“Never predict the end of the world. You’re almost certain to be wrong, and if you’re right, no one will be around to congratulate you.”*…
Still, as Amanda Shendruk reminds us (in her fascinating newsletter Not-Ship), sometimes incorrect predictions have something useful to teach us…
According to a paper published in one of the world’s top scientific journals exactly 66 years ago today — the world will end on Friday, Nov. 13, 2026. “At this date human population will approach infinity if it grows as it has grown in the last two millennia,” wrote Heinz von Foerster and his coauthors in Science.
But don’t cancel your plans! The end may be nigh, but it won’t be next week.
Von Foerster was worried about too many people. So, he fit a curve to 24 historical population estimates and found that population growth wasn’t just rising, it was accelerating. That was 1960, when the population was doubling about every 30 years. By his math, it would double every five years by 2016, and every six months by the end of 2025. And by Nov. 13, 2026, the global population would be doubling so fast that it would shoot off toward infinity.
Here’s what actually happened.
The doomsday formula was right — but only until the 1990s
Von Foerster’s population predictions compared with real global population numbers:
Global population growth peaked in the 1960s, just as the paper came out, and birth rates began to fall. The global birth rate today is roughly half what it was in 1960.
The authors didn’t really expect infinite people. But their worry was real nonetheless: that kind of growth couldn’t last. “Our great-great-grandchildren will not starve to death,” they wrote. “They will be squeezed to death.”
Today, “squeezed to death” isn’t the doomsday most of us fear, but it mirrored worries of the time. Runaway population was a defining anxiety of the decade, one that would peak in 1968 with The Population Bomb — an alarmist, bestselling book predicting mass famine.
Still, von Foerster’s prediction belongs to a very old tradition: Doomsday dates are poor guides to the future, but great records of the moments that produced them.
I compiled a dataset of about 280 dated doomsday predictions from Wikipedia and the Doomsday Scoreboard. There’s no way to know how complete this list is. I’m going to go with “not very.” But we can learn from it nonetheless.
The earliest is 66–70 CE, when a Jewish sect saw the revolt against Rome as the final battle. Before then, doomsday predictions lacked fixed dates. Precision arrived thanks to linear timelines, standardized calendars, and Jewish and early Christian apocalyptic literature.
The end is always near
Dated predictions of the end of the world, by decade:
It seems to me that doomsday dates are a bit like monster movies. They’re never really about the monsters, but about our shared anxieties.
For most doomsday dates, God is the destroyer. But signs that the end was near, and often the details of how it would happen, came from the world around them. When the Black Death devastated Europe in the 1340s, some took it as proof that the end times had begun. The Christians who braced for the end in 1666 were unnerved by the 666 in the date, the number of the beast. In 1856, as British colonists took Xhosa land in southern Africa, the prophet Nongqawuse foretold a day when the dead would rise and the Europeans would be swept into the sea — not the end of the world, but of colonial rule.
“Concern about the end of the world has always been with us,” said Matthew Gabriele, a medieval studies scholar and apocalypse expert, in an interview with Nautilus. “But the change from belief that something theological would drive it, to the possibility of human-made destruction, is absolutely new.”
In 1945, we got the atom bomb. And the ability to bring about our own demise. Predictions continued to mirror our anxieties: nuclear war, of course (at least 19 predictions), then aliens at the dawn of the space age (though first as messengers; only later as destroyers). There was Y2K in 2000, and in 2008, a black hole from the Large Hadron Collider.
Today, our most prominent warnings rarely come with dates. Instead, they come wrapped in the language of risk and probability. The Doomsday Clock, which the Bulletin of the Atomic Scientists adjusts each year to show how close it thinks humanity is to destroying itself, now stands at 85 seconds to midnight, the closest it has ever been. And in the AI world — where “end is nigh” headlines appear almost weekly — people speak of p(doom), or the “probability of doom.” (I’ve ranted about p(doom) before. Tl;dr, it’s a load of bunk.)
Von Foerster’s doomsday date tells us what worried people in 1960: too many people and not enough room. Our warnings today say just as much about now. We look to scientific or industry authorities, not prophets, to tell us how close the end is. And the threats they track — nuclear weapons, a warming climate, artificial intelligence — are all of our own making…
“Good news! The world won’t end on Nov. 13, 2026,” from @not-ship.com.
* John Green, The Anthropocene Reviewed: Essays on a Human-Centered Planet
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As we contemplate our concerns, we might send blotched birthday greetings to Hermann Rorschach; he was born on this date in 1884. A psychiatrist and psychoanalyst, his education in art helped to spur the development of a set of inkblots that were used experimentally to measure various unconscious parts of the subject’s personality. Rorschach knew the human tendency to project interpretations and feelings onto ambiguous stimuli and believed that the subjective responses of his subjects enabled him to distinguish among them on the basis of their perceptive abilities, intelligence, and emotional characteristics. His method has come to be known as the Rorschach test, iterations of which have continued to be used over the years to help identify personality, psychotic, and neurological disorders.
“In a hierarchy, every employee tends to rise to his level of incompetence”*…
As we ponder the impact of the defenestration of seasoned, competent leadership in so many corners of the executive branch (c.f., the Defense Department, the Department of Justice, and… well, sadly, pretty much across the board), an unfortunately timely observation from Mandy Brown (in her lovely newsletter, A Working Library)…
The Peter Principle posits that people are promoted until they reach their level of incompetence. What happens after that point? The implicit assumption is that they stop getting promoted and linger at whatever level they’ve arrived at, and surely all of us can point to mediocre leaders who seem to exhibit exactly that circumstance. But in fact, that situation is not stable but subject to career pressure, as Christian Gläßel and Adam Scharpf define it in Making a Career in Dictatorship. Unable to move up, and very possibly unable to meet expectations, the person who is subject to the Peter Principle is at risk of losing their job, or even their entire career. Which means they might take an even greater risk to save it. Gläßel and Scharpf write:
The logic of career pressure also offers a compelling twist to the Peter Principle. The Principle posits that individuals in hierarchical organizations are promoted until they reach their level of incompetence, where they can no longer perform effectively and cease to advance. In contrast, our logic shows how these individuals may surpass this point and climb the hierarchy even further. Career-pressured individuals often take on high-risk, labor-intensive, or morally contentious tasks. By loyally executing the organization’s dirty work, they can reestablish their value, regain competitiveness, and even outpace peers who were previously ahead of them. Alternatively, they may resort to sabotaging superiors or higher-ups by making false allegations or revealing secrets to create vacancies above them and forcibly advance their careers.
Gläßel and Scharpf, Making a Career in Dictatorship, page 182
In other words, someone promoted up to their level of incompetence, sensing a threat to their own livelihood, becomes a threat to everyone else. I’m reminded here of David Graeber’s theory in Bullshit Jobs, that a great many management jobs are basically feudal retainers—people kept around in order to make the folks above them look good. Very often, making someone else look good may well mean tackling the dirty work for them so that they can keep their hands clean.
The petered leader has in effect been made an imposter—a real imposter, not a “syndromed” or perceived one. They must seem competent, to preserve the appearance that they belong in the role; while in fact they are not capable of delivering on their actual responsibilities and may have been quietly tasked with unrelated services both illegal and unethical. As a result, one of the primary tools of the real imposter is obscurity. Imposters cloak themselves in a fog of obfuscation and busyness, so that the people around them can never say for sure what it is they are doing, or how well (or not) they are doing it.
What we call imposter syndrome was originally termed imposter phenomena, a term that makes plain that it is something that can be observed, not merely felt. The transformation into a syndrome shifted the experience of being impostered by a patriarchal, white supremacist society into a medicalized disorder, something for which the impostered person must be treated. That’s a neat trick: to shift the locus of treatment to the mistreated person, rather than the system that caused her harm. But perhaps there’s even another, more diabolical move here. What if the popularity of imposter syndrome is, in part, a means of obscuring the actual imposters in our midst? What are we missing when we look inward instead of around?
On the dangers of promoting people past their levels of competence at work: “Petered” from @aworkinglibrary.com.
Pair with: “How to Fix a Broken Hierarchy from the Inside.”
(Image above: source)
* Dr. Laurence J. Peter, The Peter Principle (written with Raymond Hull)
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As we fumble with fakers, we might recall that it was on this date in 1874, in Harpers Weekly, that editorial cartoonist Thomas Nast gave the Republican Party its mascot, the elephant. (Contrary to popular belief, Nast did not create Uncle Sam [the male personification of the United States Federal Government], Columbia [the female personification of American values], nor the Democratic donkey, though he did popularize those symbols through his artwork.)
“It may have been quixotic, but it was magnificent”*…
In July of 2018, Brent Underwood purchased the former mining town of Cerro Gordo alongside the abandoned Cerro Gordo Mines for $1.4 million. His plan was– and is– to develop the town into an artist destination for tourists and group events, while maintaining the historic nature of the property.
He checks in on how it’s going…
If you ever plan on riding out a pandemic in an abandoned mining town at 8,200 feet, there are a few things I’d recommend bringing: a four-wheel-drive truck, more than a few days of food, and at least some decent boots.
I brought none of these.
Instead, I arrived at Cerro Gordo, California, in the middle of a blizzard in March 2020, driving a base model Tacoma, and wearing white Allbirds, a few inches of ankle showing. A quarter mile from town, my bald tires slid off the road, the truck got stuck, and I walked the rest of the way in.
Six years later, I’m still here. I’ve traded my Allbird slippers for steel-toed boots and my Tacoma for a Tundra. And most of the things that once felt absurd—like getting buried in snow in March—barely register. My mailbox is an hour away and 4,500 feet in elevation below me. My driveway is an eight-mile dirt road that is almost never maintained. My bathroom is an outhouse where, on windy days, dust of questionable origin comes up through the floorboards. In the winter, sometimes I wake up to the thermometer reading 17 degrees. Indoors.
But the strangest thing about living here, after a decade of living in cities like New York and Austin, is how quickly it stops feeling strange at all.
Maybe I shouldn’t be surprised. Thousands of people have chosen to live in Cerro Gordo over the years. In the late 1800s, it was home to California’s largest silver mine, surrounded by hundreds of buildings and thousands of residents. There were at least five hotels, seven saloons, two brothels, three general stores, multiple restaurants, barber shops, and even doctors and lawyers—but no jail…
The rest of his remarkable report: “What It’s Actually Like to Live in an Abandoned California Ghost Town,” from @outsidemag.bsky.social.
For a reverse angle, see this story of a centuries-old practice: “How Rome’s Urban Shepherds Adapt and Survive”
* J. M. Barrie, Peter and Wendy (better known as Peter Pan)
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As we get away from it all, we might recall that it was on this date in 1217 that The Charter of the Forest was first issued by the young King Henry III (acting under the regency of William Marshal, 1st Earl of Pembroke) as a complementary charter to Magna Carta from which it had evolved. The Charter of the Forest re-established rights of access for free men to the royal forest that had been eroded by King William the Conqueror and his heirs. Many of its provisions were in force for centuries afterwards. Indeed, In 1154, King Henry II had placed new restrictions and levies on woodland, fields, and heathland.
While Magna Carta dealt largely with the rights of barons, The Charter of the Forest restored to the commoner some fundamental rights, privileges, and protections against the abuses of an encroaching aristocracy. It returned rights to the common man including access, pasture, grazing, turf cutting, and firewood collection, and ended harsh penalties.
“Fire made us human, fossil fuels made us modern, but now we need a new fire that makes us safe, secure, healthy and durable”*…
A number of entrenched interests have been playing a long game. There’s (all too appropriately) a good bit of attention trained on efforts like The Federalist Society‘s attempts to remake the legal and judicial landscape and The Heritage Foundation‘s “Project 2025” plan to reshape the federal government along right-wing lines. Both reflect the careful cultivation, over decades, first of sympathetic “scholarship” and opinion, then of political action.
But as Kate Yoder of Grist explains, there’s another– longer, deeper– program at work: from the family car to the dream kitchen, fossil fuel companies have sold a vision of prosperity that depended on their own products. Over a century of evidence shows how they have done it…
What does going on a date have to do with fossil fuels? Everything, according to the petroleum industry.
One advertisement from the pipeline company Energy Transfer follows two people getting ready for a date, the man primping with hair gel, the woman applying lipstick, before meeting at a bar. “That connection was brought to you by petroleum products,” the narrator says. “But what if we lived in a world without oil and natural gas?”
The story rewinds to the beginning, except this time, the woman looks bewildered as her phone with the dating app evaporates in her hands. She gets lipstick all over her cheek when the car she’s riding in hits the pavement without its tires. As his hair gel disappears, the man looks into the mirror, baffled. When they sit down across from each other, makeup, hair dye, and glasses fade away. In the game playing on a TV in the bar, a football disintegrates a second before the kicker swings his leg at empty air.
The commercial, which has racked up 7.4 million views since it appeared four years ago, taps into the fact that fossil fuels have become embedded in American life (what’s more American than football?). Oil and gas not only influence how people get around town, but also where they live, how they dress, and how they cook food in their kitchens. This all feels so ordinary that most people don’t notice how pervasive petroleum is, powering our lives while hiding in plain sight. Though it might seem inevitable that we live this way, a Grist investigation found that this idea of what’s “normal” was shaped by decades of advertising, public relations propaganda, and broader efforts to sway American culture, from radio programs to Hollywood films.
“Our dependence on oil didn’t just happen,” said Robert Brulle, an environmental sociologist at Brown University.
This campaign started about a century ago, when companies began strategizing how to create long-term demand for fossil fuels and the products that ran on them. Billboards showcased happy families on the road in their cars, films showed stylish women cooking over the flame of a gas stove, and magazines featured men pushing their gas-powered mowers across soon-to-be perfectly manicured lawns. The message was often left unsaid: The American way of life runs on fossil fuels.
There’s a well-documented history showing that Exxon and other oil giants understood the dangers of global warming by the 1970s based on their own research and still spent decades publicly downplaying, or outright denying, the science. But the story of how oil and gas companies coaxed Americans into accepting — even desiring — a life built on fossil fuels has largely escaped attention, buried in the pages of obscure trade journals.
In the 1940s and 1950s, the oil industry spent millions of dollars a year on campaigns to “educate” the public about the wonders of petroleum and how it advanced the “American way.” The natural gas industry promoted all-gas “dream kitchens” and paraded them in front of the editors of Better Homes and Gardens, Good Housekeeping, and The Ladies’ Home Journal. They sold a vision of the American Dream that required their products — a cultural legacy that has been devastating for efforts to rein in climate change and shift to a cleaner future.
This history offers an explanation for why Americans have had such a strong reaction to the idea of banning polluting gas ranges (“they can pry it from my cold dead hands”) or requiring more sales of electric vehicles over gas-powered cars. Is this debate really just about technology, or about what the burning of fossil fuels represents — freedom and a sense of identity? President Donald Trump’s Make America Great Again agenda is deeply nostalgic for what oil once promised: economic might, a higher standard of living, the chance to rise above your station and maybe make a fortune. In his inauguration speech last year, Trump promised a return to these ideals. “We will be a rich nation again, and it is that liquid gold under our feet that will help to do it,” he said.
As the world enters an “age of electricity” powered increasingly by solar, wind, and other low-carbon energy sources, the United States often appears to be stuck in the past. The Trump administration has prolonged the life of coal plants and opened up new areas to oil drilling, while paying companies billions of dollars to stop offshore wind projects. The explanation, when it’s offered, often returns to the same theme. Earlier this year, when the Trump administration scrapped Biden-era rules to cut emissions from cars, along with the Environmental Protection Agency’s authority to regulate climate change, it touted the effort as “restoring the American Dream.” White House spokesperson Taylor Rogers summed up the sentiment this summer: “President Trump was given a mandate to roll back the radical climate policies that are burning a hole through taxpayers’ wallets and jeopardizing the American Dream for future generations.”
The words echo what the oil industry has been telling Americans for a century: Without fossil fuels, there is no “good life,” only sacrifice…
Read on for the remarkable tale: “The hidden history of how Big Oil invented the American Dream,” from @grist.org.
See also: “How Big Oil Shaped America’s Parks.”
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As we look behind the curtain, we might that it was on this date in 1895 that George Selden was granted the first patent for a gasoline-powered automobile. In the patent, which Seldon had filed several years earlier, on May 8, 1879 (eight years before the public introduction of the Benz Patent Motorwagen in Europe, built in 1886 and widely considered the first automobile), he described not only the engine but also a complete automobile incorporating such features as a clutch, compressed air self-starter, and steering system (No. 549,160). He maintained that it was the combination of these elements, together with his engine, made the road-engine patentable. Selden’s day job was as a patent attorney, so he knew to delay the issue of the patent by sending amendments and other communications every two years. Meanwhile, others did the hard work of developing the automobile, and his patent became more valuable. Years of legal wrangling for profits followed.
“Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.”*…
The estimable Tim O’Reilly argues that, while we’re right to be concerned with AI as it’s currently being developed, we needn’t resort to a Butlerian Jihad…
In 2019, the computer scientist Richard Sutton wrote a short essay called “The Bitter Lesson.” Looking back over seventy years of AI research, he concluded that general methods that scale with raw computation beat methods built on human expertise, and by a large margin. The AI industry as a whole is bitter-lesson-pilled, to borrow the internet’s slang for a true believer. I am human-pilled.
I believe in the bitter lesson. Brute force wins out in the end, and I have wrestled with that fact throughout my career. But we humans still have to figure out our role in the AI economy, and I find it rather disturbing that the AI community isn’t spending more time asking how to make this future friendlier for humans. The labs think about how to keep AI from going rogue, and their chief economists study job losses from AI. They don’t study what mechanisms we could introduce to create new economic opportunities for humans.
The question matters to us at O’Reilly Media. We don’t think of ourselves as a publisher, a conference producer or an online learning platform so much as a company that transfers knowledge from the people at the cutting edge to the people who want to follow them. So we are very interested in what human expertise is worth, even in the face of AI.
Human expertise was used to train AI, which makes AI a fabulous cultural technology. Thousands of years ago, the only knowledge people had was what they could keep in their heads. Since then we have built better and better ways to share knowledge across time and space, and AI continues that. With each of these transitions, we have to ask again what the truly human is still worth. People often say judgment, or taste. That’s true, but I think it goes deeper:
AI is a sea of possibility. All of that knowledge sits in the model, and nothing sets it in motion except a human. (That is changing a little as we start giving AI agents long-running tasks, and we will have to come to grips with it.) If you and I used Claude or ChatGPT for the same research project, we would each bring different experience to it, tell it what we want and why, and give it context in different ways. I see that as the unique focusing power of human consciousness and attention.
Earlier this year I spent an hour or so talking this question over with Claude. When it offered to write up a first draft for me, I said no. I wanted an essay from its own point of view about what it took away from our conversation. The result, “Why AI Needs Us”, opens, “He came in through Powys,” and a few paragraphs later says, “The choice of entry point was already the argument.” That captured what I’m getting at. I had come into the conversation through a book I love, John Cowper Powys’s The Meaning of Culture, which distinguishes culture from education. Powys quotes one definition: “Culture is what is left over after you have forgotten all you have definitely set out to learn.” It is what you absorbed. Each of us absorbs a small and particular slice of everything. Large language models (LLMs) try to absorb everything, and so become overly general; we give them focus. Human knowledge provides them with a lens...
[Tim unpacks his thesis, explaining both what he means and how he came to believe it…]
… I think a lot about a concept from economics called mechanism design, which has also been described as reverse game theory. Game theory asks how players will act under a given set of rules. Mechanism design starts from the outcome you want and works backward to rules that will tend to produce it as people pursue their own interests. Al Roth, who shared a Nobel Prize for this kind of work, used it to design a kidney exchange that more patients and donors would join. Economists mostly apply the idea to auctions and similar markets, but I think every new technology requires new mechanisms, and the best ones are not invented by economists. We tend to fall into them without really understanding them.
Think about the economic mechanisms that made earlier technological revolutions work. The web gave us the ability to connect computers, but it took inventions like PageRank to help people find what they were looking for, pay-per-click advertising to connect businesses with customers, and online payments to make transactions easy. Smartphones gave us GPS, but somebody had to invent the matching marketplaces that made Uber and Lyft possible. YouTube’s Content ID turned copyright infringement from a problem of takedowns into an opportunity for creators and rights holders to get paid. These weren’t simply technological advances. They were new arrangements of incentives that made entirely new markets possible.So what are the equivalents for AI? How will agents discover the best expertise and skills, establish their quality, and pay the people who created them? How will they find services without the platform steering them toward whoever pays the most? We don’t know the answers yet. And that’s precisely the point. We’re spending enormous amounts of money making AI more capable, but comparatively little effort figuring out the mechanisms that will turn those capabilities into opportunities for everyone else.
Take open source software. Most people think its mechanism was the free software licenses, but I was around before those licenses, and people were already sharing software. Ken Thompson and Dennis Ritchie created Unix, the predecessor of Linux, at Bell Labs under AT&T’s proprietary license. They designed it to run on many kinds of hardware, and without meaning to, they built what I came to call an architecture of participation…
[Tim explains Model Context Protocol (MCP) and the way that it can bring participation to AI…]
…
With MCP, which lets any model connect to anyone’s tools and data, the emerging web of AI agents began to take on the shape of the participatory economy of open source software and the web. People can bring their own models and infrastructure as an alternative to the big centralized players.
This is another side of grappling with the bitter lesson: what are the missing mechanisms of the human-AI economy? Some of them are standards, because standards and protocols are a big part of what makes an architecture of participation. I have a group working on portable memory, so that what your AI knows about you can move with you from one tool to another.
That story is just beginning. We are in an exploratory phase of mechanism design for AI, and it would be a mistake to settle on answers too early. Somebody is going to figure out the next mechanism, and we’ll all say, “Oh, that’s so damn obvious.”
Mechanism design starts by asking what outcome you want. So what game ought we to be playing in the AI market?
We’ve been told that the game is the quest for superintelligence: a winner-takes-all contest that will bring untold riches to the winners and maybe impoverish, or even kill, everyone else. We’ve seen mechanisms designed to support that game. Massive investments in data center capacity mean that only the well-capitalized can play. There is colonialist-style appropriation of existing intellectual property. Access to models is sold through expensive APIs, and closed models come with terms of service that forbid others from learning from their outputs. Advertising may be creeping into answers, and memory locks your context inside one provider’s walls. Is that the game we ought to be playing?
I think the game we should be playing is human flourishing, and I think the game of human flourishing is creating new affordances for people to create and share with each other. We’re a social species. History teaches us that we will find our way there, but we can get there more quickly or more slowly depending on the choices we make. Getting there takes mechanisms that create a circulatory economy of value exchange between humans and their agents, with humans in partnership with AI rather than replaced by it.
The big labs, whatever the motives of the people who run them, are all driven by the imperatives of our financial system. They have raised enormous sums that they somehow have to pay back, so building a hugely successful business has to be their overriding goal, and that shapes all their incentives. In some ways AI was enshittified from the beginning, because it was built on the backs of so much capital that has to be repaid. That’s why open source AI matters so much, and sovereign AI too, built and controlled within individual countries. The open models, which anyone can download and run, aren’t as capable as the best closed models yet, but they keep getting closer, and they are already good enough for a lot of productive work.
Open source, open protocols, and open memory give users the freedom to shape and control the technology they use. The game I want us to play is one in which all of us build the future together, so that we control this wonderful new wave of technology for ourselves. I’m starting to see the shape of an economy where AI belongs to people rather than to the big labs, where we have personal AI agents working for us and with us, and we build an economy around that. I think it’s going to happen not because of the big labs, but in spite of them…
AI for everyone- the Bitter Lesson meets Architectures of Participation: “Human-Pilled,” from @timoreilly.bsky.social.
Pair with this consideration (from Henry Farrell) of AI’s current “flavor of the month”: “Agentic AI is myth set in motion.”
* Frank Herbert, Dune
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As we open AI, we might recall that it was on this date in 1952 that the National Security Agency– the NSA– was formed by President Harry Truman. Created from a unit that deciphered coded communications in World War II, it had predecessors (with names like “Cipher Bureau,” “Black Chamber,” and “MI-8”) that date back to World War I. Charged with global monitoring, collection, and processing of information and data for intelligence and counterintelligence purposes, specializing in a discipline known as signals intelligence (SIGINT), the NSA is also tasked with the protection of U.S. communications networks and information systems.
By the end of the Cold War, the NSA had became the largest of the U.S. intelligence organizations in terms of personnel and budget. While it appears that CIA may now have surpassed it, the NSA has some 32,000 employees.
The NSA’s actions have been a matter of political controversy on several occasions, including its role in providing intelligence during the Gulf of Tonkin incident, which contributed to the escalation of U.S. involvement in the Vietnam War. Declassified documents later revealed that the NSA misinterpreted or overstated signals intelligence, leading to reports of a second North Vietnamese attack that likely never occurred. The agency has also received scrutiny for spying on anti–Vietnam War leaders and the agency’s participation in economic espionage. In 2013, the NSA had many of its secret surveillance programs revealed to the public by Edward Snowden, a former NSA contractor. According to the leaked documents, the NSA intercepts and stores the communications of over a billion people worldwide, including United States citizens. The documents also revealed that the NSA tracks hundreds of millions of people’s movements using cell phone metadata. Internationally, research has pointed to the NSA’s ability to surveil the domestic Internet traffic of foreign countries through “boomerang routing.”
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