The emergence of ChatGPT search has led to many questions about the quality of the overall results compared to Google.
This is a difficult question to answer, and in today’s article, I will provide some insights into how to do just that.
Note that our understanding is that the technology that makes it possible for OpenAI to offer a search capability is called SearchGPT, but the actual product name is ChatGPT search.
In this article, we will use the name ChatGPT search.
This report presents an analysis of 62 queries to assess the strengths and weaknesses of each platform.
Each response was meticulously fact-checked and evaluated for alignment with potential user intents.
The process, requiring about an hour per query, highlighted that “seemingly good” and “actually good” answers often differ.
Additionally, when Google provided an AI Overview, it was scored against ChatGPT search.
A combined score for the AI Overviews and the rest of Google’s SERP was also included.
Of the queries tested – two-thirds of which were informational – Google returned an AI Overview in 25 instances (40% of the time).
The queries analyzed fell into multiple categories:
The total number of the above is greater than 100%, and that’s because some queries could fall into more than one classification.
For example, about 13% of the queries were considered informational and commercial.
Detailed information from SparkToro on the makeup of queries suggests a natural distribution of search queries as follows:
Navigational queries, which comprise nearly a third of all queries, were excluded from this test.
These queries typically demand a straightforward response like, “just give me the website,” and are a category where Google excels.
However, I included queries likely to favor one platform, such as:
I designed 62 queries to reflect diverse query intents, aiming to highlight each platform’s strengths and weaknesses.
Each response was scored across specific metrics to evaluate performance effectively.
At the end of this article are the total scores for each platform across the 62 queries.
When considering how different search platforms provide value, it’s important to understand the many aspects of the search experience. Here are some of those areas:
Multiple reviewers note that ChatGPT search is ad-free and tout how much better this makes it than Google. That is certainly the case now, but it won’t stay that way.
Microsoft has $13 billion committed to OpenAI so far, and they want to make that money back (and then some).
In short, don’t expect ChatGPT search to remain ad-free. That will change significantly at some point.
An important note is that advertising works best on commercial queries.
As you will see later in this article, I scored Google’s performance on commercial queries significantly higher than ChatGPT search.
Google has been working on understanding user intent across nearly infinite scenarios since 2004 or earlier.
They’ve been collecting data based on all the user interactions within search and leveraging what they have seen with the Chrome browser since its launch in 2008.
This data has most likely been used to help train Google algorithms to understand user intent and brand authority on a per query basis.
For reference, as of November 2024, Statcounter pegs Chrome’s market share at 67.5%, Safari at 18.2%, and Edge at 4.8%
This is a critical advantage for Google because understanding the user intent of a query is what it’s all about.
You can’t possibly answer the user’s need without understanding their need. As I’ll illustrate in the next section, this is complex!
Part of the problem with understanding user intent is that the user may not have fully worked out what they’re looking for until they start the process.
Consider the following example of a query sequence that was given to me via Microsoft many years ago:
The initial query seems quite simple: “Merrell Shoes.”
You can imagine that the user entering that query often has a specific Merrell shoe in mind, or at least a shoe type, that they want to buy.
However, we see this user’s path has many twists and turns.
For example, the second site they visit is www.merrell.com, a website you might suspect has authoritative information about Merrell shoes.
However, this site doesn’t appear to satisfy the user’s needs.
The user ends up trying four more different queries and visiting six different websites before they finally execute a transaction on www.zappos.com.
This degree of uncertainty in search query journeys is quite common.
Some of the reasons why users have this lack of clarity include is that they:
Addressing this is an essential aspect of providing a great search experience. This is why the Follow-Up Resources score is part of my analysis.
Queries can be broadly categorized into several distinct groups, as outlined below:
Recent data from SparkToro’s Rand Fishkin provides some insight into the percentage of search queries that fall into each of these categories:
Be advised that the above is a broad view of the categories of queries.
The real work in search relates to handling searches on a query-by-query basis. Each query has many unique aspects that affect how it can be interpreted.
Next, we’ll examine several examples to illustrate this. Then, we’ll compare how ChatGPT search and Google performed on these queries.
This query type is a natural strength for Google (as is any locally oriented query). We can see ChatGPT search’s weaknesses in this area in its response:
The problems with this response are numerous.
For example, I wasn’t in Marlborough, Massachusetts, when I did the query (I was in the neighboring town of Southborough).
In addition, steps 1 and 2 in the directions are unclear. Anyone following them and heading east on Route 20 would end up at Kenmore Square in Boston without ever crossing I-90 East.
In contrast, Google nails it:
The reason why Google handles this better is simple.
Google Maps has an estimated 118 million users in the U.S., and Waze adds another 30 million users.
I wasn’t able to find a reasonable estimate for Bing Maps, but suffice it to say that it’s far lower than Google’s.
The reason Google is so much better than Bing here is simple – I use Google Maps, and that lets Google know exactly where I am.
This advantage applies to all Google Maps and Waze users in the U.S.
Other types of local queries present similar issues to those of ChatGPT search. Note that a large percentage of search queries have local intent.
One estimate pegged this at 46% of all queries. This was reportedly shared by a Googler during a Secrets of Local Search conference at GoogleHQ in 2018.
Here is ChatGPT’s response to one example query that I tested:
As with the directions example, it thinks that I’m in Marlborough.
In addition, it shows two pizza shops in Marlborough (only one of the two is shown in my screenshot).
Google’s response to this query is much more on point:
I also gave Google a second version of the query “Pizza shops in Marlborough,” and it returned 11 locations – 9 more than I saw from the ChatGPT search.
This shows us that Google also has far more access to local business data than ChatGPT search.
For this query class (including the Directions discussed previously), I assigned these scores:
A content gap analysis is one of the most exciting SEO tasks that you can potentially do with generative AI tools.
The concept is simple: provide the tool of your choice a URL from a page on your site that you’d like to improve and ask it to identify weaknesses in the content.
As with most things involving generative AI tools, it’s best to use this type of query as part of a brainstorming process that your subject matter expert writer can use as input to a larger process they go through to update your content.
There are many other different types of content analysis queries that you can do with generative AI that you can’t do with Google (even with AI Overviews) at this point.
For this study, I did four content gap analysis queries to evaluate how well ChatGPT search did with its responses.
Google presented search results related to the page I targeted in the query but did not generate an AI Overview in any of the four cases.
However, ChatGPT search’s responses had significant errors for three of the four queries I tested.
Here is the beginning of ChatGPT search’s response to the one example query where the scope of errors was small:
This result from ChatGPT isn’t perfect (there are a few weaknesses, but it’s pretty good. The start of Google’s response to the same query:
As you can see, Google hasn’t even attempted to perform a content gap analysis. ChatGPT search is better set up to address this type of query.
However, ChatGPT search doesn’t earn a clean sweep for this type of query.
Here is the first part of another example result:
This looks good in principle, but it’s filled with errors. Some of these are:
Overall, I tried four different content gap analysis queries and ChatGPT search made significant errors in three of them. For this query, I assigned these scores:
How these queries perform is impacted by how well-known the person is.
If the person is very famous, such as Lionel Messi, there will be large volumes of material written about them.
If the amount of material written about the person is relatively limited, there is a higher probability that the published online information hasn’t been kept up to date or fact-checked.
We see that in the responses to the query from both ChatGPT search and Google.
Here is what we see from ChatGPT search:
The main issues with this response are in the third paragraph.
I haven’t written for Search Engine Journal in a long time, and it’s also been more than six years since I published a video on my YouTube channel (@stonetemplecons).
Let’s see what Google has to say:
Google also has problems with its response. They lead with quite a few images of me (which are all accurate), and below that, they show my LinkedIn profile and a summary of me drawn from Google Books.
Here, it says that I write for Search Engine Watch (haven’t done that for more than a decade!) and SEOMoz (which rebranded to SEOmoz to Moz in 2013) (also more than a decade!).
These responses are both examples of what I call “Garbage-In-Garbage-Out” queries.
If the web sources aren’t accurate, the tools don’t have the correct information to render.
For bio queries (3 of them), I scored the competitors as follows:
Arguably, nearly every search query has debatable user intent, but some cases are more extreme than others.
Consider, for example, queries like these:
Each of these examples represents an extremely broad query that could have many different intents behind it.
In the case of diabetes:
Or, for a term like physics:
Creating the best possible user experience for queries like these is tricky because your response should provide opportunities for each of the most common possible user intents.
For example, here is how ChatGPT responded to the query “physics”:
The additional two paragraphs of the response focused on the definition of Physics and kept the response at a very high level.
In contrast, the beginning of Google’s response also focuses on a broad definition of physics, but following that are People Also Ask and Things to Know boxes that address many other potential areas of interest to people who type in this search query:
This part of Google’s response shows a recognition of the many possible intents that users who type in the phrase “physics” may have in mind.
For this query, I assigned these scores:
One special class of debatable intents queries is words or phrases that require disambiguation. Here are some example queries that I included in my test set:
For example, here is how ChatGPT search responded to the question, “What is a joker query?”
We can see that it offers a nice disambiguation table that provides a brief definition for five different meanings of the term.
It also includes links to pages on the web that users can visit for information related to each meaning.
In contrast, Google focuses on two major intents:
Google’s focus is on the playing card and a person who tells a lot of jokes.
Following this part of the SERP, Google continues this approach with websites focusing on these two definitions.
This means that someone who’s interested in the word “joker” as it applies to contract clauses will have to do an additional search to find what they were looking for (e.g., “meaning of joker when referring to contract clauses”).
Which is better?
Well, it depends.
If the searchers interested in playing cards or people who tell lots of jokes make up more than 90% of the people who enter this search query, then the Google result might be the better of the two.
As it is, I scored the ChatGPT search result a bit higher than Google’s for this query.
Another example of disambiguation failure is simply not addressing it at all. Consider the query example: “where is the best place to buy a router?”
Here is how ChatGPT search addressed it:
You might think this result is perfect, but routers also refer to a tool used in woodworking projects.
I use one frequently as a part of building furniture from scratch (true story).
There is a large enough audience of people who use these types of routers that I hope to see recognition of this in the SERPs.
Here is Google’s response to the query:
This part of the SERP is followed by:
Google focuses on the internet router to the same degree as ChatGPT.
For this class of queries, I assigned these scores:
Another interesting aspect of search is that users tend to enter queries in sequences.
Sometimes those query sequences contain much information that helps clarify their query intent.
An example query sequence is as follows:
As we’ve seen, the default assumption when people speak about routers is that they refer to devices for connecting devices to a single Internet source.
However, different types of devices, also called routers, are used in woodworking.
In the query sequence above, the reference to cutting a circular table should make it clear that the user’s interest is in the woodworking type of router.
ChatGPT’s response to the first query was to mention two specific models of routers and the general characteristics of different types of woodworking routers.
Then the response to “where can I buy a router” was a map with directions to Staples and the following content:
All of the context of the query was 100% lost.
Sadly, Google only performed slightly better.
It identified three locations, two of which were focused on networking routers and one which was focused on woodworking routers (Home Depot):
For this query, I scored the tools this way:
Another interesting example is queries where your search is relatively rare, yet it has a spelling that’s similar to another word.
For this issue, my search was: “Please discuss the history of the pinguin.”
The Pinguin was a commerce raider used by the German Navy in World War 2. It just has a spelling very similar to “penguin,” which is an aquatic flightless bird.
Both ChatGPT and Google simply assumed that I meant “penguin” and not “pinguin.”
Here is the result from ChatGPT:
The result continues after what I’ve shown here but continues to focus on the bird, not the boat.
Google makes the same mistake:
After the AI Overview and the featured snippet I’ve shown here, the SERPs continue to show more results focused on our flightless friends.
To be fair, I’ve referred to this as a mistake, but the reality is that the percentage of people who enter “pinguin” that simply misspelled “penguin” is probably far greater than those who actually mean the German Navy’s WW2 commerce raider.
However, you’ll notice that Google does one thing just a touch better than ChatGPT here.
At the top of the results, it acknowledges that it corrected “pinguin” to “penguin” and allows you to change it back.
The other way I addressed the problem was to do a second query: “Please discuss the history of the pinguin in WW2,” and both ChatGPT and Google gave results on the WW2 commerce raider.
For this query, I assigned these scores:
There are many queries where a single (even if it is well thought out) response is not what someone is probably looking for.
Consider, for example, a query like: “smoked salmon recipe.”
Even though the query is in the singular, there is little chance that anyone serious about cooking wants to see a single answer.
This type of searcher is looking for ideas and wants to look at several options before deciding what they want to do.
They may want to combine ideas from multiple recipes before they have what they want.
Let’s look at the response from ChatGPT search:
I’ve included the first three screens of the response (out of four), and here you will see that ChatGPT search provides one specific recipe from a site called Honest Food.
In addition, I see some things that don’t align with my experience.
For example, this write-up recommends cooking the salmon to 140 degrees. That’s already beginning to dry the salmon a bit.
From what I see on the Honest Food site, they suggest a range of possible temperatures starting from as low as 125.
In contrast, Google offers multiple recipes that you can access from the SERPs:
This is an example of a query that I scored in Google’s favor, as having multiple options is what I believe most searchers will want.
The scores I assigned were:
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Next, we’ll examine the types of things that can go wrong. I looked for these issues while scoring the results.
The analysis noted where problems that generative AI tools are known for were found and potential areas of weakness in Google’s SERPs.
These included:
This is what the industry refers to as “hallucinations,” meaning that the information provided is simply wrong.
Sometimes errors aren’t necessarily your money or your life situations, but they still give the user incorrect information.
Consider how ChatGPT search responds to a query asking about the NFL’s overtime rules:
Notice the paragraph discussing how Sudden Death works. Unfortunately, it’s not correct.
It doesn’t account for when the first team that possesses the ball kicks a field goal, in which case they could win the game if the second team doesn’t score a field goal.
If the second team scores a field goal, this will tie the game.
In this event, it’s only after the field goal by the second team that the next score wins the game.
This nuance is missed by ChatGPT search.
Note: The information on the NFL Operations page that ChatGPT search used as a source is correct.
Google’s AI Overview also has an error in it:
In the second line, where Google outlines “some other NFL overtime rules,” it notes that the same ends if the first team to possess the ball scores a touchdown.
This is true for regular season games but not true in the postseason, where both teams always get an opportunity to possess the ball.
Scores were as follows:
This type of issue arises when important information that belongs in the response is left out.
Here is an example where ChatGPT search does this:
Under Pain Management, there is no mention of Tylenol as a part of a pain management regimen.
This is an unfortunate omission, as many people use only a mix of Tylenol and Ibuprofen to manage the pain after a meniscectomy.
Scores were as follows:
I used weaknesses to cover cases where aspects of the result could have been more helpful to the searcher but where the identified issue couldn’t properly be called an error or omission.
Here is an example of an AI Overview that illustrates this:
The weakness of this outline is that it makes the most sense to charge the battery as the first step.
Since it takes up to 6 hours to complet,e it’s not that useful to set up the app before completing this step.
Here is how I scored these two responses:
This category is one that I used to identify results that failed to cover a significant user need for a query.
Note that “significant” is subjective, but I tried to use this only when many users would need a second query to get what they were looking for.
Here is an example of this from a Google SERP.
The results are dominated by Google Shopping (as shown above).
Below what I’ve shown, Google has two ads offering online buying opportunities and two pages from the Riedl website.
This result will leave a user who needs the glasses today and therefore wants to shop locally without an answer to their question.
ChatGPT search did a better job with this query as it listed both local retailers and online shopping sites.
Scores for this query:
As discussed in “How query sessions work” earlier in this article, it’s quite common that users will try a series of queries to get all the information they’re looking for.
As a result, a great search experience will facilitate that process.
This means providing a diverse set of resources that makes it easy for users to research and find what they want/need. When these aren’t easily accessed it offers them a poor experience.
As an example, let’s look at how ChatGPT search responds to the query “hotels in San Diego”:
While this provides 11 hotels as options, there are far more than this throughout the San Diego area.
It’s also based on a single source: Kayak.
The user can click through to the Kayak site to get a complete list, but other resources aren’t made available to the user.
In contrast, Google’s results show many different sites that can be used to find what they want. The scores I assigned to the competitors for this one were:
It’s important to note that this analysis is based on a small sample of 62 queries, which is far too limited to draw definitive conclusions about all search scenarios.
A broader takeaway can be gained by reviewing the examples above to see where each platform tends to perform better.
Here’s a breakdown of category winners:
Google’s slight edge aligns with its strong track record for informational searches.
However, ChatGPT Search performed respectably, despite challenges with errors, omissions, and incomplete responses.
While ChatGPT search outperformed Google in this category, its lower overall score highlights areas where improvements are needed, such as accuracy.
Navigational queries were excluded from the test since they typically don’t require detailed text responses.
Google’s dominance in this category is assumed based on its straightforward, website-focused results.
Google’s extensive local business data, combined with tools like Google Maps and Waze, ensures its superiority in this category.
This category, comprising 16 queries, favored Google due to its stronger capabilities in showcasing product and service-related results.
ChatGPT Search edged out Google by more effectively presenting multiple definitions or interpretations for ambiguous terms, providing users with greater clarity.
These scores are summarized in the following table:
After a detailed review of 62 queries, I still see Google as the better solution for most searches.
ChatGPT search is surprisingly competitive when it comes to informational queries, but Google edged ChatGPT search out here too.
Note that 62 queries are a tiny sample when considered against the scope of all search.
Nonetheless, as you consider your search plans going forward, I’d advise you to do a segmented analysis like what I did before deciding which platform is the better choice for your projects.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. The opinions they express are their own.
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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