that drove their early success. When mainstream customers start adopting the entrants’ offerings in volume, disruption has occurred.

In fact, many of technology’s most successful companies, both old and new, have started (or remain) at the high end — the opposite of a Christensen disruptor. Apple is the most famous example, and both the rise and durability of the iPhone in particular point to two big holes in the theory:

• First, Christensen categorizes all innovations as being either “disruptive” or “sustaining”; according to disruption theory the former are ignored by incumbents, giving space for new companies to develop, while the latter are adopted by incumbents who eventually crush new entrants. This is why Christensen was infamously bearish on the iPhone: it was a superior product that Nokia et al would surely respond to.

  In reality, though, the iPhone was not disruptive nor sustaining: it was Obsoletive, a term I coined back in 2013:

  The problem for Nokia and BlackBerry was that their specialties – calling, messaging, and email – were simply apps: one function on a general-purpose computer. A dedicated device that only did calls, or messages, or email, was simply obsolete.

  An even cursory examination of tech history makes it clear that “obsoletion” – where a cheaper, single-purpose product is replaced by a more expensive, general purpose product – is just as common as “disruption” – even more so, in fact.

  Disruption is a bottoms-up strategy; obsoletion is a top-down one.

• Secondly, Christensen still thinks the iPhone is ultimately in trouble because it is an integrated offering competing against modular competitors. However, as I noted in What Clayton Christensen Got Wrong, it’s not clear that “good enough” always wins in consumer markets:

  Modularization incurs costs in the design and experience of using products that cannot be overcome, yet cannot be measured. Business buyers — and the analysts who study them — simply ignore them, but consumers don’t. Some consumers inherently know and value quality, look-and-feel, and attention to detail, and are willing to pay a premium that far exceeds the financial costs of being vertically integrated.

  Indeed, eight years on the iPhone is stronger than its ever been; skeptics may be concerned about growth, but no one (rightly) expects Apple to lose customers to Android.

Still, neither critique is incompatible with disruption theory; they were, and are, presented as ways the theory could be made better, part of an endeavor Christensen himself has been engaged in for the last twenty years. However, understanding the success of Uber, the company at the center of Christensen’s latest article, is another matter entirely.

Disruption: A One Way Street

As noted, disruption is a bottom-up process, and from The Innovator’s Dilemma on Christensen has made clear disruption always starts on the low-end. Christensen wrote in a chapter entitled “What Goes Up, Can’t Go Down”:

Three factors — the promise of upmarket margins, the simultaneous upmarket movement of many of a company’s customers, and the difficulty of cutting costs to move downmarket profitably — together create powerful barriers to downward mobility. In the internal debates about resource allocation for new product development, therefore, proposals to pursue disruptive technologies generally lose out to proposals to move upmarket. In fact, cultivating a systematic approach to weeding out new product development initiatives that would likely lower profits is one of the most important achievements of any well-managed company.

Indeed, for all that the iPhone has done to confound Christensen’s theory, it has, as predicted, never gone down-market. Christensen makes the same critique about Uber, claiming the company is not disruptive because it didn’t start out by undercutting taxis:

A disruptive innovation, by definition, [originate in low-end or new-market footholds] footholds. But Uber did not originate in either one. It is difficult to claim that the company found a low-end opportunity: That would have meant taxi service providers had overshot the needs of a material number of customers by making cabs too plentiful, too easy to use, and too clean. Neither did Uber primarily target nonconsumers—people who found the existing alternatives so expensive or inconvenient that they took public transit or drove themselves instead: Uber was launched in San Francisco (a well-served taxi market), and Uber’s customers were generally people already in the habit of hiring rides.¹

In fact, Christensen understates his case: Uber started by offering black car service at a price significantly higher than taxis; it was only with the introduction of UberX a full three years after the company was founded that the service became competitive with taxis on a price basis. Now, though, UberX is not only often cheaper, UberPool always is; the company is actually moving in the exact opposite direction of a disruptor, which means Christensen is quite justified in claiming that Uber is not disruptive.

And yet, the devastating impact Uber is having on the industries it is competing with looks an awful lot like disruption’s aftermath: the market for ride-sharing is far larger than the taxi-market ever was (a la new market disruption), and incumbents are not only losing riders but are also seeing the value of their most prized assets (taxi medallions) plummeting. To simply say that Uber is a sustaining innovation is to dramatically undersell what is happening.

Top-Down Aggregation

Disruptive Technologies: Catching the Wave, the Harvard Business Review article where Christensen first laid out disruption theory, came out 20 years ago; it was, in my estimation, the pinnacle of management theory. Christensen’s core insight was that the managers of disrupted companies were not stupid, but rather exceedingly rational:

Using the rational, analytical investment processes that most well-managed companies have developed, it is nearly impossible to build a cogent case for diverting resources from known customer needs in established markets to markets and customers that seem insignificant or do not yet exist. After all, meeting the needs of established customers and fending off competitors takes all the resources a company has, and then some.

A manager’s calculus looked something like this:

Every additional customer accrued a cost, whether that be the marginal cost of serving them or the opportunity cost of not serving a different customer. Given that, it was, as Christensen noted, perfectly rational to focus on the most profitable ones.

However, something else momentous happened around 20 years ago: the emergence of the Internet. As I’ve written repeatedly, including two weeks ago in Selling Feelings and this summer in Aggregation Theory, the Internet has completely transformed business by making both distribution and transaction costs effectively free. In turn, this has completely changed the calculus when it comes to adding new customers: specifically, it is now possible to build businesses where every incremental customer has both zero marginal costs and zero opportunity costs.

This has profound implications: instead of some companies serving the high end of a market with a superior experience while others serve the low-end with a “good-enough” offering, one company can serve everyone. And, given the choice between a superior experience and one that is “good-enough,” of course the superior experience will win.

To be sure, it takes time to scale such a company, but given the end game of owning the entire market, the rational approach is not to start on the low-end, but rather the exact opposite. After all, while marginal costs may be zero, providing a superior experience in the age of the Internet entails significant upfront (fixed) costs, and while those fixed costs are minimized on a per-customer basis at scale, they can have a significant impact with a small customer base. Therefore, it makes sense to start at the high-end with customers who have a greater willingness-to-pay, and from there scale downwards, decreasing your price along with the decrease in your per-customer cost base (because of scale) as you go (and again, without accruing material marginal costs).

This is exactly what Uber has done: the company spent its early years building its core technology and delivering a high-end experience with significantly higher prices than incumbent taxi companies. Eventually, though, the exact same technology was deployed to deliver an lower-priced experience to a significantly broader customer base; said customer base was brought on board at zero marginal cost (to be sure, there is more to Uber’s success than that; I laid out in Why Uber Fights how Uber’s aggregation of riders gives them leverage in bringing drivers onto their platform in a virtuous cycle).

Disrupting Disruption

I stated above that disruption was the pinnacle of management theory, and I chose my tense carefully: the truth is that Christensen’s attempt to demarcate what is and isn’t disruption perhaps has far deeper implications for the theory than he realized. Many of the most important new companies, including Google, Facebook, Amazon, Netflix, Snapchat, Uber, Airbnb and more are winning not by giving good-enough solutions to over-served low-end customers, but rather by delivering a superior experience that begins at the top of a market and works its way down until they have aggregated consumers, giving them leverage over their suppliers and the potential to make outsized profits.

And, by extension, incumbent companies are actually in far more trouble than they were 20 years ago: the issue isn’t that they are constrained by the profit motive from going down-market, but rather that the distinction between up-market and down-market is, from a cost basis anyways, increasingly non-existent. All that matters is the quality of the experience and the ability to scale, two skills that company’s founded on controlling distribution and segmenting customers are fundamentally deficient in.

That’s not to say that disruption theory doesn’t still have its place: while the iPhone may not have been disruptive to phones (it obsoleted them), Christensen has noted that the iPhone did in fact disrupt the PC. Similarly, while I agree that Uber is not disruptive to taxis (it is winning through aggregation), what could potentially happen to the personal automobile industry happily fits the theory perfectly.

1. Note: I think that calling San Francisco a “well-served taxi market” is overstating things, but that actually strengthens the point that Uber was a superior offering [↩]

One of the more famous marketing frameworks is the Marketing Mix, also known as “The Four P’s.” According to the framework there are four key components to a marketing plan:

• Product (what is actually sold)
• Price (how much the product is sold for)
• Promotion (how customers find out about the product)
• Place (where the product can be found)

Of these four the most difficult and expensive — and thus, the greatest barrier to entry (i.e. the biggest moat) — was place. Actually getting your product in front of customers required relationships with wholesales and retailers, not to mention significant investments in logistics. Indeed, the companies who controlled distribution were often the most profitable of all.

Consider the media industry: broadcast networks had rights to the airwaves, cable networks needed to get carriage (which itself was offered by private companies, earning them tremendous profits), newspapers owned printing presses and delivery trucks, music companies printed albums and got them into stores, publishers did the same with books. From a business-model perspective all of these companies were similar: by controlling distribution they collected rents on what was actually distributed.

It’s not just media, though. Selling anything — clothes, shoes, pots and pans — depended on actually getting your product on the shelves, which meant dealing with wholesalers, retailers, shippers, etc., all of whom extracted their chunk of flesh. Your typical manufacturer would be lucky to get 40% of the retail price of an item, and often far less — and that is if said manufacturer could get their item in a store in the first place.

The Good Old Days

In short, starting a new business in any industry was really, really hard: simply getting your foot in the door required not just a great product but also a massive investment in getting that product in front of customers, and we haven’t even gotten to promotion (much less a price that pays for it all).

This ultimately benefited the largest players: Proctor & Gamble, for example, could leverage its relationships with retailers who already sold Tide laundry detergent and Pampers diapers to get shelf space for a new product line. Big department store chains could demand exclusivity for new apparel or drive down the price. Media companies could pick and choose who to feature, and on their terms. The payoff for actually getting a business off the ground was that once you made it things got a lot easier:

This is what the “good old days” looked like: pre-existing businesses at best competed with a known set of peer companies, or as was often the case, dominated individual markets, limited only by their ability to scale. Of course things weren’t so good for the folks who couldn’t manage to get distribution: at best they could throw their product over the wall and hope for whatever crumbs got tossed back for their trouble, while customers had to settle for products that tended to serve the lowest common denominator.

The Connection Between Price and Place

This context is why I tend to roll my eyes at, for example, complaints about the 30% commission charged by app stores. It used to be that publishing a piece of software was only partially about creating said software: just as important, if not more, was getting said software onto shelves where customers could actually pick them up, and a publisher was lucky to keep 30% of the retail cost for the privilege.

App stores changed everything: now anyone with a developer account could publish an app on the exact same terms as anyone else; Apple and Google could afford to do that because the Internet made shelf space effectively infinite. The wall was gone!

The problem, as App Store developers have increasingly realized, is that the existence of that old distribution wall was directly tied to the existence of profits on the other side: when anyone can sell software — when the place is open to all — no one can make a profit, because the price goes to zero.

The Problem With Selling Apps

I’ve been a longstanding critic of Apple’s approach to the App Store, most recently in From Products to Platforms. Specifically, I think the App Store’s refusal to support trials makes it difficult for superior products to differentiate themselves and thus charge a higher price, and the absence of upgrade pricing and customer data makes it difficult to get more money from a developer’s existing user base.

Still, I’ve long been cognizant that even were Apple to change its policies developers would be rolling the proverbial rock uphill. Back in 2013 I noted in Open Source Apps:

What makes the software market so fascinating from an economic perspective is that the marginal cost of software is $0. After all, software is simply bits on a drive, replicated at the blink of an eye. Again, it doesn’t matter how much effort was needed to create said software; that’s a sunk cost. All that matters is how much it costs to make one more copy: $0.

The implication for apps is clear: any undifferentiated software product, such as your garden variety app, will inevitably be free. This is why the market for paid apps has largely evaporated. Over time substitutes have entered the market at ever lower prices, ultimately landing at their marginal cost of production: $0.

Still, that doesn’t mean it’s impossible to make money.

Differentiated Games

Note the key adjective there: “undifferentiated.” What does it mean to be differentiated? There’s no question it has something to do with that first ‘P’, product. A differentiated product is “better” in some way, but all too often putting your finger on exactly what is better is a frustrating exercise. It just “feels” better, or, to switch that around, it’s about how it makes you feel. I’ve written extensively about the importance of the user experience and this gets at the same point: delivering an experience is less about features than it is the entirety of the experience, including approachability, usability, and even things like status or fitting in.

Consider the one app category that continues to succeed wildly on the App Store: free-to-play games like Candy Crush or Clash of Clans. Critics complain that they are manipulative, extracting money from culpable players in exchange for a worthless digital good that delivers little more than a sense of accomplishment to the buyer — a shot of dopamine, basically. But, if I may put on my contrarian hat, so what? Is said shot of dopamine any different than that obtained by any number of other means, many of which cost money? If differentiation is more about how something makes you feel and less about features then why the special bias simply because one particular something happens to be created in software? And, I’d add, digital dopamine results in a far more equitable business model for the developer: the more a user plays the more money a developer earns.

An even more extreme example is free-to-win games that are increasingly popular on the PC (yes, it’s still a thing!). Chris Dixon wrote a must-read post entitled Lessons From the PC Video Game Industry that described this business model:

The PC gaming world has taken the freemium model to the extreme. In contrast to smartphone games like Candy Crush that are “free-to-play,” PC games like Dota 2 are “free-to-win.” You can’t spend money to get better at the game — that would be seen as corrupting the spirit of fair competition. (PC gamers, like South Park, generally view the smartphone gaming business model as cynical and manipulative). The things you can buy are mostly cosmetic, like new outfits for your characters or new background soundtracks. League of Legends (the most popular PC game not on Steam) is estimated to have made over $1B last year selling these kinds of cosmetic items.

I know many of you are rolling your eyes — selling digital clothes for a digital avatar, and to the tune of a billion dollars? How silly must you be? Well, how silly must you be to carry a $5,000 handbag with far less functionality than another a fraction of the price, or wear a $10,000 watch or $200 necktie? What about flying first class or staying in a five-star hotel — you can’t take either with you! It’s completely irrational.

Or, rather, it’s irrational if you only look at features. The entire point is how these purchases make you feel, and it’s that feeling, whether it be an appreciation for craftsmanship, status, or simply being pampered, that provides the sort of differentiation that makes all of these products profitable. One could argue that an insistence on limiting the calculation of value to items that are permanent, physical, and easily listed on a spreadsheet is the real irrationality.

Make Your Market

In the case of those PC games, what the developers have done is actually exceptionally impressive, and something that should serve as a model for all sorts of businesses. Instead of trying to make money in a market — paid PC games — where making money is all but impossible thanks to the competition unleashed by the Internet, the developers effectively created an entirely new market — a virtual world filled with people lured in through free access and quality gameplay — and then leveraged their ownership of that market to fulfill the same sort of needs that fashion-focused businesses have been fulfilling forever. The need to look cool, or the need to stand out. The need to impress your friends, or simply to like how you look.

It doesn’t matter that it’s digital, by the way: any one person’s reality is ultimately wherever they choose to focus their attention and time, which makes games like League of Legends far more real to their inhabitants than the fashion boutiques in Paris would ever be — and far more exclusive. After all, there is only one seller.

Plus, just as is the case with free-to-play games, the economics are all in alignment: creating the market is a fixed cost which means it has no impact on the marginal cost of one more player. Why not add the maximum number of players (by making it free) and then develop a different revenue stream that pays out continuously the longer a player plays the game, ensuring the developer captures value as it is realized? Sure, said value may only be captured from some, and in relatively tiny increments, but remember we’re dealing with the Internet: you can make it up in volume.

Moreover, I think the model is broadly applicable. I wrote two weeks ago about how the future of publishing will not be about monetizing pure words but rather about using words to gain fans that can be monetized through other harder-to-discover media. Time and attention remain precious commodities and earning trust in one area gives you the right to make money from it in another. Similarly, as I wrote last week, software generally should be seen as a lever to solutions that are much more meaningful to customers, and much more difficult to copy. After all, as noted above, software is infinitely copyable: better to use that quality to your advantage than to base your business model on fighting gravity.¹

More broadly, the fact remains that business is difficult — it was difficult before the Internet, and it’s difficult now — but the nature of the difficulty has changed. Distribution used to be the hardest thing, but now that distribution is free the time and money saved must instead be invested in getting even closer to customers and more finely attuned to exactly why they are spending their money on you. Any sort of software — or writing, or music, or video, or clothing, or anything else — has never been purchased for its intrinsic value but rather because of what it did for the buyer — how it made them feel (informed, happy, relaxed, etc.). Create the conditions where the need might manifest itself and then meet that need, and not only will your business succeed, it will, in all likelihood, succeed to an even greater extent than the physically-limited lowest common denominator winners from the “good old days.”

1. Like me… [↩]

Ten years ago Bill Gates suggested that open source software was the province of “modern-day sort of communists” whose views on intellectual property were hopelessly outdated:

The idea that the United States has led in creating companies, creating jobs, because we’ve had the best intellectual property system — there’s no doubt about that in my mind, and when people say they want to be the most competitive economy, they’ve got to have the incentive system. Intellectual property is the incentive system for the products of the future.

Gates’ perspective was understandable: he had built Microsoft into the biggest company in technology and one of the biggest in the world by, for all intents and purposes, selling licenses to text. Sure, that’s a dramatic over-simplification of Windows and the other software Microsoft sold, but that didn’t change what a seachange the Redmond-based company seemed to represent: one where the pure expression of ideas could make you the richest person in the world. Yet those antediluvian open-source zealots wanted to simply give it all away.

The Open-Sourcing of TensorFlow

Microsoft is still a big company — their market cap was $427 billion at yesterday’s market close — but an even bigger company today is Alphabet ($506 billion), which has a decidedly different approach:¹ earlier this week its Google subsidiary announced it was open-sourcing TensorFlow, its formerly proprietary machine learning system. From the official Google blog:

Just a couple of years ago, you couldn’t talk to the Google app through the noise of a city sidewalk, or read a sign in Russian using Google Translate, or instantly find pictures of your Labradoodle in Google Photos. Our apps just weren’t smart enough. But in a short amount of time they’ve gotten much, much smarter. Now, thanks to machine learning, you can do all those things pretty easily, and a lot more. But even with all the progress we’ve made with machine learning, it could still work much better.

So we’ve built an entirely new machine learning system, which we call “TensorFlow.” TensorFlow is faster, smarter, and more flexible than our old system, so it can be adapted much more easily to new products and research. It’s a highly scalable machine learning system — it can run on a single smartphone or across thousands of computers in datacenters. We use TensorFlow for everything from speech recognition in the Google app, to Smart Reply in Inbox, to search in Google Photos. It allows us to build and train neural nets up to five times faster than our first-generation system, so we can use it to improve our products much more quickly.

We’ve seen firsthand what TensorFlow can do, and we think it could make an even bigger impact outside Google. So today we’re also open-sourcing TensorFlow. We hope this will let the machine learning community — everyone from academic researchers, to engineers, to hobbyists — exchange ideas much more quickly, through working code rather than just research papers. And that, in turn, will accelerate research on machine learning, in the end making technology work better for everyone.

Machine learning is super important to Google; just a couple of weeks ago, on Alphabet’s Q3 earnings call, Google CEO Sundar Pichai stated in his opening remarks, “I also want to point out that our investments in machine learning and artificial intelligence are a priority for us”, and followed that up with a series of examples where machine learning was serving as a differentiator for Google. Pichai later added, in response to a question:

Machine learning is a core transformative way by which we are rethinking everything we are doing. We’ve been investing in this area for a while. We believe we are state-of-the-art here. And the progress particularly in the last two years has been pretty dramatic. And so we are thoughtfully applying it across all our products, be it search, be it ads, be it YouTube and Play et cetera. And we are in early days, but you will see us in a systematic manner, think about how we can apply machine learning to all these areas.

At a superficial level, this doesn’t make sense: if machine learning is core to Google’s future, then what is the point of giving it away? Does the company not care about making money? Are they — gasp — communists, or more charitably, as former head of Google’s Webspam team Matt Cutts put it, “releasing technology so that the entire world can benefit, not just Google”?

To be sure, there is a lovely PR angle to this news, but I think Google’s thinking is a lot more strategic than that. Open-sourcing TensorFlow makes a ton of sense, and the lessons as to why are broadly applicable.

Differentiating Software

Think carefully about what differentiates today’s technology companies. To take a few of the most prominent examples:

• Apple’s devices are differentiated first and foremost by their software, but the company makes money by selling hardware. Or, to be more precise, they make money by selling devices at scale that integrate software, hardware, and services, and to do that requires not simply an operating system but also a world-class industrial design team and an all-but-impossible-to-replicate supply chain that stretches from well over 200 suppliers all over the world to almost 500 Apple retailer stores and tens of thousands of resellers
• Amazon isn’t simply a website but also a massive logistics network that connects tens of thousands of vendors to customers via nearly 100 distribution and sortation centers in North America alone, while AWS probably has nearly another 100 data centers with millions of servers and an increasingly rich ecosystem of partners dedicated to getting companies onto AWS’ cloud
• Facebook’s value comes not from its software but from the fact that the social network has over 1.5 billion monthly active users, over a billion of whom use the service daily, plus three other services (WhatsApp, Messenger, and Instagram) with active user bases numbering in the (high) hundreds of millions; all of those users are connected to each other

The examples go on-and-on: companies that are built on software but differentiated by a difficult-to-replicate complement to said software. And this, I think, is the way to understand Google’s decision to open-source TensorFlow.

Google’s Machine Learning Advantage

I’m hardly qualified to judge the technical worth of TensorFlow, but I feel pretty safe in assuming that it is excellent and likely far beyond what any other company could produce. Machine learning, though, is about a whole lot more than a software system: specifically, it’s about a whole lot of data, and an infrastructure that can process that data. And, unsurprisingly, those are two areas where Google has a dominant position.

Indeed, as good as TensorFlow might be, I bet it’s the weakest of these three pieces Google needs to truly apply machine learning to all its various business, both those of today and those of the future. Why not, then, leverage the collective knowledge of machine learning experts all over the world to make TensorFlow better? Why not make a move to ensure the machine learning experts of the future grow up with TensorFlow as the default? And why not ensure that the industry’s default machine learning system utilizes standards set in place by Google itself, with a design already suited for Google’s infrastructure?

After all, contra Gates’ 2005 claim, it turns out the value of pure intellectual property is not derived from government-enforced exclusivity, but rather from the complementary pieces that surround that intellectual property which are far more difficult to replicate. Google is betting that its lead in both data and infrastructure are significant and growing, and that’s a far better bet in my mind than an all-too-often futile attempt to derive value from an asset that by its very nature can be replicated endlessly.

The Android Example

Indeed, Google has already demonstrated that this approach can be devastatingly effective. Gates was right to fear the open-source threat to Windows: in the smartphone era Google took Microsoft’s former position as the default operating system for the masses by open-sourcing Android. I absolutely believe that Android would not have achieved the dominant position that it has without that step, and Google was able to do so because its business model of advertising was a complement to its software, not because it sold software itself.

It’s fair to object that open-sourcing Android ensured it would never be a real money-maker for Google; I’ve made that case myself. But remember, Android was originally intended as a defensive measure for Google’s search business. And, from that perspective, it wildly succeeded.

I suspect open-sourcing TensorFlow will have a far more positive effect on Google’s bottom-line. Google is approaching machine learning from a position of strength: the company already has the most data and the most imposing infrastructure, and as noted open-sourcing TensorFlow accelerates the removal of the primary limitation to leveraging that advantage: the quality of the system itself.

Broader Lessons

There’s a parallel to be drawn to my piece last week about Grantland and the (Surprising) Future of Publishing. The fundamental nature of the Internet makes monetizing infinitely reproducible intellectual property akin to selling ice to an Eskimo: it can be done, but it better be some really darn incredible ice, and even then the market is limited. A far more attainable and sustainable strategy is to instead focus on monetizing complements to said intellectual property, resulting in an outcome where everyone wins: intellectual property consumers, intellectual property copiers, and above all intellectual property creators.

1. Microsoft has since embraced open source [↩]

It’s dangerous, I suspect, to draw too many lessons from the ignominious end of Grantland, the high-brow sports and culture site that ESPN shuttered this weekend, several months after parting ways with Bill Simmons, the site’s founder and editor-in-chief. Much of what transpired seems to have clearly been personal, not only the rancorous way in which it ended but also how it began: was ESPN every truly committed to a brand-building endeavor that didn’t even have the ESPN name, or was Grantland a pet project meant to keep Simmons, the most influential and famous sportswriter of his generation, in the fold?

Conventional wisdom is that both are worse off for having split: ESPN for having lost said writer and, in the end, a truly remarkable online magazine, while Simmons lost access to ESPN’s massive audience. Still, there is no question that the numbers didn’t add up: Deadspin reported in May that Grantland had 6 million unique visitors in March, a relatively meager sum that in no way shape or form could support a team of over 50 writers, editors, and back-end staff, even if Grantland had been much more aggressive in monetizing through advertisements (the site usually carried at most one banner ad plus a “You might be interested in…” block at the end of articles). And, so, as Chris Connelly, who replaced Simmons as interim editor-in-chief, noted in an interview with Sports Illustrated:

When you are doing a site that you understand is not making money, you kind of understand when times get challenging or there is a new economic climate, you will be scrutinized very closely. I think the site continued to do fantastic editorial, for which I want to be sure not to take credit. That was the product of the editors and writers who were there every day of the week. But in this economic climate you will be very closely scrutinized if you are not a money-making operation.

Indeed, Grantland was not the only cut at ESPN: the network also recently laid off around 300 people in the face of rising costs and declining subscriber numbers; there’s not much room for brand-building when you’re already showing valuable employees the door.

Grantland’s Value

There’s no question that Grantland was an editorial success, at least in terms of quality. The site garnered three National Magazine Award nominations, won a primetime Emmy, and was nominated for and won a number of Sports Emmys, Webby Awards, Eppy Awards, and more.¹ Speaking personally, the site was one of only a handful I visited directly daily; if an article was on Grantland I presumed it was worth reading.

To put it another way, Grantland was a “Destination Site.” I first defined this term in an article this spring about Facebook’s Instant Articles, and noted:

It’s really hard to become a destination: you need compelling content of consistently high quality. Notice, though, that that is precisely the opposite of what most online publications have focused on: in their race for ever more content and ever more clicks most publications have lowered their quality bar.

That right there is the rub, as Todd VanDerWerff lamented on Vox:

The problem is scale. A larger, general-interest site can’t be built purely atop longform, because longform takes time — both for writers to produce and readers to read. Therefore, as both Buzzfeed and Gawker realized early on, well-done longform could be the steak, but it couldn’t be the meal. (Grantland perhaps realized this too late.)

The non-steak portion of the “meal” that VanDerWerff refers to are those quick-hitting posts that, to use his employer (Vox.com) as an example, embed and summarize an interview with a pop singer, do the same embed and summarize routine about a $1500 sandwich, and rewrite a news story about Jon Stewart going to HBO that countless other sites covered as well. And that’s just (a small selection from) Tuesday! In fact, the majority of stories — including the most popular ones of the day — could have been written by anyone anywhere.

As a reader this is frustrating: Vox stripped down to the political and policy coverage that is its raison d’être would almost certainly rate as a destination site for me; instead, faced with a deluge of rewrites and summarized videos, I miss most of the good stuff save for what I stumble across on social media. I’m certainly not going to waste my time wading through the filler on the homepage.

Grantland’s Worth

Still, the fact remains that Vox is by all accounts thriving while Grantland, which eschewed filler, is dead. Moreover, the only destination sites that really seem to be working either have massive brand equity that is being leveraged into subscriptions (The New York Times, The Wall Street Journal, The Financial Times), or are tiny one-person operations that leverage the Internet to keep costs sustainably low while monetizing through small-scale native advertising (e.g. Daring Fireball and the now-retired Dooce.com, for example) or subscriptions (e.g. The Timmerman Report and the site you’re reading). It certainly seems that the lesson of Grantland is that there is no room in the middle: not enough scale for advertising, and costs that are far too high for a viable subscription business.²

The Publishing Curve Revisited

But remember my initial warning: it’s possible to read too much into Grantland. Last year I wrote about Publishers and the Smiling Curve, characterizing publishers as being akin to original equipment manufacturers assembling computers and phones at cost, while profits flowed to aggregators on one side (Facebook, Google, etc.) and to “stars and focused publications” on the other.

I certainly think I got the aggregator part right (several months later I generalized the concept to Aggregation Theory), but it wasn’t clear in the article — or ultimately to me — how exactly the “stars and focused publications” would profit outside of one-man operations.

Simmons, though, just as he pioneered online sports writing, may be leading the way once again in demonstrating how writing can be monetized: by not even trying.

Writing As Lead Generation

Simmons is pursuing two endeavors post-ESPN: he’s creating a television show for HBO Now (although it will reportedly air on HBO proper as well) and he’s recording The Bill Simmons Podcast. The business models — and thus the incentives — for both couldn’t be more different than those for successful online publishers:

• HBO Now, like all subscription services, has to surmount the far stiffer challenge of convincing customers to get out their credit cards, not just click a link (or, in television terms, watch an ad-supported show). This changes the focus from common-denominator low-cost fare like reality TV or game shows (or video summaries or news rewrites) to focused, expensive “destination shows” that fewer people watch, but those who do care intensely.

  One show in one niche isn’t enough though; the best balance between revenue and costs is found through bundling: people may be willing to pay a lot for one show and a little for several others, which means a well appointed bundle can ultimately get more revenue per customer from a wider base of customers. In the case of Simmons, he brings a younger male audience that cares about sports, and who may also be interested in, say, Game of Thrones or Hard Knocks, or perhaps a bit of comedy from, say, Jon Stewart; it’s the collection that makes said audience willing to pay $15/month, even though they may have been willing to pay Simmons alone less.

  Why, though, does Simmons have those fans in the first place? Because of his writing. The flipside of writing being hard to monetize is that it is the most digestible and sharable medium, allowing folks to accrue large audiences that they can leverage elsewhere.

• Podcasts run on an advertising model, but said advertising is far more valuable than display ads in particular. They are truly native: Simmons, like most podcast hosts, reads the ads himself in the middle of his podcast, meaning they are more likely to be heard and are more engaging to boot.

  The trouble with podcasts is that they are difficult to grow: while text can be shared and consumed quickly, a podcast requires a commitment (which again, is why advertising in them is so valuable). Simmons, though, by virtue of his previous writing, is already averaging over 400,000 downloads per episode.³ Podcast rates are hard to come by, but I’m aware of a few podcasts a quarter the size that are earning somewhere in excess of $10,000/episode; presuming proportionally similar rates (which may be unrealistic, given the broader audience) The Bill Simmons Podcast, which publishes three times a week, could be on a >$6 million run rate, which, per my envelope math in the footnote above, could nearly pay for a 50-person staff a la Grantland.

To be sure, as I noted above, Grantland was certainly much more expensive, and it’s not clear just how far podcasting can scale (but I think that’s only a matter of time), but Grantland actually had an entire stable of podcasts, several of which were quite popular because they featured writers whose writing was popular.⁴ Just as with HBO, it turns out writing is great lead generation for an actual monetizable business.

The Grantland Lesson

To be sure, it’s tempting to pull a “That’s Fine for Bill”; the guy has been writing online for eighteen years (and, technically, he’s not writing now).⁵ It’s a fair point but I think there’s room for another, equally compelling one: too much of the debate about monetization and the future of publishing in particular has artificially restricted itself to monetizing text. That constraint made sense in a physical world: a business that invested heavily in printing presses and delivery trucks didn’t really have a choice but to stick the product and the business model together, but now that everything — text, video, audio files, you name it — is 1’s and 0’s, what is the point in limiting one’s thinking to a particular configuration of those 1’s and 0’s?

In fact, it’s more than possible that in the long-run the current state of publishing — massive scale driven by advertising on one hand,⁶ and one-person shops with low revenue numbers and even lower costs on the other — will end up being an aberration. Focused, quality-obsessed publications will take advantage of bundle economics to collect “stars” and monetize them through some combination of subscriptions (less likely) or alternate media forms. Said media forms, like podcasts, are tough to grow on their own, but again, that is what makes them such a great match for writing, which is perfect for growth but terrible for monetization.

That’s why the lesson to be learned from Grantland may be the exact opposite of what it seems: the problem isn’t that ESPN spent too much money on a web site that couldn’t monetize, it’s that the web site should only have been step one to a multi-media endeavor that converted visitors to fans willing to invest time in formats that can actually pay the bills.⁷

1. Via the afore-linked Deadspin article [↩]
2. Some back of the envelope figures:

   Presuming a cost per employee of $100,000 (which, including benefits, is probably on the low side):

   • A one person site needs $100,000 in revenue, which means 1,000 subscribers at $100/year
   • A two person site needs $200,000 in revenue, which means 2,000 subscribers at $100/year
   • A 50 person site (ignoring Simmons’ reported $5 million salary, plus the fact the site included former Pulitzer Prize winners) needs $5 million in revenue, which means 50,000 subscribers at $100/year. Add in Grantland’s other expenses and the number is likely double that

   Keep that $5 million figure in mind [↩]

3. I’ve heard the number now is actually closer to 500,000 [↩]
4. The podcast point is the most legitimate of all of Simmons’ complaints about ESPN: there was clearly minimal effort made to monetize the B.S. Report or the other Grantland podcasts [↩]
5. This is also the part where I note that the reason I and so many other online writers revere him, despite his warts, is that he was a true pioneer: no one had made a career on the Internet before him, and now lots of us have [↩]
6. Indeed, I’ve not only written repeatedly that a traditional advertising model only works at scale, but also argued that Facebook and Google are on pace to capture ever more of it: the two Internet giants have better user data, better tracking, and better inventory, and relatively unlimited inventory (at least compared to a medium like TV, which certainly took a chunk of advertising revenue from traditional publishers, but only a chunk). [↩]
7. One more thing: I don’t think linear TV is that format. Fans want to read/see/listen to stars, but time is a constraint; on-demand a la HBO Now or podcasts is a much better fit [↩]