Monday, August 31, 2020

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.

Saturday, August 01, 2020

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.