EP 438: AI News That Matters - January 13th, 2024

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.

Is Microsoft laying off thousands of people because of AI? How did one small box from Nvidia change the future of work? What are Google's big AI shakeups that they just announced? And why is OpenAI getting into humanoid robots, y'? All. So many AI questions that might be swirling around in your head right now. We've got your AI answers. So every week, every day, you could spend hours trying to answer these questions, right? And more importantly, how they impact your company, your career, your growth. Don't do that. Don't spend hours a day trying to keep up, trying to worry about it. Join us. This is what we do. Well, we do it every day, but on Mondays, we bring you the AI news that matters. What's going on, y'? All? My name is Jordan Wilson, and welcome to Everyday AI. This is your daily podcast live stream in free daily newsletter, helping everyday people like you and me not just keep up with AI, but how we can be the smartest person in AI at your company and grow your company and your career. So if that's kind of what you're doing, right, trying to keep up, trying to get ahead, trying to be the smart person in AI at your company, welcome. This is your new home. All right, so if you haven't already, please go to your everydayai.com there you can sign up for our free daily newsletter. In that daily newsletter, we not only recap each and every podcast, podcast, slash, live stream every single Monday through Friday, but we also break down everything that you need to know that's going on in the world of AI in that newsletter. Also on our website, if you didn't already know, there's like 430 some back episodes that you can listen to from some of the smartest people in AI, all sorted by category. So if you care about legal, tech, enterprise, marketing, whatever, go click that category and you can go listen to the world's leading experts dish it all, all for free on our website. All right, so I'm excited to announce this, though, before we get into the AI news that matters next week. All right, so starting January 20th, all week, we got something special for you. So we have our 2025 AI predictions and roadmap series. So five straight days, five banger episodes, y'. All. I can't tell you how excited I am for this. So I did this last year. I did it in December, actually, with A, you know, kind of our 2024 AI predictions. And I was still getting people reaching out in October, November, nine to 10 months after the episodes saying how helpful it was. So what we decided to do was go even more in depth, double down on not just predictions. Right. That's not necessarily what this is about, but really the road map and how you can plan and make use of all of these predictions. So we're going to be doing 25 of these in just five a day quick shows. We're going to keep them. They're not going to be an hour long for those of you that, you know are out walking your dog or on the treadmill listening to this show. So they should be fast, action packed. I cannot wait. So mark your calendars right now. If you're a listener of the podcast, make sure you listen to every single one. So that's next week. Stay starting Monday, January 25, or sorry, Monday, January 20 through Friday, January 24. Keep an eye out for that. All right, enough chit chat. Let's get into the AI news that matters for January 13th for this week. And hey, live stream audience, thank you all for tuning in. Michael Joining on YouTube, Brian, Jackie, Christopher, Zofia, Gene, Joe, Michael. Big crew today, Tim. Let's, let's, let's go. Michael says, all right, let's do it. So Nvidia, Nvidia. Nvidia. All right, gonna have a lot of that today. So while Open AI and Google had a straight up slug fest in December that ultimately is going to change the way we work, Nvidia said, all right, y', all, hold my AI beer. And Nvidia Straight up went to town during their CEO Jensen Zuong, Jensen Wong's keynote address at the CES so the Consumer Electronics show conference in Las Vegas. So this is now, it seems like old news, but it happened on Monday night after our last AI News that Matters. So we've been covering a lot of this in our newsletter, but we have to bring it to you on our AI News that Matters show because it's a lot of AI news that matters. Some a little dorky, some not so much. So first, let's talk GPUs. All right, this is if you didn't know, GPUs is what makes everything go, go round in this AI world, right? So the big companies, right, that are, you know, running your AI chatbot or running whatever AI your company's using, they're using GPUs and then people locally, right? Like if you want to run local models, if you Want to have the best tech, the best hardware on your computer. To take advantage of this AI, you probably need one of Nvidia's GPUs, actually. So Nvidia unveiled its highly anticipated RTX 50 series GPUs at the CES keynote. So the new lineup includes the RTX 5090, RTX 5080, the 5070, and prices starting at $549 for the base new GPU. All right, so these GPUs boast pretty impressive speed increases, promising to double the performance of their predecessor, thanks to some updated technology like DLSS4. So the series introduces a more compact design, making high performance GPUs accessible also for smaller PCs. So Nvidia is extending its RTX 50 series to laptops. Yes, you don't have to have a, a big at home box. And that's the availability for. Availability for that is beginning in March. So basically it's this, their new 549 RTX 50 Series GPU 550 Wild. Because it offers pretty much the same performance as their previous Nvidia 4090 GPU, which was $1600. All right, you might be wondering, like, okay, how is that even possible? Well, I don't know. Nvidia's some source, some sort of, I don't know, a bunch of wizards running around there. But one, one thing is they're using generative frames now. So, you know, this is something that is going to be a growing trend for, you know, GPUs and gaming, video, animation, all of those things has generative frames. So essentially, you know, you might have, previously, you might have needed more kind of onboard power to generate more. But now we're using generative AI to essentially generate frames between or to upscale frames. So that's one of the reasons this is even possible and that we're able to get these huge, hugely powerful GPUs on smaller devices. A lot of Nvidia digits. I'm freaking excited about this. I don't personally have $3,000 to burn, but I might have to find $3,000 to burn. So that's because that's the price tag for the new Nvidia project digits. So this was announced also at CES. This is essentially the first purse, Nvidia's first personal AI super computer. So it starts at $3,000 and it's designed to bring AI supercomputing capabilities to the desktop, making it accessible to developers, data scientists and smaller organizations. So the new device is powered by Nvidia GB10, which is its new Grace Blackwell super chip combining a powerful Blackwell GPU with a 2020 Core Grace CPU. All right, so essentially a bunch of, bunch of crazy hardware tech all in one box. 128 gig of memory, if you're a dork like me and care about those things. And it also boasts one petaflop of AI performance. Yes, that's a petaflop. We're in a new, a new tier of flops. Apparently we're in the petaflop tier now. So this essentially allows users to handle complex AI tasks and large language models locally. That's the big thing here that were previously limited to larger supercomputers. So yeah, you put together like as an example two of these things because you can chain them, interconnect them and you can run as an example like llama 405B. Right, a 405 billion parameter model. In theory, on one of these you could run GPT4O. Yeah, let me repeat that. On one of these, in theory you could run GPT4O. The world's probably one of the world's top, top two, top three most powerful large language models. Obviously you can't download a proprietary model like OpenAI's GPT4O, but what this is the largest. I don't think large language models, especially open source ones are getting larger. They're actually getting smaller. So there will be no large language model open one that you cannot run on this first version of Digits. And this is going to be the worst this thing is ever going to be. In a year or two it's going to be cheaper, it's going to be more powerful, but that's what this signifies. This changes how we work. Here's something important to know as well that I think a lot of people are overlooking because right now, yes, Project Digits can function as a standalone Linux based workstation. So yeah, you can, you know, plug in a keyboard and a mouse and all that stuff. But it can also connect to a different primary computer for additional AI computing power for your current machine. So you don't have to be running on Nvidia's kind of Linux powered operating system. You can hook it up to your computer, your company's computers, whatever, and it can run with, alongside any Windows or Mac computer. So if you are running it just as its own computer, it runs on Nvidia's DGX operating system, which like I said, is Linux based, but it can operate and extend your current Mac or PC system. That's the thing that people are kind of overlooking, but Y' all think of this. I talk about AI every day. Yeah, I'm a big Nvidia fan. All right, I told you all like two years ago when hardly no one had heard of Nvidia. I said they're the most important company in the world no one's ever heard of, right? And they've obviously risen to prominence and at times, you know, they're usually top three. But at times they were the number one biggest company in the world by market cap. One, from relatively unknown two, biggest company in the world. I told y' all that that was gonna happen, all right? It's because every company, every person needs GPUs. The future of work is generative AI, large language models. And you need power, you need gpus. That's why this digits is extremely impressive, right? Live stream audiences. Anyone gonna go drop, drop three grand on this? Michael says Nvidia needs to send you one. Yeah. Hey, friends at Nvidia, I know a couple of you are listening. Yeah, you have my address. Go ahead and send me one. I'll, I'll review it on the show. But it's, it's wild, it's, it's. How do I even. Do you understand this, right? Two, three, four years ago, no one would have thought this is possible to run. In theory, it has the capability to run the world's most powerful large language models locally. So then you don't have to worry as much about data security, sending everything to the cloud, extended inference times, because you're running everything on your own machine, right? I think even as Edge AI or on device, AI was more of a trending topic a year and a half ago, two years ago, maybe we were talking about small language models, right? We were talking about the many, many versions. Now we have the capability in the future, at least open source models that you can download, right? So when we talk about Meta's Llama or Nvidia's Nemotron, which we're going to be talking about here, running it locally. Wow. All right, more Nvidia news. Cosmos. So Nvidia has unveiled Cosmos, all right? So that is designed to enhance capabilities in humanoids, industrial robots and self driving cars. So according to Nvidia, Cosmos is capable of generating images and 3D models of the physical world, unlike language models that focus more on text generation. So Jensen Huang in his keynote, which if you haven't listened to the keynote yet, I highly recommend you do if you want to know where AI is going and AI is heading. So he was talking about this, demonstrating Cosmos's use in simulating warehouse activities showcasing its training on 20 million hours of real world footage. Let me say that again. Cosmos was trained on 20 million hours of real world footage. Wow. All right, so the goal of Cosmos is to enable AI to better understand and interact with the physical world more effectively, rather than just creating content. So companies like Agility, Figure AI, Uber Wave and all these others are already employing Cosmos to advance their robotics in autonomous vehicles technology. Nvidia also announced some enhancements and updates to its Isaac robot simulation platform, which is essentially a gym or a workout place for AI robots. They go into Nvidia's cloud and go work out and learn. So that will now help robots better learn tasks more efficiently by generating synthetic data from limited examples. So the introduction of Cosmos and updates to Isaac are expected to appeal to businesses aiming to develop and deploy humanoid robots in various settings. Yeah, we're going to be hearing a lot about humanoid robots in the very near future. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you. You can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen. One last, one last Nvidia news story, y'. All. Nvidia also launched Nemotron models. So they unveiled their new Nemotron large language family of models. All right, so previously Nvidia essentially sent out a blog post when they kind of announced or unveiled their one variation of Nemotron, but now it got super official at ces. It's a whole family now. So Nemotron, which you're going to be hearing a lot more about, is built on Meta's Llama foundation models. All right, so these large language Models are designed to power robust enterprise grade AI solutions. So Jensen Wong highlighted the transformative potential of these new models in software coding, predicting that AI assistance will become essentially the same thing as coding. Right, so I'll leave. Well, actually I have another news story on that later, so I won't give too much of my hot take on that now. So the Llama Nemotron models are available in small, medium and large sizes, suitable for deployment on PCs, edge devices and cloud environments. So why is Nvidia doing this? Why are they essentially forking matters Llama models? Well, they're really trying to get even more of a hold in enterprise AI. Right. You know, they had their chat with RTX platform, which I think was good, but you had to have only a specific GPU on your computer to run it, and it was somewhat limited. So by essentially Nvidia saying they're going all in on forking Meta's Llama. But I think it's smart because they can now more quickly enter the enterprise AI space with powerful tools customized for businesses needs. And if you follow things like, I don't know, model rankings, benchmarks. Right. Actually Meta's Nemotron, originally their first variation of it, kind of ranked or scored higher than the model it was based off. Right. So all the smart engineers over there in the San Jose area in California somehow whipped up a better version of Llama themselves. Also, this boosts demand for their hardware. So these AI models, especially the medium and large one, require significant computational power to run effectively. So businesses using Llama Neon will likely need Nvidia's GPUs. Right. Or as an example, their new digits. Right. So smart here, Nvidia really doubling down on their investment on this open. It's not truly open source, but open source esque variation of Llama. I do expect once we get benchmarks, I expect some pretty impressive benchmarks. And I do express some enterprise companies to see this and be like, yeah, we're gonna be at least putting this in our LLM stack almost immediately. So it does make sense for Nvidia so to probably try to improve existing open source software that's out there, because if companies latch onto it, they're probably going to need even more GPUs. All right, we got away from the Nvidia news, y', all, and live stream audience, podcast audience too. Let me know what you think. Were you impressed with what Nvidia announced at ces? The thing I'm more shocked by, if I'm being honest, they're going to be doing this Nvidia is going to be doing this all again in like three months at their GTC conference, which I attended last year. I may or may not attend this year. We'll see how things go with my schedule. But the fact that they're probably going to be announcing a slew of new updates in like three months, I don't comprehend how they can innovate this quickly. All right, speaking of innovating, Google DeepMind is creating a new team of AI researchers focused on developing world models to simulate physical environments, according to a recent announcement from Tim Brooks. So Tim Brooks talked about on the show last week, or maybe it was the week before, previously was a co lead. This is why it's important. He was previously a co lead for OpenAI's Sora, their AI video tool. So he joined DeepMind in October and is leading this new initiative. With DeepMind doubling down even more on world models, essentially trying to advance Google's AI capability in simulating real world scenarios. So world models are cutting edge development in AI, trying to revolutionize, yes, things like video games, movies and realistic training environments for robots. But even more so than that, this is ultimately for all of these companies that are pushing toward artificial general intelligence. These world models are a big gap in what they need, right. Essentially all these large language models have been trained on the entirety of the Internet, scraped copyrighted content, everything, but not what actually happens out in the real world, right? So essentially, large language models have never went out and touched grass, right? I'm sure there's some people that tell me that internally they're like, Jordan, you should probably go outside and touch grass. You're in front of the computer too much talking about AI too much, right? This is the equivalent, right, of large language models going outside and touching grass. It's world models, right? So real world physics, the relationships between things in the real world, right? This is what these world models are for. So this initiative, Google's kind of new forming team, is part of their broader strategy to achieve artificial general intelligence, AGI before its competitors, emphasizing the importance of scaling, pre training on video and multimodal data, not just text only. So the project is in competition with other AI advancements like we talked about, sora, Nvidia's Cosmos platform like we talked about, and World Labs. So DeepMind's new team will collaborate with existing Google AI product projects, including the Gemini AI models, VO video generator and Genie, which simulates 3D environments in real time. So according to job descriptions, DeepMind is seeking research engineers and scientists to tackle challenges related to training at massive scale in integrating world models with multimodal language models. It was also announced that the teams at or sorry the teams that develop Google AI Studio and the Gemini Developer API will be Moving under Google DeepMind. So little footnote there but extremely important all right, more large language model news Microsoft has released its open source Phi 4 model so Microsoft has released its 5.4model as a fully open source project on hugging face. So the 5. 4 and you know, make sure to go check it out in the newsletter. But if you're just wondering if you want to look it up, it's Phi 4 all right. It is a 14 billion parameter model offering powerful reasoning capabilities while still being efficient in resource usage. So according to Microsoft, this update so from the Phi 3 makes the model accessible to a wider audience, including commercial applications under the popular permissive MIT license. So5.4 excels in benchmarks scoring over 80% in tests like math and MGSM outperforming larger models such as Google's Gemini Pro. So you know you're not going to see smaller models like 5.4 outperforming the world leading models like Google's Gemini Pro Ultra Open OpenAI's GPT 4.0 on all benchmarks. But it is pretty impressive that a much smaller model is already competing with the world class models on certain metrics in an open model nonetheless. So Microsoft's decision to open source the model aligns with an ongoing trend of fostering innovation and transparency in AI development. So the move could impact the AI landscape right? By making advanced AI capabilities more accessible to organizations with more limited resources. Joe says can't wait to get my hands on five. Four to test it is available now will be leaving the link in our newsletter. You have to have a pretty powerful setup right now to run it locally, but it is available. Actually no, it shouldn't be that powerful. If you have ah pretty Pretty new computer, 1632 gig of RAM you should be able to do it. I don't think any of my computers will do it. Well maybe my new Copilot plus PC I'll have to see if that can run it. All right next Microsoft, according to reports is already is reporting reportedly planning to lay off about 1% of its workforce which could result in thousands of jobs. So the layoffs are said to be performance based and span across the security division and other departments. So despite these layoff, Microsoft tends to backfill roles vacated for performance reasons, which means the overall employee count at Microsoft might remain stable. So as of last reports which I believe was in June, Microsoft had approximately 228 full time employees. So the move follows previous workforce reductions at Microsoft, including a notable cut of 10,000 employees in January 2023, which at the time was less than 5% of their workforce at the time. So the layoffs come as Microsoft's Microsoft continues to invest heavily in the emerging artificial intelligence market, aiming to maintain its leadership position. Microsoft is also reportedly focusing on retaining key talent, particularly in its AI initiatives, by offering retention bonuses in the form of stock or cash reward. So an internal document, according to reports, shows that Microsoft managers are prompted to evaluate the potential harm of losing specific employees, especially those critical to AI efforts. So a question about an employee's contribution to AI projects has reportedly been added to performance evaluations targeting a specific large group within the company. The focus on AI talent retention highlights Microsoft's strategic shift toward AI, with some employees being moved from the team's chat app to AI projects like Copilot. We're going to see this all across the board, y'. All. I'm not going to be the AI doom and gloom guy, but I don't know, I literally said this back in 2023, early 2023, that I said quarter four, 2024 and thereafter I said that's when the layoffs are going to start to hit. Because what happens is first big tech companies like Microsoft, like Google, like Amazon, like Meta, they're going to stop hiring new employees as the technology grows, Right. I think they're going to not replace people that leave and they might hire some, but I think we're going to see at the big tech company going to see overall headcount kind of go down, right? Yes, a lot of them were a little overinflated thanks to post pandemic or mid pandemic over hiring. But I do think that we are going to see a lot of layoffs. So 2024 was a record breaking year for tech layoffs and I think we are going to see that trend continue even More so in 2025. And unfortunately it is a lagging effect. So the rest of the business enterprise world, especially here in the US is going to follow suit later in the year. Speaking of that, Meta. Yeah. So Meta CEO Mark Zuckerberg announced that Meta plans to automate the work of mid level software engineers using AI, potentially changing the entire landscape of the tech industry. Yeah, Zuckerberg has gone full all like 180 it seems in the last month or two. Right. So Matt is also doing some things that some people are looking at as controversial Right. Kind of de emphasizing some initiatives internally that were previously emphasized, getting rid of human fact checkers and instead using a community note system. But Zuckerberg straight up said, yeah, we're going to start to automate a lot of the, that our mid level software engineers were doing and we're going to use AI to do that. What's interesting here is he emphasized mid level engineers, not entry level. Right. So that shows the people building the technology know what large language models are capable of. This isn't always just the low hanging, quote unquote entry level easy jobs. It's always sometimes mid level jobs. So according to Zuckerberg, Meta and other tech giants as the key there, are aiming to have AI that functions as mid level engineers capable of writing code effectively by 2025. So the transition to AI driven coding might initially be costly, but Zuckerberg believes it will eventually lead to all coding in Meta's apps and being done by AI. Currently, mid level software engineers at Meta earn close to mid six figures. Not close to six figures, close to mid six figures. That means most of them are earning about a half million a year. So interesting, right? And I do think, as we see, it's just a wind of change that we've seen in the coding space, right? I actually think it was Nvidia CEO Jensen Wong that kind of started this conversation last year saying like, hey, yeah, I wouldn't teach kids how to code, right? And everyone's like, whoa, what do you mean, right? Shouldn't everyone learn how to code? Isn't coding become, isn't coding becoming more accessible? Right? And it's like, yeah, but it's also becoming natural language, right? You can literally code an app, quote, unquote, code an app without knowing anything about code. You can just use something like Microsoft's GitHub, Copilot, something like Cursor, something like Lovable, Windsurf, etc. And just be like, yo, go code me this, or hey, here's a screenshot of something I use. Here's what it does. Go build me this, right? And you can, if you know what you're doing, one shot, an entire app, right? You could clone something, something that you need. That's the future, right? And I know I've been saying it for a while, you know, some of the, you know, tech leaders have been saying it for a while, but I think we have to actually pay attention here when we get official. And companies, and leaders at these companies are literally saying, yeah, AI is going to be doing most of our coding even at A kind of mid engineer level, right? Not just entry level stuff. Michael saying, I've used replit and it's incredibly simple and intuitive. Dennis says as Gardner predicted in recent Trend reports, you will begin to see knowledge workers unionized. Yeah, I, I, I do think that's coming, Dennis. And I think we're going to have an AI researcher from Gartner on the show here in the coming, coming weeks, FYI. All right, OpenAI going back all in on robotics. So we talked about initial rumors on the show a couple of weeks ago, but now OpenAI has made those rumors official and has announced the revival of its robotics department, aiming to develop a general purpose, adaptive and versatile robotic fleet. So according to a social media post by OpenAI's hardware director, the company plans to create robots with a custom sensor suite and AI models developed internally. So job listings reveal that OpenAI's robotics team will focus on integrating cutting edge hardware and software to explore various robotic form factors. The company is looking to employ contract workers to test robotic prototypes, suggesting the potential inclusion of robots with limbs. Yay. I don't know, I don't, I don't, I don't, I'm weird. I don't really want a bunch of AI humanoid robots, but it doesn't matter if any of us want them or not, they're clearly coming. So Open AI has explored building humanoid robots with aspirations for full scale production in the future. The robotics sector, yeah, obviously been booming recently and has seen some significant investments, raising over 6.4 billion from VC firms last year, highlighting the growing interest in robotics technology. So companies like X1 and Figure, backed by OpenAI, Nvidia and just about everyone else are working on creating humanoid robots, though the challenges remain significant. And y' all, AI not a new sector. Robotics not a new sector. Autonomous, you know, Humanoids, not a new sector. Why it matters now? It's the intersection of all of the work that has been done in the prior decades. But obviously with the emergence and the ever growing capabilities of large language models, Right, So large language models are no longer text based, Right? So previously, like I said, this is not new. There's been great autonomous robots used in factories before chat, GPT even was a thing. But this is how it changes. It brings the cost down, capabilities go up, so the possibilities expand, right? Because these large language models in 2025 and beyond are multimodal by default. Right? Whereas before, as an example, let's just look at GPT4, right? Technically it was three different models kind of working together on the under the hood so things like processing, you know, a photo or producing an audio output, essentially the way it worked is three different models would kind of pass information to each other and that created more latency. It created sometimes errors or an increased hallucination rate. So now models are multimodal by default. What that means is they are much faster, much more capable, and less error prone when dealing in multimodal facets. All right, so if you're like wondering, like, okay, what's all this about robotics? Robotics aren't new. Well, robotics plus multimodal gen AI is extremely new. Right. And that's why the sector is exploding. All right, our last piece of AI news for the day. OpenAI has released a 15 page document to entitled Economic Blueprints emphasizing the need for the US to secure investments and supportive regulations to maintain its lead in AI over China. So the document highlights the significance of chips, data and energy as crucial elements for winning the AI race, urging immediate action to establish nationwide rules. This announcement comes just before President elect Donald Trump takes office here in the US with expectations of a tech friendly administration. OpenAI CEO Sam Altman did contribute approximately $1 million as a lot of the leading tech, you know, tech guys out there in Silicon Valley did to Trump's inaugural fund, indicating a strategic move to strengthen tides with the new administration. So the blueprint which we will be linking to in our free daily newsletter warns that an estimated $175 billion in global funds will are ready for AI investments which could potentially flow to China if not attracted by the US therefore, increasing China's global influence. OpenAI proposes export controls on AI models to prevent access by adversary nations that might misuse the technology. An event is planned in Washington D.C. later this month by OpenAI to discuss these proposals further. All right, all I'm going to say is a lot of today's news stories align perfectly with the series that we're launching next week. All right, but thank you for tuning in, y'. All. I'm gonna quickly recap everything the AI news that matters. So Nvidia with a lot at CES and their keynote from Jensen Wong. Four big stories we covered today. Their new 50 series GPUs, their new project digits, a personal AI supercomputer. They unveiled Cosmos, kind of a new era, what they hope to be a new era for AI, robotics and automation. Then their Llama Nemotron models, small, medium and large. Then we saw DeepMind assembling a team to develop AI world models as well as a shake up in their structure there. Microsoft released their open source Phi 4 model. Microsoft also reportedly are planning to lay off up to a thousand employees amid AI investments and a kind of refocus on AI talent internally. Meta's Mark Zuckerberg said there's a push toward AI automation in their software engineering. And then open AI is reviving their robotics efforts and just unveiled their strategic blueprint to maintain USAI leadership. All right, I hope this was helpful. If it was, I'm just gonna say this. Mark your freaking calendars. All right? If one of your big goals in 2025 is to better understand the AI landscape, maybe you want a raise at your company, maybe you're trying to land a new AI job. Maybe you just feel overwhelmed, right? And you want to secure your career. You want to lead your department. You're a small business owner. You're trying to make sense of everything. I don't know if anyone knows this. This is what I do every day, right? And I've been doing this for, I don't know, long time. I think more than two years, almost two years now, right? So I've talked to, on this show, hundreds of AI leaders from big tech companies like Google, Microsoft, IBM, OpenAI, right? And then small startups, and every day, hundreds of just enterprise business leaders. I've learned a lot from them. And sometimes it's not until later when I can start connecting all these dots, right? It's like I get these little breadcrumbs from people, and then eventually, as days become weeks and as weeks become months, as months become quarters, I start to piece these things together and pick up on trends that maybe no one else is picking up on, if I'm being honest, right? I'm not trying to toot my own horn, but go back and look at my 2024 predictions from December 2023. They were eerily spot on, right? I gave 24 of them. Depending on how you look at it, about 21 of them technically came true or didn't go false. Maybe I was slightly off on one. I said we would have more AI agents in 2024 than humans, although we got a lot of reports that even single people had tens of thousands of AI agents. We didn't see, like, an official number, but for the most part did a very good job of predicting what 2024 would look like. All right? And I literally had people reaching out in September, October, November, who had just listened to that episode and said, this changes my perspective on everything. And all of a sudden, I am understanding things at a much higher level and telling me the impact that has had on their career or their company. So 2025, I'm changing it. Five episodes. You need to tune in. I don't say that a lot, right? More. More or less. I literally tell people all the time, people will reach out and they're like, oh, Jordan, I listen to the show every day. I'm like, why? Right? I made this. Maybe you tune in, you know, two or three times a week, maybe two or three times a month, and that's okay. I never say you need to listen to every single episode. No, listen when you want. Listen when something catches your eye. All of you, if you're listening to this right now, do not miss a single episode, period. I'm gonna say that. All right. If you want to get ahead in 2025, literally, we are laying out the blue, the blueprint for you, the road map for you. Based on hundreds of conversations, thousands of hours of research, we're laying it all out for you. So I hope this was helpful. If so, tell someone about it. If you're listening on the podcast, make sure to subscribe so you don't miss our new series launching January 20th. Please leave us a rating if you find it helpful. 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And that's 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 youreverydayai.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.