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Everyday AI Podcast – An AI and ChatGPT Podcast
Ep 734: Meta’s making AI job cuts and investments, NVIDIA’s big plays, Google brings Gemini everywhere and more AI news
00:37:50
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This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.
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Buying AI tools is the easy part.
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Getting your employees to actually use them for anything meaningful. That's where most companies fall apart. Section's own research shows over 90% of employees only use AI for basic tasks.
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That's not ROI.
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Section coaches every employee on real role specific use cases, tracks who's driving AI impact, and gives you the data to prove it's working. For more, check out section@sectionai.com.
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While it was a relatively Quiet week for two AI heavyweights in OpenAI and Anthropic, their competitors and financial backers made plenty of noise. Meta was all over the AI news this week and depending on how you look at it, it might have been for both good and, well, definitely bad reasons. Nvidia made plenty of billion dollar splashes over the past few days as its annual GTC conference kicks off in hours and Perplexity is trying to bring back the personal computer while Google quietly shipped useful AI everywhere from your car to your Google Docs. All right, I hope you're excited to get into all the AI news this week. I am as well. And if you miss anything that happens in the AI world, don't worry. That's what we're here for in our weekly AI news show on Monday called AI News that Matters. Well, if you're brand new here, welcome to the AI News that Matters on Everyday AI. My name is Jordan Wilson and well, Everyday AI, it's for you. If you're struggling to keep up, but you want to get ahead with everything that's happening in the world of AI, you tune into our daily livestream, podcast and free daily newsletter. Helping everyday business leaders like you and me make sense of all of this to get ahead and grow our companies and careers starts here like I said Monday through Friday with the unedited, unscripted live stream podcast. But to be the smartest person in AI at your company, our website is your cheat code your everydayai.com all right, so in today's newsletter we're going to have all the other AI happenings. But let's get into the biggest AI news stories of the week. And probably one of the biggest ones that no one was really talking about was one with a 26 billion with a B26 billion dollar price tag on it. That's because according to interviews and financial filings found by Wired, Nvidia has just announced a $26 billion investment in open AI models. So open weight AI models, not OpenAI. They've invested plenty of money in OpenAI, actually, more on that in a second. So Nvidia is announcing plans to invest $26 billion over the next five years to develop open AI models. So. And that's according to Wired. And this massive investment positions Nvidia as a direct competitor to leading AI firms like OpenAI, Anthropic and Deep Seek as the company expands beyond its dominant role in AI hardware. So Nvidia's move into open weight or open source AI models could accelerate innovation and lower barriers for companies and developers wanting to build on advanced AI technology. The company's strategy raises questions about market competition since Nvidia both manufactures the actual hardware that's powering AI systems and now, well, they're planning to produce leading AI software that could compete against those very companies as well. So some industry voices worry this could give Nvidia an unfair advantage as it can optimize its own models to run better on its own hardware versus its rival models from companies like, like OpenAI, Google or Anthropic. So AMD's CEO has weighed in, suggesting open source approaches are key to remaining competitive in the AI market, signal signaling intensifying rivalry among chip makers and AI software developers. So this one is interesting for a couple of reasons. Well, number one, you can't overlook the fact that Nvidia is a huge investor in some of Those companies like OpenAI and Anthropic that are building these closed source, proprietary, proprietary models. And there's always all these numbers that you hear in AI, right? All these valuations and funding rounds, but $26 billion is huge, right? So for Nvidia to say that they're going to be investing $26 billion over the next five years to develop open Windows weight AI models, that's no small feat. That is the equivalent of what you would be investing to get multiple state of the art frontier models, right? So investing that much money on the open source side is a really big deal, not just for open source, but also for closed source and proprietary models. Because if as an example, right, whether it's their future version is called Nemotron or something else, right, that's the version that they just released not too, not too long ago. Regardless, it's going to put a lot of pressure on the open AI anthropic in Google's of the world to build better proprietary models. Because as the technology shrinks, right, that's the other thing that people are overlooking Right. Like, you know, to have a 1 gigabyte hard drive 20 years ago probably took up 20 times the space it does today and cost 100 times as much. So you have to think the same to be true in 3, 5, 10 years when it comes to AI models and GPUs. Right. As an example, you could probably have something that's GPT 5, 4 level or Gemini 3. One level running on an iPhone. On an older iPhone. Right. So it does make sense for, from Nvidia's perspective to, well, they're going to be caching checks in both hands because the, the big AI labs to keep up with whatever Nvidia puts out on the open source end, they're going to have to continue to invest in bigger, better models, which means using Nvidia's GPU chips for inference and training. And then Nvidia is going to be bigger, building the open platforms and well, you might be saying, okay, how does Nvidia ultimately gain money from that? Well, that's because just about everyone will be buying probably Nvidia specific hardware to run these new models. So if Nvidia's open source models in the near future become the premier open source models, there's a good chance that they're going to be optimized to run really well on Nvidia's GPU chips in their hardware. Like, you know, as an example, the DGX Sparks of three to five years from now. So very interesting play here. I think Nvidia is actually not just squeezing it on both sides, they're actually winning in three ways. Or they could be winning in three different ways. Right. One, the companies are like OpenAI and Anthropic are going to have to pay Nvidia more to compete with Nvidia. Weird, right? But that's also why those companies are starting to invest in their own infrastructure. So that's the number one way. Number two, well, they're gonna. The open source side, that's huge. Right? For to push that boundary, that pushes all other companies like Meta as an example, which we have a related story here in a second. All the other open source companies are going to have to pay more to compete with the closed source and the open source. And then last but not least, you're going to have probably millions of new customers, mainly consumers who are going to want to be running these models locally and they're probably going to be buying specialized Nvidia hardware to do so. All right, speaking of the AI chip race, our next piece of AI news, well, Meta is launching Four of their own in house AI chips maybe, so they don't have to pay Nvidia so much in the future. So according to reports, Meta has introduced four new processors. So here's the names and what it stands for. It's the mtia, which is the Meta training and inference accelerator family. So the MTIA 300, 400, 450 and 500, those are the new processors that they introduced. So they are used obviously and designed for generative AI and recommendation models. And they can be scaled up in server racks with up to 72 chips, just like Nvidia's NVL 72 and AMD's Helios racks. So Meta claims the MTIA 400 is its first chip to deliver both cost savings and performance competitive with top commercial products directly targeting Nvidia and AMD's offerings. So the 450 and 500 build on the MTIA 400 offering faster and higher capacity memory for more demanding AI workloads. So according to Reuters reports, Meta has already started using some of these chips and plans broader deployment in 26 and 2027, with all models sharing a unified infrastructure for easy upgrades. So Meta moves follows, well, strategies by all the other big tech companies like Google, Amazon and Microsoft, who have developed their own chips to power their AI models and reduce dependencies on third party suppliers, mainly Nvidia. So Google and Amazon also rent out their chips to companies like Anthropic and Meta, who recently signed a multi billion dollar to use multi billion dollar deal to use Google's processors as well. So in 2026 alone, Amazon, Google, Meta and Microsoft plan to spend a combined $650 billion on capital expenditures, with most of that going toward AI infrastructure. All right, more Meta news. So the first one might have been a little positive, right? Oh, cool. Meta's building out all these, you know, AI chips, which can be great for the industry, great for local job production, right? Well, maybe not so much. That's because another recent Reuters report said that Meta is preparing for its largest workforce reduction ever as the company pivots heavily toward AI to streamline operations. So according to reports, Meta Meta is considering cutting 20% or more of its workforce, which could affect over 15,000 employees. The layoffs come as Meta plans to invest $600 billion in new data centers by 2028, a move intended to support its AI ambitions. So yeah, this is kind of similar to what we heard from Amazon about six. No, it was about four months ago, Right? This kind of shift from OPEX to Capex or thousands of people to AI factories and chips, and that looks like Meta might be going down the same route. So no official date has been set for the layoffs, and the final number of cuts is still being determined. According to reports, CEO Mark Zuckerberg has been aggressively recruiting top AI talent, offering compensation packages worth reportedly hundreds of millions of millions of dollars over four years. So Meta's shift toward AI is expected to create efficiencies, with projects previously requiring large teams, now handled by fewer highly skilled employees. The company also acquired Multbook this past week. If you read our newsletter, you saw that one, and that's a social networking platform for AI agents. And spent about $2 billion to buy Chinese AI startup Manuscript. So they've been acquiring a lot and spending a lot in the AI space, but, well, apparently not their own employees. So Meta's previous restructuring in late 2022 and early 2023 resulted in a layoff of 21,000 employees, or about a quarter of its workforce at the time. So Meta's AI efforts follow setbacks with its Llama 4 models last year, including criticism of misleading benchmarks, misleading benchmark results, and the cancellation of its largest model, which we never got. Behemoth. And the company's new superintelligence team is working on a model called Avocado. But performance has not yet met expectations so far and the model has reportedly now been delayed until May. So what's Meta doing here? We're not sure. Right. It's been now near nearly a year since we got our last models from Meta. It's been nine or so months since Meta spent $15 billion to essentially aqua higher scale AI's leadership team and its CEO. So I think most of the AI industry was expecting something probably by the end of 2025 from Meta. So it's kind of surprising that, number one, not only have we not seen anything with these large AI investments, aside from a couple of acquisitions, but now, reportedly, Meta's next model, which is codenamed Avocado for now, is delayed yet again. All right, something that is not delayed. Microsoft. They just launched Copilot Health, a new feature inside its Copilot chatbot designed to help make users help them better understand their medical records, wearable device data, and help them make better decisions regarding their health. So Microsoft's move comes as the company's survey found that health related questions are the most common topic for mobile Copilot users. So Copilot Health brings together data from smart watches, fitness rings and uploaded medical records, offering personalized insights and support. But it is not intended to diagnose or treat medical conditions. Yeah, that's always interesting when, you know, these companies now are coming out specifically with health products, but they're like, yeah, this isn't, you know, to actually diagnose anything, right? It's like, yeah, you have to put that giant asterisk, even though that's a hundred percent what people are going to be using this for. So the tool was developed with input from both Microsoft's in house clinicians and an external panel of hundreds of doctors across 24 countries. So copilot Health uses the National Academy of Medicine's standards for credible sources and includes information's license from Harvard Medical School and that started in 2025. Users can easily connect records from multiple doctors, hospitals and labs through a third party program called Health X and can delete their health data at any time with a simple toggle. Microsoft emphasized the health information in Copilot Health is kept separate from regular chatbot conversations and is not used to train AI models, but it is not protected under HIPAA privacy laws. So the tool helps users prepare for doctor visits by generating questions, breaking down lab results and finding providers who accept their insurance. But you cannot diagnose or prescribe medication. So right now Copilot Health is launching first for adults in the US With English as the only language right now. And interested users right now can sign up for a waitlist. This is no surprise, right? Help is obviously a huge play, especially on the consumer end. And you know Microsoft's large scale study that we talked about in our newsletter, I believe it was two weeks ago. Well, they found maybe a little bit surprising at the time that this is overwhelmingly one of the most popular use cases for consumers using Copilot right now. And I know there's probably some doctors out there listening that aren't going to want to hear this, but I've been saying this for a long time. Any profession that is high priced, right, just straight up knowledge based work like healthcare in doctors, accounting, consulting, those are industries that are going to be disrupted here fairly quickly as the models that we use get better at reasoning, they're faster, more accurate and more transparent. So I would expect, right? We saw this from ChatGPT, they came out with their health product Anthropic, went more of kind of a plug in route and now Microsoft going all in with Copilot Health. So an interesting space and one that we'll continue to keep an eye on. All right, we're going to take a quick break for a word from our partners. Here's A harsh truth.
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Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value.
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Most employees are still just using AI
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to summarize Meeting Notes if you're the one responsible for AI adoption at your company, you need section Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value.
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The result?
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You go from rolling out tools to
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driving measurable AI value.
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Your employees move from meeting summaries to solving actual business problems and you can prove the roi. Stop guessing. If your AI investment is working, check out section@sectionai.com that's S E C T I O-N A I.com
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all right, renowned AI scientist Yann Lecun has officially unveiled his AI startup after more than a decade at Meta as he's now launched Ami, a French startup focused on building AI that understands the physical world. So LeCun is Meta's former chief AI scientist and obviously a leading figure in the field and he's co founded AMI and left Meta. Obviously we've been reporting on that for a couple of months now, left meta after 12 years to pursue the new project. So AMI stands for Advanced Machine Intelligence and the company focuses on a fundamental shift in AI development, moving away from standard large language models toward world models. And well, they started with a pretty big splash in the tune of $1 billion in its first funding round, marking one of Europe's largest early stage investments in AI ever. Investors include five major funds and corporate giants such as Toyota, Nvidia and Samsung, along with tech leaders and former Google CEO Eric Schmidt and AMAZ CEO Jeff Bezos. So ami, well if you're wondering what the heck are these world models, well they're AI systems that understand the environment like humans and animals, moving beyond text based language models. So Lecun will serve as the company's non executive chairman. Well, Alexander Lebron is CEO and the team plans to hire 20 to 30 people immediately to accelerate research and development. So AMI's work continues as research that Lecune started at Meta, including a new architecture called JEPA designed for real world understanding. So within three to five years, AMI plans to deliver broadly capable AI for tests including autonomous driving, robotics and complex system analysis. You even got French President Emmanuel Macron that publicly praised LeCun's move, highlighting France's growing leadership in AI research. So for the past few years, LeCun has argued that today's large language models are kind of a dead end in terms of a path toward human intelligence because they don't have enough data. And, you know, he's essentially called them a powerful pattern matcher. So, you know, although he's obviously one of the most prominent names in AI, I think of a lot of today's, you know, current researchers are kind of butting heads with LeCun because they're saying, okay, well, these large languages models are clearly more than just pattern matching as they're able to produce economically viable work. But, well, he's betting with AMI and their world models that they're able to compete in areas where today's large language models aren't yet, such as robotics in manufacturing. So we'll see in the coming months and years if AMI is able to cash in on that. All right, well, speaking of cashing in, Perplexity is looking to cash in on the open claw trend as they've essentially, well, they've tried to resurrect the personal computer and maybe go after that open claw crowd a little bit here. So a new AI system was released from Perplexity called Personal Computer, and they promised to automate complex tasks on Mac devices, potentially changing how a lot of people could do their work if they get their way. So Perplexity launched Personal Computer. It is an AI powered autonomous agent that runs right now on Mac computers. So unlike typical AI assistants that wait for user prompts, Personal Computer is one that operates persistently in the background on a local machine, but also using Perplexity's hybrid architecture online, so it can just carry out these tasks independently once it's given a goal. So we've covered Perplexity's computer, which is its series of autonomous agents. So it essentially uses 19 different Frontier models to accomplish different tasks and it can do so autonomously. Right. But this is all done in the cloud and. Right. What's super hot and trending and, well, makes sense right now is using local machines. That's one of the reasons why OpenClaw has gone on to become, well, by definition, the most popular open source software ever. So Perplexity Computer, well, is trying to kind of get in on the game. So using their very impressive. Right. I was actually extremely impressed when I did a run through of Perplexity Computer a couple of weeks ago on our AI at Work on Wednesday series. But this brings its capabilities, well, to your actual computer. So Perplexity here trying to kind of redefine what the personal computer is now, which is funny in 2026 as personal computers have been around for decades. But essentially all this is is, well a couple things. It's marrying this new technology of computer their hybrid autonomous architecture with the local machine, right? So now Perplexity Computer personal computer will be able to still use the power of its hybrid cloud architecture, but also be able to run tasks locally, right? To be able to save files locally on a machine, to be able to read files locally on on a machine. So kind of what Perplexity is saying, a more secure and sandbox version of something like Open Claw. So Perplexity says the system can handle long running assignments, remaining active for hours or days until objectives are completed. So integration right now comes with productivity tools like Gmail, Slack, GitHub and Notion. That means it can, well kind of manage most people's day to day workflows. You don't need to buy new hardware right now because it can work on any existing Mac, making advanced automation accessible without having to have that extra investment. And right now, unfortunately this is only available to Perplexities users on their max plan and is wait list only. All right, and one or two more big pieces of AI news this one not a lot was written or talked about this one surprisingly. Maybe it's just with my background I find it interesting. But I think you should know about this. That's because the US Senate has officially approved staff use of generative AI chatbots with Senate data, marking a major shift in government tech policy. So according to Fed Scoop, Senate staff can now use Microsoft Copilot, Google Gemini and OpenAI's ChatGPT with official Senate data. Following approval from the Senate Sergeant at Arms Chief Information Officer, each Senate employee will be eligible for one license to either Gemini or ChatGPT at no cost, with further details on licensing expected within 30 days. Microsoft Copilot is already integrated into the Senate's Microsoft 365 Environment and can be accessed via mobile apps or office tools like Word and Excel. So the Senate's AI policy includes a two tier risk assessment system, with these approvals being the first for Tier two covering official Senate data and the approval processes in full. Senate AI policy will remain undisclosed, raising concerns about transparency and accountability among tech advocacy groups. So multiple AI vendors are offering discounted access to federal agencies, but it's unclear if similar deals will apply to Congress. So Copilot does not automatically access internal Senate resources. Right now it only uses data explicitly shared in prompts meeting federal cyber security requirements. Here's why this is interesting, y'. All, Number one, I hope. I hope the US Government and the Senate takes training seriously, because I'm just going to be honest here. You like, I think a lot of people, if you don't follow government and if you take off your politics hat, Senators are not exactly always the smartest people in the room. They're not. You would think they are. But let's just look at some recent history. Right? Especially when it comes to senators, many of them on the older side. Let's just call it out. Not really understanding technology. The reality is many members of Congress don't understand a technology, let alone AI. So, you know, like when a senator thought the Internet was a series of tubes, or when another senator didn't understand how Facebook made money, you know, with ads, or how, yes, this is real. How a senator asked Google's CEO why his granddaughter was receiving notifications on her iPhone, not knowing that Google made. Didn't make iPhones. Right. So right now, the average age of a senator is 64 years old, and more than a third are 70 or older. So I'm not saying that older generations shouldn't use AI. I think it's great that they do. But I think that this is just going to create a onslaught of essentially work slop in the government. Right. Which is what you don't necessarily want. In the same way how I think, you know, AI slop has taken over social media. Right. Yeah. I think it might start unfortunately making its way into politics, which unfortunately means that it could start making its way into actual legislation, which is not always a good thing, especially if you do not prioritize and emphasize training. So, please, US Government actually train these senators on how to use AI, Please. All right, in our last big piece of AI news, saving it for last because I think it's a big deal. Google is launching new, powerful Gemini AI features across its workspace apps. So Google has announced that Gemini will now integrate directly into Docs, sheets, slides, and drive, making it easier to start and organize projects using information pulled from emails, chats, and files. So users can prompt Gemini to draft documents, spreadsheets, or slides by referencing specific emails, meeting notes, or files, reducing the need for manual information gathering. So, in Google Docs as an example, Gemini can generate first drafts, rewrite highlighted sections to match desired tone or professionalism, and even format documents to align with reference notes. Google Sheets users can ask Gemini to create checklists, contact lists, and track quotes by pulling data directly from Gmail and drive. Google's Drive search now features an AI overview Right. So if you've ever done those AI overviews in Google Search and you're like, oh, this is pretty cool, aside from when that one time it, you know, recommended using glue sticks on pizza or something like that. But since it's gotten much better, Google's, you know, having that AI overview in Google Drive is pretty cool because it can pull and cull through relevant files, including citations. And users can ask Gemini questions about selected files, emails or calendar entries, such as tax related inquiries. So Gemini's features are accessible via a new prompt bar in each workspace app. So yeah, like as an example, if you look at the, if you're staring at a blank Google Doc, well, it's not as blank anymore because you will see this new feature there at the bottom. So the new Gemini powered tools are rolling out in beta first to Google AI Ultra and Pro subscribers with docs sheets and slide features, first rolling out globally in English and then drive features launching initially in the US for now. All right, so that is it for our main stories, but we have a lot for what's new and what's next. So yeah, for the most part, we on the main show bring you anywhere from seven to 10 big AI news stories, but there's always a ton that's happening in the world of AI. And hey, FYI, we just started a new series as well on Friday. So let me just quickly tell you what the rundown is, right? Monday we bring you the AI news that matters. Wednesday we go deep with one new AI feature, a new large language model, right? Hands on, very much in depth. And then Friday we started something new. Because what I realized is right aside from that one, you know, big in depth dive on Wednesdays, most of what we talk about on the show ends up right here at the end of our Monday show, which is the what's new and what's next, just these little bullet points. So if you're hearing something in the what's new and what's next and you're like, oh my gosh, that's huge. I need to know about that for my company. Well, tune in on Fridays because that's what we're going to be doing now is going over kind of our Friday features. All right, anyways, here's what didn't make the AI news roundup in our what's new and what's next. So Nvidia launched Nemotron. It's open Source. So the Nemotron 3 super model for scalable agentic AI systems meta, like we said earlier, acquired Multbook the social network for open claw agents. Google yeah, they're new cinematic video overviews are being rolled out to pro users, not just Ultra. I actually just stumbled upon that myself a couple hours ago. So Nvidia is also reportedly launching Nemo Claw, an open claw platform for enterprise. So yeah, I'm sure we'll hear more news out of that this week at Nvidia GTC Oracle is reportedly cutting up to 30,000 jobs amid costly AI data center expansion. Canva launched AI powered Magic Layers for editable AI designs. GLM5 Turbo was released, which is Zai's quicker version of GLM5 built for agents like OpenClaw. So yeah, if you're an OpenClaw user, you might want to check out GLM5. Google launched Gemini powered Ask Maps chatbot for personalized navigation in US and India. Yes, Google literally bringing out Gemini to your docs and your car. Anthropic launched their code review review tool for cloud code. Nvidia and Thinking Machines partnered on a gigawatt scale. Vera Rubin AI deployment starting in 2027 that is with former former OpenAI co founder Mira Murati, the Adobe's CEO resigned as investors. Pressure over unclear AI strategy Next the Pentagon is reportedly rolling out Gemini AI agents to automate tasks for more than 3 million federal employees. ChatGPT released a new feature that lets you interact with math and science visuals in real time. Claude now builds interactive charts and diagrams and that's in beta right now on all plans, including free plans. Google released Gemini Embedding 2, which lets you search and analyze text, images, video, audio and docs all at once. Anthropic launched Anthropic Institute to research societal, economic, legal AI risks. OpenAI is reportedly delaying the rollout of its adult mode. I'm fine with that. I don't know why people are so excited about that. Claude For Excel and PowerPoint now share full context and support reusable skills. So that's really cool that Claude in Excel and PowerPoint can now talk to each other. YouTube expanded deep fake detection to protect politicians and journalists from AI impersonation. Runway launched internal incubator labs to explore generative video applications. Here's a fun one. Peacock launched an AI Andy Cohen avatar curating personalized Bravo short form video feeds and and OpenAI will reportedly integrate Sora directly into ChatGPT's interface. We made it. I just lost my voice again at the end. I love still being sick randomly, right? Yeah, I'm ready for it to be warm here in Chicago so I can stop being sick so often. But that is a wrap for all of the AI news that matters. Like I said, if you don't have hours every single day to keep up with the headlines, the releases, the features, hey, just join us on Mondays as we lay it all off for you. Wednesdays we're going to go pretty deep hands on with probably one of these things and then go over our features on Friday. And we'll obviously have other shows for you on Tuesday and Thursday as well. I hope this one was helpful. If so, please go to our website your everydayai.com Sign up for the free daily Newsletter. Thanks for tuning in. We'll see you back tomorrow in Every Day for more Everyday AI. Thanks y'. All.
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If you're leading AI at your company and your employees are barely scratching the surface of what AI can actually do, you need a better plan. Section Coaches employees on real role Specific use cases tracks adoption across your entire organization and helps you prove the ROI to your CEO, your board, or whoever's asking.
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That's the job.
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Section helps you do it. Check out more on section@cellai.com and that's
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a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.