Noticias
This Fitness Expert Used ChatGPT to Create an AI Workout Program
I’ve never been the type to make internet friends.
I have very little interest in conversing back and forth through a screen (if you’re waiting on a text back from me, don’t). Yet there I was, wrapped up in bed with my iPhone, giving away my life story (and workout history), for free, to a stranger on the internet, like a scene straight out of Catfish.
The interlocutor on the receiving end of my messages wasn’t a swoon-worthy love interest. It was ChatGPT, and I was hiring everyone’s favourite do-everything generative AI (an artificial intelligence programme that produces responses to specific prompts, which in this case means my questions about fitness) as my new personal trainer.
Why? Because I’d been lagging in the gym lately and was craving the structure that comes with a coach. I’ve had coaches in the past, and the accountability is helpful. But as a fitness connoisseur (with a bachelor’s degree in Kinesiology, five years of personal and group training experience, and two years of fitness and health reporting at MH under my belt, if we’re checking resumes), I’ve struggled to find coaches who are up to my standards, but not out of my price range. Too often, I find myself calling bullshit on sloppy programmes.
I’m not alone in this struggle: Plenty of people can’t find fitness trainers at affordable prices. Google “personal trainer near me,” and you’ll likely be quoted prices range anywhere from $50 to more than $300 an hour. Working out three times a week with a high-end trainer could cost more than Manhattan rent.
This all helps explain the interest around AI bots as trainers. For the low low cost of zero dollars and an intimate conversation, you too can have a customized program almost immediately. But will that program actually get you to your fitness goals? I tried out CoachGPT for a month to find out, and walked away with some lessons you can use with your AI trainer, or in any of your other training, too.
Week 1: A Rough Start
To properly tell this story, I should note that I have zero experience with ChatGPT, or any other AI chatbot. All I know about generative AI is this: You can ask it to do anything, and it’ll respond with… something.
This lack of knowledge led me to make a grave mistake on take one: I didn’t log into the system. I had my lovely little onboarding chat with CoachGPT, and I left the page, without screenshotting it. All I remember was something about a plate raise—a strange exercise choice, I thought. When I went to tackle my first workout, I returned to the page to re-familiarise myself with the programming, and it was gone. Our first meeting, carelessly erased—like it meant nothing.
I had to go through the whole process again. I provided information on how much experience I have in the gym, how often and how long I want my workouts to be, what kind of equipment I have available, my injuries, and my fitness goals. Given that information, here’s what it provided as my workout:
There’s one fishy thing about all of this: I provided all the same information in our first discussion as in our second. It asked me the exact same questions. Somehow, the workout that CoachGPT gave me the second time around was different than the first.
That plate raise was gone. Not that I minded, because the original move bothered the trainer in me (there are more effective shoulder exercises out there). The workout changes got wilder, too: The dedicated shoulder and ab day was gone, replaced by a secondary leg day. I also distinctly remember there being only one programmed leg day the first time around. Even computers hate leg day.
I didn’t type the exact same sentences that second time, but the important pieces of information were all the same. How could the programming be that different? Answer: This is a generative AI, not a trainer. It was giving me a workout plan that fit my parameters, not necessarily the optimal plan.
Regardless, I rolled through CoachGPT’s exercises and workouts exactly as they were prescribed. Most of the workouts felt like they did the job. My legs were shot by the end of my first lower-body day; I could barely hold weights on the last few sets of weighted walking lunges. (At the end of the workout? Sadistic, Coach GPT.)
Week 1 Takeaways
I’m a natural optimist, so let’s start with the good. The exercise selection was fine; ChatGPT assigned me compound movements like squats and chest presses with some efficient isolation moves. It doesn’t get too complicated or unnecessarily fancy—a common mistake lousy trainers make to look smart.
The set and rep suggestions, typically 3 sets of 8 to 12 reps, are good for muscle growth, which is the goal I set for myself. It’s exactly what I would program for an advanced trainee looking to build muscle, like myself.
The downsides: I knew immediately that these workouts were going to be way longer than I asked for in the initial query. I was in the gym for nearly an hour and a half for the first leg day—a luxury I can’t afford with my workaholic lifestyle.
Second, the order of the exercises was a bit off. On the first leg day, the leg press is programmed to come second in the lineup. In general, you’re always better off putting machine-driven moves like leg presses after free weight moves. They’re generally safer and easier to bail out of, so it’s better to use fresh legs on bigger free weight compound lifts, where it’s easier to make injury-provoking mistakes (like the Romanian deadlifts that were scheduled directly after).
Did CoachGPT know something I didn’t by programming these early? Not exactly: After I did the leg presses on Day 1, my legs were so tired that I had to back down my other exercises. I could barely load the walking lunges. Third, there were some straight up mistakes. The leg extension, a quad isolation movement, was programmed on a day that’s supposed to be for hamstrings, glutes, and calves? Come on, Coach.
If I wasn’t testing this hardcore, I would have tweaked the programming—which is what you should do if you try this. CoachGPT generally gives good exercise recommendations, so research each (and watch some MH videos) to determine how to use them to your advantage, and where they belong in a session. Think about picking 3 or 4 of ChatGPT’s recommendations for your workout, and don’t be afraid to cut any additional accessory moves. You won’t lose out on gains—especially since CoachGPT programs so many calf exercises. Does ChatGPT have a calf fetish?
Week 2: The Honeymoon Phase
I was dedicated to the cause on Week 1. I stuck around for every minute of my lengthy programmed workouts. Week 2, I was running out of time.
I had to start skipping sets. I asked it which ones I should skip:
Sound advice. I did what it suggested, and still had pretty solid workouts.
You might have also noticed that the bot didn’t specify what equipment to use, or what style to do for a handful of the bigger moves. ‘Squats’ were not limited to back squats, goblet squats, or air squats.
This was actually a great perk, but only because I know my body well. I know I get pain in my right shoulder when I try to barbell bench press, so I swapped it for the more shoulder-friendly dumbbell bench press, instead.
This was also convenient because I was able to remain flexible when equipment wasn’t available and I was on a tight schedule. If the rack wasn’t free, I could swap to goblet squats (and I did).
Week 2 Takeaways
Some level of flexibility in a workout routine can be nice. Things come up, injuries happen, equipment becomes unavailable, or you run short on time and have to cut something from your routine.
Whether you’re dealing with ChatGPT or a real coach (oops, I said it), the programmed moves won’t always work for you. If an exercise hurts or doesn’t feel right, it’s on you to start the conversation. One of the best parts of ChatGPT: It reminded me that the client (in this case, me) always has to start that process.
If I hadn’t asked CoachGPT what to skip, it would’ve never known that I was running low on time in my workouts. The same is true with an IRL coach: how are they supposed to help you change things around if they don’t know you’re struggling?
Of course, having too much flexibility can be a bad thing. If I’m constantly changing up my style of squat, I won’t be able to properly progress movements. Yes, the strength gains you’ll get from a goblet squat will translate into your ability to do a barbell front squat, but there are small nuances that make them different. Those weights are going to be different, too, so changing them around constantly means you won’t really be able to evaluate how that load feels from one week to the next.
The moral of the story is to start conversations with your coach when you’re confused or stuck, then stick with what you’re taught.
Week 3: Losing Steam
I got my choice of squat this week because my equipment was not specified for me by CoachGPT ahead of time.
I know the benefit of both the front squat (strengthen the core and polish form) and the back squat (heavier loads). So, for the first two weeks, I ripped front squats to get myself into the groove. Rolling into Week 3, I felt ready to challenge my loads more, and swapped it for a back squat. Should I have done that after only two weeks of training front squats? Probably not—but, I haaaaaate front squats.
My front squat laziness, of course, was a lack of motivation. And that happened again on back/biceps day, when I ditched bent-over barbell rows for bench-supported rows.
Should I have been a better person and done the hard thing even if I didn’t feel like it? Absolutely. Would a real-life, good coach have forced me to push myself in that instance? Absolutely.
Week 3 Takeaways
Following any kind of online training program takes some level of discipline. An in-person coach is there to push you just as much as they are there to teach you—and that’s the kick in the ass that most people need.
If you don’t have the money for that, there are ways to bridge the gap with CoachGPT. You’ll have to ask for it. If you do, this is what you’ll get:
Semi-helpful for getting into the gym—but not super helpful for when you’re in the gym and dreading your heavy squat set.
I don’t know about you, but that wouldn’t motivate very much. Maybe I’d get some low-load reps in, which is better than nothing. But it certainly wouldn’t be enough for me to really push myself.
What I really wish I’d done was find a partner so we could take on the workouts together. A reliable gym buddy is an extremely underrated tool. You have someone who will keep you accountable for showing up as well as challenge you to push weight. Plus, you have a built in spotter—something CoachGPT will never be able to do.
Week 4: Red Flags Wave
I’ve long dealt with an issue in my right knee. I couldn’t tell you what happened to it, or when it originated, but towards the end of Week 3, that old nagging pain was starting to rear its ugly head on my leg day workouts.
Of course, I consulted my coach about how I should address it. Here’s what it said:
Swapping in hip thrusts straight into glute bridges seems a bit unnecessary to me. If I’m tackling the hip thrusts with the intensity I should be, I shouldn’t have a ton of juice left in me to hit glute bridges directly after.
I did it anyway. To be fair, it did help a bit. I wasn’t feeling my knee as much as I was prior, and was still feeling like I could overload my legs. I took ChatGPT’s bait on recovery tips, too. Here’s what it provided:
Week 4 Takeaways
I find it rather alarming that the bot didn’t mention speaking to a doctor at all. It mentions going to see a physical therapist, but that’s after seven(!) other suggestions on how to first handle it (which included wearing a knee brace, embracing an anti-inflammatory diet, and reintroducing high impact activities—all things you should be discussing with a doctor first).
In the bot’s defence, I never said it was a serious issue. Some information that it spit out probably would have been my first line of defence, too, if something like this happened to me: R.I.C.E. it, and hit the doctor if all else fails.
But, “find a medical professional” should probably be listed number one if you’re a computer. The extra suggestions can be helpful, but they should come second.
The Breakup
One of the hardest parts of having a real-life coach is ending things. Like all relationships, sometimes the partnership just isn’t meant to be. The greatest perk of CoachGPT is I didn’t have to go through the disingenuous “it’s not you, it’s me” breakup conversation.
I need someone who will understand my strengths and weaknesses, and harness them to help push me to be better. I can confidently say I enjoyed my time with the machine and learned a lot—but I’m ready to move back to someone (a real trainer?) who puts a bit more effort in.
But is CoachGPT the cost-effective fitness hack you’ve been looking for? Maybe, if you’re very experienced, extremely self-motivated, and if you understand workouts enough to make your own tweaks.
If you’re not any of those things, maybe save ChatGPT for its intended uses, like pulling research for a paper or project, or drafting a perfect breakup text.
Cori Ritchey, C.S.C.S., is an Associate Health & Fitness Editor at Men’s Health, a certified strength and condition coach, and group fitness instructor. She reports on topics regarding health, nutrition, mental health, fitness, sex, and relationships. You can find more of her work in HealthCentral, Livestrong, Self, and others.
Noticias
Apocalipsis Biosciencias para desarrollar Géminis para la infección en pacientes con quemaduras graves
– Esta nueva indicación es otro paso para desbloquear todo el potencial de la plataforma Gemini –
San Diego-(Business Wire)-$ Revb #GÉMINIS–Apocalipsis Biosciences, Inc. (NASDAQ: RevB) (la “empresa” o “revelación”), una compañía de ciencias de la vida de etapas clínicas que se centra en reequilibrar la inflamación para optimizar la salud, anunció una nueva indicación de objetivo para Géminis para la prevención de la infección en pacientes con quemaduras graves que requieren hospitalización (el Gema-PBI programa). El uso de Géminis para la prevención de la infección en pacientes con quemaduras severas, así como la prevención de la infección después de la cirugía (el Gema-PSI programa) son parte de la revelación familiar de patentes anteriormente con licencia de la Universidad de Vanderbilt.
“Estamos muy contentos de colaborar con el equipo de Apocalipsis para el avance de Géminis para la prevención de la infección en esta población de pacientes desatendida”, dijo Dra. Julia BohannonProfesor Asociado, Departamento de Anestesiología, Departamento de Patología, Microbiología e Inmunología, Universidad de Vanderbilt. “Creemos que la actividad de biomarcador clínico observada con Gemini se correlaciona fuertemente con nuestra experiencia preclínica en modelos de quemaduras de infecciones”.
El equipo de investigación de Vanderbilt demostrado El tratamiento posterior a la quemadura reduce significativamente la gravedad y la duración de la infección pulmonar de Pseudomonas, así como un nivel general reducido de inflamación en un modelo preclínico.
“La prevención de la infección en pacientes severamente quemados es un esfuerzo importante y complementa que la revelación laboral ha completado hasta la fecha”, dijo “, dijo”, dijo James RolkeCEO de Revelation “El programa Gemini-PBI puede ofrecer varias oportunidades regulatorias, de desarrollo y financiación que la compañía planea explorar”.
Sobre quemaduras e infección después de quemar
Las quemaduras son lesiones en la piel que involucran las dos capas principales: la epidermis externa delgada y/o la dermis más gruesa y profunda. Las quemaduras pueden ser el resultado de una variedad de causas que incluyen fuego, líquidos calientes, productos químicos (como ácidos fuertes o bases fuertes), electricidad, vapor, radiación de radiografías o radioterapia, luz solar o luz ultravioleta. Cada año, aproximadamente medio millón de estadounidenses sufren lesiones por quemaduras que requieren intervención médica. Si bien la mayoría de las lesiones por quemaduras no requieren ingreso a un hospital, se admiten alrededor de 40,000 pacientes, y aproximadamente 30,000 de ellos necesitan tratamiento especializado en un centro de quemaduras certificadas.
El número total anual de muertes relacionadas con quemaduras es de aproximadamente 3.400, siendo la infección invasiva la razón principal de la muerte después de las primeras 24 horas. La tasa de mortalidad general para pacientes con quemaduras graves es de aproximadamente 3.3%, pero esto aumenta al 20.6% en pacientes con quemaduras con lesión cutánea de quemaduras y inhalación, versus 10.5% por lesión por inhalación solo. La infección invasiva, incluida la sepsis, es la causa principal de la muerte después de la lesión por quemaduras, lo que representa aproximadamente el 51% de las muertes.
Actualmente no hay tratamientos aprobados para prevenir la infección sistémica en pacientes con quemaduras.
Sobre Géminis
Géminis es una formulación propietaria y propietaria de disacárido hexaacil fosforilada (PHAD (PHAD®) que reduce el daño asociado con la inflamación al reprogramarse del sistema inmune innato para responder al estrés (trauma, infección, etc.) de manera atenuada. La revelación ha realizado múltiples estudios preclínicos que demuestran el potencial terapéutico de Géminis en las indicaciones objetivo. Revelación anunciado previamente Datos clínicos positivos de fase 1 para el tratamiento intravenoso con Géminis. El punto final de seguridad primario se cumplió en el estudio de fase 1, y los resultados demostraron la actividad farmacodinámica estadísticamente significativa como se observó a través de los cambios esperados en múltiples biomarcadores, incluida la regulación positiva de IL-10.
Géminis se está desarrollando para múltiples indicaciones, incluso como pretratamiento para prevenir o reducir la gravedad y la duración de la lesión renal aguda (programa Gemini-AKI), y como pretratamiento para prevenir o reducir la gravedad y la duración de la infección posquirúrgica (programa GEMINI-PSI). Además, Gemini puede ser un tratamiento para detener o retrasar la progresión de la enfermedad renal crónica (programa Gemini-CKD).
Acerca de Apocalipsis Biosciences, Inc.
Revelation Biosciences, Inc. es una compañía de ciencias de la vida en estadio clínico centrada en aprovechar el poder de la inmunidad entrenada para la prevención y el tratamiento de la enfermedad utilizando su formulación patentada Géminis. Revelation tiene múltiples programas en curso para evaluar Géminis, incluso como prevención de la infección posquirúrgica, como prevención de lesiones renales agudas y para el tratamiento de la enfermedad renal crónica.
Para obtener más información sobre Apocalipsis, visite www.revbiosciences.com.
Declaraciones con avance
Este comunicado de prensa contiene declaraciones prospectivas definidas en la Ley de Reforma de Litigios de Valores Privados de 1995, según enmendada. Las declaraciones prospectivas son declaraciones que no son hechos históricos. Estas declaraciones prospectivas generalmente se identifican por las palabras “anticipar”, “creer”, “esperar”, “estimar”, “plan”, “perspectiva” y “proyecto” y otras expresiones similares. Advirtemos a los inversores que las declaraciones prospectivas se basan en las expectativas de la gerencia y son solo predicciones o declaraciones de las expectativas actuales e involucran riesgos, incertidumbres y otros factores conocidos y desconocidos que pueden hacer que los resultados reales sean materialmente diferentes de los previstos por las declaraciones de prospección. Apocalipsis advierte a los lectores que no depositen una dependencia indebida de tales declaraciones de vista hacia adelante, que solo hablan a partir de la fecha en que se hicieron. Los siguientes factores, entre otros, podrían hacer que los resultados reales difieran materialmente de los descritos en estas declaraciones prospectivas: la capacidad de la revelación para cumplir con sus objetivos financieros y estratégicos, debido a, entre otras cosas, la competencia; la capacidad de la revelación para crecer y gestionar la rentabilidad del crecimiento y retener a sus empleados clave; la posibilidad de que la revelación pueda verse afectada negativamente por otros factores económicos, comerciales y/o competitivos; riesgos relacionados con el desarrollo exitoso de los candidatos de productos de Apocalipsis; la capacidad de completar con éxito los estudios clínicos planificados de sus candidatos de productos; El riesgo de que no podamos inscribir completamente nuestros estudios clínicos o la inscripción llevará más tiempo de lo esperado; riesgos relacionados con la aparición de eventos de seguridad adversos y/o preocupaciones inesperadas que pueden surgir de los datos o análisis de nuestros estudios clínicos; cambios en las leyes o regulaciones aplicables; Iniciación esperada de los estudios clínicos, el momento de los datos clínicos; El resultado de los datos clínicos, incluido si los resultados de dicho estudio son positivos o si se puede replicar; El resultado de los datos recopilados, incluido si los resultados de dichos datos y/o correlación se pueden replicar; el momento, los costos, la conducta y el resultado de nuestros otros estudios clínicos; El tratamiento anticipado de datos clínicos futuros por parte de la FDA, la EMA u otras autoridades reguladoras, incluidos si dichos datos serán suficientes para su aprobación; el éxito de futuras actividades de desarrollo para sus candidatos de productos; posibles indicaciones para las cuales se pueden desarrollar candidatos de productos; la capacidad de revelación para mantener la lista de sus valores en NASDAQ; la duración esperada sobre la cual los saldos de Apocalipsis financiarán sus operaciones; y otros riesgos e incertidumbres descritos en este documento, así como aquellos riesgos e incertidumbres discutidos de vez en cuando en otros informes y otras presentaciones públicas con la SEC por Apocalipsis.
Contactos
Mike Porter
Relaciones con inversores
Porter Levay & Rose Inc.
Correo electrónico: mike@plrinvest.com
Chester Zygmont, III
Director financiero
Apocalipsis Biosciences Inc.
Correo electrónico: czygmont@revbiosciences.com
An illustration photograph taken on Feb. 20, 2025 shows Grok, DeepSeek and ChatGPT apps displayed on a phone screen. The Justice Department’s 2020 complaint against Google has 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.
Michael M. Santiago/Getty Images/Getty Images North America
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Michael M. Santiago/Getty Images/Getty Images North America
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.
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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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Drew Angerer/Getty Images/Getty Images North America
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.
Noticias
API de Meta Oleleshes Llama que se ejecuta 18 veces más rápido que OpenAI: Cerebras Partnership ofrece 2.600 tokens por segundo
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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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