When the U.S. Department of Justice originally brought — and then won — its case against Google, arguing that the tech behemoth monopolized the search engine market, the focus was on, well … search.

Back then, in 2020, the government’s antitrust complaint against Google had few mentions of artificial intelligence or AI chatbots. But nearly five years later, as the remedy phase of the trial enters its second week of testimony, the focus has shifted to AI, underscoring just how quickly this emerging technology has expanded.

In the past few days, before a federal judge who will assess penalties against Google, the DOJ has argued that the company could use its artificial intelligence products to strengthen its monopoly in online search — and to use the data from its powerful search index to become the dominant player in AI.

In his opening statements last Monday, David Dahlquist, the acting deputy director of the DOJ’s antitrust civil litigation division, argued that the court should consider remedies that could nip a potential Google AI monopoly in the bud. “This court’s remedy should be forward-looking and not ignore what is on the horizon,” he said.

Dahlquist argued that Google has created a system in which its control of search helps improve its AI products, sending more users back to Google search — creating a cycle that maintains the tech company’s dominance and blocks competitors out of both marketplaces.

The integration of search and Gemini, the company’s AI chatbot — which the DOJ sees as powerful fuel for this cycle — is a big focus of the government’s proposed remedies. The DOJ is arguing that to be most effective, those remedies must address all ways users access Google search, so any penalties approved by the court that don’t include Gemini (or other Google AI products now or in the future) would undermine their broader efforts.

Department of Justice lawyer David Dahlquist leaves the Washington, D.C. federal courthouse on Sept. 20, 2023 during the original trial phase of the antitrust case against Google.

Jose Luis Magana/AP/FR159526 AP

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Jose Luis Magana/AP/FR159526 AP

AI and search are connected like this: Search engine indices are essentially giant databases of pages and information on the web. Google has its own such index, which contains hundreds of billions of webpages and is over 100,000,000 gigabytes, according to court documents. This is the data Google’s search engine scans when responding to a user’s query.

AI developers use these kinds of databases to build and train the models used to power chatbots. In court, attorneys for the DOJ have argued that Google’s Gemini pulls information from the company’s search index, including citing search links and results, extending what they say is a self-serving cycle. They argue that Google’s ability to monopolize the search market gives it user data, at a huge scale — an advantage over other AI developers.

The Justice Department argues Google’s monopoly over search could have a direct effect on the development of generative AI, a type of artificial intelligence that uses existing data to create new content like text, videos or photos, based on a user’s prompts or questions. Last week, the government called executives from several major AI companies, like OpenAI and Perplexity, in an attempt to argue that Google’s stranglehold on search is preventing some of those companies from truly growing.

The government argues that to level the playing field, Google should be forced to open its search data — like users’ search queries, clicks and results — and license it to other competitors at a cost.

This is on top of demands related to Google’s search engine business, most notably that it should be forced to sell off its Chrome browser.

Google flatly rejects the argument that it could monopolize the field of generative AI, saying competition in the AI race is healthy. In a recent blog post on Google’s website, Lee-Anne Mulholland, the company’s vice president of regulatory affairs, wrote that since the federal judge first ruled against Google over a year ago, “AI has already rapidly reshaped the industry, with new entrants and new ways of finding information, making it even more competitive.”

In court, Google’s lawyers have argued that there are a host of AI companies with chatbots — some of which are outperforming Gemini. OpenAI has ChatGPT, Meta has MetaAI and Perplexity has Perplexity AI.

“There is no shortage of competition in that market, and ChatGPT and Meta are way ahead of everybody in terms of the distribution and usage at this point,” said John E. Schmidtlein, a lawyer for Google, during his opening statement. “But don’t take my word for it. Look at the data. Hundreds and hundreds of millions of downloads by ChatGPT.”

Competing in a growing AI field

It should be no surprise that AI is coming up so much at this point in the trial, said Alissa Cooper, the executive director of the Knight-Georgetown Institute, a nonpartisan tech research and policy center at Georgetown University focusing on AI, disinformation and data privacy.

“If you look at search as a product today, you can’t really think about search without thinking about AI,” she said. “I think the case is a really great opportunity to try to … analyze how Google has benefited specifically from the monopoly that it has in search, and ensure that the behavior that led to that can’t be used to gain an unfair advantage in these other markets which are more nascent.”

Having access to Google’s data, she said, “would provide them with the ability to build better chatbots, build better search engines, and potentially build other products that we haven’t even thought of.”

To make that point, the DOJ called Nick Turley, OpenAI’s head of product for ChatGPT, to the stand last Tuesday. During a long day of testimony, Turley detailed how without access to Google’s search index and data, engineers for the growing company tried to build their own.

ChatGPT, a large language model that can generate human-like responses, engage in conversations and perform tasks like explaining a tough-to-understand math lesson, was never intended to be a product for OpenAI, Turley said. But once it launched and went viral, the company found that people were using it for a host of needs.

Though popular, ChatGPT had its drawbacks, like the bot’s limited “knowledge,” Turley said. Early on, ChatGPT was not connected to the internet and could only use information that it had been fed up to a certain point in its training. For example, Turley said, if a user asked “Who is the president?” the program would give a 2022 answer — from when its “knowledge” effectively stopped.

OpenAI couldn’t build their own index fast enough to address their problems; they found that process incredibly expensive, time consuming and potentially years from coming to fruition, Turley said.

So instead, they sought a partnership with a third party search provider. At one point, OpenAI tried to make a deal with Google to gain access to their search, but Google declined, seeing OpenAI as a direct competitor, Turley testified.

But Google says companies like OpenAI are doing just fine without gaining access to the tech giant’s own technology — which it spent decades developing. These companies just want “handouts,” said Schmidtlein.

On the third day of the remedy trial, internal Google documents shared in court by the company’s lawyers compared how many people are using Gemini versus its competitors. According to those documents, ChatGPT and MetaAI are the two leaders, with Gemini coming in third.

They showed that this March, Gemini saw 35 million active daily users and 350 million monthly active users worldwide. That was up from 9 million daily active users in October 2024. But according to those documents, Gemini was still lagging behind ChatGPT, which reached 160 million daily users and around 600 million active users in March.

These numbers show that competitors have no need to use Google’s search data, valuable intellectual property that the tech giant spent decades building and maintaining, the company argues.

“The notion that somehow ChatGPT can’t get distribution is absurd,” Schmidtlein said in court last week. “They have more distribution than anyone.”

Google’s exclusive deals 

In his ruling last year, U.S. District Judge Amit Mehta said Google’s exclusive agreements with device makers, like Apple and Samsung, to make its search engine the default on those companies’ phones helped maintain its monopoly. It remains a core issue for this remedy trial.

Now, the DOJ is arguing that Google’s deals with device manufacturers are also directly affecting AI companies and AI tech.

In court, the DOJ argued that Google has replicated this kind of distribution deal by agreeing to pay Samsung what Dahlquist called a monthly “enormous sum” for Gemini to be installed on smartphones and other devices.

Last Wednesday, the DOJ also called Dmitry Shevelenko, Perplexity’s chief business officer, to testify that Google has effectively cut his company out from making deals with manufacturers and mobile carriers.

Perplexity AIs not preloaded on any mobile devices in the U.S., despite many efforts to get phone companies to establish Perplexity as a default or exclusive app on devices, Shevelenko said. He compared Google’s control in that space to that of a “mob boss.”

But Google’s attorney, Christopher Yeager, noted in questioning Shevelenko that Perplexity has reached a valuation of over $9 billion — insinuating the company is doing just fine in the marketplace.

Despite testifying in court (for which he was subpoenaed, Shevelenko noted), he and other leaders at Perplexity are against the breakup of Google. In a statement on the company’s website, the Perplexity team wrote that neither forcing Google to sell off Chrome nor to license search data to its competitors are the best solutions. “Neither of these address the root issue: consumers deserve choice,” they wrote.

Google and Alphabet CEO Sundar Pichai departs federal court after testifying in October 2023 in Washington, DC. Pichai testified to defend his company in the original antitrust trial. Pichai is expected to testify again during the remedy phase of the legal proceedings.

Drew Angerer/Getty Images/Getty Images North America

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What to expect next

This week the trial continues, with the DOJ calling its final witnesses this morning to testify about the feasibility of a Chrome divestiture and how the government’s proposed remedies would help rivals compete. On Tuesday afternoon, Google will begin presenting its case, which is expected to feature the testimony of CEO Sundar Pichai, although the date of his appearance has not been specified.

Closing arguments are expected at the end of May, and then Mehta will make his ruling. Google says once this phase is settled the company will appeal Mehta’s ruling in the underlying case.

Whatever Mehta decides in this remedy phase, Cooper thinks it will have effects beyond just the business of search engines. No matter what it is, she said, “it will be having some kind of impact on AI.”

Google is a financial supporter of NPR.

Meta anunció hoy una asociación con Cerebras Systems para alimentar su nueva API de LLAMA, ofreciendo a los desarrolladores acceso a velocidades de inferencia hasta 18 veces más rápido que las soluciones tradicionales basadas en GPU.

El anuncio, realizado en la Conferencia inaugural de desarrolladores de Llamacon de Meta en Menlo Park, posiciona a la compañía para competir directamente con Operai, Anthrope y Google en el mercado de servicios de inferencia de IA en rápido crecimiento, donde los desarrolladores compran tokens por miles de millones para impulsar sus aplicaciones.

“Meta ha seleccionado a Cerebras para colaborar para ofrecer la inferencia ultra rápida que necesitan para servir a los desarrolladores a través de su nueva API de LLAMA”, dijo Julie Shin Choi, directora de marketing de Cerebras, durante una sesión de prensa. “En Cerebras estamos muy, muy emocionados de anunciar nuestra primera asociación HyperScaler CSP para ofrecer una inferencia ultra rápida a todos los desarrolladores”.

La asociación marca la entrada formal de Meta en el negocio de la venta de AI Computation, transformando sus populares modelos de llama de código abierto en un servicio comercial. Si bien los modelos de LLAMA de Meta se han acumulado en mil millones de descargas, hasta ahora la compañía no había ofrecido una infraestructura en la nube de primera parte para que los desarrolladores creen aplicaciones con ellos.

“Esto es muy emocionante, incluso sin hablar sobre cerebras específicamente”, dijo James Wang, un ejecutivo senior de Cerebras. “Openai, Anthrope, Google: han construido un nuevo negocio de IA completamente nuevo desde cero, que es el negocio de inferencia de IA. Los desarrolladores que están construyendo aplicaciones de IA comprarán tokens por millones, a veces por miles de millones. Y estas son como las nuevas instrucciones de cómputo que las personas necesitan para construir aplicaciones AI”.

Breaking the Speed ​​Barrier: Cómo modelos de Llama de Cerebras Supercharges

Lo que distingue a la oferta de Meta es el aumento de la velocidad dramática proporcionado por los chips de IA especializados de Cerebras. El sistema de cerebras ofrece más de 2.600 fichas por segundo para Llama 4 Scout, en comparación con aproximadamente 130 tokens por segundo para ChatGPT y alrededor de 25 tokens por segundo para Deepseek, según puntos de referencia del análisis artificial.

“Si solo se compara con API a API, Gemini y GPT, todos son grandes modelos, pero todos se ejecutan a velocidades de GPU, que son aproximadamente 100 tokens por segundo”, explicó Wang. “Y 100 tokens por segundo están bien para el chat, pero es muy lento para el razonamiento. Es muy lento para los agentes. Y la gente está luchando con eso hoy”.

Esta ventaja de velocidad permite categorías completamente nuevas de aplicaciones que antes no eran prácticas, incluidos los agentes en tiempo real, los sistemas de voz de baja latencia conversacional, la generación de código interactivo y el razonamiento instantáneo de múltiples pasos, todos los cuales requieren encadenamiento de múltiples llamadas de modelo de lenguaje grandes que ahora se pueden completar en segundos en lugar de minutos.

La API de LLAMA representa un cambio significativo en la estrategia de IA de Meta, en la transición de ser un proveedor de modelos a convertirse en una compañía de infraestructura de IA de servicio completo. Al ofrecer un servicio API, Meta está creando un flujo de ingresos a partir de sus inversiones de IA mientras mantiene su compromiso de abrir modelos.

“Meta ahora está en el negocio de vender tokens, y es excelente para el tipo de ecosistema de IA estadounidense”, señaló Wang durante la conferencia de prensa. “Traen mucho a la mesa”.

La API ofrecerá herramientas para el ajuste y la evaluación, comenzando con el modelo LLAMA 3.3 8B, permitiendo a los desarrolladores generar datos, entrenar y probar la calidad de sus modelos personalizados. Meta enfatiza que no utilizará datos de clientes para capacitar a sus propios modelos, y los modelos construidos con la API de LLAMA se pueden transferir a otros hosts, una clara diferenciación de los enfoques más cerrados de algunos competidores.

Las cerebras alimentarán el nuevo servicio de Meta a través de su red de centros de datos ubicados en toda América del Norte, incluidas las instalaciones en Dallas, Oklahoma, Minnesota, Montreal y California.

“Todos nuestros centros de datos que sirven a la inferencia están en América del Norte en este momento”, explicó Choi. “Serviremos Meta con toda la capacidad de las cerebras. La carga de trabajo se equilibrará en todos estos diferentes centros de datos”.

El arreglo comercial sigue lo que Choi describió como “el proveedor de cómputo clásico para un modelo hiperscalador”, similar a la forma en que NVIDIA proporciona hardware a los principales proveedores de la nube. “Están reservando bloques de nuestro cómputo para que puedan servir a su población de desarrolladores”, dijo.

Más allá de las cerebras, Meta también ha anunciado una asociación con Groq para proporcionar opciones de inferencia rápida, brindando a los desarrolladores múltiples alternativas de alto rendimiento más allá de la inferencia tradicional basada en GPU.

La entrada de Meta en el mercado de API de inferencia con métricas de rendimiento superiores podría potencialmente alterar el orden establecido dominado por Operai, Google y Anthrope. Al combinar la popularidad de sus modelos de código abierto con capacidades de inferencia dramáticamente más rápidas, Meta se está posicionando como un competidor formidable en el espacio comercial de IA.

“Meta está en una posición única con 3 mil millones de usuarios, centros de datos de hiper escala y un gran ecosistema de desarrolladores”, según los materiales de presentación de Cerebras. La integración de la tecnología de cerebras “ayuda a Meta Leapfrog OpenAi y Google en rendimiento en aproximadamente 20X”.

Para las cerebras, esta asociación representa un hito importante y la validación de su enfoque especializado de hardware de IA. “Hemos estado construyendo este motor a escala de obleas durante años, y siempre supimos que la primera tarifa de la tecnología, pero en última instancia tiene que terminar como parte de la nube de hiperescala de otra persona. Ese fue el objetivo final desde una perspectiva de estrategia comercial, y finalmente hemos alcanzado ese hito”, dijo Wang.

La API de LLAMA está actualmente disponible como una vista previa limitada, con Meta planifica un despliegue más amplio en las próximas semanas y meses. Los desarrolladores interesados ​​en acceder a la inferencia Ultra-Fast Llama 4 pueden solicitar el acceso temprano seleccionando cerebras de las opciones del modelo dentro de la API de LLAMA.

“Si te imaginas a un desarrollador que no sabe nada sobre cerebras porque somos una empresa relativamente pequeña, solo pueden hacer clic en dos botones en el SDK estándar de SDK estándar de Meta, generar una tecla API, seleccionar la bandera de cerebras y luego, de repente, sus tokens se procesan en un motor gigante a escala de dafers”, explicó las cejas. “Ese tipo de hacernos estar en el back -end del ecosistema de desarrolladores de Meta todo el ecosistema es tremendo para nosotros”.

La elección de Meta de silicio especializada señala algo profundo: en la siguiente fase de la IA, no es solo lo que saben sus modelos, sino lo rápido que pueden pensarlo. En ese futuro, la velocidad no es solo una característica, es todo el punto.

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El ritmo rápido de la innovación generativa de IA coloca en los proveedores que empujan nuevos modelos de idiomas grandes (LLM) aparentemente sin pausa.

Entre estos destacados proveedores de LLM está Google. Su familia Gemini Model es el modelo de lenguaje sucesor de Pathways (Palm). Google Gemini debutó en diciembre de 2023 con el lanzamiento de 1.0, y Gemini 1.5 Pro siguió en febrero de 2024. Gemini 2.0, anunciado en diciembre de 2024, estuvo disponible en febrero de 2025. El 25 de marzo de 2025, Google anunció Gemini 2.5 Pro Experimental, continuó el ritmo rápido de la innovación.

El modelo Google Gemini 2.5 Pro ingresó al panorama de LLM a medida que su mercado cambia hacia modelos de razonamiento, como Deepseek R1 y Open AI’s O3, así como modelos de razonamiento híbridos, incluido el soneto 3.7 de Anthrope’s Claude.