Illustrated by Jiani Hou


“Well, in our country,” said Alice, still panting a little, “you’d generally get to somewhere else—if you run very fast for a long time, as we’ve been doing.”

“A slow sort of country!” said the Queen. “Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”

 Lewis Carroll, Through the Looking-Glass


The Biology of Business

In business as in biology, primary producers occupy the lowest level in the food chain.  In biology, as organisms rise up the food chain they rise in trophic level: for example, from plants to herbivores.  Similarly, in business, as products rise up the value chain they also rise in trophic level: for example, from raw materials to manufactured goods.  In biology, carnivores are the apex predators sitting atop the food chain.  What defines the analogue apex predator in business?

Arguably, the most successful companies in any industry today are Silicon Valley titans Amazon, Google and Facebook (and their fellow Chinese titans, Baidu, Alibaba and Tencent).  Though each of the Silicon Valley giants is nominally in a different business – e-commerce, search, social networking – all three are unified by a common business model.  All three are in the business of collecting as much data as possible, by every means possible, and making money off that data.  Period.  This is all they do.  This is what might be known as the “Amazoogle” business model.

Although the mechanics (front-end) of how Amazon, Google and Facebook collect their data may differ, all three derive strategic and business value from the semantics (back-end) of their growing-by-the-split-second troves of data.  The competitive power of the Amazoogle business model lies in the simple fact that the more data you accrete, the keener the insights you can drive, essentially allowing Amazoogle companies to both predict future behavior and even drive future behavior.  Brands happily pay for both results.

And since Amazoogle decrees that data is good, then certainly more data must be even better.  Hence the additional data streams enabled by Facebook’s Like button, Google’s self-driving cars, and Amazon’s purchase of Whole Foods.  Crucially, because Amazoogle data grows continuously, both its efficacy and its competitive barrier grow apace.

Data-driven Amazoogle business models are now deconstructing every legacy business, whether in finance, transportation, retail, or now even in healthcare.  Consequently, the apex predator that legacy companies in each of these industries faces is not from their traditional legacy competitors but from business model disruptions fueled by Amazoogle.


 Data: The Anti-Commodity

The problem with any manufactured product (even software) is that it’s commoditized.  For example, the only real way to differentiate autos in the minds of the buying public is through vast advertising expenditure.  The only real way to differentiate relational database platforms to an enterprise buyer is through pricing.

But just today, we have finally passed out of the Industrial Age and truly into the Information Age.  It’s now the information itself — not the manufactured good, even if that manufactured good happens to be a really sophisticated electronic device, software platform or chemical compound — that fuels the predominant business model.  Business models will no longer be predicated on the manufacturing of a thing — mobile phone, automobile, pharmaceutical — but on the value and understanding of the data that each of these things creates.  This represents the most fundamental change in business model since the rise of manufacturing and the Industrial Age.

The mobile phone industry provides a ready example of the impact of the Amazoogle business model today.  Mobile phones are the result of incredibly expensive R&D and manufacturing processes.  Vendors must also incur the marketing, distribution and support costs in getting their product into the hands of consumers in a cut-throat industry, at hardware-product margins.  These devices then freely spill vast volumes of data 24×7 into Amazoogle’s business model.  In which part of this business model equation would you rather be?  Even heretofore invincible Apple is starting to feel Amazoogle’s effects.

Embracing Amazoogle, however, doesn’t mean that if you’re a vendor of a “device” that you should abandon what you’re doing.  You (we!) still need that device.  It’s just that you need to emulate Amazoogle (indeed, anticipate Amazoogle’s competitive entry into your market) and realize that your device is part of a holistic product with data at its foundation.

In the business environment of the future everything is commoditized already or will soon become so.  The sole competitive differentiator in a commoditized world lies in being able to predict the future.  The more data you have the more accurately you can predict the future.  Whoever controls the data wins.


The Red Queen

The Red Queen Hypothesis was put forward by University of Chicago biologist Leigh Van Valen in his seminal 1973 paper on “A New Evolutionary Law”.  In this hypothesis, Van Valen posited that organisms must constantly adapt and evolve because they live in an ever-evolving ecosystem, competing for survival against other ever-evolving organisms.  Everything is competitive, and nothing is constant; it’s explicitly a zero-sum game, and stasis means extinction.  Just as in the Red Queen’s quote to Alice in Through the Looking-Glass.

In business, the Red Queen says that it’s not enough that your company is running as fast as possible, you need to run fast relative to your competition.  With data-driven Amazoogle business models moving at breakneck speeds, how fast is your company running?  If you’re not positioning yourself to out-Amazoogle your Amazoogle competition, then you’re positioned for irrelevance at best and extinction at worst.

It’s a truism that for any type of company, whether two-person start-up or Fortune 500 behemoth, it’s the business model that drives success, not technology.  The world is awash in technology, but only those technologies that can exploit an inefficiency in — and change — a business model hold promise for creating new industries and companies.

You can certainly go ahead and invent <insert name of electro-mechanical device, enterprise software, or chemical compound here>.  Designing the next great commodity product — no matter how technologically cutting-edge — will not immunize you to the depredations of Amazoogle.  Eventually, patents expire and/or someone (perhaps even your company!) manufactures the product ever more cheaply, resulting in the steady compressing of your profit margins.  In the end, anything that’s a manufactured product, no matter how sophisticated, is fated to become a commodity.

For legacy industries, using data and machine learning to improve productivity is a nice starting point, but is far short of being enough.  Doing so only helps fuel race-to-the-bottom pricing: the more efficient you become at making something, the more commoditized it becomes, eventually leading to a lowering of the margins in your business.  So using machine learning for reservoir simulation or drug discovery might only be a short-term salve.  You may have addressed innovating your technology, but not have addressed the more fundamental innovation of your business model.

If business models win, not technology, then it’s not sufficient to use data to innovate your technology alone.  You need to use data to innovate your business model.  Tactical battles are won or lost on technology.  Strategic battles are won or lost on business model.

If history has taught us one thing it’s that the future will not look like the past.  Consequently, it’s suicidal to merely focus on making yourself competitive against your past competition, even if you’re comforting yourself that you’re now using the latest (machine learning and Big Data) technology to improve how you do so.  It will still be yesterday’s battle you’ll be fighting.  You’ll just become more efficient at fighting it.  Rather, if the Red Queen is right, your efforts must be expended in using data to re-envision your business and preparing yourself for combat with tomorrow’s fast-moving Amazoogle competitors.

Put another way, given the strategic imperatives above, whose stock would you rather own?  Commoditized 20th Century business models such as Big Ag?  Big Auto?  Big Pharma?  Big Box retail?  Or 21st Century business models built on Big Data?



 “The railroads are in trouble today not because the need was filled by others (cars, trucks, airplanes, even telephones), but because it was not filled by the railroads themselves. They let others take customers away from them because they assumed themselves to be in the railroad business rather than in the transportation business. The reason they defined their industry wrong was because they were railroad-oriented instead of transportation-oriented; they were product oriented instead of customer-oriented.”

 Theodore Levitt, “Marketing Myopia”, Harvard Business Review, July-August 1960

The most dangerous word in business today is “yabut”, as in “Yeah, but do we really…?”  Unfortunately, uttering “yeah, but” in any business conversation might only be a convenient way of justifying conservatism, dodging responsibility for looking ahead (painful as that may be), and affirming the need to continue to pursue the comfortable and familiar course of business as usual.

But start-ups seeking long-term investment as well as established companies seeking long-term relevance need to pay heed to Levitt and Van Valen.  Who’s moving the business model the fastest today, the fintech start-up or the nationwide bank?  The connected car company or the global auto manufacturer?  The Big Pharma behemoth or Google’s Verily?  With this in mind, realize that “yabut” generally presages an existential threat.

The insurance industry, for example, has to date had a business model of insuring physical assets.  In the data-driven world of Amazoogle, with virtualized transportation being provided by fleets of self-driving cars or via ride-hailing services, how does an insurance company abandon “yabut” concerns about existing revenue streams and monetize data assets instead?  Companies who do not heed the clarion call to re-examine their business models in this way risk falling victim to the “fatuous contentment” in Levitt’s polemic, and thereby “guarantee premature senescence”.  This applies to all industries, even information technology.  Silicon Valley is replete with its own examples of companies that were felled by the “marketing myopia” warned by Levitt.

The technology industry has by now seen the passing of “peak hardware” and “peak software”.  Is Amazoogle also fated to pass its moment of apotheosis?  The answer is no.  Human technology has replicated physical labor through the invention of machines, and replicated the process of thinking through the invention of software.  Machine learning driven by Big Data now finally replicates — and, for good or bad, will soon surpass — the final piece: the human capacity to learn, create, and anticipate the future.  There’s now nothing left to replicate.  If you want the ultimate business model — the apex predator — it must by definition be a data-driven Amazoogle business model.

Nokia once thought they were in the cell phone business.  Yahoo thought they were in the search engine business.  Toys “R” Us thought they were in the toy store business.  Amazoogle showed each of these companies otherwise.  Business leaders wanting to avoid a similar fate should read Levitt and Van Valen.  Consider the implications of business model stasis on your long-term viability.  And then ask yourself what business you’re in.  The Red Queen warns: Amazoogle is coming.