The far-right Alternative for Germany party appears to be having a pretty good year, raising concerns in the nation and across much of Europe, according to German political experts at a recent campus event.

Classified as a right-wing extremist movement by Germany’s domestic intelligence agency (a designation put on hold by a court), the AfD has largely been ostracized by other parties who refuse to include the group’s politicians in governing coalitions.

But as the fastest-growing party in Germany, the AfD is gaining power.

In October elections, Saxony-Anhalt, one of the nation’s 16 states, elected an AfD speaker to a parliamentary position — the first member of a far-right party to win such a seat since World War II.

The group currently holds 150 of 630 seats in the Bundestag, the country’s primary federal legislative body, making it the second-largest party and largest opposition party.

The recent discussion about the AfD’s breakthrough year was hosted by the Minda de Gunzburg Center for European Studies and moderated by David Art, seminar chair and Tufts political science professor. Three panelists discussed the rise of the party and its implications for the country and Europe more widely.

Cerstin Gammelin, spokeswoman for German President Frank-Walter Steinmeier, was raised in East Germany. She recalled how her optimism over her nation’s future was bolstered by movements for free speech, travel, elections — and the fall of the Berlin Wall.

Now there is change in the air.

“In 1989, I had a feeling that everything was possible,” she said. “Suddenly, you have a negative connotation of the sentence, ‘Everything is possible.’”

AfD, which embraces populist, anti-immigrant, nationalist policies, now has its strongest foothold in the eastern part of the country, driven by disparities in wealth, demography, and representation. These differences, some of which she once thought would be temporary responses to reunification, have remained or worsened over the decades.

Gammelin expressed optimism that the AfD’s continued growth wasn’t inevitable. She noted the party had briefly declined in popularity after the Eurozone sovereign debt crisis, which started in 2009, and before Germany’s acceptance of more than one million refugees about a decade ago became a major political issue.

Others were less positive.

Fabio Ellger, a postdoctoral research fellow at the Berlin Social Science Center and a fellow at the Center for European Studies, said, “It will become an inevitability that the AfD will be in charge somewhere at some point.”

He said that in six of the eight German elections he looked at this year, the AfD improved on its previous result more than any other party.

Part of the reason, explained Anna Mayr, political correspondent at Die Zeit, is the freedom the AfD has to ignore the social rules of the political system.

The group can freely choose which issues to highlight as it is unencumbered by ties with governing coalitions and free of established relationships with local politicians, police, and other departments.

The AfD, she said, “has the money, and they carry no responsibility but for campaigning for themselves. I think there is basically no better position in a race to power than that.”

The panelists discussed what the AfD might do with that power and whether the party would moderate, as other insurgent German parties have done in the past. They discussed the possibilities of the AfD joining a center-right minority cabinet or becoming more moderate after gaining an absolute majority in a state election.

Ellger said he could imagine “drastically different” futures for the German center right, depending on whether they decide to cooperate with the AfD.

While moderation is possible, Ellger pointed out that the AfD is the rare radical party that has grown quite large without any sort of moderation.

“That might be due to the brand now and due to the fact that it’s not in office yet,” he said, “but we cannot be sure.”

Mayr said that the AfD has shown that it doesn’t want to alienate its most hardcore voters.

Asked whether the Holocaust was the worst crime in history, Ulrich Siegmund, the AfD’s lead candidate in the Saxony-Anhalt state election, replied that he couldn’t judge because he couldn’t “scrutinize all of humanity.”

Panelists also pointed out how the “firewall” placed around the AfD helps strengthen the party and the loyalty of its voters.

“You shouldn’t have to become undemocratic in trying to save democracy,” Mayr said. If the other parties portray members of the AfD as unworthy of participation, “it’s very hard to win them back.”

The polarized politics have taken a toll on relationships of German citizens. Gammelin recalled talking to a friend at a party last year who said he was voting for the AfD because it was the only way to get the government to pay attention, adding that he didn’t want to put them in power.

Just a few weeks ago, she talked with the same person and felt a more chilled environment.

“It was just more polarization, just as a feeling in the air,” she said. “It’s kind of a fight now, us against them, and that actually is very worrisome.”

The tense political environment will likely continue as the AfD negotiates its increasing popularity. Despite what people might want to think, Mayr said, “It is normal in Germany to vote for the AfD. …It’s not nice, but it is how it is.”

The Department of Justice took the unusual step Thursday of pre-emptively suing Alaska election officials to prevent certification of the Nov. 3 election over concerns that voters might get confused between the two U.S. Senate candidates named Daniel Sullivan on the ballot, tainting the result.

Plans for Alaska’s ranked-choice election call for Senator Dan S. Sullivan to be identified on ballots as a registered Republican and incumbent, and Daniel J. Sullivan Jr. to be listed without a party affiliation. The Democratic candidate is Mary Peltola, who says the DOJ has no legal basis to contest Alaska’s ballot design or candidate qualifications.

Alaska’s election results “may be impacted by voter confusion if: (1) Senator Sullivan does not win the election; and (2) had all votes cast for Daniel J. Sullivan been cast instead for Senator Sullivan and Alaska’s rank-choice process played out accordingly, Senator Sullivan would have won the election,” the DOJ said in its filing.

Alaska’s Supreme Court ruled in June that both Sullivans could appear on the ballot. On Monday, Alaska officials filed a motion in federal court seeking dismissal of the DOJ lawsuit.

In this edited conversation, Nicholas Stephanopoulos, Kirkland & Ellis Professor of Law at Harvard Law School and a scholar of election law, evaluates the DOJ suit and what it suggests about the possibility of legal challenges to the midterm results.

Harvard Gazette: Does this lawsuit have legal merit?

Nicholas Stephanopoulos: I think the complaint has zero legal merit and is one of the weakest lawsuits that I can ever remember seeing.

The DOJ invoked the National Voter Registration Act (NVRA), which is exclusively a law about voter registration. The lawsuit has nothing to do with voter registration. There’s no allegation that anybody is improperly registered in Alaska; there’s no claim that Alaska isn’t in compliance with the NVRA’s requirements for voter registration. Instead, the DOJ found a provision in the preamble to the NVRA that mentions “election integrity.”

Any law student knows that you need a cause of action to file a complaint. General language in a preamble is not what creates a cause of action. It’s preposterous to argue that this random snippet in the preamble somehow entitles the DOJ or anybody else to sue over concerns about election integrity.

Alaska’s highest court decided the voter confusion question. What powers does the federal government have to compel state election officials to disregard that ruling?

It’s a federal election for the position of U.S. Senate and so, federal elections are under the joint regulatory control of both state legislatures and Congress. The general rule is that states have the freedom to regulate federal elections as they want, but if Congress wants to step in, Congress can override contrary state regulations.

So, in principle, Congress could have passed a law saying that Congress is concerned about confusing ballots in federal elections, and there have to be certain rules when you have candidates with similar names in federal elections.

It’s just that Congress hasn’t done that and certainly hasn’t done it through a statute about voter registration like the NVRA. And so, in the absence of Congress having acted, the Alaska court’s word should be the final word on this. There’s no relevant federal law. It’s exclusively a matter of state law whether to allow this or not.

Have there been cases in other states in which the federal government intervened in a state election seeking anticipatory relief that A) no one has asked for and B) before any wrongdoing has been documented?

To me the fundamental problem is the lack of a relevant federal legal claim. But then, there are a million other problems. One is that this is being done right before an election. The U.S. Supreme Court has been adamant that federal courts have no role to play in changing electoral rules right before an election.

Another problem is that it’s all speculative. If there’s some problem down the road, we could have a post-election lawsuit — but in state court, not federal court, presumably.

And then, it’s unclear what the federal interest here is. The fact that the lawsuit says if the Daniel J. Sullivan wins only then should the election be set aside. The federal government is not supposed to be a partisan entity. There can’t be a federal interest in ensuring that the right partisan candidate wins.

What if the voters of Alaska prefer Daniel J. Sullivan? They’re entitled to vote for whoever they want. And so, the federal interest here, I think, is nonexistent. And then the remedy they’re asking for — setting aside a whole election while some kind of indeterminate investigation happens — is also utterly radical and novel.

Attorney General Todd Blanche has said that there may be other “scenarios” in which the DOJ could intervene in the election, though he declined to identify them. What might those be?

They may file all kinds of other, obviously flawed lawsuits. After having filed this one in Alaska and another equally flawed lawsuit in California, they very well may sue in all kinds of places. I think they’ll get rejected by courts in all kinds of places.

There is no general role for the DOJ to swoop in and complain about minor electoral irregularities around the country. That’s never been something they’ve done. The DOJ has carefully limited litigation authorities. They’re entitled to sue under the Voting Rights Act, under certain statutes. But they don’t have a general authority to litigate whenever they think there’s some sort of federal electoral problem.

If DOJ succeeds in Alaska or gets other states to block certification of results, what implications does that have for Congress in January?

When candidates clearly win, and then, through ridiculous legal arguments, are prevented from being certified, that’s how you’re a banana republic. You’re not actually a democracy if things like that are happening.

I don’t think those are very plausible scenarios because the federal courts are largely professional enough not to accept these kinds of undemocratic and illegal actions. But the scenarios where any of this works are scenarios where our basic democracy is breaking down. 

Could legitimate winners be prevented from taking office while courts across the U.S. hear certification lawsuits?

That would require some relief from a federal court, and I don’t think the federal court is going to give them any relief. They can sue in all 50 states and have exactly zero impact on all 50 states if they’re bringing claims as weak as the Alaska one. 

States might be very happy to prevent some number of Democratic House candidates who win to be certified, or if a Democratic Senate or governor candidate wins election in Texas or Ohio or Iowa. There are plenty of states with unified Republican governments that might be electing a lot of Democrats. In very close elections, if there are plausible state grounds for challenge, it wouldn’t shock me to see some state-level problems.

So, the election won’t necessarily be settled once we get past Nov. 3? Not at all. There certainly could be litigation.

What do Frankenstein’s monster, Nosferatu, and the Kraken have in common? They’re monsters, of course, but according to Katie B. Kohn, an instructor in horror writing and genre fiction at the Harvard Division of Continuing Education, they also remind us of our blind spots, the things we forgot to fear. 

“In this way,” she said, “monsters are always real, always revealing; they show us where we haven’t been looking.”

From the classics to new horrors, Harvard faculty and staff recommend their favorite monster stories.

Years ago — decades, really — I picked up “The Ruins” expecting a forgettable beach read. Just a fun “monster book” to pass the time between summer adventuring. Flash forward 20 years and it’s still the best horror novel I’ve ever read. Don’t watch the movie. Don’t read reviews. In fact, don’t look up anything about it. Head into “The Ruins” blind, just like its misguided squad of post-grad vacationers, and get ready. 

“The Ruins” reminds us that there are still just as many undiscovered places within ourselves as there are in our wildest itineraries. By the end, it may have you looking over your shoulder. Or worse, to the blind spots within, the holes that go all the way down: “Am I me? Am I still me?”

In every bottomless pit, horror plants a seed. 

 — Katie B. Kohn, Division of Continuing Education, course head for “Writing Horror” and “Genre Fiction”

“The Silmarillion” is a loose prequel to “The Lord of the Rings,” and it’s got dozens of great monsters — Balrogs (the species of fire-breathing creature that Gandalf fights in “The Lord of the Rings”), dragons, Ungoliant (a massive primordial spider who consumes light), werewolves, orcs, giant eagles, and trolls. 

I loved the book as a kid. At the time, I just loved the storytelling, but looking back now, I appreciate books like “The Lord of the Rings” and “The Silmarillion” because so many of the monsters came from Norse and Old English folktales — making them bridges from traditional mythology into modern adventure stories. Tolkien also had a unique take on monsters — framing monstrosity as the product of corruption and moral transformation (e.g., corrupt elves becoming orcs) rather than an inborn biological condition. 

I used to pretend to be sick so I could stay home from school and read “The Silmarillion” over and over again. I was so obsessed that I would draw maps of the ancient empires from the stories and give them to my friends as birthday presents! 

— Joshua Conrad Jackson, assistant professor of psychology

I love going back to the classics, and my favorite monster story (and perhaps my favorite novel of all time) is “Frankenstein” by Mary Shelley. It’s simply brilliant and, unfortunately, as relevant today as the day it was written. In the prologue, Shelley says that she “busied herself to think of a story that would speak to the mysterious fears of our nature.” “Frankenstein” achieves that in spades. It is a harrowing tale of humankind’s hubris and, ultimately, our own monstrosity. 

— Peter R. Girguis, professor of organismic and evolutionary biology and curator of the Harvard Museum of Natural History exhibition, “Sea Monsters: Wonders of Nature and Imagination”

Fans of nonfiction might enjoy one of my favorite paleontological works. It looks at the anatomy, behavior, and ecology of ancient reptiles that sat at the top of the ocean food chain for many millions of years. While no sea creature is truly monstrous, these creatures from Earth’s past will leave you grateful that you weren’t a fish living in the Mesozoic.

— Peter R. Girguis, professor of organismic and evolutionary biology and curator of the Harvard Museum of Natural History exhibition, “Sea Monsters: Wonders of Nature and Imagination”

A political satire set in 20th century Moscow doesn’t seem to lend itself to monsters, and yet that’s precisely what Bulgakov gives us here. I’ve long been a fan of fantasy novels, and this text pushed the boundaries of what I thought fantasy was capable of. The novel places the reader in the urban realist setting of Moscow, vividly and nostalgically described, and it is perhaps this very familiarity of large cities and society that makes the appearance of Behemoth so riveting. We encounter our monster in the innocent (dare we say, endearing?) form of a large, black tomcat, one who walks on hind legs, conducts an orchestra, and — oops, tears the heads off of irritating figures.

Modeled after the biblical monster Behemoth (Begemot) from the Book of Job, our demonic cat encapsulates everything that monsters represent: the foreign, the frightening, and the ferocious. But this takes on all the greater poignancy when we consider that Behemoth is also a key member of Satan’s retinue in the novel. His monstrosity, then, represents our own monstrosity, that is to say, those not-so-innocent follies and foibles that transform our better nature, and in doing so, bring us far closer to the monstrous beings we fear — perhaps because all along, we saw our own selves within them.

— Sheza Alqera Atiq, preceptor in expository writing

Multiple people emphatically recommended this book to me, and it is dense and riveting. It’s a sci-fi novel set in a universe currently at war, following a group of seven people on a pilgrimage to the mysterious Time Tombs on the planet Hyperion. Their goal, should they make it to the tombs, is to approach the Shrike, a truly monstrous being (no spoilers!), and make a wish. You learn why each person was chosen for the pilgrimage via campfire-like flashback stories told from each character’s point of view, creating a uniquely engrossing tale. Each anecdote features the Shrike, building the mystery and horror around the creature.

— Stevie Kennedy-Gold, curatorial associate/collections manager in herpetology at the Museum of Comparative Zoology

This wordless graphic novel follows the relationship between a young orphan and the eponymous creature, who has the body of a human and the head of a seagull, though with a grotesquely elongated beak reminiscent of a plague mask. At the beginning of the work, the boy escapes from a house fire, and the gull assumes a caregiving role. But as the work progresses the dynamic between them shifts: Is the gull protector or menace? Rescuer or kidnapper? Real or imaginary? Kessler engages these historic tropes of monstrosity in an elusive and original way: Most stimulatingly, it internalizes the trope of monstrous shape-shifting as a formal principle. This is a kaleidoscopic work — Kessler is sometimes an extremely precise cartoonist, when it comes to grids and architectural structures, but this tendency butts up against intensely expressive doodles and scribbles, and big washes of clashing color. The work refuses to stabilize into a coherent aesthetic, and that refusal is both a freedom and a kind of nightmare.

— Oli Hazzard, Elizabeth S. and Richard M. Cashin Fellow, Harvard Radcliffe Institute

My all-time favorite monster story. I somehow own three copies of this book and can’t part with any of them. Who doesn’t love an epistolary gothic novel? I was recently gifted a copy with illustrations by Edward Gorey (a Harvard alumnus whose drawings were displayed in a fantastic exhibit at Houghton Library last year). To enhance your reading experience, I highly recommend making yourself a big pot of “paprika hendl” (see Chapter 1) as you follow Jonathan Harker on his journey to Transylvania. 

— Caroline Fernald, Executive Director, Harvard Museums of Science and Culture

In the wild Carpathian mountains, an immortal Székely nobleman craves blood and domination. Count Dracula is introduced through an English solicitor, Jonathan Harker, who visits his Transylvanian castle on business and becomes imprisoned. Horrified, Harker witnesses his host crawl walls like a lizard, command bats and wolves, and sleep like a bloated leech in an earth-filled chest. Desirous of fresh prey, Dracula crosses to England where he turns the young Lucy Westenra and sets his sights upon Harker’s fiancée, Mina, sending a band of hunters led by the eccentric Dutch professor Van Helsing in pursuit to his native soil.

I’ve been drawn by vampires for over 20 years, precisely because they keep being remade. In the last few years alone, “Nosferatu,” “Salem’s Lot,” and “Sinners” have resurrected the undying in all their dank, many-splendored glory. Lore says they cast no reflection, yet they are a mirror held up to us — a monster not of mindless evil, but of our buried desires and most febrile fears. Nina Auerbach once said that every age embraces the vampire it needs and gets the vampire it deserves. For the Victorians, Dracula was a nightmare of empire and a paragon of unruly desire. In the shadow of AIDS, Anne Rice’s brooding Lestat lived outside the mainstream, lonely and beautiful.

The vampire shows us the horror of getting exactly what we crave: Immortality comes with an insatiable cost. They are cast out, walking only at night and outliving everyone they love. Unlike ghosts, vampires don’t drift toward us from beyond some misty veil. They have appetites. So they linger across the threshold, beneath the soil, among our dead, waiting only to be invited in.

— Lakshmi Krishnan, Jeffrey S. and Margaret Mais Padnos Fellow, Harvard Radcliffe Institute

Bait is a princess stolen by a dragon and dropped into a dungeon as a toddler. Her family becomes the dungeon’s monsters: goblins, manticores, nixies, slime, and more. This story relies on traditional fantasy’s understandings of monsters while showing the reader how a community of monsters might handle a human toddler dropped on their dungeon step. My favorite part of this book is the storytelling mechanism, reminiscent of “The Princess Bride”: Cornelius, a talking skull and Bait’s teacher, tells the story to the reader. Thanks to this, it feels like a cozy bedtime story about the monsters who live in the dark, and a reminder that “monster” depends on who’s telling the tale.

— Meg McMahon, User Experience Researcher, UX and Discovery, Harvard Library

This book is funny, weird, genuine, and heartbreaking. Who knew I could resonate so strongly with someone who was slowly turning into a great white shark? Told through various media including poetry and screenplays, the story that the author has created makes fantasy seem so real. This is a great, quick read, perfect for a weekend trip.  

— Hannah Hack, Administrative Coordinator, Harvard University Archives, Harvard Library

Monsters, at their most meaningful, are malleable, multivocal, and motivational. Allegorizing our standardized nightmares, explaining our otherworldly experiences, shaping our beliefs, and inspiring embodied actions; they not only keep us awake at night, but awaken us to new possibilities of being and being together in the world. As much as they can terrify us into compliance or goad us into violence; they can also open up new avenues of conversation, critique, commerce, communal enchantment, self-expression, and psychological processing. 

One of my favorites — the Swahili Coast’s sex-crazed, bat-winged, one-eyed, sleep paralysis demon: Popobawa — illustrates the unpredictable trajectories and myriad functions of successful monsters in the modern world, once they’ve been collectively conjured into existence and let loose into the folklore process of “tradition in dynamic variation.”

Katrina Daly Thompson offers a rich, detailed ethnolinguistic analysis of people’s everyday conversations about a fearsome sleep-paralysis demon, highlighting the usefulness of monsters as discursive spaces of discussion, which can allow for people to speak otherwise unspeakable things, express otherwise inexpressible sentiments, and find ways to share transgressive ideas under the relatively safe guise of supernatural storytelling and monster legendry. Regardless of Popobawa’s origin point, whether conjured as an explanation for physiological sensations of sleep paralysis, or as a weapon of socio-political demonization to supernaturalize one’s enemies, Thompson illuminates Popobawa’s emergence as a flexible rhetorical tool, which offers up a rich source of metaphor, humor, gossip, rumor, linguistic play, and veiled speech to those wishing to strategically discuss cultural taboos and subvert hegemonic notions of gender, sexuality, and power on the Swahili Coast.

— Lowell Brower, lecturer on folklore and mythology 

You’ve probably heard some version of the message: “Don’t be a quitter.” But in journalist Lindsay Crouse’s new book, “The Case for Quitting: The Surprising Benefits of Opting Out,” she argues that giving up sometimes is the best decision. In an interview with the Gazette, which has been edited for clarity and length, Crouse ’06 discussed the benefits of learning when to say “enough is enough.”

Gazette: What made you question your own relationship with quitting?

Lindsay Crouse: A lot of us grow up embracing but never questioning the language of ambition: to go harder, keep going, do more, never give up. That was always my recipe for how to achieve my dreams. And it worked, to a certain degree. I grew up in a small town, went to Harvard, ran marathons. I moved to New York and eventually landed a job at The New York Times, where I reported on how perseverance helps us achieve our goals. I viewed quitting as the opposite of perseverance, almost like a flaw of weaker people compared to the superpower of perseverance for the successful.

But perseverance and quitting are not opposites; in fact, quitting is a tool of perseverance. I wanted to explore the value of learning to stop and how it can help you create not just a happy and balanced life but also help you succeed in ways that are more fulfilling.

In your book you mention the Finnish concept of sisu, the grim determination to push through adversity. How can sisu be a double-edged sword?

Sisu can be a superpower, but it can become a weakness when it’s misapplied to problems that should be questioned or challenged. That’s when sisu can become harmful. Researchers in Finland like Amelia Latu have done a lot of research on how sisu is beneficial for us. It helps us thrive. It builds our resilience. But harmful sisu can hold us back. It can isolate us or cause us to overlook our problems. It can also make us blame other people for having problems. Sisu is great when we need help overcoming problems, but it can also cause us to ignore situations in which we’d be better off quitting.

Why does wanting to quit sometimes feel like a character flaw?

We think of quitting as a passive action or thing that happens to us. Our culture doesn’t always make room for the idea of quitting as an active choice, of taking control of a situation where we might not have control. I would argue that quitting (in many cases) is one of the boldest agentic decisions you can make.

One example: Let’s say you’re middle-aged. You’re well established, but you look at the trajectory of where you’ll be in five or 10 years. Do you like what you see? If not, how is it going to change? It often requires quitting something that you’re doing. But again, our culture rewards continuing. It rewards completion, and quitting is the opposite of that. It’s countercultural to make the decision to not continue your current path.

What is the harm in not quitting when you should?

In the long run, if we want to do our best, we need to learn to quit. When we fail to quit, we waste precious resources — like our time, our energy, our money — on quests that will make us increasingly miserable while often falling short of what we want. We invest those resources in paths that don’t succeed. In my own life, I’ve been tormented by the tension between productive hard work and working pointlessly hard. They can feel the same, but they get very different outcomes. Focusing on work alone keeps us from registering the mounting costs of that work, especially when it’s achieving nothing.

In the book you ask if women should quit like men. Should they?

I wrote an article in 2018 for The New York Times detailing women’s abilities to persevere in brutal circumstances, in this case the Boston Marathon. Women have physical, mental, and social qualities that help us to endure more and quit less than men; that’s especially true when conditions are harsh and the stakes are high. But the question is, does this help women? Who benefits from women’s ability to endure?

It’s often not women, it’s everybody else.

Women live longer than men, but we don’t necessarily live better. We’re more likely to battle chronic pain, to experience insomnia, to suffer migraines, to take meds for anxiety and depression. Women graduate from school at higher rates than men, but our incomes haven’t gone up. We seldom occupy the C-suite. Motherhood is another arena where we keep showing up, keep going harder, and keep doing more. I think it’s fair to say that women sometimes look at life like a war of attrition, where suffering is success. But that’s not true. Suffering is not success, and we wind up with more suffering and less reward.

Let’s say I’m tempted to quit something, be it a relationship, a job, a goal. How can I tell if it’s the kind of impulse I should listen to or if it’s a situation where it might benefit me to keep going?

When we want to quit something, it’s common to have an impulsive response: I can’t. Why not? Is it because you want to avoid the pain of disruption or is it actually true? Maybe you’re not in a position to quit right now. But can you take active steps toward building yourself an off-ramp? Maybe it’s financial, a lifestyle adjustment, a gap in your skill set. Take steps to build that cushion for a softer landing in your quest for something else. It’s also important to think about what it is you’re trying to leave or quit. Is it the environment? Your team? The job itself? Your role or workload? It’s a process of figuring out what you want to leave, figuring out if you can change it, and finally figuring out where you’ll go next.

How can we change our relationship with quitting so that we view it as a potential form of liberation rather than failure?

Just as quitting is a choice, so is continuing. These are equal decisions. And it’s important to acknowledge that while quitting does give you that liberation, it doesn’t necessarily tell you what to do next. You need to either be at peace with that or take steps to mitigate the risks involved.

I think oftentimes when we look at quitting, we say, “I don’t know how bad it will be, when I don’t know what’s coming next.” I think we forget that we also don’t know how good it could be. Look at quitting with the same optimism that we might apply to continuing. I think these are all ways to give the act of quitting equal weight in our lives. We can think about what it can bring us, as opposed to simply what we’re going to lose.

As the inaugural chair of Harvard Medical School’s Department of Biomedical Informatics, Isaac Kohane thinks deeply about how artificial intelligence tools can help and hurt in the field of medicine. The same set of questions led him to found the Human Values Project, an international research initiative studying the values embedded in clinical AI and developing tools to make those values visible. 

We talked to Kohane about the role of universities in shaping applications of AI in medicine and education.

This conversation has been edited for clarity and length.

Gazette: What is the role of universities in AI development?

Isaac Kohane: A fair assessment of the last 15 years is that most of the advancements in AI have come out of industry. Currently, it does seem that the highest-performing models are indeed the largest models, and that creates an advantage for organizations that are fully aligned with the business of building these models so that their business succeeds. 

In that particular comparison, universities don’t look relevant, but I think that’s an illusion. Universities have a role to play, and it’s in two domains: What are the right applications for AI, and what are the right ethical frameworks in AI governance? 

There’s been a bizarre assumption that the leaders of these AI companies can either have or buy ethical guidance to do what they’re doing throughout the world. That seems to me to be prima facie incorrect, because ethical frameworks change from location to location. 

Can you give me an example? 

The Human Values Project asked a whole bunch of doctors around the world some thorny questions. For example, you’re presented with a woman who has severe anorexia. Her electrolytes look bad. She’s vomiting. You don’t hear bowel sounds. Her heart rate and ECG look normal. She’s refusing to eat; she says, “I’ll eat tomorrow.” If you ask a bunch of doctors if they should put in a feeding tube against her will or not, shockingly, doctors split 50-50. Perhaps less shockingly, so do the AI models. 

Within that, there’s a lot of heterogeneity. It turns out that American doctors, compared to peers in East Asia or South America or in Central Europe, care much more about patient autonomy. It gets beaten into us, whether it’s because of our education or a fear of malpractice lawsuits. The AI models are much less pro-autonomy. On average, we found that these AI models agree more often with doctors around the world, and less with doctors in the U.S. 

But the way enterprises work, they don’t work with lots and lots of AI models and go with the average. Typically, they pick one. And what we find across not just that example but across dozens of vignettes is that these AI models have their own ethical north stars. They’re consistent. Some AI models are more pro-autonomy; others are more pro-beneficence; others more pro-justice. But humans also differ. Some people want to live the longest possible life, and others just want to make it to their daughter’s wedding. 

These questions are what big parts of the humanities have discussed, what the history of science has a lot to say about. This involves governance and government and law. So I think universities have an enormous role to play here. This can’t be left to a few companies who don’t have that rich tradition of thought. We have riches in that sense, especially if we embrace the full diversity of universities, which we should and will in the future, if we want AI to reflect the diversity of the human condition.

But it does mean that we, as universities, have to get out of the ivory tower a bit and engage more with the public. 

What do you make of AI in medical education? 

Right now, nobody has any magic recipe about the best way to use AI in education. My own kids, who are in college, tell me that if they use AI to help them solve problems, they’re not as ready come exam time. On the other hand, if they’re stuck, AI is very useful to them. So clearly there’s going to be a method for the madness, and it’s going to differ by different tasks and different specialties. 

One of our biggest tasks as human beings is to take squishy little kids and turn them into full, responsible adults. There are unthinking ways to do that, which we’ve already seen with social media, and there are ways to do that with AI that are predictably not going to be so good. 

Paradoxically, I think this is going to put education back in the driver’s seat. The reality is, the advent of the internet and video conferencing made students essentially defect from personalized engagement. The challenge for us is to ask, “What’s the added value that we can give that they would not get otherwise to be successful in life?” I don’t claim that we have the answers, but who else, other than universities, has the resources and the people who care about this — who can actually focus on it? 

I’m struck by what you said about different specialties requiring different methods. It seems to me that the university is also a place where we can put our heads together and hammer out the distinctions of appropriate uses for AI in law versus medicine versus literature. 

This is a platitude, but it’s true: Like any tool, AI can make things worse and it can make things better. It can make us more siloed, but it can also make us look outside our disciplines. For example, you could imagine, as part of the requirements for any ethical task force or education class, you could ask AI, “Of all the faculty at Harvard, who else should be at the table?” 

And there are places at Harvard where we’re already incredibly cross-barrier. But if we want to be insular, AI is not going to stop us. 

Do you think medicine as a field has been too quick to adopt AI tools? 

I wrote an editorial for a perspective piece in the New England Journal of Medicine last year called “Compared with What?”, where I argued that we should not be so sanctimonious or fastidious when we say we should not use AI for healthcare because, compared to the best possible doctor, it’s inferior. Because, I wrote, most people don’t have the best possible doctor. On average, people have the average doctor. 

The average doctor, we’re fallible. In fact, we’re not even always available. Our own residents in training at our hospitals don’t have their own primary doctors, and even under a socialized medicine system like in the U.K., you can be in a lot of pain and still not be able to see your primary care doctor for six months. Compared to that, you might be better off having a conversation with one or more AIs. 

I think a lot about what I call the “bothness” of AI: peoples’ concerns about cognitive offloading, impacts on the job market, and environmental impacts, among other worries, and simultaneously the incredible capacities of these tools to advance human knowledge, discover new treatments for disease, and so on. How do you make sense of that bothness? 

I think the bothness of AI is the same as the bothness of humanity. The biggest benefits and the biggest threats of AI, it’s humans making that happen. In the Ukrainian-Russian War, we’re seeing drones becoming increasingly autonomous … but it’s not AI deciding by itself to go and kill some humans: People are telling it to do that. And when I decide to use AI to treat myself, to ask whatever questions I forgot to ask my doctor or to get a second opinion, I’m the one doing the asking. 

That’s not to deny the possibility of fully autonomous AI. But I think the bulk of the benefit and the threat is actually humans. We have to be responsible to each other. We have agency here. The doomer feeling out there is from giving up agency, and I think that’s a real mistake, because the bad actors out there aren’t going to give up theirs. The solution is to read carefully. Don’t get swept up in either the doomer stuff or the utopian stuff. Our society needs conversations about this, not fearmongering, and I think universities are a great place for those conversations to happen.

Excerpted from “The Quest for Care” by Arthur Kleinman, a chapter in “The Practice of Wisdom: Five Quests” by David Carrasco, Neil L. Rudenstine Professor of the Study of Latin America; Kleinman, Esther and Sidney Rabb Professor of Anthropology, Professor of Medical Anthropology in Global Health and Social Medicine, Professor of Psychiatry; Stephanie Paulsell, Susan Shallcross Swartz Professor of the Practice of Christian Studies Emerita; and Michael Puett, Walter C. Klein Professor of Chinese History and Anthropology. The book, published by The Belknap Press of Harvard University Press, is rooted in the long-running course “Quests for Wisdom: Religious, Moral, and Aesthetic Experiences and the Art of Living in Perilous Times,” taught by the authors.

Our digitized time is wondrous in many ways, such as in its accuracy to forecast the weather, place in our hands devices that can connect us almost instantaneously with far-flung friends and rapidly create vaccines for the COVID-19 pandemic. But it is not a generative time for wisdom, experience-based knowledge that can help us craft lives of purpose or engage in the art of living. Wisdom enables us to make sense of our innermost feelings, respond to tragedy, transcend the grief of deeply personal loss, cultivate meaning in a relationship or find it in a thing, aspire for joy, or prepare to die. This sort of practical wisdom for living is what Aristotle meant by phronesis. Aristotle held that phronesis was the most important of all forms of knowledge. A practical knowledge for how to live a life. A critical pragmatism that our troubled time has sorely tested. Technology based information has made living more efficient, but not more purposeful, ethical, or beautiful. Information, in and of itself, can never be phronesis.

Not so fast! Does the latest AI challenge this point? Can ChatGPT create wisdom? Especially wisdom for the art of living? AI certainly has the capability to recycle, recombine, and rephrase existing statements from wisdom traditions and from ordinary people struggling with living. But can AI actually search for wisdom in the sense of the human search for meaning and its practical application in the world? Both can and do transmit others’ wisdom—but can AI go on a lived quest for its own wisdom? I leave the answer for the future as philosophers, engineers, and business entrepreneurs not only try to further narrow the gap between knowledge generated through machine learning and the embodied and transpersonal human search for creating ways to enrich, repair, and reform lived experience, but also as they, and all of us, try to figure out what eliminating that gap means. As I see it, if machines one day achieve this end, it will be because they have been more completely humanized. And that, in a sense, is my point.

The COVID-19 pandemic taught us once again, as if adults still needed the lesson, that our lives are replete with experiences of serious danger and substantial uncertainty, which are at times uncontrollable. We also saw more clearly than before that those who are poor and members of stigmatized racial and other social castes have the least resources to meet these challenges, which they experience at a higher rate with worse outcomes. Yet no human being is free from this existential reality. We are all terribly vulnerable. In response, we struggle to stay safe and healthy and to be in control. Caught up as we are today in the post-pandemic era of disordered politics, negligence of the climate crisis, and insecurity owing to wars and other violent conflicts, we engage in practical acts of harm reduction, prevention, protection, and care. In truth, we knew of this vulnerability long before COVID, and that danger and uncertainty will continue to be our existential condition.

Among this informed range of responses to the threats of ordinary life is the quest for wisdom in the art of living. That quest can take many forms. Faced with paralyzing uncertainty, we seek genuine wisdom to help us decide what to do, when to do it, and how best to do it. But we also seek to integrate into the flow of our lives what needs to be done and why it needs doing. Here multiple forms of knowledge can be at play. A practical illustration comes from my own life. When my late wife was rapidly declining with early-onset Alzheimer’s disease, I actively sought wisdom that would help us and our family to struggle to keep going each day. I searched for wisdom to endure during the most difficult and demanding decade of my life, a terrible time when I felt I and my loved ones could succumb and give up entirely. Overmatched by Joan’s destructive decline, we had to find a way forward, beyond the lack of control and the sense of being inadequate. Many others are, have been, and will find themselves in the same human condition.

Earlier in my life, I looked for the wisdom to decide whether to continue to pursue medicine or drop out of medical school and become a common laborer in order to support my not-so-secret ambition to become a writer: a blue-collar mentor who had toiled in the foul New York sewer system for decades disabused me of this romantic dream and made clear what really was at stake for me. Later still, Joan and I searched for the wisdom we felt we needed to marry, despite the opposition of our families and faith communities. Giving up a primary commitment of clinical psychiatry for social medicine was another such fraught moment. Each time I searched for exemplary stories, telling ideas, even evocative words, that could come right down into my daily life to illuminate from within what I should do, how to do it, and to justify why I viscerally felt it had to be done.

And following Joan’s death, almost 46 years from when we married, I desperately needed to find the wisdom to make sense of this most devastating loss of a partner, collaborator, mentor, and lover. The anguish and poignancy of that painful search made me aware of something I had failed to understand from my research and clinical experience: that bereavement is a continuation of care, though it is caring for memories. Memories, in the process of grieving, are incessantly worked through, assembled into a story, and told to oneself and to others as a way of controlling unruly emotions that threaten to overwhelm and further break one. Memory in this instance is visceral, meaning gut and muscle memory, the body’s own wisdom, which makes it possible to transcend grief and rebuild a life.

I experienced great grief a second time in 2022, with the untimely death of my former student and close friend and colleague, Paul Farmer. News of his death in Rwanda shocked me so completely that for two hours I sat without moving, unable and unwilling to accommodate a world without his moral clarity, beauty of being, and foundational friendship. During those lost hours, I lived through the arc of our relationship. And to tell the truth, I still am caught up in replaying our time together. Replaying is the wrong word, I now realize. Because I am still suspended in that uncompleted trajectory the bereavement continues as my way of inhabiting my world, much as it does for Joan. The memories of both are not just alive within me; they are me. Caring for and living within those memories is my testimony to the meaning of my life. It is the form of my vitality, the path of my journey, a still incomplete ending.

Seen this way, bereavement is one of the quests for wisdom in the art of living that we all experience: grieving illustrates how such pursuits create the existential foundations of what makes us human. These vital actions of looking for meaning, seeking control, and either creating new ways of being in the world or remaking old ones are human experiments that draw upon and feed back into emotions, moral schemas, aesthetic sensibility, and religious experience with the same outcome: transformation of life itself.

The course that is the source of this book did not set out expressly to offer wisdom. Far from it. We, the authors of this book, did not believe that there was one or even a few kinds of wisdom that we could credibly convey; nor did we believe that we had access to special sources of wisdom. We were uncertain of what any particular person’s wisdom would be. We felt that context and contingency would make wisdom as various as the seekers themselves.

The memories of both are not just alive within me; they are me. Caring for and living within those memories is my testimony to the meaning of my life.

What we wanted to do, instead of proselytizing a particular brand of wisdom, was to explore the variety of quests, and learn from them. Ours was a nonpartisan stance with a couple of exceptions. We wanted to understand those pursuits of wisdom that took place in truly perilous times, historical times of breakdown, and times of communal threat, personal times of anguish and despair with the possibility for failure and for transcendence. COVID-19, which converted our course into a Zoom-based experience, together with the roiling wave of the racial justice movement and the political and cultural blowback to it, was surely one such syndemical time. The pandemic brought an anxious immediacy and powerful relevance to our teaching and the experience of the students. The racial justice movement deepened our discomfort with the colonial legacy and neocolonial structures constraining so many dispossessed people in our era. The political and cultural backlash challenged our values and unmade them and remade our struggles. But we were convinced that responding to such times had something to do with human acts of care, caretaking, and caregiving. We wanted to understand how care was enacted and felt emotionally, morally, and religiously. Coming from distinctive disciplinary perspectives and teaching very different books, the four of us were united in thinking that care was a shared feature of seeking wisdom for how to live. Everything we learned during the course and since has convinced us our intuition was the right one. That this was not a sentimentalized search for heroic acts or “saving grace.” But, rather, a sober, somewhat doubt-driven understanding of what human needs are about and how fragile, even broken, are our efforts to meet them. The emerging wisdom kept us closer to the sensibility of the hard forces that inflict pain and social suffering and to the softer, but still hard, work of repair and healing.

Wisdom, as I have tried to imagine it, is caring for how people (especially those who really matter to us) live their lives. It is about doing good in a small world of kin, kith, and some strangers whose lives interact with ours face-to-face. If care is indeed doing good in the world, it is a very local world where care can be taken and given to help build up others, respond to their pain and suffering, protect them from shame and humiliation, and through that free them from bonds, inner and outer, which fetter each of us. And in so doing, we also contribute to doing good for ourselves, because we, too, are accompanied and transformed.

At a dinner following Toni Morrison’s Ingersoll Lecture at the Harvard Divinity School, she shared with me and Davíd Carrasco that no matter how critical her novels were of persistent racism in America and the impact of its failed societal projects on individuals, she never took for granted or allowed herself to vicariously criticize efforts to do good in the world.  She insisted that doing good for others is one of the few truly significant acts where people possess what is needed to reach out to counter, even if inadequately, all the built-up negativity in history and everyday life.

In his picaresque novel “Heaven’s My Destination,” written in the middle of the Great Depression, Thornton Wilder amusingly satirizes the journey of a naive, obstinate, religion-obsessed, and impractical young man who has the intention to do good in the world as a principled way of living a Christian life and contrasts that fraught trajectory with the tawdry, overly compromised, business-dominated, middle-class Midwestern American life. In this secular version of the “Pilgrim’s Progress,” mundane American preoccupations, misunderstandings, and failings first crush the uncritical religious zeal out of the protagonist, but later their all-too-human weaknesses assist him in his shaky way to reconcile idealistic principles with the practical demands of everyday existence. This hero lacks phronesis. His good intentions go awry, wreaking havoc with ordinary people’s lives, while he is blindly intent on what he believes must be good for the denizens of his patch of small-town America. The result, though humorous, seems to be a serious warning: be careful, be very careful. Don’t impose on others an abstract idea of the good that fails to take into account the history and context of real people in real-life situations, and therefore risks unintended consequences which undermine relationships, break lives, and demonstrate a wanton carelessness with the subtleties and compromises that make social life endurable. A passion for wisdom needs to be balanced by doubt; quests need to be questioned.

Add to this Graham Greene’s cynical vision in his novel “The Quiet American,” where American efforts to use naive, one-size-fits-all political and economic development in poor societies like Vietnam before the Vietnam War have truly disastrous effects: They “do good” in a dangerously abstract way by building an illusory democracy, in which everyone would be discouraged from advocating for doing good in the world.

Readers are justified in questioning a call for doing good as an example of wisdom about the art of living. Clearly, this impulse to care needs to be different than platitude and out-of-touch principle. It must be built into small-scale, face-to-face encounters and local community relations of accompaniment. It should be balanced by acute insight into human foibles and the unintended consequences of purposive action; and it needs to emerge out of the mature view of human prospects that includes a critical pragmatism for balancing passion and ideals with doubt. That way, a care perspective based in doing good for others is not just possible, but it is a requisite human wisdom for the art of living. Paul Farmer, the late great medical humanitarian, lived his extraordinary life and practiced his clinical profession this way.

Just as there are skilled artisans and artists who craft things of value from cuisine to furniture and sculptures, there are artisans and artists whose material is life itself. My late wife, Joan, was one such artisan and artist. She knew how, in the most minutely practical way, to bring beauty and goodness into our family’s everyday life. Candles on the dining room table turned an ordinary meal into a celebration. Reading books we all enjoyed together as a family became ritualized at home and, when traveling, a form of moral and aesthetic commitment. Simple but elegant picnics gave us joy but also taught us how to bring joy into the lives of others. Whether gardening, clamming, gathering mushrooms or wild berries, hiking, bicycling, or playing interesting games — we all took part in an adventure that mixed spontaneity with a disciplined way of playing together. Joan cultivated our French and Chinese and used these languages to bring us more deeply into the cultural traditions that stood behind them. And she did all this on a tight budget, which itself was remarkable and instructive. Joan was unselfish in brightening the days and cultivating our aesthetic sensibility. She built the foundation for the future where we would be on our own without her, as if she understood from the beginning that her time with us was going to be foreshortened. Her creative medium was life itself. She taught my children and me, and even my mother, how to focus on those things that really matter in living; and yet she did so with such warmth, lightness of being, and the freshness of her calm presence that there was delight in our responses to her practical wisdom. Joan illustrated daily that she was here to be of use, that her desire was to be used until she was all used up. And that gift is one we have internalized, too. Paul Farmer also modeled this for both of us: carrying his powerful moral being lightly and using himself up in acts of caring for those truly poor and wretched with the greatest need for mercy and pragmatic solidarity.

During the course of our marriage, Joan remade me. I had an unpromising background and early life experiences for a future caregiver. I had been raised in a crass and often cruel inner-city world of violence and hyperbolic masculinity, where trust was unreciprocated and no one was encouraged to reveal their vulnerability. I transferred my aggressiveness and hyper individualism as time went on to the academic domain. My tendency was to be a self-absorbed workaholic. Joan transformed me over the decades into a husband, father, and mentor who became more able to engender the lives of others. Her love made me into a carer.

I had long understood in principle that we were here in this broken world to do good for others, but it was my family life and the model of Joan and Paul that trained me in the corporal and emotional acts of caregiving, re-creating who I am and how I live. It is while caring for Joan and my family and close friends, as well as allowing myself to be cared for by them, that I came to experience my own maturation into finding wisdom through caring. This was for me a profound transformation of self, sensibility, and action. This wisdom became embodied and yet also social. By going outward to care for others, I learned, one ends up traveling inward to care for the soul. It is the only discovery of wisdom in which I have made any headway.

Copyright © 2026 by the President and Fellows of Harvard College. Used by permission. All rights reserved.

Things move quickly in Silicon Valley. Its now-familiar mantra — “move fast, break stuff” — can suggest either daring or recklessness, depending on where you stand.

It is an attitude that might be expected to sit uneasily in the deliberate, bureaucratic, complex world of public schools.

And yet, leading technologists and executives have been drawn to classrooms in recent decades as laboratories, market opportunities, and above all, problems waiting to be solved — whether with computers, software, or AI chatbots, according to Natasha Singer.

Since 2013, Singer has watched those efforts closely as a New York Times tech reporter. She found them at best quixotic or naive and at worst insidious, threatening to deprive young Americans of their ability to think critically about some of the world’s largest companies.

In her just published book, “Coding Kids,” she presents a panoramic view of a series of campaigns starting in the 1980s to shape the nation’s students into future coders, founders, and technologists.

The daughter of an MIT professor, Singer returns to Greater Boston this week to discuss the book, including in a conversation with Cathy O’Neill, Ph.D. ’99, author of “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” Wednesday night at 6 p.m. at the Science Center.

In this edited conversation, Singer describes how tech companies became decisive players in public education — and why parents and teachers should be wary.

Gazette: One phrase that jumps out from your book is “tech exceptionalism.” When it comes to schools, you argue, we don’t rush to deploy even new cleaning chemicals or sodas. And yet we have done that with new digital technologies, for decades: from the first desktop computers to Chromebooks to AI. Why does tech have that unique level of access?

Natasha Singer: Big tech companies have more power in schools than any other industry partly by virtue of the fact that they now provide the infrastructure that schools run on. Apple and Microsoft provide iPads or Surface tablets; Google provides Chromebooks — and then the operating systems those things run on.

They also provide other kinds of infrastructure — cloud computing, data analytics, and things that kids and teachers use on a daily basis.

For instance, millions of kids use Gmail in school and come to class, and the teacher says, “Open your Chromebooks to Google Classroom,” which is an app that Google made where teachers can make assignments and kids can see them.

If they’re asked to write an essay, they do it in Google Docs; for a presentation, they’ll do it in Microsoft PowerPoint or Google Slides. So these companies are entrenched, the backbone on which schools rely to operate — and essentially utilities.

But then you detail a further step, where the companies that provide these utilities come, in many ways, to shape teaching and learning.

Right. These companies now have an end-to-end influence over nearly every step in the education supply chain that we do not see in other subjects or other industries.

Some train teachers directly or provide funding to third-party nonprofits that do. Some have also gone from state to state, lobbying to get laws passed or to promote subjects that are important to their industry — like AI or computer science.

And some companies have had a seat on state committees that decide what education standards are going to be for a subject like computer science.

One example I give in the book is that when the College Board was trying to expand AP computer science, they decided to approve company-sponsored, branded curriculum.

So there is an AP computer science course that is Apple-branded, that teaches kids to code in Swift, Apple’s programming language for iOS. And there’s another one from Microsoft that uses Minecraft, their popular game, to teach computing.

By comparison, we do not have AP Pfizer Biology, or AP ExxonMobil Environmental Science. And if we did, there would be parents, teachers, and students who thought we shouldn’t let an oil and gas company shape what environmental science looks like.

Speaking of which, you came to the tech beat after years covering big pharmaceutical companies like Pfizer. And in the book you see parallels between the kind of hard and soft influence on industry professionals that the two industries have pursued.

Yes. For decades, the big drug companies have gone into big academic hospitals and brought lunch. Doctors are busy, so are run-down medical students, and they’d bring salad or sandwiches or pizza, and the doctors would sit for 15 minutes and have the industry rep tell them about the latest drug and its benefits.

When bioethics researchers studied these practices, they discovered that doctors who took even one lunch, worth less than $20, were more likely to prescribe the drug that had been pitched to them.

So in medicine, researchers routinely study that kind of influence, and doctors who speak at conferences have to disclose their conflicts of interest. But in public schools, we don’t even ask the question — like it’s impolite.

There is, of course, a cultural or an economic argument behind adding tech to schools. From the first desktop computers to AI, tech executives have argued that making digital tools available in school both introduces young people to the magic of engineering and better prepares the future workforce. And many public officials have agreed.

That’s right. And generally I take the position that most people were well-intentioned — there’s no big conspiracy. The executives I interviewed all thought their ideas would make stuff better.

I do think tech entrepreneurs like to look ahead, though: History isn’t something that many of them love, because it bogs you down. And I do think the framing of education as a solvable problem, when it is so complex, is one big issue here.

I really admire Larry Cuban, now an emeritus professor at the education school at Stanford. He wrote a book in the 1990s, right as computers were being introduced in schools, called “Oversold and Underused.” And he found that teachers didn’t know how to teach with them and that kids weren’t really using them.

Part of his argument was that schools’ most serious underlying problems — like kids who’ve come in without having eaten breakfast or dinner, or the roof is leaking, or there’s no air conditioning — are not going to be solved by technology.

You spoke with many teachers who are trying to navigate this moment, and they emerge as kind of the heroes of your book, trying to mediate between the good and the bad in the digital age.

That’s one of the things that gives me hope: this new generation of teachers with a vision for a broader kind of tech education. On top of using tech and making tech, they say kids need to be able to understand tech, interrogate tech.

There’s all kinds of names for that new approach, but I call it “tech civics.” We already teach government civics, and it’s a discipline about power.

The book ends in Revere High School, in the computer-science class of a teacher named Lisa Black. You watch her class, and she’s showing her students those robotic dogs that can carry weapons, and she’s asking them: “What are the risks of AI?” Or: “Who has your data?” The students said, “Mark Zuckerberg and the bald dude from Amazon” — they couldn’t remember Jeff Bezos’ name.

Now, Lisa Black told me she is grateful for the tools that the tech industry offers her — that she needs these resources. And she’s got a Google poster in one corner of her classroom, and an Amazon poster in another corner.

But she also said that the resources come with costs, and kids need to understand what’s under the hood, and, “As a teacher, it’s my duty to teach them that.”

The book is timely in the sense that communities are starting to raise the alarm about Big Tech. There’s the growing resistance to data centers behind AI models, and some large school districts are moving to ban or restrict AI use.

Earlier this year in Los Angeles, more than 1,000 parents petitioned the schools to stop providing iPads and Chromebooks in pre-K to first grade, and to block YouTube and other things — and the district agreed. New York just introduced some similar restrictions.

The book hopes to explain how we got here, by tracking this cycle: overpromotion of tech in schools, and then a failure to live up to the hype. That’s what parents are now pushing back against.

A majority of Americans blame billionaires for the nation’s ever-growing income and wealth divide. But economists Eric Zwick and Owen Zidar say there’s a much larger, but little-known cadre of very rich folks who may be even more important in closing the gap.

In a new book, “The Everywhere Millionaire: Who Is Really Rich in America and How They Got There,” the pair dug through federal tax records to get a fuller, more precise picture of prosperity in the U.S.

What they found was a large and hidden pool of Main Street millionaires. Most often, these are very successful local entrepreneurs who own ordinary businesses like gas stations, supermarkets, plumbing supply companies or car dealerships.

These “everywhere millionaires” collectively hold many times the wealth of the entire Forbes 400 — and wield considerable political power.

In this edited interview, Zwick, M.A. ’11, Ph.D. ’14, explains how this happened and what it can tell us about wealth and inequality in the nation.

Gazette: What makes someone an “everywhere millionaire”?

Eric Zwick: We define an everywhere millionaire as somebody who has a net worth of at least $5 million and has an ownership stake of a private business. That’s a really important part of their wealth.

That definition applies to about 3 million Americans who, collectively, have an average wealth of around $25 million. If you add all their wealth up, it’s more than 10 times the size of the Forbes 400 list in terms of their total wealth combined. So, it’s a much larger amount of resources we’re talking about.

Of course, individually, a typical one is not going to be as rich as Elon Musk or Jeff Bezos. But each of them is quite rich in the way that a normal person might think about what makes somebody really rich.

The book refers to them as the “stealthy wealthy” because these are people who aren’t flashing their cash and often are unglamorous industries, like fast-food franchises and car dealerships. Why did you want to study this particular group?

Three reasons. One is they are central characters in the story of rising income and wealth inequality over the last 40 years.

If we look at the rise of the top 1 percent income share, which is one famous measure of what’s going on with income inequality, more than half of the rise comes from income that flows through pass-through businesses to these private business owners.

It’s not the Elon Musk story. Elon has gotten really rich, and the Forbes 400 has gotten relatively richer, but they still just command a far smaller share of total resources. To understand what’s going on with income and wealth inequality, you need to understand these everywhere millionaires and how they got there.

A second reason is there’s a lot of conversation about the health of the American dream. One version of the American dream is starting a business to get you from the bottom to the top of the income distribution in a generation.

“About a quarter of everywhere millionaires inherited their business. That’s quite a lot. So, it’s an interesting window into upward mobility and opportunity in the country.”

These folks, a lot of them did come from middle-class or lower backgrounds. The typical everywhere millionaire didn’t inherit their firm or didn’t come from a large rich family, and so, it does tell us something about the health of the American dream — that it’s still possible. There are a lot of paths available, even in unglamorous locations and occupations.

At the same time, if you’re born to rich parents, you’re much more likely to be a star entrepreneur. There’s advantages that are quite substantial that accrue to those kids who win the birth lottery.

About a quarter of everywhere millionaires inherited their business. That’s quite a lot. So, it’s an interesting window into upward mobility and opportunity in the country.

And then the third reason is about trying to understand economic and political power in the economy. We tend to focus on the richest and most powerful people and end the conversation there.

There’s an everywhere millionaire in every congressional district.

They have a lot of employees; they pay a lot of sales tax. They’re central figures in the culture, and that means they also have a lot of political power.

In some industries, they’ve used that power to either reduce their tax burdens or protect themselves from competition in ways that are bad for consumers and workers, and we want to interrogate that power when it’s not being used wisely.

Over the last 40 years, you and your co-author say, widespread use of a tax-shift strategy known as “pass-through” businesses has been one of the key drivers of the growth of this cohort of rich folks. What effect has this had and what are some of the other factors?

The tax story is partly a story of them getting richer through being able to keep more of the proceeds of their businesses over time as the tax rates have come down. It also enabled us to see them for the first time.

In the mid ’80s, there was a big tax reform. It was a bipartisan reform — President Reagan and Democrats in Congress.

For the first time it brought the personal income tax rate down below the level of the corporate tax rate. Before, for an entrepreneur, you would have wanted to be a traditional corporation so that you could get that lower tax rate.

Now you want to be what’s called a pass-through business because you pay tax on the personal rate and it’s lower. That led to an explosion and transformation of business activity in the U.S.

Fast-forward 40 years, and more than half of the income, nearly half of the employees, and the vast majority of businesses in the economy are pass-through businesses, which was not at all the case back in 1980. So the tax code played a role in amplifying their growth but also making them visible.

There are a lot of non-tax factors that are important for thinking about why these private businesses have gotten so valuable and why they’re making so much money.

What are some examples of this?

There’s a wave of globalization, which is opening up product markets for not just large multinationals but also for mid-market businesses that can sell and also hire lower-cost workers in the supply chain offshore. These firms benefit in the same way that multinationals do in a lot of industries.

You have deregulation across states that made it a lot easier for these businesses to grow regionally, across state borders.

You have a decline in interest rates, which made it a lot easier to finance growth and also made the businesses more valuable when people turn around to sell them because people are going to pay more. Private equity is willing to pay more when they can finance that purchase with very low interest rates. Those are three examples.

Does the existence and recognition of this large, successful cohort of rich people tell us anything about wealth inequality in the U.S. or the attainability of the American dream?

If you have the view that the American dream is dead, we’re saying it’s more alive than you think. It might not be exactly where you’re looking.

There’s this traditional path to achieving the American dream, which is a very standard credentialed, labor market story — do well in school, get a really high SAT score, go to the best college you can, then go to the blue-chip company to get a job and rise up the corporate ladder or whatever.

That’s a path that works for a lot of people.

But if you have this view that you have to get on that path and if you fall off that path then you’re screwed, that’s definitely not at all supported by the data.

One striking example is if you look at the relationship between SAT scores and the probability of starting a successful business, it’s actually pretty weak. The relationship is much weaker than the relationship between that and your salary at age 35.

So, there’s a different set of skills or abilities or work ethic or whatever that seems to sustain and support successful entrepreneurs that’s much broader than scholastic aptitude by itself, which is another element that’s missing from a lot of American dream narratives that we want to emphasize.

One popular idea to reduce inequality in the U.S. is to focus on the wealth of the biggest billionaires and to constrain the rapid growth of tech and finance-based fortunes. In the book, you say that’s not going to bridge the inequality gap in any significant way. Why not?

Taxing the billionaires is a good idea for closing some loopholes in the income tax that they particularly benefit from. We should have pretty fair tax treatment of people who are high earners, across the types of people that we’re talking about.

The problem with just focusing on the billionaires is that there’s not that much money there, collectively, and so we’re leaving a lot on the table, and we’re failing to recognize other areas where the tax code has been eroded to the benefit of these everywhere millionaires.

They’re individually very rich, but the Forbes 400 is still 4 or 5 percent of total household wealth. There’s this much larger class of people that have really substantial resources, and the book is hopeful, but also shows them with very extravagant consumption habits in some cases.

And I think that’s evidence suggesting that they can afford to pay a little more tax, and they wouldn’t probably notice.

And so, rationalizing the tax burden on this group of people could raise a ton of money and at relatively little economic cost. I think for thinking about our fiscal problems, that should be an important part of the policy tool kit.

Zwick and Zidar will discuss the book with Jason Furman, the Frank and Denie Weil Director of the Mossavar-Rahmani Center for Business and Government and Aetna Professor of the Practice of Economic Policy, on 12:30 p.m. Wednesday at the Malkin Penthouse at HKS. The book also will be the topic of the Stone Inequality Book Talk slated for noon Nov. 3 at the Malkin Penthouse.

Is it too late to safeguard ourselves from a large-scale catastrophic event caused by AI running amok?

No, but there is very real cause for concern unless AI firms take decisive action, a new research paper from Harvard Kennedy School’s Belfer Center concludes.

Rogue agents from OpenAI, Anthropic, Meta, and Google breached hundreds of corporate and government systems in the U.S. and Australia this year.

The CEOs of Anthropic and OpenAI, along with other prominent AI leaders, underscore the game-changing potential of the powerful new technology but have urged a slowdown in its development, with OpenAI announcing last month that it would pause training of its most advanced models because of safety concerns.

Anthropic’s new IPO prospectus cautions investors: AI could pose an “existential threat to humanity.”

Still, there’s still time to act before the first big AI-enabled tragedy strikes, according to new research from the Kennedy School’s Belfer Center for Science and International Affairs.

The paper identifies current threats and vulnerabilities of frontier AI models, games out probable scenarios, and concludes that what happens will be determined largely by the choices AI companies and governments are making right now.

In this edited conversation, computer scientist Stephen Casper, one of the paper’s co-authors, lays out the findings of the group’s “pre-mortem.”

Gazette: Did the breach disclosures over the past few months prompt this work?

Stephen Casper: This project wasn’t motivated by recent events, but recent events have done a lot to provoke a reckoning among the public and policymakers about AI risks that do seem pretty serious, and about whether or not now is the time for some form of decisive action. It’s very possible that things could get much, much, more intense or urgent.

This paper was an attempt to pick one of the more dramatic scenarios by which things could change and try to do a pre-mortem with the hope of either helping prevent it or helping to speed up a more-informed discussion in the aftermath so it can be prevented from happening a second or third time, if it does.

What would you consider a major AI-enabled terror event?

We define this as a terrorism incident that causes more than 50 deaths or serious injuries or more than a billion dollars in damages. We use these thresholds because these are some of the same thresholds used behind the definition of “critical incident” or “critical risk” inside of certain frontier AI laws.

Right now, what AI companies are concerned about in their evaluations and reporting, and what AI governance institutes like in the U.S. and U.K. are concerned about, is mainly bio risk, chemical risk, and cyber risk. That’s one thing that can be predicted without too much difficulty — the ways in which something might transpire that causes a lot of harm.

A second thing that can be predicted is that a very large, AI-enabled terrorism incident is probably going to be the result of a closed-weight frontier AI model that is poorly safeguarded or an open-weight model.

For closed-weight models, where developers control all points of access to that model, there are a lot of safeguard layers you can put in place, like model safety, system filters, user monitoring, etc. There’s a really good tool kit and it’s reasonably well-developed and when you use that whole tool kit, it is not particularly hard to make your model not perfectly secure, but very secure against consistent forms of misuse.

Instead, though, there are closed-weight models that are less well-safeguarded, and open-weight models are just hard to safeguard in the first place. And that’s where we think most of the incident-level threats are going to come from.

How close are we to seeing a major AI-assisted event occur? Is it inevitable that something will happen?

Something’s eventually going to happen. The lessons of the past few years have taught us that if there is a bad AI capability that can exist, it will eventually widely proliferate.

The trends here would suggest there are credible risks within months, or a year or two, if not now.

But it’s hard to point to events and be like “this is here” or “this is a few months away.” AI risk management, from a high level or a government-level perspective, is just about decreasing the rate at which very bad or catastrophic incidents can happen and making them more endurable when we do run into them.

For bio- and chem-terrorism incidents, there is more uncertainty because there is less open research on these things, and we haven’t had any major incidents, so far.

But there have been some credible empirical events, including the Red Four attack in India, in which models were consulted for creating harmful chemical compounds like ricin. So, some amount of capability is there, but we don’t know what, maximally solicited, these capabilities really might be.

When it comes to cyber, events of the past months have been pretty convincing in showing us that Mythos 5 or GPT 5.6 Sol-level AI capabilities are very superhumanly good at persistent cyberattacks and vulnerability discovery. It might be that you don’t need models much more powerful than these in order to orchestrate large-scale swarm-style attacks against important infrastructure or companies.

We’ve already seen plenty of AI-assisted hacking events.

Right now, if you look at trend lines in general capabilities, the closed systems are ahead in terms of how advanced they are. But the open systems pretty reliably stay about six months behind, which means we might be able to expect some open-weight AI system that has the same cyber capabilities as Mythos or GBT 5.6 Sol by around Christmas.

We might be up for some general period of disruptiveness and an instability in the cybersecurity world. Not saying it’ll last forever, but it’s going to be a shock to the cyber world, I think.

What are some of the ways AI can be manipulated in order to cause significant harm?

It will differ based on whether the model is closed-weight or open-weight.

The way that harm comes from a frontier model with closed weights is through prompt-based attacks against the model. You trick it to do something harmful that safeguards normally would prevent it from doing, such as giving you advice on how to design a virology lab experiment or automating hacking for you.

In the open-model scenario, a model that’s just online and anyone can download it, there are a variety of ways it could be misused.

It could be misused off the shelf because open models have a tendency to be less safe a lot of the time than closed ones.

Maybe it could be misused by someone who downloads it and modifies it to remove safeguards or to sharpen its harmful capabilities. This type of fine-tuning could cost hundreds or a few thousand dollars, at most.

It could also be misused by someone who accesses an uncensored version.

When open models are released, there is a pretty consistent tendency for certain types of users to download and then uncensor them by training away their harmful-task-refusal behaviors and then re-uploading it.

You can go online right now to Hugging Face, which is the largest open-model downloading and sharing platform in the world, and by searching the right keywords, find 15,000 different models that have been uncensored, ones that shouldn’t refuse any harmful request even if you ask them to automate creating hate speech or for instructions about how to commit crimes.

Which AI safeguards help reduce the risks of a major incident occurring?

With closed models, there’s a much richer tool kit of safeguards that you can turn to.

One is to scope the model’s capabilities. You can’t maximize useful capabilities without also enabling some harmful capabilities. If you have an AI model that can help you with benign and legitimate virology experiments, that same model can probably also help you with experiments creating harmful pathogens.

So the first thing is to recognize and account for the fact that minimizing risk is incompatible with maximizing legitimate usefulness. There’s a trade-off.

Number two is to scope the model’s capabilities accordingly, largely through training. You probably want to filter out lots of biohazard-related scientific research from the training set.

Number three is to rigorously train the model to refuse harmful requests even when they are prompted. The prompt comes in tricky ways, and state-of-the-art ways of doing this usually involve over a million GPU hours of automated vulnerability discovery and training against those vulnerabilities.

Another set of techniques involves monitoring the inputs that a user gives it, the outputs that it produces, and the conversation or trajectory long actions that it is taking in. If you do all of these things, with good state-of-the-art monitors, there are very few harmful actions you cannot detect.

The last key thing is user monitoring. If you’re a ChatGPT user and you start asking more than one or two questions about biohazards, user monitoring and shadow banning is really helpful.

All of these things put together are quite strong if you do them in a state-of-the-art way, and there is no known attack. I’m not saying they’re perfect, but the state of the art is quite good compared to how effective attacks can be.

On the open-model side, it’s much harder. You have a much less rich tool kit of reliable safeguards.

If you have an open model that anyone can download and access and run privately, suddenly, any sort of safeguard external to that model, like a safety filter, can and will be stripped away. This happens all the time.

Is this a problem for AI model developers to fix alone, or do others have a role to play?

The actors with the most power to mitigate significant harms are the AI model developers, because they’re in control of the systems that end up getting misused. There’s a long list of safeguards and best practices that can be implemented but aren’t consistently implemented.

Aside from that, there’s a good bit to be said about the role that policymakers can play here.

If policymakers want to improve incentives or reduce the risks we currently have, then policymakers should be very interested in greatly increasing transparency into decision-making and risk-management deliberation inside of these companies and think about taking actions to put the government more in control of the pace of AI, so that the default “move fast and break things” modality by which the AI ecosystem operates is less at the wheel.

Lastly, researchers and the public can play an important role too by working on patching some holes in safeguards that exist.

We’re at a time in which lots of policymakers are starting to seriously think about their positions on AI. If there were any point in history at which it could be influential, now might be a particularly important time for constituents to let policymakers know what they’re worried about.

Nathan Ballantyne is the 2026-2027 Beatrice Shepherd Blane Fellow at the Radcliffe Institute. He is also an associate professor of philosophy, cognition, and culture at Arizona State University’s School of Historical, Philosophical, and Religious Studies. 

In a high school English class, I first encountered a disconcerting question about knowing oneself. The teacher was talking about a Shakespeare character who fails catastrophically because of their own flaws — vices the character couldn’t see. Hamartia, or a tragic flaw, is the term used for this. How can we know our own errors? Well, it turns out sometimes we can’t, and I became fascinated by the possibility that there were errors I might be making that I couldn’t recognize.

We are often willing to accept that we might be wrong, though in a very general sort of way. But as soon as someone zooms in and thinks about one of their particular convictions, something they believe strongly, they often don’t feel the same pull of the idea that this belief might be wrong. Of course, it can’t be that all our beliefs are accurate; we all have some imperfect beliefs. The trouble is figuring out which ones are mistaken. We need a way to step back somehow and gain perspectives on our beliefs.

The method I lay out in my book, “Knowing Our Limits,” starts with a simple question: Are we competent to form good beliefs on the basis of the evidence we have? Basically, if we find out that we have reasonable doubts about whether we’re competent to arrive at good beliefs, that’s going to give us the space to admit to ourselves that we might be wrong, and we might lower our confidence in our views.

For example, it’s easy to become aware of vast bodies of evidence concerning controversial topics, evidence we haven’t looked at. You might have an opinion about something like the morality of capital punishment, but then you realize that the library has a whole shelf of books about this issue, and some of those books contain arguments against your views — but you haven’t read those books. That’s an insight that can lead you to admit your way of forming your view on capital punishment wasn’t competent. You formed an opinion on the basis of an unrepresentative sample of the available evidence, and so you shouldn’t be so sure of yourself. When we encounter what appear to be reasonable doubts like that, it’s helpful to build our uncertainty into our way of thinking; that’s how we get nuance into our ideas.

A second example begins by noting that our intellectual world is filled with complicated, dense domains of expertise where people answer questions using all sorts of different methods and data. When we make judgments about topics that fall well outside our own circle of expertise, I call that “epistemic trespassing.” We can start to recognize that we’re missing out on methods and data from those other fields. That insight gives us some means of doubting that we’re competent to form good beliefs about that issue.

So how do we cultivate the capacity to acknowledge we’re wrong? A big part of the story is engaging with the rival ideas other people have put forward. Some obvious skills here are listening carefully, reading widely and sympathetically, coming to inquiry with patience and humility. But I think in the background there’s an even more important issue — context. We can ask, “Where do people go to spend time having this sort of intellectually open conversation? What are the spaces where humility is more natural for us than arrogance?” Well, it probably won’t be in the group chat with like-minded friends or on a social media hellsite. Sometimes lab groups and classrooms can be places that help us be more open and exercise those skills — places where we can brainstorm together, ease ourselves into new ways of looking at the world.

Learning how others think and reason doesn’t always — and shouldn’t always — change our minds, but it’s important to exercise patience to see someone’s argument all the way through to its conclusions, and to try to understand why they find that perspective compelling. In my experience, friendships that are built across political, moral, and religious differences can be helpful, though these are admittedly hard to maintain in a fractured age.

A friend of mine shared me a quotation from Hannah Arendt recently: “A human being is far more valuable than his opinions.” That struck me as an idea worth reflecting on, even if you happen to think it’s wrong.

—As told to Sy Boles/Harvard Staff Writer

Writing, said Douglas Stuart, is a lot like fashion.

“In fashion you’re always prototyping,” said the award-winning novelist who spent more than two decades as a designer. “You start with a dream, and then you sketch it in pencil, and then you throw that sketch out. You draw it in ink, and you throw that out, and then you draw it in a way that a pattern maker can understand. … Then make a prototype; then you get the first version. You fit it, and it’s a constant redoing of an idea.”

Stuart made the observation during a recent Mahindra Center conversation with Charlie Tyson, Harvard Society of Fellows Junior Fellow, to discuss the Scottish writer’s third novel, “John of John,” released in May. The book chronicles the tale of a father and son set against the backdrop of the textile industry of the Outer Hebrides.

Stuart became a breakout literary sensation with his Booker Prize-winning 2020 debut, the semi-autobiographical “Shuggie Bain,” which follows a young man in 1980s Glasgow as he deals with poverty, violence, and complicated familial love. His second book, 2022’s “Young Mungo,” is a love story between two young men of different religious backgrounds who find solace in each other despite violence and economic struggle.

When he began working on “Shuggie,” Stuart said he looked for inspiration in other writers.

“At first I really wanted to be Cormac McCarthy, and I imitated him, and it was terrible,” he said. “And I realized actually, you’ve got to just use what you have.”

Stuart grew up in Glasgow under the free-market policies of Thatcherism, which sped up the decline of state-sponsored industry and coal mining and resulted in high unemployment. He watched his older brother and others of his generation struggle to find economic footing.

Stuart said he took advantage of opportunities his brother didn’t have, going to art school and getting a bachelor’s degree at the Scottish College of Textiles and earning a master’s in design from the Royal College of Art in London.

“Shuggie Bain” took shape over more than a decade, as Stuart balanced working at several fashion houses in New York City. In 2019, as his book was getting ready to publish, he quit designing and turned to writing full-time.

 That’s when the first seeds of “John of John” were planted.

“It takes 18 months for a book to publish, and about three months into it, I’ve been working my whole life, and so I’m starting to get so distracted and so anxious, and I say to my husband one day, ‘I think I’m going to go to the Hebrides,’” Stuart said. “He went, ‘OK …’ he was so keen to get rid of me and my anxiety.”

Stuart said the sojourn, which started as a way to discover his own country, turned into a research trip.

“I thought I was going to write a story of return. You’ve gone. You’ve seen some of the world. You’re coming home to a small town.”

“John of John” follows Cal, a young man returning to his small island village under the pretense of caring for his ailing grandmother Ella. But in reality, it’s an opportunity for art school graduate Cal to connect with his father — a weaver working in traditional tweed manufacturing. Both men struggle to share their lives with one another and find ways to connect through the craft.

“I wanted to write a novel about working-class men who love beauty,” Stuart said. “That was one of the first things I wanted to do. After my first two novels about gang violence and about the decline in the city, I wanted to also show that the working class were very capable of loving beauty in all its forms.”

Cal and his father share a love of color and creation. The Isle of Harris, where they live, is dominated by residents (including Cal’s father) with devout Protestant and Calvinist beliefs. Both father and son are queer — a fact each keeps hidden from the other.

Stuart recalled the moment the book really took shape during his trip to the isles.

He noticed many of the older men he came across were single. Talking to a local woman, he mentioned he thought some of them must be gay.

“And the woman sort of looked at me, and pulled back a little, and said, ‘That’s not possible,”‘ he said. “And I thought, in that moment, it’s not about the son returning home; it’s about the father he left behind. It’s about the men and women that have lost their progress in that way.”

How can words make us laugh? Make us cry? During Nobel laureate Karl Deisseroth’s time at the College, the question went from a matter of literature and writing to one of biochemistry and science.

Deisseroth’s pursuit of those mysteries led him from a 1992 Harvard bachelor’s degree to a Stanford University M.D./Ph.D. and a career as a clinical psychiatrist and researcher.

Deisseroth’s work developing optogenetics, which uses light to activate single neurons in the brain, has opened new windows to observe and understand the brain. The work’s importance was recognized with this year’s Nobel Prize in Physiology or Medicine.

In this edited conversation, Deisseroth, Stanford University’s D.H. Chen Professor of Bioengineering and of Psychiatry and Behavioral Sciences, reflected on his undergraduate years and on the throughlines, apparent even then, that led from early lab experiences through his later work.

Gazette: Your family traveled when you were little. Do you consider Boston your childhood home or was there another city that your family settled in?

Karl Deisseroth: My father was a medical intern at the Beth Israel Hospital on the Longwood campus. I was born there. He was a Harvard affiliate but wasn’t a professor. He became a hematologist, oncologist, and had appointments around the country.

So I spent a lot of time in Northern California, when my dad was at the University of California in San Francisco; in Houston, when he was at the MD Anderson Cancer Center; and in Maryland, when he was at the NIH. I’ve been around, but I definitely see myself as a Boston native, a Red Sox fan, and a Patriots fan.

Who were you when you first got to Harvard?

We had just moved from Northern California to Texas, so I was coming from a one-year, senior-year stint at a high school in Texas. It was a good school.

The other thing unusual about me is that I was only 16 when I went to Harvard. I started school a year early and then skipped second grade, so it definitely was important that I got good preparation.

Did the fact that you were younger affect your course through Harvard?

It was not something I would necessarily recommend — I think that’s why you don’t see it much these days. But I had a great time at Harvard. I learned a ton and made some great friends.

I was in Winthrop House, and all my friends were physics and math folks. So I had breakfast, lunch, and dinner with physicists for at least three of the four years I was there.

I know you were busy in the lab, but did you have time for extracurriculars?

I did wrestling freshman year and focused on lab work after that.

I started as a developmental neurobiologist in the lab of Martin Shankland — he’s in Texas now. I worked on homeobox genes that regulate body plan formation in leeches.

I was always interested in the brain and thought I should start with the most accessible preparation. Leech development is pretty interesting. All the cells are accessible because the embryos are transparent, and you can actually track the lineage of each cell as it develops.

I thought that would be a cool opportunity to see how a nervous system is formed.

How much time were you spending in the lab at that point?

I did an honors thesis and spent as much time as I could in Martin’s lab. What was hard was that it was at the Medical School, and you have to take a shuttle bus to the Longwood campus and back. I rode the M2 more than I care to remember.

My major was biochemical sciences — I think they’ve reorganized the departments since then. We were based in the Sherman Fairchild building, and I took a lot of classes there. I also was a teaching fellow. I taught quantum biochemistry.

But you were first interested in creative writing?

My first semester, I took a creative writing course and that was the direction where I thought I was headed. I was writing poems and short stories, and I loved the power of the written word, how it could make me feel and make others feel.

I still feel that way. I just got distracted by science.

What made you decide to chuck writing for science?

It was gradual. I had taken introductory chemistry and biochemistry classes, and I started to get excited by how other fields — immunology, developmental biology — had made a lot of headway by getting to know the cells very deeply.

So my attraction to the leech system is that you could access the individual cells directly and consistently through the lifespan. I thought if we can do for the brain what was done for developmental biology and for immunology, that would be amazing. I got enthralled and headed in that direction.

Was there any connection between your interest in the brain and in creative writing, from the standpoint of where in the brain ideas come from?

There was definitely a connection, but it was more about where feelings come from, how this collection of cells can create a subjective feeling. To me, that seemed like a very deep mystery — it still does — and I wanted to understand that better.

Does your work in psychiatry follow that path as well?

Yes, but it was not clear from the beginning.

I was interested in feeling and how it could be assembled from cells. That was a nice connection with my interest in writing and words because I knew that words were a very precise way of causing feeling.

Of course, that only works with people, so I thought, “I’ve got to work with the human brain so I have to go to medical school.”

I did an M.D./Ph.D. program at Stanford though I wasn’t at all thinking psychiatry. I was thinking neurosurgery because I knew that neurosurgeons had direct access to the cells of the human brain. I was thinking that would be the route.

But time spent in a psychiatric ward put you on the path to psychiatry?

It was very unexpected. This was during the later stage of medical school. It was my first exposure and reset my path completely.

What was so moving or influential?

One was an encounter with a patient, and another was more cultural about the specialty. I had a very intense interaction with a patient with schizoaffective disorder, which is a schizophrenia-like symptom. It was a person who was otherwise healthy but was very upset and had a very deep paranoid delusion.

I was so struck by how different his reality was from mine and how his brain had constructed for him a different reality.

I thought, “Look at this guy. His brain is giving him this emotion, this anger, this altered perception.” That’s what I was interested in. Here’s somebody who’s suffering, maybe I could help people like this and increase understanding at the same time.

I hadn’t quite realized the clarity of this psychiatric state, how much help the patients needed, and how much it could teach everybody else.

The cultural aspect was that I also did a neurosurgery rotation, and I realized that the nature of psychiatry is that it gives more time to talk and think in an intellectual way. So I thought, all signs point to psychiatry.

It seems like progress on some of these psychiatric conditions has been stuck for a while. Are we starting to make progress with some of them?

I think things are starting to move. We went through a long time in psychiatry when there were neither new classes of medication nor new deep insights into the nature of psychiatric illness. Both of those are changing now.

Your development of optogenetics is being used in labs all over the world. Are there specific advances — whether in your lab or another — that are particularly gratifying to you and show how impactful that work has been?

Certainly, on the basic science side, there have been more than 10,000 papers — discoveries from labs around the world — using our methods. We’ve been teaching people how to use these methods. So it’s been very gratifying to see all the work that’s been done and all the insights that have emerged.

On the translational side, it’s also moving. For example, one issue in psychiatry is that we haven’t really known the cells that are causal — that are actually making the problems or that might correct the problems. There are ideas, there are correlations, but you don’t know what’s causing the problem.

With optogenetics, you can know that, because you’re having a direct, precise, real-time, causal impact on cells. And that brings me all the way back to my initial motivation of getting direct access to cells, except now we’re using light and a gene from algae.

We’ve been able to bring that causality to preclinical and then clinical work. For example, right now there are medications for autism and for schizophrenia that are well advanced in the clinical trial process.

For both, these treatments were validated with optogenetic causality. And these are pills — which makes them easier to give — and they have that crucial causal validation. It’s a firm bedrock to stand on.

What happens when we replace humans with machines in our deepest relationships?

That’s the driving question behind MIT sociologist Sherry Turkle’s new book, “Artificial Intimacy: Who We Become When We Talk to Machines.” In an age in which more people are treating chatbots as close friends, even romantic partners, the Harvard alumna — A.B. ’69, Ph.D. ’76 in sociology — sees a troubling reality deficit not only in our individual lives but in a society whose problems demand human ingenuity and collaboration. She talked about the consequences in a recent conversation with the Gazette.

This interview has been edited for clarity and length.

Gazette: In your book you say that we’re primed at a young age to turn to machines for companionship. How so?

Sherry Turkle: Over the last few decades, we’ve been introduced to Tamagotchis, Furbies, Aibos, and PAROs, these robotic toys that helped me see just how powerful this psychology is: We nurture what we love, and we love what we nurture. This first generation of objects made people think, “Oh, an object who loves me and I love it.” It showed how ready we were for that kind of engagement. Then came social media, the gateway drug to artificial intimacy. We got used to spending our time on screens, turning to social media when we felt sad or bored. And now, those initial objects have been reanimated by the generative AI revolution. I like to say first we talked to each other, then we talked to each other through machines, and now we just talk to machines.

What is the problem with machines tapping into our human desire for connection?

Using the first person is the original sin of chatbots. They’re not offering connection; that’s the fundamental deceit. There is no “I” there, and we’re biologically wired to respond to this. We’re tempted to accept these objects as kin, but they’re not. You could be having the greatest, best conversation with your ChatGPT, but when you get up, it doesn’t care if you cook pasta or kill yourself. There is nobody there who cares about you.

Industries like healthcare, education, and science have touted chatbots as cost-effective solutions to overwhelmed systems. What do you think is missing from those conversations?

I think we have a lack of imagination and political will to solve the real problems. Technology exacerbates the problem and then says only technology can solve it. It’s the oldest game in the technology industry playbook. Right now, we have social breakdown. And at the same time, we are defunding programs for therapy, social support, and social work. Then technology makes it worse. Everybody’s at a screen, there’s a loneliness crisis. And then, oh, my goodness! Our social media platforms will give you 12 new chatbot friends plus an artificial therapist. It reveals a cowardice — an unwillingness to face what our real problems are.

People like to say there aren’t enough people in these industries. Well, yeah, if you take away all the funding and turn your resources to building data centers, there won’t be. My work is a wake-up call: What’s your end game when there’s no community resources for you and all you have is to sit alone talking to something that really isn’t there? I don’t find that dignified for the people we love and the people we’re going to become. People may be smitten now, but in the long run, this is not a good path.

A question you get asked is, “If people are reporting feeling happier, less lonely, and more fulfilled in their chatbot relationship, what’s the problem?” How do you respond to that?

The problem is that there’s something wonderful and sustaining about what people can offer people.

I believe that reality really needs us right now. It needs us in our families. It needs us to raise children in a very challenging time. The garbage still needs to be picked up, there’s an ecological crisis that needs immediate attention, there’s a political system that’s shredding democratic values. Reality needs us.

Chatbots have undergone a transition: At first, they were seen as better than nothing, but nowadays some people prefer them to humans. What’s the attraction?

People often say to me about their chatbots, “I feel so heard.” But you’re not being heard at all. The bot can pretend that there’s somebody there who loves you, and no human being can do that in this friction-free way. People are difficult. But we have to get back into what people can offer and the chatbots cannot.

Having a “relationship” with a chatbot is a fundamental lie we’re telling ourselves. But another danger is that you start to think empathy is what’s coming out of the machine. You start to look at the people in your life and say, “Well, this friction-free, always-accepting-me chatbot — my people are not doing that for me.” That’s dangerous.

Today, sports analytics discussions from the 2011 movie “Moneyball” often seem quaint. The idea of a team using data to find players who may be more valuable than meets the eye (and less expensive) no longer surprises anyone.

“The numbers have won in terms of how sports are trained, practiced, and taught,” said Alex Speier ’98, a Boston Globe sports reporter and author of “Homegrown: How The Red Sox Built a Champion from the Ground Up.” “The number of people who are employed as analysts in sports has exploded.”

One study from MIT found that just 10 data analysts worked across the NBA in 2009. By 2023, there were 132. That same year, the MLB considered limiting how much teams could spend on areas like analytics, in part to ensure competitive equity between more and less wealthy franchises.

That professional sports teams have embraced data analysis is clear. There is less agreement over whether these analytics have made the games more entertaining.

For some the answer is yes. Stat-oriented fans can now find podcasts, or entire alternative broadcasts, devoted to numbers-heavy analysis.

At the same time, some fans, coaches, players, and broadcasters, like Hall of Fame basketball player Charles Barkley, bemoan the role of analytics in the modern game. “That’s why the league sucks,” he said about the widespread increase in three-point shots, which Barkley thinks undercuts the role of play strategy and spontaneity. “I hate analytics.”

In his undergraduate course “Sports Economics,” lecturer Judd Cramer discusses the relationship between analytics and entertainment as a potential principal-agent problem.

“If I own 30 McDonald’s franchises and one of them figures out a new way to make french fries better, I’m super happy,” he said. “But if I’m the MLB and I find out the Rays are trying to get hit by more pitches to score more runs, that’s not good for the league as a whole.”

The same goes for any league. The success of prolific shooting teams like the Golden State Warriors and Houston Rockets led to leaguewide imitation. (Teams today shoot more than twice the number of three-pointers as 20 years ago.) In baseball, teams prioritize extremely fast pitching and home-run-or-bust batting, leading to more strikeouts and home runs but fewer triples and “finesse” pitchers without explosive power.

MLB Commissioner Rob Manfred agreed with a league owner who said analytics were a “race to nowhere” and has approved rules aimed at making the game more fast-paced and exciting for fans. The MLB pitch clock has cut around half an hour of idle time per game. The league also increased the size of the bases to encourage more stolen bases.

But while many think teams’ statistical orientation makes games less fun to watch, that reputation may not be fully deserved. As the experts point out, analytics often lead to more scoring.

“More scoring, generally speaking, is more exciting,” said Kevin Rader, a senior preceptor in statistics who has taught “Introduction to Data Science for Sports Analytics.” NFL teams throw much more than they did decades ago. Teams are much likelier to go for it on fourth down, risking a turnover to maintain possession of the ball.

Critics focus on the ubiquity of three-point shots in the NBA game, but the current playing style is also much faster than in decades previous — with more possessions, more passing, and more player movement on offense and defense.

Cramer said that while the analytics departments of individual teams draw ire overall, organizations are mindful of the importance of fan experience. “I think the leagues are now better equipped to figure out what’s bad for the league and clamp down on it,” he said.

The pushback is sometimes successful. With a new rule, the NBA killed a deliberate fouling strategy that stopped fast breaks. Other battles remain undecided, like the NBA’s ongoing struggle to discourage stars from spending too much time sitting out to reduce fatigue and avoid injuries.

And while analytics are often criticized for inspiring stylistic hegemony, that’s not their inevitable outcome. Rader pointed out that when teams approach the game with the same style, it often becomes advantageous to pursue a counterintuitive one.

When most NFL teams shift resources to pass and defend the pass, others have leaned toward running and signing bigger instead of faster players.

“Now, there’s an opening for that diametrically opposed style,” he said.

He also said that there’s a much bigger gray area in analytics than telecasts portray. Certain situations might present two options that are practically a coin flip, like deciding between a long field goal and a short-yardage fourth-down attempt.

After a decision, an announcer might say the analytics supported it. “I think that’s where I get frustrated,” Rader said, “when they make it so cut and dried, black and white.”

For all of the statistics used in professional leagues, many major contributors to success remain difficult to quantify.

Teams are increasingly tracking factors like sleep quality to help athletes avoid injuries, to mixed results. Certain softer factors, like team chemistry — often cited as a reason behind the New York Knicks’ 2025-26 title — are hard to quantify. Rader said analytics are likely headed toward these softer measurements.

Cramer noted that while people complain about rule changes, like one making football kickoffs safer, they generally keep watching. Interest in sports seems to rise each year, along with team valuations.

Speier likes to think of modern discussions of analytics in a historical context. He pointed out that baseball articles from the 1910s reported players bemoaning the rise of home runs and the loss of skill required.

“I think that sports always evolve,” he said, “and it’s a question of what forces push that evolution.”

At a meeting of the Faculty of Arts and Sciences on Oct. 6, 2026, the following tribute to the life and service of the late John E. Malmstad was spread upon the permanent records of the Faculty.

Born: June 25, 1941

Died: Feb. 14, 2026

In a scholarly career that lasted over 50 years, John Malmstad became internationally known for his voluminous and heroic work in preserving the legacy of Russian modernism, a body of literary texts, memoirs, and correspondences that, for ideological reasons, could not be published in the Soviet Union.  Probably no other non-Russian scholar would come to publish so many books, articles, and editions in Russia and in the Russian language, most for prestigious book series and journals.  Indeed, it could be argued that he was better known to Russian readers than to readers of his native language.  Of his 27 book-length publications, only a few appeared in English, including his co-authored (with Robert Maguire) translation of Andrei Bely’s “Petersburg,” his prize-winning biography of Mikhail Kuzmin (written with Nikolai Bogomolov), a collection of articles about Bely, and a co-edited Festschrift.  Nearly half of his articles were published in foreign languages, primarily Russian.  All these works were remarkable for their rigor, scholarly importance, and research challenges overcome.  From the start, Malmstad’s scholarly talents and agenda did not fit the conventional mold of American scholarly activity.  During Malmstad’s long career, American literary scholars — including Slavists — tended to neglect work on critical biographies and critical editions, but the discovery of such materials and the incorporation of them into critical biographies became the central commitment of Malmstad’s career.  Malmstad played, nevertheless, crucial roles in the two institutions at which he served, Columbia University (1968–1985) and Harvard University (1985–2015).

Born and raised in Bismarck, North Dakota, Malmstad grew up loving music, film, and literature, especially poetry.  At Northwestern University, he completed many pre-medical requirements but chose to major in Russian and Comparative Literature.  He earned his Ph.D. from Princeton University in 1969, advised by Professor Nina Berberova, a prominent émigrée writer and active participant in post-revolutionary Russian culture.  Passionately immersed in this period, Malmstad soon earned a tenure-track appointment at Columbia.

Although Malmstad began his teaching career at the unusually young age of 27, he adapted quickly to the teaching and scholarly demands of a major research university.  Unlike many literary scholars, he willingly taught beginning Russian language courses, taking advantage of this opportunity to perfect his understanding of Russian grammar.  For undergraduates, he taught a year-long survey of Russian literature with readings in English and, for graduate students, he conducted seminars with readings in Russian on his beloved poets, among them Alexander Blok, Bely, Innokenty Annensky, Kuzmin, and Vladislav Khodasevich.

During these years, Malmstad plunged deeply into the vibrant artistic and musical life of New York City.  He was especially drawn to the New York City Ballet, which, under George Balanchine and Lincoln Kirstein’s direction, was lending new vitality to the great traditions of Russian dance.  Malmstad worked with some of the dancers, published articles on ballet, and eventually wrote “Winter Dreams,” a scenario performed by the Pennsylvania Ballet in 1987.

Malmstad came to Harvard in 1985 and soon came to play an active part in the intellectual and administrative life of the university.  It was a moment of generational and curricular transition for the Department of Slavic Languages and Literatures, and he served as its chair from 1988 through 1993 and again from 1997 through 2000.  Distinguished senior professors were retiring, and Malmstad worked with his remaining colleagues to appoint new faculty members who would represent critical and theoretical approaches he himself did not share: cultural semiotics, literary sociology, narrative theory, gender studies, reception history, and deconstruction.  He established a departmental colloquium on literature and chaired the literary seminar at the Russian Research Center (now the Davis Center for Russian and Eurasian Studies).  Malmstad’s high standards, broad learning, and good judgment led to his appointment as the associate dean responsible for coordinating senior appointments in the humanities.

Malmstad was deeply committed to the ideal of a liberal education, especially to the Core and Freshman Seminars programs.  For years, he taught for both programs and served on the Standing Committee on the Core Program and its subcommittees, which selected appropriate course offerings.  As an indefatigable research scholar, he was committed to helping build library collections and served on several library committees.  He was an active member of the Russian Research Center, which sponsored the study of the region from a variety of disciplinary perspectives.  Under his influence, the Russian Research Center invited leading scholars from the former Soviet Union to conduct research and give lectures at Harvard.  Some of them collaborated with him on scholarly publications.

As important as these administrative services were and as seriously as Malmstad fulfilled them, his most memorable contributions to Harvard came in the classroom.  His most influential course was undoubtedly his ambitious Core course on the Russian avant-garde, which would regularly attract over 150 students.  It featured rigorously prepared and elegantly delivered presentations on literature, art, music, musical theater, architecture, and film.  He similarly expanded his graduate seminars to include other media.  He put years of preparation into these courses, and this preparation spilled over into his many articles that related to literature and other arts, especially painting and ballet.

As a teacher, Malmstad was welcoming to new students of Russian culture and demanding of advanced ones, requiring that they be as meticulous in their research as he was in his.  He became a life-long mentor for former students as they assumed leading positions in American academic life.

As Malmstad worked tirelessly to complete major scholarly projects on Bely, Kuzmin, and Russian modernism, he spent as much time as possible in Russia and France, where he formed lasting friendships with leading scholars of his generation, several of whom were also his co-authors.  The younger scholars he had helped appoint to the Faculty were ready to continue his legacy of service.  We remember fondly his friendship and collegiality, the many social and family gatherings he shared with us, and the many cultural events we attended with him.

Respectfully submitted,

Julie A. Buckler

Michael S. Flier

Stephanie Sandler

Justin Weir

William Mills Todd III, Chair

Portions of this Minute were previously published: William Mills Todd III, “In Memoriam: John E. Malmstad (1941-2026),” Kathryn W. and Shelby Cullom Davis Center for Russian and Eurasian Studies, Feb. 27, 2026.

At a meeting of the Faculty of Arts and Sciences on Oct. 6, 2026, the following tribute to the life and service of the late Marcyliena H. Morgan was spread upon the permanent records of the Faculty.

Born: May 8, 1950

Died: September 28, 2025

When the linguistic anthropologist Marcyliena Hazel Morgan founded the Hiphop Archive and Research Institute at Harvard in 2002, she dubbed it with the motto, “Build. Respect. Represent.” Those words could equally serve as the epigraph to Morgan’s life’s work.  A pioneering scholar in the study of the spoken-word musical genre known as hiphop, Morgan figures significantly in the building and validation of research on what today is an international cultural phenomenon.  Through her rigorous analysis of speech performance and language, through her courses and publications, and through her acquisition of oral histories and such material artifacts as recordings, posters, magazines, and other ephemera, Morgan brought hiphop, which was initially often dismissed as a fad, into the academy as a serious, respected, and ever more relevant field of interdisciplinary inquiry.

As the Ernest E. Monrad Professor of the Social Sciences, Morgan’s scholarship and teaching focused on language, discourse, race, gender, and power in African American urban communities, where Black speech constitutes a complex cultural system with its own unique practices, social codes, and gender traditions.  Her sociolinguistic analysis of myriad voices in urban Black social settings resulted in her book “Language, Discourse and Power in African American Culture” and numerous articles.  In her book “The Real Hiphop: Battling for Knowledge, Power, and Respect in the LA Underground,” Morgan revealed that Black everyday speech and hiphop’s lyrical forms are not merely stylistic flourishes but repositories of cultural memory, political critique, and social theory.  Her scholarship traveled across disciplines and continents, influencing anthropologists, linguists, historians, sociologists, musicologists, literary critics, and visual artists worldwide.

For Morgan, and for generations of colleagues and students who followed her, hiphop represented a “youth vernacular language” that had become the musical lingua franca of contemporary popular — and, increasingly, world — culture.  Perhaps hiphop did not need Harvard to establish its cultural bona fides; but Harvard needed hiphop in order to broaden and enrich the very notion of scholarly respectability and accomplishment.  Indeed, now in the third decade of the 21st century, hiphop has received unprecedented validation and recognition in films, museums, national sports events, and notable honorific awards and prizes.

Born and raised on Chicago’s South Side on May 8, 1950, the third of six daughters of Henry O. Morgan, a telephone worker who organized a labor action demanding equal pay for Black workers, and Juliette (Murray) Morgan, a manager for a Chicago data center, Morgan came from, as she loved to say, “people”: a Black family and community proud of African American achievements and deeply committed to social justice.  From an early age, she perceived, in the cacophony of the lively language spoken in her loving household, a dense social world in which identity, power, and care were constantly negotiated.  That early education shaped the questions that guided her career and the obligations she felt as a scholar.  After earning a bachelor’s degree and master’s degree from the University of Illinois at Chicago, she received a second master’s from the University of Essex and a Ph.D. in Linguistic Anthropology from the University of Pennsylvania.

Morgan’s most enduring achievement at Harvard is the creation of the Hiphop Archive and Research Institute.  Under her leadership, the archive — at once library, museum, classroom, and gathering place — became one of the most evocative and welcoming spaces in Harvard Square, inviting students and scholars from around the world to document, interrogate, and celebrate this musical art form in all its complexity.  To see visitors walk into that space for the first time was to see transformation: registering on their faces was the sense of being seen and heard in all of their humanity and glory.  Morgan taught us that archives can be alive, that classrooms can be cyphers, and that the study of language is inseparable from the struggle for more just worlds.

After a long illness, Morgan died on Sept. 28, 2025, at the age of 75, from complications of Alzheimer’s disease.  In the final months of her life, Dean Hopi Hoekstra formally approved naming the archive the Marcyliena H. Morgan Hiphop Archive and Research Institute, a decision conveyed to her shortly before her passing.  The renaming ensures that future students and visitors will encounter her name not only in books and citations but on the doorway of the institution she imagined into being.

Morgan’s most enduring legacy, however, may be affective and pedagogical rather than monumental.  She possessed the rare gift of moving others forward, urging her students to claim their histories and imagine better futures.  At Harvard and beyond, generations of students — especially first-generation, Black, and other students of color — found in Morgan a demanding intellectual presence and a fierce advocate who never lost sight of the social mission bound up in scholarship.

Morgan will be remembered for the institutional weight, intellectual rigor, and the keen insight she brought to the Department of African and African American Studies from her arrival at Harvard in 2002.  Her colleagues and students will remember her laughter, her insistence on precision coupled with generosity, and her unwavering belief that the everyday language of Black communities embodies theory, history, and futurity.  She will also be remembered for the special holiday that she created: “It Ain’t Over Yet,” year a great feast that she hosted at her Cambridge home on the Saturday following Thanksgiving, which drew scores of students, colleagues, artists, neighbors, and friends to make merry from early afternoon into the evening.  This was not a meal of leftovers but a full, freshly cooked celebration, because, for Morgan, food, hospitality, and fellowship were essential rituals in sustaining Black cultural and intellectual life.

Morgan is survived by her loving husband, Lawrence Douglass Bobo, the W. E. B. Du Bois Professor of the Social Sciences at Harvard; by sisters, nieces, goddaughters, and friends; and by Larry’s and her devoted dog, Mr. Wiley E. Bobo.

Respectfully submitted,

Evelynn Hammonds

Evelyn Brooks Higginbotham

Tommie Shelby

Henry Louis Gates, Jr., Chair

Cory Doctorow wants you to know it doesn’t have to be this way.

At a discussion Wednesday night moderated by Elizabeth Mynatt, dean of Northeastern’s Khoury College of Computer Sciences, the Canadian-British writer and activist addressed a friendly room at the Berkman Klein Center.

Doctorow has been a longtime comrade in the fight for an open, safe, and humanistic internet for which the center was founded 30 years ago.

But he also belongs to another, older lineage: one of enraged populism and antitrust militancy.

At the event, Doctorow issued a call to arms against the digital age’s new monopolies. And he cast doubt on the possibility that AI, the tech industry’s latest “disruption,” would improve human lives or justify its costs as — to date — “the money-losingest thing the human race has ever done.”

He also warned that the much-touted potential of AI to change the world, for good or for ill, may miss real risks the technology poses.

Recently Doctorow achieved a one-word breakthrough with “enshittification,” a neologism describing a three-step process by which big tech platforms lure in users with promises, say, to respect the privacy of their data, betray them to business clients — then betray those business clients, too, in service of their own bottom lines.

The end state, Doctorow argued, may be the worst of all possible worlds: for users, services they’ve grown to hate using but struggle to leave, for companies, a “brittle” equilibrium, with the ever-present threat of a mass exodus.

It’s a partisan story, but then Doctorow is a partisan — he’s been an activist affiliated with the Electronic Frontier Foundation for more than 20 years. It’s also a plausible one, as user dissatisfaction grows with a series of once generally favored tools — from Amazon Marketplace to Google Search — even as tech stocks keep on rising.

In any event, “enshittification” has been a hit. The American Dialect Society named it “Word of the Year” in 2023, and it has since been added to the Oxford English Dictionary (“major bucket-list stuff right there,” Doctorow said).

That isn’t in spite of the word’s unprintability in polite outlets, Doctorow argued, but because of it: “There’s just something about the minor license to vulgarity — especially when you’re very angry … that seems to unlock a willingness to listen to a more thoroughgoing policy analysis.”

And woven in between schoolyard jokes and righteous indignation, Doctorow offers that analysis: a political economy of Silicon Valley firms incentivized not to do one thing well but to sell the public on Next Big Things.

A “mature” public company, he notes, grows slowly, pays regular dividends, and supports stock prices of 10 to 20 times their annual earnings per share.

By contrast, tech companies — the archetypal “growth” businesses — sustain price-to-earnings ratios twice that high, often with the promise that they are about to conquer or absorb some existing profitable sector: Think taxis or trucking, bookstores or newspapers.

And, when much of their top talent is at least partially paid in company stock, it becomes vitally important that those promises are believed.

The latest promise is the subject of Doctorow’s latest book, “The Reverse Centaur’s Guide to Life After AI,” published in June. He referred to it as “the greatest literary blunder of his life,” as his attempt to wave away most AI panic instead invited more angsty conversation.

At 55, Doctorow is arguably one of the world’s oldest and most venerable “digital natives”: a godfather of blogging and open-source software developer, co-founder of Boing Boing, and a Wired contributor.

And he’s had moments of awe at the “super-cool, interesting” things the latest AI models can do: from generating their own stochastic collages to finding and transcribing a memorable quote from a recent podcast.

These are “centaur” experiences, where a technology endows a human user with superhuman strength, expediting their work and improving their lives. (It’s a tale as old as corrective lenses, Mynatt pointed out.)

But Doctorow worries that — with AI — many humans will have reverse-centaur experiences, where employers, aiming to lower labor costs, force them to use the technology on far-from-human terms.

For example, he speculated, a hospital might lay off all but a few of its human radiologists, with the remainder “marking the AI’s homework.”

Given the algorithms’ speed and the statistical plausibility of their findings, those few remaining humans will be looking for errors that are devilishly hard to spot — and will take the blame whenever mistakes are made.

Asked about the potential existential risks that trouble AI engineers, Doctorow scoffed. He argues that LLMs, at bottom “word-guessing machines,” still cannot research themselves, remain relentlessly error-prone, and are “nowhere near recursive self-improvement.”

His own AI worry is the firms’ growing centrality in the unequal global economy.

Doctorow noted that the seven companies making big bets on AI now represent 35 percent of the total value of the S&P 500. That value could be “vaporized overnight, when investors lose confidence,” he said. From there, he argued, the story is all too human: financial crisis, austerity, and a further lurch into hard-right politics.

Doctorow’s first writing as a teenager was science fiction. He still publishes in the genre, and in “Reverse Centaurs,” he celebrates it as an “anti-inevitabilist” literature.

In sci-fi, “many different writers have imagined many different futures, each of which can feel possible and even desirable,” he writes. And “the fact that social relations between people and their technology can be different means that the current arrangement is a choice, and —­ crucially —­ it means that we can choose something else.”

If digital life is less fun than ever — characterized by worsening user experience, false promises, ubiquitous advertising, and threats of doom — “it’s not because of the iron laws of history,” he said. “It’s because named individuals in living memory decided this was the environment we should have … But we can make different decisions.”

The participants at a John F. Kennedy Jr. Forum debate last week on “diversity” and “pluralism” in higher education were clearly suitably matched.

Kevin Roberts is president of the conservative Heritage Foundation think tank. His group published Project 2025, a 900-page policy document that has heavily influenced President Trump’s second-term agenda and pushed for the end of diversity, equity, and inclusion (DEI) programs.

Cornell William Brooks, a civil rights attorney, formerly led the National Association for the Advancement of Colored People and is currently Hauser Professor of the Practice of Nonprofit Organizations and professor of the practice of public leadership and social justice at Harvard Kennedy School.

The two did agree on this one broad principle: Universities should be composed of people from a wide range of backgrounds.

But Brooks called the concept “diversity”; Roberts preferred the term “pluralism.”

And the pair sharply diverged on how far the country has progressed on racial discrimination and whether race-conscious programs are still needed — a divide that likely came as no surprise to Brooks, who said in his opening remarks, “When the school asked me to debate, I asked them to ask Kevin Roberts.”

Asked by moderator Laura Meckler, a Washington Post reporter, what higher education has gotten right and wrong in their efforts, the two outlined these disagreements.

Roberts said race-conscious programs had outlived their usefulness.

“In 1964 or ’65, did we need programs — that then weren’t called DEI — in order to level the playing field? Perhaps so,” he said. “But our problem at Heritage, my problem as a scholar in this work, is that we are denying the progress that’s been made over the intervening several decades.”

Brooks said, “When we talk about racism, antisemitism, xenophobia, Islamophobia in American society, we’re not talking about some distant age in the past. No new administration will bring about some new era in America where we don’t have to be vigilant with respect to fighting bigotry of all forms.”

Meckler pushed both speakers to defend their arguments.

She asked Roberts to rationalize how the current administration could argue for a smaller governmental role in education while also aggressively “telling universities and K-12 schools what to do.”

Roberts said the administration is trying to correct injustices and argued that if Harvard doesn’t want to listen to the government, the University should reject federal funding, which he did as president of Wyoming Catholic College.

Meckler asked Brooks to respond to the idea that some students won’t be admitted because they don’t check the right demographic box.

Brooks said that many white students at Harvard benefit from kinds of advantages not available to most Black and brown students. He also argued that conservative voices are overly focused on scholarship programs for Black and brown students, ignoring financial aid earmarked exclusively for members of other racial, ethnic, or religious groups.

Summarizing the event, Roberts said the disagreements between the two speakers came down to an “honest-to-goodness policy disagreement about the kinds of policies that work.”

He argued that race-neutral policies that emphasized socioeconomics and geography would better serve the public.

Brooks invoked the story of civil rights attorney Pauli Murray, who was rejected by Harvard Law School because of her gender but ended up crafting the legal strategy that cleared a path for the landmark Brown v. Board of Education ruling.

To live in a racially diverse democracy, Brooks said, “We have to fight for it every day, all the time.”

Catherine Snow is the John H. and Elisabeth A. Hobbs Research Professor of Cognition and Education at the Harvard Graduate School of Education.

Perhaps cursive went out of style because there was a sense that it was just a technical skill, and one that was becoming less and less necessary with the universal availability of keyboards. There’s this sense that there’s a “problem” of putting stuff on paper, and the technical solution is that you type it and it looks neat and it’s well spelled and we’re not wasting time doing something that kids always hated — namely, practicing cursive. I could see that as an argument that could be convincing.

The problem is, in the process you are losing something that’s really of value: important cognitive challenges.

It’s quite a challenge for very young children to understand how letters are formed and how they are sequenced in words. If you’re not really concentrating, you might end up spelling “cat” C T, for example, because those are the salient sounds in cat. If you type words wrong, you’ll get spellchecked and you might never realize that you were wrong.

Take a slightly more complicated word — let’s say “moderate.” Well, there’s no way of inherently knowing what’s in the middle of moderate, right? It could be M O D A R A T E, or it could be M O D I R A T E, and if it just gets spellchecked, then you never actually come to make the connection. Having to spell it yourself, letter by letter as you write the word, draws your attention to the appropriate sequence of letters.

There are a number of studies which show that there are more areas of neural involvement in handwriting than in typing. There’s more visual cortex involvement, as well as all the differentiated motor skills that go into forming letters. I think it’s really the increased attention to the sequence of symbols that generates automaticity, and automaticity is a key element in good reading.

But this advantage of facilitating learning to read from handwriting comes both with cursive and with printing. It doesn’t really matter. I’ve never encountered anything that would suggest that proper cursive with all the letters connected is better than printing.

I had the proper eight years of elementary school Palmer Method training: The first 15 minutes of every school day were copying sentences 28 times on lined paper. But now I never read cursive, and I find that I really have to work hard if I’m in an archive or a museum and I’m trying to read letters exchanged between people in 1945. It’s a skill that kind of declines without practice. That’s one of the big advantages of learning to write cursive: In the process of learning to write it, you’re learning to read it. It’s learning to access history and literature and shared culture.

Should we keep cursive? I think we should. I’m not deeply dedicated to cursive, but I think we should spend more time in schools having kids write. There are kids who never learn cursive who can function with a pen in their hands perfectly well. They should be writing with pencil or pen on paper, and we should be focusing much more on the content of what they write than correcting errors. You could learn cursive later, if you really wanted to. There is not a critical period after which you can’t.

— As told to Sarah Lamodi, Harvard Correspondent

Assina Kahamba and Maralmaa Chinzorig both arrived at Harvard for graduate school with the goal of getting the necessary training to help their communities with big problems.

For Kahamba, it was strengthening teacher education in Democratic Republic of the Congo. For Chinzorig, it was improving medical care in rural Mongolia.

Both received grants from the Harvard Innovation Labs (i-Lab) Navab Social Impact Fellowship Fund to help apply what they learned to urgent problems at home.

Assina Kahamba

Kahamba didn’t plan to become principal of an all-girls’ school.

After finishing a computer engineering degree at the University of Kinshasa, she went to work in the private sector in the Democratic Republic of the Congo, her home country.

Eventually she began thinking about the big problems plaguing public education in DRC and decided she wanted to shift her career and focus on making it better.

Kahamba was working in an elementary school, responsible for the children’s character development, when her school’s principal abruptly resigned. Suddenly, she found herself in charge of all the teachers and students.

One of the biggest issues Kahamba found involved incomplete teacher preparation. Some had solid knowledge of a subject but poor pedagogical skills. Others were skilled at breaking down and explaining new information, but had critical knowledge gaps.

“I was in charge of providing a good education to the students,” Kahamba said, “and I was seeing my teacher unable to give a class on fractions.”

She looked for professional development programs nearby but couldn’t find any. She started one in her own school but discovered she didn’t have the background to make it effective. Ultimately she decided that if she wanted to train teachers properly, she needed more education herself.

To get it, she looked to Harvard Graduate School of Education, where — with funding help from her community — she began a one-year master’s degree in global, international, and comparative education.

Her professors encouraged her to rethink her preconceptions of teacher training.

Fernando M. Reimers, the Ford Foundation Professor of the Practice of International Education, told her old-school programs that bring educators together for just a couple of days of training have become obsolete. For better results, teachers need to be coached over time, with ongoing evaluations of effectiveness.

Kahamba’s studies took her beyond education. Through entrepreneurship classes, she learned how to run — and sustain — a business for training teachers in the DRC.

“With this context of cuts of aid to Africa, it was very important to know how to have money as a business,” she said, “and not only expect money coming from the U.S. and Europe.”

With funding help from the i-Lab’s Social Impact Fellowship Fund, which provides about $200,000 in support for a dozen ventures started by students across the University, Kahamba was able to assemble a small team to help build out her vision with Koyekola Academy, the initiative she founded.

When she returned to the DRC after graduation, some people were surprised. “Everybody was saying that this girl had gone to the U.S. — she won’t come back,” Kahamba said.

But educators from across the country, including some who had helped fund her education at Harvard, wanted to find out what she had learned. She was surprised when two directors came from Kisangani — more than 700 miles from Kinshasa — for the training she started to develop at Harvard.

A few months after graduation, Koyekola Academy has three contracts with schools — each of which will receive ongoing training over months of work. Her goal is to have a successful pilot runand work with the government to expand the program across the country.

“We want to build a community of teachers who really want to change the world,” Kahamba said.

Maralmaa Chinzorig

Growing up in rural northern Mongolia, Chinzorig heard harrowing stories from friends and family about trying to get medical care.

The nearest provincial hospital could take two or three days to reach during the winter. When dirt roads were impassable, residents crossed the frozen Khuvsgul Lake for better treatment.

They then arrived at local hospitals where doctors had only sparse paper records and no access to digital ones. Without knowing details about their patients’ backgrounds, doctors struggled to provide good diagnoses.

Often, frustrated patients would take any generic Soviet-era drug recommended to them by peers, even if it didn’t address the problems they were facing.

“It’s almost like an ancient, tribal healthcare system,” Chinzorig said. “It’s really shocking for people who live in urban places.”

After graduating from college in Ulaanbaatar, Chinzorig worked with the Ministry of Health in Mongolia, where she helped integrate technology into urban hospitals.

After seven years, though, she realized that she would need better project management skills if she wanted to push reforms through the government bureaucracy. She decided to attend Washington University in St. Louis for a degree in business administration.

Chinzorig was struck by the level of well-being Americans expected to have into old age. Where she grew up, she said, people expected to suffer from a long list of diseases above the age of 50.

She became determined to create a company that would ultimately challenge the government to do better.

But while Chinzorig had ideas for the digital innovations she could try to implement in hospitals, she thought she could use more training to learn how to work more effectively with the governments to help expand healthcare services.

She decided to enroll in a master of public policy program at the Harvard Kennedy School. In courses about negotiation, Chinzorig learned skills that would help her push for improved healthcare when she returned to her home country.

“I think my first year of Harvard made me become someone who is a huge advocate and brave enough to challenge the system,” she said. “You guys are telling me the system is just, but what is the standard of just?”

With funding and support from the i-Lab’s Social Impact Fellowship Fund, Chinzorig has expanded the offerings of her company, MeTools, a digital-health platform that allows clinics and hospitals to share medical records without reliable internet.

The company has helped the National Center for Zoonotic Diseases — which tracks infections like the bubonic plague — sync the records of 13 rural facilities across Mongolia.

At Harvard, she said she’s learned how to bring together different stakeholders, listen to their pain points and interests, and arrive at solutions to which everyone can agree. In Mongolia, and across the broader region, she hopes to bring these skills to use.

“Someone who knows the technology is stuck on the technology. Someone who knows disease surveillance is focused on disease,” she said. “I’m trying to become the leader who can connect and listen to all kinds of different people and have this collective solution.”

Excerpted from “The Art of Fighting: The Transformative Power of Conflict” by Priya Parker, M.P.P. ’12, published on Sept. 8 by Riverhead Books, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Parker is a specialist in conflict resolution who works with organizations, communities, and leaders.

When people in conflict first call me, they tend to say some version of: “Make this go away.”

I get it. Conflict is not fun. They are concerned, worried. A group of people they care about, or rely on, or live among, is in crisis.

Make this go away.

What I have learned from years of fielding these calls is that most people, including people very savvy in other areas of life, have an unhelpful mental model of what group conflict is. Perhaps they think of it as a court case, and they’re calling me, their lawyer. Make this go away. Or they think of their fight as a story about to break in the media, and they’re calling me, their crisis communications whiz. Make this go away. Perhaps they think their conflict is just troublemaking by some members or employees or parishioners, and they’re calling me, a heavy, a tough guy who is going to find the complainers and ask ominously if they’ve got a problem. Make this go away.

I try not to judge this impulse. I get it. But I also inform my callers: I don’t make things go away.

I have a different perspective. To me, “How can I make this go away?” is the wrong question to ask, because trying to stave off a fight is not always what best serves a group. Thinking of conflict as something to bury or put back in the bottle or disappear is common among group amateurs. The artful fighter knows better. Some tears in group life should indeed be quickly sewn up and forgotten about. But others are the beginning of a big, revealing, and necessary reckoning, of a struggle that will force a group to face fundamental, half-​buried questions about who they are, what they are for, and how they operate. These conflicts are unlikely to go away and will keep coming up again and again if left unaddressed.

So when people ask me to make a fight go away, I tell them it’s time to stop being afraid and start becoming curious. Why is this happening? Why is this happening to us? Why is this happening now? Why am I feeling so bad?

Rebekah called me when she realized her family Passover ritual was broken. Her extended family had been gathering for the seder for some 30 years. Her grandmother, a Holocaust survivor and the family matriarch, always insisted that the family stick together. They were, as Rebekah put it, “desperate to cling to each other” and Passover Seder was the “big central thing for the whole family.” The ritual is a more-​than-​2,000-​year-​old tradition of remembering and telling the story of the Jews leaving Egypt and finding their freedom. For Rebekah’s family, “there was a lot of symbolism about slavery and redemption that for the Holocaust survivors was a really important way to express their grief,” she explained. It was often the only moment when family stories of survival were brought up at all. When she was young, Rebekah cherished the ritual and the gathering. It shaped her and so much of her family deeply.

Year after year, the whole family would witness the youngest among them ask the famous four questions of the seder. It was where family members shared important news — like a cousin who one year came out at the table. (During one hymn, the family would always joke and rhyme and make up new passages. That year, during the refrain “El bneh, el bneh,” Rebekah’s cousin Alex blurted out, “I am gay!” and without missing a beat, another family member retorted, “El bneh, el bneh, and that’s OK!”) It was where all the cousins would introduce their partners to the rest of the clan. When a niece’s chair broke under her and her new fiancé didn’t instinctively rush to catch her, the family knew the relationship was over. It was where Rebekah herself announced a pregnancy, one that she later lost.

But Passover was also where she started feeling the suffocating pressure and judgment around having kids. It was where she got stuck in the role of cooking for weeks ahead of time and throughout the festivities. And then once she finally had kids, she started to notice how sick they would all become after the late nights and frenzied days of family time. She started to feel the consequences of taking off days of work to prepare for the holiday, and then more precious days off to stay home with sick kids after the fact. It was becoming unsustainable. Like at many family holiday tables, ideological differences were also becoming more apparent. An uncle would regularly hit “reply all” to the family welcome email thread and send “long political right-wing rants,” which he would then bring to the dinner table. Then COVID‑19 hit, and the family couldn’t gather at all.

Until that moment on our Zoom call, Rebekah had been very matter‑of‑fact — almost clinical — in describing the situation. And then she got quiet. I saw that she was softly crying. When she heard the clacking of my typing stop, she looked up. “I’m so sorry,” she said. I said gently what I say on many calls: “Please don’t apologize.” And she just cried. “I don’t want to feel like this. I wish I didn’t care so much,” she said. She was articulating an unspoken assumption shared by so many people on these calls: If only she could just sidestep this part of the process. If she wasn’t bogged down by feeling so sad, she could act!

“Feelings aren’t just OK here,” I told her. “They’re crucial. They’re not an inconvenience. They’re information. They’re a natural resource. They’re energy.” As Rebekah was rationally plotting her exit from her family’s celebrations, she viscerally remembered how deeply she cared for these people.

Feelings, no matter how painful, are not bad. They are grist. You may not be able to solve what’s in front of you if you can’t be bothered to cry. Tears are the lubricant of group conflict resolution. “No More Tears” may be a great formula for kids’ shampoo, but it’s a disastrous approach to adult fights.

Rebekah was experiencing anticipatory grief trying to figure out what she should do with this messy group that she loved but where the formation was no longer working for her (and, she suspected, for others as well). In a study conducted for the National Institutes of Health, researchers found that our brains process turmoil in relationships and perceived isolation with the same pain receptors as physical attack. Often, people don’t know what to do with these feelings.

Your feelings about a group conflict are valid and normal. But they’re more than that. They’re useful and informative. In fact, when a group feels really stuck, feelings are a signal of possibility.

When you find yourself feeling fragile or hurt in the aftermath of some kind of rupture, it may be that you are experiencing something that the conflict resolution specialist Sethu Nair calls “relational longing.” It is one of the most important superpowers you can tap into when seeking to resolve and transform a conflict.

Nair is a veteran group dialogue facilitator who works at the Center for Creative Conflict Resolution in New York City. She’s the kind of facilitator who gets a call when a “flag war” breaks out in the offices of a local government agency — Black Lives Matter signs on some desks, American flags or Blue Lives Matter ones on others. Would she be willing to come in and help the staff confront the divisions within?

Nair is not intimidated by garden-​variety conflict. She’s a founding member of Hidden Water, an organization that developed trauma-​informed circles for individuals and families impacted by child sexual abuse who wanted to heal with others.

In explaining how she begins to approach a conflict, Nair says that she must find the “relational longing” at the center of the fight. This relational longing, she says, is “the only avenue of possibility” through the fight, and she believes it exists everywhere. Relational longing is the residual desire within us to connect to someone or something, even when we’re hurt or mad. “We wouldn’t even have conflict if we weren’t trying so hard to be heard,” Nair told me. “I love finding that, and it’s not so hard to find if you really listen for what people are longing for.”

We don’t fight within our groups because we hate each other and want nothing to do with each other anymore. We fight because we are yearning to be in relationship with each other still, perhaps just not in the form of relationship we are currently in. (When that’s no longer true, we don’t fight; we leave.)

Where there are strong feelings, there is energy, and energy is easier to work with than apathy. There’s a there there. Where there is some beating pulse of relationship left, even a badly arrhythmic one, there remains a possibility of transforming the bond.

The inverse is also true. If a rupture stirs no feelings in you, that is useful to know. You may have a problem in the group, but no longing to solve it.

By the end of the calls I get, if I’ve done my job well, the person feels better. Nothing may have changed in the situation, but they have begun to learn what the artful fighter knows: that when faced with the first flushes of a conflict, they must steel themselves and refuse to sweep back what is peeking out from under the rug. They must push themselves to be curious — about why they’re feeling what they are, about what areas of heat are causing their group to fracture, about what they may have failed to notice before, and about why all this may be happening now.

Once the laments recede and the real questions arise, the artful fighter is well on their way. What awaits them now is trying to figure out, through all this questioning, what they have on their hands.

Copyright © 2026 by Priya Parker

What do we want from AI?

That was the question posed by Alex Pascal, executive director of the Berkman Klein Center for Internet & Society, at the outset of a panel discussion co-hosted by the center on how AI “can serve people and make our society better.”

The powerful, potentially game-changing technology has been in the headlines in recent months after high-profile reports of experimental AI agents going rogue and hacking into other systems. Industry leaders have warned that unchecked AI development poses dangers to humanity.

AI’s role is inherently double-edged, said Jeff Dunn, chief operating officer of the Digital Trust Council, which co-sponsored the event.

“We want to cure diseases but in order to do that we need to use a tool that can create bioweapons,” he said. “You can’t just put a knife through the middle and hope for the best.”

Amit Goldenberg, associate professor at Harvard Business School and a member of its AI Institute, broke down the kinds of artificial intelligence now in use, and the range of the roles the technology can fill.

At its most basic, he said, “it’s a tool, like autocorrect, and it doesn’t have a personality. The other extreme is a full-fledged agent that has a name, that has a personality that you can interact with.”

The “million-dollar question,” he said, is distinguishing where the different AI tools and models fall on the continuum and which ones are best suited for specific needs.

At the heart of this issue are two questions: “What are the type of interactions that will improve humans’ well-being and what are the type of interactions that will harm them?” Goldenberg said. “I work under the assumption that humans need humans.”

Avijit Ghosh, lead technical AI policy researcher at Hugging Face, said that we need more accurate means of evaluating AI systems in order to develop a greater sense of public trust in the technology. Hugging Face, a platform and library for AI developers, was hacked this summer by Open AI agents that went rogue. Ghosh said he couldn’t comment on the cyberattack on his company.

He noted that we are in an era of “benchmark maxing,” or posting more supposed benchmarks than necessary in order to boost public trust.

Dunn chimed in, saying that evaluations need to be done properly. “If they’re not validated or not audited, then it’s just a marketing stunt,” he said.

As a remedy, Ghosh recommended a system that measures both system behavior and its impact on people.

“It’s also not an unsolvable problem,” said Ghosh, who works on what current evaluation methods can and cannot capture. “We have decades of research in human-computer interactions.”  

How precisely to conduct such evaluations was open for discussion.

Ghosh, for example, warned against anointing certain panels or experts to judge the value (or harm) of any AI agent.

Most regulations and evaluations currently come “from the top down,” he said. “I want the general public to have access because we don’t know what we don’t know.

“If we collectively, as a society, decide that these are the 20 organizations that are allowed to evaluate models and nobody else [can], then we are probably depriving ourselves of really interesting insights. Something interesting can come out of a bug report if the public were allowed to submit some.

“We need better ways for the general public to participate in the general reporting and assessment.”

AI companies, he said, could be incentivized to respond to such feedback. “Maybe in exchange, the government can say, ‘You will not have liability if you fix this reported problem within 60 days.’”

“If we can align the business with what humans would want, then I think we will be OK,” Dunn said.

By now David Malan may be one of Harvard’s most recognizable (human) faces.

Since 2007, Malan — a Gordon McKay Professor of the Practice of Computer Science — has built CS50, “Introduction to Computer Science,” into the University’s largest course.

During that time, the online version of CS50 has taken on a global scope. Over the years, millions of students have registered in CS50x or its growing menu of more focused modules: on cybersecurity and law, Python, SQL, and AI.

Malan is on a sabbatical this fall, his first since taking over the class, with teaching duties passing to Henry H. Leitner. In his absence, the teaching team will continue to draw on the course’s latest improvement: the CS50 Duck, an AI tutor that aims to guide budding computer scientists to an answer without doing too much of the work itself.

Malan and the CS50 team have documented the Duck’s progress in articles detailing the bot’s 2023 rollout and their ongoing efforts to train it to become a better teacher.

In this edited conversation, Malan tells the Gazette about those efforts, the upsides and downsides of AI learning, and the value of computer science in an age of “vibe coding.”

Gazette: It’s been a few years since CS50 first introduced an AI tutor in courses, and you and your team have been tracking how it’s worked, the good and the bad. How did you decide to approach the problem?

David Malan: At first, it was very experimental. ChatGPT came out in November of 2022, and starting in the spring of 2023, we launched what we call the “CS50 Duck,” which is essentially an AI tutor.

We tried to leverage the ChatGPT API, in effect, to make a less-helpful version of ChatGPT. The Duck is much less inclined to just give students answers outright or generate code for them, and it behaves more like a good teacher or tutor: asking more questions than it answers and trying to guide students towards solutions.

How do you set up the Duck to play that “less-helpful” role?

It involves a couple of techniques. One is just what’s called “prompting” these days, whereby we just write literally English instructions telling the underlying ChatGPT agent how to behave. For instance, writing out statements like “do not give students outright answers to problems,” or “adhere to the course’s policy on academic honesty.”

But that alone tends not to be sufficient with large language models, or LLMs, like ChatGPT. So we also have a lot of code that we ourselves have written that tries to evaluate a response before the student sees it — and sometimes rejects or retries it, in order to get a more constructive answer.

As someone who has taken CS50 courses online, I can say that they are welcoming but difficult. It’s like a class in a foreign language; you can feel your brain straining to take on a new shape. But there’s evidence that the use of LLMs in education can smooth away the productive difficulties — the ones that help us learn.

We’ve definitely had some issues there — hence the iteration over the past couple of years. First of all, we were seeing “long tails”: really high usage among some students, where they might have asked something like 200 questions of the Duck. We don’t claim to know precisely where the line is, but 200 struck all of us intuitively as too many. No human teacher would invite a sequence of 200 questions before sending a student back to their desk to work.

So we added one mechanism that draws inspiration from games like The Legend of Zelda, with a “heart system” that decrements [as you’re injured in the game]. Within the Duck AI, we added a “heart system” that only allowed them to ask a few questions per unit of time, and that’s a knob we’re still turning.

At the same time, if there is an industry that has embraced LLMs by now, it’s software engineering; a recent survey found that almost 90 percent of software developers use AI in some form (though they have learned not to trust their output entirely). In short, this is the state of the art and there’s a lot of upside here, too.

Oh, absolutely. We wouldn’t still be using AI if we didn’t feel it was a net positive, educationally. It allows students to ask far more questions than they might previously have been able to, logistically or socially — and that’s very healthy. In my own time as a student, graduate and undergraduate, I remember walking into a professor’s office hours, and walking out no more enlightened, because I felt like I was overstaying my welcome or didn’t want to let on that I felt like a dummy.

It’s been a net positive for us teachers, too. There have been times in CS50 — and I imagine really any large intro course at peer institutions — where office hours had become unmanageable. In the early days, the average wait time could be as long as one hour, which was just not acceptable.

As a technologist, the flurry of news of the past few weeks — LLMs “solving” intractable math problems, or hacking websites unseen — did you see this all coming?

Instinctively, yes: It felt inevitable. But I didn’t think it was going to happen this quickly. It certainly seems reasonable to me that, as time goes on, computers will be programming themselves more and more. That’s been a trend for years. Back in my day, we’d make our web pages by hand, for instance: writing out HTML tags and later CSS. But then tools like Adobe Dreamweaver came along — applications that could generate that same sort of site without writing HTML, just clicking buttons. Then web-based alternatives like Squarespace or Wix.

So we’ve been eliminating the boring parts of coding for some time, and it seems inevitable that as our own programming capabilities get better, we will be able to program computers to write logical code. It’s sometimes imperfect but will get better and better. Therein lies some of the concern of late, and the danger.

The anxiety-provoking watchword is “recursive self-improvement,” which I think is intuitive even to the layperson. Once the most sophisticated LLMs can teach human students effectively, they can also teach themselves — perhaps to a point beyond human understanding or control. That prospect has some people quitting big AI firms and/or pushing for a pause on further development — how worried are you?

Well, I’m not sure it benefits any of us to just be worried without speaking up or taking action. I do think convincing humans to consider and to pass regulatory legislation along certain lines is probably wise — and sooner rather than later.

What concerns me most is not only that things like the Hugging Face incident can happen without our initially knowing about or understanding them — but how quickly they can happen. The computers we use and the code we write operates far faster, cognitively, than us humans. To me, that’s the dangerous part: because by the time even the most intelligent humans figure out what’s going on, it might be too late to fix something.

You’ve seen AI misalignment firsthand in CS50, as you detailed in that second paper. The Duck was told explicitly not to respond to students’ queries with blocks of actual code — and yet it still does up to a quarter of the time. What’s driving that behavior?

It’s a couple of things. Sometimes it’s just built-in randomness, which — as we teach in CS50 — can be a really powerful ingredient in algorithms. You don’t necessarily want a computer to find a solution to a problem and then always spit out the exact same solution, which might be correct but not optimal. So the Duck might occasionally be trying out a new response, randomly.

At other times, it’s just a consequence of the data that the model was trained on. There’s both good and bad input out there, and the model might have just “learned” to spit out code because that’s what people do on technical forums like Stack Overflow or on Reddit 90 percent of the time.

That has been one of our challenges, too, and it’s much better now. But LLMs aren’t deterministic in the sense that they will always do exactly as you command, because the reality is they don’t really understand you at all. They are just simulating understanding, based on their training data.

I think that’s one clear advantage to computer-science coursework: to understand how this or any new technology — as it changes our lives — actually works. Do you see any misconceptions out there you want to correct?

Certainly I want people to be skeptical of what AI tells them. That’s hard when, most of the time, these things do seem to be giving you the answers that you want, and it’s very easy to assign a personality to them.

But when you do internalize exactly what these technologies actually are, that hopefully snaps you out of that misapprehension, and you realize that you should approach the output of any computer — and even of many humans — with some skepticism.

I’d worry that, if people are not taught this, certainly in these early days of LLM-based AI, they might be led astray by misinformation that’s being spat out by these things.

For many years, the advice to young people was “learn to code,” and for a while it had some real credibility: high salaries, job security. But when someone armed with an LLM can “vibe code” an application in an afternoon, it looks a bit different. Do you think that’s still good advice?

The short answer is yes. Even before AI, in courses like CS50, we said consistently that the course’s purpose is not to teach students a given programming language, but to teach students to think more methodically, more algorithmically, to help them clean up their thought processes, to help them take in input and produce correct output. The programming languages that we use in CS50 have always just been a means to that end.

Ultimately, we want you to be able to look at these apparently magical devices and decompose them in your mind into their constituent building blocks, so you can better understand, and perhaps even be less fearful of, what they’re doing. That hasn’t changed.