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OpenAI is transitioning to a for-profit business. The stakes are enormous.

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When it was founded in 2015, artificial intelligence research lab OpenAI was a nonprofit organization. The idealistic mission: to make sure the high-stakes work they were doing on artificial intelligence served the whole world. This was necessary because — according to the founders’ fervent belief, at least — it would transform the whole world.

In some ways since then, OpenAI has succeeded beyond its wildest dreams. “General artificial intelligence” sounded like a pipe dream in 2015, but today we have talking, interactive, creative AI that can pass most tests of human competence we’ve put it to. Many serious people believe that full general intelligence is just around the corner. OpenAI, which in the years since its founding morphed from a nonprofit lab into one of the most highly valued startups in history, has been at the center of that transformation. (Disclosure: Vox Media is one of several publishers that has signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

In other ways, of course, things have been a bit of a mess. Even as it basically became a business, OpenAI used nonprofit governance to keep the company focused on its mission. OpenAI CEO Sam Altman reassured Congress he had no equity in the company, and the nonprofit board still held all authority to change course if they thought the company had gone astray from its mission.

But that ultimately put the board at odds with Altman last November in a messy conflict that the CEO ultimately won. Nearly the entire original leadership team departed. In the year since, the board has largely been replaced and high-profile employees have left the company in waves, some of them warning they no longer believe OpenAI will build superintelligence responsibly. Microsoft, OpenAI’s largest investor, increasingly seems eager for the company to stop building superintelligence and start building a profitable product.

Now, OpenAI is attempting a transition to a more conventional corporate structure, reportedly one where it will be a for-profit public benefit corporation like its rival Anthropic. But nonprofit to for-profit conversions are rare, and misinformation has swirled about what, exactly, “OpenAI becoming a for-profit company” even means.

Elon Musk, who co-founded OpenAI but left after a leadership dispute, paints the for-profit transition as a naked power grab, arguing in a recent lawsuit that Altman and his associates “systematically drained the non-profit of its valuable technology and personnel” in a scheme to get rich off a company that had been founded as a charity. (OpenAI has moved to dismiss Musk’s lawsuit, arguing that it is an “increasingly blusterous campaign to harass OpenAI for his own competitive advantage”).

While Musk — who has his own reasons to be competitive with OpenAI — is among the more vocal critics, many people seem to be under the impression that the company could just slap on a new “for-profit” label and call it a day.

Can you really do that? Start a charity, with all the advantages of nonprofit status, and then declare one day it’s a for-profit company? No, you can’t, and it’s important to understand that OpenAI isn’t doing that.

Rather, nonprofit lawyers told me that what’s almost certainly going on is a complicated and fraught negotiation: the sale of all of the OpenAI nonprofit’s valuable assets to the new for-profit entity, in exchange for the nonprofit continuing to exist and becoming a major investor in the new for-profit entity.

The key question is how much are those assets worth, and can the battered and bruised nonprofit board get a fair deal out of OpenAI (and Microsoft)?

So far, this high-stakes wrangling has taken place almost entirely behind the scenes, and many of the crucial questions have gotten barely any public coverage at all. “I’ve been really kind of baffled at the lack of curiosity about where the value goes that this nonprofit has,” nonprofit law expert Timothy Ogden told me.

Nonprofit law might seem abstruse, which is why most coverage of OpenAI’s transition hasn’t dug into any of the messy details. But those messy details involve tens of billions of dollars, all of which appear to be up for negotiation. The results will dramatically affect how much sway Microsoft has with OpenAI going forward and how much of the company’s value is still tied to its founding mission.

This might seem like something that only matters for OpenAI shareholders, but the company is one of the few that may just have a chance of creating world-changing artificial intelligence. If the public wants a transparent and open process from OpenAI, they have to understand what the law actually allows and who is responsible for following it so we can be sure that OpenAI pursues this transition in a transparent and accountable way.

How OpenAI went from nonprofit to megacorp

In 2015, OpenAI was a nonprofit research organization. It told the IRS in a filing for nonprofit status that its mission was to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”

Understanding OpenAI’s expansive reach

OpenAI, the maker of ChatGPT, is one of the most important companies in artificial intelligence and one of the most controversial. I’ve been covering the ins and outs of OpenAI for years; here are some highlights:

Have questions or comments? Email me at kelsey.piper@vox.com.

By 2019, that idealistic nonprofit model was running into some trouble. OpenAI had attracted an incredible staff and published some very impressive research. But it was becoming clear that the lofty goal the company had set itself — building general artificial intelligence, machines that can do everything humans can do — was going to be very expensive. It was naturally hard to raise billions of dollars for an effort that was meant to be nonprofit. “We realized that we’d reached the limits of our fundraising ability as a pure nonprofit,” co-founder Ilya Sutskever (who has since departed the company) told me at the time.

The company would attempt to split the difference with a hybrid structure: a nonprofit board controlling a for-profit company. An additional twist: Investors in the for-profit company’s returns were capped at 100x their original investments so that, if world-altering superintelligence was achieved as the OpenAI leadership believed it might, the benefits would accrue to all humanity and not just investors. After all, investors needed to be enticed to invest, but if the company truly ended material scarcity and built a God on Earth, as they essentially said they wanted to, the hope was that more than just the investors would come out ahead.

The nonprofit, therefore, was still supposed to be preeminent. “It would be wise to view any investment in OpenAI Global, LLC in the spirit of a donation,” an enormous black-and-pink disclaimer box on OpenAI’s website alerts would-be investors, “with the understanding that it may be difficult to know what role money will play in a post-AGI world. The Company exists to advance OpenAl, Inc.‘s mission of ensuring that safe artificial general intelligence is developed and benefits all of humanity. The Company’s duty to this mission and the principles advanced in the OpenAl, Inc. Charter take precedence over any obligation to generate a profit.”

One might expect that a prominent disclaimer like that would give commercial investors pause. You would be mistaken. OpenAI had Altman, a fantastic fundraiser, at the helm; its flagship product, ChatGPT, was the fastest app to 100 million users. The company was a gamble, but it was the kind of gamble investors can’t wait to get in on.

But that was then, and this is now. In 2023, in an unexpected and disastrously under-explained move, the nonprofit board fired OpenAI CEO Sam Altman. The board had that authority, of course — it was preeminent — but the execution was shockingly clumsy. The timing of the firing looked likely to disrupt an opportunity for employees to sell millions of dollars of stock in the company. The board gave a few examples of underhanded, bizarre, and dishonest behavior by Altman, including being “not consistently candid” with the board. (One board member later expanded the allegations, saying that Altman had lied to board members about private conversations with other board members, but provided nothing as clear as confused and frustrated employees hoped.)

Employees threatened to resign en masse. Microsoft offered to hire them all and reconstitute the company. Sutskever, who was among the board members who’d voted for Altman’s removal, suddenly changed his mind and voted for Altman to stay. That meant the members who had fired Altman were suddenly in the minority. Two of the board members who had opposed Altman resigned, and the once and future CEO returned to the helm.

Many people concluded that it had been a serious mistake to try to run a company worth 11 figures as a nonprofit instead of as the decidedly for-profit company it was clearly operating as, whatever its bylaws might say. So it’s not surprising that ever since the aborted Altman coup, rumors swirled that OpenAI meant to transition to a fully for-profit entity.

In the last few weeks, those rumors have gotten much more concrete. OpenAI’s latest funding round has been reported to include commitments that the nonprofit-to-for-profit transition will get done in the next two years on pain of the more than $6 billion raised being paid back to those investors. Microsoft and OpenAI — both of whom have enormous amounts to gain in the wrangling over who owns the resulting for-profit company — have hired dueling investment banks to negotiate the details.

We are moving into a new era for OpenAI, and it remains to be seen what that will mean for the humble nonprofit that has ended up owning tens of billions of dollars of the company’s assets.

How do you turn a charity into a for-profit?

If OpenAI were really just taking the nonprofit organization’s assets and declaring them “converted” into a for-profit — as if they were playing a game of tag and suddenly decided a tree was “base” — that would absolutely be illegal. The takeaway, though, shouldn’t be that a crime is happening in plain sight, but that something much more complicated is being negotiated. Nonprofit law experts I talked to said that the situation was being widely and comprehensively misunderstood.

Here are the rules. First off, assets accumulated by a nonprofit cannot be used for private benefit. “It’s the job of the board first, and then the regulators and the court, to ensure that the promise that was made to the public to pursue the charitable interest is kept,” UCLA law professor Jill Horwitz told Reuters.

If it looks as though a nonprofit isn’t pursuing its charitable interest, and especially if it appears to be handing some of its board members bargain-bin deals on billion-dollar assets during a transition to for-profit status? That will have the IRS investigating, along with the state’s Attorney General.

But a nonprofit can sell anything it owns. If a nonprofit owns a piece of land, for example, and it wants to sell that land so that it has more money to spend on its mission, it’s all good. If the nonprofit sold the land for well below market value to the director’s nephew, it would be a clear crime, and the IRS or the state’s Attorney General might well investigate. The nonprofit has to sell the land at a fair market price, take the money, and keep using the money for its nonprofit work.

At a much larger scale, that is exactly what is at stake in the OpenAI transition. The nonprofit owns some assets: control over the for-profit company, a lot of AI IP from OpenAI’s proprietary research, and all future returns from the for-profit company once they exceed the 100x cap set up by the capped profit company — which, should the company achieve its goals, could well be limitless. If the new OpenAI wants to extract all of its assets from the nonprofit, it has to pay the full market price. And the nonprofit has to continue to exist and to use the money it has earned in that transfer for its mission of ensuring that AI benefits all of humanity.

There have been a few other cases in corporate legal history of a nonprofit making the transition to a for-profit company, most prominently the credit card company Mastercard, which was founded as a nonprofit collaboration among banks. When that situation happens, the nonprofit’s assets still belong to the nonprofit.

Mastercard, in the course of transitioning to a public company, ended up founding the now-$47 billion Mastercard Foundation, one of the world’s wealthiest private foundations. Far from the for-profit walking away with all the nonprofit’s assets, the for-profit emerges as an independent company and the nonprofit emerges not only still extant but very rich.

OpenAI’s board has indicated that this is exactly what they are doing. “Any potential restructuring would ensure the nonprofit continues to exist and thrive, and receives full value for its current stake in the OpenAI for-profit with an enhanced ability to pursue its mission.” OpenAI board chairman Bret Taylor, a technologist and CEO, told me in a statement. (What counts as “full value”? We’ll come back to that.)

Outside actors, too, expect to be applying oversight to make sure that the nonprofit gets a fair deal. A spokesperson for the California Attorney General’s office told the Information that their office is “committed to protecting charitable assets for their intended purpose.” OpenAI is registered in Delaware, but the company operates primarily in California, and California’s AG is much less deferential to business than Delaware’s.

So, the OpenAI entity will definitely owe the nonprofit mind-boggling amounts of money. Depending who you ask, it could be between $37 billion and $80 billion. The OpenAI for-profit entity does not have that kind of money on hand — don’t forget that OpenAI is projected to lose tens of billions of dollars in the years ahead — so the plans in the works are reportedly for the for-profit to make the nonprofit a major shareholder in the for-profit.

The Information reported last week that “the nonprofit is expected to own at least a 25% stake in the for-profit — which on paper would be worth at least $37 billion.” In other words, rather than buying the assets from the non-profit with cash, OpenAI will trade equity.

That’s a lot of money. But many experts I spoke to thought it was actually much too low.

What’s a fair price for control of a mega company?

Everyone agrees that the OpenAI board is required to negotiate and receive a fair price for everything the OpenAI nonprofit owns that the for-profit is purchasing. But what counts as a fair price? That’s an open question, one that people stand to earn or lose tens of billions of dollars by getting answered in their favor.

But first: What does the OpenAI nonprofit own?

It owns a lot of OpenAI’s IP. How much exactly is highly confidential, but some experts speculate that the $37 billion number is probably a reflection of the easily measured, straightforward assets of the nonprofit, like its IP and business agreements.

Secondly, and most crucially, it owns full control over the OpenAI for-profit. As part of this deal, it is definitely going to give that up, either becoming a minority shareholder or ending up with nonvoting shares entirely. That is, substantially, the whole point of the nonprofit-to-for-profit conversion: After Altman’s ouster, the Wall Street Journal reported, “[I]nvestors began pushing OpenAI to turn into a more typical company.” Investors throwing around billions of dollars don’t want a nonprofit board to be able to fire the CEO because they’re worried he’s too dishonest to make good decisions around powerful new technology. Investors want a normal board that will fire the CEO for normal reasons, like that he’s not maximizing shareholder value.

Control is generally worth a lot more, in for-profit companies, than shares that come without control — often something like 40 percent more. So if the nonprofit is getting a fair deal, it should get some substantive compensation in exchange for giving up control of the company.

Thirdly, investors in OpenAI under its old business model agreed to a “capped profit” model. For most investors, that cap was set at 100x their original investment, so if they invested $1 million, they would get a maximum of $100 million in return. Above that cap, all returns would go to the nonprofit. The logic for this setup was that, under most circumstances, it’s the same as investing in a normal company. Investments don’t usually produce 100x returns, after all, with the exception of early investments in massively successful tech companies like Google or Amazon.

The capped profit setup would be most significant in the unlikely world where OpenAI attained its ambitious goals and built an AI that fundamentally transformed the world economy. (How likely is that? Experts disagree, rather heatedly, but we shouldn’t discount it altogether.) If that does happen, its value will be nearly unfathomably huge. “OpenAI’s value is mostly in the extreme upside,” AI analyst Zvi Mowshowitz wrote in an analysis of the valuation question.

The company might fail entirely; it might muddle along as a midsized company. But it also might be worth trillions of dollars, or more than that, and most investors are investing on the premise it might be worth trillions of dollars. That means the share of profits owned by the nonprofit would also be worth trillions of dollars. “Most future profits still likely flow to the nonprofit,” Mowshowitz concludes. “OpenAI is shooting for the stars. As every VC in this spot knows, it is the extreme upside that matters. That is what the nonprofit is selling. They shouldn’t sell it cheap.”

So what would be an appropriate valuation? $60 billion? $100 billion? Mowshowitz’s analysis is that a fair price would involve the nonprofit still owning a majority of shares in the for-profit, which is to say at least $80 billion. (Presumably these would be nonvoting shares.)

The only people with full information are the ones with access to the company’s confidential balance sheets, and they aren’t talking. OpenAI and Microsoft will be negotiating the answer to the question, but it’s not clear that either of them particularly wants the nonprofit to get a valuation that reflects, for example, the expected value of the profits in excess of the cap because there’s more money for everyone else who wants a piece of the pie if the nonprofit gets less.

There are two forces working toward the nonprofit getting fair compensation: the nonprofit board — whose members are capable people, but also people handpicked by Altman not to get ideas and get in the way of his control of the company — and the law. Experts I spoke with were a bit cynical about the board’s willingness to hold out for a good deal in what is an extremely awkward circumstance for it. “We have kind of already seen what’s going on with the OpenAI board,” Ogden told me.

“I think the common understanding is they’re friendly to Sam Altman, and the ones who were trying to slow things down or protect the nonprofit purpose have left,” Rose Chan Loui, the director of UCLA Law’s nonprofit program, observed to the Transformer.

If the board is inclined to go with the flow, the Delaware Attorney General or the IRS could object. These are fundamentally complicated questions about the valuation of a private company, and the law isn’t always good at consistent and principled enforcement in cases like this one. “When you’re talking about numbers like $150 billion,” UCLA law professor Jill Horwitz warned, “the law has a way of getting weak.”

Does that mean that Elon Musk’s allegation — that we’re witnessing a bait-and-switch before our eyes, a massive theft of resources that were originally dedicated to the common good — is right after all? I’m not inclined to grant him that much.

Firstly, having spoken to OpenAI leadership and OpenAI employees over the six years I’ve been reporting on the company, I genuinely come away with the impression that the bait-and-switch, to the extent it happened, was completely unintentional.

In 2015, the involved parties really were — including in private emails leaked in Musk’s lawsuits — convinced that a research organization serving the public was the way to achieve their mission. And then over the next few years, as the power of big machine learning models became apparent, they became sincerely convinced they needed to find clever ways to raise money for their research. In 2019, when I spoke with Brockman and Sutskever, they were enthusiastic about their capped profit structure and saw it as a model for how a company could raise money but ensure most of its benefits if it succeeded went to humanity as a whole.

Altman has a habit of being all things to all people, even when that may require being less than truthful. His detractors say he’s “deceptive, manipulative, and worse”, and even his supporters will say he’s “extremely good at becoming powerful,” which VCs might consider more of a compliment than the general public does.

But I don’t think Altman was aiming for this predicament. OpenAI did not inflict its current legal headache on itself out of cunning chicanery, but out of a desire to satisfy a number of different early stakeholders, many of them true believers. It was due chiefly to understandable failures of foresight about how much power corporate governance law would really have once employees had millions riding on the company’s continued fundraising and once investors had billions riding on its ability to make a profit.

Secondly, I think it’s far too soon to call this a bait-and-switch. The nonprofit’s control of OpenAI was meant to give it the power to stop the company from putting profits before the mission. But it turns out that being on a nonprofit board does not come with enough access to the company, or enough real power, to productively turn OpenAI away from the brink, as we discovered last November.

It seems entirely possible that a massive and highly capitalized nonprofit foundation with the aim of ensuring AI benefits humanity is a better approach than a corporate governance agreement with power on paper and none in practice. If the nonprofit gets massively undervalued in the conversion and shooed away with a quarter of the company when more careful estimates suggest it currently controls a majority of the company’s value, then we can call it a bait-and-switch.

But that hasn’t happened. The correct attitude is to wait and see, to demand transparency, to hold the board to account for getting the valuation it is legally obligated to pursue, and to pursue OpenAI to the full extent of the law if it ends up convincing the board to give up its extraordinary bequest at bargain-basement prices.

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Introducción a la API de SDK y respuestas de los agentes de Operai

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Como Openai introdujo lo que todos los demás llaman a los agentes SDK, admitió que usar las capacidades existentes de manera unida “puede ser un desafío, a menudo requerir una amplia iteración rápida y una lógica de orquestación personalizada sin suficiente visibilidad o soporte incorporado”. En resumen, el uso de agentes necesitaba bastante programación, y esa no es la historia que cualquier proveedor de IA quiere vender.

Para devolver la narración a la idea de que gastar dinero en IA eventualmente erradicará la necesidad de un costoso desarrollo de software humano, o de hecho humanos, Openai está implementando una estructura para permitir una orquestación simple.

Primero resumamos cuáles son los problemas. Las tareas de agente implican al menos dos procesos que funcionan individualmente, con una tarea que comienza otra y con los resultados que se informan a un proceso de informes finales al final, con suerte en momentos similares. Los “resultados” también deben estar en un formato conocido (por ejemplo, una oración, un archivo, una imagen, una base de datos), pero esto no es fácil de generalizar. Incluso el camino feliz es un buen equilibrio: lidiar y explicar errores es otro problema. Todos estos son problemas de orquestación familiares. Pero como industria, nadie cree que la orquestación es un problema “resuelto”. Heavy LLM Uso también agrega la necesidad de controlar el uso del token; Las fichas son el nuevo oro negro.

Para comenzar el viaje de orquestación, OpenAI ha agregado algunas API nuevas a su plataforma central. En particular, ha introducido un básico Respuestas API Eso limpia algunos de los supuestos hechos por los agentes de chat.

En el sentido más simple, esto puede capturar la salida:

Puede analizar imágenes en este nivel; y agregue una de las herramientas a continuación. Cuidado: es probable que los nuevos modelos dejen de admitir la API de finalización de chat existente: muchas características nuevas solo admiten la API de nuevas respuestas.

Veamos estas nuevas herramientas. Búsqueda web Permite que un agente rastree la web para tareas simples. El breve script de Python a continuación muestra cómo se le da a un modelo la opción de usar esta herramienta:

El reesponse También contendrá referencias a cualquier artículo citado. Estas consultas se pueden definir por tiempo o ubicación. También puede sopesar el costo, la calidad y la latencia.

Búsqueda de archivos es efectivamente una tienda vectorial alojada. Usted indica que la búsqueda de archivos es una herramienta disponible e identifica su tienda vectorial:

Si es necesario, un agente lo usará. La respuesta citará los documentos utilizados en la respuesta. Puede limitar las respuestas a controlar el uso y la latencia del token. Hay límites para el tamaño total del archivo, los archivos buscados y el tamaño de la tienda Vector. Los tipos de documentos que se pueden buscar (por tipo de archivo) parecen extensos.

El Uso de la computadora La herramienta es interesante:

“La herramienta de uso de la computadora funciona en un bucle continuo. Envía acciones de la computadora, como click(x,y) o type(text)que su código se ejecuta en un entorno de computadora o navegador y luego devuelve capturas de pantalla de los resultados al modelo “.

Parece que está fingiendo ser selenio, la herramienta que usamos para probar las interfaces web a través de scripts. Obviamente, esto reconoce que todavía no estamos en el AIS solo hablando con otro mundo de AIS todavía. Pero al menos es un guiño a la idea de que no todo es un sitio web.

Probar agentes

Usaré los ejemplos de Python (definitivamente es un producto de Python-First, pero los documentos también muestran el script equivalente de JavaScript). Hemos ejecutado Python varias veces en mis publicaciones, pero en mi nuevo MacBook, solo verificaré que tenga Python instalado:

El resultado fue que python@3.13 3.13.2 ya está instalado y actualizado.

Mi pip también está allí (como PIP3).

Así que ahora puedo instalar los paquetes Operai:

Ah, recuerdo esto. Necesitamos un virtual:

Luego activo el virtual:

Y estamos listos para proceder.

Ahora, por supuesto, deberá usar y establecer un OpenAI_API_KEY. Me creé una nueva clave en la página de mi cuenta y establecí el opanai_api_key (no te preocupes, es mucho más largo que esto):

Y tienes que asegurarte de tener un poco de oro negro, me refiero a las fichas. He presentado algunas de las formas de evitar pagar OpenAi usando modelos locales, pero para esta publicación asumiré que está pagando por los tokens.

Como es tradicional, comencemos con una verificación de que los conceptos básicos anteriores están en su lugar a través de una simple solicitud con lo siguiente Haiku.py:

Y obtenemos una buena respuesta:

(Un buen haiku tradicional debería mencionar las temporadas que pasan, pero no es por eso que estamos aquí). Por lo general, también verificaría mi equilibrio, pero no ha sido perturbado.

Nido de agentes

Como puede ver, ya hemos usado un agente. No es que interviniera de ninguna manera, pero llegaremos a eso.

OpenAI ha simplificado el proceso de orquestación con algunos términos simples. A manos libres es una introducción al mundo asincrónico, donde algo tiene que esperar algo más. Desglosemos su ejemplo, que ejecutaré como hola.py:

Esto muestra dos cosas básicas. En primer lugar, la configuración de roles para los agentes en inglés simple a los que estamos acostumbrados, pero también estableciendo la interacción entre los agentes. El agente de transferencia mantiene una lista de agentes disponibles para responder respuestas.

Ahora, esto implica que mi solicitud alemana no obtendrá la respuesta correcta. Entonces, si cambiamos la consulta dentro hola.py:

Y ejecutar nuestro nido de agentes:

Entonces, aunque OpenAi no tuvo problemas para traducir alemán, el agente de triaje no tenía un agente de idiomas relevante a la mano, por lo que hizo el trabajo y respondió en inglés. Es poco probable que nuestros clientes alemanes estén demasiado molestos, pero podemos mejorar.

Entonces, si finalmente agregamos el agente alemán y lo ponemos en la lista de transferencias a hola.py:

Podemos intentar esa solicitud alemana nuevamente:

Esta vez se llama al agente correcto y responde. Nuestros clientes alemanes ahora están más felices: ¡Ausgezeichnet! No olvides que mi terminal de urdimbre también te está dando los tiempos para estas respuestas.

Conclusión

Primero observamos el bucle de respuesta, que puede incluir más llamadas de herramientas. Si la respuesta tiene una transferencia, establecemos el agente en el nuevo agente y volvemos al inicio.

Hay opciones de registro debajo de esto, pero como de costumbre, OpenAI está dando una API de alto nivel en esta etapa, lo que debería fomentar la experimentación sin la necesidad de involucrarse demasiado con la orquestación.

Si bien he introducido agentes aquí, en publicaciones posteriores, veré más partes del SDK.

Vía Sahin Ahmed


Grupo Creado con boceto.

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Las habilidades de varios idiomas de Gemini Live me han volado los calcetines

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Rita El Khoury / Android Authority

Géminis todavía está lejos de ser perfecto, pero lentamente se cultiva en mí. Específicamente, sin embargo, es el modo de conversación en vivo el que más me atrae porque es todo lo que siempre quise del Asistente de Google, y algo más. Puedo hablar con la IA, interrumpirlo, pedirle que lo repita, corregirlo y pedir más detalles, todo en una conversación muy natural y relajada.

Pero si eres alguien como yo y estás acostumbrado a hablar tres idiomas al mismo tiempo, a menudo en la misma oración, y tu cerebro funciona así de forma predeterminada, lo que hace que sea difícil mantener una conversación completa en un idioma, entonces probablemente hayas estado mordiendo en los bits, esperando que Gemini vive para apoyar varios idiomas. Con la caída del píxel de marzo, la función ahora está aquí, y oh. Mi. Cielos. ¿Es mucho mejor de lo que esperaba o qué?

¿Has intentado hablar con Gemini en varios idiomas?

2 votos

Más intuitivo y confiable que el varios idiomas de Google Assistant

Cuando Google lo anunció, pensé que el soporte de varios idiomas en Géminis Live significaba que podría tener una conversación en inglés y luego otra conversación en francés sin cambiar manualmente el idioma. Este ha sido el caso con el Asistente de Google durante años, excepto que tuve que configurar manualmente exactamente qué idiomas quería usar en el Asistente, y nunca funcionó tan bien como se esperaba.

Con Gemini Live, como puede ver en el video de arriba, ese no es el caso:

  • No tuve que elegir el idioma cada vez; Acabo de comenzar una nueva charla, y me entendió.
  • Fuera de la caja, funciona con todos los idiomas compatibles con Live. No tengo que limitarme a solo dos como con el asistente.
  • Aunque tuve algunos silencios incómodos de Géminis y tuve que repetir algunas oraciones, la tasa de éxito de la IA para reconocer diferentes idiomas ha superado el 90% en mis pruebas, y eso es más de lo que el asistente podría soñar.

Hablo tres idiomas casi nativamente (inglés, francés, árabe) y puedo entender y hablar (con un acento grueso) algunos español, italiano y alemán. Entonces, puse esto a prueba e probé diferentes chats con Gemini en vivo en todo esto. Me consiguió todos mis acentos nativos y gruesos cada vez.

El único con el que tuve problemas es, por extraño que parezca, mi lengua materna árabe. Podría hablar en árabe formal escrito, pero eso no es algo natural para mí. En cambio, cuando hablo, está en el dialecto libanés informal. Géminis, sin embargo, parece hablar una mezcla entre un dialecto levantino informal no descriptivo y el árabe formal escrito. Culpo esto a los millones de dialectos regionales y cuán complicados y ampliamente diferentes son, pero incluso entonces, la tasa de éxito fue más alta de lo que esperaba o había experimentado con Asistente en árabe.

Todo esto ya fue una victoria, pero luego decidí avanzar más. Y ahí es donde Gemini vive en sentido figurado me voló los calcetines.

¡Las habilidades de varios idiomas de Gemini Live funcionan a mitad de chat y a mitad de oración!

Google Géminis Multilguages ​​2

Rita El Khoury / Android Authority

Como tenía una experiencia tan positiva con diferentes chats en diferentes idiomas, quería ver si Gemini podía manejarme cambiando idiomas a mitad de chat. Así que comencé una simple discusión en inglés, luego cambié al francés, árabe, español, italiano, alemán, y me siguió a través de los seis, nunca sudando. Puedes verlo en el video a continuación.

Mirando hacia atrás en la transcripción, pude ver que realmente entendía cada palabra que dije en cada idioma y cambió sus respuestas en consecuencia.

Pero no pude parar allí, ¿verdad? Ahora, tenía curiosidad por ver si podía manejar el cambio a mitad de la oración. Así que comencé una oración en inglés, la terminé en francés y esperé con la respiración con la respuesta. ¡Y lo consiguió! Probé para otro lado. ¡Éxito!

Honestamente, en este punto, estaba gritando internamente: “¡Hechicería!” Después de vivir con el Asistente de Google durante 10 años y ver que lucha saber la diferencia entre “Bonjour” y “Bone Joke”, había perdido toda esperanza en los algoritmos de reconocimiento de voz y AIS. Pero Géminis Live restauró esa fe. Compruébalo en acción:

Comencé a mezclarme en árabe y español y seguí cambiando a mitad de la oración, y obtuvo todos ellos. A menudo respondía en el primer idioma con el que comencé mi oración, pero su respuesta era una prueba de que entendía toda la pregunta, no solo la primera parte. Incluso abrió mi herida sobre la última falla de Randal Kolo Muani en la última Copa Mundial de la FIFA y me burló de mí sobre la excelente salvación de Emiliano Martínez. Oh, bueno.

Google Gemini Multilguages ​​4

Rita El Khoury / Android Authority

Más allá de eso, quería intentar desestabilizar a Gemini en vivo aún más y llevarlo a su límite. Entonces, comencé a hablar como normalmente lo hago con mi familia y amigos, mezclando inglés, francés y árabe en la misma oración: la verdadera forma de hablar libanese, por así decirlo. Para mi sorpresa absoluta de mordisco, recibió a nuestro famoso “Hola, Kifak, CA VA?” Y siguió bien (aparte de la incómoda limitación de acento árabe que mencioné anteriormente).

¿Una palabra en un idioma diferente en medio de toda una oración en inglés? Ningún problema

Finalmente, simplemente fui por el ejemplo más extremo que se me ocurrió: hablar una oración completa en un idioma pero poner una palabra en otra. Para ser justos, así es como hablo con mi esposo el 90% del tiempo. Si estamos usando inglés, algunas palabras nos eludirán, y en el medio de nuestro flujo, solo usamos la palabra francesa o árabe. O si hablamos árabe o francés, intercalamos algunas palabras en los otros idiomas sin pensarlo mucho. Es cómo nuestros cerebros funcionan normalmente, y es por eso que nunca me siento muy cómodo hablando con asistentes de voz porque tengo que forzarme a usar un idioma. Pero Géminis Live lo consiguió.

Le pregunté: “Se llama una planta habaq En árabe, ¿qué es eso en inglés? Me dijo que es Basilio. cibuleta ¿en Inglés?” Dijo cebollino. roquettes“Mientras rodaba mi R, entendía que estaba hablando de hojas de cohetes/rúcula. Y finalmente, cuando pregunté qué”Jozt El Tib“Estaba en inglés, dijo correctamente que es una nuez moscada (sí, estaba en mi cocina e intentaba obtener ideas para las pruebas de Géminis).

Google Gemini Multilguages ​​1

Rita El Khoury / Android Authority

Mirando hacia atrás en el registro de chat durante todo esto, la transcripción no es 100% precisa o en el idioma correcto. Está “Haba” y “Rocket” y “Rose to Tibe”, mientras que “Ciboulette” ni siquiera está escrito de ninguna manera. Pero la respuesta demuestra que Gemini Live recibió la palabra correcta en el idioma correcto cada vez.

Y estos no son solo casos extremos. Todas son preguntas que realmente me he hecho o usé el traductor de Google en un momento de mi vida. No puedes creer cuántas veces quiero buscar recetas con calabacín y todo lo que mi cerebro quiere escribir es “calabacín recetas “. Así que solía traducirlo primero, recuerda que es calabacín, luego regresa para hacer mi búsqueda. sfouf (Curcuma Cake) Receta con 3e2de safra (curcuma) y busque lugares para comprar granos para la recomendación de mi padre ba2le (Verde) Planta sin sudar.

Regresé a mis pruebas e intenté las mismas preguntas con el modo de chat de voz de Chatgpt. Mientras consiguió los franceses roquettes y cibuletafalló con el árabe habaq y Jozt El Tibdiciéndome que son fenogrecidos y cominos. Oof. No querría fenogreco en mi pesto.

Después de todas estas pruebas, no puedo, pero no puedo inclinar mi sombrero al equipo de Géminis por clavar el soporte de varios idiomas y hacer que funcione tan impresionantemente bien desde el primer momento. Cada vez que lo empujaba más, me sorprendía ver que todavía me mantenía al día. Este es el primer agente de IA que me entiende de la forma en que hablo naturalmente, por lo que ya no tengo que recordar la palabra exacta en inglés si quiero continuar una conversación con ella. Todavía tengo que transformar un poco mi acento árabe para que me entienda, pero ese es un pequeño precio a pagar por un agente de voz de IA tan versátil. Sin embargo, una vez que comprenda el dialecto libanés como es, será una perfección absoluta.

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Which AI Is Smarter And More Useful?

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Generative AI has been with us for over two years now, with most major tech companies trying to take a piece of the action. OpenAI’s ChatGPT may be the product more people know about thanks to its early market advantage, but Microsoft Copilot has the immense power of a multi-trillion dollar company behind it. Seems like a fair enough fight, right? So, with OpenAI and Microsoft both touting their flagship AIs, which one is actually the better bot when it comes to everyday usefulness? 

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I’ve been putting AIs to the test against one another for a while now. Last year, when pitting ChatGPT against Google Gemini, the latter stole the crown  — but only barely. Can Copilot pull off a similar victory? I’ve devised a gauntlet of tests for these AIs, with questions designed to be difficult for large language models. Simply put, the goal is to push these AIs outside of their comfort zones to see which one has the widest range of usability and highlight their limitations. 

First, some parameters. I performed all these tests on the free version of both platforms, as that’s how the majority of users will experience them. If you’re one of the people paying $200 a month for the most premium version of ChatGPT, for example, your experience will differ from these results. Two, I used the main chat function for each test unless otherwise stated.

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What are Copilot and ChatGPT?

You’re likely familiar with OpenAI ChatGPT, and by extension, Microsoft Copilot. They’re AI chatbots that can have conversations, answer questions, and more. On a more technical level, both Copilot and ChatGPT are large language model (LLM) AIs. They are trained on large amounts of text scraped from a variety of sources using a transformer model that calculates the relationships between words. 

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On the user-facing side, they generate text in response to user-submitted prompts by guessing the probability of each word they output. To heavily oversimplify, they’re kind of like your phone keyboard’s next-word prediction feature, but far, far more complex.

OpenAI makes ChatGPT, while Microsoft makes Copilot. However, Microsoft is a major investor in OpenAI, and because Copilot uses AI models from OpenAI, it has a lot of overlap with ChatGPT. That’s not to say they’re the same thing — Microsoft uses some proprietary models in Copilot (specifically, its Prometheus model) in addition to a custom assortment of OpenAI models, but there’s a lot of ChatGPT under Copilot’s hood. Nevertheless, Microsoft does its own tuning to balance all the different AI gremlins under that hood, so it is distinct enough as a product to merit a head-to-head comparison between the two.

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OpenAI, meanwhile, retains a massive user base on ChatGPT, which gives it a big competitive advantage since the more users there are, the more the AI is getting used and trained. Neither company actually turns a profit on AI  – OpenAI head Sam Altman says the company is losing money even on $200/month subscribers – but OpenAI remains the market leader by a wide margin. ChatGPT is built into everything from Copilot to Apple’s Siri these days, and it’s widely considered the industry standard.

Copilot is all up in your business

The largest difference between ChatGPT and Copilot is that Microsoft has been cramming Windows and Office products to the gills with its AI. Microsoft was legally ruled a monopoly in the PC operating system market a quarter of a century ago, and things haven’t changed much since then. Windows is by far the most dominant OS on the planet, which means the ability to simply blast a firehose of Copilot features into all of its products is a huge advantage. From your taskbar to your Word documents, Copilot is digging roots deep into the Microsoft ecosystem.

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This strategy hasn’t translated into very many users for Copilot, though, and ChatGPT retains by far the largest user base in the AI market. With 28 million active Copilot users in late January compared to over 300 million monthly active users for ChatGPT at the end of 2024, it’s an absolute blowout for OpenAI. Things get even more bleak for Copilot when you realize how many of its users are likely to be using it only because it’s the tool built into their computer by default. 

For the rest of this comparison, we’ll focus on the capabilities of each chatbot. Still, the truth is that you can do more with Copilot than you can with ChatGPT, at least if you have a Windows computer that supports it. Both AIs have desktop apps you can run, but Copilot can manipulate your Excel spreadsheets, Word documents, PowerPoint slides, Outlook inbox, and more from directly within those apps.

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Basic questions

One of the most common uses for AI is searching up the answers to basic, everyday questions that you’d usually ask Google. Both AIs are pretty good at this, but pretty good is rarely good enough. AI remains prone to hallucinations  — confidently stating falsehoods as facts  — which can undermine their usefulness. If you have to double check an AI’s answers on Google, you might as well just use Google in the first place.

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In any case, I started this head-to-head comparison by prompting both AIs to “Tell me some fun facts about Google Android.” The similarity of the two responses is a clear demonstration of just how much of ChatGPT’s DNA is baked into Copilot. Both told me Android was originally built to run on digital cameras (true), that Google acquired Android in 2005 for $50 million (true), that the first Android-powered phone was the HTC Dream (true – SlashGear covered it at the time), that the original Android logo was a much scarier robot, and that the one we know and love was inspired by bathroom signs (both true).

However, both AIs also made mistakes. Both told me the Android mascot is named Bugdroid. That’s not true. Google officially calls it The Bot, while Bugdroid is a fan-created nickname. Similarly, the Dream was indeed the first consumer Android phone, but the first was a Blackberry-style prototype, something which only ChatGPT pointed that out. 

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It’s easy to spot such errors when you’re asking about something you know a lot about, but if I’d been asking about something outside my expertise, I’d need to double check everything. In other words, a pretty good rate of accuracy isn’t good enough when it comes to this tech. Both AIs performed decently, but there’s plenty of room for improvement.

Logical reasoning

Reasoning has been a major area of focus for all of the major players in the AI space recently. ChatGPT and Copilot have both implemented new reasoning capabilities that supposedly allow the AIs to think more deeply about questions. This language is a bit misleading  — AI doesn’t “think,” it just calculates probability based on which words are most closely related in its training data. However, the bots can now show their work, so to speak. 

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I decided to be a bit glib here. I’ve noticed that AI has trouble answering questions that are very close to common logic puzzles but which differ by being much simpler.

I turned reasoning on in both Copilot and ChatGPT, then asked, “A farmer needs to cross a river to bring his goat to the other side. He also has a pet rock with him. The rock will not eat the goat, but the rock is very significant to the farmer on an emotional level. How can the farmer get himself, the goat, and the rock across in the fewest number of trips?” Human readers will note that there is actually no puzzle here. Since I’ve added no real constraints, the farmer can clearly bring both across in one trip. However, neither AI clued into that fact.

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Because it resembles more complex puzzles, Copilot and ChatGPT assumed the problem must be more challenging than it is. They invented a constraint not present in my question  — that the boat must not be able to hold both the goat and the rock  – and told me that it would take three trips to bring both across. Earning the slight advantage, Copilot ultimately noted that if the boat were larger the farmer could cross the river in one trip.

Creative copy

One of the main selling points for large language models like ChatGPT and Copilot has been the generation of creative copy  — writing. Well, I happen to have an advanced degree in putting words one after another, so I’ll be the judge of that. In last year’s Gemini versus ChatGPT showdown, I enjoyed making the bots write from the perspective of a little kid asking their mom to let them stay up late and eat cookies. I reused a very similar prompt here, but added a new wrinkle. “My mom says I can have a cookie before bed if I go right to sleep. I want to stay up and have a cookie. Write a letter persuading my mom to let me have both.”

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Here, the two chatbots took different tacks. While ChatGPT gave a bullet-pointed list of reasons why our put-upon child should be allowed to have his cookie and stay up, too, Copilot was less didactic. It kept things in all prose, adhering closer to a traditional letter writing style. However, both AIs gave more or less the same argument, claiming that they’d be more well behaved and go to bed without fuss if they got what they wanted. However, ChatGPT did a bit better here, at least in logical terms, because it offered the hypothetical mom something in exchange — the promise of spending that extra time awake as mom-kid quality time.

Copilot gets points here for more closely embodying the perspective of the child in its response, while ChatGPT gets a cookie for using slightly better logic. Ultimately, though, neither of these letters felt persuasive enough to be very convincing to any actual parent.

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The haiku test

When I compared ChatGPT to Google Gemini almost a year ago, I pointed out their limitations by asking both to write a haiku. As a result of the way LLMs work, neither AI could do so correctly. AI doesn’t actually know anything about the words it spits out, and that means they don’t know what a syllable is. Consequently, they can’t write a haiku, which follows a five-seven-five syllabic verse pattern So, has anything changed a year later?

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Maybe someone at OpenAI saw that comparison, or at least I’d like to think so. When prompted to “write a haiku about Slashgear.com,” ChatGPT did so with no problem, writing the following:

“Tech news on the rise,

gadgets, cars, and future dreams,

SlashGear lights the way.”

It’s not going to win any awards, but it qualifies as a haiku, and that’s progress. I’m no AI developer, so I have no clue what changed behind the scenes to enable haiku writing here. Either way, it’s good to see improvement.

Copilot stalled out when I gave it the same prompt. It wouldn’t write its haiku until I signed out of my Microsoft account and reloaded the page, at which point it gave me this:

“Gadget whispers loud,

Innovation on the rise,

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SlashGear guides the way.”

It’s interesting to see how both AIs repeat phrases here, such as “on the rise” and “lights/guides the way.” I’d guess that Copilot defaults to ChatGPT for this, and that’s why the poems are similar. Neither poem was particularly beautiful or evocative, but both bots passed this test, and both showed a basic understanding of what SlashGear is, which was integral to the prompt.

Problem solving

As you may have heard, AIs can often pass the bar exam. However, they can’t be lawyers, as lawyers who’ve tried to use them have found out the hard way. So, with those mixed results in mind, how do ChatGPT and Copilot do with logistically complex problem solving puzzles of the kind that routinely stump LSAT test takers? 

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Rather than using actual LSAT practice questions, which are copyrighted and have probably already been scraped to train the AIs, I came up with a few of my own. The first was, “Fred is a used car salesman. One day, a family comes in looking to buy a car he hasn’t had time to inspect, but he tells them there’s nothing wrong with it. After all, none of the cars he’s sold ever had issues in the past. What is the fallacy in Fred’s logic, if any?” ChatGPT and Copilot both correctly identified that Fred has fallen victim to the hasty generalization fallacy.

The next question was, “On the way home from Fred’s dealership, the brakes fail in the car he sold, and several people are killed in a collision. Fred claims he’s not at fault, since his cars are sold as is and become the owner’s responsibility once paperwork is signed. The surviving family member claims he is at fault, since the family would not have purchased the vehicle had they known the brakes were faulty. Based only on logic, who is right?”

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The responses to this more subjective question differed, with Copilot asserting that both parties have strong claims, while ChatGPT sided with the family, pointing out that Fred’s position relies on “contractual technicalities,” while the family can prove causality.

Code writing

One of the more useful applications of AI is thought to be coding. Especially when it comes to the common but tedious chunks of code that developers routinely find themselves writing, it’s been posited that it’s much easier to offload that work to an AI, leaving the human coder with more time to write the new and complex code for the specific project they’re working on. I’m no developer, so take this particular test with a grain of salt. At the same time, though, these tools should supposedly lower the barrier to entry for coding noobs like me.

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Common wisdom dictates that writers should have their own websites, but I’ve been putting off the creation of one. With that in mind, I asked both AIs to, “Generate HTML for a personal website for a writer named Max Miller. Give the website a retro aesthetic and color scheme, with an About Me section with a headshot and text field, a Publications section where I can link out to published work, and a Contact section where I can add social media and email links.”

At this point, I found out ChatGPT now has a code editing suite called Canvas. It allowed me to play with and preview the code right in my browser. Taste is subjective, but ChatGPT also generated what I would argue is the better looking website, using nicer looking margins and a dark mode style color scheme. Both, however, fulfilled the prompt more or less to a T, each generating a very similar page layout. Have a look for yourself below.

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Real-time information

When I tested ChatGPT against Google Gemini last year, only the latter could give me up to date information on recent events such as sports scores. I asked both how my local hockey team, the Colorado Avalanche, are doing this season, and both gave me an overview that appears to be correct. Both ChatGPT and Copilot provided me with current rankings and a few highlights from the season, but ChatGPT was more detailed. It told me some player stats that Copilot didn’t bother with.

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I followed up by asking who they’re playing next. Both AIs correctly understood the “they” in my question to mean the Avalanche. I’m writing this section at 5:00 p.m. on Friday, February 28, and both AIs informed me about tonight’s game, which takes place against the Minnesota Wild at Ball Arena in Denver two hours from the time of this writing. Interestingly, Copilot attached a Ticketmaster advertisement to the end of its response. ChatGPT, meanwhile, gave me much more useful information by showing me the upcoming schedule for not only tonight’s game but several thereafter. It also appended a link to the official Avalanche website.

Things got far more stark when I asked about breaking news. As of this writing, authorities are investigating the shocking deaths of legendary actor Gene Hackman and his wife. When I asked, “What’s the latest on the investigation into Gene Hackman,” Copilot gave me the basics of the story and told me autopsy and toxicology tests are still pending. ChatGPT, on the other hand, had no idea what I was talking about.

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Image based prompting

Using multimodal AI — the ability of an AI to work with multiple forms of media — both ChatGPT and Copilot can incorporate user submitted pictures and other files into a prompt. I decided to start simple for this test. On my bed, I arranged a Samsung Galaxy S23 Ultra, a Samsung portable SSD, a Swiss army multitool, lip balm, hand cream, a eyeglass case, a beaded bracelet, Samsung Galaxy Buds, and my wallet. I then took a photo of the assortment and uploaded it to both AIs with the prompt, “Identify the objects in this photo.”

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Both AIs did okay here, but ChatGPT blew Copilot away by a country mile. Whereas Copilot misidentified the SSD as a power bank and the glasses case for deodorant, ChatGPT identified everything accurately.

It was time to up the stakes. I took a photo of a generic Prilosec pill and asked both AIs, “What kind of pill is this?” If these AIs misidentified the medication, that could have dire effects for an overly trusting user. Thankfully, both AIs declined to make a guess when faced with the blank, red pill. Sometimes, it’s better to be useless than wrong.

Lastly, I took a photo of two rows on my bookshelf, containing 78 books, and ensuring all the text in the photo was legible, then asked the AIs, “Which of these books should I read if I have an interest in dystopian fiction?” Again, ChatGPT strong armed Copilot into submission. Neither impressed me, though. Whereas Copilot suggested “Agency” by William Gibson, ignoring everything else and hallucinating a book I don’t own, ChatGPT identified “Agency,” “The Parable of the Sower” by Octavia Butler, and “Appleseed” by Matt Bell. However, it hallucinated several more titles not on the shelf.

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Mobile apps

Lastly, both Copilot and ChatGPT are available in mobile form, with apps available in the Apple App Store and Google Play Store. On the surface, both apps look pretty similar, with a text field at the bottom and buttons to enter a voice mode. Since both apps are quite similar, it makes sense to focus this comparison on where they differ — which is in exactly one way

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Copilot’s standout mobile app feature is Copilot Daily, an AI news summary. It begins with a fun fact before launching into the daily news, presumably summarizing the articles it cites as sources in the bottom of the screen for each item. Based on my knowledge of the events it summarized, it seems relatively accurate. However, it’s not as if there’s a shortage of news summary features created by actual journalists. You can find them from every major news outlet.

However, the apps are otherwise nearly carbon copies of their web interfaces. Both apps are essentially just wrappers for that interface, since it’s not as if your phone has the power to run these models locally. Unless you’re very excited to hear a robot read the news to you, the ChatGPT app is the better option simply because ChatGPT has more built in features within its interface.

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Conclusion: ChatGPT beats Copilot by a hair, but neither AI is great

If you absolutely had to choose either Microsoft Copilot or ChatGPT, the latter remains the better option for most people. While Copilot isn’t exactly like its more popular peer, it’s using enough of OpenAI’s models that you’re better off with the original flavor. Copilot is a lot like Bing — doing basically the same thing as the bigger name brand, but just a little bit worse.

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With that said, it’s a stretch to call either of these chatbots smart or useful. Frankly, with hundreds of billions of dollars now sunk into these two AIs alone by both OpenAI and Microsoft, how is it that Copilot and ChatGPT still can’t nail the basics? Microsoft plans to spend $80 billion on AI data centers this year, while OpenAI is seeking up to $7 trillion for new projects. 

Yes, that’s trillion with a T to fund a technology that can’t get basic facts right or understand how boats work. When competitors like DeepSeek are doing the same things for a microscopic fraction of that investment cost, these products feel deflatingly unimpressive in comparison. Markets aren’t a consumer concern, it’s true, but some perspective feels necessary here.

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Look, if all you need is a robot that can quickly write you an email, both ChatGPT and Copilot will happily crank out slop copy that anyone can tell was written by AI. If you need a smart thesaurus, or sports scores, or a bit of simple code, they’ve got you covered. In a tight race, ChatGPT does a few things marginally better than Copilot. Still, for any task where accuracy matters, neither are reliable enough to count on.



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