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Major AI Moves: Meta, NVIDIA, and Google Reset the Business Value Equation
A recent episode of Everyday AI dissected a week that quietly reshaped the business AI landscape—unpacking how tech giants are layering new investments, cutting workforces, and embedding advanced AI directly into mainstream tools. Decision makers face tangible impacts in both operational efficiency and strategic road-mapping. Here’s a breakdown of what business leaders need to note, with specific takeaways for capital planning, competitive positioning, and process automation.
NVIDIA’s $26 Billion Bet: Implications for AI Infrastructure and Competition
NVIDIA’s announcement of a $26 billion investment in open weight AI models over five years marks a pivotal shift from being the hardware enabler to competing directly in AI model development. This strategy gives NVIDIA a triple-pronged advantage: cementing its grip on AI hardware, extracting additional margin from in-house models optimized for its chips, and capturing new customers who seek local, high-performance deployment.
For business leaders: The competitive edge will increasingly hinge on which AI models are not only most advanced but most cost- and compute-efficient. Enterprises now face a future where open source models could match current closed, frontier AI offerings—possibly available to run on existing hardware with minimal incremental investment. Those deploying AI must monitor NVIDIA’s Nemotron and anticipate heightened pressure on both pricing and pace of innovation from proprietary vendors like OpenAI, Anthropic, and Google.
Meta’s Duality: Chip Development and Massive Workforce Cuts
Meta’s introduction of four proprietary AI processors under its MTIA family signals an aggressive strategy to reduce dependency on NVIDIA and AMD. These processors—scalable up to 72 chips per server rack—aim to directly compete with established AI hardware, with claims of cost savings and competitive performance.
However, this technological advancement is accompanied by plans for a major strategic pivot: a reported 20% workforce reduction, potentially impacting over 15,000 employees, with capital redirected toward $600 billion in new data centers by 2028. This parallels Amazon’s earlier shift to favor capex-heavy AI infrastructure development over traditional operational spend.
For business leaders: The era of AI workforce efficiency is here. Roles handling repetitive or lower-value tasks will likely be compressed, even as demand surges for high-skill AI talent. As projects previously requiring larger teams move to smaller, more specialized units, companies must recalibrate hiring strategies and internal skill-building to maximize output from high-performing teams and heavier automation.
Google Gemini Everywhere: AI Embedded in Productivity Ecosystems
Google’s direct integration of Gemini AI features across Docs, Sheets, Slides, and Drive streamlines information gathering and process automation within its workspace suite. Users can generate first drafts, synthesize notes and emails, format documents, and derive instant AI overviews from Drive—all by referencing pre-existing company data.
For business leaders: This is not about abstract AI potential; it’s a present opportunity to strip manual copy-pasting and error-prone aggregation from business workflows. Teams can now launch projects, consolidate insights, and create decision-ready documents in moments, not hours. The net benefit is faster cycles and reduced administrative drag, especially for project managers, sales, and operations leaders. Adoption will require tailored staff onboarding to avoid shallow use cases (e.g., only summarizing meeting notes) and drive tangible ROI on subscribed licenses.
Enterprise and Government Adoption: U.S. Senate and Microsoft Copilot Health
The U.S. Senate’s formal move to approve generative AI chatbot use—across Microsoft Copilot, Google Gemini, and ChatGPT—signals that regulated entities are now embracing AI at scale. This follows Microsoft’s launch of Copilot Health, a feature that organizes health data, generates personalized insights, assists with doctor visit prep, and centralizes medical records. While Copilot Health is not for diagnosis, the real value lies in reclaiming staff time from administrative healthcare interactions and lowering the data barrier for informed decision-making.
For business leaders: AI’s entry into both sensitive government workloads and regulated industries like healthcare means the risk-benefit analysis has shifted. Organizations must double down on AI literacy and compliance trainings to ensure productivity gains don’t inadvertently create liabilities—especially around privacy regulations and data governance.
New Entrants and Open Source: Yann LeCun’s AMI and Perplexity’s Personal Computer
Yann LeCun’s exit from Meta to launch AMI, with a billion dollars in funding, reflects both the technical and geographic expansion of AI R&D. By focusing on "world models" for real-world understanding, and drawing capital from Toyota, Nvidia, Samsung, and tech visionaries, AMI aims to attack AI use cases where text-based large language models underperform—such as autonomous driving and robotics.
Simultaneously, Perplexity’s new "personal computer" provides Mac users with a persistent, AI-powered agent that automates workflows locally and in the cloud, without additional hardware investment. It supports integrations with Gmail, Slack, GitHub, and Notion, and is designed to remain active on long-term assignments—a move similar to the trajectory of OpenClaw and other open-source communities.
Business impact: The proliferation of local-first and open-source AI agents creates new competitive vectors for digital transformation. Existing staff and IT leaders can trial advanced automation without expensive infrastructure upgrades. Companies should evaluate which workflows can meaningfully benefit from persistent background AI for process automation, and assess local compute strategies in parallel with traditional SaaS deployments.
Strategic Takeaways for Decision Makers
- Capital spends are shifting from workforce-heavy models to investments in data-center infrastructure and specialized chips.
Competitive pressure is rising as both open source and proprietary model providers accelerate development; monitoring the model landscape is critical for AI strategy alignment.
Embedded AI productivity features offer immediate efficiency gains for teams, provided there is company-wide onboarding and adoption beyond superficial use.
Government and healthcare adoption underscores the need for robust staff training and compliance controls as AI’s reach extends into sensitive or regulated operations.
Open local AI agents democratize access to automation and should be seen as a lower-barrier pilot for digital transformation.
Adapting strategies to these AI developments enables sustained relevance—and competitive edge—as the cycle of innovation continues to accelerate.
Topics Covered in This Episode:
- NVIDIA's $26B Open Weight AI Investment
- NVIDIA Competing Against OpenAI, Anthropic, DeepSeek
- Meta Launches Four New In-House AI Chips
- Meta Plans Massive AI-Driven Job Cuts
- Meta's AI Acquisitions: Multbook and Manus
- Microsoft Copilot Health AI Launch Details
- Yann LeCun Launches AMI World Model Startup
- Perplexity's Personal Computer AI Agent for Mac
Episode Transcript
Jordan Wilson [00:00:19]:
Well, 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. Alright. 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.
Jordan Wilson [00:01:12]:
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 in to 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 our 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, youreverydayai.com. Alright.
Jordan Wilson [00:01:56]:
So in today's newsletter, we're gonna 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 26,000,000,000 with a b, $26,000,000,000 price tag on it. That's because according to interviews and, financial filings found by Wired, NVIDIA has just announced a $26,000,000,000 investment in OpenAI models. So Openweight 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 $26,000,000,000 over the next five years to develop open weight AI models.
Jordan Wilson [00:02:49]:
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 DeepSeek 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 manufacturers 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 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.
Jordan Wilson [00:04:10]:
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 $26,000,000,000 is huge. Right? So for NVIDIA to say that they're going to be investing $26,000,000,000 over the next five years to develop open 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 gonna put a lot of pressure on the open AI, anthropic, and Google's of the world to build better proprietary models.
Jordan Wilson [00:05:30]:
Because as the technology shrinks, right, that's the other thing that people are overlooking. Right? Like, you know, to have a one gigabyte hard drive twenty years ago probably took up 20 times the space it does today and cost a 100 times as much. So you have to think the same to be true in three, five, ten years when it comes to AI models and GPUs. Right? As an example, you could probably have something that's GPT five four level or Gemini three one level, running on an iPhone, on an older iPhone. Right? So it does make sense form from NVIDIA's perspective to well, they're gonna be cashing 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 gonna 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 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.
Jordan Wilson [00:06:44]:
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 gonna 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 gonna 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.
Jordan Wilson [00:07:24]:
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 gonna have to pay more, to compete with the closed source and the open source. And then last but not least, you're gonna have probably millions of new customers, may may mainly consumers who are gonna wanna be running these models locally, and they're probably gonna be buying specialized NVIDIA hardware to do so. Alright. Speaking of the AI chip race, our next piece of AI news, well, Meta's launching four of their own in house AI chips, maybe so they don't have to pay NVIDIA so much in the future.
Jordan Wilson [00:08:23]:
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, four fifty, 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 the top commercial products directly targeting NVIDIA and AMD's offerings. So the four fifty and five hundred 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.
Jordan Wilson [00:09:41]:
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 multibillion dollar to use multibillion dollar deal to use Google's processors as well. So in 2026 alone, Amazon, Google, Meta, and Microsoft plan to spend a combined $650,000,000,000 on capital expenditures with most of that going toward AI infrastructure. Alright. More meta news. So the first one might have been a little positive. Right? Oh, cool. That is building out all these, you know, AI chips, which can be great for the industry, great for local job production.
Jordan Wilson [00:10:41]:
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 is considering cutting 20% or more of its workforce, which could affect over 15,000 employees. The layoffs come as Meta plans to invest $600,000,000,000 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 oh, 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.
Jordan Wilson [00:11:47]:
CEO Mark Zuckerberg has been aggressively recruiting top AI talent, offering compensation packages worth reportedly hundreds 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 $2,000,000,000 to buy Chinese AI startup, Manus. 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 twenty twenty two and early twenty twenty three 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 four 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.
Jordan Wilson [00:13:18]:
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 $15,000,000,000, to essentially acquire Scale AI's leadership team and its CEO. So I think most of the AI industry was expecting something probably by the 2025 from Meta. So it's kinda 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. Alright. 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.
Jordan Wilson [00:14:32]:
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 smartwatches, 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 100% 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.
Jordan Wilson [00:15:23]:
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 HealthX 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 wait list. This is no surprise. Right? Health 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.
Jordan Wilson [00:16:53]:
And I know, there's probably some doctors out there listening that aren't going to wanna 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 health care and doctors, accounting, consulting. Those are industries that are gonna 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 Chan GPT. 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.
Jordan Wilson [00:17:38]:
So an interesting space and one that we'll continue to keep an eye on. Alright. We're gonna take a quick break for a word from our partners. Here's a harsh truth. 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. Most employees are still just using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need Section.
Jordan Wilson [00:18:09]:
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. The result? You go from rolling out tools to driving measurable AI value. 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 at sectionai.com. That's sectionai.com. Alright.
Jordan Wilson [00:18:51]:
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, Lacun is Meta's former chief AI scientist and, obviously, a leading figure in the field, and he's cofounded AMI and left Meta. Obviously, we've been reporting on that for a couple of months now. Left Meta after twelve 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 while they started with a pretty big splash in the tune of 1,000,000,000 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 Amazon 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.
Jordan Wilson [00:20:23]:
So Lacun will serve as the company's nonexecutive chairman, while 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 Lacun 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 tasks, including autonomous driving, robotics, and complex system analysis. You even got French president Emmanuel Macron that publicly praised Lacun's move, highlighting France's growing leadership in AI research. So for the past few years, Lacun has argued that today's large language models are kind of a dead end in terms of a path towards 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 Lacun because they're saying, okay. Well, these large language 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.
Jordan Wilson [00:21:58]:
So, we'll see in the coming months and years, if AMI is able to cash in on that. Alright. 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 the 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.
Jordan Wilson [00:23:11]:
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.
Jordan Wilson [00:24:05]:
So perplexity here trying to kinda redefine, where 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 of 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 a on a machine. So, kind of what perplexity is saying a more secure and sandbox version of something like OpenClaw. So, perplexity says the system can handle long running assignments remaining active for hours or days until objectives are completed.
Jordan Wilson [00:25:06]:
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 perplexity's, users on their max plan and is wait list only. Alright. 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.
Jordan Wilson [00:25:52]:
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 FedScoop, senate staff can now use Microsoft Copilot, Google Gemini, and OpenAI's chat g b t 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 Chat GPT at no cost with further details on licensing expected within thirty days. Microsoft Copilot is already integrated into the senate's Microsoft three sixty five 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.
Jordan Wilson [00:27:18]:
So Copilot does not automatically access internal senate resources right now. It only uses data explicitly shared in prompts, meeting federal cybersecurity 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 gonna be honest here. You've 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 that 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.
Jordan Wilson [00:28:42]:
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 four 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 the good thing, especially if you do not prioritize and emphasize training.
Jordan Wilson [00:29:45]:
So please, US government, actually train these senators on how to use AI, please. Alright. 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 in organized 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.
Jordan Wilson [00:30:57]:
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 pull 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 slides features. First, rolling out globally in English and then drive features launching initially in The US for now.
Jordan Wilson [00:32:07]:
Alright. 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.
Jordan Wilson [00:32:43]:
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 gonna be doing now, is going over kind of our Friday features. Alright. Anyways, here's what didn't make the AI news roundup in our what's new and what's next.
Jordan Wilson [00:33:22]:
So NVIDIA launched Nemotron. It's open source. So the Nemotron three supermodel for scalable agentic AI systems. Meta, like we said earlier, acquired MoltBook, the social network for open claw agents. Google, yeah, their new cinematic video overviews are being rolled out to pro users, not just Ultra. I actually just stumbled upon that, myself 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.
Jordan Wilson [00:34:03]:
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. GLM five turbo was released, which is ZAI's quicker version of GLM five built for agents like Open Claw. So, yeah, if you're an Open Claw user, you might wanna check out GLM five. Google launched Gemini powered ask maps chatbot for personalized navigation in US and India. Yes. So Google literally bringing out Gemini to your docs and your car. Anthropic launched their code review, review tool for Cloud Code.
Jordan Wilson [00:34:47]:
NVIDIA and Thinking Machines partnered on a gigawatt scale. They're Rubin AI deployment starting in 2027. That is with former, former OpenAI cofounder, Mira Muradi. The Adobe's CEO resigned as investors pressure over unclear AI strategy. Next, the Pentagon is reportedly rolling out Gemini AI agents to automate automate tasks for more than 3,000,000 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 two, which lets you search and analyze text images, video, audio, and docs all at once.
Jordan Wilson [00:35:43]:
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.
Jordan Wilson [00:36:20]:
Here's a fun one. Peacock launched an AI Andy Cohen av avatar, curating personalized bravo short form video feeds, and OpenAI will reportedly integrate Sora directly into chat GPD'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.
Jordan Wilson [00:36:53]:
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 gonna 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, youreverydayai.com. Sign up for the free daily newsletter. Thanks for tuning in. We'll see you back tomorrow in everyday for more everyday AI.
Jordan Wilson [00:37:27]:
Thanks, y'all.