Alexa: Amazon’s Operating System

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The concept of an operating system is pretty straightforward: it is a piece of software that manages a computer, making said computer’s hardware resources accessible to software through a consistent set of interfaces.

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Operating systems have a special allure to technology companies because of the unique properties that come from being at the center of this diagram:

  • First, by abstracting away the hardware an operating system reduces the plane of competition for hardware providers to pure performance (as opposed to, say, lock-in). In the short term this increases competition amongst hardware providers, which benefits the operating system, and in the long run, when performance becomes “good enough”, hardware is effectively commoditized allowing the operating system to capture the majority of profits in the value chain.
  • Second, by providing a consistent set of interfaces for software, operating systems create network effects: the more users there are of an operating system the more software applications that are developed for that operating system; this in turn drives more users which increases the addressable market for developers further still. In the long run this results in lock-in for both developers and users.
  • Third, operating systems by definition have a direct interface with end users, and owning the user relationship is massively valuable for the leverage it creates over entire ecosystems.

Much of the history of technology, particularly in the consumer space, is about owning the operating system.

Windows: The Perfect Business Model

The most famous operating system, of course, is Windows, which remains the best example of just how powerful owning an operating system can be:

  • Windows fostered and benefited from competition for nearly every piece of hardware in PCs, resulting in massive increases in performance and massive decreases in price.
  • Meanwhile, thanks to IBM, Windows (well, DOS to be precise, Windows’ command line interface-driven predecessor) was the default operating system for enterprise, which meant there was nearly immediately a huge and rapidly growing market for developers, which increased the desirability of Windows in the sort of virtuous cycle I described above.
  • Windows then leveraged its ownership of users to build out two other massive businesses: first its Office franchise, and then its Windows Server line of products.

The end result was one of the most perfect business models ever: commoditized hardware vendors competed to make Windows computers faster and cheaper, while software developers simultaneously made those same Windows computers more capable and harder to leave. And, all along, Microsoft collected a licensing fee that was basically pure profit.1

Mobile Operating Systems

On mobile Microsoft tried to repeat the trick, only to have its market stolen by Google’s Android, which was not only better than Windows Mobile but also free; unfortunately for Google, Android was so successful in its goal of ensuring Microsoft could never profit from the operating system chokepoint on mobile that Google itself was handicapped when it came to making money. Android provides valuable data and indirectly contributes to Google’s search-based profit-engine, but it is not nearly the business that Windows was.

Apple, meanwhile, has always had a different business model: selling hardware. That hardware, though, is differentiated by its own operating system; thanks to the sheer size of the smartphone market this has led to far greater revenue and profits than even Microsoft in its heyday, but the model is ever so slightly more fragile than Windows’ was: Apple has to not only bear the risk inherent to building hardware, but also by definition can only ever own a minority of the market. First, no company could ever build enough phones for the world, and secondly, to serve every customer would ruin the profit margins that make the business model so successful. That, by extension, has meant a duopoly with Android, resulting in most developers serving both markets; Apple still has a moat, but it’s not nearly as deep as Microsoft’s used to be.

Google and the Internet Operating System

This brief history of consumer operating systems is less complete than it seems: Android and iOS have replaced Windows in importance, but in fact Windows lost its lock-in well before Steve Jobs launched the modern smartphone era in 2007. The Internet made the operating system of the computer used to access it irrelevant, and the most dominant company on the Internet was Google.

Of course Google is not an operating system according the strict definition of the term, but in effect Google was the operating system of the Internet. Consider the qualities of operating systems I noted above:

  • While websites could be accessed directly by typing a URL, in practice most websites in the desktop era were reached via search, akin to how computer hardware was accessed via a common operating system. And, just as hardware vendors had no choice but to commoditize themselves, websites had no choice but to make themselves as Google-friendly as possible.
  • The interplay between developers and users created a virtuous cycle that created Windows lock-in; in the case of Google the interplay was between users and the data they generated. Suppose you took two otherwise identical search engines and give one 51% of searches and the other 49%: the former would steadily become better than the latter simply by virtue of having more data on which to iterate. The reality in the case of Google was much more extreme: the company started out with a technological and engineering advantage over its rivals, which earned it market share, which then gave the company data with which to increase its quality lead even further, earning it even more market share; the end result was a monopoly built on user choice.
  • Over time Google has leveraged its relationship with users to build out its own suite of products — or, in many cases, acquired companies that gave it new opportunities to grow.

Google could afford the acquisitions thanks to a new business model for “operating systems”: advertising. Advertising doesn’t make much sense for traditional computer operating systems, which need to be platforms for applications — there is no room for the ads. Google, though, was a platform for attention, not applications, and attention is exactly what advertisers crave. To that end, the business model wasn’t so different after all: operating systems are the chokepoint of the value chain in which they operate, and money always flows to the chokepoints.

Facebook’s Lucky Break

On mobile the most important chokepoint is Facebook (and WeChat in China): the average user spends nearly an hour a day on Facebook, Messenger, and Instagram, and the results are predictable:

  • Facebook’s “suppliers”, in this case publishers, have fully commoditized themselves by not only putting their content on Facebook but even using Facebook’s preferred format; they have no choice.
  • Facebook’s network effect is perhaps the most straightforward of all: it is the people you know (which is one of many reasons why Snapchat is such a threat).
  • Facebook’s ownership of users pays off with its business model as well: not only does Facebook own attention for nearly two billion people, it also has better data about who we are and what we like than any company ever; after all, we told the company ourselves.

What is so fascinating about Facebook’s dominant position on mobile is that it was in many respects a lucky accident: Facebook on the desktop had designs on being something much more akin to a computer operating system, abstracting away the underlying operating system and building an application platform on top. And, when mobile rose to prominence, Facebook tried to build their own phone, convinced that was the only way to own users.

As I just noted, though, an application platform is fundamentally incompatible with an advertising-based business model; by extension, an advertising-based business is not necessarily in conflict with the operating system on which it runs. In the case of Google, the company made its fortune on top of Windows; the dominance of iOS and Android made Facebook just an app, which was the best possible thing that could have happened to the company.

Amazon’s Phone Failure

Amazon made the same mistake as Facebook: convinced it needed its own operating system and the direct access to users that entailed, the company made one of the worst phones in history. The product was misguided for all kinds of reasons, most of them predictable: iOS and Android may have been a duopoly, but their shared developer lock-in was arguably no less imposing than Windows’ had been (as Microsoft itself found out).

More fundamentally, Amazon sought to sell the phone through hardware and OS differentiation, much like Apple, but the company could not be more different organizationally and culturally from the iPhone maker; you don’t make good products because you really want to, you make good products by fostering the conditions in which great products can be made, and Amazon’s deeply rooted culture of modularity and services was completely ill-suited for building a highly differentiated physical product.

One of the things that makes Amazon such an impressive company, though, is that modularity and willingness to make multiple bets: on October 24, 2014 Amazon took a $170 million write-off on the Fire Phone business; two weeks later, the company launched the Amazon Echo.

Amazon’s Operating System

It was apparent on day one that the Echo was a much more compelling product than the Fire Phone:

  • The physical device (the Echo) was simply a conduit for Alexa, Amazon’s new personal assistant. And critically, Alexa was a cloud service, the development of which Amazon is uniquely suited to in terms of culture, organizational structure, and experience.
  • The Echo created its own market: a voice-based personal assistant in the home. Crucially, the home was the one place in the entire world where smartphones were not necessarily the most convenient device, or touch the easiest input method: more often than not your smartphone is charging, and talking to a device doesn’t carry the social baggage it might elsewhere.
  • There was an ecosystem to assemble: more and more “smart” products, from lightbulbs to switches, were coming on the market, but nearly every company trying to be the centerpiece of the connected home was relying on the smartphone.

Amazon seized the opportunity: first, Alexa was remarkably proficient from day one, particularly in terms of speed and accuracy (two factors that are far more important in encouraging regular use than the ability to answer trivia questions). Then, the company moved quickly to build out its ecosystem in two directions:

  • First, the company created a simple “Skills” framework that allowed smart devices to connect to Alexa and be controlled through a relatively strict verbal framework; in a vacuum it was less elegant than, say, Siri’s attempt to interpret natural language, but it was far simpler to implement. The payoff was already obvious at last year’s CES: Alexa support was everywhere.
  • Secondly, “Alexa” and “Echo” are different names because they are different products: Alexa is the voice assistant, and much like AWS and Amazon.com,2 Echo is Alexa’s first customer, but hardly its only one. This year CES announcements are dominated by products that run Alexa, including direct Echo competitors, lamps, set-top boxes, TVs, and more.

In short, Amazon is building the operating system of the home — its name is Alexa — and it has all of the qualities of an operating system you might expect:

  • All kinds of hardware manufacturers are lining up to build Alexa-enabled devices, and will inevitably compete with each other to improve quality and lower prices.
  • Even more devices and appliances are plugging into Alexa’s easy-to-use and flexible framework, creating the conditions for a moat: appliances are a lot more expensive than software, and much longer lasting, which means everyone who buys something that works with Alexa is much less likely to switch

That leaves the business model, and this is perhaps Amazon’s biggest advantage of all: Google doesn’t really have one for voice, and Apple is for now paying an iPhone and Apple Watch strategy tax; should it build a Siri-device in the future it will likely include a healthy significant profit margin.

Amazon, meanwhile, doesn’t need to make a dime on Alexa, at least not directly: the vast majority of purchases are initiated at home; today that may mean creating a shopping list, but in the future it will mean ordering things for delivery, and for Prime customers the future is already here. Alexa just makes it that much easier, furthering Amazon’s goal of being the logistics provider — and tax collector — for basically everyone and everything.

  1. Remember, software has basically no marginal costs ↩

  2. To be clear, AWS was not built using spare Amazon.com capacity, but was built to provide a services infrastructure for Amazon.com ↩

The 2016 Stratechery Year in Review

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2016 has been quite the year for the world at large and for tech specifically; it has certainly been a productive one for Stratechery. This year I wrote 143 Daily Updates (including tomorrow) and 46 Weekly Articles, and, as per tradition, today I summarize the most popular and most important posts of the year.

You can find previous years here: 2015 | 2014 | 2013

Here is the 2016 list.

The Five Most-Viewed Articles:

  1. Dollar Shave Club and The Disruption of Everything — Dollar Shave Club is a textbook example of how the new Internet economy will destroy value in incumbent industries.
  2. It’s a Tesla — Tesla is not a disruptor, but then again, neither is Apple, the closest comparison: both succeed by building a brand around being the best (Editor’s Note: That’s not to say that Tesla will have Apple’s success; there are lots of reasons for skepticism ($) especially after the unjustifiable Solar City acquisition ($)).
  3. Apple’s Organizational Crossroads — A core part of what makes Apple Apple is its organization structure; Tim Cook has said it will never change. However, if Apple is serious about being a services company, change it must.
  4. How Google is Challenging AWS — AWS seems to have a dominant position in enterprise computing, but Google is trying to change the rules to favor their inherent strengths; they just might succeed (see also: Google’s Go-to-Market Gap and Google and the Limits of Strategy).
  5. The Future of Podcasting — Podcasting is stuck between the open model of the past and the push for monetization in the future. Might there be a third way that actually benefits publishers?

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Five Big Ideas

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Five Posts About Media and Politics

  • The Voters Decide — An apolitical analysis of what is happening in U.S. politics through the lens of Aggregation Theory (Editor’s Note: I’m biased but believe more than ever that this is a critical piece to understanding what is happening in western democracies).
  • The Brexit Possibility — Brexit’s downsides are clear; might tech help realize upsides in building something new based on a new world order? (Editor’s Note: This could have been written after Donald Trump’s election as well).
  • Antitrust and Aggregation — The European Commission’s antitrust case against Google is likely to be the first of many against aggregators, because the end game of Aggregation Theory is monopoly.
  • The Reality of Missing Out — Tech is entering a period of inequality where the big winners lift the sector as a whole even as smaller companies suffer. The best example is Facebook, Google, and digital advertising (Editor’s note: Over the last year this has gone from projection to reality).
  • Fake News — Facebook is under fire for fake news and filter bubbles; they are a problem, but most of the proposed solutions are far worse (see also: The Real Problem with Facebook and the News and Why Twitter Must Be Saved).

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Five Company-Specific Posts

  • The Amazon Tax — Amazon is building a lot of businesses that look like AWS: taxes on major industries that work to everyone’s benefit. The reason, though, is that AWS is a lot like Amazon itself.
  • Snapchat’s Ladder — Snapchat is on the verge of conquering the toughest messaging market in the world: the United States. The way they did it is by laddering-up (see also: Snapchat Spectacles and the Future of Wearables).
  • Beyond the iPhone — Apple’s event may have been lacking on the surface, but it laid the groundwork for innovations that will be revealed in time. And yes, it was courageous.
  • Facebook, Phones, and Phonebooks — There are two types of social networks, and Facebook wants to be both. The problem is that the company already chose public sharing over private communication (See also: The Audacity of Copying Well).
  • Oracle’s Cloudy Future — Larry Ellison has declared that Oracle is a cloud company, but their customer offering seems more suited to the world that was.

See also: The FANG Playbook

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Fifteen Daily Updates

I slightly expanded this list this year, in part because Daily Updates have continued to become even more in-depth; they are still very timely in covering the news of the day but contain their own strategic insights as well (please note that these are subscriber-only links; you can sign-up here).

  • January 4 — Augmented vs Virtual Reality
  • January 7 — Netflix Goes Global
  • February 16 — Kanye West and Tidal, The Problem with Exclusivity
  • February 17 — Apple Versus the FBI, Understanding iPhone Encryption, The Risks for Apple and Encryption (See also this Weekly Article: Apple, the FBI, and Security)
  • February 25 — Stripe Atlas, An Interview with Stripe CEO Patrick Collison
  • March 7 — Amazon Echo Expands, The Nest Failure
  • March 21 — The Significance of AlphaGo, Google to Sell Boston Dynamics, Google’s Self-Driving Car Will Take Awhile
  • April 28 — Facebook and New Market Disruption
  • May 4 — Doubting the iPhone Revisited, What Has Changed, On Being Bearish
  • May 16 — Apple, Didi, and Occam’s Razor; Uber in China (See also: August 1 — Didi Acquires Uber China, Why Uber China Was Doomed, Was Uber China Worth It?)
  • June 9 — Apple Makes Major Changes to App Store, The App Store and Apple’s Nature, Additional Notes
  • September 20 — Does Uber Have a Strategy Problem?, Netflix and Aggregation Theory
  • October 3 — Nutanix and Hyper-Convergence, The Conservation of Attractive Data-Center Profits
  • October 10 — Coupa IPOs — and Pops, Why (Most) IPOs are Under-Priced, Why the IPO Process Doesn’t Change
  • November 9 — Donald Trump is the President-Elect, Tech Under Trump, The Big Picture
  • December 20 — Uber Losses (But China Gains?), Uber and Unit Economics, Reconsidering Uber

Merry Christmas and Happy New Year. I’m looking forward to a great 2017!

The State of Technology at the End of 2016

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This is the third year I have written an article summarizing the state of technology. In 2014 I described the three historical epochs of consumer technology — the PC, the browser, and mobile — and the outline of the fourth, which I suggested was messaging; in 2015 I refined the fourth to be Facebook specifically (it is clearly WeChat in China) and wondered if Slack could form a similar foundation for the enterprise.

Each of these epochs laid the ground work for what followed: PCs were where browsers ran, and browsers enabled the build-out of cloud services that made mobile so compelling. Then, the omnipresence of mobile devices created the conditions for social media, specifically Facebook, to dominate a staggering amount of attention. What, then, has Facebook wrought?

Well, Donald Trump, for one.

From Startup to Establishment

For most of its existence Silicon Valley has been synonymous with startups; even during the rise of the PC it was Redmond-based Microsoft that became the establishment. The scrappy underdogs were clustered alongside the San Francisco Bay, and disruption was their mantra. And disrupt they have: while the IT era was about making existing companies more efficient, the Internet era has shaken the very foundations on which those companies rest. The media is the starkest example: Google and Facebook didn’t create better newspapers, but rather reduced newspapers — and all other forms of media — to just another piece of content no better or worse than cat GIFs or baby pictures jostling for attention in their carefully manicured gardens.

The pervasiveness of those gardens — not just Facebook and Google, but Apple and Amazon as well, and Microsoft at work — can at times seem overwhelming; these companies are anything but scrappy startups, and their relative market caps — five of the top eight (and the top five at the end of last quarter) — confirm that Silicon Valley is very much the establishment. That establishment, though, is of a far different nature than what came before.

Facebook, the Media, and Trump

The dispute about Facebook and fake news is the most obvious example: while previous claims of bias were about sins of commission — editors and journalists fitting the news to their preconceived notions — Facebook and Google both are being accused of a sin of omission: not actively removing purposeful mistruths from their platform. Critics contend that Facebook in particular bears some responsibility for recent election results, that absent “fake news” Donald Trump would not be the president-elect.

In fact, Facebook is responsible for Trump’s election, but not because of fake news, at least not directly. Rather, as I explained in the spring, the devolution of traditional media driven by Facebook’s commodification of content was not an isolated event: the power of America’s political parties stemmed directly from the fact that media (and its paid advertising model) was the gatekeeper of information. From that piece:

[Facebook has created] a curious dynamic in politics in particular: there is no one dominant force when it comes to the dispersal of political information, and that includes the parties described in the previous section. Remember, in a Facebook world, information suppliers are modularized and commoditized as most people get their news from their feed. This has two implications:

  • All news sources are competing on an equal footing; those controlled or bought by a party are not inherently privileged
  • The likelihood any particular message will “break out” is based not on who is propagating said message but on how many users are receptive to hearing it. The power has shifted from the supply side to the demand side

Screen Shot 2016-03-03 at 12.35.04 AM

This is a big problem for the parties as described in The Party Decides. Remember, in Noel and company’s description party actors care more about their policy preferences than they do voter preferences, but in an aggregated world it is voters aka users who decide which issues get traction and which don’t. And, by extension, the most successful politicians in an aggregated world are not those who serve the party but rather those who tell voters what they most want to hear.

Telling users what they want to hear is, of course, the real reason fake news gains traction: Facebook has monetized confirmation bias with its singular focus on engagement, and while I count that as a lesser evil than active political censorship, the broader point is that everyone and everyone — mainstream media, Macedonian teens, even political parties — are competing on a playing field where by default no one has a louder megaphone than anyone else.

The Rise of E-Commerce

This leveling of the playing field is not limited to media; I wrote this summer about the rise of Dollar Shave Club which leveraged YouTube, social media marketing, and e-commerce to challenge P&G’s Gillette in the market for U.S. razors. It didn’t matter that P&G had the money to build brand affinity with endless advertising and the leverage with retailers to ensure that Gillette dominated shelf space: virality is free, targeted advertising excels at niches, and when it comes to e-commerce, shelf space is effectively infinite.

Amazon is seeking to do the same thing for all physical goods, and the potential effects are massive. Just consider Amazon Prime’s two headline features — free shipping and Prime Video — and then consider three of the primary industries in the consumer economy: consumer packaged goods companies leverage their size to secure shelf space in big box retailers; both are reached by people driving their cars. These three industries are the largest TV advertisers, and Prime is taking aim at all three: Prime Video and its competitors are increasingly dominating non-live television viewing, and there are no commercials, while Amazon Prime obviates the need to visit retailers, and its infinite shelf space means niche products are much more viable.

This transformation echoes the impact of Facebook on the media: a dramatic leveling of the playing field. The advantages of scale that guaranteed success in the post-war era just don’t matter very much when advertising is cheap, shelf space is infinite, and shipping is free. And, just as Facebook’s breakdown of the media broke down the political parties, Amazon’s break-down of physical retail will have its own knock-on effects: carrying household supplies is a major reason to own a car, for example, which means Amazon is a laying the foundation for a service like Uber to shift from being a car supplement to a full-on substitute.1

New Opportunities

There is a certain symmetry to Dollar Shave Club and Donald Trump: both began by targeting niches and leveraging social media, but more importantly, the companies and institutions most invested in stopping them found themselves powerless to do so because their point of leverage had been circumvented by the Internet. That certainly ought to strike fear into the heart of any executive or politician whose institution is predicated on the old world order, but it is also an unprecedented opportunity to build something new.

To that end the most exciting companies in technology are those enabling just that: new companies for a new world without gatekeepers. Companies like Square, making it easy to accept payments offline, and Stripe doing the same online; both are expanding into adjacent services that make it easier to get a business off the ground. It has been encouraging to see Zenefits get its act together: the HR company and its competitors make it far easier and cheaper to grow a new business. And today’s giants play a role as well: AWS has made it dramatically cheaper to start any sort of company with an Internet component, and Amazon.com’s marketplace model means small companies can leverage the billions of dollars the company has invested in fulfillment — or they can go to Etsy, holding off the giants, with a share price that is up 50% on the year. Facebook has a role to play as well: a Facebook page is much easier for a small business to set up and is much more discoverable than a web page, and its advertising products are approachable and measurable in a way that even newspaper ads never were.

Who is Being Disrupted?

One of the ironies of The Innovator’s Dilemma being Silicon Valley’s favorite business book is that Clayton Christensen was writing for a completely different audience than your stereotypical techie. With his theory of disruption Christensen offered a compelling explanation for how it was big company managers, equipped with the best education and aided by the best consultants armed with the latest in best practices, fell prey again and again to upstart competitors with seemingly inferior products; technologists took it as a manual.

What remains so compelling about Christensen’s work is not its analysis of disk drives or steel mills but rather the focus on incentives, and how managers failed precisely because they did what they were supposed to do: meet the needs of their best customers in a way that ensured the viability and continued growth of their business — at least in the short term. The problem came when new technologies that were inferior for current customers’ needs made it possible to serve completely new customers at lower prices; rational managers at incumbent companies dismissed those technologies, allowing new entrants to serve those markets, but given that inferior technologies improve far more quickly than user needs grow, those new entrants eventually threaten incumbents with cheaper technology that is just as good, if not better.

The risk for the technology industry is that we are now the incumbents: we have a stake in keeping things exactly as they are, and we build products for ourselves — we’re our own best customers. That, though, cedes the future to the powerless — those with nothing to lose under the current system will by sheer necessity build the new.

That is why we need more companies like those above, ones that work for everyone, enabling the application of human creativity and ingenuity to the creation of a new world order. I know at this moment in history that seems optimistic, but the truth is that a new world order is inevitable; the question now is who will shape it.

  1. This is already reality in major Chinese cities ↩

Opendoor: A Startup Worth Emulating

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I suppose I appreciate the efficiency with which Techcrunch expressed its skepticism for tech’s latest unicorn, Opendoor; it’s right there in the headline: Online real estate service Opendoor raises $210M Series D despite risky financing model. And, to be fair, Opendoor’s approach is risky. Here’s a summary from a feature in Forbes magazine:1

Opendoor is betting that there are hundreds of thousands of Americans who value the certainty of a sale over getting the highest price. The company makes money by taking a service fee of 6%, similar to the standard real estate commission, plus an additional fee that varies with its assessment of the riskiness of the transaction and brings the total charge to an average of 8%. It then makes fixes recommended by inspectors and tries to sell the homes for a small premium. Buyers get to shop on their own timetable, using key codes for access to the properties, and they receive a 30-day guarantee that Opendoor will buy it back if they’re not satisfied and a two-year warranty on the electrical system and major appliances…

To succeed, it has to price the homes it buys accurately, without seeing them, and it has to sell them quickly to minimize the costs of carrying them. As interest rates rise or housing prices fall, Opendoor will have to figure out how to respond. When market risk increases, the company may charge a higher fee, its own version of surge pricing.

To paraphrase that Techcrunch headline, what were those investors thinking?

Zillow and Redfin

Real estate has long been an appealing market for investors, and for good reason: there is a lot of money at stake. The United States alone has $25 trillion worth of housing, and $900 billion of that changes hands every year; that means well over $50 billion in real estate fees and even more in mortgage interest. It is a market tailor-made for those ubiquitous “If we get X% of the market” pitches that have characterized many a startup business plan.

And yet, the most successful real estate startup, Zillow (which acquired its largest competitor Trulia a couple of years ago), is little more than a glorified marketing tool: the company makes most of its revenue by getting real estate agents — the ones collecting 6% of fees, split between the buying and selling agents2 — to pay to advertise their houses on the site. Certainly a free tool that makes it easier to find houses in a more intuitive way is valuable — Zillow has acquired the sort of userbase that allow it to build an advertising business for a reason — but at the end of the day the company is a tax on a system that hasn’t really changed in decades.

Redfin, meanwhile, which came up with the original houses on a map idea, is taking real estate agents on directly by being a real estate agent itself: their hook is that other tech monetization favorite, being cheap. Redfin keeps half as much as a typical selling agent3 and pays its agents a salary instead of commission. Both limit growth: regular agents with a stake in the current system steer home buyers away from Redfin properties, and hiring and training agents who aren’t interested in the upside from commission takes a lot of time and money.

The Opendoor Model

Opendoor is unique in two respects:

  • First, Opendoor is focused on sellers, the party with the least leverage in a typical residential transaction. Real estate agents sell lots of houses, buyers can choose from lots of houses, but sellers only have a single house to sell, which means the cost of any delay or complication falls on their shoulders. Opendoor relieves that burden by making an offer for the house based solely on an address and questionnaire; if the seller accepts Opendoor will send an inspector to verify the house, agree on any necessary repairs (which are paid by the seller), and close the deal as soon as the seller wishes.
  • Second, Opendoor explicitly charges sellers for having replaced total uncertainty with a bank wire: not just the same 6% that typically goes for buyer and seller agent fees, but also an additional 0-6% for “market risk” — i.e. dealing with the uncertainty of actually showing and selling the house — along with the aforementioned repair costs.

After that the house is on the market like any other, listed in the Multiple Listing Service (MLS), and freely accessible to regular real estate agents to whom Opendoor will pay the customary 3% fee a selling agent pays the buying agent. There are some additional niceties uniquely enabled by technology that both enhance the buying experience and keep prices down, particularly 24-hour access to listed homes with only a smartphone (and cameras to keep a watchful eye), but unlike Redfin, Opendoor isn’t seeking to compete with agents on price, giving them no reason to retaliate; remember, Opendoor actually charges more!

The Ingredients of Disruption

This is a dynamic that Redfin never understood: using technology to list houses was a sustaining advantage that was trivially co-opted by Zillow et al working hand-in-hand with realtors, while competing on price incentivized independent realtors to effectively collude against Redfin without any overt organization.

More broadly, technology alone is rarely sufficient when it comes to entering existing markets: at best a company focused on technology can skim a tax from incumbents, either through advertising like Zillow or service fees; taxes, though, by definition only capture a slice of the value being generated. To truly disrupt a market requires both a differentiated means of meeting the needs of an underserved market and a new business model.

Given that, look again at Opendoor’s two unique features:

  • Sellers are uniquely disadvantaged under the current system, which is another way of saying they are an underserved market with unmet needs
  • Opendoor has a new business model: taking advantage of a theoretical arbitrage opportunity (earning fees on houses sold at a slight mark-up) by leveraging technology in pursuit of previously impossible scale that should, in theory, ameliorate risk

Unlike Zillow and Redfin, Opendoor has the pieces in place to actually disrupt the market over the long run.

Opendoor Risk

There’s no question Opendoor’s approach is, to use Techcrunch’s term, risky. The company needs to accurately price homes sight unseen, carry them on the balance sheet while waiting for them to sell, and bear the risk of market collapses, and that’s above-and-beyond the usual startup risks: finding customers, scaling the product, dealing with regulations and paperwork that differ in every market they enter. It’s a whole lot less risky to just build a fancy search engine and charge advertising, or try to win on price.

Risk, though, is not only about downside; it’s about upside. More than that, the level of downside risk is correlated to upside risk: Opendoor has many more reasons why it might fail than Zillow or Redfin, but its potential upside is far greater as a result. First is the immediate opportunity: sellers who can’t wait. However, as Opendoor grows its seller base, especially geographically, its risk will start to decrease thanks to diversification and sheer size; that will allow it to lower its “market risk” charge which will lead to more sellers. More sellers means both less risk and an increasingly compelling product for buyers to access, first with a real estate agent and eventually directly. More buyers will mean lower marketing costs and faster sell-through, which will lower risk further and thus lower prices, pushing the cycle forward. It’s even possible to envision a future where Opendoor actually does uproot the anachronistic real estate agent system that is a relic of the pre-Internet era, and they will have done so with realtors not only not fighting them but, on the buying side, helping them.

Or, in the next downturn, the entire company might go bust.

Opendoor’s Potential Impact

There is a deeper reason why I am excited about Opendoor, and it too is related to how the company’s approach differs from Zillow’s and Redfin’s. While Zillow makes it easier to look for new houses, and Redfin promises to save sellers a few bucks, making it trivial to sell a house has the potential to fundamentally impact our economy at a time we desperately need exactly that. Many, including myself, have written about how globalization and technology are upending the job market; one particular challenge is that often new jobs are created in different geographic areas than where job seekers are located.

To that end the potential for Opendoor to dramatically increase liquidity in the housing market by buying direct from sellers is not just a business opportunity, but one with the potential to increase dynamism in the job market. Granted, it will take a long time for Opendoor to move into the towns where this sort of service is most needed, but the idea is very much a move in the right direction.

I suspect that this positive impact is related to Opendoor’s business model: by identifying a market need and offering a paid service to alleviate that need, Opendoor is creating value as opposed to taxing a few bucks off the top of an existing market or simply trying to be cheap. While I am hardly anti-advertising, the fact of the matter is that it is a zero-sum market: advertising has been just over 1% of U.S. GDP for a century, which means advertising-based businesses are by-and-large stealing value from other advertising-based business, not creating their own, at least from a dollars-and-cents perspective (end users have been the real beneficiaries, as most of the value creation of services like Google and Facebook is consumer surplus). Meanwhile, simply being cheaper is certainly a viable business model, but it is on the whole deflationary, which is to say value-destructive; sure, money saved can be deployed elsewhere, but the long-term benefits are much more difficult to trace.

To that end I hope Opendoor succeeds simply so it can be a role model for tech: taking on big risks for big rewards that create real value by solving real problems is the best possible way our industry can create benefits that extend beyond investors and shareholders; for too long too much money and talent has been poured into low-risk digital-only businesses that aspire to little more than leeching off of value creators that already exist, or at best cut them off at the knees with low prices, with the assumption that someone else will pick up the pieces. And while I am all for appropriate skepticism, to not consider upside is to be just as oblivious to risk as those who don’t consider what might go wrong.

  1. I can’t quote the Techcrunch article as it fails to explain the business model properly ↩

  2. This is unique to North America; in most countries the selling agent works alone. However, there is little incentive to change the system because the buyer doesn’t pay for their agent, the seller does via the selling agent ↩

  3. Which means they charge the seller 4.5%; the buying agent still gets 3%. If a buyer goes through Redfin they also get a partial rebate of the fee ↩

How Google Is Challenging AWS

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Big companies are often criticized for having “missed” the future — from the comfortable perch of a present where said future has come to pass, of course — but while the future is still the future incumbents are first more often than not. Probably the best example is Microsoft: the company didn’t “miss mobile” — Windows Mobile came out in 2000 — but rather was handicapped by its allegiance to its license-based modular business model and inability to envision a world where its core product (Windows) was a planet orbiting mobile’s sun; everything about Windows Mobile’s design presumed the exact opposite.

One could make the same argument about Google and the enterprise; both G Suite (née Google Apps for Your Domain) and Google Docs launched a decade ago and enjoyed modest success, particularly in smaller businesses and education; unsurprisingly, both markets share broadly similar characteristics to Google’s core consumer user base — limited configurability and a low price were good things. Traction was harder to come by in larger enterprises, though, and in fact over the last few years Office 365 has well out-paced G Suite, not only growing faster but winning back customers.

Still, for all the success Microsoft has had with Office 365, the real giant of cloud computing — which is to say the future of enterprise computing — is, as is so often the case, a company no one saw coming: the same year Google decided to take on Microsoft Amazon launched Amazon Web Services. What makes AWS so compelling is the way that it reflects Amazon itself: it is built for scale and with clearly-defined and hardened interfaces. Customers — first Amazon but also companies around the world — access “primitives” that can be mixed-and-matched to build a more efficient, scalable, and secure back-end than nearly any company could build on its own.

AWS’ Primitives

Earlier this year in The Amazon Tax I explained how Amazon’s AWS strategy sprang from the same approach that made the company successful in the first place:

The company is organized with multiple relatively independent teams, each with their own P&L, accountabilities, and distributed decision-making. [The Everything Store author Brad] Stone explained an early Bezos initiative (emphasis mine):

The entire company, he said, would restructure itself around what he called “two-pizza teams.” Employees would be organized into autonomous groups of fewer than ten people — small enough that, when working late, the team members could be fed with two pizza pies. These teams would be independently set loose on Amazon’s biggest problems…Bezos was applying a kind of chaos theory to management, acknowledging the complexity of his organization by breaking it down to its most basic parts in the hopes that surprising results might emerge.

Stone later writes that two-pizza teams didn’t ultimately make sense everywhere, but as he noted in a follow-up article the company remains very flat with responsibility widely distributed. And there, in those “most basic parts”, are the primitives that lend themselves to both scale and experimentation. Remember the quote above describing how Bezos and team arrived at the idea for AWS:

If Amazon wanted to stimulate creativity among its developers, it shouldn’t try to guess what kind of services they might want; such guesses would be based on patterns of the past. Instead, it should be creating primitives — the building blocks of computing — and then getting out of the way.

Steven Sinofsky is fond of noting that organizations tend to ship their org chart, and while I began by suggesting Amazon was duplicating the AWS model, it turns out that the AWS model was in many respects a representation of Amazon itself (just as the iPhone in many respects reflects Apple’s unitary organization): create a bunch of primitives, get out of the way, and take a nice skim off the top.

AWS’ offering has certainly expanded far beyond infrastructure like (virtualized) processors, hard drives, and databases, both in terms of further abstraction (e.g. Lambda “serverless” computing) and up the stack into platform and software services, but the foundation of its success continues to be Amazon’s pure platform approach: they provide the pieces for enterprises to build just about anything they want.

Google is a Product Company

Google, meanwhile, has never really been a platform company; in fact, while Google is often cast as Apple’s opposite — the latter is called a product company, and the former a services one — that only makes sense if you presume that only hardware can be a product. A more expansive definition of “product” — a fully realized solution presented to end users — would show the two companies are in fact quite similar.

Make no mistake: the differences between cloud services and hardware are profound (which I explored at length in Apple’s Organizational Crossroads), but so are the differences between being a product company and being a platform one. The ideal product, whether it be a smartphone or a search box, achieves simplicity and a great user experience through tremendous effort in design and engineering that, ideally, is never seen by the end user. Indeed, this is why integrated products win in consumer markets, and make no mistake, Google’s consumer-focused services have traditionally been as integrated on the back-end as iPhones are.

Note, though, that this is the exact opposite of the model employed by not just Amazon but also Microsoft, the pre-eminent platform company of the IT era: instead of integrating pieces to deliver a product AWS went in the opposite direction, breaking down all of the pieces that go into building back-end services into fully modular parts; Microsoft did the same with its Win32 API. Yes, this meant that Windows was by design a worse platform in terms of the end user experience than, say, Mac OS, but it was far more powerful and extensible, an approach that paid off with millions of line of business apps that even today keep Windows at the center of business. AWS has done the exact same thing for back-end services, and the flexibility and modularity of AWS is the chief reason why it crushed Google’s initial cloud offering, Google App Engine, which launched back in 2008. Using App Engine entailed accepting a lot of decisions that Google made on your behalf; AWS let you build exactly what you needed.

Google’s Platform Antidote

The Windows example is instructive when it comes to thinking about how Google has since changed its approach: the massive ecosystem built around Microsoft’s extensive API ended up being the ultimate lock-in. Most obviously the apps built for Windows were not easily ported to other operating systems, but just as important was the huge network of partners and value-added resellers that made Windows the only viable choice for enterprise. Amazon is hard at work building the exact same sort of ecosystem.

And yet, it has never been more viable to not use Windows, first for consumers but also for enterprise, and the reason is the web: here was a new runtime that sat on top of Windows but did not depend on it,1 and on the consumer side Google was the biggest winner. Indeed, the rise of the browser explains AWS as well: any new business application is built for the web (including apps that run on web-based APIs) and it is accessible on any device.

It turns out that over the last couple of years Google has undertaken a sort of browser approach to enterprise computing . In 2014 Google announced Kubernetes, an open-source container cluster manager based on Google’s internal Borg service that abstracts Google’s massive infrastructure such that any Google service can instantly access all of the computing power they need without worrying about the details. The central precept is containers, which I wrote about in 2014: engineers build on a standard interface that retains (nearly) full flexibility without needing to know anything about the underlying hardware or operating system (in this it’s an evolutionary step beyond virtual machines).

Where Kubernetes differs from Borg is that it is fully portable: it runs on AWS, it runs on Azure, it runs on the Google Cloud Platform, it runs on on-premise infrastructure, you can even run it in your house. More relevantly to this article, it is the perfect antidote to AWS’ ten year head-start in infrastructure-as-a-service: while Google has made great strides in its own infrastructure offerings, the potential impact of Kubernetes specifically and container-based development broadly is to make irrelevant which infrastructure provider you use. No wonder it is one of the fastest growing open-source projects of all time: there is no lock-in.

But how does that help Google? After all, even if Kubernetes becomes the standard for enterprise clouds Amazon’s broader ecosystem lock-in is still present (and the company has its own container strategy that further locks customers into AWS); Google needs a differentiator.

Costs Versus Experience

Here again the desktop is instructive: the open nature of the web running on platform-agnostic browsers did not make Google successful per se; rather, the openness of the web created the conditions for the best technology to win. And not only did Google have the best search engine, but the reason it was the best — its reliance on links instead of simply page content — meant that as the web got bigger Google, unlike its competitors, got better.

I think this is an idea that can be abstracted to be broadly applicable; indeed, it’s a core piece of Aggregation Theory: as distribution (or switching) costs decrease, the importance of the user experience increases. To put it another way, when you can access any service, whether that be news or car-sharing or hotels or video or search etc., the one that is the best will not only win initially but will see its advantages compound.

This is Google’s bet when it comes to the enterprise cloud: open-sourcing Kubernetes was Google’s attempt to effectively build a browser on top of cloud infrastructure and thus decrease switching costs; the company’s equivalent of Google Search will be machine learning.

Machine Learning and Data

It seems certain that machine learning will be increasingly dominated by cloud services: both are about processing scale and massive amounts of data, and only a select few behemoths will have the financial capability to not only build out the infrastructure required but also have the wherewithal to employ the best machine learning engineers in the world. That, by extension, means that for most enterprises the differentiation arising from machine learning will derive first and foremost from whether or not their data is in the cloud (there will be on-premise solutions, but I expect them to fall more and more behind over time), but secondly from which cloud provider they choose.

That raises the stakes for cloud providers themselves; superior machine learning offerings can not only be a differentiator but a sustainable one: being better will attract more customers and thus more data, and data is the fuel by which machine learning improvement comes about. And it is because of data that Google is AWS’ biggest threat in the cloud.

I described how Google’s enterprise business was limited by its consumer focus above, but the big advantage that Google has is that it has been working with massive amounts of data for nearly two decades, and developing powerful machine learning algorithms for the last several years. Still, it’s the data that matters most-of-all, and the best evidence that is the case came last year when Google open-sourced TensorFlow, a blueprint for machine learning: as I noted in TensorFlow and Monetizing Intellectual Property Google’s willingness to share its approach was an implicit admission that its superior data and processing infrastructure was a sustainable advantage.

We’re just now starting to see that advantage applied to Google’s cloud offering. Just before Thanksgiving Google made a series of product announcements that clearly leveraged its data advantage:

  • The Cloud Natural Language API, which uses machine learning to analyze text, graduated to general availability
  • A premium edition of the Cloud Translation API, which uses machine learning to massively improve accuracy in translating eight languages (above-and-beyond the standard edition that supports over 100 languages)
  • A big price reduction for the Cloud Vision API, which uses machine learning to analyze images
  • A new Cloud Jobs API that uses machine learning to match potential employees with jobs

These four join the Cloud Prediction API that uses machine learning to, well, make predictions. It, along with the first three APIs above, is clearly derived from various Google consumer products; the Jobs API likely builds on an internal Google tool, as well as Google’s wealth of data from all over the web. In each case Google has spent years honing its algorithms so that by the time they are applied to a corporate data set the results are very likely superior, or at least far down the training funnel. I expect this advantage to persist and be meaningful.

Still, Google will have to do more, which is why the other big announcement was the creation of the Google Cloud Machine Learning group headed by Fei-Fei Li and Jia Li: this group will be charged with building new machine learning APIs specifically for business; to put it another way, they are tasked with productizing Google’s machine learning capabilities.

That, in a roundabout way, gets to the genius of Google’s strategy: the company was outpaced by Amazon in the first wave of cloud computing because success rested on being the best platform; by open-sourcing Kubernetes in an attempt to shift the industry to vendor-agnostic containers, Google is trying to move the plane of competition to products. After all, it’s often easier to change the rules of competition than to change your fundamental nature as a company.

To be sure, Google’s success is not assured: the company still has to grapple with a new business model — sales versus ads — and build up the sort of organization that is necessary for not just sales but also enterprise support. Both are areas where Amazon has a head start, along with a vastly larger partner ecosystem and a larger feature set generally.

And, of course, AWS has its own machine learning API, along with IBM and Microsoft. Microsoft is likely to prove particularly formidable in this regard: not only has the company engaged in years of research, but the company also has experience productizing technology for business specifically; Google’s longstanding consumer focus may at times be a handicap. And as popular as Kubernetes may be broadly, it’s concerning that Google is not yet eating its own dog food.

Still, Google will be a formidable competitor: its strategy is sound and, perhaps more importantly, the urgency to find a new line of business is far more pressing today than it was in 2006. Most importantly, the shift to cloud computing is still in its beginning stages, and while Amazon seems to be living the furthest in the future, the future has not happened yet; it will be fascinating to watch Google’s attempt to change the rules under which said future will operate.

  1. ActiveX notwithstanding ↩

Fake News

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Between 2001 and 2003, Judith Miller wrote a number of pieces in the New York Times asserting that Iraq had the capability and the ambition to produce weapons of mass destruction. It was fake news.

Looking back, it’s impossible to say with certainty what role Miller’s stories played in the U.S.’s ill-fated decision to invade Iraq in 2003; the same sources feeding Miller were well-connected with the George W. Bush administration’s foreign policy team. Still, it meant something to have the New York Times backing them up, particularly for Democrats who may have been inclined to push back against Bush more aggressively. After all, the New York Times was not some fly-by-night operation, it was the preeminent newspaper in the country, and one generally thought to lean towards the left. Miller’s stories had a certain resonance by virtue of where they were published.

It’s tempting to make a connection between the Miller fiasco and the current debate about Facebook’s fake news problem; the cautionary tale that “fake news is bad” writes itself. My takeaway, though, is the exact opposite: it matters less what is fake and more who decides what is news in the first place.

Facebook’s Commoditization of Media

In Aggregation Theory I described the process by which the demise of distribution-based economic power has resulted in the rise of powerful intermediaries that own the customer experience and commoditize their suppliers. In the case of Facebook, the social network started with the foundation of pre-existing offline networks that were moved online. Given that humans are inherently social, users started prioritizing time on Facebook over time spent reading, say, the newspaper (or any of the effectively infinite set of alternatives for attention).

It followed, then, that it was in the interest of media companies, businesses, and basically anyone else who wanted to get the attention of users, to be on Facebook as well. This was great for Facebook: the more compelling content it could provide to its users, the more time they would spend on Facebook; the more time they spent on Facebook, the more opportunities Facebook would have to place advertisements in front of them. And, critically, the more time users spent on Facebook, the less time they had to read anything else, further increasing the motivation for media companies (and businesses of all types) to be on Facebook themselves, resulting in a virtuous cycle in Facebook’s favor: by having the users Facebook captured the suppliers, which deepened their hold on the users, increasing their power over suppliers.

This process reduced Facebook’s content suppliers — media companies — into pure commodity providers. All that mattered for everyone was the level of engagement: media companies got ad views, Facebook got shares, and users got the psychic reward of having flipped a bit in a database. Of course not all content was engaging to all users; that’s what the algorithm was for: show people only what they want to see, whether it be baby pictures, engagement announcements, cat pictures, quizzes, or, yes, political news. It was, from Facebook’s perspective — and, frankly, from its users’ perspective — all the same. That includes fake news too, by the way: it’s not that there is anything particularly special about news from Macedonia, it’s that according to the algorithm there isn’t anything particularly special about any content, beyond the level of engagement it drives.

The Media and Trump

There has been a lot of discussion — in the media, naturally — about how the media made President-elect Donald Trump. The story is that Trump would have never amounted to anything had the media not given him billions of dollars worth of earned media — basically news coverage (as opposed to paid media, which is advertising) — and that the industry needed to take responsibility. It’s a lovely bit of self-reflection that lets the industry deny the far more discomforting reality: that the media couldn’t have done a damn thing about Trump if they had wanted to.

The reason the media covered Trump so extensively is quite simple: that is what users wanted. And, in a world where media is a commodity, to act as if one has the editorial prerogative to not cover a candidate users want to see is to face that reality square in the face, absent the clicks that make the medicine easier to take.

Indeed, this is the same reason fake news flourishes: because users want it. These sites get traffic because users click on their articles and share them, because they confirm what they already think to be true. Confirmation bias is a hell of a drug — and, as Techcrunch reporter Kim-Mai Cutler so aptly put it on Twitter, it’s a hell of a business model.

Why Facebook Should Fix Fake News

So now we arrive at the question of what to do about fake news. Perhaps the most common sentiment was laid out by Zeynep Tufekci in the New York Times: Facebook should eliminate fake news and the filter effect — the tendency to see news you already agree with — while they’re at it. Tufekci writes:

Mark Zuckerberg, Facebook’s chief, believes that it is “a pretty crazy idea” that “fake news on Facebook, which is a very small amount of content, influenced the election in any way.” In holding fast to the claim that his company has little effect on how people make up their minds, Mr. Zuckerberg is doing real damage to American democracy — and to the world…

The problem with Facebook’s influence on political discourse is not limited to the dissemination of fake news. It’s also about echo chambers. The company’s algorithm chooses which updates appear higher up in users’ newsfeeds and which are buried. Humans already tend to cluster among like-minded people and seek news that confirms their biases. Facebook’s research shows that the company’s algorithm encourages this by somewhat prioritizing updates that users find comforting…

Tufekci offers up a number of recommendations for Facebook, including sharing data with outside researchers to better understand how misinformation spreads and the extent of filter bubbles, 1 acting much more aggressively to eliminate fake news like it does spam and other objectionable content, rehiring human editors, and retweaking its algorithm to favor news balance, not just engagement.

Why Facebook Should Not

All seem reasonable on their face, but in fact Tufekci’s recommendations are radical in their own way.

First, there is no incentive for Facebook to do any of this; while the company denies this report in Gizmodo that the company shelved a change to the News Feed algorithm that would have eliminated fake news stories because it disproportionately affected right-wing sites, the fact remains that the company is heavily incentivized to be perceived as neutral by all sides; anything else would drive away users, a particularly problematic outcome for a social network.2

Moreover, any move away from a focus on engagement would, by definition, decrease the time spent on Facebook, and here Tufekci is wrong to claim that this is acceptable because there is “no competitor in sight.” In fact, Facebook is in its most challenging position in a long time: Snapchat is stealing attention from its most valuable demographics, even as the News Feed is approaching saturation in terms of ad load, and there is a real danger Snapchat will beat the company to the biggest prize in consumer tech: TV-centric brand advertising dollars.

There are even more fundamental problems, though: how do you decide what is fake and what isn’t? Where is the line? And, perhaps most critically, who decides? To argue that the existence of some number of fake news items amongst an ocean of other content ought to result in active editing of Facebook content is not simply a logistical nightmare but, at least when it comes to the potential of bad outcomes, far more fraught than it appears.

That goes double for the filter bubble problem: there is a very large leap from arguing Facebook impacts its users’ flow of information via the second-order effects of driving engagement, to insisting the platform actively influence what users see for political reasons. It doesn’t matter that the goal is a better society, as opposed to picking partisan sides; after all, partisans think their goal is a better society as well. Indeed, if the entire concern is the outsized role that Facebook plays in its users’ news consumption, then the far greater fear should be the potential of someone actively abusing that role for their own ends.

I get why top-down solutions are tempting: fake news and filter bubbles are in front of our faces, and wouldn’t it be better if Facebook fixed them? The problem is the assumption that whoever wields that top-down power will just so happen to have the same views I do. What, though, if they don’t? Just look at our current political situation: those worried about Trump have to contend with the fact that the power of the executive branch has been dramatically expanded over the decades; we place immense responsibility and capability in the hands of one person, forgetting that said responsibility and capability is not so easily withdrawn if we don’t like the one wielding it.

To that end I would be far more concerned about Facebook were they to begin actively editing the News Feed; as I noted last week I’m increasingly concerned about Zuckerberg’s utopian-esque view of the world, and it is a frighteningly small step from influencing the world to controlling the world. Just as bad would be government regulation: our most critical liberty when it comes to a check on tyranny is the freedom of speech, and it would be directly counter to that liberty to put a bureaucrat — who reports to the President — in charge of what people see.

The key thing to remember is that the actual impact of fake news is dependent on who delivers it: sure, those Macedonian news stories aren’t great, but their effect, such as it is, comes from confirming what people already believe. Contrast that to Miller’s stories in the New York Times: because the New York Times was a trusted gatekeeper, many people fundamentally changed their opinions, resulting in a disaster the full effects of which are still being felt. In that light, the potential downside of Facebook coming anywhere close to deciding the news can scarcely be imagined.

Liberty and Laziness

There may be some middle ground here: perhaps some sources are so obviously fake that Facebook can easily exclude them, ideally with full transparency about what they are doing and why. And, to the extent Facebook can share data with outside researchers without compromising its competitive position, it should do so. The company should also provide even more options to users to control their feed if they wish to avoid filter bubbles.

In truth, though, you and I know that few users will bother. And that, seemingly, is what bothers many of Facebook’s critics the most. If users won’t seek out the “right” news sources, well, then someone ought to make them see them. It all sounds great — and, without question, a far more convenient solution to winning elections than actually making the changes necessary to do so — until you remember that that someone you just entrusted with such awesome power could disagree with you, and that the very notion of controlling what people read is the hallmark of totalitarianism.

Let me be clear: I am well aware of the problematic aspects of Facebook’s impact; I am particularly worried about the ease with which we sort ourselves into tribes, in part because of the filter bubble effect noted above (that’s one of the reasons Why Twitter Must Be Saved). But the solution is not the reimposition of gatekeepers done in by the Internet; whatever fixes this problem must spring from the power of the Internet, and the fact that each of us, if we choose, has access to more information and sources of truth than ever before, and more ways to reach out and understand and persuade those with whom we disagree. Yes, that is more work than demanding Zuckerberg change what people see, but giving up liberty for laziness never works out well in the end.

For more about how the Internet has fundamentally changed politics, please see this piece from March, The Voters Decide.

  1. Facebook has done a study about the latter, but as Tufekci and others have documented, the study was full of problems  ↩

  2. Indeed, it wasn’t that long ago that I was making this exact argument in response to those who insisted Facebook would alter the News Feed to serve their own political purposes ↩