If you’ve read Shoshana Zuboff’s Surveillance Capitalism, you likely agree that the most important asset for a data-driven advertising platform is consumer engagement. That engagement throws off data, that data drives prediction models, those models inform algorithms, those algorithms drive advertising engines, and those engines drive revenue, which drives profit. And profit, of course, drives stock price, the highest and holiest metric of our capitalistic economy.
So when an upstart company exhibits exponential growth in consumer engagement – say, oh, 3,000-percent growth in a matter of two months – well, that’s going to get the attention of the world’s leading purveyors of surveillance capitalism.
As the coronavirus crisis built to pandemic levels in early March, a relatively unknown tech company confronted a defining opportunity. Zoom Video Communications, a fast-growing enterprise videoconferencing platform with roots in both Silicon Valley and China, had already seen its market cap grow from under $10 billion to nearly double that. As the coronavirus began dominating news reports in the western press, Zoom announced its first full fiscal year results as a public company. The company logged $622.7 million in revenue, up 88 percent from the year before. Zoom’s high growth rate and “software as a service” business model guaranteed fantastic future profits, and investors rewarded the company by driving its stock up even further. On March 5th, the day after Zoom announced its earnings, the company’s stock jumped to $125, more than double its price on the day of its public offering eleven months before. Market analysts began issuing bullish guidance, and company executives noted that as the coronavirus spread, more and more customers were flocking to Zoom’s easy-to-use video conferencing platform.
But as anyone paying attention to business news for the past month knows, it’s been a tumultuous ride for Zoom ever since. As the virus forced the world inside, demand for Zoom’s services skyrocketed, and the company became a household name nearly overnight. Zoom’s “freemium” model – which offers a basic version of its platform for free, with more robust features available for a modest monthly subscription fee – allowed tens of millions of new users to sample the company’s wares. Initially, Zoom was a hit with this new user base – stories of Zoom seders, Zoom cocktail parties, and even Zoom weddings gave the company a consumer-friendly vibe. Just like Google or Facebook before it, here was the story of a scrappy Valley startup with just the right product at just the right time. According to the company, Zoom’s monthly users leapt from 10 million to more than 200 million – an unimaginable increase of 2,000 percent in just one month.
A new year brings another run at my annual predictions: For 17 years now, I’ve taken a few hours to imagine what might happen over the course of the coming twelve months. And my goodness did I swing for the fences last year — and I pretty much whiffed. Batting .300 is great in the majors, but it kind of sucks compared to my historical average. My mistake was predicting events that I wished would happen. In other words, emotions got in the way. So yes, Trump didn’t leave office, Zuck didn’t give up voting control of Facebook, and weed’s still illegal (on a federal level, anyway).
Chastened, this year I’m going to focus on less volatile topics, and on areas where I have a bit more on-the-ground knowledge — the intersection of big tech, marketing, media, and data policy. As long time readers know, I don’t prepare in advance of writing this post. Instead, I just clear a few hours and start thinking out loud. So…here we go.
Facebook bans microtargeting on specific kinds of political advertising. Of course I start with Facebook, because, well, it’s one of the most inscrutable companies in the world right now. While Zuck & Co. seem deeply committed to their “principled” stand around a politician’s right to paid prevarication, the pressure to do something will be too great, and as it always does, the company will enact a half-measure, then declare victory. The new policy will probably roll out after Super Tuesday (sparking all manner of conspiracies about how the company didn’t want to impact its Q1 growth numbers in the US). The company’s spinners will frame this as proof they listen to their critics, and that they’re serious about the integrity of the 2020 elections. As with nearly everything it does, this move will fail to change anyone’s opinion of the company. Wall St. will keep cheering the company’s stock, and folks like me will keep wondering when, if ever, the next shoe will drop.
Netflix opens the door to marketing partnerships. Yes, I’m aware that the smart money has moved on from this idea. But in a nod to increasing competition and the reality of Wall St. expectations, Netflix will at least pilot a program — likely not in the US — where it works with brands in some limited fashion. Mass hysteria in the trade press will follow once this news breaks, but Netflix will call the move a pilot, a test, an experiment…no big deal. It may take the form of a co-produced series, or branded content, or some other “native” approach, but at the end of the day, it’ll be advertising dollars that fuel the programming. And while I won’t predict the program augurs a huge new revenue stream for the company, I can predict that what won’t happen, at least in 2020: A free, advertising-driven version of Netflix. Just not in the company’s culture.
CDA 230 will get seriously challenged, but in the end, nothing gets done, again. Last year I predicted there’d be no federal data privacy legislation, and I’m predicting the same for this year. However, there will be a lot of movement on legislation related to the tech oligarchy. The topic that will come the closest to passage will be a revision to CDA 230 —the landmark legislation that protects online platforms from liability for user generated content. Blasphemy? Sure, but here we are, stuck between free speech on the one hand, massive platform economics on the other, and a really, really bad set of externalities in the middle. CDA 230 was built to give early platforms the room to grow unhindered by traditional constraints on media companies. That growth has now metastasized, and we don’t have a policy response that anyone agrees upon. And CDA 230 is an easy target, given conservatives in Congress already believe Facebook, Google, and others have it out for their president. They’ll be a serious run at rewriting 230, but it will ultimately fail. Related…
Adversarial interoperability will get a moment in the sun, but also fail to make it into law. In the past I (and many others) have written about “machine readable data portability.” But for the debate we’re about to have (and need to have), I like “adversarial interoperability” better. Both are mouthfuls, and neither are easy to explain. Data governance and policy are complicated topics which test our society’s ability to have difficult long form conversations. 2020 will be a year where the legions of academics, policy makers, politicians, and writers who debate economic theory around data and capitalism get a real audience, and I believe much of that debate will center on whether or not large platforms have a responsibility to be open or closed. As Cory Doctorow explains, adversarial interoperability is “when you create a new product or service that plugs into the existing ones without the permission of the companies that make them.” As in, I can plug my new e-commerce engine into Amazon, my new mobile operating system into iOS, my new social network into Facebook, or my new driving instruction app into Google Maps. I grew up in a world where this kind of innovation was presumed. It’s now effectively banned by a handful of data oligarchs, and our economy – and our future – suffers for it.
As long as we’re geeking out on catchphrases only a dork can love, 2020 will also be the year “data provenance” becomes a thing. As with many nerdy topics, the concept of data provenance started in academia, migrated to adtech, and is about to break into the broader world of marketing, which is struggling to get its arms around a data-driven future. The ability to trace the origin, ownership, permissions, and uses of data is a fundamental requirement of an advanced digital economy, and in 2020, we’ll realize we have a ton of work left to do to get this right. Yes, yes, blockchain and ledgers are part of the discussion here, but the point isn’t the technology, it’s the policy enabling the technology.
Google zags. Saddled with increasingly negative public opinion and driven in large part by concerns over retaining its workforce, Google will make a deeply surprising and game changing move in 2020. It could be a massive acquisition, a move into some utterly surprising new industry (like content), but my money’s on something related to data privacy. The company may well commit to both leading the debate on the topics described above, as well as implementing them in its core infrastructure. Now that would really be a zag…
At least one major “on demand” player will capitulate. Gig economy business models may make sense long term, but that doesn’t mean we’re getting the execution right in the first group of on demand “unicorns.” In fact, I’d argue we’re mostly getting them wrong, even if as consumers, we love the supposed convenience gig brands bring us. Many of the true costs of these businesses have been externalized onto public infrastructure (and the poor), and civic patience is running out. Plus, venture and public finance markets are increasingly skeptical of business models that depend on strip mining the labor of increasingly querulous private contractors. A reckoning is due, and in 2020 we’ll see the collapse of one or more larger players in the field.
Influencer marketing will fall out of favor. I’m not predicting an implosion here, but rather an industry wide pause as brands start to ask the questions consumers will also be pondering: who the fuck are these influencers and why are we paying them so much attention? A major piece of this — on the marketing side anyway — will be driven by a massive increase in influencer fraud. As with other fast growing digital marketing channels, where money pours in, fraud fast follows — nearly as fast as fawning New York Times articles, but I digress.
Information warfare becomes a national bogeyman. If we’ve learned anything since the 2016 election, it’s this: We’ve taken far too long to comprehend the extent to which bad actors have come to shape and divide our discourse. These past few years have slowly revealed the power of information warfare, and the combination of a national election with the compounding distrust of algorithm-driven platforms will mean that by mid year, “fake news” will yield to “information warfare” as the catchphrase describing what’s wrong with our national dialog. Deep fakes, sophisticated state-sponsored information operations, and good old fashioned political info ops will dominate the headlines in 2020. Unfortunately, the cynic in me thinks the electorate’s response will be to become more inured and distrustful, but there’s a chance a number of trusted media brands (both new and old) prosper as we all search for a common set of facts.
Purpose takes center stage in business. 2019 was the year the leaders of industry declared a new purpose for the corporation — one that looks beyond profits for a true north that includes multiple stakeholders, not just shareholders. 2020 will be the year many companies will compete to prove that they are serious about that pledge. Reaction from Wall St. will be mixed, but I expect plenty of CEOs will feel emboldened to take the kind of socially minded actions that would have gotten them fired in previous eras. This is a good thing, and likely climate change will become the issue many companies will feel comfortable rallying behind. (I certainly hope so, but this isn’t supposed to be about what I wish for…)
Apple and/or Amazon stumble. I have no proof as to why I think this might happen but…both these companies just feel ripe for some kind of major misstep or scandal. America loves a financial winner — and both Amazon and Apple have been runaway winners in the stock market for the past decade. Both have gotten away with some pretty bad shit along the way, especially when it comes to labor practices in their supply chain. And while neither of them are as vulnerable as Facebook or Google when it comes to the data privacy or free speech issues circling big tech, both Apple and Amazon have become emblematic of a certain kind of capitalism that feels fraught with downside risk in the near future. I can’t say what it is, but I feel like both these companies could catch one squarely on the jaw this coming year, and the post-mortems will all say they never saw it coming.
So there you have it — 11 predictions for the coming year. I was going to stop at 10, but that Apple/Amazon one just forced itself out — perhaps that’s me wishing again. We’ll see. Let me know your thoughts, and keep your cool out there. 2020 is going to be one hell of a year.
I’ve been quiet here on Searchblog these past few months, not because I’ve nothing to say, but because two major projects have consumed my time. The first, a media platform in development, is still operating mostly under the radar. I’ll have plenty to say about that, but at a later date. It’s the second where I could use your help now, a project we’re calling Mapping Data Flows. This is the research effort I’m spearheading with graduate students from Columbia’s School for International Public Affairs (SIPA) and Graduate School of Journalism. This is the project examining what I call our “Shadow Internet Constitution” driven by corporate Terms of Service.
Our project goal is simple: To visualize the Terms of Service and Data/Privacy Policies of the four largest companies in US consumer tech: Amazon, Apple, Facebook, and Google. We want this visualization to be interactive and compelling – when you approach it (it’ll be on the web), we hope it will help you really “see” what data, rights, and obligations both you and these companies have reserved. To do that, we’re busy turning unintelligible lines of text (hundreds of thousands of words, in aggregate) into code that can be queried, compared, and visualized. When I first imagined the project, I thought that wouldn’t be too difficult. I was wrong – but we’re making serious progress, and learning a lot along the way.
I’ll never forget a meal I had with a senior executive at Facebook many years ago, back when I was just starting to question the motives of the burgeoning startup’s ambition. I asked whether the company would ever support publishers across the “rest of the web” – perhaps through an advertising system competitive with Google’s AdSense. The executive’s response was startling and immediate. Everything anyone ever needs to do – including publishing – can and should be done on Facebook. The rest of the Internet was a sideshow. It’s just easier if everything is on one platform, I was told. And Facebook’s goal was to be that platform.
Those words still ring in my ears as we celebrate the 30th anniversary of the web today. And they certainly should inform our perspective as we continue to digest Facebook’s latest self-involved epiphany.
This is an edited version of a series of talks I first gave in New York over the past week, outlining my work at Columbia. Many thanks to Reinvent, Pete Leyden, Cap Gemini, Columbia University, Cossette/Vision7, and the New York Times for hosting and helping me.
If predictions are like baseball, I’m bound to have a bad year in 2019, given how well things went the last time around. And given how my own interests, work life, and physical location have changed of late, I’m not entirely sure what might spring from this particular session at the keyboard.
But as I’ve noted in previous versions of this post (all 15 of them are linked at the bottom), I do these predictions in something of a fugue state – I don’t prepare in advance. I just sit down, stare at a blank page, and start to write.
So Happy New Year, and here we go.
1/ Global warming gets really, really, really real. I don’t know how this isn’t the first thing on everyone’s mind already, with all the historic fires, hurricanes, floods, and other related climate catastrophes of 2018. But nature won’t relent in 2019, and we’ll endure something so devastating, right here in the US, that we won’t be able to ignore it anymore. I’m not happy about making this prediction, but it’ll likely take a super Sandy or a king-sized Katrina to slap some sense into America’s body politic. 2019 will be the year it happens.
2/ Mark Zuckerberg resigns as Chairman of Facebook, and relinquishes his supermajority voting rights. Related, Sheryl Sandberg stays right where she is. I honestly don’t see any other way Facebook pulls out of its nosedive. I’ve written about this at length elsewhere, so I will just summarize: Facebook’s only salvation is through a new system of governance. And I mean that word liberally – new governance of how it manages data across its platform, new governance of how it works with communities, governments, and other key actors across its reach, and most fundamentally, new governance as to how it works as a corporate entity. It all starts with the Board asserting its proper role as the governors of the company. At present, the Board is fundamentally toothless.
3/ Despite a ton of noise and smoke from DC, no significant federal legislation is signed around how data is managed in the United States. I know I predicted just a few posts ago that 2019 will be the year the tech sector has to finally contend with Washington. And it will be…but in the end, nothing definitive will emerge, because we’ll all be utterly distracted by the Trump show (see below). Because of this, unhappily, we’ll end up governed by both GDPR and California’s homespun privacy law, neither of which actually force the kind of change we really need.
4/ The Trump show gets cancelled. Last year, I said Trump would blow up, but not leave. This year, I’m with Fred, Trump’s in his final season. We all love watching a slow motion car wreck, but 2019 is the year most of us realize the car’s careening into a school bus full of our loved ones. Donald Trump, you’re fired.
5/ Cannabis for the win. With Sessions gone and politicians of all stripes looking for an easy win, Congress will pass legislation legalizing cannabis. Huzzah!!!! Just in time, because…
6/ China implodes, the world wobbles. Look, I’m utterly out of my depth here, but something just feels wrong with the whole China picture. Half the world’s experts are warning us that China’s fusion of capitalism and authoritarianism is already taking over the world, and the other half are clinging to the long-held notion that China’s approach to nation building is simply too fragile to withstand democratic capitalism’s demands for transparency. But I think there may be other reasons China’s reach will extend its grasp: It depends on global growth and optimistic debt markets. And both of those things will fail this year, exposing what is a marvelous but unsustainable experiment in managed markets. This is a long way of backing into a related prediction:
7/ 2019 will be a terrible year for financial markets. This is the ultimate conventional wisdom amongst my colleagues in SF and NY, even though I’ve seen plenty of predictions that Wall St. will have a pretty good year. I have no particular insight as to why I feel this way, it’s mainly a gut call: Things have been too good, for too long. It’s time for a serious correction.
8/ At least one major tech IPO is pulled, the rest disappoint as a class. Uber, Lyft, Slack, Pinterest et al are all expected this year. But it won’t be a good year to go public. Some will have no choice, but others may simply resize their businesses to focus on cash flow, so as to find a better window down the road.
9/ New forms of journalistic media flourish. It’s well past time those of us in the media world take responsibility for the shit we make, and start to try significant new approaches to information delivery vehicles. We have been hostages to the toxic business models of engagement for engagement’s sake. We’ll continue to shake that off in various ways this year – with at least one new format taking off explosively. Will it have lasting power? That won’t be clear by year’s end. But the world is ready to embrace the new, and it’s our jobs to invest, invent, support, and experiment with how we inform ourselves through the media. Related, but not exactly the same…
10/A new “social network” emerges by the end of the year. Likely based on messaging and encryption (a la Signal or Confide), the network will have many of the same features as the original Facebook, but will be based on a paid model. There’ll be some clever new angle – there always is – but in the end, it’s a way to manage your social life digitally. There are simply too many pissed off and guilt-ridden social media billionaires with the means to launch such a network – I mean, Insta’s Kevin Systrom, WhatsApp’s Jan and Brian, not to mention the legions of mere multi-millionaires who have bled out of Facebook’s battered body of late.
So that’s it. On a personal note, I’ll be happily busy this year. Since moving to NY this past September, I’ve got several new projects in the works, some still under wraps, some already in process. NewCo and the Shift Forum will continue, but in reconstituted forms. I’ll keep up with my writing as best I can; more likely than not most of it will focus the governance of data and how its effect our national dialog. Thanks, as always, for reading and for your emails, comments, and tweets. I read each of them and am inspired by all. May your 2019 bring fulfillment, peace, and gratitude.
If you’re read my rants for long enough, you know I’m fond of programmatic advertising. I’ve called it the most important artifact in human history, replacing the Macintosh as the most significant tool ever created.
So yes, I think programmatic advertising is a big deal. As I wrote in the aforementioned post:
“I believe the very same technologies we’ve built to serve real time, data-driven advertising will soon be re-purposed across nearly every segment of our society. Programmatic adtech is the heir to the database of intentions – it’s that database turned real time and distributed far outside of search. And that’s a very, very big deal. (I just wish I had a cooler name for it than “adtech.”)”
But lately, I’m starting to wonder if perhaps adtech is failing, not for any technical reason, but because the people leveraging are complicit in what might best be called a massive failure of imagination.
I’m about to go on a rant here, so please forgive me in advance.
But honestly, who else out there is sick of being followed by ads so stupid a fourth grader could do a better job of targeting them?
Case in point is the ad above. I took this screen shot from my phone this past weekend while I was reading a New York Times article. The image – of a robe Amazon wanted me to buy – was instantly annoying, because I had in fact purchased a robe on Amazon several days before. Why on earth was Amazon retargeting me for a product I just bought?!
But wait, it gets worse! As I perused the next Times article, this ad shows up:
You might think this ad makes more sense. If the dude buys a robe, makes sense to try to sell him a new pair of slippers, no? Well, sure, but only if that same dude didn’t buy a new pair of slippers two weeks ago. Which, in fact, I did just do.
So, yeah, this ad sucks as well. Not only is it not useful or relevant, it’s downright annoying. The vast machinery of adtech has correctly identified me as a robe-and-slippers-buying customer. But it’s failed to realize *I’ve already bought the damn things.*
Is it possible that adtech is this stupid? This poorly instrumented? I mean, are programmatic buyers simply tagging visitors who land on ecommerce pages (male robe intender?) without caring about whether those visitors actually bought anything?
Are the human beings responsible for setting the dials of programmatic just this lazy?
I’ve been a critical observer of adtech over the past ten or so years, and one consistent takeaway is this: If there’s a way for a buyer to cut corners, declare an easy win, and keep doing things they way the’ve always been done, well, they most certainly will.
But why does it have to be this way? Digging into the examples above yields an extremely frustrating set of facts. Consider the data the adtech infrastructure either got *right* about me as a customer, or could have gotten right:
I am a frequent ecommerce customer, usually buying on Amazon
I recently purchased both a robe and some slippers
I am reading on the New York Times site as a logged on (IE data rich) customer of the Times‘ offerings
These are just the obvious data points. My mobile ID and cookies, all of which are available to programmatic buyers, certainly indicate a high household income, a propensity to click on certain kinds of ads, a rich web browsing history reflecting a thickly veined lodestar of interest data, among countless other possible inputs.
Imagine if a programmatic campaign actually paid attention to all this rich data? Start with the fact I just purchased a robe and slippers. What are products related to those two that Amazon might show me? Well, according to its own “people who bought this item also bought” algorithms, folks who bought men’s robes also bought robes for the women in their life. Now there’s a cool recommendation! I might have clicked on an ad that showed a cool robe for my wife. But no, I’m shown an ad for a product I already have.
I’ve got a few calls in to verify my hunch, but I suspect the ugly truth is pure laziness on the part of the folks responsible for buying ads. Consider: The average cost for a thousand views (CPM) of a targeted programmatic advertisement hovers between ten cents (yes, ten pennies) to $2. With costs that low, the advertising community can afford to waste ad inventory.
Let’s apply that reality to our robe example. Let’s say the robe costs $60, and yields a $20 profit for our e-commerce advertiser, not including marketing costs. That means that same advertiser is can spend upwards of $19.99 per unit on advertising (more, if a robe purchaser turns out to be a “big basket” e-commerce spender). So what does our advertiser do? Well, they set a retargeting campaign aimed anyone who ever visited our erstwhile robe’s page. With CPMs averaging around a buck, that robe’s going to follow nearly 20,000 folks around the internet, hoping that just one of them converts.
Put another way, programmatic advertising is a pure numbers game, and as long as the numbers show one penny of profit, no one is motivated to make the system any better. I’ve encountered many similar examples of ad buyers ignoring high-quality data signals, preferring instead to “waste reach” because, well, it’s just easier to set up campaigns on one or two factors. Inventory is cheap. Why not?
This is problematic. What’s the point of having all that rich (and hard won) targeting data if buyers won’t use it, and consumers don’t benefit from it? An ecosystem that fails to encourage innovation will stagnate and lose share to walled gardens like Facebook, Google, and others. If the ads suck on the open web (and they do), then consumers will either install ad blockers (and they are), or abandon the open web altogether (and they are).
Well, Walmart vs. Amazon is all about big business – a platform giant (Amazon) disrupting an OldBigCo (Walmart and its kin). Over the past two decades, Amazon bumped Walmart out of the race to a trillion-dollar market cap, and the OldCo from Bentonville had to reset and play the role of the upstart. The Token Act levels the playing field, forcing both to win where it really matters: In service to the customer.
But while BigCos are sexy and well known, it’s the small and medium-sized business ecosystem that determines whether or not we have an economy of mass flourishing. So let’s explore the Token Act from the point of view of a small business startup, in this case, a new neighborhood restaurant. I briefly touched upon this idea in my set up post, Don’t Break Up The Tech Oligarchs. Force Them To Share Instead. (If you haven’t already, you might want to read that post before this one, as I lay out the framework in which this scenario would play out.) What I envision below assumes the Token Act has passed, and we’re at least a year or two into its adoption by most major data players. Here we go…
Fresh off her $2,700 win from Walmart, Michelle decides she’s ready to lean into a lifelong dream: Starting a restaurant in her newly adopted neighborhood of Chelsea in New York City. Since moving to the area from California, she’s noticed two puzzling trends: First, a dearth of interesting mid- to high-end dinner spots walking distance from her new place, and second, what appears to be higher-than-average vacancy rates for the retail storefronts in the same general area. It appears to be a buyer’s market for retail restaurant space in Chelsea. So why aren’t new places launching? She read the Times’ piece on vacancies a few years ago (before the Token Act passed) and was left just as puzzled as before – seems like there’s no rhyme or reason to the market.
Michelle wants to start a high end American gastro pub – the kind of place she loved back when she lived in Northern California (she’s fond of Danny Meyers’ Gramercy Tavern, pictured above, but it’s a bit too far away from her new place). She has a strong hunch that such a place would be a hit in her new neighborhood, but she’s not sure her new neighbors will agree.
Now starting a restaurant requires a certain breed of insanity – they say the best way to make a small fortune in the business is to start with a large one. The truth is, launching restaurants has historically been a crap shoot – you might find the best talent, the best designer, and the best location – but if for some reason you don’t bring the je ne sai quois, the place will fail within months, leaving you and your partners millions of dollar poorer.
It’s that je ne sai quois that Michelle is determined to reveal. The tools she will leverage? The newly liberated resources of data tokens.
Before we continue, allow me to draw your attention back to the rise of search, indeed, the very era which begat Searchblog in the early 2000s. Google Adwords launched in 2000, and within a few years, the media world had been turned upside down by what I termed The Database of Intentions. As if by magic, people everywhere could suddenly ask new kinds of questions, finding themselves both surprised and delighted by the answers they received.
A Gates-Line compliant ecosystem quickly developed on top of this new platform, driven by an emerging industry of search engine marketing and optimization. SEO/SEM sprung into existence to help small and medium sized businesses take advantage of the Google platform – by 2006 the industry stood at nearly $10 billion in spend, growing more than 60 percent year on year. Adwords grew from zero to millions of advertisers by connecting to a long tail of small businesses that took advantage of an entirely new class of revealed information: The intents, desires, and needs of tens of millions of consumers, who relentlessly poured their queries into Google’s placid and unblinking search box.
Were you a limo service in the Bronx looking for new customers? It paid huge dividends to purchase Adwords like “car service bronx” and “best limo manhattan.” Were you a dry cleaner in West LA hoping to expand? Best be first in line when customers typed in “best cleaners Beverly Hills.” Selling heavy machinery to construction services in the midwest? If you don’t own keywords like “caterpillar dealer des moines” you’d lose, and quick, to whoever did optimize to phrases like that.
My point is simply this: Adwords was a freaking revolution, but it ain’t nothing compared to what will happen if we unleash data tokens on the world.
Ok, back to Michelle and her new restaurant. Of course Michelle will leverage Adwords, and Facebook, and any other advertising service to help her new business grow. But none of those services can help her figure out her je ne sai quois – for that, she needs something entirely novel. She needs a new question machine. And the ecosystem that develops around data tokens will offer it.
Thanks to her Walmart experience, Michelle has become aware of the power of personal data. She’s also read up on the Token Act, the new law requiring all data players at scale to allow individuals to create machine-readable data tokens that can be exchanged for value as directed by the consumer. After doing a bit of research, she stumbles across a startup called OfferExchange, which manages “Token Offers” on behalf of anyone who might want to query TokenLand. OfferExchange is a spinout from ProtocolLabs, a pioneer in secure blockchain software platforms like Filecoin. It’s still early in TokenLand, so an at-scale Google of the space hasn’t emerged. OfferExchange works more like a bespoke yet platform-based research outfit – the firm has a sophisticated website and impressive client list. It uses Facebook, Twitter, LiveRamp, and Instagram to identify potential token-creating consumers, then solicits those individuals with offers of cash or other value in exchange for said tokens.
Michelle does a Crunchbase search for OfferExchange and sees it’s backed by Union Square Ventures and Benchmark, which gives her some comfort – those firms don’t fund fly-by-night hucksters. And OfferExchange site is impressive – in less than five minutes, it guides her through the construction of an elegant query. Here’s how the process works:
First, the site asks Michelle what her goal is. “Starting a restaurant in New York City,” she responds. The site reconstructs around her answer, showing suggested data repositories she might mine. “Restaurants, New York City,” reads the top layer of a directory-like page. Underneath are several categories, each populated with familiar company names:
Restaurant Reservation and Review Services
OpenTable Google Resy Yelp Eat24 Facebook (more)
Food Delivery Services
GrubHub Uber Eats PostMates InstaCart (more)
Uber Lyft Juno Via (more)
Real Estate Services (Commercial)
LoopNet DocuSign CompStak (more)
Foursquare Uber Lyft Google NinthDecimal (more)
American Express Visa Mastercard Apple Pay Diners Club (more)
And so on – if she wished, Michelle could dig into dozens of categories related to her initial “restaurant New York City” search.
Michelle’s imagination sparks – the kinds of queries she could ask of these services is mind blowing. She could limit her query to people who live within walking distance of her neighborhood, asking her *actual neighbors* for tokens that tell her what restaurants they eat at, when they eat there, the size of their checks, related reviews, abandoned reservations, the works. She might discover that folks like Indian takeout on Mondays, that they rarely spend more than $100 on a meal on Tuesdays, but that they splurge on the weekends. She could discover the percentage of diners in Chelsea who travel more than two miles by car service to eat out at a place similar to the one she has in mind, and what the size of the check might be when they do. She can also check historical average rents for restaurants in her zip code, over time, which will certainly help with negotiating her lease. The possibilities are endless.
Put another way, with OfferExchange’s services, Michelle can litigate the merde out of her je ne sai quois.
This post is getting long, so I’ll stop here and pull back for a spot of Thinking Out Loud. I could continue the story, imagining the process of the token offer Michelle would put out through OfferExchange’s platform, but suffice to say, she’d be willing to pay upwards of $5-20 per potential customer for their data. The marketing benefit alone – alerting potential customers in the neighborhood that she’s exploring a new restaurant in the area – is worth tens of thousands already. And of course, OfferExchange can connect anyone who offers their tokens to Michelle’s new project a discount on their first meal at the restaurant, should it actually launch. Cool!
But let’s stop there and consider what happens when local entrepreneurs have access to the information currently silo’d across thousands of walled garden services like Uber, LoopNet, Resy, and of course Facebook and Google. While better data won’t insure that Michelle’s restaurant will succeed, it certainly increases the odds that it won’t fail. And it will give both Michelle and her investors – local banks, savvy friends and family members – much more conviction that her new enterprise is viable. Take this local restaurant example and apply it to all manner of small business – dry cleaners, hardware stores, bike shops – and this newly liberated class of information enables an explosion of efficiency, investment, and, well, flourishing in what has become, over the past four decades, a stagnant SMB environment.
Is this Money Ball for SMB? Perhaps. And yes, I can imagine any number of downsides to this new data economy. But I also believe the benefits would far outweigh the downsides. Under the Token Act as I envision it, co-creators of the data – the services like Uber, OpenTable, or Facebook – have the right to charge a vig for the data being monetized. Sure, it’d be possible for an entrepreneur to steal customers via tokens, but I’m going to guess the economic value of allowing your customers to discover new use cases for their data will dwarf the downside of possibly losing those customers to a new competitor. Plus, this new competitive force will drive everyone to play at a higher level, focusing not on moats built on data silos, but instead on what really matters: A highly satisfied customer. That’s certainly Michelle’s goal, and the goal of every successful local business. Why shouldn’t it also be the goal of the data giants?
Social conversations about difficult and complex topics have arcs – they tend to start scattered, with many threads and potential paths, then resolve over time toward consensus. This consensus differs based on groups within society – Fox News aficionados will cluster one way, NPR devotees another. Regardless of the group, such consensus then becomes presumption – and once a group of people presume, they fail to explore potentially difficult or presumably impossible alternative solutions.
This is often a good thing – an efficient way to get to an answer. But it can also mean we fail to imagine a better solution, because our own biases are obstructing a more elegant path forward.
This is my sense of the current conversation around the impact of what Professor Scott Galloway has named “The Four” – the largest and most powerful American companies in technology (they are Apple, Amazon, Google, and Facebook, for those just returning from a ten-year nap). Over the past year or so, the conversation around technology has become one of “something must be done.” Tech was too powerful, it consumed too much of our data and too much of our economic growth. Europe passed GDPR, Congress held ineffectual hearings, Facebook kept screwing up, Google failed to show up…it was all of a piece.
The conversation evolved into a debate about various remedies, and recently, it’s resolved into a pretty consistent consensus, at least amongst a certain class of tech observers: These companies need to be broken up. Antitrust, many now claim, is the best remedy for the market dominance these companies have amassed.
It’s a seductive response, with seductive historical precedent. In the 1970s and 80s, antitrust broke up AT&T, ultimately paving the way for the Internet to flourish. In the 90s, antitrust provided the framework for the government’s case against Microsoft, opening the door for new companies like Google and Facebook to dominate the next version of the Internet. Why wouldn’t antitrust regulation usher in #Internet3? Imagine a world where YouTube, Instagram, and Amazon Web Services are all separate companies. Would not that world be better?
Perhaps. I’m not well read enough in antitrust law to argue one way or the other, but I know that antitrust turns on the idea of consumer harm (usually measured in terms of price), and there’s a strong argument to be made that a free service like Google or Facebook can’t possibly cause consumer harm. Then again, there are many who argue that data is in fact currency, and The Four have essentially monopolized a class of that currency.
But even as I stare at the antitrust remedy, another solution keeps poking at me, one that on its face seems quite elegant and rather unexplored.
The idea is simply this: Require all companies who’ve reached a certain scale to build machine-readable data portability into their platforms. The right to data portability is explicit in the EU’s newly enacted GDPR framework, but so far the impact has been slight: There’s enough wiggle room in the verbiage to hamper technical implementation and scope. Plus, let’s be honest: Europe has never really been a hotbed of open innovation in the first place.
But what if we had a similar statute here? And I don’t mean all of GDPR – that’s certainly a non starter. But that one rule, that one requirement: That every data service at scale had to stand up an API that allowed consumers to access their co-created data, download a copy of it (which I am calling a token), and make that copy available to any service they deemed worthy?
Imagine what might come of that in the United States?
I’m not a policy expert, and the devil’s always in the details. So let me be clear in what I mean when I say “machine-readable data portability”: The right to take, via an API, what is essentially a “token” containing all (or a portion of) the data you’ve co created in one service, and offer it, with various protections, permission, and revocability, to another service. In my Senate testimony, I gave the example of a token that has all your Amazon purchases, which you then give to Walmart so it can do a historical price comparison and tell you how much money you would save if you shopped at its online service. Walmart would have a powerful incentive to get consumers to create and share that token – the most difficult problem in nearly all of business is getting a customer to switch to a similar service. That would be quite a valuable token, I’d wager*.
Should be simple to do, no? I mean, don’t we at least co-own the information about what we bought at Amazon?
Well, no. Not really. Between confusing terms of service, hard to find dashboards, and confounding data reporting standards, The Four can both claim we “own our own data” while at the same time ensuring there’ll never be a true market for the information they have about us.
So yes, my idea is easily dismissed. The initial response I’ve had to it is always some variation of: “There’s no way The Four would let this happen.” That’s exactly the kind of biases I refer to above – we assume that The Four control the dialog, that they either will thwart this idea through intensive lobbying, clever terms of service, and soft power, or that the idea is practically impossible because of technical or market limitations. To that I ask….Why?
Why is it impossible for me to tokenize all of my Lyft ride data, and give for free it to an academic project that is mapping the impact of ride sharing on congestion in major cities? Why is it impossible for a small business owner to create an RFP for all OpenTable, Resy, and other dining data, so she can determine the best kind of restaurant to open in her neighborhood? I’m pretty certain she’d pay a few bucks a head for that kind of data – so why can’t I sell that information to her (with a vig back to OpenTable and Resy) if the value exchange is there to be monetized? Why can’t I tokenize and sell my Twitter interactions to a brand (or more likely, an agency or research company) interested in understanding the mind of a father who lives in Manhattan? Why can’t I tokenize and trade my Spotify history for better recommendations on live shows to see, or movies to watch, or books to read? Or, simply give it to a free service that’s sprung up to give me suggestions about new music to check out?
Why can’t an ecosystem of agents, startups, and data brokers emerge, a new industry of information processing not seen since the rise of search optimization in the early aughts, leveraging and arbitraging consumer information to create entirely new kinds of businesses driven by insights currently buried in today’s data monopolies?
Such a world would be fascinating, exciting, sometimes sketchy, and a hell of a lot of fun. It’d be driven by the individual choices of millions of consumers – choosing which agents to trust, which tokens to create, which trades felt fair. There’s be fails, there’d be fraud, there’d be bad actors. But over time, the good would win over the bad, because the decision making is distributed across the entire population of Internet users. In short, we’d push the decision making to the node – to us. Sure, we’d do stupid things. And sure, the hucksters and the hustlers would make short term killings. But I’ll take an open system like this over a closed one any day of the week, especially if the open system is governed by an architecture empowering the individual to make their own decisions.
It’s be a lot like the Internet was once imagined to be.
I’ve been noodling on such an ecosystem, and I’m convinced it could dwarf our current Internet in terms of overall value created (and credit where credit is due, The Four have created a lot of value). It’d run laps around The Four when it comes to innovation – tens of thousands of new companies would form, all of them feeding off the newly liberated oxygen of high quality, structured, machine readable data. Trusted independent platforms for value exchange would arise. Independent third party agents would munge tokens from competing services, verifying claims and earning the trust of consumers (will Walmart really save you a thousand bucks a year?! We can prove it, or not!). Huge platforms would develop for the processing, securitization, permissioning, and validation of our data. Man, it’d feel like…well, like the recumbent, boring old Internet was finally exciting again.
There’s no technical reason why this world doesn’t exist. The progenitors of the Web have already imagined it, heck, Tim Berners Lee recently announced he’s working pretty much full time on creating a system devoted to the foundational elements needed for it to blossom.
But until we as a society write machine-readable data portability into law, such efforts will be relegated to interesting side shows. And more likely than not, we’ll spend the next few years arguing about breaking up The Four, and let’s be honest, that’s an argument The Four want us to have, because they’re going to win it (more money, better lawyers, etc. etc.). Instead, we should just require them – and all other data services of scale – to free the data they’ve so far managed to imprison. One simple new law could change all of that. Shouldn’t we consider it?
*In another post, I’ll explore this example in detail. It’s really, really fascinating.