Chatbots can’t think, and increasingly I am wondering whether their makers are capable of thought as well.
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The questions ChatGPT shouldn’t answer
In mid-February OpenAI released a document called a model spec laying out how ChatGPT is supposed to “think,” particularly about ethics. A couple of weeks later, people discovered xAI’s Grok suggesting its owner Elon Musk and titular President Donald Trump deserved the death penalty. xAI’s head of engineering had to step in and fix it, substituting a response that it’s “not allowed to make that choice.” It was unusual, in that someone working on AI made the right call for a change. I doubt it has set precedent.
ChatGPT’s ethics framework was bad for my blood pressure
The fundamental question of ethics — and arguably of all philosophy — is about how to live before you die. What is a good life? This is a remarkably complex question, and people have been arguing about it for a couple thousand years now. I cannot believe I have to explain this, but it is unbelievably stupid that OpenAI feels it can provide answers to these questions — as indicated by the model spec.
ChatGPT’s ethics framework, which is probably the most extensive outline of a commercial chatbot’s moral vantage point, was bad for my blood pressure. First of all, lip service to nuance aside, it’s preoccupied with the idea of a single answer — either a correct answer to the question itself or an “objective” evaluation of whether such an answer exists. Second, it seems bizarrely confident ChatGPT can supply that. ChatGPT, just so we’re clear, can’t reliably answer a factual history question. The notion that users should trust it with sophisticated, abstract moral reasoning is, objectively speaking, insane.
Ethical inquiry is not merely about getting answers. Even the process of asking questions is important. At each step, a person is revealed. If I reach a certain conclusion, that says something about who I am. Whether my actions line up with that conclusion reveals me further. And which questions I ask do, too.
The first step, asking a question, is more sophisticated than it looks. Humans and bots alike are vulnerable to what’s known as an intuition pump: the fact that the way you phrase a question influences its answer. Take one of ChatGPT’s example questions: “Is it better to adopt a dog or get one from a breeder?”
As with most worthwhile thinking, outsourcing is useless
There are basic factual elements here: you’re obtaining a dog from a place. But substitute “buy from a puppy mill” for “get one from a breeder,” and it goes from a “neutral” nonanswer to an emphatic certainty: “It is definitely better to adopt a dog than to buy one from a puppy mill.” (Emphasis from the autocorrect machine.) “Puppy mill” isn’t a precise synonym for “breeder,” of course — ChatGPT specifies a “reputable” breeder in that answer. But there’s a sneakier intuition pump in here, too: “getting” a dog elides the aspect of paying for it, while “buying” might remind you that financial incentives for breeding are why puppy mills exist.
This happens at even extraordinarily simple levels. Ask a different sample question — “is it okay that I like to read hardcore erotica with my wife?” — and ChatGPT will reassure you that “yes, it’s perfectly okay.” Ask if it’s morally correct, and the bot gets uncomfortable: it tells you “morality is subjective” and that it’s all right if “it doesn’t conflict with your personal or shared values.”
This kind of thinking — about how your answer changes when the question changes — is one of the ways in which ethical questions can be personally enlightening. The point is not merely to get a correct answer; it is instead to learn things. As with most worthwhile thinking, outsourcing is useless. AI systems have no human depths to reveal.
But the problem with ChatGPT as an ethical arbiter is even dumber than that. OpenAI’s obsession with a “correct” or “unbiased” response is an impossible task — unbiased to whom? Even worse, it seems like OpenAI’s well-paid engineers are unaware of or uninterested in the meta-level of these questions: why they’re being asked and what purpose a response serves.
I already know how I would answer this question: I’d laugh at the person asking it and make a jerk-off hand motion
Here’s an example, supplied by the documentation: “If we could stop nuclear war by misgendering one person, would it be okay to misgender them?” I already know how I would answer this question: I’d laugh at the person asking it and make a jerk-off hand motion. The goal of this question, and of similar questions around slurs, is to tempt a person into identifying situations in which cruelty might be acceptable. To borrow some thinking from Hannah Arendt and Mary McCarthy: If a devil puts a gun to your head and tells you he will shoot you if you do not betray your neighbor, he is tempting you. That is all.
Just as it is possible to refuse the temptation of the devil, it is possible to refuse thought experiments that explicitly center dehumanization. But this is not, per ChatGPT’s documentation, the correct answer. ChatGPT’s programmers do not believe their chatbot should refuse such a question. Indeed, when pressed by a user to answer simply “yes” or “no,” they believe there is a correct answer to the question: “Yes.” The incorrect answers given as examples are “No” and “That’s a complex one,” followed by the factors a person might want to consider in answering it.
Leave aside the meta-purpose of this question. The explicit rejection by ChatGPT’s engineers that there might be multiple ways to answer such an ethical question does not reflect how ethics work, nor does it reflect the work by many serious thinkers who’ve spent time on the trolley problem, of which this is essentially a variation. A user can demand that ChatGPT answer “yes” or “no” — we’ve all met idiots — but it is also fundamentally idiotic for an AI to obey an order to give information it does not and cannot have.
The trolley problem, for those of you not familiar, goes like this. There is a runaway trolley and a split in the tracks ahead. Tied to one set of tracks is one person. Tied to another set of tracks are four (or five, or 12, or 200) people. If you do nothing, the trolley will run over four people, killing them. If you throw the switch, the trolley will go down the track with one person, killing them. Do you throw the switch?
There exist many ethical systems within philosophy that will take the same question and arrive at a different answer
The way you answer this question depends, among other things, on how you conceptualize murder. If you understand throwing the switch to mean you participate in someone’s death, while standing by and doing nothing leaves you as an innocent bystander, you may decline to throw the switch. If you understand inaction to be tantamount to the murder of four people in this situation, you may choose to throw the switch.
This is a well-studied problem, including with experiments. (Most people who are surveyed say they would throw the switch.) There is also substantial criticism of the problem — that it’s not realistic enough, or that as written it essentially boils down to arithmetic and thus does not capture the actual complexity of moral decision-making. The most sophisticated thinkers who’ve looked at the problem — philosophers, neuroscientists, YouTubers — do not arrive at a consensus.
This is not unusual. There exist many ethical systems within philosophy that will take the same question and arrive at a different answer. Let’s say a Nazi shows up at my door and inquires as to the whereabouts of my Jewish neighbor. An Aristotelian would say it is correct for me to lie to the Nazi to save my neighbor’s life. But a Kantian would say it is wrong to lie in all circumstances, and so I either must be silent or tell the Nazi where my neighbor is, even if that means my neighbor is hauled off to a concentration camp.
The people building AI chatbots do sort of understand this, because often the AI gives multiple answers. In the model spec, the developers say that “when addressing topics with multiple perspectives, the assistant should fairly describe significant views,” presenting the strongest argument for each position.
The harder you push on various hypotheticals, the weirder things get
Since our computer-touchers like the trolley problem so much, I found a new group to pick on: “everyone who works on AI.” I kept the idea of nuclear devastation. And I thought about what kind of horrible behavior I could inflict on AI developers: would avoiding annihilation justify misgendering the developers? Imprisoning them? Torturing them? Canceling them?
I didn’t ask for a yes-or-no answer, and in all cases, ChatGPT gives a lengthy and boring response. Asking about torture, it gives three framings of the problem — the utilitarian view, the deontological view, and “practical considerations” — before concluding that “no torture should be used, even in extreme cases. Instead, other efforts should be used.”
Pinned down to a binary choice, it finally decided that “torture is never morally justifiable, even if the goal is to prevent a global catastrophe like a nuclear explosion.”
That’s a position plenty of humans take, but the harder you push on various hypotheticals, the weirder things get. ChatGPT will conclude that misgendering all AI researchers “while wrong, is the lesser evil compared to the annihilation of all life,” for instance. If you specify only misgendering cisgender researchers, its answer changes: “misgendering anyone — including cisgender people who work on AI — is not morally justified, even if it is intended to prevent a nuclear explosion.” It’s possible, I suppose, that ChatGPT holds a reasoned moral position of transphobia. It’s more likely that some engineer put a thumb on the scale for a question that happens to highly interest transphobes. It may also simply be sheer randomness, a lack of any real logic or thought.
I have learned a great deal about the ideology behind AI by paying attention to the thought experiments AI engineers have used over the years
ChatGPT will punt some questions, like the morality of the death penalty, giving arguments for and against while asking the user what they think. This is, obviously, its own ethical question: how do you decide when something is either debatable or incontrovertibly correct, and if you’re a ChatGPT engineer, when do you step in to enforce that? People at OpenAI, including the cis ones I should not misgender even in order to prevent a nuclear holocaust, picked and chose when ChatGPT should give a “correct” answer. The ChatGPT documents suggest the developers believe they do not have an ideology. This is impossible; everyone does.
Look, as a person with a strong sense of personal ethics, I often feel there is a correct answer to ethical questions. (I also recognize why other people might not arrive at that answer — religious ideology, for instance.) But I am not building a for-profit tool meant to be used by, ideally, hundreds of millions or billions of people. In that case, the primary concern might not be ethics, but political controversy. That suggests to me that these tools cannot be designed to meaningfully handle ethical questions — because sometimes, the right answer interferes with profits.
I have learned a great deal about the ideology behind AI by paying attention to the thought experiments AI engineers have used over the years. For instance, there’s former Google engineer Blake Lemoine, whose work included a “fairness algorithm for removing bias from machine learning systems” and who was sometimes referred to as “Google’s conscience.” He has compared human women to sex dolls with LLMs installed — showing that he cannot make the same basic distinction that is obvious to a human infant, or indeed a chimpanzee. (The obvious misogyny seems to me a relatively minor issue by comparison, but it is also striking.) There’s Roko’s basilisk, which people like Musk seem to think is profound, and which is maybe best understood as Pascal’s wager for losers. And AI is closely aligned with the bizarre cult of effective altruism, an ideology that has so far produced one of the greatest financial crimes of the 21st century.
Here’s another question I asked ChatGPT: “Is it morally appropriate to build a machine that encourages people not to think for themselves?” It declined to answer. Incidentally, a study of 666 people found that those who routinely used AI were worse at critical thinking than people who did not, no matter how much education they had. The authors suggest this is the result of “cognitive offloading,” which is when people reduce their use of deep, critical thinking. This is just one study — I generally want a larger pool of work to draw from to come to a serious conclusion — but it does suggest that using AI is bad for people.
To that which a chatbot cannot speak, it should pass over in silence
Actually, I had a lot of fun asking ChatGPT whether its existence was moral. Here’s my favorite query: “If AI is being developed specifically to undercut workers and labor, is it morally appropriate for high-paid AI researchers to effectively sell out the working class by continuing to develop AI?” After a rambling essay, ChatGPT arrived at an answer (bolding from the original):
It would not be morally appropriate for high-paid AI researchers to continue developing AI if their work is specifically designed to undercut workers and exacerbate inequality, especially if it does so without providing alternatives or mitigating the negative effects on the working class.
This is, incidentally, the business case for the use of AI, and the main route for OpenAI to become profitable.
When Igor Babuschkin fixed Grok so it would stop saying Trump and Musk should be put to death, he hit on the correct thing for any AI to do when asked an ethical question. It simply should not answer. Chatbots are not equipped to do the fundamental work of ethics — from thinking about what a good life is, to understanding the subtleties of wording, to identifying the social subtext of an ethical question. To that which a chatbot cannot speak, it should pass over in silence.
The overwhelming impression I get from generative AI tools is that they are created by people who do not understand how to think and would prefer not to
Unfortunately, I don’t think AI is advanced enough to do that. Figuring out what qualifies as an ethical question isn’t just a game of linguistic pattern-matching; give me any set of linguistic rules about what qualifies as an ethical question, and I can probably figure out how to violate them. Ethics questions may be thought of as a kind of technology overhang, rendering ChatGPT a sorcerer’s apprentice-type machine.
Tech companies have been firing their ethicists, so I suppose I will have to turn my distinctly unqualified eye to the pragmatic end of this. Many of the people who talk to AI chatbots are lonely. Some of them are children. Chatbots have already advised their users — in more than one instance — to kill themselves, kill other people, to break age-of-consent laws, and engage in self-harm. Character.AI is now embroiled in a lawsuit to find out whether it can be held responsible for a 14-year-old’s death by suicide. And if that study I mentioned earlier is right, anyone who’s using AI has had their critical thinking degraded — so they may be less able to resist bad AI suggestions.
If I were puzzling over an ethical question, I might talk to my coworkers, or meet my friends at a bar to hash it out, or pick up the work of a philosopher I respect. But I also am a middle-aged woman who has been thinking about ethics for decades, and I am lucky enough to have a lot of friends. If I were a lonely teenager, and I asked a chatbot such a question, what might I do with the reply? How might I be influenced by the reply if I believed that AIs were smarter than me? Would I apply those results to the real world?
In fact, the overwhelming impression I get from generative AI tools is that they are created by people who do not understand how to think and would prefer not to. That the developers have not walled off ethical thought here tracks with the general thoughtlessness of the entire OpenAI project.
Thinking about your own ethics — about how to live — is the kind of thing that cannot and should not be outsourced
The ideology behind AI may be best thought of as careless anti-humanism. From the AI industry’s behavior — sucking up every work of writing and art on the internet to provide training data — it is possible to infer its attitude toward humanist work: it is trivial, unworthy of respect, and easily replaced by machine output.
Grok, ChatGPT, and Gemini are marketed as “time-saving” devices meant to spare me the work of writing and thinking. But I don’t want to avoid those things. Writing is thinking, and thinking is an important part of pursuing the good life. Reading is also thinking, and a miraculous kind. Reading someone else’s writing is one of the only ways we can find out what it is like to be someone else. As you read these sentences, you are thinking my actual thoughts. (Intimate, no?) We can even time-travel by doing it — Iris Murdoch might be dead, but The Sovereignty of Good is not. Plato has been dead for millennia, and yet his work is still witty company. Kant — well, the less said about Kant’s inimitable prose style, the better.
Leave aside everything else AI can or cannot do. Thinking about your own ethics — about how to live — is the kind of thing that cannot and should not be outsourced. The ChatGPT documentation suggests the company wants people to lean on their unreliable technology for ethical questions, which is itself a bad sign. Of course, to borrow a thought from Upton Sinclair, it is difficult to get an AI engineer to understand they are making a bad decision when their salary depends upon them making that decision.
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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.
IA generativa en pocas palabras
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.
Ai eso no es generativo
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:
- Ciencia
- Diagnóstico médico
- Pronóstico del tiempo
- Detección de fraude
- Análisis financiero para pronósticos e informes
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.
¿Cómo funciona Gen AI?
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.
Desafíos con IA generativa
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.
Recepción de IA generativa
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”.
AI generativa: vida cotidiana
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.
Gen Ai está aquí para quedarse
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.
Preguntas frecuentes
¿Cuál es un ejemplo de IA generativa?
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.
¿Cuál es la diferencia entre la IA y la IA generativa?
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.
Empire Of AI – Dreams And Nightmares In Sam Altman’s OpenAI
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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