227 | AI is now outperforming top human experts in coding & real-world tasks 🤯 Sam Altman's favorite ChatGPT feature just dropped, $100Bs pouring into AI infra, and the economy may never be the same — key AI news for the week ending on September 26, 2025

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What happens when AI not only matches but beats the best human minds?

OpenAI and Google DeepMind just entered  and won the "Olympics of coding", outperforming every top university team in the world… using off-the-shelf models. Now, combine that with agents, robotics, and a trillion-dollar infrastructure arms race, and business as we know it is about to change — fast.

In this Weekend News episode of Leveraging AI, Isar Meitis breaks down the real-world implications of AI’s explosive progress on your workforce, your bottom line, and your industry’s future.

Whether you’re leading digital transformation or trying to stay ahead of disruption, this episode delivers the insights you need — minus the fluff.

In this session, you'll discover:
01:12 – AI beats elite humans at coding using public models
05:15 – OpenAI’s GDP-VAL study: AI outperforms humans in 40–49% of real-world jobs
12:56 – KPMG report: 42% of enterprises already deploy AI agents
18:02 – Allianz warns: 15–20% of companies could vanish without AI adaptation
29:22 – OpenAI + Nvidia announce $100B+ infrastructure build
33:30 – Deutsche Bank: AI spending may be masking a U.S. recession
43:15 – Sam Altman introduces “Pulse”: ChatGPT gets proactive
and more!

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Transcript

Speaker 2: 0:00

Hello and welcome to a Weekend News episode of the Leveraging AI Podcast, a podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Metis, your host, and we have so much to talk about today. First of all, we're going to talk about a really big and interesting milestone that AI has achieved this week when it comes to competing with humans on performing tasks, and what might be the implications of that and of, related research from open AI on the global job market. Then we're going to dive into the topic of agents and how they are impacting the world New agent type of releases, new research about agents and so on. So, a lot of agentic relevant, interesting news. The third big topic of today is gonna be the massive investments that are happening right now in AI infrastructure and what is planned for the next few years and what might be the implications of that. And then we have a very long list of rapid fire items, including really interesting new releases of features and capabilities and models, including a feature of ChatGPT that Sam Altman calls his favorite AI feature. So stick around. There's a lot to cover. Let's get started. Last week was the International College eight Programming contest, also known as ICPC was held in Baku Azerbaijan last week., in that contest, which is considered the Olympics of programming for colleges from all around the world. The top and most brightest programming students from 139 universities from over a hundred countries compete to try to solve 12 really complex problems in programming within five hours. There were two AI companies that participated in this contest this year. One was Google DeepMind with Gemini 2.5 Deep Think, and the other is Open AI with GPT-5. Open AI models were able to solve 12 out of the 12 problems, which not a single human team were, was able to do. GPT-5, the regular GPT-5 that you and I have access to nailed 11 of the problems in the first try, and then an experimental model was working on the toughest program and was able to solve it after nine attempts. Google deepminds Gemini 2.5 deep think again, a model you and I have access to solve. 10 out of the 12 problems, including the most complex problem that no student team was able to solve. Both these achievements earned the gold medal equivalent if it was a human team actually competing in the contest. Now, in addition to the fact that he was able to solve all these problems, Gemini solved eight of the problems in just 45 minutes and then the two additional problems in three additional hours. Still way ahead of the five hour limit that most participants have to use all of it in order to compete in an effective way. Now, if you remember, not too long ago, we reported that a model from ChatGPT and a model from DeepMind has successfully competed and earned gold medals in the mathematical Olympics that was held earlier this summer. However, both of the models that participated in the Olympics were experimental models, which are not the commercially available models that you and I use in this particular one. In the coding one, they were using, as I mentioned, for most of the staff, the same exact models that we use, meaning they were not being specifically trained in order to solve these kind of problems, and yet they were able to beat all the human participants in this contest. Now, who else won gold in this Olympics? Well, the top human teams are from St. Petersburg's State University, the University of Tokyo and two Chinese universities ong University Singha University as well. I potentially mispronouncing them. So All these universities won gold and Harvard and MIT earned silver, but none of the teams were able to solve 12 out of the 12 problems. and The open AI model was the only one that was able to do this. Now, what does this mean? It means that enclosed loop, well-defined problems. AI currently beats the best humans on the planet that are training for months to compete in this exact field. Now, as I mentioned, the recent one, the coding one compared to the math one is even more interesting because it was using available models, the same models that are released and available on open AI and Gemini's platform for UE me to use every single day. So this is not a custom effort to try to beat this particular use case, but it's a generalized model that can do a gazillion other things that can beat the top humans in the world in complex coding problems. no, To be fair, we gotta remember that both math and coding are a very well-defined universes, right? So it's not they have a lot less of the complexities of real life and real business because they're very structured and rigid kind of environment. And AI definitely excels in these environments. However, the trajectory of not having any tool that does it two years ago to it beats the top teams on the planet to two and a half years later is a very fast trajectory. And all the other fields will just follow. It's just gonna take longer. But it is very, very clear that it's going to be better than all humans at anything, sometime within the next few years. But the next question that this raises is, what is the implication or application that on real world problems, so not a contest with a five hour limit with a very well-defined set of requirements, but in real life with actual jobs? Well, we got the answer to that as well. This week, OpenAI introduced what they call GDP Valve, which is a process that they have created in order to perform research to understand what are going to be the implications of AI on actual real world utility across multiple industries. So the benchmark is designed in order to assess the AI performance. On real world tasks from 44 different occupations across nine key US GDP sectors. So this was very much US centric. However, it is very similar definitely to the Western hemisphere because the same sectors and the same industries are aligned with what you'll see in the rest of the world, or at least of the, or at least the developed world. So how did this benchmark work? Well, the GDP valve encompasses 1,320 tasks total with 220 in what they call the gold subset, which is the key more important aspects. It's covering the majority of the work activities for over 44 high wage knowledge occupations in nine different sectors. And the selection process for the specific tasks in the benchmark involve choosing sectors that each contribute over 5% of the U-S-G-D-P. And these sectors include real estate and rental leasing, government manufacturing, professional, scientific and technical services, healthcare and social assistance, finance and insurance, retail trade, wholesale trade and information. And collectively, these nine sectors represent 75.7% of us, GDP. So basically three quarters of us, GGDP is represented by these tasks and sectors that were evaluated in this new evaluation criteria. Now, the tasks themselves are derived from real work products, so legal briefings, engineering blueprints and so on. Actual use cases that are used by real people in real industries. And they were presented by professionals averaging 14 years of experience. Obviously, including multimodality across files and images and charts and so on. And it took weeks to set it up and it took multiple experts to review the outputs of the models. So how did the valuation process work? Well, very similar to the LM Chat arena. The experts received two answers, one from humans and the other from different models, and they had to grade them across a detailed rubric, across several different aspects of how good the answer was compared to other answers. And the models they evaluated were GPT-5 with thinking capabilities and Claude 4.1. So 5GPT-5 with an enhanced thinking version matched or surpassed human experts in 40.6% of tasks across these 44 different occupations. Claude Opus 4.1 are preferred humans in 49% of tasks. Now the other interesting thing, they also tested older models. So they also tested GPT-4 as an example, and what they've learned is that the performance has doubled in success rate from GPT-5 4.0, which came out in the spring of 2024 to GPT-5, which we got in the summer of 25. So within one year, the results in real life work related tasks has doubled in its success rate. Now, in addition, models have completed the task in approximately a hundred times faster and a hundred times cheaper than the human experts. Now that speed does not include the time that will requires humans to oversight and review the information. but the AI part of the task was done at a hundred times faster and a hundred times cheaper than the humans. Now, they also categorize the success of the models. We're saying that Opus 4.1 excels in aesthetics and formatting of the results while GT five shines in accuracy and domain knowledge. But the trajectory is very clear. So going back to how we started, we talked about a closed loop, well-defined contest, and in this case, this is the real work done to generate 75% of the GDP value of the United States of America economy. And right now with the current leading models, the models are performing about half the tasks better than human experts with 14 years of experience, and they're doubling their capability every year, again, based on real research with real people doing a blind test. So in theory, if this trajectory continues, and I dunno if you'll continue in the same exact trend, but I think the direction is clear by a year from now. So the summer or the fall of 2026, these models will be able to do 70 to 80% of the tasks better than human experts in these fields on the stuff that drives the US economy. So what does that mean? It means that the future in which there is very serious competition between employing people and just using AI to solve problems is potentially in a significantly shorter timeline than everybody thought before. It also means, it means that right now you can do about half the tasks in most companies in the US with AI better than humans can do it right now. Now, yes. Does that require human oversight? A hundred percent. Will it require less human oversight in the future? A hundred percent. What does that mean to our economy? What does that mean to the unemployment rate? What does that mean to the livelihood of people who their jobs will go away and potentially their life Ewings will go away? Uh, very big questions that I don't think anybody has answers to, but I think this very important research we will hopefully sound the alarm much louder than anything that we've seen before. I really hope that people in the government will start paying more and more attention to this kind of research. This is the best research that I have seen so far that looks at it from a broad, professional done research perspective to show what might be the implications of AI on the economy And the job market. And it scares the hell out of me to be completely frank. I wanna update you on something that is very important that could dramatically help you learn AI faster, and that is the fact that we just relaunched our self-based AI business transformation course. This course is based on the course that I'm teaching, live on Zoom, with the benefit that you can take it at your spare time and break it down into specific sessions. In addition, we are not launching another course probably until the beginning of 2025, and there's gonna be a wait list open in the immediate feature. But if you want to learn everything that we have been teaching companies and individuals and leadership teams across multiple industries with thousands of people around the world who are successfully transforming their businesses on what they've learned, you now have the opportunity to do this at your own pace. In addition, I just finished teaching a cohort of the course and we just finished updating the course to the latest and greatest. So the self-paced course that you can take right now, there's gonna be a link in the show notes is updated as of September of 2025, which is right now. And so you're not gonna be using a recording that is six months old or a year old, but it's actually from the last few weeks with all the relevant updates on the models and capabilities and tools and so on. So if you want to learn how to use AI effectively across multiple aspects of the business, and this is not a fluff course, this is not talking about the history of AI or the technology of ai, it's actual practical use cases, and how to apply different AI tools in order to achieve business goals. Go check it out. There's a link in the show notes, and you can sign up and start taking the lessons right now. And from that, let's continue to the age agentic news of the week. There's a lot of interesting things, uh, in the age agentic world that has happened. So first of all, KPMG which is a large consulting company, has released their Q3 AI quarterly pulse survey that they've been running for about two years right now. And the survey is based on KPMG surveying 130 C-suite executives at companies that are at least a billion dollar in revenue. So these are large enterprises and it gives you a great idea on what's happening in there when it comes to AI and agent implementation. And what they found is absolutely stunning. 42% of organizations now deploy at least one kind of AI agent. That's up from 11% two quarters ago. So that's four x the amount of agentic being actually deployed. So these are not tests, these are things in deployment, running, doing actual work for X in six months. From the survey in Q1 of this year now from the organization surveyed, they plan to pour a joint$130 million into AI in the next year significantly up from Q1 levels and 57% of these c-suite leaders anticipate measurable ROI within 12 months of deployment of the AI solutions. And they're basing that on quantifiable gains with 97% of surveyed people say they've seen improved productivity in their companies, 94% saying they saw enhanced profitability because of using ai. And these are two very different things, right? So on one hand, profitability may mean you're getting efficiencies, meaning you're cutting the bottom line. But a lot of them mentioned that they're actually able to grow sales and grow the top line while using AI strategically, which I talk about a lot when I do workshops for C-Suite and leadership teams, talking about the two potential benefits of ai, not just being cost cutting and efficiencies, but also being able to address new kind of markets with new products that you couldn't do profitably before AI existed and now you can, which opens many, many opportunities for more or less every company on the planet. Now, when it comes to what's slowing companies down, 82% of companies flag poor data as the biggest AI obstacle. This is a jump from 56% just last quarter. I think this is a realism that companies, As they're starting to deploy actual solutions versus testing small scales of them, the data gap becomes more and more obvious. And the second most mentioned issue is cybersecurity worries, which hits 78% of the people surveyed. And that is very, very clear because now you're exposing your data to a lot more connections and so on, which leads to a lot more loopholes in your data security. the two very interesting pieces of information that came out of the survey. 78% of leaders admit that old school KPIs miss the full potential of the AI transformation. Meaning the way we're measuring return right now may not fully cover everything that people are seeing. A lot of it is because the small day-to-day stuff that people are gaining as far as benefits by using models to answer emails, summarize meetings and so on, is very hard to capture. And it is very, very real in the value that it provides the organization and the economy as a whole. And the other really interesting piece of information is that the resistance of employees to the AI implementation has plummeted from 47% to 21%. And Steve Chase, the US Vice Chair and Global Head of AI and Digital Innovation at KPMG said, and I'm quoting, agents are taking on repeatable, measurable work where time and cost savings show up directly in the metrics organization track today. That clarity is why leaders feel so confident about achieving ROI in the next 12 months. The results are visible, tangible, and compounding quickly. Now, what is that leading to? It's leading to the expected outcomes. 56% of those being surveyed have said that they're planning entry level hiring tweaks next year. What is that leading to? Well, it's leading to very predictable results. 56% of the people surveyed are planning entry level hiring tweaks within this coming year. We're already seeing that, right? We talked about this in this podcast multiple times. Why in entry level jobs? Well, because these are the areas that the easiest for AI to do consistently. Think about what we talked about before. These multiple tasks that open AI have reviewed in the research, they compared it to people with 14 years of experience, not to entry level people. If the research would've been to entry level people, the answer is instead of AI being able to do it about 50% of the time, it will probably be AI can do it about 85% of the time, which by the way might be the case next year for everyone, or maybe in two years for everyone, but this is where it's going. So it is definitely impacting already the entry level hiring, and it will probably impact all hiring as AI evolves and gets better. The other thing that it does is 82% of leaders foresee their industry's landscape radically altered within the next 24 months. And 93% of people are crediting Gen AI for making these differences and providing a competitive edge to their companies. So think about what that means. These are people from more or less every industry and 82% of them are thinking that the industry landscape, what we know as the big companies, small companies, successes and failures and so on, is going to change within the next two years. Now we got another confirmation or data point for the disruptive nature and the transformative nature of AI on the future global economy. So virgin Mazen View, which I'm probably butchering the name and I apologize for that. The Chief Information Officer for the Global Equities, Allianz Global Investors forecast that 15 to 20% of currently listed companies in the world could vanish within the next five years due to failure to adapt to ai. They're calling it digital Darwinism. So basically they're saying that the companies who will not effectively adapt to ai, 15 to 20%, one in every five or six companies in publicly traded successful businesses will disappear within the next five years. They mentioned that as part of the Bloomberg's Investment Management Summit in London, now, they said that the AI driven equity surge is not a bubble, but, and I'm quoting Fragile Boom, underpinned by fundamentals. Basically what they're saying is that the underlying fundamentals are real. Companies are actually growing, they're selling more stuff. They're generating crazy revenue. They're growing really, really fast, and so that's not what you see in a regular bubble. I would say that the fact that he added the word fragile in the beginning tells you that he still has significant worries about the volume and the speed that the investments are going. More on that shortly in the next segment of this episode. But for now, let's continue with agents. So notion just released Notion three, which is unleashing AI agents across everything notion. But the AI agents and I'm quoting Notion is saying, can do everything a human can do in Notion which is autonomously, building pages and databases, searching across workspaces, slack and the internet, and executing plans for up to 20 minutes across hundreds of pages all at once. So these notion agents are basically connected to everything notion. They remember user preferences and context and content preferences and it knows where everything is stored and it can apply everything it knows about the user as well as everything it knows about the data in order to do tasks that humans can do. Inside of Notion, what does that mean? It means that you can run your notion operations significantly faster with these agents because they can do the stuff that humans used to take multiple minutes or sometimes hours to do. The agents can do in seconds or minutes, basically cutting the effort to achieve specific goals by probably 10 x. Or the way notion define it. This is an instant teammate that is available that is highly effective and can do things for you within the notion environment, but also beyond the notion environment because it integrates with Slack and the internet and can collect information beyond the standard default notion dataset. Another company that did a big splash when it comes to Agent this week is Citigroup. So Citigroup is starting a test with 5,000 Citigroup employees that will test what they call the stylus workspaces, agentic features, which is their internal system. Which is a homegrown agent environment that can do a lot of things across the city universe. So what can these agents do? Well, they can autonomously handle multi-stage tasks like client research across data sets, profile, building, documentation, translation, and all from a single prompt. So the goal is to basically have a sidekick or a partner to every employee out of those 5,000 or multiple of these subset agentic employees under every employee to be able to perform tasks that they otherwise would've done manually and now the AI is going to do for them. Behind the scenes, stylists integrates both Google, Gemini, and philanthropic Claude for a flexible solution that will allow it to maximize the benefits of each and every one of these models. And. Deliver nuanced results across all the different tasks that it needs to deliver. Now, Citigroup has put very strict cap limits on how much compute these agents can actually use, but that being said, their chief technology officer, David Griffith says, as model pricing decline rapidly, traditional return on investment models may become outdated quickly, making long-term planning difficult. So there are two lines here. One is the amount of compute you need to run more and more agents, which is a line that's going up and up and up. But on the other hand, the cost of compute is going down, down, down. And what is the combination and the trajectory of these lines moving forward when you try to combine them together is very, very hard to predict. Making it very hard for companies to figure out where this is going. But doing a large scale test like this one with 5,000 employees, with multiple agents for each one that can do multiple jobs is a great way to figure it out. Now what they're saying is that they're putting this experiment under a microscope, and that they're going to track the behavior, the output boosts and the cost value ratios to decide on how and if they're going to scale this, or what aspect of it they're going to scale and so on. But they also said that the level of reliability that they're seeing from these agents have seen a significant boost in the past six months, which has now enabled them to drive more and more automation and autonomous behaviors from these agents. Now, per Griffith this level of automation promises that I'm quoting a massive boost of capacity. But he also said it's too early to predict the effect on jobs. And I would say it is very easy to predict the effect on jobs because they have two options and two options only, or combining both of them. Option number one is to grow faster than the efficiencies that you're getting, right? So if you're getting a 30% efficiency, if you can grow by using that extra capacity of employees by 40%, then it makes sense. You provide value back to your shareholder, your stock price goes up, everybody's happy. However, if you cannot grow by 40% and you have a 30% savings, then some people will have to go. Now this is just looking at efficiencies. If you look at the broader picture, there needs to be a return on the cost of setting these systems in place and of running these systems. So you need to look at the ROI beyond just the savings of time over the specific individuals and being a publicly traded company that is scrutinized for their profitability. They will have to show positive ROI, which means if they will have to cut staff in order to justify the investments in capacity for the ai, they will do that. Now, put that in perspective of what we shared before with the digital Darwinism from Allianz, and you understand that each and every one of these large companies is. That is potentially facing extinction. If you are following the Darwinism concept, then they will invest everything they can into AI and will cut everything they need in order to be able to afford that. Just to reduce the risk of being eliminated or becoming less relevant and less competitive in the next few years. Anybody that says otherwise, like in this very gentle thing, it's too early to predict the effects on jobs. It is not too early. The effect is very obvious. It is already happening and it's going to be amplified dramatically, even just based on the results that we have today and that we learned about from the research that we shared with you earlier in this episode. Staying on Ag Agentic News, perplexity just released a email assistant that can handle many different tasks in your inbox, such as verifying email, checking them, drafting responses, setting up meetings, and so on. It is currently only available to their max tier so that people are paying them$200 a month. But just like many other previous releases from perplexity and from other companies, this might be just a first step before a broader deployment to everybody that uses perplexity. As part of the launch of this product, currently they are integrated with Gmail and Outlook, which obviously covers the vast majority of email users in the world, but they're planning to connect it to more email platforms and other systems as well in order to provide a more comprehensive solution for our day-to-day workspace. So again, more agents in more places, connecting to more and more of the day-to-day stuff that we're doing, coming from every single direction from multiple companies, integrating with the systems that we use the most today, such as our Outlook, Gmail account, and notion, et cetera. But the transformation does not stop there. So these are white collar jobs, but blue collar jobs are gonna get hit as well. And we've talked about this in the past before that robotics is coming and that it's coming fast, but it's just a few years behind. So in a very good example of where this is going, a new collaboration between the American Bureau of Shipping, known as a BS and Persona ai, which is a robotics company. They just signed a long-term partnership in order to provide robots to the shipyard operations. Now over there, it's actually probably not going to replace employees, or not exactly, shipyards currently face attrition of 22, 20 5% among average workers, and 30, and sometimes 50 to 60% attrition on first year employees. So they are shorthanded all the time. A lot of people are leaving because it's not an easy job to do and people are looking for other things. And so this new robotic approach, the idea is to replace the employees that they're lacking instead of the employees that they have right now. And their initial focus for these robots is data collection for ship construction and classification. This means that a BS standards on how to do data collection, data analysis, and so on are going to change in order to make them a better fit for these robotics. So the way the shipyard work is being handled is going to change dramatically. So it's not just, oh, let's look for efficiencies, is let's find different, more innovative ways to do this because now we're gonna have scales that were not available before. I shared with you a few months back when a big infrastructure project was happening in my community and there were dozens of people walking around the streets with metal detectors and spray cans spraying where different lines are running. I'm like, there is no way robots are not gonna take this over and probably can take this over right now of just collecting the data of where the different lines are passing. So when you're digging, you're not running into them. This is just one very simple example that I saw with my own eyes, but these kind of things are gonna get replaced by robots probably faster than we can even think. Now if you think about a shipyard environment, and I obviously never worked at a shipyard, but I can definitely imagine how it looks like it's a hectic and dynamic environment that requires adaptability and humanoid robots with quote unquote thinking brains is the perfect solution in order to be able to fit into the existing operation without making significant infrastructure changes. Now speaking of robot and changes that are coming, Chinese startup, a head form has unveiled a humanoid robot head that mimics human emotions and it's really weird to look at it. So they released, two different videos of their robots. One looks like a human, the other one looks like an elf. They call it the Elf series. So it makes sense that just has incredibly realistic facial expressions and it is blinking and move its head and lips and everything in a way that is. Nothing but weird and scary to me. But what they're saying is even weirder and scarier. They are claiming that yes, they can't anticipate exactly the full trajectory on the development of this and the deployment of this, but they anticipate that within no more than 20 years, robots will be able to look exactly like people and we won't, and we won't be able to tell the difference. This is the mother of all science fiction, right? If you think about a lot of movies that we've seen where we can't differentiate between the robots and the humans, they're claiming this is coming within less than 20 years. Now, if we think about any other technological prediction that happened in the past that always fell short, that might be 15 years or 10 years, where we're gonna have robots that are indistinguishable from actual humans. I don't like this thought at all, but the technologies are being developed right now, and with the right business model, this is where it is going to go. So now after talking about agents and robots and how they're gonna impact the workforce, let's talk about why I think this is not stopping and this is the crazy investments that are happening right now in the infrastructure that is supposed to drive all of this. So OpenAI just announced that as part of their target initiative they are going to be building a capacity of seven gigawatts of new data center in the near future. So they're building five new sites, plus an expansion of their Abilene, Texas, and core weave projects. And they're doing it way ahead of their initial schedule. So we shared with you their crazy partnership and deal with Oracle for$300 billion worth of extra capacity, 4.5 gigawatts of extra capacity to be specific across several different locations. And as I mentioned, part of it is just the expansion of their existing cloud setup in Abilene, Texas, which could be housing 400,000 GPUs by mid 2026. So less than a year from today. Now a quick recap on what is Stargate and how it was announced. So it was launched in January at the White House with President Trump, and it's a partnership between Oracle, SoftBank and OpenAI in order to create significantly more compute for OpenAI to keep them competitive, presumably against China. So if you remember, Ted Cruz said, we should have a very light touch regulatory approach because we are in a race with China. It basically tells you this current administration will do everything required in order to keep the US companies ahead, which means allowing them to build, consume a huge amount of power and generate significantly more environmental impact than probably any project in human history. Now we have a new deal from this week where Nvidia is going to invest a hundred billion dollars in OpenAI via a phased approach that will generate 10 gigawatts of GPU powered data centered owned by OpenAI, right? So the goal here is that Nvidia is going to provide these data centers or these GPUs to the data centers that OpenAI is going to own, versus the compute that they rent right now from Microsoft and Oracle and so on. So how is this still going to work? Nvidia is going to fund 4.5 million new GPUs for a 10 gigawatt data centers starting in the second half of 2026. Just to put things in perspective, the deal with Oracle is for 4.5 gigawatts, so they're talking about 10. That more than doubles the amount of compute just from this crazy large Oracle infrastructure that they are planning now there are a lot of discussions about the bubble and how crazy these investments are and so on. There are a lot of people that are saying, oh, so the way this is going to work is that Nvidia is going to invest a hundred billion in open ai, so they have a hundred billion to actually pay Oracle out of the$300 billion that they need to pay Oracle that they don't have. And this way Oracle will be able to make a lot more money that will be able to invest in Nvidia and doing the circular generation of money out of thin air with billions and billions of dollars. The reality is, I don't think this is completely accurate because I think this, for several different reasons. One, this is presumably on top of the previous investment, so these a hundred billion dollars in NVIDIA new chips is supposed to be new processing capability, not as part of the previous project. And the other is, it was made very, very clear by Nvidia that these are gonna be owned by Open AI and not by Oracle and or somebody else. So while I understand the concerns and while I understand the fears of the crazy bubble that is potentially shaping in front of us, I don't think this is exactly the right place to say, oh, here's a smoking gun of what's actually happening. As another data point on how crazy is the need for ai compute is right now AI chipmaker cerebra, which makes the world's fastest inference chips right now. So, putting things in perspective, the GPUs that Nvidia is generating that has grown them to be the most valuable company in the world is used right now, both for training AI models as well as for inference. Inference is when the AI is generating outputs. It's called inference. And so the fastest inference chip right now is by, there's another company called Grok, uh, grok with a Q, not grok with a K to be separated from X AI's, grok large language model. So Cereus is currently raising$1 billion at a$8 billion valuation in a move that is showing you very, very clearly what is the demand for what they're doing. There's definitely a future in which GPUs are gonna be used more for training new models and less for inference because these inference chips are significantly cheaper and run significantly faster, providing higher efficiency at the inference time. And if you think about it, inference is gonna be the core of compute in the future as we'll. Need to train less models because the existing models will just gonna be good enough, not necessarily the existing today, but the ones that will exist in the future are gonna be good enough for most things. And then just using them is gonna be the need. And for that, there are better chips than the Nvidia GPUs, at least right now. Now, in addition to this crazy investment by OpenAI on new compute to deliver everything that they need to deliver, whether it's training models or creating inference or building devices and so on. OpenAI is also committed to investing a hundred billion dollars in backup servers. So this is stacked on top of the$350 billion of rental of compute that they have between now and the end of 2030, combined with the additional a hundred billion from nvidia. You kind of get the point. These are numbers we've never, ever, ever seen in any industry, in any infrastructure of anything ever before, but this is what OpenAI are committed to as of right now. But they are not alone. So Microsoft just announced that they are converting the Mount Pleasant Wisconsin Foxconn eighth Wonder of the World Building into a powerhouse AI data center campus. Putting things in perspective. They're taking an existing building that was built to do something else and failed and turning it into a high capacity data center with hundreds of thousands of Nvidia gb, 200 GPUs and fiber connections across everything to make it extremely fast. They're claiming by the way, that the amount of fiber optics there is 180,000 kilometers, which is four and a half times, which means it can go around Earth four and a half times. Now the good news is the cooling for this facility is all closed system liquid cooled, which is presumably cooling 90% of the space without wasting any new water, which is good news from an environmental perspective. However, the energy that this will consume is 250 megawatts solar farm matched by fossil fuel usage and other renewables. To put things in perspective, a few green organizations from Wisconsin are saying this equals to over 4.3 million homes. And to put that into perspective, Wisconsin as a whole has 2.8 million homes. So this one facility will require more electrical power than one and a half times the entire homes of the state. Now, here's another data point for you for how much investment is going into this ai boom. A new report that was published on CNBC talks about the seven elite private tech companies that has completely sky rocketed in the past two years to a combined value of 1.3 trillion. These are not publicly traded company, these are private companies, so obviously the biggest one is open AI with a valuation somewhere between 350 billion to 500 billion. Elon Musk's Xai with a$200 billion valuation, who by the way just raised another$10 billion just shortly after the previous$10 billion raise earlier this year, and the valuation went up from 150 billion to 200 billion in just a few months. Anthropic with 178 billion valuation, which is kind of weird when you think about there being behind, uh, XAI when it comes to valuation. But Elon is Elon and he can do stuff that a lot of other people cannot do when it comes to valuation of companies. Databricks after that with a hundred billion dollars in valuation. And three additional non-AI companies such as SpaceX, Stripe, and Unreal. Now, to explain how much investment these companies has racked up, AI startups have vacuumed$65 billion across 19 companies just this year. That is 77% of all private capital. have to say this again, 19 companies has gotten$65 billion, which is 77% of all private capital this year. This tells you the crazy amount of concentration of investment that's going into the more promising AI companies out there. That being said, the CEO of Forge investment, Kelly Rodriguez says, we've not seen this in any private market ever. Companies that are growing a hundred, 200, 300% on numbers that are already pretty big, and she doesn't only just means the valuation, she means the revenue as well. Let's just think about Anthropic. Philanthropic went from 1 billion in revenue at the end of last year to$5 billion right now, not even a year later. bUt with all that money, most of it going to infrastructure, Deutsche Bank has sound, the alarm as far as the really scary underlying economy problems that this is hiding. So their head of research, George Velas, has mentioned that AI CapEx has outpaced all consumer spending to drive the US GDP growth this year, but in order for the tech cycle to continue contributing to GB to GDP growth capital investment needs to remain parabolic. This is highly unlikely or basically what he's saying. He's saying that if you take out the crazy investments that happen in AI in 2025 in the US and you take that out of the current equation, the US economy would've been in a recession. So while we're seeing the economy growing and we see GDP growing, what is basically saying that is fueled by ai and AI infrastructure and the rest of the economy is actually shrinking right now, which is definitely not good news unless the output of the AI will generate significant actual growth in other industries, that will then compensate for that really big jump in investment, specifically in AI infrastructure. In a Bain and Company report that was released this past week, they project that AI needs to generate$2 trillion in annual revenue by 2030 in order to just fund the 200 gigawatts of global compute that it is committing to right now. And even with all the savings that they're seeing right now, they see a shortfall of$800 billion in the equation as of right now. Do I agree or disagree? It's very hard for me to say. These people are way more knowledgeable than me when it comes to doing these kind of calculations. I would just say that I think AI will drive such an incredible shift. In everything we know about most industries and most markets that we can't project how it will actually impact the economy because we're walking into a uncharted territory. But it's so far from chartered territory that is very, very hard to project. Even for people who are highly experienced in doing these kind of projections, There are also optimistic groups like Goldman Sachs that think that AI will eventually create a significant productivity across multiple markets. But there are also other negative views that are saying that the s and p 500 right now, as an example, has a huge exposure to ai. And if the AI boom collapses, anybody who's an investor in the s and p 500 is gonna get a very serious hit. So you hear voices on both side of that equation. I think both of them are guessing and speculating based on a lot of unknowns. And we'll just have to play this out and see how this works. So that's it for the deep dives, that what we've learned from them is that there is the greatest investment in history in AI infrastructure right now, more than any investment in anything else in the past, in a really, really high speed, at a really high scale, fueled by a global race to get first to A-G-I-A-S-I and beyond, and that the companies that are behind it are investing amounts of money that sound completely legendary. However, each and every one of these things is leading to results that potentially put at risk every single job or many, many jobs across the known economy without anybody knowing what other jobs, if other jobs will emerge to replace them. So overall, this doesn't look too promising to human jobs in the, at least near future until we figure out what other jobs we can do in the AI era. Now let's switch to rapid fire items. There's a lot of stuff to cover. The first segment in the Rapid Fire is gonna be about new releases and new capabilities and existing models. The most interesting one this week is that Xai announced Grok for Fast. And Grok for Fast is one of those mini models like we've seen before with O three Mini and so on that are almost as good as the existing model. So Grok four Fast performs almost as good as Grok four across multiple benchmarks, but it comes at a 98% price cut. So you're gonna pay 2% to get almost the same exact outcome. And in addition, this new model comes with a massive 2 million tokens cost context window, which is the top of what is being offered right now. Which is at the high end of the scale, similar to Gemini 2.5 Pro and way above, uh, ChatGPT as an example. It is also very good at multi-modal capabilities, and it has improved dramatically in code generation and basically everything else you want to use AI for. And in addition, based on third party analysis. It is currently by far the frontier runner when it comes to cost efficient intelligence. If you want to get solid intelligence at a very low price, grok four fast is your number one option, and I'm sure a lot of people that are developing against APIs are gonna switch to it at least until the next thing comes over. So right now you can get a top of the line capability for 20 cents for 1 million input tokens and 50 cents for 1 million output tokens, which is way more competitive than any other platform out there right now. Now speaking of compute and investments and how much more of it will become available and what will enable and being able to run models in a more effective way and so on. Leads us to the next announcement. Sam Altman, like he likes to do, dropped a cryptic X post this week that talks about a new compute intensive offering. So I will read the exact quote from there. Over the next few weeks, we are launching some new compute intensive offerings because of the associated cost. Some features will initially only be available to pro subscribers and some new products will have additional fees. Our intention remains to drive the cost of intelligence down as aggressively as we can and make our services widely available, and we are confident we will get there over time. But we also want to learn what's possible when we throw a lot of compute at today's model's cost at interesting new ideas. What does that mean? Well, nobody knows. There are a lot of speculations, which leads us to the next topic, which is several different people have been reporting that they're seeing a new selection of models in the open AI dropdown menu and in their API, that is called alpha models. Under these alpha models, two separate agent models appeared. One is called agent with truncation and the other one is called agent with prompt expansion. These are not very good names, but A, there's a very good chance that was not supposed to be exposed to the public, and b, open AI has not been very good at making up names in general, so that would surprise me if that ends up being the final name of these new features. When using these features, what it was activating is agent mode, but in a very specific setup that will still allow it to run browser and use tools and so on, but with more capabilities than it had before. Now, shortly after these reports came out, these models were rolled back and were not available anymore. But is this what Sam Altman was referring to? New agent capabilities running within the chat GPT agent mode? Nobody knows, but it feels like this is the direction that they're going, which is deploying more agent capabilities. That by definition will require a lot more compute. But as Sam mentioned, we will know in the next few weeks. And I promise to keep you posted as things are happening. But there is a feature that Opener did roll out this week, which is a feature that Sam Altman calls and I'm quoting today, we are launching my favorite feature of Chachi Piti, so far, called Pulse. This also points to what I believe is the future of ChatGPT, a shift from being a reactive to being significantly proactive and extremely personalized. So what is this feature and how can you get it? So first of all, it has been deployed, first and foremost to the pro subscribers, the$200 a month people, and only on the mobile application. It is generating between five and 10 personalized cards as you open chat GPT-5, every single day. And these include an image in a little bit of text. And when you click on it, it takes you to a more detailed view of the same topic. And these are gonna be personalized topics that you care about. This could be soccer updates or travel plans for a specific destination, or a diet friendly menu and so on and so forth. And you click on those and it will take you to that specific information. The other thing is it is capped by the number of these cards that you're going to see. At the end of it, there's a statement saying, great, that's it for today. And the idea is intentionally to move away from the endless scroll time waste of social media. So basically is to give you high value, organized and highly personalized information that you need to start your day. This could be personal, this could be business related. Now, this feature integrates with their existing connectors such as Gmail and calendar parsing, which means it can surface emails and agendas and so on, and it is connected to the chat's long-term memory for context and understanding of what you're working on, what you like and so on, which will become more and more personalized over time. In the demo that was provided by Christina Wadsworth Kaplan, she showed how Pulse Auto added London running routes for her based on her jogging history and also offered pescatarian tweaks to her dinner reservations in order to help her find the right restaurants open AI's new CEO of applications, Fiji CMO Blogged we're building AI that lets us take the level of support that only the wealthiest have been able to afford and make it available to everyone over time. And ChatGPT Pulse is the first step in that direction, starting with pro users today, but with a goal of rolling out this intelligence to all. But I wanna go back to Sam's tweet and I'm gonna read the second half of it again, and then I'm gonna refer to it. This also points to what I believe is the future of ChatGPT, a shift from being all reactive to being a significant, proactive, and extremely personalized. What does that mean? It means we get a glimpse into the future. A AI that is not a chat, but that actually is proactive and participates in our day-to-day activities across the entire day. From personalized to business things fully connected to the entire universe of our personal knowledge. Again, both personal and work. Now, if you combine this with wearable devices such as glasses or whatever it is that OpenAI is developing. And that means we're gonna have devices that are always aware, always collecting information about what we do. And you understand that these tools will become extremely powerful. It will know everything about us. It can become the most incredible support sidekick that we ever could dream about. It can help promote healthy habits, so don't eat this or eat that, or go work out more frequently, or get up and walk a little bit or connect with friends and so on. But it can also do better time management that we're doing right now. Allow us to focus when we need to focus. Allow us to plan things that we're currently finding hard or don't have time to plan as deeply as we wanted to, and assist with personalization and prioritization of things, and so on and so forth, across basically everything that we're doing. This is. Really exciting and really, really scary at the same time. A, because the complete invasion of privacy of technology into everything in our lives, which is just gonna be an extension of what we know today. From, as an example, Google knowing everything about me because I have all my data in the Google Universe and I use an Android phone. So it's just gonna be a very significant expansion of that. But in addition, it is scary because I feel that we will become completely dependent on this technology. If this technology will be assisting us with making every decision along the day, how will we know to make our own decisions without this technology? And I think very quickly will become addicted to this. While ChatGPT is saying, this is gonna be different than social media because you won't be able to score to scroll forever. I think the negative impact might be much more significant than social media because it will be integrated into literally everything that we're doing. Now from open AI to anthropic in a topic that's actually related to open ai and Anthropic has got a lot of bad press in the last month or so for things not working well with anthropic code generation capabilities, which was the most advanced and most capable code generation tool. So far, it has been the default for most developers around the world and it has fueled the crazy growth of philanthropic from$1 billion in revenue to$5 billion in revenue in less than a year. Well, there's been a lot of speculation around what's actually happening with the Claude models. A lot of people were speculating that Anthropic is throttling down the models in order to save money and so on and so forth. Well, philanthropic just came out with a report that is sharing that they found three significant bugs that has together led to the outcome that Claude responses were suboptimal. I'm not gonna dive into the exact technicalities of these bugs, but the reality is the three bugs combined could have affected up to 30% of cloud code users at the end of August through the beginning of September, when they start deploying solutions for that, that took them about two weeks to deploy. So through mid-September, they were able to roll out the entire solution for all these different issues, which presumably is supposed to, to solve the problem. They swore they're not throttling the models and that they're providing the best capabilities that they can to everybody all the time. The reality is that the big winner out of this is open ai, and the reason for that is as people were getting very unhappy with the degraded results from Claude, GPT-5 has been shining as the most capable code generation tool right now. And I assume, and again, there's no statistics that I found, but I assume that a lot of people have jumped ship from running anthropic behind the scenes on the development processes to running GPT-5. Which leads me to one of the most interesting new phenomenas of the AI era, which is how easy it is to switch from one platform to the other. So if you think historically, if a critical infrastructure of your company you were not happy about it, and you wanted to switch, let's say you wanted to switch from SAP to Oracle or the other way around, it would have required months of planning, more months of execution, and millions of dollars in investments to switch from one core infrastructure to the other. Right now, switching from Claude to Chachi, PT to X and vice versa in your. As the underlying infrastructure in your tech stack takes seconds and practically has no cost. Like in your IDE as an example, your co-generation environment, you can go in the backend drop, click the dropdown menu and select the different model and be up and running, and that's it. No cost, no investment, no infrastructure change. And in this reality, it needs to be very scary to the companies that create this infrastructure because until they have very significant integrations into companies systems, they will be able to be replaced with a simple dropdown menu and with no cost to the company. So there's significantly less stickiness than we are used to in the tech industry. Another big interesting release this week comes from bike dance from China. They just released Sea Dream four, which is the latest image generation model that is supposed to be as good as Gemini nano banana, and it is supposed to be competing directly with that. That's why they released this model. It is very good at generating images. It is very good at generating text. It is very good at generating at editing images, and it runs 10 x faster than their previous model, so a lot to look for. I haven't tested it myself, but it comes at a very attractive price point of$30 for a thousand generations at a very high speed and at a higher resolution than Gemini nano Banana. It is currently ranking second on the LM Arena, text to image ranking, but a very, very close second. So Nano Banana has 1,152 points and Seed Dream four has 1,151 points. So this is a fraction of a percent difference in the models, and it is cheaper to run C Dream 4.2. Now, if you're just generating it on your regular chat, it doesn't really matter, but if you want to use it through the API, then it matters a lot because it's gonna be significantly cheaper to generate very similar results. It is also ranking second, but with a bigger spread on image editing capabilities with Gemini. Nano Banana with 1337 points and C Dream with 13. 13. Again, still a relatively small spread, but not as close as the other one. And they are all far ahead of all the other models behind them. Another interesting feature that was released this week was released by Meta. Meta, just launched Vibes on September 25th, which is a dynamic feed of AI generated short videos that invites users to browse, create, and remix content and music and styles to generate new content out of that. So you can either create videos from scratch or remix existing content from all the other platforms and reshare them back to the other platforms. So if you see a meta AI video on Instagram as an example, you can tap to bring it into the Meta AI app, remix it, add music, change the style, and repost it back to Instagram, Facebook, et cetera. So this is a whole new interactive environment for generating, editing and remixing existing AI generated videos. Users have generated over 10,000 videos in just the first 24 hours of this feature. Do I think it's gonna take over our feed? Probably not. Do I think it's gonna play role in the future? Feed a hundred percent. Because you'll be able to create really cool videos or just not possible before, unless you are Disney and were willing to invest millions of dollars in generating something cool, but now anybody can generate them. And so I have zero doubt that they will be a part of every feed that we see in the future. But I do think people are still wanna know what other people are doing, what people are achieving, and so on and so forth. So I don't know how big this will turn. Time will tell. And let's switch gears from talking about features and releases to really big strategic partnerships that have rolled out this week. The first one is Lex Share, which is a key supplier to Apple that saw their shares surge 10% after there's been reports of a partnership with OpenAI on their next AI driven device. So this Chinese manufacturer that has been assembling the Apple AirPods and the Vision Pro is working on a prototype device together with ChatGPT, which is leading to a lot of speculations. Whether that's the device they're developing with Johnny Ive, or not, it is not completely clear. But what is clear is that OpenAI is going all in on devices. The information shared this week that in addition to the crazy investment in Johnny, ive and his team. Yeah, just as a quick reminder, that was$6.5 billion early this year. Open AI has been recruiting aggressively hardware engineer and designers directly from Apple. So while there's been 10 previous Apple Engineers in OpenAI in 2024, they've recruited over two dozens in 2025. ANd in addition to Lux Share, open Air has approached other suppliers of Apple devices for potential components such as components to a speaker model in one of the future devices. The latest rumors is that the first open AI device that is targeting a release in late 26 or early 27 is going to be a speaker without a screen. But either way, it puts a lot of pressure on Apple, not just because OpenAI has been stealing some of their top engineers, but because of what it means to Apple. Apple is a device company. They're a hardware company. They've built an incredible brand that millions and millions of people in the world are dependent on, but they've been failing in the AI race time and time again in the last few years. And with the soaring popularity of ChatGPT as a tool that everybody knows as ai and with the soaring and with the growing popularity of wearable devices, definitely the glasses from Meta. But again, once OpenAI comes up with a ChatGPT based device, X number of millions of people are gonna buy it. And eventually these will start taking market share from iPhones and other Apple devices. And I'm certain Apple is aware of it, the window of opportunity is closing because if OpenAI really releases this product within about a year from now, this puts a very, very serious pressure on Apple to do something and do something successful and do it fast, which they were not able to do in the past two years. Another huge partnership this week that actually connects to one of the earlier topics that we discussed in this episode is OpenAI. And Databricks has forged a multi-year, a hundred million dollar deal to enable a Newgen development platform based on OPI tools inside the Databricks environment. So the goal of the partnership is to enable enterprises to easily develop AI agents using the open AI capabilities while connecting it to their proprietary data that is hosted on the Databricks platform. This obviously makes perfect sense to both companies and it will provide a lot of value to the companies that are using the Databricks platform. Another big announcement this week when it comes to partnership, the general services administration as also known as GSA, has signed a deal with Xai for 18 months agreement to deliver Groks latest models to anybody in the government for 42 cents per organization in the government. In addition to delivering the models for practically free, XAI is committing engineers to help agencies implement grok rapidly and successfully. Plus, they're suggesting training for the employees in order to explain to them how to best use the models in a good way. We reported about similar deals that were signed with Microsoft and OpenAI just in the past few months, but my biggest question really goes back to training, right? So providing the models to the government is great. Uh, the fact that the companies are willing to do it for free, I don't think they're doing it out of generosity. I think they just understand that after the free segment is done, they will be able to make billions of dollars from government, employees being used to using their models and having it fully integrated into multiple different processes within the government. The government being the largest employer in the US makes it all makes perfect sense. Also, it's good relationships with the administration, so it all makes great sense. The problem is training. Who is going to provide the training to the government employees in order to use and leverage these tools effectively? That still stays unclear. What I see in many companies that I work with is like, oh yeah, we're investing a lot of ai. We got everybody licenses for whatever, you know, whether it's ChatGPT, or Microsoft Copilot and so on. But they're not seeing any results, and in many cases they're seeing negative impact because there's no proper training to the employees. And I have a feeling that a similar thing on a much larger scale is gonna happen in the US government. So I really hope that the government will figure out how to provide adequate and continuous training to government employees on how to leverage AI effectively and safely so we can all benefit from this of potentially getting better government services cheaper and faster. From government. Let's switch to legal for a minute. Xai just filed a lawsuit on September 25th accusing OpenAI of stealing trade secrets via hired away employees. Now, this is not the first and probably not the last lawsuit between Elon Musk and Sam Altman, or between Xai and OpenAI. It is very clear that this is battle is not going to stop. In this particular case, as I mentioned, Musk is claiming that Sam is stealing talent, but not just the talent, but they are actually using trade secrets that were developed inside of Xai as they're getting the employees into OpenAI. The lawsuit names three former XAI employees, two engineers, and one senior executive that allegedly passed along proprietary source code and other business secrets as they were joining open ai. Now, I'm not going to dive into the whole history between these two individuals, but this thing doesn't seem to quiet down. It's just getting worse and worse. I don't know if to say that's getting ridiculous is the right word to use, but it is getting ridiculous. They're acting like five-year-olds and mostly Elon is acting like a five-year-old. The other thing that this brought me to think about is that engineers are jumping ship in this. Race all the time from one company to the other. And if this somehow makes it to court and somehow makes it to trial and somehow gets to a situation where the court is saying, no, you might, you cannot do this or you have to do that, then this is going to stop something that's been happening for decades and definitely happening in the last two years, which is people that are moving from one company to the competition, it is very, very hard to draw the line in the sand on what they know versus what is considered stealing trade secrets. Uh, and it's gonna be very interesting to see how this evolves. Again, I don't think this is going to go very far. I definitely think this is just a personal battle between Elon and Sam, and this is just another way for Elon to put another stick in the wheels of OpenAI. Again, I don't personally think this is gonna go very far. Another big partnership that we actually announced a couple of weeks ago, but is now actually available to the public, is Microsoft 365. Co-pilot now includes Claude Sonnet four and Claude Sonnet Opus 4.1. As part of its selection of models, there's even a try Claude button in the researcher app inside of copilot, and it allows the users to switch to cloud to Claude Opus 4.1. Deep reasoning tasks for deep reasoning tasks. Replacing the chat GPT models. One of the several different reports have showed that Anthropic models significantly outperform open AI when it comes to integration with Excel and PowerPoint. So that's another reason to, for them to be well integrated into the environment. And it makes perfect sense to anthropic because it provides them access to huge enterprise audience. And it makes perfect sense to Microsoft because it allows them to diversify and reduce their dependency on open AI models. I would not be surprised if other companies are rolled into there, if Xai is going to really be continuing at the trend they are right now. They're probably going to be next. That's it for this weekend, we'll be back on Tuesday with another how to episode. In this particular case, we're going to show you how to create incredible videos that can promote your brand across social media and other channels in seconds without teams and people and influencers and so on all on your own while using AI with all the pros and cons. That comes, uh, with doing what I just said. If you have been enjoying this podcast and finding value in it, please hit the subscribe button and please share it with other people that can benefit from it. Literally just click on share right now and just think of a few people that can benefit from listening to this podcast and just send it to them. I'm sure they will. Thank you. I will thank you as well. And for now, keep on exploring ai. Keep sharing what you learn with me and with other people wherever you can. It will help all of us to reduce the potential negative outcomes out of ai and have an amazing rest of your weekend.