Early Indications October 2014: Who’s Watching?

I didn’t really go looking for this particular constellation of ideas, but several good pieces really got me connecting the dots and this month’s letter represents an effort to spell things out with regard to surveillance.

1) The Economist published one of its special reports on September 13 on online advertising. Entitled “Little Brother,” the report argues that mobile devices combined with social networks are providing advertisers — and more importantly, a complex ecosystem of trackers, brokers, and aggregators illustrated in Luma Partners’ now-famous eye-chart slides — with unprecedented targeting information. One prominently quoted survey asserts that marketers have seen more change in the past two years than in the previous 50. Among the biggest of these shifts: programmatic ad buying now works much like algorithmic trading on Wall Street, with automated ad bidding and fulfillment occurring in the 150 milliseconds between website arrival and page load on the consumer device.

[As I type this, Facebook announced that the firm made $3.2 billion in one quarter, mostly from ads, nearly $2 billion of it from mobile.]

Given that surveillance pays dividends in the form of more precise targeting — one broker sells a segment called “burdened by debt: small-town singles” — it is no surprise that literally hundreds of companies are harvesting user information to fuel the bidding process: online ad inventory is effectively infinite, so user information is the scarce commodity and thus valued. This marks a radical reversal from the days of broadcast media, when audience aggregators such as NBC or the New York Times sold ad availability that was constrained by time or space. Thus the scarcity has shifted from publishers to ad brokers who possess the targeting information gleaned from Facebook, GPS, Twitter, Google searches, etc. Oh, and anyone who does even rudimentary research on the supposedly “anonymous” nature of this data knows it isn’t, really: Ed Felten, a respected computer scientist at Princeton, and others have repeatedly shown how easy de-anonymization is. (Here’s one widely cited example.)

2) In another sign that surveillance is a very big deal, not only for advertising, the always-astute security guru Bruce Schneier announced that his next book Data and Goliath, due out in March, addresses this issue.

3) Robots, which for our purposes can be defined as sensor platforms, are getting better — fast — and Google has acquired expertise in several forms of the discipline:

-the self-driving car (that has severe real-world limitations)

-Internet of Things (Nest and Waze)

-autonomous military and rescue robots (Boston Dynamics and Schaft).

4) A September 28 post by Steve Cheney raised the prospect of Google moving some or all of the aforementioned robot platforms onto some version of Android. While he predicted that “everything around you will feel like an app,” I’m more concerned that every interaction with any computing-driven platform will be a form of surveillance. From garage-door openers and thermostats to watches to tablets to “robots” (like the one Lowes is prototyping for store assistance) to cars, the prospect of a Google-powered panopticon feels plausible. (I looked for any mention of robotics in the Google annual report but all the major acquisitions were made in this fiscal year, so next year's 10-K will bear watching on this topic.)

5) Hence Apple’s recent positioning makes competitive sense. When Tim Cook said “A few years ago, users of Internet services began to realize that when an online service is free, you’re not the customer. You’re the product.” he was ahead of the curve, I believe: according to the Economist report, only .00015% of people use those little triangle things to opt out of online ad tracking. In Cook’s and Apple’s narrative, premium prices implicitly become more reasonable to those who value privacy insofar as there is no “audience commodity” as at eBay, Amazon, Google Twitter, or Facebook.

6) One other thing to consider here is how that information is being processed at unprecedented scale. When The Economist noted the likeness of ad-buying to algo trading, we enter the world of artificial intelligence, something Google counts as a core competency, with 391 papers published not to mention untold portions of secret sauce.

Some very smart people are urging caution here. Elon Musk was at MIT for a fascinating (if you’re a nerd) discussion of rockets, Tesla, the hyperloop, and space exploration. Thus for someone serious about a Mars space base to warn against opening an AI Pandora’s box was quite revealing:

“I think we should be very careful about artificial intelligence. If I were to guess like what our biggest existential threat is, it’s probably that. So we need to be very careful with the artificial intelligence. Increasingly scientists think there should be some regulatory oversight maybe at the national and international level, just to make sure that we don’t do something very foolish. With artificial intelligence we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water, it’s like yeah he’s sure he can control the demon. Didn’t work out.”

(The complete MIT talk is here)

Musk is not alone. The University of Oxford’s Nick Bostrom recently wrote Superintelligence (maybe best thought of as an alternative excursion into Kurzweil-land), a book that quite evidently is grappling with the unknown unknowns we are bumping up against. He knows of what he speaks, but the book is, by his own admission, a frustrating read: no generation of earth’s population has ever had to ask these questions before. The book’s incompleteness and tentativeness, while making for a suboptimal read, are at the same time reassuring: someone, both informed and set in a broad context, is asking the questions many of us want on the table but lack the ability, vocabulary, and credibility to raise ourselves.
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In a nutshell, there it is: mobile devices and social networks generate data points that supercomputing and sophisticated analytical tools turn into ad (or terrorist, or tax-cheat) profiling data. Computing liberated from desktop boxes and data centers moves in, and acts on, the physical world, extending surveillance further. Apple positions itself as a self-contained entity selling consumers stuff they pay for, not selling eyeballs/purchase histories/log-in fields/expressed and implied preferences to advertisers. In sharp contrast, Google has repeatedly shown — with Streetview, wi-fi access point mapping, Buzz, and Google+ — a desire to collect more information about individuals than many of those individuals would voluntarily reveal. With AI in the picture, the prospect of surveillance producing some very scary scenario — it may not accurately be called a breach, just as the flash crash wasn’t illegal — grows far more likely. Human safeguards didn’t work at the NSA; why should they work in less secure organizations? Like Bostrom, I have no ready answers other than to lead a relatively careful digital existence and hope that the wisdom of caution and respect for privacy will edge out the commercial pressures in the opposite direction.

Next month: unexpected consequences of a surveillance state.

Early Indications September 2014: Alternatives to Industry

In classic business school strategy formulation, a company’s industry is taken as the determining factor in cost structures, capital utilization, and other constraints to the pursuit of market success. Nowhere is this view more clearly visible than in Michael Porter’s seminal book Competitive Strategy, in which the word “industry” appears regularly.

I have long contended that Internet companies break this formulation. A series of blog posts (especially this one) in the past few weeks have crystallized this idea for me. The different paths pursued by Apple, Amazon, and Google — very different companies when viewed through the lens of industries — lead me to join those who contend that despite their different microeconomic categories, these three companies are in fact leading competitors in important ways: but not of the Coke/Pepsi variety.

Let us consider the traditional labels first. Amazon is nominally a retailer, selling people (and businesses) physical items that it distributes with great precision from its global network of warehouses. Its margins are thin, in part because of the company’s aggressive focus on delivering value to the customer, often at the cost of profitability at both Amazon itself and its suppliers.

Apple designs, supervises the manufacture of, and distributes digital hardware. Its profit margins are much higher than Amazon’s, in large part because its emphasis on design and usability allows it to command premium prices. Despite these margins and a powerful brand, investors value the company much less aggressively than they do Amazon.

Google, finally, collects vast sums of data and provides navigation in the digital age: search, maps, email. Algorithms manage everything from web search to data-center power management to geographic way-finding. In the core search business, profit margins are high because of the company’s high degree of automation (self-service ad sales) and the wide moats the company has built around its advertising delivery.

Thus in traditional terms, we have a mega-retailer, a computer hardware company, and a media concern.

When one digs beneath the surface, the picture morphs rather dramatically. Through a different lens, the three companies overlap to a remarkable degree — just not in ways that conform to industry definitions.

All three companies run enormous cloud data center networks, albeit monetized in slightly different ways. Apple and Amazon stream media; Google and Amazon sell enterprise cloud services; Apple and Google power mobile ecosystems with e-mail, maps, and related services. All three companies strive to deepen consumer connections through physical devices.

Apple runs an industry-leading retail operation built on prime real estate at the same time that Amazon is reinventing the supply-chain rule book for its fulfillment and now sortation centers. (For more on that, see this fascinating analysis by ChannelAdvisor of the Amazon network. In many cases, FCs are geographically clustered rather than spread more predictably across the landscape) Both of these retail models are hurting traditional mall and big-box chains.

At the most abstract but common level, all three companies are spending billions of dollars to connect computing to the physical world, to make reality a peripheral of algorithms, if you will. Amazon’s purchase of Kiva and its FC strategy both express an insistent strategy to connect a web browser or mobile device to a purchase, fulfilled in shorter and shorter time lags with more and more math governing the process. In the case of Kindle and streaming media, that latency is effectively zero, and the publishing industry is still in a profoundly confused and reactive state about the death of the physical book and its business model. The Fire phone fits directly into this model of making the connection between an information company and its human purchasers of stuff ever more seamless, but its weak market traction is hardly a surprise, given the strength of the incumbents -- not coincidentally, the other two tech titans.

Apple connects people to the world via the computer in their pocket. Because we no longer have the Moore’s law/Intel scorecard to track computing capacity, it’s easy to lose sight of just how powerful a smartphone or tablet is: Apple's A8 chip in the new iPhone contains 2 Billion (with a B) transistors, equivalent to the PC state of the art in 2010. In addition, the complexity of the sensor suite in a smartphone — accelerometers, microphone, compasses, multiple cameras, multiple antennae — is a sharp departure from a desktop computer, no matter how powerful, that generally had little idea of where it was or what its operator was doing. And for all the emphasis on hardware, Nokia’s rapid fall illustrates the power of effective software in not just serving but involving the human in the experience of computing.

Google obviously has a deep capability in wi-fi and GPS geolocation, for purposes of deeper knowledge of user behavior. The company’s recent big-bet investments — the Nest thermostat, DARPA robots, Waze, and the self-driving car team — further underline the urgency of integrating the world of physical computing on the Android platform(s) as a conduit for further and further knowledge of user behavior, social networks, and probably sentiment, all preconditions to more precise ad targeting.

Because these overlaps fail to fit industry definitions, metrics such as market share or profit margin are of limited utility: the three companies recruit, make money, and innovate in profoundly different ways. Amazon consistently keeps operating information quiet (nobody outside the company knows how many Kindle devices have been sold, for example) so revenue from the cloud operation is a mystery; Google’s finances are also somewhat difficult to parse, and the economics of Android for the company were never really explicated, much less reported. Apple provides most likely the most transparency of the three companies, but that’s not saying a lot, as the highly hypothetical discussion of the company’s massive cash position would suggest.

From a business school or investor perspective, the fact of quasi-competition despite the lack of industry similitude suggests that we are seeing a new phase of strategic analysis and execution, both enabled and complicated by our position with regard to Moore’s law, wireless bandwidth, consumer spending, and information economics. The fact that both Microsoft and Intel are largely irrelevant to this conversation (for the moment anyway) suggests several potential readings: that success is fleeting, that the PC paradigm limited both companies’ leaders from seeing a radically different set of business models, that fashion and habit matter more than licenses and seats, that software has changed from the days of the OSI layer cake.

In any event, the preconditions for an entirely new set of innovations — whether wearable, embedded/machine, algorithmic, entertainment, and/or health-related — are falling into place, suggesting that the next 5-10 years could be even more foreign to established managerial teaching and metrics. Add the external shocks — and shocks don’t get much more bizarre than ebola and media-savvy beheadings — and it’s clear that the path forward will be completely fascinating and occasionally terrifying to traverse. More than inspiration or insight from our business leaders, we will likely need courage.