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Things are moving quickly in AI—and if you’re not keeping up, you’re falling behind.
Two recent developments are reshaping the landscape for developers and enterprises alike: DeepSeek’s R1 model release and OpenAI’s new Deep Research product. Together, they’re redefining the cost and accessibility of powerful reasoning models, which has been well reported on. Less talked about, however, is how they’ll push companies to use techniques like distillation, supervised fine-tuning (SFT), reinforcement learning (RL), and retrieval-augmented generation (RAG) to build smarter, more specialized AI applications.
After the initial excitement around the amazing achievements of DeepSeek begins to settle, developers and enterprise decision-makers need to consider what it means for them. From pricing and performance to hallucination risks and the importance of clean data, here’s what these breakthroughs mean for anyone building AI today.
The headline with DeepSeek-R1 is simple: It delivers an industry-leading reasoning model at a fraction of the cost of OpenAI’s o1. Specifically, it’s about 30 times cheaper to run, and unlike many closed models, DeepSeek offers full transparency around its reasoning steps. For developers, this means you can now build highly customized AI models without breaking the bank—whether through distillation, fine-tuning, or simple RAG implementations.
Distillation, in particular, is emerging as a powerful tool. By using DeepSeek-R1 as a “teacher model,” companies can create smaller, task-specific models that inherit R1’s superior reasoning capabilities. These smaller models, in fact, are the future for most enterprise companies. The full R1 reasoning model can be too much for what companies need – thinking too much, and not taking the decisive action companies need for their specific domain applications. “One of the things that no one is really talking about in, certainly in the mainstream media, is that actually the reasoning models are not working that well for things like agents,” said Sam Witteveen, an ML developer who works on AI agents, which are increasingly orchestrating enterprise applications.
As part of its release, DeepSeek distilled its own reasoning capabilities onto a number of smaller models, including open-source models from Meta’s Llama family and Alibaba’s Qwen family, as described in its paper. It’s these smaller models that can then be optimized for specific tasks. This trend toward smaller, fast models to serve custom-built needs will accelerate: there will be armies of them. “We are starting to move into a world now where people are using multiple models. They’re not just using one model all the time,” said Witteveen. And this includes the low-cost, smaller closed-sourced models from Google and OpenAI as well. “Meaning that models like Gemini Flash, GPT-4o Mini, and these really cheap models actually work really well for 80% of use cases,” he said.
After the distilling step, enterprise companies have a few options to make sure the model is ready for their specific application. If you’re a company in a very specific domain, where details around the domain are not on the web or in books – where LLMs can train on them – you can inject it with your own domain-specific data sets, in a process called supervised fine tuning (SFT). One example would be the ship container-building industry, where specifications, protocols and regulations are not widely available.
DeepSeek showed that you can do this well with “thousands” of question-answer data sets. For an example of how others can put this into practice, Chris Hay, an IBM engineer, demonstrated how he fine-tuned a small model using his own math-specific datasets to achieve lightning-fast responses—outperforming OpenAI’s o1 on the same tasks (See his hands-on video here)
Additionally, companies wanting to train a model with additional alignment to specific preferences – for example making a customer support chatbot sound empathetic while being concise – will want to do some reinforcement learning (RL) on the model. This is also good if a company wants its chatbot to adapt its tone and recommendation based on a user’s feedback. As every model gets good at everything, “personality” is going to be increasingly big, said Wharton AI professor Ethan Mollick on X yesterday.
These SFT and RL steps can be tricky for companies to implement well, however. Feed the model with data from one specific domain area, or tune it to act a certain way, and it suddenly becomes useless for doing tasks outside of that domain or style.
For most companies, however, retrieval-augmented generation (RAG) is the easiest and safest path forward. RAG is a relatively straight-forward process that allows organizations to ground their models with proprietary data contained in their own databases — ensuring outputs are accurate and domain-specific. Here, an LLM feeds a user’s prompt into vector and graph databases, in order to search information relevant to that prompt. RAG processes have gotten very good at finding only the most relevant content.
This approach also helps counteract some of the hallucination issues associated with DeepSeek, which currently hallucinates 14% of the time compared to 8% for OpenAI’s o3 model, according to a study done by Vectara, a vendor that helps companies with the RAG process.
This distillation of models plus RAG is where the magic will come for most companies. It has become so incredibly easy to do, even for those with limited data science or coding expertise. I personally downloaded the DeepSeek distilled 1.5b Qwen model, the smallest one, so that it could fit nicely on my Macbook Air. I then loaded up some PDFs of job applicant resumes into a vector database, and then asked the model to look over the applicants to tell me which ones were qualified to work at VentureBeat. (In all, this took me 74 lines of code, which I basically borrowed from others doing the same).
I loved that the Deepseek distilled model showed its thinking process behind why or why not it recommended each applicant — a transparency that I wouldn’t have gotten easily before Deepseek’s release.
In my recent video discussion on DeepSeek and RAG, I walked through how simple it has become to implement RAG in practical applications, even for non-experts. Sam Witteveen also contributed to the discussion by breaking down how RAG pipelines work and why enterprises are increasingly relying on them instead of fully fine-tuning models. (Watch it here).
While DeepSeek is making reasoning models cheaper and more transparent, OpenAI’s Deep Research announced Sunday, represents a different but complementary shift. It can take RAG to a new level by crawling the web to create highly customized research. The output of this research can then be inserted as input into the RAG documents companies can use, alongside their own data.
This functionality, often referred to as agentic RAG, allows AI systems to autonomously seek out the best context from across the internet, bringing a new dimension to knowledge retrieval and grounding.
Open AI’s Deep Research is similar to tools like Google’s Deep Research, Perplexity and You.com, but OpenAI tried to differentiate its offering by suggesting its superior chain-of-thought reasoning makes it more accurate. This is how these tools work: A company researcher requests the LLM to find all the information available about a topic in a well-researched and cited report. The LLM then responds by asking the researcher to answer another 20 sub-questions to confirm what is wanted. The research LLM then goes out and performs 10 or 20 web searches to get the most relevant data to answer all those sub-questions, then extract the knowledge and present it in a useful way.
However, this innovation isn’t without its challenges. Amr Awadallah, the CEO of Vectara, cautioned about the risks of relying too heavily on outputs from models like Deep Research. He questions whether indeed it is more accurate: “It’s not clear that this is true,” Awadallah noted: “We’re seeing articles and posts in various forums saying no, they’re getting lots of hallucinations still and Deep Research is only about as good as other solutions out there on the market.”
In other words, while Deep Research offers promising capabilities, enterprises need to tread carefully when integrating its outputs into their knowledge bases. The grounding knowledge for a model should come from verified, human-approved sources to avoid cascading errors, Awadallah said.
The most immediate impact of DeepSeek’s release is its aggressive price reduction. The tech industry expected costs to come down over time, but few anticipated just how quickly it would happen. DeepSeek has proven that powerful, open models can be both affordable and efficient, creating opportunities for widespread experimentation and cost-effective deployment.
Awadallah emphasized this point, noting that the real game-changer isn’t just the training cost—it’s the inference cost, which for DeepSeek is about 1/30th of OpenAI’s o1 or o3 for inference cost per token. “The margins that OpenAI, Anthropic, and Google Gemini were able to capture will now have to be squished by at least 90% because they can’t stay competitive with such high pricing,” Awadallah said.
Not only that, but those costs will continue to go down. Dario Amodei, CEO of Anthropic said recently that the cost of developing models continues to drop at around a 4x rate each year. It follows that the rate that LLM providers charge to use them will continue to drop as well. “I fully expect the cost to go to zero,” said Ashok Srivastava, chief data officer of Intuit, a company that has been driving AI hard in its tax and accounting software offerings like TurboTax and Quickbooks. “…and the latency to go to zero. They’re just going to be commodity capabilities that we will be able to use.”
This cost reduction isn’t just a win for developers and enterprise users; it’s a signal that AI innovation is no longer confined to big labs with billion-dollar budgets. The barriers to entry have dropped, and that’s inspiring smaller companies and individual developers to experiment in ways that were previously unthinkable. Most importantly, the models are so accessible that any business professional will be using them, not just AI experts, said Srivastava.
Most importantly, DeepSeek has shattered the myth that only major AI labs can innovate. For years, companies like OpenAI and Google positioned themselves as the gatekeepers of advanced AI, spreading the belief that only top-tier PhDs with vast resources could build competitive models.
DeepSeek has flipped that narrative. By making reasoning models open and affordable, it has empowered a new wave of developers and enterprise companies to experiment and innovate without needing billions in funding. This democratization is particularly significant in the post-training stages—like RL and fine-tuning—where the most exciting developments are happening.
DeepSeek exposed a fallacy that had emerged in AI—that only the big AI labs and companies could really innovate. This fallacy had forced a lot of other AI builders to the sidelines. DeepSeek has put a stop to that. It has given everyone inspiration that there’s a ton of ways to innovate in this area.
While DeepSeek and Deep Research offer powerful tools, their effectiveness ultimately hinges on one critical factor: data quality. Getting your data in order has been a big theme for years, and accelerated over the past nine years of the AI era. But it has become even more important with generative AI, and now with DeepSeek’s disruption, it’s absolutely key. Hilary Packer, CTO of American Express, underscored this in an interview with VentureBeat yesterday: “The AHA moment for us, honestly, was the data. You can make the best model selection in the world… but the data is key. Validation and accuracy are the holy grail right now of generative AI.”
This is where enterprises must focus their efforts. While it’s tempting to chase the latest models and techniques, the foundation of any successful AI application is clean, well-structured data. Whether you’re using RAG, SFT, or RL, the quality of your data will determine the accuracy and reliability of your models.
And while many companies aspire to perfect their entire data ecosystems, the reality is that perfection is elusive. Instead, businesses should focus on cleaning and curating the most critical portions of their data to enable point AI applications that deliver immediate value.
Related to this, a lot of questions linger around the exact data that DeepSeek used to train its models on, and this raises questions about the inherent bias of the knowledge stored in its model weights. But that’s no different from questions around other open source models, such as Meta’s Llama model series. Most enterprise users have found ways to fine-tune or ground the models with RAG enough so that they can mitigate any problems around such biases. And that’s been enough to create serious momentum within enterprise companies toward accepting open source, indeed even leading with open source.
Similarly, there’s no question that many companies will be using DeepSeek models, regardless of the fear around the fact that the company is from China. Though it’s also true that a lot of companies in highly regulated companies such as finance or healthcare are going to be cautious about using any DeepSeek model in any application that interfaces directly with customers, at least in the short-term.
DeepSeek and OpenAI’s Deep Research are more than just new tools in the AI arsenal—they’re signals of a profound shift, where enterprises will be rolling out masses of purpose-built models, extremely affordably, competent, and grounded in the company’s own data and approach.
For enterprises, the message is clear: the tools to build powerful, domain-specific AI applications are at your fingertips. You risk falling behind if you don’t leverage these tools. But real success will come from how you curate your data, leverage techniques like RAG and distillation, and innovate beyond the pre-training phase.
As AmEx’s Packer put it, the companies that get their data right will be the ones leading the next wave of AI innovation.
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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