In 2020, Edge Cable Holdings, a Facebook subsidiary, was burying a new fiber-optic cable into the seabed near Tierra Del Mar, Oregon. Working beneath a rugged mixture of basalt rock mounds, unconsolidated sands, and sandstone bedrock, the company’s drilling operation went awry. Stalled out, they ditched their metal pipes, drilling fluids, and other construction materials in the ocean: Out of sight, out of mind.

When Oregon’s Department of State Lands learned of the abandonment, they ordered Edge Cable Holdings and Facebook (now Meta) to pay a fine. But the damage was done. Two sinkholes formed along the installation path and most of the materials will remain lodged in the seafloor forever. These items, and thousands of gallons of drilling fluid, pose an ongoing risk to the surrounding seafloor ecosystem. Despite public outrage, the company returned to complete the cable in 2021, with debris from the first attempt still lodged in the seabed.

The cable was not the first to slither into Oregon’s stretch of the Pacific Ocean, and it’s by no means the last. Big technology companies including Amazon, China Mobile, and Google are flocking to Oregon’s coastline to land transpacific fiber-optic cables. Most recently in August 2023, the Department of State Lands approved a 9,500-mile fiber-optic cable connecting Singapore, Guam, and the United States.

What has transformed Oregon into an undersea cable hotspot—and how is the installation process affecting a vibrant ocean ecosystem? The explanation resides in tax breaks, swift permitting processes, cheap energy, vast amounts of open land for data centers, and a historical carelessness for the environment shared by the state and tech companies alike.

Fiber-optic cables transmit data with pulses of light through thin glass fibers. In 2022, they provided over 98 percent of the world’s internet services and international phone calls. There are more than 745,000 miles of submarine fiber-optic cables in operation around the world—that’s enough cable to wrap around the Earth’s equator more than 29 times. It’s the work of cables, not satellites, that connect us on a global scale.

Although undersea cables seem to be torn from the pages of a futuristic science fiction novel, they aren’t a new technology. The first functional telegraph cables crossed the Atlantic seabed in the 1860s.

The Pacific, a wider and deeper ocean basin and therefore more difficult to wire, received its first transoceanic cable in 1902. By the early 1900s, the global seafloor hosted around 200,000 miles of telegraph cables. And by the 1950s, that number reached nearly 500,000 miles of telephone and telegraph cables, with fiber-optic cables first joining the mix in the 1980s.

Back then, many transpacific cables landed in California, Washington, and British Columbia, where they could link up with transportation hubs and industrial centers on land. That began to change in 1991, when Oregon landed its first transpacific fiber-optic cable. Called the North Pacific Cable, the privately owned line connected Oregon to Alaska and Japan. In the three decades since, the state has welcomed a new fiber-optic cable every four or five years, in tandem with new data centers—large, high-security buildings that store rows of servers. These servers host the internet’s millions of websites.

There are significant onshore incentives for cable owners to land their lines in Oregon. Oregon’s “enterprise zones” tax-exemption program allows individual towns to negotiate property tax breaks for big construction projects, thereby saving companies millions of dollars each year. In exchange for the tax breaks, tech companies provide a small influx of jobs and tax revenue to small communities hurting from the decline of the timber industry. In 2015, Oregon lifted its cap on enterprise zones to attract even more data centers, just as more cables arrived along the shoreline.

Consider Meta, which owns a 4.6 million square foot data center complex in rural Prineville, Oregon. Although it’s far from the ocean in a former timber town, this data center connects to a network of underground fiber-optic cables, including the controversial undersea cable installed near Tierra del Mar. In 2015, the Oregonian reported that the data center complex received $30 million in tax breaks that year alone.

For Meta, as well as Amazon, Google, and Apple, Oregon offers a win, win, win.

So who exactly is losing?

The coastal ecosystem. During installation, it’s standard practice to bury cables multiple feet into the seabed to avoid snags by fishing vessels. The most common burial method is plowing, during which a remotely operated vehicle cuts a ditch into the seafloor and inserts the cable into the trough. Another method, jetting, uses high-pressure fluids to liquefy sediments on the seafloor, easily slicing a clean line into the seabed in which the cable can burrow. Companies also use directional drilling to bore diagonally into the seabed from the shore. All of these methods squish or displace any worms, crabs, sea stars, urchins, anemones, corals, or sponges living within the trenching path.

Once installed, submarine cables settle into the seafloor ecosystem. In search of hard substrate to call home, marine life will colonize the cable’s exterior. After a few decades of service, cable owners have historically abandoned their lines in the ocean, a decision that is both cheaper for companies and often results in less disturbance for colonizing species. Inert but not biodegradable, most dead cables will sit in the ocean indefinitely, hidden from the public who is usually none the wiser.

The 2020 Facebook/Edge Cable Holdings abandonment prompted Oregon to pass a 2021 law instituting firmer planning and decommissioning regulations for new undersea cable projects. Still, the increasing scrutiny doesn’t appear to be slowing the big tech companies. As Amazon builds its recently approved line to Guam and Singapore, the tech giant is also building another data center in Umatilla, Oregon, a small town on the Columbia River.

Data centers are no better for terrestrial environments than submarine cables are for marine. The buildings suck significant amounts of power from the grid. Oregon’s renewable energies, like hydroelectric dams on the Columbia River, can’t cover data centers’ growing energy demands, meaning utility providers must tap into fossil fuels and increase their greenhouse gas emissions. Despite Oregon’s efforts to decrease the state’s carbon footprint, some regions are moving backward in the fight against climate change. Big tech companies, and their big buildings, are spurring that reversal.

Across Oregon, communities and ecosystems are confronting the physical impacts of a world that runs on internet—impacts that our regulatory systems have yet to reckon with.

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Will was a web designer living in Los Angeles and supporting his wife, an aspiring actress. He couldn’t shake the idea that he, too, should pursue his passion. So he started a side business on a new digital platform, AllDone (a pseudonym), which connected skilled service providers with customers. Will created a profile to offer guitar lessons, quickly landed a couple of students, and signed up for a subscription that allowed him to respond to potential clients for a flat fee of $20 per month.

Will’s business grew, and he quit web design to teach full-time. A few months later, he got a call from a customer support agent at AllDone. She had bad news: The platform would no longer offer subscriptions. Will would now have to pay a fee to respond to each potential client—adding up to an unsustainable hundreds of dollars a month. Will panicked and pleaded with the customer service agent to let him keep his subscription. “You guys have shattered my dreams!” he cried, when she told him the decision came from management, and there was nothing she could do.

I learned about Will’s story when I was conducting sociological research inside AllDone, one of many Silicon Valley startups aiming to profit from “disrupting” existing industries and building a new, digitally backed gig economy. Like so many workers who rely on apps to make a living, Will had invested in his business under one set of rules only for them to be suddenly and unilaterally altered. Workers criticized—and organized against—platforms like Uber and Instacart in their early years, when they repeatedly experimented with employment policies and wages in similar fashion.

There has been no shortage of debate about the role of arrogant CEOs and harmful algorithms in defining technological innovation. But we typically hear less about the systemic forces that generate those problems. A key culprit is venture capital (VC), which provides early funding for entrepreneurs who want to transform neat ideas into billion-dollar companies. For a business to have any chance of “changing the world” with its technology, it must mold itself to meet the demands of these funders. VCs have become folk heroes in Silicon Valley, widely revered for delivering innovation. Often, they help create products that succeed in short-term disruption—with questionable or even dangerous long-term effects.

VC compels startups to engage in relentless experimentation to generate exponential growth, ratcheting up their expectations at every stage of a firm’s development. The goal is to increase a startup’s valuation so the investor can sell their stake for far more than they originally paid for it. When a startup succeeds, investors’ profits can be stunning. Sequoia Capital’s initial outlay of $585,000 to Airbnb was worth $4 billion after the company went public.

Many celebrate venture capital’s role in creating a marketplace that nurtures the most innovative ideas. But the ideas that are “best” for capital markets—and for enriching the most affluent among us—aren’t always those that are best for societies. When well-connected entrepreneurs, VCs, and the wealthy institutions and individuals whose money they invest achieve massive payouts, other stakeholders are frequently left behind. For example, following Uber’s IPO in 2019, a combined $27.1 billion—about 40% of the company’s valuation—was captured by just three investment funds and the company’s two co-founders. Longtime Uber drivers, on the other hand, received bonuses that averaged $273 per person, or the equivalent of just a few cents per ride.

Tech startups that succeed are not necessarily those that figure out how to create a stable business model that yields consistent profits. Instead, the winners are often the companies that have attracted more capital than their competitors by pursuing reckless growth. Consumers and stakeholders end up missing out on some of the best, most sustainable, and perhaps even the most innovative products and services—tethered instead to the ones able to meet investors’ ever-escalating benchmarks.

The VC business model is powerful. But it is also relatively new, fueled by policies enacted in the late 1970s that incentivized startup investors, including big cuts to the capital gains tax rate and a Department of Labor ruling that allowed pension fund managers to invest in riskier assets. Since then, a small cadre of funders, over a third of whom are based in the Bay Area, have seized an outsized voice in determining the distribution of the economic risks and rewards associated with innovation.

As a new wave of generative AI startups takes center stage in Silicon Valley, venture capital is once again setting the agenda, unleashing experimental technologies that expose us all to substantial risk. How can we come together to minimize the harms generated by new technologies, while sharing their benefits—and fueling sustainable innovation—more broadly?

It’s time to look for better ways to invest in our future—ways that reward new ideas not just for their ability to inflate a startup’s valuation, but also for the benefits they bring to society as a whole. A brief survey of the tech landscape reveals numerous examples of potential alternatives to the VC model. Craigslist is privately owned and has largely resisted outside investment. Instead of constantly experimenting with its platform to increase engagement, serve advertisements, or harvest user data, the company is free to balance the profit motive with a public-service ethos.

Nonprofit video-captioning and translation platform Amara pays higher wages than similar for-profit labor platforms. Up & Go, a platform that allows customers in New York City to order house-cleaning services, is co-operatively owned and operated by the workers themselves. Ninety-five percent of the revenue generated through the platform is paid out to workers, resulting in wages about $5 per hour above the local average.

What these models have in common is the ability to reduce entrepreneurs’ dependence on external funds, and thus external control. Other measures can loosen venture capital’s grip on our innovation ecosystem, too: federal grant and loan programs that require founders to cap prices or share profits, publicly owned investment vehicles, and eliminating tax dodges.

By promoting and investing in businesses with alternative ownership structures, consumers, workers, activists, and governments can challenge venture capital’s winner-take-all model, creating ecosystems of smaller, more localized and specialized platforms that are more responsive to the people who use them and to the communities in which they are embedded. Workers like Will could build more stable livelihoods doing what they love; consumers could have more choices, and their money could directly support their neighbors and local communities instead of serving investors’ interests. Technological breakthroughs could really make life better, which is what innovation should be about. Curbing VCs’ influence isn’t about stifling innovation—it’s about making room for the rest of us to have a say in our technological future.

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Wood’s Zócalo Sketchbook imagines “what nature might look like if it insisted on innovating itself to extinction.” The series was inspired by OpenAI’s release of Sora, a way to generate photorealistic video sequences based on text prompts, earlier this year. 

“As a creative, it chilled me to my core,” Wood says. “When I saw where we are headed, I thought, Why are we so addicted to relentless innovation? Why are we absolutely determined to ‘improve’ our way to extinction? It got me thinking, Would anything else in nature act the way we humans do?”

Wood’s Sketchbook considers the impact of the increasing incursion of generative AI on society. Rich in narrative details, each of his illustrations acts like a doorway into classic science fiction stories that ask us to reflect on our own actions in the world.

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The smartphone has become a modern Swiss Army knife: driver’s license, e-payment device, camera, radio, television, map, blood pressure monitor, workstation, babysitter, pocket AI, and general gateway to the internet. And now consumers are leaving their smartphones behind to sport lightweight smartwatches with equivalent functionality. With every update, our devices inch closer to us—our bodies, our minds. From the handheld, to the wearable, to the … What next?

Nearly 20 years ago, in 2006—before X, Amazon Web Services, iPhone, Fitbit, Uber, or ChatGPT—M.G. Michael was faced with a similar question. He was guest lecturing on the “Consequences of Innovation” at the University of Wollongong in Australia, focusing on emerging technologies in security. A student asked: “So then, where is all this surveillance heading?”

For a couple of years already, we had noticed hints of an ultimate destination in patents, pilots, and proposed products and services: chips implanted inside the human body to identify people and offer them digital services on demand. Hardware placed in the arm might let one pay at the checkout simply by waving a hand, or allow a first responder to scan a patient’s vital signs and medical records in an emergency. Such implants brought with them a perceived increase in security. They remained inside the body, hidden from view, and could not be stolen, or accidentally left behind.

M.G. searched for a word that would summarize what he was seeing emerge in these fields, and all around us. He imagined a coming together of Orwell’s Big Brother, microchip implants, radio frequency identification devices (RFID), Global Positioning Systems (GPS), apocalypticism, and Nietzsche’s idea of the ultimate, superior, progressed human form, the Übermensch. On the spot, he called it “uberveillance.” The neologism soon entered the lexicon.

Uberveillance is fundamentally an above and beyond, exaggerated, almost omnipresent 24/7 electronic surveillance. It is not only always on but also always with you. Like an airplane flight recorder, a personal “black box.” Or, if you prefer, it is like Big Brother on the inside looking out.

This kind of bodily and hyper-invasive monitoring is not risk-free, and won’t necessarily make us safer and more secure. Omnipresence in the physical world does not equate with omniscience. Despite their tremendous data gathering capacities, there is a real concern that implantable devices will breed misinformation, misinterpretation, and information manipulation, all of which may lead to misrepresentations of the truth.

In our original conception, uberveillance was multidimensional. A tiny RFID transponder, implanted in the arm, would connect with sensors such as accelerometers, gyroscopes, and magnetometers. Uberveillance would use GPS and other technologies to allow designated authorities to understand the who, where, when, why, what, and how. We imagined government authorities would use it in the context of civilian, commercial, or national security—as a find-me alert. For example, monitoring people living with dementia or on extended supervision orders; tracking suspects in crimes, parolees, or notable public figures or dignitaries; and allowing access to secured buildings or rooms.

Yet despite the perceived benefits, even in the early 2000s, we couldn’t ignore the sinister undertones. How far would this go? Was uberveillant technology too alluring—difficult to resist because of its ease of use? What if it did not always work as it should, proving subject to tampering, data bias, and inference?

Constructing a verifiable digital end-to-end cyber-physical-social reality is impossible. There is no substitute for real life. Recorded data—incomplete, from multiple sources and without necessary quality checks—are not always accurate. Global positioning coordinates may lack precision when tall buildings obstruct a line-of-sight between the handheld or wearable technology and satellites. There are black spots in networks when an individual leaves an urban space, or moves between locations.

Furthermore, uberveillant systems leave out context. An image of an altercation may seem to provide evidence that implicates an individual, but snapshots of moments prior may show that they were acting in self-defense. Near real time is not real time. This is the great flaw in uberveillance.

Without capturing context, an accurate chronicle of activity is unattainable. And a flawed chronicle of activity can be devastating. GPS coordinates with a lag may tie a user to a suspicious event; facial recognition algorithms may identify a passerby as an individual of interest; implants that have been spoofed may appear in multiple hit lists, cloaking the identity of the bona fide individual at a given location; and biometric data could be interpreted to indicate distress when a subject may simply have been in reflection.

Your cell service provider or smartwatch manufacturer might assure you they’ll only use your data for research. But they may also inform you they have no control over how their partners might use the biometric and other data downstream. Your wearable data could end up in an AI model one day, or used by a prospective employer during a hiring process, or be presented as evidence in a court of law. The wrong data might render you unemployable, uninsurable, and ineligible for government benefits. In an instant you could become persona non grata.

Uberveillance advances the idea of “us” versus a series of “thems”—data brokers, Big Tech companies, government agencies, hackers, secret intelligence, first responders, caregivers, and others. In doing so, “they” have power over how others perceive us and use our data, potentially building multiple black boxes containing intricate profiles—limited accounts of what makes us “us.” This technology is not free, and will not set us free.

Today, as in 2006, this strikes us as technology’s natural trajectory. From the moment the first programmable general purpose digital computer, the ENIAC, was dubbed “an electronic brain,” it was always going to fuse with the body at its ultimate technological potential.

The paradox of all this pervasive vigilance is that the more security we hope for, the less we get.

“Nothing was your own except the few cubic centimeters inside your skull,” Orwell ominously wrote in 1984. And yet uberveillance threatens that too: An embedded “smart” black box in the human body would encroach on a last fragment of private space. An internal closed-circuit television feed could bring about the most dehumanizing of prospects—a total loss of control and dignity, if used to surveil thoughts, rituals, habits, activities, appetites, urges, and movements. Such dystopian scenarios are no longer sci-fi imaginings alone.

This has ontological implications, directly to do with the nature of being. It could represent the consequential deconstruction of what it means to be human, to have agency, and to make choices for oneself. If uberveillance is to expand and forge ahead on its current path, the scenarios are countless and potential consequences staggering. At that point, we will have surrendered more than just our privacy.

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The Supreme Court struck down race-based affirmative action in university admissions early this summer thanks in large part to the charge that Harvard’s practice of race-conscious admissions discriminates against Asian Americans. Whether or not the decision changes the college application process for Asian Americans, it certainly obscures the more insidious and widespread forms of bias that many Asian Americans face. It also opened up pathways to dismantling race-conscious policies elsewhere—particularly in the workplace, where assumptions about what makes a good leader are couched in ideas of race.

Asian Americans are not underrepresented in university classrooms, including at Harvard. They account for 7.2% of the U.S. population, yet 29.9% of Harvard’s incoming class. Where they are underrepresented is in the boardroom and the C-suite. Among the Fortune 500, only 2.4% of CEOs are Asian, two-thirds of whom are South Asian (with roots in the Indian subcontinent, and mainly from India). Many Asian Americans—and especially East Asians (with origins in China, Korea, and Japan)—find themselves hitting a “bamboo ceiling” akin to the glass ceiling that women face. It’s here, in the workplace, where affirmative action has an important role to play in the lives and livelihoods of Asian Americans—one that the Court has put in jeopardy.

Asian Americans lag behind all racial, ethnic, and gender groups in promotion to managerial and executive ranks in spite of their education, work experience, and job performance. Even in fields in which Asians are overrepresented, such as technology, medicine, the natural sciences, engineering, and law, they are rare in leadership.

In top technology firms in Silicon Valley, white men and women are twice as likely as Asian men and women to advance into the executive ranks. Between 1997 and 2008, Asian Americans made up 20% of medical school faculty—yet there were no Asian American deans. And while Black and Latino physicians are underrepresented in the field, Asian Americans are the only racial group that accounts for a much smaller share of medical school department chairs than their percentage of the faculty in medical schools.

A similar pattern emerges in law: Asians comprise 10% of graduates of top-30 law schools, but only 6.5% of all federal judicial law clerks. And while Asians are the largest non-white group in major law firms, they have the highest attrition rates and the lowest ratio of associates to partners of all groups at four-to-one, compared to two-to-one for Blacks and Latinos, and parity for whites. Even in academia, where Asian Americans are overrepresented as students in top universities, they are nearly absent in leadership ranks, comprising only 1.5% of college presidents.

So what forms the branches of the bamboo ceiling?

Some argue that racial and gender stereotypes—technically strong but socially weak, mathematically and scientifically inclined rather than verbally gifted—hinder Asians’ advancement in the workplace. Employers may recognize Asian Americans for their hard work, dedication, and effort without seeing them as innately brilliant, visionary, or skilled to lead.

Asian American women are doubly disadvantaged in this regard: They are the least likely group to be promoted to leadership positions, and to be perceived as fit for leadership roles regardless of their education, experience, and behavior.

Where do these stereotypes come from, and what can be done to combat them? A new strand of research points to differences in culture, and, more specifically, differences in verbal assertiveness between East Asian and white Americans. Western corporate culture prizes individual assertiveness and achievement, whereas East Asian culture promotes harmony and the stability of interpersonal relationships.

To buttress this point, researchers find that South Asians are more verbally assertive than East Asians, and, despite still not being as represented as white men in top positions, South Asian men are now even more likely than white men to attain leadership positions—pointing to a unique pattern of “South Asian exceptionalism.” A similar pattern emerges in law and business schools, where South Asians outperform East Asians in leadership, strategy, and marketing—courses in which verbal assertiveness is prized and class participation accounts for a larger percentage of the final grade. The branches of the bamboo ceiling begin to grow in the classroom.

South Asian exceptionalism may also be explained by Americans’ understanding of who counts as Asian. In the U.S., “Asian” is often shorthand for East Asian, and most Americans—including most Asian Americans—exclude South Asians from the fold. If the stereotypical perception of Asian men (i.e., East Asian men) is that they are diffident, passive, and distant, South Asian men (who are not perceived as Asian) may not be hampered by a social identity that presumes these qualities. The absence of the stereotype may change both their behavior and the way others interpret that behavior.

But a larger question underlying this debate is why we assume that leaders must be bold, brash, and assertive to be effective. Some of the country’s top CEOs have been described as listeners first, and team players who are empathetic, thoughtful, steady, and measured. Columbia University’s new president, Minouche Shafik, is the first woman to lead the university in its 269-year history. When asked about her leadership style, she quoted the 6th-century B.C. Chinese philosopher Lao-tzu: “A leader is best when people barely know they exist … When the leader’s work is done, the people will say, ‘We did it ourselves.’”

Thinking more expansively about the qualities that make a good leader while recognizing that different leadership models may be just as effective (if not more so) than traditional Western ones will broaden leadership opportunities for not only East Asians, but also women, and for many of us who do not fit the prototype of what an American leader looks or acts like. It would also benefit the members of such leaders’ organizations, who may work more effectively with more diverse managers and styles. Leadership comes in many forms, and recognizing and rewarding this will better prepare us to lead and serve the diverse country that we are.

It is the recognition of race, ethnicity, and gender that enables us to identify biases in our understanding of who makes a suitable CEO, president, chair, dean, or manager. Affirmative action policies in the workplace give us the tools to address these biases and remove the barriers they create. Now, even these policies are coming under attack, led by no less than the same conservative advocate who engineered the lawsuit against Harvard.

The fight to dismantle affirmative action in university admissions was never about protecting Asian Americans, yet profiling them abetted the demise of the policy. It also veiled the more rampant forms of bias that Asian Americans face that impede their career mobility. Affirmative action in the workplace paved the way for white women to shatter and break through the glass ceiling. It can help non-white professionals—including Asian Americans—do the same.

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One of my best friends died recently.

It still doesn’t feel real. The last time I saw her was the day after the Fourth of July. Her smile always lit up the room, but that night, the joy seemed to seep out of her, so much so that even the person who took our order at dinner commented on it.

Life was coming together. She’d gone back to school to become a speech-language pathology assistant and was on track to complete her program in the fall. Unfailingly patient, positive, and compassionate, her teachers said she had everything it took to excel in the field. She’d just become an aunt for the first time, too, and made the four-hour drive from Ventura to San Diego whenever she could to get to know this three-month-old with a gummy smile and a pompadour, who was now a part of her. And she’d recently fallen in love, with a guy from Missouri, whose Hinge profile she’d shown me last Thanksgiving. They were talking about moving in together after she graduated. Our high school friend group had yet to meet him, but she promised we would soon.

We never got the chance before she left us. It was a prolonged sinus infection that progressed into fatal meningitis. A “perfect storm” of events, a nurse later said. Everything went so wrong so fast that she was still wearing the magnetic eyelashes she’d put on to see the Barbie movie when she was brought to the hospital.

Perhaps inescapably, because we met in the 2000s, when social media was just taking off and phones had become cameras (and vice versa), the grief has taken on a digital dimension. To stop myself from being consumed by the questions around her death, the hows and whys of what happened, I’ve been trying to focus on remembering her life through these memories preserved in pixelated resin.

There is an overwhelming number of them to choose from, but I can’t help but feel what is missing. The Facebook replies I can no longer access because I deleted my account. The texts and videos I never backed up on the cloud. Obsolete media whose formats are no longer supported today. Underlying this sense of absence, of course, is the knowledge that as much as there is, there won’t be more coming.

Much of the digital ephemera I’ve come across so far I remember, even if the memories of what we were doing or where we were when we made them are just glimmers. But going through our old texts the other day, I found a few unopened voice messages I must have forgotten to play. Because I’d waded through so much of the annals of our lives at that point, I thought I was prepared for anything. But I haven’t been able to bring myself to listen to those recordings yet.

I think it’s because the medium feels like it picks up a conversation in real time. It’s the message in the bottle of the digital age. You share a thought without knowing when, where, or in what time zone it will find its recipient. In that way, voice messages feel alive in a way that video or a photo—where a haircut, a t-shirt, or a setting betrays its time stamp—does not.

Voice messages are relatively new. WeChat, the Chinese instant message and social media app, introduced them in 2011, and they have been available on Apple’s iMessage since June 2014. Over the past decade, the technology, which allows you to send voice recordings over messenger apps, has rapidly gained popularity. According to a recent YouGov poll for Vox, 62% of Americans say they’ve sent a voice message (or voice memo or voice note), and around 30% communicate this way “weekly, daily, or multiple times a day.”

But the basic idea behind the technology has arguably been with us since the mid-1800s when Édouard-Léon Scott de Martinville invented the phonautograph, the first machine to document sound. This soon gave way to Thomas Edison’s phonograph, which allowed people to record and playback sound on cylinders, opening up the commercial possibilities of the audio medium.

It doesn’t surprise me that once people could get their hands on the phonograph, they instantly saw its potential for preserving the voices of loved ones beyond the grave.

“The phonograph was linked with death from the very beginning,” according to Jonathan Scott’s Into the Groove: The Story of Sound From Tin Foil to Vinyl, which notes that the “idea of the preservation of a voice after death was a common trope in the phonograph’s advertising copy.” Most famously, the iconic trademark and logo of Victor Talking Machine Company, later RCA Victor, seemingly depicts a dog listening to a recording of his late owner.

Nipper, the dog gazing at the brass horn of a phonograph in English artist Francis Barraud’s painting “His Master’s Voice,” was the real-life companion of the artist’s recently departed brother, Mark Henry. While it’s been debunked that Nipper was actually listening to Mark’s recorded voice in Francis’ original rendering, recording the “last words” of dying individuals was a real trend, as detailed in newspaper accounts, like this 1889 piece in the San Francisco Examiner about a family who took a phonograph to the hospital to “cheer their mother on during her long illness and also to preserve the tones of her voice to comfort them after her death.”

It makes sense that Victorians embraced a technology for preserving the voices of the departed. The culture was steeped in death due to high mortality rates, and from funerals to fashion, Victorians came up with a dizzying number of ways to commemorate those who had passed (what historian James Steven Curl has characterized as a “celebration of death”).

Rapid scientific advancement during the era, which comingled with a burgeoning spiritualist movement, seemingly made the Great Beyond more tangible to mourners. The invention of X-ray machines made the invisible visible. Modern camera techniques like double exposure allowed for “spirit photographs,” which hinted at a world beyond this one. The phonograph presented just another way to thin the veil between the living and the dead, to help those grieving find new ways to connect with those who were gone.

Historian of sound John M. Picker has also made the case that because the phonograph was the first technology that let people record sound at home, its embrace by Victorians was “inherently more personal and interactive” than consumer responses to audio technology that followed (such as the gramophone, which allowed playback only).

We’ve come a long way from that initial liberation of the voice from the constraints of time and space. But holding my iPhone in 2023, the distance to these earliest phonographic recordings feels closer.

Like the Victorians, and many, many people since, I share that same human want that drove us to record sound from the beginning: to hold on to those we’ve loved and lost and miss.

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Comedian Steve Martin once said that teaching is like show business. Keeping this metaphor in mind, I try to approach each of my lectures like a live set. The idea is to keep my students present and engaged so that we can learn together in real time.

But what happens when the entertaining professor gets upstaged by a chatbot that can produce the lecture as well as write student papers and take the final exam? Does the college class become a meaningless joke?

Well, no.

There are people who fear that ChatGPT, Bard, and other generative AI bots will let students outsource their own learning. But I teach media history. I know that new media technologies do not make people obsolete. Video did not kill the radio star.

So rather than slip some language about ChatGPT in the policy section of my syllabus about plagiarism (which won’t stop students who know about the apps that can rewrite papers to evade detection), my plan this fall is to focus on creating interactive lessons that incorporate chatbots directly into my teaching.

Instead of letting chatbots change the learning process, I’ll show my students that anything that chatbots can do, they can do better.

Many of my students were already trying ChatGPT out last year. Because chatbots can be especially useful for performing routine tasks, one student explained that she had started to use ChatGPT at her job in customer service to generate quick responses to complaints, which she would then rewrite to improve.

While chatbots are able to do that kind of task well, more complicated tasks, such as historical essays, can be a disaster. But these limitations also open the door to teaching exercises that show students how to utilize this technology in their work.

Professors teaching writing skills can have chatbots generate outlines, drafts, and other lists of ideas. Then, the professor can direct students to work in small groups to rewrite the text for greater originality.

Chatbots also offer an opportunity to teach critical thinking and media literacy skills. ChatGPT is prone to making up false information out of the data-driven cloud—a phenomenon its handlers euphemistically call “hallucinations.” This means that students have to learn how to check facts and verify information, using citable sources and databases.

Professors can also teach students to be alert to the systemic racism and sexism that AI bots can perpetuate and amplify because of the source texts they’re drawing from. I once asked ChatGPT to write a list of some of the leading scholars of the U.S. Constitution and the First Amendment. Its response only included white men—as if no person from another background, ethnicity, or gender ever studied the U.S. Constitution.

A solution to this problem? Show students how they can give the chatbot follow-up prompts that generate more complete answers—say, specifically to include persons of color, different genders, and diverse backgrounds. When I did this, ChatGPT readily listed Kimberlé Crenshaw, Ange-Marie Hancock, and other prominent constitutional scholars.

For my classes this fall, I’m also creating “AI Moments,” where my students will get a chance to see who does it better: the robot or the professor.

After I present a new lesson and talk about it with my students, I’ll prompt ChatGPT to give a lecture on the very same subject.

To test out this idea over the summer, I asked ChatGPT to rewrite my short lecture on the history of broadcast media. Unsurprisingly, the text it generated was horrible. Just one cliché after another. It was as cold and dull as that slice of ham still relaxing in my refrigerator from the Fourth of July. Now there’s an unexpected image for you—the kind of surprise turn that ChatGPT will never accomplish. The AI-generated draft also made bad word choices—replacing the word “media” with “platform” (not all media are platforms).  It also changed my question, “Did the emergence of broadcast TV mean the end of going to the movies?” and instead asked “whether the emergence of broadcast TV resembled the demise of cinema attendance caused by the rise of radio.” This word choice altered the meaning of the point, which is that new media does not replace the old.

When I recreate this exercise in my classroom, I plan to have my students search ChatGPT’s lecture for bad writing that they will rewrite, turning each cliché into original imagery and poor word choices into something more precise. I’ll also ask them to find and eliminate bias and fact-check for inaccuracies.

What I learned from my practice matches with ChatGPT is that I know more about teaching journalism, writing, and media history—even though the chatbot can draw from vast amounts of information on the internet. And more importantly, it cannot share ideas accurately or in a creative and engaging way.

This is the kind of realization I want my students to have this fall when we engage with the AI-generated text, openly and transparently. My hope is that they will learn to learn to use AI effectively since these tools will become ever more common and maybe even indispensable in workplaces and in education. But also that through this they realize that when it comes to the contest of students versus robots, they will always come out on top.

The post Anything ChatGPT Can Do, My Students Can Do Better appeared first on Zócalo Public Square.

The fight over Dianne Feinstein’s future is a foolish waste of time. Both sides in the controversy over whether she should resign her seat now, or serve out her term, seem to be missing the point.

Some Californians say our senior U.S. senator must step down as soon as possible so that Gov. Gavin Newsom can appoint a politician who is healthy and young enough to show up in person for all votes. Others say Feinstein was elected to the post, and is the best judge of when she should leave.

But this is a false choice. In fact, it’s easy to give both sides what they want. We can make sure California is always represented in the Senate, while Feinstein can remain in the world’s supposedly greatest deliberative body for as long as she likes.

How? Two words:

Artificial intelligence.

Yes, we now have AI technology more than advanced enough to do a senator’s job, and with far greater flair and efficiency than Feinstein, who turns 90 next month, or many of the other older politicians who occupy seats in the upper chamber.

Indeed, we could create an artificial intelligence based on Feinstein—DiFi—that could be just as moderate and maddening as the human original.

AI could easily handle all the fundamental tasks of a U.S. senator.

Fundraising? DiFi could reach more people in more places faster (without all the carbon pollution of airplane flights)—and write less annoying email spam.

Legislation? An algorithm could introduce sensible gun-control legislation that gets ignored by red state senators, way faster than the human Feinstein.

Voting the party line? So easy that your 12-year-old could build an AI to do that in just a few minutes.

And with some voice software add-ons, the AI could deliver the same meandering, repetitive remarks in committee rooms that have become Feinstein hallmarks.

But why stop at handling official duties? A DiFi AI could solve political problems far outside the scope of a living, breathing Feinstein.

Right now, Gov. Newsom has to worry about the tricky politics of whom he might choose to replace Feinstein if she leaves early or dies; the AI spares him that choice.

And in 2024, three Democratic contenders to replace Feinstein will raise tens of millions of dollars to spend against one another—money that donors instead could devote to beating Republican senate candidates in swing states. Better for Democrats to unite behind the DiFi AI and spare themselves the costly internal warfare.

A DiFi AI also could expand the power of democracy, in a historic way. Right now, we can only vote for representatives who are alive at the time the ballots are printed. But a DiFi AI would allow Californians to keep Feinstein, or at least her algorithmic essence, in office long after she’s dead.

Yes, there might be knee-jerk objections to letting the dead represent the living, but those objections are easily dismissed, especially in this country. The United States is so good at killing people before their time—endless wars, ceaseless gun violence, misinformation-fueled pandemic responses—that the dead really need more representation from their own.

And Californians can hardly object to governance by ghosts. Today’s citizens do less governing of our state than ballot measures approved many decades ago—like 1978’s Proposition 13—by electorates of voters who are mostly dead now. And attempts to alter such measures are routinely dismissed as politically unrealistic.

Of course, there would be some technical challenges. Replacing a human senator with an AI of the same human senator doesn’t conform with the outdated rules of the Senate. But it wouldn’t be hard to convince senators to change those rules. U.S. senators have huge egos and think of themselves as indispensable. They also are old: The average age in the Senate is 64. An amendment making it possible for them to serve long after they’ve shuffled off this mortal coil might achieve rare bipartisan—even unanimous—passage.

Filling the Senate with AIs also might be good for the public discourse. After all, human senators are hard for constituents and reporters to get on the phone or text, but AI senators would only be a click away. Since AIs adapt to new information—unlike aging senators—AI politicians might be more accountable, too.

I’d benefit personally. Sen. Feinstein doesn’t talk to me—or many reporters—but, with some guidance from an AI-savvy friend, I recently asked the GPT-4 multimodal model, from Open AI,  to pretend it was a 90-year-old senator from California facing questions from a “provocative California columnist” about whether she should leave office and who should replace her.

Here’s a portion of the AI-generated chat:

Columnist: Could an AI replace you?

Senator: Replace me? Well, that’s an intriguing idea. Can an AI understand the needs of the single mother in Fresno, or the plight of the immigrant family in San Diego? Can it empathize with the struggles of the small business owner in San Francisco? Can it sit across a table from a foreign leader and negotiate peace?

Columnist: Well, with advancements in technology, they could probably do all of that.

Senator: But there’s a difference between doing and understanding. It’s one thing to process data, quite another to feel the weight of human experiences. And politics, my dear columnist, is not just about decisions. It’s about the people behind those decisions. AI may have my knowledge, but it can’t have my heart.

I marveled at the savvy of this DiFi AI. She seemed to make the case for human emotions—without actually answering the question. And she didn’t count herself out of the race, either.

Could the human Feinstein do any better?

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