Leveraging AI

245 | AI Tsunami warning! 11 % of jobs replaceable today, CEOs admit 20 % staff over-capacity, Claude 4.5 outcodes humans, Harvard’s PopEVE cracks rare genes and more AI news for the week ending on Nov 28, 2025

• Isar Meitis • Season 1 • Episode 245

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Are you preparing for AI disruption or are you already behind the curve without realizing it?

While hiring slows and layoffs remain low, AI is quietly carving out $1.2 trillion worth of human labor and you may not see it until it's too late.

In this Weekend News episode of Leveraging AI, Isar Meitis unpacks eye-opening data from MIT, PWC, McKinsey, and more, all pointing to a looming shift in the global labor market. The twist? Most of the risk is still hidden below the surface.

Whether you're a CEO, department leader, or simply trying to future-proof your team, this episode gives you the data, context, and strategies to stay competitive as AI transforms every corner of the white-collar world.

In this session, you’ll discover:

  • The Iceberg Index: MIT's simulation of the U.S. workforce and why visible AI disruption is just the tip
  • The surprisingly low adoption of AI among global workers — and why that’s both a risk and an opportunity
  • Why entry-level jobs are vanishing — and what that means for workforce development
  • How businesses are navigating 10–20% overcapacity while still starving for AI talent
  • Why reimagining work, not just automating it, is the only viable strategy
  • What the Claude 4.5 Opus and Flux 2 releases tell us about the next wave of AI capabilities
  • A sober look at the flawed optimism in McKinsey’s and PWC’s economic projections
  • Why AI fluency is now the #1 most in-demand skill — and how to get ahead of the curve
  • Plus: How OpenAI and Perplexity just turned holiday shopping into an AI-powered game

About Leveraging AI

If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Speaker:

Hello and welcome to a Weekend News episode of the Leveraging AI Podcast, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This Isar Metis, your host, and this week we're going to start with focusing on the impact of AI on the global and US job markets. There has been several different studies from leading organizations such as MIT and the Department of Labor and PWC have been released in this past week related to the impact of AI on the job market. And we're going to dive into those. We're going to talk about new releases. The two most interesting ones are Claude 4.5, Opus and Flux two. We're also going to talk about the impact of the release of Gemini three on open ai, and we're going to end with AI assistance for holiday shopping, which is already in full swing, and so we have a lot to cover. So let's get started. We are going to start with a new tool that was developed by MIT and Oak Ridge National Laboratory. And what it helps to do is to get a reality check on the impact of AI on the US labor market. The way they've done the research, which is by itself really fascinating, is they built basically a labor simulation that looks at 151 million US workers, each as individual agents mapping over 32,000 scales across 923 occupations in 3000 counties in the us. With the goal of comparing each and every one of these data points to the current existing and future capabilities of ai. Or as the r and l director and co-leader of the research said, basically, we are creating a digital twin of the US labor market. So they are simulating everything as far as down to the most simple task across almost everything in the US labor market. Again, 32,000 skills. So what did they find? They found that current AI systems, the tools we have access to right now can replace 11.7% of the tasks performed by the US workforce. That is approximately$1.2 trillion in annual wages that again, AI can do right now. Now, in this study, they have coined a phrase called the iceberg index, which is basically highlighting the big difference between what we're seeing clearly right now as far as AI impact versus what's actually happening below the surface. That is the real truth, basically like the tip of the iceberg. Based on their finding the visible disruption right now of ai, what they call the primary disruption, which is clear in technology computing, and it accounts for just 2.2% of the total wage exposure. But the overwhelming majority of risk is below the waterline, basically like a iceberg. And it is embedded into more or less every white collar routine function across major sectors like finance, healthcare, professional services, et cetera. And in roles such as hR, logistics, finance, office admin, et cetera. Now they've built this tool for state and government use cases, and there are several states who's already paying attention and starting to take action based on this research. So Tennessee cited the Iceberg Index in an official AI workforce action plan. Utah, North Carolina are working on similar reports, and so this effort is going to have real impact on at least state level decision making processes. But the finding again, is staggering. It is showing that right now with current tools, without any future additional development in ai, more than 11% of the tasks that are performed across multiple industries and roles in the US economy can be replaced by ai. This is a very, very significant number Which, as I mentioned many times before, if companies learn how to really benefit from that, that means they can either grow by more than 11%, or they will eventually have to let go of 11% of their workforce. Otherwise they will not be competitive and they will risk the entire business. And so this number, while not being very high, it is very clear of how much risk there is right now developing with the current AI capabilities. Another research that was released earlier this month was released by PWC. It wasn't specifically about ai. It is called PWCs Global Workforce Hopes and Fears Survey 2025. The survey itself was actually conducted earlier this year with nearly 50,000 responders spanning over 28 sectors in 48 major economies. So this is a global survey, not just around the us. What they found when it comes to AI is actually really surprising on two aspects of the spectrum. One is how low is the global adoption of AI is so far? So only 14% of global workers use genai daily at work, so the numbers grow dramatically if we look at their weekly numbers, but only 14% use AI every single day up from 20% in 2024. I must admit, I'm completely shocked and blown away by these numbers, and they sound crazy low to me because every company that I interact with, and in the last two and a half months, I've been interacting with a different company every single week. And in some cases when I did keynotes, multiple, sometimes hundreds or thousands of different CEOs and people in leadership positions, everybody seems to be using AI at a relatively high frequency. So 14% sounds really low, but that's what the research has actually found. But the flip side of that is that those 14% of people that say that they use AI daily report, a 92% productivity boost, and 70% of them report excitement about AI's impact on their job. Now the other interesting parameter is that optimism when it comes to AI currently is higher than the anxiety that comes from the AI impact on future jobs. So 47% of workers that responded to the survey are curious about ai and 38% think it will have a positive impact on their job. However, there's a very big difference between managers and non-managers. Non-managers, only 43% of non-managers have a positive feeling about the job future related to AI versus 72% of executives. Same thing when it comes to the provided resources for learning. 51% of non-managers feel that they get the right resources for learning and training about AI versus 72% of senior leaders. There are also very big differences between different sectors. So tech leads with 35% daily gen AI usage among the different AI adopters with 71% of tech people saying they're getting learning career boosting skills out of using ai, and that is provided by their companies. This is significantly higher than in other defense sectors. And as we've seen in research in previous weeks, and we're gonna talk about this more again this week entry level workers face the highest level of uncertainty with managers of these people in these kind of positions are split with their prediction. 38% of these managers predict job cuts and 30% predict job gains when it comes to entry level positions. But either way, it is very clear that entry level positions are at risk with the introduction of ai. Again, more about this shortly. McKinsey also just released a new report related to this topic. It was called Agents, robots, and US Skilled Partnership in the Age of ai. And the narrative behind this report is saying that AI is not necessarily going to displace jobs, but instead it's going to define a new future in which humans and ai, both agents for white collar jobs and robots for blue collar jobs, are going to partner together to achieve whatever the goal is of that specific type of work. Now in their research, they're not necessarily looking at the current level of exposure, but potentially at the future level of exposure. And they are estimating that AI with robots has the opportunity to automate 57% of current US work hours. That's more than half. Now, they're doing that without too much research. They're trying to extrapolate from the current capabilities and trying to figure out what they will be in the future. This means that by 2030, the US economy, can unlock an estimated 2.9 trillion in annual economic value. What they're saying is, is in order to capture this value, the economy as a whole and companies and individuals will have to completely redesign processes around collaborations in humans and machines versus trying to automate individual tasks, which is what most companies and organizations are doing right now. I'm talking about this a lot when I work with senior executives about the fact that you have to reimagine how your company works. You have to, in many cases, reimagine the goods and services that you're delivering by how AI will impact their value in the future with AI and the abundance it's going to provide to your customers. And so it is definitely a big challenge to organizations to try to reimagine how they've done business in the past 10, 20, 30, 50 years, and try to think about it in a completely different way. But if they will, the games can be very significant. Now, the report also finds not surprisingly skyrocketing demand for AI skills. Or how they defined it, the demand for AI fluency that they're defining as the ability to use and manage AI tools, and that has grown sevenfold in just two years, outpacing every other skill in the US job posting right now. This is basically signaling to anybody out there that if you don't know how to use AI effectively, you should start doing that because it's the main thing that companies are looking for right now across the board in all different industries. The report also introduces a new index they call the skill change index, or CSI. Which provides a tool to track or measure the exposure of different roles to automation. Not surprisingly, the highest exposure right now is in digital and information processing skills. While interpersonal skills like coaching and negotiating and caring are at much lower risks. Again, not a big surprise there. Now, as I mentioned, the report distinguishes between agents, which are machines that automating non-physical work and robots, which are machines that are automating physical work. And they're stating that right now, two thirds of all US work hours are non-physical work, meaning AI agents are the primary immediate driver, and to be fair, they are way more ready for this kind of work. So their humanoid robots, are developing and they're developing fast, but they're currently too expensive and not available in large enough scales to take over the physical work. They're saying that right now the average cost of these robots are 150 to$500,000 per robot. Where in order to be more dramatic in their impact on the economy, there needs to be between 20 and$50,000. But that's definitely within reach. Now, I must admit that I don't agree with their conclusion that the future will be collaboration versus replacement of jobs and I say that because of several different reasons. Reason number one is that there is limited demand, right? So let's say that you can do things way more efficiently by working collaboratively with AI, and you can grow your business. Well, not all businesses can grow by 20, 30, 50, a hundred percent because the demand is limited. And once you reach that demand level, then it doesn't matter how much more efficient you can become, you will not be able to sell any more. And so at that point, you go into competition with other people in your industry that are also becoming better and better because they're using ai, which means some people will be let go because of the gained efficiencies of this collaboration between AI and humans. Now, if we're analyzing the example that this study gives, it is claiming that the replacement theory doesn't work. They cite that between 2017 and 2024, the employment of radiologists grew by 3% annually despite rapid AI advancements in image analysis. They're using this as a proof to the fact that AI is just augmenting roles rather than replacing it. And I find some very serious flaws in this flaw. Number one is that they're looking at data from 2017 to 2024. When we look at 24 to 25, we see the huge spike in the ability of AI to analyze information and definitely to analyze images, to understand that what happened in the previous seven years is not the same as what's happened in the last two years as far as AI capabilities to make these changes. The other thing they're not taking into consideration is the timing. It takes systems to adjust, especially really large system like the healthcare system in the us. And yes, it takes time for these systems to adjust, but they will adjust eventually. And so while I agree that the future depends on human's ability to collaborate physical machines or thinking machines, I do not think that will not have impact on job displacement. I think it will have a very significant job displacement impact because of what I said earlier. But I do agree that the current approach of most companies, at least in the initial stages of trying to automate specific tasks versus looking at the bigger picture, is missing the bigger point. Or as the author's state and I quote, integrating AI will not be a simple technology rollout by re-imagining of work itself, redesigning processes, roles, skills, culture, and metrics. So people, agents, and robots create more value together. Another survey from a company called Bearing Point. Has surveyed over a thousand global executives. Have found that 50% of C-Suite leaders report that their organizations currently have 10 to 20% of workforce over capacity, which is directly attributed to early stage automation, and the failure to redesign roles and processes effectively around these new changes. Now to make this more extreme, the findings show that nearly half, 45% of the people that were surveyed are expecting staggering 30 to 50% excess capacity in the next few years. So I assume you're asking yourself, why aren't these people let go? If an organization has a 10 to 20% over capacity, why are they keeping the people? And it connects back exactly to the point from the previous article. Most of these organizations are trying to figure out the new. Way to do business. Instead of letting go of good people that have experience, that knows the industry, that knows the organization and so on, they're trying to figure out what is the new way of running a business with these new capabilities. Initially, without letting people go, which is a good news, at least in the short run. Or like one of the partners at Bearing Point that was a part of this research has said, and I'm quoting rather than layering AI onto outdated functions, they are beginning to deconstruct traditional role definitions and rebuild them around human agent collaboration. Cool. Now the report also mentioned another interesting balancing act that these companies have to do. On one hand, they're quote unquote stuck with over capacity on some aspects of the company. And on the other hand, all of these companies are suffering from big shortage in AI talent and AI critical domains. And so on one hand, they wanna hire more people to help them figure out ai. On the other hand, they have too many people on some aspects, not exactly knowing how to rebalance them. And this learning period, if you want, represents serious challenges for senior leaders across different companies and different industries around the world. Now, I can tell you that in the past two and a half months, almost every single week, I've either done a keynote at a con, at some kind of a professional conference or have done specific workshops to different companies around the world. And in all of them, this is very clear that senior leaders are not exactly certain how is the future going to evolve, and hence how they need to plan for that across multiple different aspects of the organization from the operational side, and definitely on customer service call centers, data analysis and stuff like that, which are very clearly more prone to AI automat. And I think this uncertainty will be a part of the internal communication and strategy in senior leadership across multiple industries around the world for the next few years until the dust settles, if it will settle and we'll have a clear view of how the future of work is going to be done, how this partnership is going to look like, how will that impact supply and demand? How will that impact competition between different companies but if there's something that is very clear is that companies that figure this out first are going to have huge benefits and incredible competitive advantages. And individuals who can develop these scales are at a very high demand right now, more or less in every industry around the world, which by the way might be the biggest recommendation for people getting out of colleges and looking for their first jobs. As more and more reports are showing that finding entry level jobs is getting harder and harder, a new CNBC report reveals that the traditional path of how we learn on the job. Basically, you get hired for an entry level job. This is how you learn the industry, the company, the roles and you progress from there is disappearing or at least shrinking very fast. The data comes from a research done by Venture Capital firm, signal Fire, and what they found is that the share of entry level hires at Top Tech companies has crashed by 50% since 2019. If you look at more recent data, the recent graduates account for just 7% of new hires in 2024 compared to 11% in 2022. So in just two years, the percentage of new hires and company has shrunk by 33%, again from 11 to 7% of the global hiring. Another data point comes from research company LIO Labs, which found that job posting for entry-level positions in the US has fallen by approximately 35% since January of 2023. So finding a job straight out of college is becoming harder, but it also raises a very big concern to the future of these companies and how will people grow within these businesses. And the report details, what they call the breaking of the unwritten covenant between employers and grads, where previously companies provided training, basically showing people how to get into the workforce and work within companies in exchange for affordable labor. That was basically the deal. Now with AI being able to do weeks worth of entry level jobs in just seconds or minutes, businesses are less incentivized to keep their side of the deal. Why would I hire a person and teach them when I can have AI just complete the job? The question becomes how will these companies develop the next level of mid-level people and more advanced capabilities, and eventually senior leaders if they don't have people learning how the company works straight from the beginning, starting at the bottom and working the way up to the top. But while all of this is happening, while the reports are very, very clear on the future impact and the current potential impact of AI with its current capabilities on the current workforce, the discussion at the top of the economic forums in the US are looking at current numbers versus future impact, which is really, really scary. A, B, c. News just released an article that shows that the number of Americans that are applying for unemployment benefits fell by 6,000 to only 216,000 for the week ending of November 22. This figure is significantly lower than the expected 225,000. Again, the number was actually 216,000, but it also the lowest level of claims since April of 2025. So in the last six months. The same is true for the four week moving average has declined, which is sowing a trend of less people that are seeking to get unemployment benefits. So less people are losing their job than they did earlier this year and even a few weeks ago. But at the same time, the same report finds that it's harder to find a job. So the number of people receiving benefits after the initial week has rose by 7,000, which means people who have been unemployed finding it harder to find a new job right now than they did previously. So this basically suggests that companies maybe are not firing people, but they're also not hiring people. So there's basically a hiring freeze in the economy right now. And this is obviously a broad average painting the picture with a very broad brush, but this is very clear from all the recent surveys and reports that we just reviewed. Companies are not letting people go because they're trying to figure out how to move forward, but they're also not hiring new people and definitely not hiring new entry level jobs. But my biggest problem with this report from CNBC is that the conversation is about what should happen in the economy based on the current unemployment levels. And they're even suggesting whether the Fed should or should not cut rates in the next cycle. And this really scares me because they're looking at the current numbers of unemployment, trying to understand what is coming in the future, and this is a very bad way to measure that and to plan what's coming in the future. Every research out there, I just gave you three or four different highly respected sources of research, is showing that AI will have a significant impact on future tasks and future roles across more or less every aspect than every role in the economy, whether in the US or around the world. And yet the people in charge of macroeconomics, including the Fed, are looking at the current unemployment numbers in order to make their decisions. This connects very clearly in my mind to the opening, the prerecorded opening of this podcast that talks about the earthquake that's already happened in the middle of the ocean that is starting at tsunami, and yet the leaders of the economic forums are looking at what people are doing at the beach right now, and now they're having fun or not to decide what's gonna happen in the future. The tsunami hasn't hit the coast yet, but it is definitely coming and not being prepared for it because the people on the beach right now are having fun is not a very good way to prepare for what's coming, at least in my eyes. So what is the bottom line of all of this? And I'm gonna give you another point afterwards when we start talking about Anthropic and their research. So before I give you my summary, let me give you one more data point from Anthropic Anthropic just released their latest version of the potential impact of AI on jobs that they have released several times in the past. By reviewing how people actually engage with Claude. So they have looked at a hundred thousand sample conversations from Claude and they using that to extrapolate how will that impact different jobs and different tasks. And what they found that the AI assistant reduces individual task completion time by an average of 80%. What they found is that people typically use Claude for complex tasks that would take approximately 90 minutes and that the AI assistant can complete in seconds or just a minute or two. As expected, these are not the same across different domains. So, healthcare assistant tasks saw a 90% speed increase while task related to hardware issues. Saw only a 56%, time savings. But either way, these are very significant. By the way, the longest tasks that people used this for was for management tasks, basically making strategic decisions in legal, which they estimate that would've taken over two hours for humans to complete without the assistance. Now they're mentioning by the way, that there are limits to the way they analyze, because all they know is what's happening inside the chat itself. They don't know how much time people are actually investing in checking the output of the ai. So the time saving is probably not the real numbers. They're probably smaller because people have to verify the outputs. But still, let's say it's not 90% or 50%, let's say it's 20%, that is still very significant. So what's the bottom line of all of this research? The bottom line is that we have a new technology that can replace or dramatically augment a growing percentage of tasks in the economy. It will require companies and individuals to completely reimagine how work is currently being done. So instead of trying to automate simple steps of what we're doing right now is think about what is it that we're trying to do from a jobs to be done perspective and how can AI assist, replace, augment most of the aspects of that process and then rebuild it from the ground up around this new capability. Now this need to reimagine how work is done. Today is more or less the only thing that is slowing down the AI revolution that is happening because big organizations cannot change fast. It is very hard for large systems to change, and hence this will take a while and it is slowing down the capabilities of the technology and the real impact that we're seeing on the actual world and workforce. But it is coming and it is inevitable because eventually everybody will figure this out. Now if you want, the biggest proof for that is all you have to do is take the parameter that I shared with you a few weeks ago. The concept of revenue per employee is a great way to measure efficiency of companies. the average revenue per employee of traditional SaaS companies is around$250,000 per employee on average. If you compare that to the top AI native companies out of Silicon Valley, right now, they have about$2.4 million in revenue per employee. That's 10 x. That means that companies that are restructured around AI collaboration in this case, they don't have to restructure. They just structure from the beginning around that can be 10 x more productive with the current level of ai. Which means, again, if you do this right now and you move quickly, you can gain amazing market share as these companies are growing to hundreds of millions of dollars in revenue with a really small number of employees. But as everybody figures this out, the it will level the playing field and as I mentioned previously, the demand is finite, meaning not everybody can grow to 10 x size, they are right now, which means just from the competition perspective, this will force companies to let people go and to do 10 x with less people. They will force companies to let people go and instead of doing 10 X with the current number of people, they will do two x with half the people or something like that, which means there are going to be a lot of people unemployed. And that will put a very big question mark on the potential economical impact of ai. Because if nobody can buy the goods and services well, you can't really sell them, which then can collapse the entire global economy because there's not going to be demand because people will not have money. While most of the research that I shared with you today is optimistic about the potential impact of this on the world economy, I don't really see how that's possible. Again, because I don't see that there's infinite demand for everything. This just cannot happen. So while the MIT research mentions 1.2 trillion in wages that can be done by ai and the McKinsey research talks about unlocking trillions in economic value. To gain those trillions, we actually need people buying those goods and services. Even if people don't have money, then who's gonna buy these goods and services? And if they cannot, then there is no growth in trillions. So I don't see how this can play out the way these research are showing. Again, I'm not taking anything away from McKinsey or MIT or PWC. They all have way more people and way smarter people than I am. I just think they're looking at this from a pure mathematical perspective and not from an economical viability perspective. And I haven't yet seen one report that talks about that aspect of it. So either I'm delusional or these reports are very optimistic when it comes to what can be, at least from a short term perspective, the impact of figuring out how to apply AI in a collaborative way or however else they want to describe it. But there's one thing that is clear from all of these reports, which is the big lack of understanding of how to apply AI effectively. Both inside of these companies to define strategy as well as the huge need for these skills when these companies are hiring. And it all comes down to training, education, and AI fluency. Inside the companies. It has to do with training people and leadership in order to figure this out. And outside of the companies, it comes to the individuals you to train yourself on how to use these tools effectively in order to get hired and get paid more money by these companies who are struggling to find people with better AI skills. So if you want some specific quotes from them, in the McKinsey Global Institute research, they said, and I'm quoting, the outcomes for firms, workers and communities will ultimately depend on how organizations and institutions work together to prepare people for the job of the future. Another quote from the same article. Integrating AI will not be a simple technology rollout by re-imagining of work itself, redesigning processes, roles, skills, culture, and metrics, so people, agents, and robots create more value together. Again, it's all about redesigning role skills, culture, which all comes back to training. Another quote says, demand for AI fluency, the ability to use and manage AI tools has grown sevenfold in two years faster than any other skill in US job posting. In the bearing point study, the quote is workforce planning, talent development and organizational design will need to be rethought from the ground up. You get the point. It's all focused about understanding AI deeper, finding talent that currently knows AI in order to reimagine the work, in order to gain the benefits from ai. This is what we have been focused on for the last two and a half years with company tailored workshops and open to the public courses. So if you are in a leadership position in an organization and you still do not have a plan on how to figure out AI for your company, for your industry, for your department, for your team, whatever the case may be, reach out to me. There's a link in the show notes to set up a meeting with me on my calendar so you can discuss your current needs and I can see if I can help you or at least guide you in the right direction. And if you are an individual and you understand the need that is coming in the market and you want to build your personal future, come and join our courses. We have the next cohort of the AI Business Transformation Course, which I have been teaching for over two and a half years now, with thousands of business people who have transformed their personal wellbeing as well as the success of their teams and the people that they manage. The next cohort of the AI Business Transformation Course is opening on January 20th, 2026. It is literally the best way for you to take the first steps and accelerate your understanding of AI across multiple aspects of AI related, specifically two aspects of business. So this is not a theoretical course. We're not gonna talk about concepts of ai, but all of it is around practical use cases across multiple aspects of the business. And if you are looking for ways to improve your career success, this is an amazing way to do that. And there is no better time than the beginning of 2026 to make that next step. So if you want to see more details and join us in January, there's a link for that in the show notes as well. But since we mentioned Anthropic and the research, let's continue with Anthropic. Anthropic just released Claude Opus 4.5. Those of you who need some background, all the models that Claude release have three different variations with Haiku, sonnet, and Opus. A few weeks ago anthropic released Sonnet 4.5, but they did not release Opus 4.5. Sonnet 4.5 took over the entire leaderboard, more or less for every single aspect of AI usage. But since then, several models came out, including Gemini three and Grok 4.1. Both of them, took leading positions over Claude. So now 4.5 Opus, which is the largest model, is trying to reclaim the throne and to be fair, not completely successfully yet. That being said, it is definitely a better model than sonnet 4.5 and as an example, they have let Opus 4.5 take a take home exam that is designed for prospective performance engineering candidates and Claude Opus 4.5 scored higher than any human candidate ever scored on that test within the two hour time limit. This model outperforms sonnet 4.517 out of eight programming languages on different benchmarks, and it comes at a new pricing scheme that makes it cheaper than the previous Opus model, with$5 per million input tokens and$25 per million output tokens, it is also built to run a lot more efficiently. Anthropic also added a new feature into the API calls for this model called Effort Parameter. This effort parameter allows to control how much Claude actually thinks in order to deliver different tasks. And what they're saying is that in the medium effort settings, the new Claude Opus 4.5 does better than sonnet 4.5, while using 76% fewer output tokens. So it's not only that they made the tokens cheaper, they can achieve the same results while using significantly less tokens, which overall would make the effort significantly cheaper than previously possible. Now while coding is a big deal because it drives a huge percentage of the revenue for Claude, this model is also supposed to be better than a previous model. In vision, reasoning, mathematical skills and overall outperform the sonnet 4.5 across multiple benchmarks and Anthropic being anthropic and focusing on safety, they're saying it's that their most robustly aligned model, yet with lower chances of prompt injection attacks can be applied to this model. So how is this model doing on the LM Arena board? Well, it is currently ranked number three on text after Gemini three Pro and Grok 4.1 thinking. It is ranked number one for web development. If you look at the overall table, it is ranked third after Gemini three Pro and Grok 4.1 thinking where it is sharing the first position across several different aspects and ranked lower on several other aspects. Overall, still reigning supreme with number one across the board on everything is Gemini three Pro, another. Very interesting thing that Anthropic has released today is that they have developed a new methodology that allows AI to run effectively across multiple chats. Now, what is this coming to solve? The core challenge is obviously the context window. So those of you who don't know and have not been listening to this podcast for a while, every chat that you run has a limited memory that can be used in a single chat. It is called the context window. you try to run bigger and bigger tasks, you come to the end of the context window and then you have to start from the beginning. Well, what Anthropic has developed right now is a twofold solution with two types of agents. One is called the initializer agent, and the other is a coding agent. And what the Initializer agent does is it creates a summary basically of every chat in A-J-S-O-N format that can be delivered to the next chat with clear instructions on how to continue from the previous chat. In addition, it gives very specific instructions to the coding agent on exactly what feature to work on at every single time, out of the bigger tasks, being more like a manager versus just a deliverer of information. And what this new methodology enables is to run a process of developing multiple components in a single shot, as the AI on its own will continue across multiple different chats, but continue the development in a structured and well organized way. Now this follows a very similar release from OpenAI that I reported last week. So the ability to run across multiple chats while continuing. If you want the thought process between the different chats. This is just the beginning, but as this evolves in the next weeks and months, we might get into a situation where the concept of a limited context window will be a thing of the past, meaning a. AI will be able to complete tasks across multiple hours and maybe days and maybe even weeks if it can keep a coherent flow and continue working without basically the limits of the context window as it can deliver the data from one context window to the other and know exactly what to work on while keeping the process going in an effective way. This is currently built specifically both in OpenAI and in anthropic for coding. But as they figure this out for coding, there's zero reason why this will not be effective for more or less everything else. What does that mean? It means a complete game changer to the level of sophistication, complexity, and length of tasks that AI can perform. Connecting it back to the previous points of what type of tasks can AI perform today, what it will be able to perform in the very near future is gonna be a very, very big gap between these because of this limitation that will go away, AI will be able to perform things that are far beyond what it can perform right now, which will have an even bigger impact on how companies can use it and on the overall economy. Sticking with new releases, but switching to a different company in a different field. Black Forest Labs has released Flux Two, so those of you who don't know Flux is the best open source image generation model out there. It is extremely capable and it is very good across multiple different functions while Flux Two was just released, and it is almost as good at almost everything as Gemini Dano Banana Pro. So what did they improve from Flex One? It is significantly better at editing images, including editing high resolution, real images, and being able to make changes to them while preserving detail and coherence with the original image. It has multi reference support, very similar to the new nano banana, meaning you can reference up to 10 other images simultaneously and still keep consistency of character, product style, et cetera, from all the different images into a new image that you are generating. It is very good at generating complex typography, including generating infographics and UI mockups for entire websites, and it is much better at adhering to the structure of prompts and to brand guidelines if these are brought to its attention. I loved using the original version of Flux. I think it's an incredibly powerful model and it has the ability to train the model for your needs. It's called Building a LoRa, which allows you to train the model on a specific style or a specific product, or a specific person, which enhances the capabilities even further, which is a functionality that has, as far as I know, doesn't exist in Gemini right now. Now in testing that I've seen different people do online right now, there's still an edge for the recent nano Banana Pro over flux. When it comes to the fine details of the outcome, especially when it comes to multiple aspects of text, like creating an entire dashboard or a infographics, however, it is doing it significantly faster and way cheaper. So from a cost to quality ratio, flux is in a much better position than nano banana. And in many use cases, it will be good enough, it will be able to deliver the work faster and at a much cheaper rate, which for many of us is the better trade-off versus the slightly higher capability. And it's just gonna depend on your particular use case. So the good news is within two weeks we got extremely capable image generation and image editing models. One is open source one from Google, and they are both at the level that enable actual real company work and can dramatically accelerate the professional aspect of image generation. Most people who use flex use it through third party tools and through their API. This is how I use it. However, if you want to just test it out, you get 50 free renderings per day on the Black Forest Lab playground website, which everybody has access to. Staying on the topic of new releases, Harvard Medical School researchers just developed and released an AI model called Pop Eve or something like that, which stands for Population Calibrated Evolutionary Variant Effect. This is a complete mouthful, but what it does is nothing short of magical. It is designed to replace or address the extremely time consuming effort of diagnosing rare signal variant genetic diseases. When they tested the model, they gave it access to over 30,000 patients with undiagnosed severe developmental disorders. The model on its own was able to diagnose. One third of the cases, again, that was not diagnosed by people before. Even more impressive, is the model was able to identify 123 genes that are linked to developmental disorders that has not been previously known to cause such a disease. Now, since the paper was published, 25 of these genes out of the 123 found was verified and independently confirmed by other research labs. And these are the things that excite me the most about AI is its ability to really address real problems that are currently unad addressable by the technologies we had so far. Being able to help people with really serious diseases by being able to diagnose exactly what's the problem with them at a scale and speed that is unmatched, can really help us solve really big problems in the world, whether it's genetic illnesses or global warming, or access to clean water, clean energy, et cetera. The biggest problems that the world has today and AI should be able to, and hopefully will help us solve. Before we switch to some interesting insights from Sam Altman and OpenAI related to the release of Gemini three, and before we talk about shopping, I wanna share with you some insights from the interesting interview of Ilia Sr, who was one of the co-founders of OpenAI, and now is the co-founder of SSI, which stands for Safe Super Intelligence. He was interviewed by Ddus on the D dsh podcast about the current state of AI and the work that they're doing at SSI. This interview is way more technical than it probably should have been, which some of these interviews are. So I. Don't necessarily recommend you listening to the entire thing because there's a lot of segments that are highly technical. But that being said, they touched on a few very interesting points. One was the question of how can these models do so good at some of these evals and then when you actually come to use them, they are either worthless or significantly less capable. And what Ilia suggested is, what I have been claiming on this podcast all along is that the models are trained to be good at the evals because this is one of the benchmarks that they can be tested on. So the reinforcement learning is rewarding them for being good at the evals. So they become very good at the evals. That doesn't necessarily mean they'll be really good at real life. So think about a high school student that is awesome at solving the test he was being taught for the entire year on how to solve specific types of questions. That doesn't mean he's gonna be really good at the actual application of the topic. And it is the same exact thing here. these models are rewarded to be good at the evals because this is one of the ways to train them. And so they become very good at that, but necessarily at the more generalized list, rigid and structured version, which is real life of solving problems in a specific category. So this makes perfect sense to me, and this is what I assumed all along, and this just verifies what I thought about this topic. The other interesting aspect in the interview talked about the transition from the age of scaling to the age of research. What Ilia is saying is that in the early days of AI research, or not very early days, but let's say, Five to 10 years ago, the main focus was about research because nobody had enough compute and you had to invest a lot in research in order to come up with solutions. Then we went in the last few years through the age of scaling, all the companies were able to invest billions or hundreds of billions into more and more compute, and they were able to solve a lot of problems by scaling. Well, what he's saying right now is that this age of scaling is getting to the edge of its capacity, which will force companies and organizations and research labs to go back into research and investing ways to better solve and better teach the models versus just brute force of providing more compute, and he's making a very interesting claim. He's saying that when you think about how humans work, humans are currently way more capable than these machines when it comes to generalized intelligence. Despite the fact we have significantly less knowledge. So the idea behind scaling was let's take more and more and more knowledge in the pre-training phase, and that will generate better and better models, which was conceptually true and was true. And yet, right now, these models have significantly more knowledge than humans, and yet they're significantly less capable than us across multiple different aspects, especially when you're generalizing intelligence or the way IA talks about it. He talks about robustness of human understanding much more deeply and exhibits his ability to analyze and understand things while having a tiny fraction of the data that AI has access to in its training process. So when ESH was pushing him to try to explain exactly what they're trying to work on and what super intelligence looks like, and how they're going to release that in a safe way to the world. What they ended up with, I actually really like what he's saying is that what he's trying to build is not necessarily super intelligence, but a super learner ai basically developing an AI that learns much better than the way current AI learns. And he's saying that will put us on the path for a GI and A SI. And once they figure out how to make the AI a much better learner than it is today, they will start releasing versions of that to the world that will allow people to figure out how to use this in a safe way. I personally find this very interesting. Again, we are in an era where everything was focused around let's get more and more and more compute, and because it was obvious, as we get more compute in a much larger data set, these models become smarter. But it is obvious that this is not the final way to move forward. And I find this exciting because it means that we will need potentially less resources, which would be less demanding on our planet in order to achieve better results with ai. And just the fact that he was able to raise billions to focus purely on research says that there's other people who believe that might be the right path forward instead of just billions investing hundreds of billions in compute. So overall, I think this is an interesting path, and I really hope this will yield successful results. Now as I promise the impact of the Gemini three release on OpenAI in a candid internal memo that was obtained by the information Sam Altman has alerted the employees of OpenAI to brace for a period of turbulence, or as he said, and I'm quoting temporary economic headwinds because of the success of Gemini three. Alman was very open about the success of Google and how well they built the model. With the memo stating, and I'm quoting, google has been doing excellent work recently in every aspect, and it's specifically relating to their advancement in pre-training of models, which connects back to our previous point. They have found more effective ways to do pre-training, which saves them time and money and yields better results. This drove open AI to define a new project, which I am not sure I'm pronouncing correctly. It's called Shallow Pit or something like that. And that new model, or that new project, is supposed to address the deficiencies they have in pre-training right now compared to Google with the goal to reclaim their superiority in the AI domain. Now the report highlights something that I've been talking about on this podcast for a very long time, that it is not a fair fight. Google has a full vertical and horizontal integration in their approach to ai. They have a full stack of everything. They control their own chips, so they're running on TPUs, which they are developing, specifically optimized for what they're trying to do. They have their own data centers, they have their own distribution platforms across the board from Android, to Chrome to Google Workspace, et cetera And OpenAI relies on a lot of other companies to provide most of these capabilities. In addition, from a financial perspective, there is a huge, huge difference with OpenAI making 13 to$20 billion this year. There's different rumors with different numbers, but this is the ballpark, 13 to 20 billion while losing a huge amount of money burning through a lot of investor cash. While on the other hand, Google generated$70 billion of free cash flow over this past year. So Google has everything they need in order to keep on pushing in that direction without raising crazy amounts of money. And OpenAI will have to rely on other companies to deliver many of these capabilities, whether building the data centers or creating and delivering GPUs in their perspective. And obviously providing funding for all of this to work where Google has all of that ready to go, including the ability to distribute that across multiple tools and gain the knowledge from people using it across all these different tools. So do I think AI can beat Google in this race? I doubt it. But can they stay a significant competitor to Google? A hundred percent they can, and I'm sure they will stay in there as long as they can manage their cash flows compared to the amount of money they can raise in an effective way. Since we are getting into the most wonderful time of the year, at least from a retail shopping perspective, I want to share with you the two different companies, OpenAI and Perplexity, have released shopping research assistance to their tools. So right now, inside of Chachi piti, inside of the selection of what you wanna select, such as deep research, you can now select. Shopping research. I've actually used it to shop for a TV in this Black Friday week, and I found it to be very, very helpful. It is really good at understanding what your needs are and then curating results based on very complex ideas, concepts, and queries that you wanna look for. So you don't necessarily have to look for a specific kind of a tv, but just to find what your needs are, what your budget is, and it will go and help you find this specific thing you are shopping for. It can even help you with ideas. So let's say you're looking for something cute for a five-year-old girl. It can give you ideas of what you can shop for, and then it can shop, and then it can help you find the information and compare different aspects of this to help you make an educated decision on what you are trying to purchase. I found this to be very helpful and it saved me hours of doing research on my own. And OpenAI emphasized that the shopping results are organic and unsponsored, meaning they are ranked purely based on relevance to the user's needs and requirements and not based on paid placements. Like on most platforms out there, whether you're shopping on Google or you're shopping on Amazon, most of the stuff that you're seeing is sponsored. And here this is not the case. They're trying to show you exactly what you're looking for based on your defined needs, which again, I tested and I found very helpful. A very similar thing was released by Perplexity. So if you are a heavy perplexity user or if not an open AI user, you can go to the free perplexity right now and use it to help you in your holiday shopping. Very similar concepts. In both cases you can check out on the platform if it is connected to a checkout process that they already have integrated with. And this gives us a great glimpse of the future that we're walking or running or sprinting into where AI agents are going to help us with more or less everything that we're doing and how we engage with the world, and definitely with the digital world in this particular case, just in shopping. But I think this will evolve over time to more and more aspects of our engagement with our job and our personal lives, and how we engage with the world through digital interfaces. As I mentioned, my very first personal experience was very positive. There are many other aspects of this news this week that we're not going to dive into some really fascinating battles that are going on behind the scenes from a chip wars between Google and Nvidia new company and investment by Jeff Bezos and many other good things that they're just not time to cover all of it. And if you wanna see all of it, you can sign up for our newsletter. It has all the stuff that we didn't cover today or that we don't cover every single week. We usually include about 30. Topics that we cover, maybe a little less, but there's usually about 50 that we curate every single week. And if you wanna know the rest of them, you can just sign up for our newsletter. It also includes all the events that we're running, the training that we're providing, the workshops, the free stuff. Everything you want is all in that newsletter. So go sign up for that. And while you're looking for the links for that, you can also look at the links for the workshops and for the courses that are upcoming, which will allow you to prepare yourself and or your company for 2026 and beyond. If you are enjoying this podcast, please hit the subscribe button on your podcast player so you don't miss any episode that we release. And while you're at it and you have your podcast player open, please rate this podcast on Apple Podcast or Spotify. This helps us reach more people and I actually read all of these reviews and get the feedback and try to give you more of the content that you are looking for. So if you have specific things you're looking for, please include that in your review. That is it for today. Have an amazing rest of your weekend, and we'll be back with another how to episode on Tuesday.

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Isar Meitis

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