Two of them were sprawled out on a long concrete bench in front of the main Haverford College library, one scribbling in a battered spiral-ring notebook, the other making annotations in the white margins of a novel. Three more sat on the ground beneath them, crisscross-applesauce, chatting about classes. A little hip, a little nerdy, a little tattooed; unmistakably English majors. The scene had the trappings of a campus-movie set piece: blue skies, green greens, kids both working and not working, at once anxious and carefree.
I said I was sorry to interrupt them, and they were kind enough to pretend that I hadn’t. I explained that I’m a writer, interested in how artificial intelligence is affecting higher education, particularly the humanities. When I asked whether they felt that ChatGPT-assisted cheating was common on campus, they looked at me like I had three heads. “I’m an English major,” one told me. “I want to write.” Another added: “Chat doesn’t write well anyway. It sucks.” A third chimed in, “What’s the point of being an English major if you don’t want to write?” They all murmured in agreement.
What’s the point, indeed? The conventional wisdom is that the American public has lost faith in the humanities—and lost both competence and interest in reading and writing, possibly heralding a post-literacy age. And since the emergence of ChatGPT, which is capable of producing long-form responses to short prompts, colleges and universities have tried, rather unsuccessfully, to stamp out the use of what has become the ultimate piece of cheating technology, resulting in a mix of panic and resignation about the impact AI will have on education. But at Haverford, by contrast, the story seemed different. Walking onto campus was like stepping into a time machine, and not only because I had graduated from the school a decade earlier. The tiny, historically Quaker college on Philadelphia’s Main Line still maintains its old honor code, and students still seem to follow it instead of letting a large language model do their thinking for them. For the most part, the students and professors I talked with seemed totally unfazed by this supposedly threatening new technology.
The two days I spent at Haverford and nearby Bryn Mawr College, in addition to interviews with people at other colleges with honor codes, left me convinced that the main question about AI in higher education has little to do with what kind of academic assignments the technology is or is not capable of replacing. The challenge posed by ChatGPT for American colleges and universities is not primarily technological but cultural and economic.
It is cultural because stemming the use of Chat—as nearly every student I interviewed referred to ChatGPT—requires an atmosphere in which a credible case is made, on a daily basis, that writing and reading have a value that transcends the vagaries of this or that particular assignment or résumé line item or career milestone. And it is economic because this cultural infrastructure isn’t free: Academic honor and intellectual curiosity do not spring from some inner well of rectitude we call “character,” or at least they do not spring only from that. Honor and curiosity can be nurtured, or crushed, by circumstance.
Rich private colleges with honor codes do not have a monopoly on academic integrity—millions of students and faculty at cash-strapped public universities around the country are also doing their part to keep the humanities alive in the face of generative AI. But at the wealthy schools that have managed to keep AI at bay, institutional resources play a central role in their success. The structures that make Haverford’s honor code function—readily available writing support, small classes, and comparatively unharried faculty—are likely not scalable in a higher-education landscape characterized by yawning inequalities, collapsing tenure-track employment, and the razing of public education at both the primary and secondary levels.
When OpenAI’s ChatGPT launched on November 30, 2022, colleges and universities were returning from Thanksgiving break. Professors were caught flat-footed as students quickly began using the generative-AI wonder app to cut corners on assignments, or to write them outright. Within a few weeks of the program’s release, ChatGPT was heralded as bringing about “the end of high-school English” and the death of the college essay. These early predictions were hyperbolic, but only just. As The Atlantic’s Ian Bogost recently argued, there has been effectively zero progress in stymying AI cheating in the years since. One professor summarized the views of many in a recent mega-viral X post: “I am no longer a teacher. I’m just a human plagiarism detector. I used to spend my grading time giving comments for improving writing skills. Now most of that time is just checking to see if a student wrote their own paper.”
While some institutions and faculty have bristled at the encroachment of AI, others have simply thrown in the towel, insisting that we need to treat large language models like “tools” to be “integrated” into the classroom.
I’ve felt uneasy about the tacit assumption that ChatGPT plagiarism is inevitable, that it is human nature to seek technological shortcuts. In my experience as a student at Haverford and then a professor at a small liberal-arts college in Maine, most students genuinely do want to learn and generally aren’t eager to outsource their thinking and writing to a machine. Although I had my own worries about AI, I was also not sold on the idea that it’s impossible to foster a community in which students resist ChatGPT in favor of actually doing the work. I returned to Haverford last month to see whether my fragile optimism was warranted.
When I stopped a professor walking toward the college’s nature trail to ask if ChatGPT was an issue at Haverford, she appeared surprised by the question: “I’m probably not the right person to ask. That’s a question for students, isn’t it?” Several other faculty members I spoke with said they didn’t think much about ChatGPT and cheating, and repeated variations of the phrase I’m not the police.
Haverford’s academic climate is in part a product of its cultural and religious history. During my four years at the school, invocations of “Quaker values” were constant, emphasizing on personal responsibility, humility, and trust in other members of the community. Discussing grades was taboo because it invited competition and distracted from the intrinsic value of learning.
The honor code is the most concrete expression of Haverford’s Quaker ethos. Students are trusted to take tests without proctors and even to bring exams back to their dorm rooms. Matthew Feliz, a fellow Haverford alum who is now a visiting art-history professor at Bryn Mawr—a school also governed by an honor code—put it this way: “The honor code is a kind of contract. And that contract gives students the benefit of the doubt. That’s the place we always start from: giving students the benefit of the doubt.”
Darin Hayton, a historian of science at the college, seemed to embody this untroubled attitude. Reclining in his office chair, surrounded by warm wood and, for 270 degrees, well-loved books, he said of ChatGPT, “I just don’t give a shit about it.” He explained that his teaching philosophy is predicated on modeling the merits of a life of deep thinking, reading, and writing. “I try to show students the value of what historians do. I hope they’re interested, but if they’re not, that’s okay too.” He relies on creating an atmosphere in which students want to do their work, and at Haverford, he said, they mostly do. Hayton was friendly, animated, and radiated a kind of effortless intelligence. I found myself, quite literally, leaning forward when he spoke. It was not hard to believe that his students did the same.
“It seems to me that this anxiety in our profession over ChatGPT isn’t ultimately about cheating.” Kim Benston, a literary historian at Haverford and a former president of the college, told me. “It’s an existential anxiety that reflects a deeper concern about the future of the humanities,” he continued. Another humanities professor echoed these remarks, saying that he didn’t personally worry about ChatGPT but agreed that the professorial concern about AI was, at bottom, a fear of becoming irrelevant: “We are in the sentence-making business. And it looks like they don’t need us to make sentences any more.”
I told Benston that I had struggled with whether to continue assigning traditional essays—and risk the possibility of students using ChatGPT—or resort to using in-class, pen-and-paper exams. I’d decided that literature classes without longer, take-home essays are not literature classes. He nodded. The impulse to surveil students, to view all course activity through a paranoid lens, and to resort to cheating-proof assignments was not only about the students or their work, he suggested. These measures were also about nervous humanities professors proving to themselves that they’re still necessary.
My conversations with students convinced me that Hayston, Benston, and their colleagues’ build-it-and-they-will-come sentiment, hopelessly naive though it may seem, was largely correct. Of the dozens of Haverford students I talked with, not a single one said they thought AI cheating was a substantial problem at the school. These interviews were so repetitive, they almost became boring.
The jock sporting bright bruises from some kind of contact sport? “Haverford students don’t really cheat.” The econ major in prepster shorts and a Jackson Hole T-shirt? “Students follow the honor code.” A bubbly first-year popping out of a dorm? “So far I haven’t heard of anyone using ChatGPT. At my high school it was everywhere!” More than a few students seemed off put by the very suggestion that a Haverfordian might cheat. “There is a lot of freedom here and a lot of student autonomy,” a sophomore psychology major told me. “This is a place where you could get away with it if you wanted to. And because of that, I think students are very careful not to abuse that freedom.” The closest I got to a dissenting voice was a contemplative senior who mused: “The honor code is definitely working for now. It may not be working two years from now as ChatGPT gets better. But for now there’s still a lot of trust between students and faculty.”
To be sure, despite that trust, Haverford does have occasional issues with ChatGPT. A student who serves on Haverford’s honor council, which is responsible for handling academic-integrity cases, told me, “There’s generally not too much cheating at Haverford, but it happens.” He said that the primary challenge is that “ChatGPT makes it easy to lie,” meaning the honor council struggles to definitively prove that a student who is suspected of cheating used AI. Still, both he and a fellow member of the council agreed that Haverford seems to have far fewer issues with LLM cheating than peer institutions. Only a single AI case came before the honor council over the past year.
In another sign that LLMs may be preoccupying some people at the college, one survey of the literature and language faculty found that most teachers in these fields banned AI outright, according to the librarian who distributed the query. A number of professors also mentioned that a provost had recently sent out an email survey about AI use on campus. But in keeping with the general disinterest in ChatGPT I encountered at Haverford, no one I talked with seemed to have paid much attention to the email.
Wandering over to Bryn Mawr in search of new perspectives, I found a similar story. A Classics professor I bumped into by a bus stop told me, “I try not to be suspicious of students. ChatGPT isn’t something I spend time worrying about. I think if they use ChatGPT, they’re robbing themselves of an opportunity.” When I smiled, perhaps a little too knowingly, he added: “Of course a professor would say that, but I think our students really believe that too.” Bryn Mawr students seemed to take the honor code every bit as seriously as that professor believed they would, perhaps none more passionately than a pair of transfer students I came across, posted up under one of the college’s gothic stone archways.
“The adherence to it to me has been shocking,” a senior who transferred from the University of Pittsburgh said of the honor code. “I can’t believe how many people don’t just cheat. It feels not that hard to [cheat] because there’s so much faith in students.” She explained her theory of why Bryn Mawr’s honor code hadn’t been challenged by ChatGPT: “Prior to the proliferation of AI it was already easy to cheat, and they didn’t, and so I think they continue not to.” Her friend, a transfer from another large state university, agreed. “I also think it’s a point of pride,” she observed. “People take pride in their work here, whereas students at my previous school were only there to get their degree and get out.”
The testimony of these transfer students most effectively made the case that schools with strong honor codes really are different. But the contrast the students pointed to—comparatively affordable public schools where AI cheating is ubiquitous, gilded private schools where it is not—also hinted at a reality that troubles whatever moralistic spin we might want to put on the apparent success of Haverford and Bryn Mawr. Positioning honor codes as a bulwark against academic misconduct in a post-AI world is too easy: You have to also acknowledge that schools like Haverford have dismantled—through the prodigious resources of the institution and its customers—many incentives to cheat.
It is one thing to eschew ChatGPT when your professors are available for office hours, and on-campus therapists can counsel you if you’re stressed out by an assignment, and tutors are ready to lend a hand if writer’s block strikes or confusion sets in, and one of your parents’ doctor friends is happy to write you an Adderall prescription if all else fails. It is another to eschew ChatGPT when you’re a single mother trying to squeeze in homework between shifts, or a non-native English speaker who has nowhere else to turn for a grammar check. Sarah Eaton, an expert on cheating and plagiarism at Canada’s University of Calgary, didn’t mince words: She called ChatGPT “a poor person’s tutor.” Indeed, several Haverford students mentioned that, although the honor code kept students from cheating, so too did the well-staffed writing center. “The writing center is more useful than ChatGPT anyway,” one said. “If I need help, I go there.”
But while these kinds of institutional resources matter, they’re also not the whole story. The decisive factor seems to be whether a university’s honor code is deeply woven into the fabric of campus life, or is little more than a policy slapped on a website. Tricia Bertram Gallant, an expert on cheating and a co-author of a forthcoming book on academic integrity, argues that honor codes are effective when they are “regularly made salient.” Two professors I spoke with at public universities that have strong honor codes emphasized this point. Thomas Crawford at Georgia Tech told me, “Honor codes are a two-way street—students are expected to be honest and produce their own work, but for the system to function, the faculty must trust those same students.” John Casteen, a former president and current English professor at the University of Virginia, said, “We don’t build suspicion into our educational model.” He acknowledged that there will always be some cheaters in any system, but in his experience UVA’s honor-code culture “keeps most students honest, most of the time.”
And if money and institutional resources are part of what makes honor codes work, recent developments at other schools also show that money can’t buy culture. Last spring, owing to increased cheating, Stanford’s governing bodies moved to end more than a century of unproctored exams, using what some called a “nuclear option” to override a student-government vote against the decision. A campus survey at Middlebury this year found that 65 percent of the students who responded said they’d broken the honor code, leading to a report that asserted, “The Honor Code has ceased to be a meaningful element of learning and living at Middlebury for most students.” An article by the school newspaper’s editorial board shared this assessment: “The Honor Code as it currently stands clearly does not effectively deter students from cheating. Nor does it inspire commitment to the ideals it is meant to represent such as integrity and trust.” Whether schools like Haverford can continue to resist these trends remains to be seen.
Last month, Fredric Jameson, arguably America’s preeminent living literary critic, passed away. His interests spanned, as a lengthy New York Times obituary noted, architecture, German opera, and sci-fi. An alumnus of Haverford, he was perhaps the greatest reader and writer the school ever produced.
If Jameson was a singular talent, he was also the product of a singular historical moment in American education. He came up at a time when funding for humanities research was robust, tenure-track employment was relatively available, and the humanities were broadly popular with students and the public. His first major work of criticism, Marxism and Form, was published in 1971, a year that marked the high point of the English major: 7.6 percent of all students graduating from four-year American colleges and universities majored in English. Half a century later, that number cratered to 2.8 percent, humanities research funding slowed, and tenure-line employment in the humanities all but imploded.
Our higher-education system may not be capable of producing or supporting Fredric Jamesons any longer, and in a sense it is hard to blame students for resorting to ChatGPT. Who is telling them that reading and writing matter? America’s universities all too often treat teaching history, philosophy, and literature as part-time jobs, reducing professors to the scholarly equivalent of Uber drivers in an academic gig economy. America’s politicians, who fund public education, seem to see the humanities as an economically unproductive diversion for hobbyists at best, a menace to society at worst.
Haverford is a place where old forms of life, with all their wonder, are preserved for those privileged enough to visit, persisting in the midst of a broader world from which those same forms of life are disappearing. This trend did not start with OpenAI in November 2022, but it is being accelerated by the advent of magic machines that automate—imperfectly, for now—both reading and writing.
At the end of my trip, before heading to the airport, I walked to the Wawa, a 15-minute trek familiar to any self-respecting Haverford student, in search of a convenience-store sub and a bad coffee. On my way, I passed by the duck pond. On an out-of-the-way bench overlooking the water feature, in the shadow of a tree well older than she was, a student was sitting, her brimming backpack on the grass. There was a curl of smoke issued from a cigarette, or something slightly stronger, and a thick book open on her lap, face bent so close to the page her nose was almost touching it. With her free hand a finger traced the words, line by line, as she read.
Ya sea que se dé cuenta o no, la inteligencia artificial está en todas partes. Se encuentra detrás de los chatbots con los que hablas en línea, las listas de reproducción que transmites y los anuncios personalizados que aparecen en tu desplazamiento. Y ahora está tomando una personalidad más pública. Piense en Meta AI, que ahora está integrado en aplicaciones como Facebook, Messenger y WhatsApp; o Géminis de Google, trabajando en segundo plano en las plataformas de la compañía; o Apple Intelligence, lanzando a través de iPhones ahora.
AI tiene una larga historia, volviendo a una conferencia en Dartmouth en 1956 que primero discutió la inteligencia artificial como una cosa. Los hitos en el camino incluyen Eliza, esencialmente el primer chatbot, desarrollado en 1964 por el informático del MIT Joseph Weizenbaum y, saltando 40 años, cuando la función de autocompleta de Google apareció por primera vez en 2004.
Luego llegó 2022 y el ascenso de Chatgpt a la fama. Los desarrollos generativos de IA y los lanzamientos de productos se han acelerado rápidamente desde entonces, incluidos Google Bard (ahora Gemini), Microsoft Copilot, IBM Watsonx.ai y los modelos de LLAMA de código abierto de Meta.
Desglosemos qué es la IA generativa, cómo difiere de la inteligencia artificial “regular” y si la Generación AI puede estar a la altura de las expectativas.
En esencia, la IA generativa se refiere a sistemas de inteligencia artificial que están diseñados para producir un nuevo contenido basado en patrones y datos que han aprendido. En lugar de solo analizar números o predecir tendencias, estos sistemas generan salidas creativas como texto, música de imágenes, videos y código de software.
Algunas de las herramientas de IA generativas más populares en el mercado incluyen:
El principal entre sus habilidades, ChatGPT puede crear conversaciones o ensayos similares a los humanos basados en algunas indicaciones simples. Dall-E y MidJourney crean obras de arte detalladas a partir de una breve descripción, mientras que Adobe Firefly se centra en la edición y el diseño de imágenes.
No toda la IA es generativa. Si bien Gen AI se enfoca en crear contenido nuevo, la IA tradicional se destaca por analizar datos y hacer predicciones. Esto incluye tecnologías como el reconocimiento de imágenes y el texto predictivo. También se usa para soluciones novedosas en:
La IA que venció a los grandes campeones humanos en el ajedrez y el juego de mesa no fue una IA generativa.
Es posible que estos sistemas no sean tan llamativos como la Generación AI, pero la inteligencia artificial clásica es una gran parte de la tecnología en la que confiamos todos los días.
Detrás de la magia de la IA generativa hay modelos de idiomas grandes y técnicas avanzadas de aprendizaje automático. Estos sistemas están capacitados en grandes cantidades de datos, como bibliotecas completas de libros, millones de imágenes, años de música grabada y datos raspados de Internet.
Los desarrolladores de IA, desde gigantes tecnológicos hasta nuevas empresas, son conscientes de que la IA es tan buena como los datos que lo alimenta. Si se alimenta de datos de baja calidad, la IA puede producir resultados sesgados. Es algo con lo que incluso los jugadores más grandes en el campo, como Google, no han sido inmunes.
La IA aprende patrones, relaciones y estructuras dentro de estos datos durante el entrenamiento. Luego, cuando se le solicita, aplica ese conocimiento para generar algo nuevo. Por ejemplo, si le pide a una herramienta Gen AI que escriba un poema sobre el océano, no solo extrae versos preescritos de una base de datos. En cambio, está usando lo que aprendió sobre la poesía, los océanos y la estructura del lenguaje para crear una pieza completamente original.
Es impresionante, pero no es perfecto. A veces los resultados pueden sentirse un poco apagados. Tal vez la IA malinterpreta su solicitud, o se vuelve demasiado creativo de una manera que no esperaba. Puede proporcionar con confianza información completamente falsa, y depende de usted verificarla. Esas peculiaridades, a menudo llamadas alucinaciones, son parte de lo que hace que la IA generativa sea fascinante y frustrante.
Las capacidades generativas de IA están creciendo. Ahora puede comprender múltiples tipos de datos combinando tecnologías como el aprendizaje automático, el procesamiento del lenguaje natural y la visión por computadora. El resultado se llama IA multimodal que puede integrar alguna combinación de texto, imágenes, video y habla dentro de un solo marco, ofreciendo respuestas más contextualmente relevantes y precisas. El modo de voz avanzado de ChatGPT es un ejemplo, al igual que el proyecto Astra de Google.
No hay escasez de herramientas de IA generativas, cada una con su talento único. Estas herramientas han provocado la creatividad, pero también han planteado muchas preguntas además del sesgo y las alucinaciones, como, ¿quién posee los derechos del contenido generado por IA? O qué material es un juego justo o fuera de los límites para que las compañías de IA los usen para capacitar a sus modelos de idiomas; vea, por ejemplo, la demanda del New York Times contra Openai y Microsoft.
Otras preocupaciones, no son asuntos pequeños, implican privacidad, responsabilidad en la IA, los profundos profundos generados por IA y el desplazamiento laboral.
“Escribir, animación, fotografía, ilustración, diseño gráfico: las herramientas de IA ahora pueden manejar todo eso con una facilidad sorprendente. Pero eso no significa que estos roles desaparezcan. Simplemente puede significar que los creativos deberán mejorar y usar estas herramientas para amplificar su propio trabajo”, Fang Liu, profesor de la Universidad de Notre Dame Dame y Coeditor-Chief de las transacciones de ACM en las transacciones de Probabilista, contó el aprendizaje en el poderoso de la máquina probabilística, le dijo a Cetnet.
“También ofrece una forma para las personas que tal vez carecen de la habilidad, como alguien con una visión clara que no puede dibujar, pero que puede describirlo a través de un aviso. Así que no, no creo que interrumpa a la industria creativa. Con suerte, será una co-creación o un aumento, no un reemplazo”.
Otro problema es el impacto en el medio ambiente porque la capacitación de grandes modelos de IA utiliza mucha energía, lo que lleva a grandes huellas de carbono. El rápido ascenso de la Generación AI en los últimos años ha acelerado las preocupaciones sobre los riesgos de la IA en general. Los gobiernos están aumentando las regulaciones de IA para garantizar el desarrollo responsable y ético, especialmente la Ley de IA de la Unión Europea.
Muchas personas han interactuado con los chatbots en el servicio al cliente o han utilizado asistentes virtuales como Siri, Alexa y Google Assistant, que ahora están en la cúspide de convertirse en Gen AI Power Tools. Todo eso, junto con las aplicaciones para ChatGPT, Claude y otras herramientas nuevas, es poner ai en sus manos. Y la reacción pública a la IA generativa se ha mezclado. Muchos usuarios disfrutan de la conveniencia y la creatividad que ofrece, especialmente para cosas como escribir ayuda, creación de imágenes, soporte de tareas y productividad.
Mientras tanto, en la encuesta global de IA 2024 de McKinsey, el 65% de los encuestados dijo que sus organizaciones usan regularmente IA generativa, casi el doble de la cifra reportada solo 10 meses antes. Industrias como la atención médica y las finanzas están utilizando Gen AI para racionalizar las operaciones comerciales y automatizar tareas mundanas.
Como se mencionó, existen preocupaciones obvias sobre la ética, la transparencia, la pérdida de empleos y el potencial del mal uso de los datos personales. Esas son las principales críticas detrás de la resistencia a aceptar la IA generativa.
Y las personas que usan herramientas de IA generativas también encontrarán que los resultados aún no son lo suficientemente buenos para el tiempo. A pesar de los avances tecnológicos, la mayoría de las personas pueden reconocer si el contenido se ha creado utilizando Gen AI, ya sean artículos, imágenes o música.
AI ha secuestrado ciertas frases que siempre he usado, por lo que debo autocorrectar mi escritura a menudo porque puede parecer una IA. Muchos artículos escritos por AI contienen frases como “en la era de”, o todo es un “testimonio de” o un “tapiz de”. La IA carece de la emoción y la experiencia que viene, bueno, ser una vida humana y viviente. Como explicó un artista en Quora, “lo que AI hace no es lo mismo que el arte que evoluciona de un pensamiento en un cerebro humano” y “no se crea a partir de la pasión que se encuentra en un corazón humano”.
La IA generativa no es solo para técnicos o personas creativas. Una vez que obtienes la habilidad de darle indicaciones, tiene el potencial de hacer gran parte del trabajo preliminar por ti en una variedad de tareas diarias.
Digamos que está planeando un viaje. En lugar de desplazarse por páginas de resultados de búsqueda, le pide a un chatbot que planifique su itinerario. En cuestión de segundos, tiene un plan detallado adaptado a sus preferencias. (Ese es el ideal. Por favor, verifique siempre sus recomendaciones).
Un propietario de una pequeña empresa que necesita una campaña de marketing pero que no tiene un equipo de diseño puede usar una IA generativa para crear imágenes llamativas e incluso pedirle que sugiera copia publicitaria.
No ha habido un avance tecnológico que haya causado tal boom desde Internet y, más tarde, el iPhone. A pesar de sus desafíos, la IA generativa es innegablemente transformadora. Está haciendo que la creatividad sea más accesible, ayudando a las empresas a racionalizar los flujos de trabajo e incluso inspirar formas completamente nuevas de pensar y resolver problemas.
Pero quizás lo más emocionante es su potencial, y estamos rascando la superficie de lo que estas herramientas pueden hacer.
ChatGPT es probablemente el ejemplo más popular de IA generativa. Le das un aviso y puede generar texto e imágenes; Código de escritura; Responder preguntas; resumir el texto; borrador de correos electrónicos; y mucho más.
La IA generativa crea contenido nuevo como texto, imágenes o música, mientras que la IA tradicional analiza los datos, reconoce patrones o imágenes y hace predicciones (por ejemplo, en medicina, ciencia y finanzas).
Si busca “CHATGPT” en su navegador, es probable que se tope en sitios web que parecen estar alimentados por OpenAI, pero no lo son. Uno de esos sitios, chat.chatbotapp.ai, ofrece acceso a “GPT-3.5” de forma gratuita y utiliza marca familiar.
Pero aquí está la cosa: no está dirigida por OpenAi. Y, francamente, ¿por qué usar un GPT-3.5 potencialmente falso cuando puedes usar GPT-4O de forma gratuita en el actual ¿Sitio de chatgpt?
In the summer of 2023, Ilya Sutskever, a co-founder and the chief scientist of OpenAI, was meeting with a group of new researchers at the company. By all traditional metrics, Sutskever should have felt invincible: He was the brain behind the large language models that helped build ChatGPT, then the fastest-growing app in history; his company’s valuation had skyrocketed; and OpenAI was the unrivaled leader of the industry believed to power the future of Silicon Valley. But the chief scientist seemed to be at war with himself.
Sutskever had long believed that artificial general intelligence, or AGI, was inevitable—now, as things accelerated in the generative-AI industry, he believed AGI’s arrival was imminent, according to Geoff Hinton, an AI pioneer who was his Ph.D. adviser and mentor, and another person familiar with Sutskever’s thinking. (Many of the sources in this piece requested anonymity in order to speak freely about OpenAI without fear of reprisal.) To people around him, Sutskever seemed consumed by thoughts of this impending civilizational transformation. What would the world look like when a supreme AGI emerged and surpassed humanity? And what responsibility did OpenAI have to ensure an end state of extraordinary prosperity, not extraordinary suffering?
By then, Sutskever, who had previously dedicated most of his time to advancing AI capabilities, had started to focus half of his time on AI safety. He appeared to people around him as both boomer and doomer: more excited and afraid than ever before of what was to come. That day, during the meeting with the new researchers, he laid out a plan.
“Once we all get into the bunker—” he began, according to a researcher who was present.
“I’m sorry,” the researcher interrupted, “the bunker?”
“We’re definitely going to build a bunker before we release AGI,” Sutskever replied. Such a powerful technology would surely become an object of intense desire for governments globally. The core scientists working on the technology would need to be protected. “Of course,” he added, “it’s going to be optional whether you want to get into the bunker.”
Two other sources I spoke with confirmed that Sutskever commonly mentioned such a bunker. “There is a group of people—Ilya being one of them—who believe that building AGI will bring about a rapture,” the researcher told me. “Literally, a rapture.” (Sutskever declined to comment.)
Sutskever’s fears about an all-powerful AI may seem extreme, but they are not altogether uncommon, nor were they particularly out of step with OpenAI’s general posture at the time. In May 2023, the company’s CEO, Sam Altman, co-signed an open letter describing the technology as a potential extinction risk—a narrative that has arguably helped OpenAI center itself and steer regulatory conversations. Yet the concerns about a coming apocalypse would also have to be balanced against OpenAI’s growing business: ChatGPT was a hit, and Altman wanted more.
When OpenAI was founded, the idea was to develop AGI for the benefit of humanity. To that end, the co-founders—who included Altman and Elon Musk—set the organization up as a nonprofit and pledged to share research with other institutions. Democratic participation in the technology’s development was a key principle, they agreed, hence the company’s name. But by the time I started covering the company in 2019, these ideals were eroding. OpenAI’s executives had realized that the path they wanted to take would demand extraordinary amounts of money. Both Musk and Altman tried to take over as CEO. Altman won out. Musk left the organization in early 2018 and took his money with him. To plug the hole, Altman reformulated OpenAI’s legal structure, creating a new “capped-profit” arm within the nonprofit to raise more capital.
Since then, I’ve tracked OpenAI’s evolution through interviews with more than 90 current and former employees, including executives and contractors. The company declined my repeated interview requests and questions over the course of working on my book about it, which this story is adapted from; it did not reply when I reached out one more time before the article was published. (OpenAI also has a corporate partnership with The Atlantic.)
OpenAI’s dueling cultures—the ambition to safely develop AGI, and the desire to grow a massive user base through new product launches—would explode toward the end of 2023. Gravely concerned about the direction Altman was taking the company, Sutskever would approach his fellow board of directors, along with his colleague Mira Murati, then OpenAI’s chief technology officer; the board would subsequently conclude the need to push the CEO out. What happened next—with Altman’s ouster and then reinstatement—rocked the tech industry. Yet since then, OpenAI and Sam Altman have become more central to world affairs. Last week, the company unveiled an “OpenAI for Countries” initiative that would allow OpenAI to play a key role in developing AI infrastructure outside of the United States. And Altman has become an ally to the Trump administration, appearing, for example, at an event with Saudi officials this week and onstage with the president in January to announce a $500 billion AI-computing-infrastructure project.
Altman’s brief ouster—and his ability to return and consolidate power—is now crucial history to understand the company’s position at this pivotal moment for the future of AI development. Details have been missing from previous reporting on this incident, including information that sheds light on Sutskever and Murati’s thinking and the response from the rank and file. Here, they are presented for the first time, according to accounts from more than a dozen people who were either directly involved or close to the people directly involved, as well as their contemporaneous notes, plus screenshots of Slack messages, emails, audio recordings, and other corroborating evidence.
The altruistic OpenAI is gone, if it ever existed. What future is the company building now?
Before ChatGPT, sources told me, Altman seemed generally energized. Now he often appeared exhausted. Propelled into megastardom, he was dealing with intensified scrutiny and an overwhelming travel schedule. Meanwhile, Google, Meta, Anthropic, Perplexity, and many others were all developing their own generative-AI products to compete with OpenAI’s chatbot.
Many of Altman’s closest executives had long observed a particular pattern in his behavior: If two teams disagreed, he often agreed in private with each of their perspectives, which created confusion and bred mistrust among colleagues. Now Altman was also frequently bad-mouthing staffers behind their backs while pushing them to deploy products faster and faster. Team leads mirroring his behavior began to pit staff against one another. Sources told me that Greg Brockman, another of OpenAI’s co-founders and its president, added to the problems when he popped into projects and derailed long-standing plans with last-minute changes.
The environment within OpenAI was changing. Previously, Sutskever had tried to unite workers behind a common cause. Among employees, he had been known as a deep thinker and even something of a mystic, regularly speaking in spiritual terms. He wore shirts with animals on them to the office and painted them as well—a cuddly cat, cuddly alpacas, a cuddly fire-breathing dragon. One of his amateur paintings hung in the office, a trio of flowers blossoming in the shape of OpenAI’s logo, a symbol of what he always urged employees to build: “A plurality of humanity-loving AGIs.”
But by the middle of 2023—around the time he began speaking more regularly about the idea of a bunker—Sutskever was no longer just preoccupied by the possible cataclysmic shifts of AGI and superintelligence, according to sources familiar with his thinking. He was consumed by another anxiety: the erosion of his faith that OpenAI could even keep up its technical advancements to reach AGI, or bear that responsibility with Altman as its leader. Sutskever felt Altman’s pattern of behavior was undermining the two pillars of OpenAI’s mission, the sources said: It was slowing down research progress and eroding any chance at making sound AI-safety decisions.
Meanwhile, Murati was trying to manage the mess. She had always played translator and bridge to Altman. If he had adjustments to the company’s strategic direction, she was the implementer. If a team needed to push back against his decisions, she was their champion. When people grew frustrated with their inability to get a straight answer out of Altman, they sought her help. “She was the one getting stuff done,” a former colleague of hers told me. (Murati declined to comment.)
During the development of GPT‑4, Altman and Brockman’s dynamic had nearly led key people to quit, sources told me. Altman was also seemingly trying to circumvent safety processes for expediency. At one point, sources close to the situation said, he had told Murati that OpenAI’s legal team had cleared the latest model, GPT-4 Turbo, to skip review by the company’s Deployment Safety Board, or DSB—a committee of Microsoft and OpenAI representatives who evaluated whether OpenAI’s most powerful models were ready for release. But when Murati checked in with Jason Kwon, who oversaw the legal team, Kwon had no idea how Altman had gotten that impression.
In the summer, Murati attempted to give Altman detailed feedback on these issues, according to multiple sources. It didn’t work. The CEO iced her out, and it took weeks to thaw the relationship.
By fall, Sutskever and Murati both drew the same conclusion. They separately approached the three board members who were not OpenAI employees—Helen Toner, a director at Georgetown University’s Center for Security and Emerging Technology; the roboticist Tasha McCauley; and one of Quora’s co-founders and its CEO, Adam D’Angelo—and raised concerns about Altman’s leadership. “I don’t think Sam is the guy who should have the finger on the button for AGI,” Sutskever said in one such meeting, according to notes I reviewed. “I don’t feel comfortable about Sam leading us to AGI,” Murati said in another, according to sources familiar with the conversation.
That Sutskever and Murati both felt this way had a huge effect on Toner, McCauley, and D’Angelo. For close to a year, they, too, had been processing their own grave concerns about Altman, according to sources familiar with their thinking. Among their many doubts, the three directors had discovered through a series of chance encounters that he had not been forthcoming with them about a range of issues, from a breach in the DSB’s protocols to the legal structure of OpenAI Startup Fund, a dealmaking vehicle that was meant to be under the company but that instead Altman owned himself.
If two of Altman’s most senior deputies were sounding the alarm on his leadership, the board had a serious problem. Sutskever and Murati were not the first to raise these kinds of issues, either. In total, the three directors had heard similar feedback over the years from at least five other people within one to two levels of Altman, the sources said. By the end of October, Toner, McCauley, and D’Angelo began to meet nearly daily on video calls, agreeing that Sutskever’s and Murati’s feedback about Altman, and Sutskever’s suggestion to fire him, warranted serious deliberation.
As they did so, Sutskever sent them long dossiers of documents and screenshots that he and Murati had gathered in tandem with examples of Altman’s behaviors. The screenshots showed at least two more senior leaders noting Altman’s tendency to skirt around or ignore processes, whether they’d been instituted for AI-safety reasons or to smooth company operations. This included, the directors learned, Altman’s apparent attempt to skip DSB review for GPT-4 Turbo.
By Saturday, November 11, the independent directors had made their decision. As Sutskever suggested, they would remove Altman and install Murati as interim CEO. On November 17, 2023, at about noon Pacific time, Sutskever fired Altman on a Google Meet with the three independent board members. Sutskever then told Brockman on another Google Meet that Brockman would no longer be on the board but would retain his role at the company. A public announcement went out immediately.
For a brief moment, OpenAI’s future was an open question. It might have taken a path away from aggressive commercialization and Altman. But this is not what happened.
After what had seemed like a few hours of calm and stability, including Murati having a productive conversation with Microsoft—at the time OpenAI’s largest financial backer—she had suddenly called the board members with a new problem. Altman and Brockman were telling everyone that Altman’s removal had been a coup by Sutskever, she said.
It hadn’t helped that, during a company all-hands to address employee questions, Sutskever had been completely ineffectual with his communication.
“Was there a specific incident that led to this?” Murati had read aloud from a list of employee questions, according to a recording I obtained of the meeting.
“Many of the questions in the document will be about the details,” Sutskever responded. “What, when, how, who, exactly. I wish I could go into the details. But I can’t.”
“Are we worried about the hostile takeover via coercive influence of the existing board members?” Sutskever read from another employee later.
“Hostile takeover?” Sutskever repeated, a new edge in his voice. “The OpenAI nonprofit board has acted entirely in accordance to its objective. It is not a hostile takeover. Not at all. I disagree with this question.”
Shortly thereafter, the remaining board, including Sutskever, confronted enraged leadership over a video call. Kwon, the chief strategy officer, and Anna Makanju, the vice president of global affairs, were leading the charge in rejecting the board’s characterization of Altman’s behavior as “not consistently candid,” according to sources present at the meeting. They demanded evidence to support the board’s decision, which the members felt they couldn’t provide without outing Murati, according to sources familiar with their thinking.
In rapid succession that day, Brockman quit in protest, followed by three other senior researchers. Through the evening, employees only got angrier, fueled by compounding problems: among them, a lack of clarity from the board about their reasons for firing Altman; a potential loss of a tender offer, which had given some the option to sell what could amount to millions of dollars’ worth of their equity; and a growing fear that the instability at the company could lead to its unraveling, which would squander so much promise and hard work.
Faced with the possibility of OpenAI falling apart, Sutskever’s resolve immediately started to crack. OpenAI was his baby, his life; its dissolution would destroy him. He began to plead with his fellow board members to reconsider their position on Altman.
Meanwhile, Murati’s interim position was being challenged. The conflagration within the company was also spreading to a growing circle of investors. Murati now was unwilling to explicitly throw her weight behind the board’s decision to fire Altman. Though her feedback had helped instigate it, she had not participated herself in the deliberations.
By Monday morning, the board had lost. Murati and Sutskever flipped sides. Altman would come back; there was no other way to save OpenAI.
I was already working on a book about OpenAI at the time, and in the weeks that followed the board crisis, friends, family, and media would ask me dozens of times: What did all this mean, if anything? To me, the drama highlighted one of the most urgent questions of our generation: How do we govern artificial intelligence? With AI on track to rewire a great many other crucial functions in society, that question is really asking: How do we ensure that we’ll make our future better, not worse?
The events of November 2023 illustrated in the clearest terms just how much a power struggle among a tiny handful of Silicon Valley elites is currently shaping the future of this technology. And the scorecard of this centralized approach to AI development is deeply troubling. OpenAI today has become everything that it said it would not be. It has turned into a nonprofit in name only, aggressively commercializing products such as ChatGPT and seeking historic valuations. It has grown ever more secretive, not only cutting off access to its own research but shifting norms across the industry to no longer share meaningful technical details about AI models. In the pursuit of an amorphous vision of progress, its aggressive push on the limits of scale has rewritten the rules for a new era of AI development. Now every tech giant is racing to out-scale one another, spending sums so astronomical that even they have scrambled to redistribute and consolidate their resources. What was once unprecedented has become the norm.
As a result, these AI companies have never been richer. In March, OpenAI raised $40 billion, the largest private tech-funding round on record, and hit a $300 billion valuation. Anthropic is valued at more than $60 billion. Near the end of last year, the six largest tech giants together had seen their market caps increase by more than $8 trillion after ChatGPT. At the same time, more and more doubts have risen about the true economic value of generative AI, including a growing body of studies that have shown that the technology is not translating into productivity gains for most workers, while it’s also eroding their critical thinking.
In a November Bloomberg article reviewing the generative-AI industry, the staff writers Parmy Olson and Carolyn Silverman summarized it succinctly. The data, they wrote, “raises an uncomfortable prospect: that this supposedly revolutionary technology might never deliver on its promise of broad economic transformation, but instead just concentrate more wealth at the top.”
Meanwhile, it’s not just a lack of productivity gains that many in the rest of the world are facing. The exploding human and material costs are settling onto wide swaths of society, especially the most vulnerable, people I met around the world, whether workers and rural residents in the global North or impoverished communities in the global South, all suffering new degrees of precarity. Workers in Kenya earned abysmal wages to filter out violence and hate speech from OpenAI’s technologies, including ChatGPT. Artists are being replaced by the very AI models that were built from their work without their consent or compensation. The journalism industry is atrophying as generative-AI technologies spawn heightened volumes of misinformation. Before our eyes, we’re seeing an ancient story repeat itself: Like empires of old, the new empires of AI are amassing extraordinary riches across space and time at great expense to everyone else.
To quell the rising concerns about generative AI’s present-day performance, Altman has trumpeted the future benefits of AGI ever louder. In a September 2024 blog post, he declared that the “Intelligence Age,” characterized by “massive prosperity,” would soon be upon us. At this point, AGI is largely rhetorical—a fantastical, all-purpose excuse for OpenAI to continue pushing for ever more wealth and power. Under the guise of a civilizing mission, the empire of AI is accelerating its global expansion and entrenching its power.
As for Sutskever and Murati, both parted ways with OpenAI after what employees now call “The Blip,” joining a long string of leaders who have left the organization after clashing with Altman. Like many of the others who failed to reshape OpenAI, the two did what has become the next-most-popular option: They each set up their own shops, to compete for the future of this technology.
This essay has been adapted from Karen Hao’s forthcoming book, Empire of AI.
By Karen Hao
*Illustration by Akshita Chandra / The Atlantic. Sources: Nathan Howard / Bloomberg / Getty; Jack Guez / AFP / Getty; Jon Kopaloff / Getty; Manuel Augusto Moreno / Getty; Yuichiro Chino / Getty.
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