Early Indications December 2020: The bounce-back myth

Maybe it was all the Zoom toasts over the holiday pledging a return to in-person celebrations next year, but I’ve heard too much talk about the timetable for a return to “normal.” That is, there seems to be an expectation that after enough immunizations, life can somehow resume the rhythms and patterns that defined life in 2019. I don’t share this expectation: we have seen consequential political transitions in the EU, UK, and US, for starters. There has been a global financial recession. 2020 saw extreme climate events, including wildfires and hurricanes, that presage more such disasters. Nobody knows how many more businesses will close, permanently, in 2021. For one example, many people assembled Airbnb empires of multiple rental properties that now present impossible mortgage payments. It’s one thing to prevent evictions in the pandemic, but I doubt courts will stop foreclosures of such speculative properties whose impact on residential neighborhoods is not wholly positive. That’s just an initial accounting of the impact.

Here are a few questions identifying sectors, markets, and institutions that will emerge from 2020-21 forever changed.

1) What is the new media mix?

The breakout of TikTok into mass consciousness has certainly been noteworthy over the past year, but it’s not the only important story. The game platform Roblox has been around for about a decade, but its hold on the under-10 demographic is stronger now than ever. TikTok is a key player in 12-24, and podcasts consumed voraciously by middle-agers are seeing huge investment by Amazon, Spotify, and others. YouTube has gained traction among every segment from 5 to 95. Two questions: who wins 21-34, especially if Facebook must divest Instagram? Also, what happens to legacy media: radio is in trouble for sure, and it’s not clear that network television can adapt to the streaming model. ABC, given both its Disney parent and its ESPN sister network, should be in better shape than CBS, for example, but how will we know?

2) What is the future of “date-night” entertainment?

Music venues are getting some bailout money, iirc, but I’m guessing Live Nation and its kin will see money sooner than small-footprint venues. Musicians are but one class of artists hit hard by the Covid recession, and many are doing clever podcasts, instructional videos, and the like to keep the electric bill paid, but this industry will bear watching. Similarly, what is the future of movie theaters? What, in particular, is the future of movie theaters in shopping malls, which have their own existential changes to confront? Going to the mall for a meal, some strolling, and a movie will not be as appealing in 2022 as it was in 2002, for lots of reasons. Restaurants, meanwhile, are closing by the thousands: how will new entrepreneurs address the mix of meal delivery, carry-out, catering, and interior table service? Will new ethnic cuisines fill some of the empty storefronts? Will the meal-delivery apps’ attractiveness persist post-Covid? A larger question informs many economic ones: how much will our cocooning get thrown off in a collective binge of sociality, and how much will staying in (from dining, from work, from shopping) define the new normal?

3) What’s ahead for the tech sector?

Will there be a new category of device or application that rearranges the landscape as the PC, Internet browser, search engine, or smartphone did? Will so many revenue streams continue to be ad-based? How will computer/app/smartphone companies address the mobility market (see #6 below)? How many Internet platforms will survive? Is Pinterest, for example, a feature or a product? Will health tech be a major new consumer market? Will Silicon Valley maintain its primacy? HP, Oracle, and Palantir are leaving, Google is distributing its operations, and California’s many drawbacks — regulatory, climatic, and cost-of-living — provide other areas with ample opportunity to demonstrate their attractiveness, particularly in a work from home world.

4) How will governments address budget shortfalls?

Covid-related expenses coupled with drops in tax receipts are presenting many cities and states with a grim scenario for the next few years. Education, social safety nets, policing, and other functions will shrink: many states have balanced budget provisions that preclude any form of borrowing. States with large tourism sectors (Nevada and Hawaii for starters) are especially hard hit. How will private philanthropy co-evolve with shrinking state and local programs? How much seed corn will be destroyed? That is, how many qualified job applicants will lack key skills in 2 or 10 years? How many foregone investments, in whatever form, will deprive future years of returns on industrial attractiveness, after-school programs, or mass transit maintenance? How many “deaths of despair” and “diseases of despair” will plague our towns and states going forward? 

5) What happens to education?

Apart from the budget crunch noted above, schools will cope with Covid-related fallout for years. How many teachers will get sick and/or burn out after this trying year? How will pedagogy evolve to include remote learning as a routine component? At the college and university level, schools with large endowments have enjoyed robust investment returns; all institutions with seen exceptional (one-time) costs soar, particularly for investments with no long-term payoff. Plexiglas shields come to mind, but so do (in the instance of Syracuse University) hundreds of thousands of saliva tests. Academic hiring freezes are prudent, given the uncertainties involved, but are creating logjams in PhD programs in which advanced students are staying on rather than graduate, see student loans come due, and face bleak job prospects. Maybe the biggest question: how many of the critically valuable (for both their skills and their tuition dollars) international students a) will be allowed and b) will choose to attend college or graduate school in the US vs Australia, Canada, France, Germany, or elsewhere?

6) How will people get around?

Electric cars are obviously big news, but bicycles are even more interesting, especially at the global level. Paris is changing its infrastructure to be more bike-friendly, driven by 2020's modal shift from subways (where the fear of Covid dropped ridership) to cycling at a mass level. Like bikes, standing desks are enjoying a work from home sales surge, but cycling is far healthier still. As the electric conversion proceeds, charging will continue to be an issue, for urban street-parkers in particular. As work from home changes the frequency and nature of commuting, what happens to urban office space and the associated parking infrastructure? 

Many eyebrows were raised by the recent news story that Apple’s Project Titan is still targeting a consumer-facing automotive product for 2024 or so. More interesting, to me, is Amazon, which has made no announcement. Consider:

-Amazon is learning a lot about drones, including path planning, machine vision, and vehicular control: three key competences of autonomous driving.

-Amazon employs some of the best machine learning talent on the planet.

-Amazon is building a fleet of more than 100,000 delivery vehicles (for comparison, the USPS fleet numbers about 200,000). Using autonomy internally or in a B2B scenario could be a smarter play than targeting individuals: Google Glass would have fared much better had it targeted industrial and commercial use cases rather than consumer behavior, a fact not lost on Jeff Bezos I’m sure.

-Amazon has effectively infinite computing capacity (and vast programming capacity) on tap.

That’s four reasons, constituting a unique footprint (UPS lacks AI server farms; Google lacks a massive fleet of vehicles), why Amazon could shake up mobility. Oh, and they just bought these guys.

That’s a cursory list: work, dining, mobility, education, entertainment, and media are all in play. I’m similarly interested in travel (who’s buying airline or ski resort stock right now?), supply chains/manufacturing (can Boeing reclaim its mojo?), and most important, health care. Ohio State just found heart damage in 15% of athletes who had tested Covid-positive (a scary-big percentage), but there’s no diagnostic as of yet to inspect the virus’s long-term impact on the brain regardless of whether senses of smell/taste rebound.

Yes Covid-19 has a relatively low death rate, so far, but in the nightmare scenario, millions of people worldwide could be cardio-pulmonary-neurological time bombs. It’s not improbable that we’ll see lung scarring, heart malfunction, and/or cognitive/emotional damage on a massive scale, threatening long-term debilitation (possibly including accelerated onset of Alzheimer’s) of huge numbers of people, some of whom were silently infected in 2020. (I’m not free-form speculating here: see here and here for starters.) That sure doesn’t sound like “the good old days” to me.

Early Indications October 2020: Why this time will be different

Nearly 30 years ago, businesses across the world began a surge of investment in information technologies. Spurred by desktop computers that were fast enough to run graphical user interfaces, by the adoption of the Internet starting with email, and by the combination of management consultants and enterprise software vendors, companies began “reengineering the corporation,” as one best-selling book termed it. Chief Information Officers were named and, with great rapidity, often replaced. SAP, Siebel, Oracle, Sun Microsystems, and Microsoft all minted millionaires out of programmers and architects, while the big systems integrators — IBM, Accenture, and many others — hired, trained, and churned through thousands of young hires to staff the many and massive projects. As for results, opinions varied in both academic and managerial circles, but every CEO, no matter what industry, had to confront the decision as to how (not whether) to invest in IT.

There are reasons to wonder whether the time has come for a similar wave. Machine learning has dropped in price and proven its capabilities to help address new kinds of problems. Cloud services have changed the capital spending landscape sufficiently that IBM is spinning out everything else and focusing 100% on virtualized computing. Internets of things, industrial and consumer alike, create stunning possibilities for improvements in efficiency, safety, and innovation. New languages and architectures including Tensorflow, containers, adaptive processing platforms like FPGAs, and no-code development tools extend both the making and using of enterprise computing to new scenarios. And no longer are programmers limited to Microsoft, Oracle, and the usual developer toolsets: Zoom just announced it wants to be a platform, joining the likes of Slack, Dropbox, and of course Salesforce.

There’s a growing list, most recently driven by Covid-19, of consequential business and social problems that require attention. The good news is that the aforementioned technical developments make new solutions (and types of solutions) possible. Finally, businesses need the kinds of transformations that IT makes possible. Amazon’s scale, Airbnb’s flexibility, Google’s reinvention of the advertising industry: none of these competitive forces could run on 1990s computing. Whether it was the lengthening of global supply chains, the offshoring of millions of western jobs, or the complex financial instruments that made modern banking possible, modern business relies on more and more complex software applications, ideally accessible anywhere any time on any device.

Why will this time be different? I’ve spotted many reasons, and readers will no doubt have even more. Let’s look at a few.

1) The global pandemic will make capital spending extremely challenging

Yes interest rates look like they will remain low, facilitating borrowing. But the uncertainty of the pandemic’s initial duration along with the virus’s long-term effects, susceptibility to vaccines and/or therapies, and potential mutation will not clarify for years. Hard-hit industries continue to multiply: travel and tourism, hospitality, oil, retail, and the public sector only begin a list. Corporate spending on anything investigative is likely to remain constrained for 3 years, probably more.

2) The IT vendor landscape is changing

In the old days, “nobody ever got fired for buying IBM.” Later, duopolies or oligopolies emerged in sector after sector: databases (IBM, Oracle, and Sybase or another flavor of the month), enterprise operating systems (Unix variants, Windows NT), networking (Cisco and Juniper), and so on. Now, several factors represent a changed landscape from 15 years ago.

A) The installed base is a complex mess of new and old; on-premise and cloud; startups, warhorses, and unsupported failed companies; mobile and desktop; top-down and bottom-up; free and licensed; and backbones from accumulated merged entities. “Greenfield” is a ridiculous notion in this day and age.

B) Virtualization has destroyed the old ISO “layer cake” model of who did what. Is a storage area network storage or networking? Given that a social graph is a database, can our DBAs run one? Is Google Maps data or application? VMWare, a software company 80% owned by Dell, a hardware company, sells virtualization that runs on Amazon's AWS. Who's the lead dog on that sled? Figuring out which vendor can, should, and will do what, especially in the aforementioned complex legacy environments, is nontrivial.

C) The IS shop can no longer play gatekeeper. Beginning with Salesforce, business units and even departments began bypassing central IT and expensing SAAS seats that solved real problems, ramped up in days not months, and avoided the types of IT staffers who were either too intrusive (“you need to do it this way”) or unresponsive (“fill out a trouble ticket and we’ll sort them by priority”). In some companies, central IT tried to block Facebook at the firewall, smartphones notwithstanding.

D) While there are still big vendors selling IT, the buying environment is more complex. “One-stop shops” are harder to find. SAP stock fell 20% in one day a week ago. IBM is reorganizing, again. Amazon does AWS extremely well, but it’s not a holistic solution, especially absent pretty capable developers to deploy it on the client side of the relationship. The trend toward process outsourcing means that Accenture might run a big chunk of a company’s supply chain, but the outsourcer’s handoff points to both IS and internal SCM are far from standard or fixed. And what the contract says might not be how things actually work on the ground. Thus the days of Oracle, Deloitte, and Compaq teaming up on a big bid look very different: recall that a recent government cloud contract came down to AWS and Microsoft after Oracle and IBM were eliminated.

3) Today’s business problems are different than 1995’s

Whether it’s computational biology at a pharma company, chatbots in customer service, detecting synthetic media (deepfakes) in video on social media, or administering fair tests via online instruction, the enterprise software of the past decades won’t have, or be able to accommodate, the machine-learning and other computational resources to address many of these evolving business needs. The jokes about ERP and poured concrete resonated for a reason, and in a world where international trade agreements can change weekly, terrorism and sabotage take new forms, and markets can evaporate overnight (hello air travel), agility has become a watchword that big software packages typically don’t speak. 

4) Today’s organization is different from 1995’s

Remote workers and the new childcare realities that they bring, contractors with evolving legal status, offshore factories migrating closer to (maybe not “back”) home, pressure from multiple sides to address racial and gender equity issues, people at retirement age staying on given insufficient 401(k) savings, and the need to recruit millennials with different skills, norms, and values from the core staff — it’s hard to see much business as usual, and the upheaval will increase, not decrease, in the foreseeable future. The call to be “data-driven” echoes from the C-suite outward, but people with the skills to do and to manage such processes are not yet available in sufficient numbers. (If anything, scientific and quantitative literacy in the U.S. are declining.) Deciding to do the right things is a key part of management, but doing those things right is tough if the requisite skills are just not available. 

Finally, this time will be different in part because some things stay the same. People still resist change, personally and especially collectively. Most organizations, ERP investments notwithstanding, still run on Excel + email. Work/life don’t balance. Risk-taking has been suppressed by “risk management” departments to the point where the status quo becomes organizational law. 

If it’s going to be different, what should we look for this time around?

Privacy, algorithmic transparency, and naïveté all need to be addressed by substantive debate and laws with teeth: people have proven incredibly easy to game and the massive behavioral experiments being run by Google, Facebook, and Amazon have concrete consequences. In enterprise IT, meanwhile, when cash is king, customers can extract real change from vendors. Finally, maybe some organizations won’t waste a good crisis and instead begin the hard work of reinventing the cost structure rather than nibbling at it, the value proposition by truly engaging with customers, and the organization by taking a hard, fresh look at what’s possible and sustainable. (The Nordic countries are one place to start.)

Jeanne Ross is a longtime fixture at MIT’s Center for Information Systems research, having been director and now Principal Research Scientist in nearly 30 years there. In her new book Designed for Digital, she and two co-authors look at the IS organization that is emerging in the post-ERP/CRM era: these backbones are necessary but not sufficient, and IoT, machine learning, analytics, blockchain and many other emerging technologies need to be assessed and where applicable utilized. Her exemplars of “big companies that get it,” drawn from the CISR’s global roster, might be surprising but I can attest, after seeing hundreds of mid-career masters students, that Schneider Electric and Philips are in fact making positive moves. 

There is a lot to learn from in the book, but three core lessons from the leading companies apply in the context of this newsletter. 

1.  They experiment repeatedly.

2.  They co-create with customers.

3.  They assemble cross-functional development teams.

In Ross’s words, “These three challenges attack your habits because they tend to be different from what you have done.” In other words, it’s the behaviors that matter, not the change management consultants, or the CIO who has “a seat at the table” (most still don’t), or the project’s projected ROI.  

In learning from startups, Ross notes that big companies don’t really get the notion of the pivot, of starting down a road then changing course after new information becomes available. Yes the established company has channels to market, capital, and brand equity the startup lacks, but, in the end, the startup poses a threat to the incumbent for this single reason: small, open-minded organizations are better at changing their mind and shifting direction in light of experimental evidence.

Thus the short answer to the opening question — why will things be different this time? — is that size matters, and small is beautiful because agility often matters more than mass. For many reasons, look not for 5-year $50 million transformations, but rather quick-hit, small-scale pilots with the freedom to evolve and ideally pivot.

Early Indications November 2020: Intel Outside

It’s been a little over 9 years since the Silicon Valley venture capitalist Marc Andreesen proclaimed that “software is eating the world.” At the time, his credentials included playing a key role in the invention and commercialization of the web browser before becoming an investor whose portfolio companies included Facebook, Zynga, LinkedIn, Foursquare, Skype, Groupon, and Twitter. In the almost-decade since he staked out his intellectual position, software has created billions of dollars of wealth at the aforementioned Facebook, at Google, and Netflix. As Andreesen’s own list of ostensible world-eaters illustrates, however, it’s unclear (judging from Microsoft acquisitions Skype and LinkedIn, Groupon, or Foursquare) how much his thesis came true. Think back to 1994-2000, and recall the software companies that indeed turned the world upside-down: Akamai, Netscape, Google, PayPal, MySQL, VMWare, and Salesforce.

As those last two companies foretold (as did Andreesen’s second startup, Loudcloud — later called Opsware), the “where” of software was changing from running on a device or customer premise to connecting via the Internet to some vast data center at an undisclosed location. As we look at 2000 to 2020, many of the companies “eating the world” may in fact build software, but their competitive differentiation includes hefty portions of hardware and infrastructure. It’s easy to see that Amazon writes clever software at huge scale, but with dozens of data centers and more than a million employees, many of whom have nothing to do with code, it’s not meaningfully called a software company. Similarly, ByteDance writes clever algorithms to power its TikTok and other similarly addicting services around the world, but absent server farms, there’s nothing happening.

When scanning US-based innovation in the past 9+ years since Andreesen wrote, there’s really not much to see apart from Airbnb and Uber/Lyft: important companies, for sure. Using software to arbitrage everyday people’s capital (at Airbnb, this was true at the outset but less so now) is a game changer, but it remains to be seen if the three companies can be consistently profitable: right now the ride-sharing companies keep losing prodigious sums of money while compressing drivers’ wages and flexibility further each year. As I argued in a journal article a couple years ago, it remains unclear how much Uber’s notoriously bad behavior derived from a crappy culture fostered by the co-founder and how much it resulted from the realization that the entire model could never work. The whole point of taxi medallions was to limit supply to maintain profitable pricing; absent limits on supply, price will race to the bottom of a market.

All of this is a somewhat circuitous way of suggesting that while software is incredibly important, hardware not only matters, but is actually where more interesting things are currently happening. Intel is (to use this year’s most overused word, albeit correctly) an iconic company, on par with AT&T, GM, and Wal-Mart in that it helped define an entire epoch in business history. Former CEO Andy Grove’s paranoia about being disrupted in Christensonian fashion eventually came true: Arm chips, dismissed so easily at their launch given low benchmark performance alongside their power efficiency, were the final blow that broke the boulder of Intel’s industry dominance.

2020 has been a fascinating year on the microprocessor front. Softbank sold Arm to Nvidia to raise cash to help right the sinking Vision Fund ship. In October, meanwhile, AMD bought Xylinx, the dominant player in the broadly-defined system-on-a-chip market. Taiwan Semiconductor Manufacturing Company, meanwhile, is fab-to-the-world, most visibly to Apple, and its stock has more than doubled off its Covid-low in March. Like Apple, Google designs its own silicon, and outsources manufacturing to either TSMC or GlobalFoundaries, AMD’s spun-out semiconductor operation now owned by the Abu Dhabi sovereign wealth fund. Amazon, meanwhile, bought an Israeli chip-design firm in 2015 that now designs the Arm-based custom silicon (reportedly manufactured by TSMC) that powers big chunks of AWS. Facebook appears to rely heavily on Intel, with AI chips rumored to be in development.

Given how few of us work in close proximity to Google TPUs or Amazon Gravitons, Apple’s recent launch of its M1 chip is the first experience everyday people can have of this chip revolution. The M1 is in broad outlines a close relative of the A14 Bionic chip that powers the most recent iPhones. It is available in the Mac Mini and some laptop computers (that, remarkably, are on sale for Cyber Monday). Given that both chipsets are Arm-based, the recent announcement (in July) and release (in November) of Apple products that migrate off Intel processors is extremely significant. The performance gains are simply staggering: my favorite is that the M1 can emulate an Intel chip — relatively efficiently, but not as fast as Apple’s Arm-native code — faster than an Intel can run at full throttle. This performance comes with battery life measured in days rather than hours, without cooling fans, and at low price points. The fact that TSMC can produce 5-nanometer traces while Intel is announcing delays in its 7-nm products, out into 2022, helps explain the disparity. 

It’s unclear what markets Intel will have left: Apple is phasing out Intel chips, desktop computing is in the midst of a steep decline, and Intel captures little of the tablet market. The Wintel duopoly, meanwhile, is no longer an oligopoly, and Microsoft is focusing its energies on the cloud rather than the desktop. Lots of laptops were sold to support Covid-driven work-from-home initiatives, but that blip in sales will most likely not last given general economic softness and the size of this sudden (and one-time) refresh cycle.

I’ll spare my readers the fanboy-like praises of the many reviewers (here’s one summary) but it did warm my heart to remember seeing technology breakthroughs on a regular basis: the Mosaic browser, Altavista then Google search, Keyhole EarthViewer, Gapminder Trendalyzer, YouTube, the iPhone, then . . . what? The iPad, EarPods, Apple Watch, endless meal-delivery apps that will bankrupt already-drowning restaurants — none of these really count as anything that engages me the way the M1 reviewers report feeling. Just to take one example, opening the lid of an M1 Mac laptop has become a kind of sport, given the instant screen readiness. Even browser windows and simple operations are said to snap in a way that redefines the computing experience, reminding a certain kind of user why we went into this business in the first place. 

Early indications September 2020: YouTube and Drill Music

A month ago I had no idea what drill rap was. Then I read a review of a new book, Ballad of the Bullet, in The Economist. Thanks to the wonders of Covid librarianship, the school’s copy was shipped to me a few days later, and I then read a thoroughly engrossing and impressive piece of scholarship.

Eight years out of his UCLA PhD, Forrest Stuart is now a sociology professor at Stanford, but in the interim he taught at the University of Chicago and ran an after-school program aimed at helping community members cope with the violence of their surroundings. Once he was exposed to the rappers from the neighborhood who were trying to follow the path blazed by Chief Keef (Keith Cozart), Stuart embedded himself with them and saw firsthand the intersection of a sliver of opportunity amidst crushing poverty, social media popularity (driven by taunts of bravado), and street violence resulting from that bravado being challenged or usurped. The artifice of created personas, distributed via free online channels, fueled both multiple trappings of success (a new variant on sex, drugs, and rock and roll) and physical constraints on movement outside one’s turf.

Much as “reveal codes” taught a generation of people HTML 20+ years ago, so too was the YouTube/music playbook open for all to read. Cozart unleashed a fierce style of rapping, shaped by the brutality of his surroundings, that stood out from most other types. The videos reinforced the harshness of the sonics and were not the product of hundreds of thousands of dollars in production expenses. Authenticity and a Darwinian epistemology were paramount. As Stuart summarizes the movement, “If there is a dominant message running through virtually every drill song, video, and related content, it’s an appeal to superior authenticity: I really do these violent deeds. I really use these guns. I really sell these drugs. My rivals, however, do none of this.” (p. 6) Copying Cozart’s visual style, production techniques, distribution channels, and lyrical subject matter was straightforward, and Chicago became the home of a musical subgenre that has spread to London, Los Angeles, and elsewhere. As of 2016, 31 of 45 gangs in a six-square-block area had uploaded YouTube content. (p. viii)

Paradoxes abound. In contrast to the one Laptop Per Child school of thought, the teens Stuart observed were extremely adept at social media from a smartphone orientation; laptops for tasks such as video editing were hard to come by. Rather than learn conventional school subjects, the “drillers,” as Stuart calls them, were focused on social media. This focus took several forms. Primarily, one broadcast one’s persona via YouTube, Facebook, and Instagram. The fact that these were personas sometimes escaped police and prosecutors, who took social media posts literally, then used them as evidence of activity that may or may not have actually transpired. (Stuart notes the difference between black teens posing with firearms and white counterparts who were tolerated, celebrated, or applauded by police.) In addition, social media was also used as operational intelligence, predicting opposing gang members’ whereabouts, ideally unsuspecting and/or unaccompanied. Drive-by shootings could follow.

Second, for all the national fame (at different times in the book Stuart’s drillers travel to Indianapolis, Atlanta, and Los Angeles), being recognized even a block or two outside one’s home turf could be extremely dangerous. One consequence was to be “found wanting,” as one was forced on camera to renounce one’s gang superiority, walk back one’s prior claims (a certain kind of poser was known as a “computer gangster”), and commit other emasculations. 

Third, the benefits of fame tended to be more social than financial. Much as most blues musicians were never paid royalties by record labels, drillers often uploaded their videos to sites owned by videographers, producers, or other people more expert in managing Google’s revenue-sharing. Cash payouts could come when rappers were “featured” on another aspiring artist’s videos, but cash also was expended on said videographers and recording studios. In addition, other entities cashed in on the drillers, ranging from bloggers who highlighted social-media “beefs” that could in these quarters escalate to violence to Google and Facebook themselves. Instead, the benefits of local fame — “clout” in the nomenclature — could be as simple as getting respect from one’s family (one rapper had been kicked out of his mother’s house before finding fame and being let back in) or getting attention from females in the court of teen public opinion.

The book’s insights are many.  While there may be posturing that suggests drug dealing, for many teens climbing the hierarchy of the “corporate” gangs of the ‘80s is no longer an option: established dealers distance themselves from the social media frenzy and no longer stake out a newcomer to the operation with product and a market to prospect. The teens depicted in the book actually lost money in their brief experiment with dealing, through friends-and-family discounts, stolen stashes, and too many in-house samples of the product.

Rather than make money on drugs, the drillers quickly learn algorithmic scaffolding: unknown rappers can capitalize on better-known acts by posting “diss tracks” that use big viewership numbers to pull the newcomer along, albeit at real risk: you can only insult a national name so many times before retaliation comes. Elsewhere, the stereotype of the “digital divide” is tested, found wanting, and replaced by a more nuanced view of “digital disadvantage” among the urban poor. Micro-celebrity for these YouTubers is not of the same variety as that of travel “influencers” or fashion bloggers: they can’t quit if the rewards are insufficient or the blogger gets bored. This all-in pursuit of fame can be two-edged: as teens grow into adulthood and perhaps seek to leave gang life behind, erasing one’s social media presence, linked as it is to the gang, can be much more difficult than getting the tattoos removed.

There is much more to applaud. Stuart reflects carefully and usefully on the dangers of ethnography as voyeurism or self-aggrandizement. Ballad of the Bullet has much to teach about poverty and its potential remedies, about race and racism, about the atypical adoption (and consequences) of digital technologies. Most centrally, the highly consequential intersection of online fame with street-level mortality fuels new insights in the larger inquiry into online video’s many externalities. Forrest Stuart brings stand-up credibility, clear prose, and reflective insight to a corner of the Internet few adults will have encountered. His book broadened my perspective markedly, and I recommend it enthusiastically.

Early Indications August 2020: Book Review of Thomas Gryta and Ted Mann, Lights Out: Pride, Delusion, and the Fall of General Electric

As with so many aspects of economics and finance, an entity’s name often says little about what it actually does or contains. The Dow Jones Industrial average, a basket of (currently) 30 stocks, has never really been an accurate reflection of the U.S. manufacturing sector. Founded in 1896 by Charles Dow (co-founder of current-day Dow Jones), the original index was comprised of 12 companies, many of which represented what today might be called basic materials: cotton oil, sugar, tobacco, rubber, lead, and coal. The “Distilling & Cattle Feeding Co” is still with us as Jim Beam, now owned by the Japanese Suntory group. Laclede Gas still provides natural gas to communities in Missouri. Any pretext of only tracking companies that made things was quickly lost: AT&T and Western Union joined in 1916; Sears Roebuck and F. W. Woolworth were added in 1924. More industrially, General Electric was one of the original 12 companies on the Dow, dropped off two years later, and readded in 1899. 

GE’s long presence on the Dow belies the evolution of the company, which by 2000 was an advantageous blend of an investment bank and a heavy/advanced manufacturing conglomerate: in terms of naming accuracy, it was very “general” but not particularly “electric.” The business practices and assets of the factory-based side allowed the bank to borrow at the lowest possible interest rates at the same time that equity markets favorably valued the company more like a metal-bender than a bank. The double standard eventually disintegrated, and under CEO Jeff Immelt GE shed many of its financial services divisions in the midst of 370 divestitures; at the same time it acquired 380 companies. Bankers and lawyers were delighted — M&A fees ran an estimated $1.7 billion to fund the shuffle — while shareholders had less reason to cheer. This evolution eventually failed, and today GE faces fundamental reinvention. As it promises, Lights Out credibly tells the story of the fateful 20 years in which the company came to its moment of reckoning.

I had many questions that the book did not answer. For many years I taught whole sections of GE employees in my online supply chain masters classes at Penn State. Their devotion to Six Sigma/Lean Manufacturing was durable bordering on enthusiastic, but Lean is not usually a good tool for fostering radical innovation. I hope someday someone else is able to tell that story. Second, GE was a leader in adapting 3D printing to industrial production (not just prototyping) and while additive manufacturing wasn’t going to save the Titanic that was GE as of 2018, I wonder what is being salvaged from those pioneering efforts. The big question, which outsiders will never be able to answer, concerned the culture in which bad decisions went unchallenged by either the board or by middle managers with sufficient data to see pitfalls: the Alstom acquisition was built on shaky logic at the outset, and the EU’s pressure for concessions made GE leadership’s business case at closing completely fanciful.


Much of the book is devoted to the portrayal of CEO Immelt as global potentate, and rightly so. He flew with a spare business jet following him “just in case,” which cost shareholders many millions of dollars over his period in the job. Meetings with kings and presidents were part of the job; GE was one of those companies not only too big to fail but too big for political leaders to ignore. For all his skill in these settings, the job as Immelt defined it removed him still further from customers and their market realities.

Immelt made his name as a salesman, and the personality trait of not taking no for an answer (and the unshakable confidence a master closer must possess) led to multiple deals in which GE either overpaid or bought an asset for the wrong reasons: “synergies” that were so often promised rarely materialized on the income statement. More crucially, Immelt focused his attention far more on the share price than on his customers, and for all his desire to be valued like a tech company, GE appeared to have little of Apple’s human-centric design sense, Amazon’s customer-obsessiveness, or Google’s user-facing performance improvements. Knowing the complex financial structures of the business through the eyes of trusted lieutenants appeared to be far more important than step functions in the customer value proposition. Buying market share may work for a few quarters, but rarely did a Baker Hughes, an Alstom, or a Vivendi drive innovation and customer value.


One of Immelt’s highly visible missteps in his latter days was an apparently under-informed faith in the “industrial Internet.” This set of extremely expensive initiatives, designed more to goose the stock price than to deliver customer value, set out to establish GE as the Google of connected MRI machines, drilling platforms, and locomotives. It had all the signals of what a colleague in consulting once called “management by magazine,” the practice of a high-ranking executive reading some oversimplified account of computational magic and saying “make it so” in his or her company. Immelt showed no evidence of understanding cloud vs edge computing, networking and storage at massive scale, data curation, or the limits of data interoperability (standards and protocols). In and of themselves, these technical shortcomings shouldn’t be fatal: the Internet of Things is still in its early stages, and the challenge of instrumenting heavy machines and digesting the data they produce is non-trivial.

Immelt’s failing was more fundamental: he never seemed to have asked his business and technical experts for even back-of-the-envelope financial projections. Let’s assume sensors and analytics could deliver the promised 1% performance improvements — in jet engine fuel economy, in gas turbine reliability, in locomotive predictive maintenance accuracy. Who would pay for those sensors, that infrastructure, and the requisite brainpower? Companies that forked over multiple millions for a GE-made asset were (and are) not likely to pay for the privilege of letting GE siphon data from their assets only to sell it back to them in the form of more expensive service contracts: “here’s a way to run your generation facility longer between shutdowns. That’ll cost you $x million.” 


The story of GE’s embarrassing commercials featuring Owen the programmer who couldn’t lift his father’s sledgehammer and eschewed writing code for layering tropical fruits into photos of small furry animals turns out to be merely the tip of the Predix iceberg. Those commercials were intended for an ill-defined audience: 20-something data scientists weren’t turning down Facebook or Uber to work for GE, and the company’s buyers of capital equipment also did not react consistently favorably to the ads. Some contended that the campaign was intended for investors, who did not react as GE hoped, and the company’s retirees were largely furious at the tone as much as the content. Below the iceberg’s waterline, as it were, the combination of a naive (at best) business model with an insufficiently robust technical team meant that Predix was doubly doomed: making such a whiteboard vision work at scale still exceeds GE’s level of digital expertise years later, and even if the technology could have worked, there was never a realistic path to profitability in the market.

In the end, three aforementioned trends converged to bring down a mighty company: GE was dropped from the Dow in 2018, replaced by Walgreens Boots. First, the company relied more heavily on financial engineering (in the process, falling prey to Wall Street’s quarterly focus) than technical innovation. Second, GE lost focus on customer markets: Immelt bought and sold companies, not generators or drilling platforms. Finally, leadership got cute, trying to achieve with M&A what it could not with fundamental strategy and execution — while silencing voices of realism and dissent. Strategy, culture, and quality of execution all contributed to the fall of GE, which raises the question of lessons.


If we assume that companies age and decay faster than before — it’s hard to image another 120-year Dow tenure — companies like Microsoft, Google, and Apple are in mid-life. Many founders have disappeared: Apple lost its resurrected co-founder/messianic CEO almost 9 years ago, Google’s co-founders have retreated from operational responsibility, and Bill Gates is a hero of public health, not a monopolist-villain; Microsoft and Apple have both executed successful succession plans (the former after a 14-year mistake). The Economist recently profiled Google at mid-life (the jury is still out on its succession plan), and investors have to be very nervous about succession at Amazon. 


What can GE teach these companies? 1) Nothing can grow forever, and we see limits to scale: Dell peaked in 2006 and posted record net income in 2020 after six straight years of losses. IBM revenue peaked in 2011, arguably after its industry influence did. 2) Losing sight of customers and innovating to solve real issues can be fatal: Immelt and Steve Ballmer at Microsoft shared several characteristics, including being sales executives. 3) Managing to the share price is a real temptation that Jeff Bezos and Tim Cook (for very different reasons) have avoided. 4) Most important, you have to make the right technology bets and have the engineers who can make them work. GE had no signature customer-facing innovation for years, certainly nothing on the scale of Amazon’s cloud or Alexa platforms, Apple’s pivot to iPhone-linked services, or Google’s AI-powered operations in multiple markets. Call it revenge of the nerds, but the best CFO in the world can’t compensate for a shortfall of well-deployed and well-managed technical talent. Google has both a top-drawer CFO and terrific technical talent, but there are strains in the relationship between the two; Facebook too has cultural issues between programmers and management. It may turn out that keeping your engineers happy and well focused is the mark of a healthy company in the mid-21st century.

Early Indications June 2020: So many questions



I am both academically and temperamentally inclined to analyze the unintended consequences of technology innovation. That inclination can lead to some surprising juxtapositions. Right now, for example, I’m thinking that penicillin is part of the reason the current pandemic is so weird. To explain: as of 1930, with the 1918-19 influenza epidemic still fresh in memory, pretty much everybody had lived through severe illness outbreaks. Tuberculosis, polio, typhoid, malaria, and other diseases were part of the normal landscape. After DDT, antibiotics, vaccines, improved nutrition, better public infrastructure (especially water supplies), and indoor plumbing, the late 20th century saw many of these diseases recede from mass consciousness. As a result, we in 2020 lack mental models for processing what global patterns of infection look like. Accordingly, Covid-19 is fueling many (but certainly not all) of the questions we are confronting at this juncture. 


Like everyone else, I have never experienced a pandemic. While many questions (including “what do I wear?”) have sort of been answered (athleisure), the cascade of unknowns is overwhelming. Business and civic officials are faced with a huge, complex tangle of issues around which they must plan. We will have more to say about planning under uncertainty, but for now, the U.S. and most of the world confront an unprecedented (in our lifetime) web of questions.


I. Covid-19


Thinking back just 4 months is an exercise in cognitive dissonance, returning to a time when air flights, concerts and sporting events, and bustling restaurants were unremarkable. When large numbers of people can gather indoors again, it won’t be a simple reset. How different populations in different places move on will be a legacy of the pandemic, with many consequences both major and minor. For now, there's so much we don't know.


-Basic statistics
How many people have been infected or otherwise possess immunity? Of those infected, how many became symptomatic? How many people have a) directly and b) indirectly died because of Covid-19? How have the ratios of these numbers changed in the past 6 months? The one number we do seem to have — how many people have tested positive? — doesn’t really tell us much.


-Basic science
How exactly is the virus transmitted? How long will the Covid-19 virus remain stable (will there be a Covid-20 or 21 variant?)? How long does immunity remain viable? Can the virus’s traits that kill people be addressed by treatment? What if any long-term impacts will people develop in a year or five? That is, new research suggests that the virus can involve multiple body systems beyond the lungs including pancreas, brain, gut, heart, and immune system. Might a 20-year-old infected in 2020 develop debilitating lung scarring at 25, or age of childbearing, or at menopause? Might we see millions of cases of cardiac and/or cognitive damage at atypically young ages of onset? Assuming a billion people are eventually infected, and a quarter require long-term care, the costs in both money and lost human potential could be staggering. Or they might not be much more than a blip.



-Vaccination
Can a safe, reliable vaccine be developed? If so, how many people will consent to immunization, and how quickly? Most people have forgotten the 1976 flu vaccine program, which occurred during a presidential re-election landscape (Gerald Ford) and was rushed, largely unnecessary, and heavy-handedly promoted. The current public faith in the vaccine establishment is much less robust than it was 44 years ago, in part because of social media, and the understandable urgency to deploy a vaccine could lead to mistakes that a) harm people and b) drop credibility further.


-Symbolism
Mask-wearing has become politicized in ways few people could have predicted. Governors still hold considerable authority (withdrawing liquor licenses or certificates of occupancy for large venues), but the simplest tool has been taken out of play in many areas. What other responses to the virus might lead to shootings, fistfights, and other conflict?


-Economics
Travel and tourism along with hospitality have suffered substantially since March, and restaurants in particular look especially vulnerable. How many establishments can turn a profit at 1/3 or 1/2 seating capacity (after being completely out of business for a time)? Additionally, with conferences, trade shows, and large sporting events off the calendar for many more months, foot traffic will drop still further. Schools and colleges are other major economic actors that rely on large gatherings in indoor venues, and educators are becoming increasingly vocal in their refusal to return to classrooms under current plans (Fairfax, VA is an example). More than 30 football players at both LSU and Clemson have tested positive so far: even without fans in the stands, can those numbers drop to and remain at safe levels for close, hard-breathing contact for 3 hours at a shot? If not, both amateur and professional leagues could see multi-billion-dollar losses.


-The social contract
Thus far, vulnerable populations — the aged and low-wage communities of color — have suffered disproportionate fatalities in many countries. For a time, this appeared to be a policy calculus in some places. With the coming of fall, a bitter U.S. presidential election, and a projected resurgence of both Covid and influenza viruses, will those populations continue to be harder hit? Or might more affluent communities see dramatic and widespread effects, perhaps triggering tighter lockdowns than were implemented to safeguard more marginal groups? 



-Gatherings
Which venues will _stay_ open first? Which venues will people return to and which will see drops in participation? Churches, offices, bars, arenas, and trade shows will likely resume speed on different trajectories. The quick reopenings that are being followed by panicked closings (see: beaches, Florida) may fuel a more cautious ultimate return in some places than we saw in countries like New Zealand, where the governmental response was more coherent.


II. Black Lives Matter


Coincident with the pandemic is a broad-based movement to reverse centuries of institutional racism, beginning with more accountability for police officers who kill and abuse unarmed citizens without commensurate consequences. In addition to the public safety discussion, education, hiring and promotion practices, and cultural representations of people of color are suddenly front-burner issues in many places.


-What will police reform look like?
It’s easy to point to the abuses, but reversing them is complex. Different geographic units count things (or don’t count things) in different, non-standardized units: a drop in arrests could result from either bad policing or good policing. Prosecution of police officers depends on testimony from other officers, in trials initiated by prosecutors dependent on police cooperation in their everyday duties. Absent smartphone video, most policing crimes are neither reported nor prosecuted. Already police walkouts are occurring in response to even minor action by mayors. Personal safety is an emotional topic and police unions appeal to it at every opportunity.


-What constituency will drive change?
Speaking of police unions, political progressives are in a bind as they both march for police reform (or “defunding,” a word with many meanings) and seek to bolster labor union membership and bargaining power. Ethnic minorities, by definition, are minority populations even if they may be a majority in a precinct or locality. Suburban whites have marched and told pollsters they support change, but how deep or long-lived will that support be? How much management attention will be devoted to fixing policing in an economic recession in a pandemic in a period of high emotional anxiety and uncertainty? Put more bluntly, will fixing police brutality get a mayor re-elected if she doesn’t create jobs, fix potholed infrastructure, or maintain the aforementioned personal safety?


-How long will change take?
Universities pledge to hire and tenure more people of color: many groups are underrepresented in the professoriate and are rightfully speaking out. Businesses pledge to increase minority board seats and C-level appointments. Many people and groups seek to support Black-owned businesses. None of these commitments can be realized overnight. Between 2011 and 2017 the top 20 U.S. economics programs graduated a total of 15 Black PhDs. That’s collectively: in other words, about a tenth of a PhD per year per school. Filling the talent funnel will take decades, beginning in pre-K programs like Head Start. How can early gains be realized at the same time that structural reforms will necessarily take a long time to kick in?


-Where is the political will?
A divided U.S. congress has already claimed one effort to begin to address abuses in policing. State budgets are being crushed between increased Covid-related costs and decreased tax revenues. All politics may be local in the Tip O’Neill sense, but programs to equip police forces with military armament originate far away from city streets. Following the money reveals considerable cash devoted to the status quo. What coalition can change that?



-Where are the invisibilities?

There are many Americas, and residents of one can be oblivious to the others. I have only seen two or three Indian reservations that I know of (casinos notwithstanding) and that's probably by political design. Aggressive inequities in policing in Minneapolis are long-running, yet the Twin Cities routinely score well on quality of life indexes like this one: https://realestate.usnews.com/places/rankings/best-places-to-live. Ralph Ellison's _Invisible Man_ turns 70 years old in 2022, its relevance having outlived the war on poverty; Montgomery, Selma, and Memphis; the Voting Rights Act; Malcolm X; affirmative action; and so many other American beginnings that have yet to realize the fruition of true human inclusion.  

III. Macro-level dynamics


Even before the virus rearranged everyone’s lives in March, high-level social and economic winds were shifting. With new forces such as telework and telehealth suddenly accelerated by the virus response, life as we knew it in 2019 is likely gone forever in many regards. Despite lots of people talking about getting “back to normal,” our future non-pandemic life is going to require some adjustments.


-International trade
The trend toward globalization of the 1990-2019 variety was already slowing, and post-Covid-19, we will see new developments. China has become a true super-power and future negotiations will reflect that reality. Critical items like facemarks are too important to be manufactured only in plants far away from points of urgent need. A growing global middle class is loading the planet with demand for animal protein, motor vehicles, and air transportation, and every country will have to re-evaluate its role in that load. Factories can no longer be as easily relocated for convenience of low wages and lighter regulatory burdens.


-Technologies of fabrication and motion
Micro-manufacturing, micromobility, and advanced materials will transform transportation and manufacturing. Bicycles (which are selling fast these days) and pedestrians will play a bigger part in formerly car-centric urban planning. This shift in transit means more bike and light-rail factories and eventually fewer car-makers. How fast and how far air travel rebounds is another major uncertainty.


-Real estate
With the link of work and place broken, for good at some firms, real estate will be shaken up at all levels. Many malls, already an endangered species in the past few years, are going to fail sooner than they would have absent the pandemic. Office space will be scaled back in favor of telecommuting and virtual teams. People can buy houses where they want to live, to a greater extent, than they could when physical work presence was the rule. Such familiar practices as commutes, business travel, and industry gatherings will be redefined rather than simply resumed.


-Skills mismatches
Despite lots of people making creative pivots (corporate magicians and other entertainers doing Zoom sessions), the pandemic has heightened the realization that the current skills base both in corporations and coming out of universities doesn't align with what institutions (businesses, non-profits, governments) will need in 2025 or 2030. As Benedict Evans points out, internet telephony was not invented by Skype, nor could Skype dominate the market: many apps now have voice embedded. Might Zoom go the same way, breaking down an ease-of-use barrier only to see video embedded in social, learning, customer service, and other scenarios? Will resumes of the future embed the technology, essentially encapsulating the interview in the application document? The same questions can be asked of new manufacturing, advertising, retail, social service, and recreational-access technologies. Who will staff the organizations built on the legacy of the Covid-19 quarantine?


IV. How does one cope?


The general sense of anxiety is widely reported: major uncertainties cloud one’s health and safety, economic well being, kin and friendship networks, and future prospects for one’s offspring. The huge academic literature devoted to decisions under uncertainty isn’t much help. Much of it is written to support neural network and other machine learning research. Other bodies of work (including the Nobel-winning contributions of Tversky and Kahneman) show how humans revert to known patterns in the use of heuristics rather than relentless examination of the evidence. Further, many decision models are built on binary outcomes: the election will be won by a Democrat or a Republican, the student will attend college or not attend college, tomorrow it will rain or not rain. What we are faced with now is far from such simplicity: economic recession and/or recovery and policing that no longer commits the crimes of the current institution are non-binary futures. Modeling our current future is an exercise in murkiness.


Let me end with a prediction based on hope more than evidence. With travel curtailed, with commuting redefined, and with people taking a bigger role in urban transport (vis a vis automobiles), perhaps we will see a resurgence of physical community, of neighbors taking action alongside neighbors, putting aside the “virtual” social networks that have proven to be so toxic to the republic and to the body politic. If we take Tip O’Neill at his word then perhaps a corollary is that all localism can generate political change.

Early indications July 2020: Our digital twins?

Seeing the major US tech CEOs testifying before Congress earlier this week is a useful prompt to consider just what it is those companies sell to become so powerful. Amazon sells household goods, information goods, groceries, computing capability, and now eyeballs: 2019’s $14 billion in ad revenue was a 40% jump on the prior year. (For perspective, that’s more than the company made on cloud services as recently as 2016.) Apple sells high-margin hardware, and increasingly services: at about $50 billion a year (annualized), the App Store, iTunes, cloud storage, and the like outperformed most of the company’s hardware lines, but not the iPhone. Google and Facebook, however, are less diversified: they each sell some version of us.

What we will consider this month is the degree to which the digital representations of us that are modeled and manipulated by the ad giants mimic a notion with its origins in heavy industry: the digital twin. Briefly, a GE, Boeing, or Caterpillar can aspire to accumulate and crunch sensor data from thousands or millions of Internet-connected Things such as jet engines, MRI machines, airframes, or excavators. Identification of safety risks, predictive maintenance optimizations, and other business processes is the grail in this world: it’s far better for BNSF to have a digital locomotive fail in a simulation than the physical one 500 miles from a breakdown crane. As with self-driving cars, all data-powered products (think of Tesla’s over-the-air software upgrades) can theoretically be as capable as the most capable unit. As of now, the industrial digital twin is closer to whiteboard aspiration (see this new book about GE’s failures) than to profitable reality.

It’s pretty obvious after spending any time on a modern digital platform that its algorithms are easily fooled. Run a few searches for birthday presents for someone, and your ad feed quickly resembles the demographic of your giftee. Now that I have a physician in the household accessing Epic and other clinical systems, I get “overspray” in the form of ads for prescribers of IUDs by virtue of sharing a network, apparently. The reference librarian knows I’m writing a research paper on government policy regarding Puerto Rico whereas Google responds as though I want to vacation there.

At one level this slippage is reassuring: I used to get ads for industrial ropes and slings in my Gmail header, not to mention ads from malpractice lawyers representing patients with complications from the implantation of transvaginal mesh. At the same time, the uncanniness of ads has led to widespread suspicion that open microphones are capturing spoken conversation. There’s also the head-fake to consider: Target used to send coupons that too closely resembled a person’s interests or shopping list, and customers were creeped out. Target’s solution, if I recall correctly, was to add “noise” coupons to calm suspicious consumers: there’s nothing like a lawn mower ad to distract from how much the store knows about your health and beauty purchasing habits.

At the same time that we are (mis-)represented by behavioral data collected both on- and off-line, in unfathomable quantities, that can vary widely from our “real” selves, there are data representations of us of much higher fidelity. None of these are _currently_ aimed at trying to get me to do something, though as we will see, the lure of ad revenue extends farther and farther, to include ISPs for example. Verizon/AT&T have an extremely accurate map of my daily movement, provided my phone is within a few feet of my person most of the time. Smart TVs and cable boxes track viewing habits, with the data shared in sometimes-objectionable ways. (Devices from multiple manufacturers share behavioral data with Google, Facebook, and Netflix, for example, and opting out is predictably difficult.) Personal fitness trackers and exercise trainers are another source of high-fidelity data that could someday contribute to a “digital twin.”

Google and Facebook get paid when we click on ads. Ideally, Aetna should want me to live a long and healthy life with few expensive conditions/episodes and I presumably agree. How much will these two trajectories — digital twin as behavioral experiment vs digital twin as predictive maintenance — diverge vs converge?

This is pure speculation, but I think the player to watch is the richest CEO from the congressional hearing the other day. Amazon has 1) a vast store of behavioral, social network (in the form of our address books and gifting history), and purchase data, 2) unsurpassed computational power and algorithmic talent, 3) designs on medical markets, as evidenced by its PillPack acquisition, and 4) strong motivation to lower health care costs for its enormous — soon to approach one million — workforce. (I missed it, but Harvard surgeon and New Yorker author Atul Gawande left the CEO post at the Amazon/Berkshire/JPMorgan Haven Healthcare startup back in May.) Amazon warehouse workers already wear wristbands to track movement and allegedly productivity. If there’s a digital twin of an employee already built, the e-commerce behemoth is as likely as anyone else to have built it. 

Where might we go from here? Behavioral nudges — to lose 5 pounds, to get up from the desk and stretch, to eat more vegetables — would seem to be a perfect marriage of the two trajectories. Back in the days of e-commerce, Pets.com discovered a powerful predictive question: if the website visitor bought his or her animal a present on its last birthday, they were substantially more likely to make a purchase than a site visitor without that behavior. Where else can big data expose similar minimally invasive but predictively powerful indicators of long-term well-being? If I were inventing a “dream” college graduate right now, she’d have some combination of algorithmic aptitude, behavioral economics, and engineering training to understand big data, human motivation/reward, and systems thinking. 

Bearing in mind the fact that the man behind Facebook’s explosive growth between 2007 and 2011 won’t let his own children near the “short-term, dopamine-driven feedback loops that . . . are destroying how society works,” how might the future be better? The first thought is algorithmic transparency, a phrase that has yet to be operationally defined, as Microsoft’s danah boyd has shown: few of us could read the algorithm, and the algorithm is an abstraction without user data, raising privacy hurdles. Second, there has to be a working definition of ownership: at some point, a person’s data footprint should be under his or her influence rather than being remote and inaccessible. Realistically, this would mean FTC- or FDA-like regulation. Third, we need better sensors, better sensor protocols (including for privacy), and better sensor-data analytics: if AM General can’t predict when the Humvee transmission will fail in Afghanistan vs in Alabama, Mass General is still a long way from identifying when I will have a stroke. 

Last, I would be in favor of intensifying training in critical thinking. The echo chambers so powerfully created and manipulated by Facebook (among others, obviously) would gain less traction if more people sniffed out hoaxes and self-serving propaganda. Scientific literacy appears to be in retreat, in part, it appears, because of those “short-term, dopamine-driven feedback loops”: people, it turns out, are incredibly easy (and profitable) to game.

How can we as parents, as educators, as citizens, as humans demand — and model — better? It sounds paradoxical, but better critical thinking and digital literacy skills will help us build new kinds of organizations — of learning, of governance, of news/media — to replace today’s so visibly broken ones. Restoring institutional credibility and cognitive authority (in short: trust) in our institutions, nurturing humanistic leaders who grasp the realities of today’s vast machines of data collection and behavioral manipulation, will be a long road, but one I believe is worth hiking, one careful step at a time.