Using a conventional phone to access generative AI via voice-only just got a big boost via OpenAI’s … [+]
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In today’s column, I discuss the hot trend of being able to access generative AI and large language models (LLMs) via a simple phone call. No smartphone is required — just use any mobile phone or even old-fashioned landline, and you immediately have unfettered voice access to a full-on generative AI capability.
Nice.
This trend has notably been given a huge boost due to OpenAI announcing their newly available 1-800-CHATGPT (1-800-242-8478). Yes, OpenAI ChatGPT, the 600-pound gorilla or elephant in the room, has grandly made phone usage of generative AI a widespread phenomenon that is going to spur other AI makers to do likewise. This is decidedly an earth-shattering upping of the ante in the fiercely contested AI one-upmanship taking place.
In the case of ChatGPT access, you are limited to up to 15 minutes of free phone-based usage per month. No registration is required. The AI simply notes the phone number that you are calling from to keep track of your allowed usage (must be a U.S.-based line for now). If you happen to have more than one phone, voila, you can essentially get more time per month by using up the 15 minutes permitted per separate phone line. For those outside the U.S. or who otherwise don’t want to make a phone call per se, OpenAI has also established a text-message-based approach to the same catchy phone number via the use of WhatsApp.
Before you get started partying, it turns out that there is more to being astute and safe about using generative AI over the phone than perhaps meets the eye. There are certainly celebratory upsides, but lots of disconcerting downsides too.
Let’s talk about it.
This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI including identifying and explaining various impactful AI complexities (see the link here). For my coverage of the top-of-the-line ChatGPT o1 model and its advanced functionality, see the link here and the link here.
Before we dive into the pluses and minuses, let’s make sure we are all on the same page about how accessing generative AI via a regular phone takes place.
The fundamentals are straightforward.
I’m assuming that you might have already used a generative AI capability via the web or possibly downloaded an app for use on your smartphone. If so, you know that once you have reached the generative AI, perhaps having to log in first, you then enter a prompt. Based on the prompt, the generative AI generates a response. All of this is typically done on a text-to-text basis, whereby you enter text as your prompt and get a text-based response from the AI.
You normally proceed with a back-and-forth of you entering a prompt and the AI provides a response. This happens on a turn-by-turn basis. Modern-day generative AI is amazingly fluent-like. You can easy-peasy carry on a compelling written conversation with the AI.
Some generative AI now allows a voice-to-text feature. This involves you speaking to the AI and your spoken words are converted into text. The text then gets fed into the AI. All else is roughly the same thereafter. In addition, some generative AI currently allows for a text-to-voice feature. That involves the AI producing text as a response, but rather than showing you the text, the system reads aloud the text to you.
Here’s the deal with regular phone-based access to generative AI.
You dial a phone number that automatically connects you to the designated generative AI.
The AI starts the conversation by doing a text-to-voice telling you a standardized opening. For example, when calling ChatGPT you generally get this introductory salutation:
On any subsequent calls that you make to the ChatGPT phone line, the AI will detect that you previously called and will therefore start the opening by saying “Hello, again” and follow with the rest of the standard opening.
You then carry on a conversation with the AI, entirely via voice. Your voice utterances are converted into text, the AI processes the text as normally would be the case, and the AI generates a text response that is then said to you via a text-to-voice function.
Voila, this process continues until either you hang up or your time limit is reached.
In the real world, not everything is wine and roses.
Let’s see how that applies to generative AI being accessed via a regular phone.
First, some people are undoubtedly going to misdial and reach some number other than the designated one. Oopsie. The problem too is that this might occur on a massive scale. Currently, there are 300 million weekly active users of ChatGPT. How many of those users will opt to access ChatGPT via regular phone? In addition, how many new users who haven’t used ChatGPT will be attracted to using phone-based access?
We don’t know what the volume is going to be, but the odds are that it could be a rather large number. The point is that even if some small percentage misdials, there could be a slew of people calling other numbers inadvertently. On a tremendously beguiling scale. This could be an exasperating mess.
Second, and I deplore saying so, there is a distinct possibility that some evildoers will try to grab-up other phone numbers that are specifically similar to the designated phone number. Here’s their scam. They hope that people will misdial to their dastardly number. They then try to sell the person on swamp land or some other rip-off. People will be perhaps confused and assume that they have reached the correct number, a reputable number. Baddies will deceptively take advantage of them by asking for credit card numbers, social security numbers, and the like.
Sad face.
The next thing to consider is whether you are able to be adequately heard by the generative AI.
Imagine this. You tell the AI to find all the stores that are near the city named Pinole. Though voice-to-text translation is pretty good these days, there is a solid chance that your utterance will be misheard. The AI responds by telling you about the stores that are near the city named Binhole, a completely different city.
You and the AI go back and forth trying to iron this out. Meanwhile, your allotted time, let’s say 15 minutes, is rapidly dwindling. You decide this is fruitless and in anger vow to never call the number again.
Even if your commentary is readily interpretable, including an accent, there is the issue of potential background noise. Suppose you are standing on the streets of New York City and trying to get the generative AI to tell you which museum has your favorite painting. The background noise could be marring your perfect pronunciation.
Rinse and repeat.
Another frustration will be dropped lines. If you are in a rideshare car and the driver doesn’t speak your language, you might be tempted to call the generative AI and ask it to convert your destination instructions into that other language. You make the connection to the AI, which maybe can’t connect at first or at all due to a bad reception — and then start a conversation. Midway through, the line drops.
All in all, there might be a lot of angst involved.
I’ve previously covered that many of the generative AI apps stipulate in their licensing agreements that whatever text you enter is fully accessible to the AI maker, see my analysis at the link here. The issue is this. Your text-based prompts under those stipulations can be examined by the AI researchers and AI developers of the AI maker. They can also reuse your entered data to further data train the AI. Bottom-line is that you are potentially getting yourself into a privacy intrusion and undercut any semblance of confidentiality.
Most users don’t know of this.
What about phone-based access to generative AI?
It is conceivable that an AI maker will decide to employ the same licensing requirements.
Realize that your utterances are being converted into text and that the transcribed text will be stored and made available to the AI maker. Keep this in mind. You’ll need to decide what kind of remarks or commentary you are willing to say to the generative AI phone-based capability. Also, it would be wise to find out beforehand what licensing stipulations the AI maker has established for their phone-based generative AI.
An added twist occurs with the voice aspects in the context of phone-based interaction.
When you enter text into a conventional generative AI online, the only communication being conveyed is the text. Period, end of story. In contrast, when you speak via phone, your voice is being captured too.
Your actual voice.
So what?
If the licensing allows the AI maker to exploit your voice, they could potentially use it to make a synthetic voice that sounds like you. Or, if not going quite that far, they might use your voice recording to further train the AI on how to interpret voice utterances. The gist is that your voice is possibly going to be used in ways that you might not have thought would occur.
Be wary and on your toes.
I’m guessing that some might be thinking that since you don’t need to register to use the generative AI for its phone-based functionality, you are essentially acting anonymously. Ergo, it doesn’t matter what you say, nor whether they record you or not. They just have some random person’s data that they have collected.
You can go on your merry way.
Well, maybe yes, maybe not.
The usual method of tracking you is going to be via the phone number you are using to make the call. That is something you are freely giving up. They could presumably try to pair the phone number with other databases. Doing so might enable the AI maker to figure out your name, address, age, and a wide variety of personal data.
The twofer is they can match what you’ve said to who you are.
Envision that you have called the AI several times to ask questions about sailboats. The next thing you know, the AI maker behind the scenes sells your name and phone number to a company that makes and sells sailboats. Of course, they can do this with just the phone number alone, not necessarily having to go through the trouble of matching your phone number to who you are. They simply sell your phone number and the fact that you have made inquiries about sailboats, the rest is up to the buyer for that information.
An interesting angle is that if an AI maker goes that route, they are likely to be embroiled in numerous federal, state, and local laws on such matters. The FTC is already pursuing various companies for AI-based scams, see my coverage at the link here. It would seem doubtful that the major generative AI vendors would go down that bumpy path. Presumably, hopefully, not.
The other issue is that once the use of generative AI via phone becomes a common practice, fly-by-nights could set up similar arrangements. Call this or that 800 number and get a full hour with interactive AI. Call now. Don’t wait.
How can they afford to do this?
They lean into the data in the manner I’ve described above.
A typical phone-based approach is going to assume that the user has a voice-only phone.
Thus, the AI cannot ask the user to take a picture of something. Without having a visual clue of what the user is discussing, the generative AI might have a limited ability to provide on-target responses.
For example, I am walking through a beautiful outdoor park and happen to notice a plant that looks potentially dangerous. I want to quickly find out if the plant is harmful. With smartphones, you can usually load an app or make an online connection that allows you to activate the camera on your phone. If the camera isn’t activated for the app, you can typically take a photo and send the picture to the app.
In the case of a typical phone-based generative AI, you have to be good enough at describing things to do what you need to do. For the plant, I might tell the AI that it is green in color, has leaves that are three-pronged, and appears to grow near the base of trees. Is that sufficient for the AI to figure out what plant it is? Probably a stretch.
The other side of this same coin is that the generative AI cannot display to the user a result in any pictorial way. Nor can the AI give the result in a text format. Why would someone want their result in text versus machine-produced voice? It could be that the person can’t adequately hear the AI, or maybe they hear it but want to write down what the AI has said. Having a text option would be handy, but again we are assuming that the user is calling on a conventional phone that lacks a multi-modal capacity.
Gradually, you can bet that most of the phone-based generative AI offerings will readily switch to multi-modal mode if a user is calling from a suitably equipped smartphone. The moment you make the connection to the AI, it will detect what your device is and what it can do. From then on, the AI will inform you of the ways to provide input and the means of producing outputs to the device that you are using.
Gosh, some of you might be thinking, this discussion is all doom and gloom.
Isn’t there anything upbeat to say about this emerging means of using generative AI?
Yes, absolutely, there is lots to say.
The very exciting prospect is that people who have not yet experienced generative AI due to lack of an Internet connection or not having Wi-Fi will now be able to readily use generative AI. There might be millions upon millions of people who either can’t afford the equipment for such access or don’t live in a place where access is feasible.
The reach of a regular phone call is an incredible expansion of possibilities. I dare say that making phone calls is a lot simpler, easier, and readily possible. You don’t need to download anything to use the AI. You can use the AI pretty much anywhere and at any time, assuming you have access to a phone.
Some would assert that this is a vital step in the democratization of AI (read about the essentials at the link here). That’s a catchphrase that says we don’t want to end up in a situation of those that have AI and those that don’t have AI. The have-nots are presumably going to be at a disadvantage to the haves. Phone access ought to go a long way toward leveling the playing field concerning the access constraint.
All in all, you’ve got the widespread ubiquity of phones, the ease of using a regular phone, the relatively low cost of the phone and hopefully low cost for usage, and access to generative AI that only requires being able to speak. No typing skills are needed. No dealing with logins. Etc.
Just call and start using generative AI.
Boom, drop the mic.
I hope that the last bit of rah-rah gives you a sense of how important this new trend is.
And though I certainly don’t want to spoil the party, I ask that we all keep our heads and realize that in some sense we are also opening a Pandora’s box. How so? As I’ve repeatedly stated, people are using generative AI for all kinds of purposes, including mental health guidance. They simply access generative AI and start asking for therapy that would be seemingly akin to meeting with a human therapist, and most of the AI apps readily comply, see my analysis at the link here.
The good news is that phone-based access to generative AI implies that a bunch more people can now use AI for their mental health assistance. The bad news is roughly the same, namely, we are amid a massive scale experiment of people using everyday generative AI to give them mental health guidance. What if the AI isn’t doing this prudently? What if people avoid seeking human therapists since they assume AI is all they need?
The population-level consequences are potentially staggering and we upping the ante via phone-based generative AI access, see my predictions on what might arise at a population-level, at the link here.
A final contemplative thought for now.
In 1876, Alexander Graham Bell purportedly transmitted the first-ever recognizable speech message to his assistant Thomas A. Watson by saying, “Mr. Watson come here, I want you.” A stellar moment in history. Something never to be forgotten.
You might one day want to tell your kids that you were one of the first to use a phone-based generative AI. Something you’ll never forget. Think about your options, decide what seems appropriate to your needs and concerns, and make that call.
Get going and remember the date that you did so.
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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