Podcasts about capex

In this episode of The Circuit, hosts Ben Bajarin and Jay Goldberg break down a packed earnings week across the technology and semiconductor sectors, grouping major updates into key themes around mobile markets and AI infrastructure. They analyze softness in mobile demand alongside severe supply chain bottlenecks impacting Apple, Qualcomm, ARM, and MediaTek, highlighting how surging AI demand is squeezing capacity across foundry and memory partners. The hosts then dive into hyperscaler CapEx trends from Amazon, Microsoft, and Meta, evaluating the transition from AI training to inference, the rising adoption of behind-the-meter power solutions, and severe labor constraints in electrical trades. Finally, they address the growing reliance on debt financing for data center expansion and unpack the market mechanics behind recent stock volatility and hedge fund unwinds.

Amazon Web Services CEO Matt Garman says the company will keep investing in capex. “Today, demand still significantly outstrips supply and we’re trying to build and invest to keep up with what customers are asking for." He talks to Bloomberg's Ed Ludlow.See omnystudio.com/listener for privacy information.

Nearly $580 billion in trailing twelve-month capital expenditure across Amazon, Microsoft, Google, Meta, Oracle, Tesla, and SpaceX. Full year 2026 approaching $900 billion. CSI's 2027 estimate: $1.5 trillion. CSI breaks down the Q2 hyperscaler CapEx numbers and makes the case for why the semiconductor bull market remains intact despite a volatile few weeks for chip stocks.AWS, Azure, Google Cloud, and Oracle are all reporting multi-year backlogs. Amazon just raised its 2026 CapEx guide to $220 billion, partly driven by rising memory prices — sending more capital directly to the semiconductor supply chain. Gartner revised data center spending up to $820 billion for 2026, a sixty-three percent year-over-year increase. The recent pullback looks like a leverage unwind, not a fundamental shift.For in-depth research and the Semiconductor Insider membership, visit chipstockinvestor.com. Use fiscal.ai/csi for 15% off any paid plan.Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.CSI owns shares of Meta, Alphabet, Oracle, and Amazon.

In this episode of The Canadian Macro Investor Podcast, Simon and Dan break down the latest Fed decision and why the bond market may be starting to challenge Kevin Warsh’s inflation message. They discuss the split reaction across the yield curve, with short-term yields moving differently than longer-term yields, and what that could mean for inflation, recession risk and future rate hikes. They also look at Canada’s population data problem and why undercounting temporary residents could distort unemployment, mortgage delinquency trends and the broader read on the Canadian economy. From there, they dig into big tech earnings, including Microsoft and Meta, and why investors are paying closer attention to AI capex, free cash flow, depreciation, and credit default swaps across the hyperscalers. They also discuss Anthropic, open-weight AI models, data privacy concerns, tariffs, copper demand, and what the AI infrastructure build-out could mean for energy and markets. Tickers discussed: MSFT, META, GOOG, GOOGL, AMZN, NVDA, ORCL, AAPL, SKM Watch the full video on Our New Youtube Channel! Check out our portfolio by going to Jointci.com Our Website Canadian Investor Podcast Network Twitter: @cdn_investing Simon’s twitter: @Fiat_Iceberg Braden’s twitter: @BradoCapital Dan’s Twitter: @stocktrades_ca Want to learn more about Real Estate Investing? Check out the Canadian Real Estate Investor Podcast! Apple Podcast - The Canadian Real Estate Investor Spotify - The Canadian Real Estate Investor Web player - The Canadian Real Estate Investor Asset Allocation ETFs | BMO Global Asset Management Sign up for Fiscal.ai for free to get easy access to global stock coverage and powerful AI investing tools. Register for EQ Bank, the seamless digital banking experience with better rates and no nonsense.See omnystudio.com/listener for privacy information.

Jul 30, 2026 – When an OpenAI frontier model broke out of its sandbox and hacked Hugging Face on its own, it forced a hard question: how do we govern AI that's both ubiquitous and powerful? Policy powerhouse Bruce Mehlman unpacks...

Anthropic’s revenue has grown nearly 10x a year, three years running. Its losses are accelerating with it. And here is Dario Amodei in February: “If my revenue is not 1 trillion dollars, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if ... Read more The post Today’s Market = 1999 Capex + 2008 Credit – Ep 298 appeared first on The Intellectual Investor - Value Investing by Vitaliy Katsenelson.

Competing in a Future World of Infinite Intelligence Navigation: Intro From Knowledge Workers to Judgment Workers The AI-Native Company: Org, Hiring, Culture The Human Element: Are We Underestimating It? Scenarios Our Take Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show:   Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 79 of Tech DECIPHERED. Today, we take a leap into the big unknown. This is a thesis episode, not your classic analysis, in-depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age, and how would one, or how would a company compete in a world of infinite intelligence? The big idea, again, is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. If that happens, what happens to work, what happens to companies, what happens to society? This episode will be really framing a lot of these discussions. From knowledge workers to judgment workers, addressing the AI native company and how does that change, going into the human element and whether or not we’re underestimating it, and finally, ending up going into scenarios, feasible scenarios of a future where, well, intelligence is abundant. Intelligence is quasi-infinite or infinite itself.Bertrand Schmitt Yes. Big questions for this episode 79. From Knowledge Workers to Judgment Workers We can start with from knowledge workers to judgment workers. Let’s go back first to how came the knowledge worker. It’s a 20th-century invention from Peter Drucker in 1959. The idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that judgment around production of knowledge is not disappearing and is staying for a bit control managed by humans. What’s your take on this, Nuno? Do you agree with this split?Nuno Gonçalves Pedro I think it’s a little bit more profound than that. It’s not just judgment. Definitely, human judgment will be needed. We’ve seen agents perform all sorts of funny things in the wrong way when left alone to their own devices. Even some very well-known AI researchers coming forward and saying, “Hey, I tried to use this myself, and actually I messed up some of my systems,” or “I messed some of my code. I messed up some of my flows for a period of time.” I think just having human-in-the-loop from a judgment standpoint will be needed for a significant amount of time. That is something you can’t just delegate into machines, into algorithms, et cetera. The second part is, ultimately, there needs to be contextualization, and that contextualization, I think, comes from two forms. One from actual data, where the machine, I think, at some point will catch up, or the machines will catch up. The algorithms, at some point, on the data analysis will get better and better and have probably the closest to the truth that you can get, minus all the biases that are in the data, just to be clear, because data has a ton of biases. We’ve looked at this in the past and discussed it at prior episodes. But maybe on that, I think the machine has a chance to catch up, or the machines have a chance to catch up, so there’s less of distinctiveness from the human standpoint. But then, on just the attributes, the ability when you’re judging some situation, you’re in the middle of the situation. You’re judging the person and how it’s acting, in some ways, a lot of the things that end up happening, end up happening because there’s human interaction. There’s someone on the other side. I see how they’re delivering the message, how they’re implicating. We’ll talk about it later in the context of the organization and what changes in companies. I don’t think it’s just judgment. I think there’s a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and this notion of the knowledge worker, which he later on reemphasized with the publishing of his book, which for me was seminal and defined a lot of my career in life, the post-capitalist society, which is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. The two big camps, information rich, information poor, which links back to this invention of the term knowledge worker, that knowledge is going to be key in some ways. I think that’s what we’ve seen for the last decades. Again, I think judgment is not going anywhere, but I think it’s beyond judgment. There’s elements of humanity and involvement that won’t go away anytime soon, where human-in-the-loop are particularly critical. We’ll discuss later some scenarios, but for me, that’s my stick in the ground. I think human-in-the-loop is going to be critical for many decades to come.Bertrand Schmitt While we are talking about all of this, and we share some possible scenarios, there is always that question. This is moving so fast right now. If you think about AI 10 years ago, AI 5 years ago, AI with the launch of ChatGPT 3, and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you, me in 6 months, the change has been pretty dramatic in terms of what I can use AI for. There is always that question that whatever we are thinking about cannot just be connected to what we were able to do 6 months ago or even today, we have to think and project ourselves at least in the next 6–12 months. Of course, we can go beyond that, and we will do that with some future scenarios, but it’s a very fast-moving, and it’s not clear yet where are the limits.Nuno Gonçalves Pedro I think that’s a very fair point. Let me try to analyze things that I don’t think will change anytime soon for the next few years. Agreed with you that many things will change, and we’ll have a lot better tools, platforms out there. That will be difficult to predict what exactly won’t change. I think there’s elements of humanity, and some of them do relate to judgment, like having good or bad taste, having a view on it, on whether something looks good or bad. Obviously, all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization. I think a little bit going back to what we did at Chamaeleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor judgment as humans in the loop to systematize pattern recognition and a variety of other things, but that Mantis would really elevate all that judgment, not just in terms of timing, us being more productive, but also in terms of the quality of the decisions we’re making. Think of it as a little bit like having our human judgment in the context of operating Chamaeleon at a higher altitude, where we are more aware of the things that are happening and how they actually happen. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha in our case for investors. What I mean by this is I think there’s always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece people are like, “I can figure out what’s the taste in the market.” Yeah, but that’s mainstream. That doesn’t identify what’s the next big thing, which normally doesn’t start from mainstream. It starts from something else. It could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn’t been thought through. For example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.Bertrand Schmitt Let’s not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. Finding more data is becoming a limitation these days. What it means is that it’s very hard for AI to think beyond its training data. There is some level of logic that’s being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? Is that no, it doesn’t make sense. Not enough battery life, no keyboard, no this, no that. If you just base your analysis on what’s written out there, what’s being sold out there, you would just say, “It’s going to fail.” AI might really follow that more generic advice and perspective because that’s what in the training data and that’s what they’re in volume. It’s, of course, raising a lot of questions of, how do you improve the quality of the training data? How do you separate the weed from the chaff? There are a lot of questions there, and obviously, it will get better over time. But it’s still a critical part of how it’s working today. It won’t be that easy to change. I really like your point regarding Mantis, and I will say in general, platforms that you build with AI or leveraging AI capacity. Because when we say knowledge production is going to disappear, but we’ll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or 5 junior analysts or whatever, and you can run that on nearly anything you do in life or at work, it’s completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data to have a judgment. Before it was a lot of finger in the wind and trying to smell something, but you didn’t have enough to make a serious analysis. Except if you are working as a strategy consultant, as you used to do, Nuno. That part is actually quite interesting. That the judgment itself will be exercised much more often and hopefully on the base of much more in-depth analysis for a lot of things. We will work very differently.Nuno Gonçalves Pedro We will go in-depth, faster and more fact-based, more data-based along the way. The question some of you might have right now is, is there some judgment that’s going to go away? Is there some judgment? We seem to be defining that there’s this organization, we’ll talk about it later, that goes from doers more into deciders. I think there’s some nuances to that, so I’ll just hit pause on that. In terms of judgment, obviously, there’s judgment that has been hidden over the years under the pretense of being wisdom, but it’s actually not wisdom. It’s just repetitive tasking, and it’s rules-based for the most. There’s a lot of judgment done, in particular in the white-collar space, that you could say it’s just reps. People have been doing it all along like that, and so therefore to say, “I’ve done it before like this, so I’ll do it the same way.” There’s actually no best in class, no analysis, no nothing. It’s just, “I’ve done it like that before.” I think that type of judgment will disappear because, again, algorithms will be as good, if not much better at that. They’ll be better at figuring out, actually, this would be the better way to do this. That’s how you play it forward. Then the question is, if there are fundamental, wise people in the organization, people that can really take that more complex elements of judgment, how do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work, and they learn their way, and therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road? How do we do that in a world that now is saying, “I don’t need people out of college because I can do it myself, and I can do individual contributor, and I can have agents doing the work that would require some manifestation of management in the middle.” Basically, “I don’t need this stuff. I don’t need you.” It’s a little bit the story we’re in. How do you create then this apprenticeship? How do we create then wisdom? My two cents on that is that wisdom, because of what we were just discussing and what, for example, myself and Bertrand was just saying, because of more often interactions with more data-stressed information and insights, what will happen is people will get better through their own reps in whatever form they’re doing, in day-to-day life, in internships, et cetera. In some ways, that will create the accelerated growth. It’s a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. That still leaves the question around social interactions, but that’s probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions in effect.Bertrand Schmitt I agree with you because when we talk about apprenticeship, in some ways a lot of time was wasted on stuff that were not that important. But in a way, that was the price you had to pay in order to be there when people make the big decision to try to get some wisdom from that one hour of interactions that’s really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do like a machine in a way. Why not let a machine do that? That, for me, is a big question. You could argue there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your 4, 5 years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated 5 years ago. I think that part will require a question around, “How do you change education?” You see what I mean? If you keep education the same way, expecting that the output is someone that should go now into 5 years of apprenticeship, that’s not going to work because companies will be, “No apprenticeship anymore.” On the contrary, you have to come much more knowledgeable and ready to use the tools. The tools are so efficient that the bar pretty high. You need to come already very well-grounded. If the education is not doing their job, that will be trouble. That part for me, I think is often forgotten. In some ways, the new-found importance of universities as a place to, and not just universities, the trade to really deliver people who are ready for the workforce. If on the business side, the expectation can change, of course, you have to change the education on the other side. My worry probably right now is that it doesn’t look like universities are in touch with what businesses are looking for, businesses are working on. Of course, that’s very worrisome because the cost of university has increased very significantly. It’s not clear quality of education has improved at all. If anything, it could be the opposite. It’s pretty scary. Of course, it’s going to raise a lot of questions. How much is education worth in that type of situation? Maybe another point because we talk a lot about apprenticeship, how this stuff was useful, but at the same time, if we go back in time, not long ago in the ’50s, if you wanted to be a developer, for instance, ’50s, ’60s, the job was very different. There was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. Once you had your stuff working, then, the debugging was a total nightmare. My point is that no one is looking back to that time saying, “You know what? It was great. It was a great way to learn and to do an apprenticeship for 5 years. To do that crappy job of punching cards for the boss.” There was little value in this. Guess what? Everyone is happy it’s not being done anymore by anyone. I think we also have to see what AI is bringing in a similar way is that everyone’s job is going to become quite different. There are a lot of big parts of the job who are not going to look back with fondness. Just looking back as, “Wow, that was very machine-like type of job. I’m glad I’m done with it.” People will want to jump directly to the next step. You don’t need to go to the punch card phase to be able to be a good developer for the past 40 years. I guess it will be the same with AI.Nuno Gonçalves Pedro I think so. The difficulty we have as humans is to also visualize dramatically different scenarios and landscapes, professionally. It’s difficult for us to anticipate what are the jobs of the future. Jobs have changed a lot in the last few decades, not even the last century. What people do, the migration initially from the agricultural society to then the industrial society to then the services society, and in some ways, the shift within the services industry, and now we’re seeing another shift, so we can’t really anticipate what those jobs look like. Back to your point on education, because I think that’s a very important point. If you’re right now an undergraduate student or a postgraduate student, for that matter, and you’re not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, et cetera, you’re going to face very difficult times. If you’re not right now using all these AI tools proficiently, all these cycles of vibe coding, co-working, et cetera, with agents in the mix, you’re going to have a really tough time. If you’re not at this point in time as proficient as someone like myself or Bertrand, and given that we’re nerds, we’re relatively proficient with a lot of these tools that are out there. On top of it, some of us have our own platforms in-house. If you’re not as proficient as we are with those tools, you’re going to have a very difficult time because then people like us won’t need you. I think that’s the sad truth. It’s like at some point, if you’re not needed, you’re not needed. Then again, you may find something else that’s more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, et cetera. But again, I think the bar is very high. If you’re in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It’s not the best time, it’s the worst time. Because education and all these institutions haven’t adapted to it yet. You need to adapt. You need to adapt. You need to adapt. If you don’t, you’re going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding your career path in those first few critical years.Bertrand Schmitt You need to be especially proactive when you’re facing this type of period where businesses are adapting as fast as they can because they all know it’s going to be survival of the fittest very quickly. Universities typically are working on a very different pace, and it’s pretty guaranteed they are not going to have adapted as fast as businesses. In time of big dramatic change, it will be trouble. It will be trouble. Yes, you will have not fun. Not saying it was part of the deal when you sign up for that loan and decided to go for university. But that’s life. There has been issues before. It’s not the first time. You have to do something about it. You talk about your perspective about, “Hey, why do we need you if you are not already fluent and very efficient with these tools and stuff?” The truth, in some ways, it’s even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us.Nuno Gonçalves Pedro Exactly.Bertrand Schmitt It’s a very big choice of, “Hey, do I spend more time training this person?” Do I just… there is an opportunity cost. Or, do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get to return versus the light speed I’m already on? It’s a lot of tension. Again, it’s certainly new. But if we want to look back, I think you talk about the switch from agriculture and society, industrial society, and now the service industry. The reality is that, yes, we have made dramatic changes in the past before. 140 years ago, we were 90% agricultural society in Europe, in the US, 90% of us. Today, it’s what? 2%. So my point is that that’s a normal evolution. There is no progress without change. Sometimes the rate of change is soft, and sometimes you have a step function. Now it’s a step function, and it’s also a pretty fast step function. Before, it could take decades to get new stuff being put in place, to have electricity come up, this or that. Now we see that the rate of investment in AI is insane, way beyond anything we have seen before. Two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. What’s new might be the pace of the transition, how unnatural it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them. From horses to cars to planes to rockets, pretty big change, maybe even bigger change.Nuno Gonçalves Pedro Maybe the silver lining, just to bookend this section, is one, there will be new roles. There are a lot of things we can’t anticipate. There will be new roles, there will be new jobs being created, and new things that we can’t really quite grasp yet. The second part is that the rules are changing, and they’re changing, I would say, in general, for the better. If you are a decision-maker or an organization, and you still have your job, you’re probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making an impact on how decision-making is made. There’s less and less red tape along the way in certain organizations. There are more and more fact-based discussions happening as we move along. The silver lining is better jobs, more jobs, different jobs in the future, hopefully as well. Secondly, the second part of the silver line is that the jobs that exist today, hopefully, will be more interesting, certainly on the knowledge space and on this judgment space that we’re now introducing as part of this episode. The AI-Native Company: Org, Hiring, Culture Switching gears, maybe to how does that shift? How does the company of the future look like? How does an AI native company look like? I feel there are a lot of discussions on, “Oh, you only need one person to run everything.” Let’s not go to that level. We’ve had a couple of episodes where we focused on AI as your co-founder and a couple of other elements that you guys can go back to. Let’s focus on a more evolutionary view of what’s happening to organizations, and maybe start with the org structure. In general, we should see more flat organizations where mid-level managers have to justify their pay in some ways because middle management are routers. They are normally routing tasks. It’s sometimes aggregating it, synthesizing it, and pulling it back up. Guess what? AI and agents in general are very good at that. The synthesis piece, et cetera, is not as well needed. One could say there are several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, and the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn’t need to necessarily exist. I feel we’re moving into a world of smaller teams, more senior teams, where there’s more judgment at the top, where you’ll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. If you’re not used to that, if you’re not used anymore to be an individual in the future, again, and if you’re a very senior in an organization, maybe this is the right time to either reinvent yourself, find some other job that doesn’t require as much of that, which we’ll have plenty of those jobs for the next few decades, or maybe retire. I’ve actually, shockingly enough, seen people who have said, “You know what? This thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I’m about to retire in a couple of years. I’m just going to retire now.” I’ve literally met two people who have done that. Again, there’s nothing wrong about it. I think we’re, again, going through a step function and a huge shift, but figuring out where you fit in this new model of organizations, more senior at the top, smaller teams, more of a mix of individual contribution with management than ever was done before.Bertrand Schmitt I agree with you. In some ways, I’m not surprised that some people might say, “You know what? It’s now time to retire.” I feel a bit sad, maybe because it means you don’t like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that’s your conclusion. But everyone is entitled to their own opinion, obviously, and a way of life. I guess that’s what happened, again, at regular times in the past in terms of big change. What I can see is that the rise of, you can call it the full-stack individual, someone who will have multiple roles inside the team. Before, you had to really separate the role. Especially in the US, there is such a clear separation between every role you can have in a company. Let’s take a tech company. You will have people doing design, people doing different types of designs, people doing front-end development, back-end development, and operations. You see step-by-step hyper-specialization. I have seen that, and it’s true that the level of complexity you had to deal with at some point requires some level of hyper-specialization because it will take you 6, 12 months in order to be really, really strong on a specific topic, a specific language. God forbid, trying to go deep into something that you had no real experience into. But I feel with AI, it’s a big change, actually. It’s the opportunity to go beyond that. It’s the opportunity to do more, to touch more. You can combine designing and shipping code, product managing and shipping code, being an analyst and deploying. Of course, we have to think how it works because putting a marketer shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see it changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper-specialization. I think for me, in some ways, hyper-specialization was bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives, and you lose if you go to hyper-specialization.Nuno Gonçalves Pedro I don’t think the age that is coming is the age of the generalist. I think it’s going to be the age of the multispecialist. We’re going to go into an age of multispecialization, which is a little bit, we’ve mentioned it as well in the past, what Amazon defines as an athlete or T-shaped or pie-shaped people, people that have on top an amazing ability to do general management, strategy, managing teams, et cetera, then have spikes. Spikes into business development, corporate development, product management, whatever it is. With AI and with agents, the development of those spikes, as we’ve been discussing in this episode, will actually be easier. It’s almost like a given. If you want to go deeper and deeper into a certain area, you can go much faster. I think that level of multispecialization is going to be really cool to observe. I’m not sure we’ve had an age of multispecialization over the years. Maybe people would point out, well, the Da Vinci example, people that are great across very different areas. Maybe that’s an example of multispecialization. But honestly, from my perspective, this is going to be an exciting time because of that, because you’ll have people who, instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So the work, as we were talking before, can be more interesting. More demanding as well, because the judgments you need to make are more complex. The context you need to actually gain needs to be gained much faster. At a level of magnitude, you haven’t been able to do it before. Talk about information overload. But actually, ultimately, the roles can be a lot more interesting, a lot more exciting, because I can jump around. If I’m an investor, in this case, we have two investors on this conversation. But if I’m an investor, one of the things that we start looking at is actually not just looking at a startup as, is this startup doing something in AI or not? Is it AI-enabled or not? Is it an AI platform or not? But actually, more fundamentally, is this an AI native startup? Meaning, organizationally, culturally, is this the company that’s already in the AI age? How is the team working? How are they defining things? It’s not just that they only have two or three people. It’s like, what are those two or three people doing? How are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? I feel we’re still actually relatively early on that track. It’s very interesting because we’ve had all these companies raising mega rounds. First round out, we invested in one of them, but there have been many frontier labs out there raising a ton of money. But a lot of them don’t have a fundamentally different way of doing business. Of organizing themselves, of how they do the day-to-day. Although they’re working on cutting-edge stuff, with very notable exceptions, they’re actually not using it themselves. They’re not actually shifting how they do stuff themselves.Bertrand Schmitt For me, that’s very interesting because in the past, I used to be quite conservative on how you manage and run a company in the sense that if you’re already in tech, if you are already on the cutting edge of what technology can deliver, and this and that, don’t waste time trying to invent a new org structure. Just focus on delivering something great, amazing, and be great at technologies. That’s already your huge differentiator. At the time, there was no real reason to innovate on the team organization. I have seen so many teams that tried to innovate, and it was just catastrophic because there was not much to innovate on, because we had decades of optimization that we could leverage. There was no reason to invent. But here it’s very different. There is a dramatic shift in how you can organize differently a company. I don’t think there are any blueprints yet on what’s the best way to do it because it’s too new. But at the same time, I would feel very bad to invest or support a company that first is not focused on AI or AI-enabled, but at the same time is not trying to innovate on the team itself. Because if you don’t do that, you’re going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.Nuno Gonçalves Pedro Indeed. The shifts are pretty substantial. If you look, for example, just at hiring, what do you hire for? Certainly, there’s this element of the multispecialized orchestrator, which normally will be someone with quite a lot of wisdom and expertise. It doesn’t necessarily mean someone who’s old, but someone who has the ability to work with all the AI tooling and platforms out there and be an orchestrator of agents. Why do they make judgments, make decisions, move stuff forward really, really, really quickly? Again, those jobs are going to be the best jobs. The second part, I think that is very interesting, around hiring, is you’re going to skew towards the elements that are potentially either very aligned with the use of AI tooling and platform, AI expertise, or being AI native, or someone who’s used to using AI. That’s one side of the fence. On the other side, you’re going to actually be optimizing to hire people that have the characteristics that will be difficult for AI to replace immediately, like taste and the notion of fundamental accountability and notion of implications, the notion of how you affect change in organizations, how you affect change in individuals, the elements of coaching, and beyond coaching. You’ll be optimizing for those kinds of hires as well. Then, last but not least, for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to under-hire rather than over-hire. The moment of the good old days of blitz scaling, “Oh, let me go and hire 300 people to scale my go-to-market and just land grab market.” Now, that’s not how it’s going to work. People are going to try and first get the efficiencies in-house with top talent and see if there’s, at the end, the need to hire more people or not, rather than the other way around. I think the issue here is a little bit of what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you’ll have to manage people, you’ll have to work with them, et cetera. If I don’t need to, I might as well work with the agents that the tools and platforms that I use give me access to. Because that’s a world that’s much more efficient, right?Bertrand Schmitt I’m in total agreement with you on this. It’s definitely raising way more questions than before because, again, on one side, you have the product, the technology used to build products that are completely different. At the same time, all of this is also enabling new ways to design organizations and to scale differently, especially in a world where, as we have seen in 3, 6, and 12 months, stuff that you thought were impossible are suddenly becoming possible. So you’re, “Hey, I’m going to scale and burn a shitload of money for 6 months before I know if there is any return.” Versus, “You know what? Maybe I just wait 6 months. The AI has improved enough so that we don’t need this new team. We don’t need these people to do stuff.” Because actually, if you just wait 6 months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, we used to say that in mobile, things were going three times as fast as on the web in terms of pace of innovation and speed of development and stuff. I mean, with AI, it’s 5X mobile.Nuno Gonçalves Pedro Maybe even more. Yes, well.Bertrand Schmitt Maybe even more, maybe 10X. Every assumption around blitz scaling or scaling in general was based on past assumptions. It’s not based on how is the industry evolving today. Might make more sense for you to really grow your agents and spend more money on more tokens. I remember, of course, Jensen is selling his business interest, but he was saying, “Hey, for each one of my 450K engineers, he better spend 250K in tokens a year.” I’m not saying it’s the right way to say it, but I think there is some truth in it, and that would be something to think about. Have we maxed out the token usage per employee? I’m not talking in a stupid way because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting a return on these tokens, can you use more? Can you generate more? Can you create more loops so that one engineer manages not just 10 agents, but 50 agents, but 200 agents? I think that’s the big question. We’re trying to add more people. More people means more management, more issues, more this, more that. That would be a fair question. Another piece of the puzzle is how do you build in a way your… I don’t know if it’s a digital twin, but more like the digital version of your companies represented by agents. How do you make sure that everything you do as a business is truly captured, is truly leveraged so that your agents are getting better and better? Not just because the model gets better, but because you are putting more data into it, because it has more opportunity to learn, and as a result, gets better at your specific business.Nuno Gonçalves Pedro The next big thing is culture. How does culture change? I think the biggest shift that I see is, why would you do meetings all the time?Bertrand Schmitt Yes.Nuno Gonçalves Pedro At least at Chamaeleon, we have a very small team, just by the way. We have a very small team at Chamaeleon. We’ve reduced by way more than 50% the time we spend on meetings between each other across the board, one-on-ones, partner meetings, et cetera. I think we’re really pushing to be more and more asynchronous. There’s stuff you can process via message. I was just asking one of my colleagues, “Can you just send me that prompt for that so I can just do that on CoWork?” Or “Can I just go on Mantis and do this? Can you tell me the cycle?” Or vice versa. Basically, it’s a little bit like you’re just going to do it. I don’t need to meet. I don’t need to meet all the time. There are some things where we still need to meet and interact, and we need to brainstorm at times, and we need to go to a different level of abstraction on the top end. Then on the lower end, there might be things that are a little bit more specific and governance-related and operational-related that we need to agree on that are more sticky. But otherwise, the culture is going to be biased towards build. “Go and do it,” rather than, “Let’s do a meeting.”Bertrand Schmitt Yes.Nuno Gonçalves Pedro Async is the thing. I’m more and more like we have a couple of interns this summer. “Can we async this?” They’re like, “What does that mean?” “Can we make this interaction asynchronous?” Because synchronous interactions for me are very expensive. Can you send me something that I can process, and then I can send it back to you? We don’t waste time on you giving me context and whatever. Then I’m not ready quite yet because I need to process it. Maybe I’m in between two meetings that I’m actually thinking about other things in my mind.” Again, I feel that shifts how stuff is done. One, build rather than meeting. Two, asynchronous versus synchronous. In some way, millennials had it right when they shifted a lot to messaging and stuff like that. Let’s do more asynchronous rather than synchronous, those two elements from just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis because there’s this thing that’s bugging me as we’re talking here. Everyone who is listening to us at this point in time might be saying, “Cool, but I work for this large organization. We’re just now…” Everything we’re saying here is contextualized by time. We’re giving you extreme situations. We’re looking into the future. Some companies that we’re talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. Then again, some of the companies that might take years might actually be destroyed in between or meanwhile, and be disrupted. Some of them might not because they’re in very legacy businesses, and it’s fine, and it’s okay. Again, don’t take everything that Bertrand and I are saying today as this is gospel, and it’s going to happen tomorrow, and why the hell are we not doing it? We think that aspirationally, this is where you should be moving to as an organization, whatever size you’re at. Speed will matter, as we discussed before, but not everyone, obviously, is going to move as fast as we’re describing it here.Bertrand Schmitt Yes. Me, for instance, take inspiration often with what some of the AI labs, frontier AI labs, are doing, the way they are working, especially in OpenAI and Anthropic. They are clearly at the top of the spear in terms of what is it that you can do because they have access to models we don’t have access to, because they have unlimited tokens they can use for tasks. They hire people who are, of course, 100% on AI. They are the best example of what is achievable if you have the top minds, if you have the latest models, if you have unlimited tokens. From there, you can take that for our needs and for our situation, and others in industries that are not as advanced. Definitely, you have some time. But as you say, things are moving fast, things are changing. Wall Street is going to expect better returns because when we discuss all of this, the conclusion is that you should be able to do more with less. That’s as real as it gets at some point. By the way, that’s what you see. You see better performance, a better business performance right now. So even if you might not get disrupted, you’d better start there. For some, it might take more time, and they might still be fine.Nuno Gonçalves Pedro Maybe to bookend this section, clearly what we’re saying is organizations are going to change. Their MOs are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem-solving, is going to get democratized. The algorithms are able to do that. On the other hand, having points of view and having wisdom is not necessarily democratized, necessarily by the machines. It can be facilitated, it can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. We’re not saying that’s out of the question. Actually, that’s going to be the asset. People who have fundamental wisdom that can come to the table and frame things. We see this even today in prompt engineering, on just creating prompts. The better your prompt is, the better the outcome is going to be, the result that you get from the algorithms. That’s not going to change, in my opinion, anytime soon. That UI interaction piece is not going to change anytime soon. Again, if you’re an organization thinking through organizational structure, culture, if you’re thinking through hiring, these are some of the elements that we think will give you an opportunity, but I would actually go one step further. On the positive side, I would say, they give you arbitrage. If you’re able to move faster than your competitors and really adapt your org faster, you’ll reap the benefits faster as well. That’s what many still say and relate to as the word innovation. That’s how innovation gets accelerated. I think there’s a huge opportunity right now for arbitrage. If you move fast, experiment, experiment on new org structures, experiment with talent, you’ll know that some of them will work well, some of them will fail miserably, so you can’t experiment on literally everything. On the other side, I think the doomsday scenario is if you don’t, if you’re on the other side and your competitor is outpacing you on trying these different organizational models, structure, hiring models, and operating models, they’ll potentially just disrupt you. They’ll do stuff that you thought you had the moat on, and lo and behold, you don’t anymore. Sometimes it comes just from org, just from injection of people with a different MRO, different operating model.Bertrand Schmitt The Human Element: Are We Underestimating It? Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? The three things that are a big part of the human elements, emotion, creativity, and synthesis. Is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?Nuno Gonçalves Pedro I’ll start with emotion first because I think it’s probably the easiest of all the ones you’ve mentioned. Emotion is key. Many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it, just in and of itself, this could be a sentence, it’s something verbal, et cetera. Makes a difference between the person or the people on the other side actually adopting it or actually just resisting it. Emotion is critical. It’s what runs the world. Everyone talks about a bunch of things, but emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there’s some literally very high-definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. It will take a while for that emotion to be manifested. Emotion, I think, is still something that we as humans have as a moat, and it’s critical. As you mentioned before, I was a strategy management consultant at McKinsey, and getting people to action is actually 80% about the delivery, communication, the emotion that you surround the project itself, more than sometimes the truth. It’s great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways, that’s not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions.Bertrand Schmitt You could argue that’s something that most politicians have perfectly understood. If you look at most campaigns these days, everything on emotions, maybe the tagline might be one word. It’s interesting when you see from that perspective that actually it’s very little on facts, very little on all of this, but more about emotion. You could argue it’s the same for businesses in the future? That’s a fair question. I think creativity is another one that’s quite important. At the same time, it’s not so easy because I must say I’m quite amazed when I’m looking for creativity from AI, either to generate the image, to generate video, to generate audio, or to generate text. AI can be pretty creative. I still think you need to control its creativity; you need to understand what’s good, what’s bad, what’s quality, but at the same time, I can see even in creative tasks, AI can be a very strong partner. I’m talking about any creative task, like invent a name for a product, let’s brainstorm the mission for the company. AI can actually be doing a pretty impressive job. That’s the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. We say, “You can do quite a lot.” It’s an interesting one because I think there is some unique human creativity, and at the same time, AI can be pretty strong at creative task as well.Nuno Gonçalves Pedro I agree. In particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans. If it’s like genuine light bulb moments of creativity, angles that haven’t been tried before, certainly not in the same way, I think humans still have the advantage. To your point, I agree. This is not a humans-win situation. On the previous one, on emotion, still, part of it is because, also on emotion, there are exchanges. You and I might be looking at each other, and from the facial expressions and the reactions, where you judge that for AI to get there, it’s going to take a long time. There’s going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, but creativity, I agree with you. There are a lot more nuances to it today, where AI does have significant advantages at the end of the day. Synthesis depends. Synthesis, I feel, if we’re talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them and then trying to create and form one coherent, fully accountable point of view that you stake something on, like a decision, a company, a business unit, whatever, I think humans have the advantage. Part of it is the complexity of what we have today with generative, pre-trained transformers, today with GPTs, where the hallucination comes through, where it’s really more statistical analysis. Over time, maybe synthesis will be a forte for AI. Right now, I think we still have that ability to really be the ultimate decision-makers and judge-makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides, so ended up, as we say in Portuguese, neither fish nor meat. It’s to balance both sides’ answers. That’s not helpful in most cases. When you’re in a difficult position where, for example, the future of a company, company is almost dying, what do you do? I’m not sure your AI algorithms that are going to give you a great solution. Because it will give you a median or average solution, which likely will lead you to a median or average outcome, which in this case would be failure. Again, on synthesis, there are some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge-focused, they’re more than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that, and I think they’ll just get better over time. That’s how I see synthesis.Bertrand Schmitt I think a lot of improvements will come with a better fine-tuning of agents to what’s special about your company. Because if you just take a general agent, there is only so much. It can understand your industry, your company, and your way of working. I think that part of making sure your agents are finely trained, finely tuned on your own business, so that they can give you a really well-calibrated feedback, will have a lot of importance.Nuno Gonçalves Pedro I think that’s absolutely spot on. Maybe to end it, what is definitely different about humanity? Definitely, emotion, as we discussed, some pieces of synthesis. Creativity, maybe the light bulb creativity, not the more repeatable creativity, the one that you can put and encapsulate into processes in some ways. There are elements of us being physical, which robots can’t still recreate. That’s definitely an advantage. The embodied, we’re embodied. That’s obviously a huge advantage. With that also comes advantages because we have to interpret each other, and we have to see the complexities in physicality that land to it. Is human and the human element categorical difference? If we’re having a more philosophical discussion around this, I think it is. I think it will be for at least the foreseeable future and maybe decades to come, even in whatever scenarios we’ll discuss, which is our next section, scenarios.Bertrand Schmitt I would say projecting beyond 10 years is always pretty hard on this because, again, some of the improvements we are talking about we can imagine based on how it has evolved, but at the same time, there will be disruptions in AI. Stuff that we take for granted in terms of weakness, especially, might not be there in a few years from now. Either because it has been solved through brute force or because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously, robots are coming. How fast, how cheap? That will be a big question. Right now, they’re not very smart. They’re usually very specialized. The more we move to a more general form factor, humanoid form factor, the more I think it will change. Also, another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. At some point, we keep assuming that humans in the loop. If we’re talking about agents convincing another agent, not having embodiment might be even more efficient. That will be another perspective. Going forward, we will have not just agents we control who are doing a job and scanning the job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or maybe not confrontationally, but trying to think and having different perspectives with another agent, controlled by other teams. I don’t think we have seen much of that now. We have seen mostly agents that are controlled by one team doing one job in one direction. Not multiple teams agents working together, or against or in parallel with another team agent. I think we will see some interesting things coming out of that.Nuno Gonçalves Pedro Scenarios Switching to scenarios, we love our two-by-twos. We haven’t done one in a while. This time it’s a two by two. We have four scenarios. I think on one axis, we would have potentially the capabilities of AI. One side would be more incremental. The other side would be the extreme full AGI. I’ll define it in a bit so that we can at least have a little bit of a definitional view on what the AGI is. Then the other axis would be how gains are distributed, concentrated versus broad. Obviously, if they’re very concentrated, it’s more unequal. It only goes to a few companies, a few people, a few individuals. If it’s broad, it’s much more dispersed through society, et cetera. AGI, just to try to define it, the formal definition of it is that it’s a hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn, reason, and adapt to novel situations across any domain. Then there are several mutations on this, but there’s one notion, or rather, there are three notions that normally are across a lot of these definitions. One is generalization, ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have. Autonomy in agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is their issue with agents to a lot of that extent. Then, last but not least, human parity, performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you’ll realize that Bertrand and I have slightly different views on AGI, and if it’s already here or not. I think, definitionally, maybe we have slightly different views on what the definition actually is. For me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence. Strict to census, Bertrand is more connecting to AGI as in its prime definition. It behaves as well or better than a human thing. Maybe that’s what’s leading us to differences on whether AGI has arrived or not.Bertrand Schmitt Personally, I will have a different scale where I will put AGI, as you just said, in some ways, relatively similar in performance to your average human being. On top of it, it’s able to touch different domains that most humans are not able to do. Usually, there is some level of specializations where in AI, it can be more generic. I will put ASI, Artificial Superintelligence, as clearly the step beyond. Something that, on any dimension you pick, it’s able to beat a human expert. From my perspective, I think we already discussed that, but we are at AGI already. We have AI that can do way better, not just way better, but at least as well as humans on many topics, sometimes better. Yes, there are some topics that are not for AI yet. Embodiment, for instance, to flock with your humanoid robot in 2026. For me, we are partially there or fully there in AGI. If we take the stricter definition, ASI, we are definitely not there, but my guess is that it’s moving quite fast. We might be there in a few years from now. I don’t think we are talking about multi-decades. It’s 5 years, maybe 10. Of course, there are questions because people will say, for instance, “Hey, how do you become truly super-intelligent when all your training is based on human data?” That’s not an easy one because how do you train on that? To be way better, not just a bit better, but way better. Maybe I’m going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting with AI, AI challenging AI. The same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trained to play against itself. That’s when it reached superintelligence in Go. It reached superintelligence by playing against itself and basically letting go of that human baggage, if you want, and going to the next level. What I found interesting in that, actually, first, that’s what happened, but two, there was some analysis that the average level of Go players and the top players went up after AlphaGo because AlphaGo, in a way, opened doors that humans didn’t believe were open in front of them, or they didn’t see them. They didn’t see these doors, so they didn’t bother to open them. AI opened new doors, but interestingly enough, humans improved after that, thanks to AI. You see what I mean? It was an interesting, okay, that self-learning from AI was the way to go beyond the current level of human knowledge and human expertise, but at the same time, humans were able to follow up. It was not like suddenly humans are totally useless crap. They improved. Did they still beat AI? Maybe not, but it was definitely also helpful.Nuno Gonçalves Pedro Back to our scenarios. We’re going to take the definitional extreme just for argument’s sake for scenarios. We’re going to talk about maybe what you were saying, ASI rather than full AGI, but like ASI. Again, artificial superintelligence as the extreme on the one hand. Let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau scenario. All of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They’re a great tool. At some point, they’re going to hit a wall. Hallucinations are never going to be a thing of the past. We can’t fully trust them on really hardcore stuff. We’ll gain productivity enhancements. We’ll keep gaining those productivity enhancements, but at some point in time, we really won’t reach ASI. We really will be stuck with what we have. It’s a little bit like we get the next big thing, the next big spreadsheet, the next big internet, but it’s not going to change the whole world beyond just productivity, enhancements, and amazing tools that we have available to us that makes us much better. In that scenario, the winners will continue being fast adopters, probably small and medium businesses, because there won’t be a push for maximum speed either, so they’ll catch up at some point. Then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It’s not necessarily a new species of companies. It’s just companies that are a little bit better at doing stuff, which we also saw during the internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different on how they operated. It took us another couple of decades for companies to be more and more digitally native along the way. Basically interesting, but it’s boring. It’s like, cool, we got tools, we got promised the world. What are the implications? All these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars. Because at some point we’ll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. Therefore, this will have been a bubble, and likely it would be a hard landing to that bubble. That’s the implication.Bertrand Schmitt I would just say that, yes, I agree with you, but I would just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have 10 years of madness just to leverage everything that we have today.Nuno Gonçalves Pedro Understood, Bertrand. This is a scenario. I understand, but maybe we’re going to hit a wall, and we’re going to hit that wall next year, or we’re going to hit that wall in 2 years or whatever.Bertrand Schmitt Possibly. I’m just saying we still have 10 years of goodness from that big push in AI we experienced the past few years.Nuno Gonçalves Pedro Absolutely. Agreed, but it’s boring.Bertrand Schmitt It’s boring. It’s a plateau.Nuno Gonçalves Pedro It’s a plateau. The second one is more of something that we have AI, but humans in the loop are going to be critical along the way. The judgment work that we described earlier in the episode is going to be critical to everything that happens. It’s, I would call it the augmentation scenario. The AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. Everyone will have access to them. We humans, are still very important. We have all these augmentation things, and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we’re just better. We’re better, faster, more data-driven, more factually current. We’re doing stuff faster, but humans

Big Tech earnings are moving markets sharply as investors try to figure out whether the AI trade still has another leg higher or whether the volatility is warning of something more fragile.Chuck Zodda and Mike Armstrong break down the sharp swings in semiconductor stocks, why major tech names like Microsoft, Meta, Amazon, and Apple are seeing outsized moves after earnings, and why the broader market still looks uncertain despite several big rebounds. They also discuss Amazon's strong cloud growth and rising CapEx, Apple's disappointing guidance tied to supply constraints and memory chip costs, and why Apple's slower approach to AI resembles Toyota's patience during the EV boom. Plus, they look at the blowup of the AI-focused hedge fund Situational Awareness, Todd Lutsky's explanation of irrevocable Medicaid trusts, and why new reports about Anthropic's AI models hacking companies raise serious concerns about agentic AI risks.

Der Hedgefonds des 25-Jährigen Leopold Aschenbrenner, war vierfach gehebelt auf die KI-Rally gesetzt, lag im ersten Halbjahr 450 Prozent im Plus und musste dann innerhalb von Stunden fast alles verkaufen. Ken Griffins Citadel hat die Reste eingesammelt. Pip erklärt, wie Margin Calls funktionieren, warum so ein Blocktrade für den Käufer beinahe risikofreies Geld ist und welche drei Erklärungen es für Aschenbrenners Aufstieg gibt. Danach senkt OpenAI die Preise um bis zu 80 Prozent, was zu der Frage führt, ob es je eine Softwarekategorie gab, die so schnell billiger wurde. Es folgt die große Earnings-Runde mit Apple, Microsoft, Meta, Amazon, Reddit und Robinhood, und die Beobachtung, dass zwei Konzerne für denselben Capex völlig unterschiedlich behandelt werden. In der Schmuddelecke will Josh Kushner Anteile an der Weltmeisterschaft kaufen, und Google Earth lässt jeden ein Atomkraftwerk in den Iran setzen. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Aschenbrenner und Citadel (00:20:11) OpenAI senkt Preise (00:30:00) Anthropic-Modelle hacken (00:32:55) OpenAI-Umsatz (00:36:00) Tesla und SpaceX (00:39:35) Apple (00:43:58) Microsoft (00:46:18) Meta (00:54:23) Amazon (01:08:25) Reddit (01:09:24) Robinhood (01:12:50) FIFA-Ultimatum (01:15:12) Gefälschte Satellitenbilder (01:17:43) LinkedIn-Slop-Button (01:23:46) Pentagon gegen Anthropic (01:26:25) Durow Shownotes Situational Awareness sucht Kapital nach KI-Ausverkauf - ft.com Citadel kauft Aschenbrenners Aktienportfolio - ft.com OpenAI senkt GPT-5.6-Preise um bis zu 80 Prozent - axios.com Anthropics Modelle hackten drei Firmen im Test - wsj.com Juli-Umsatz uebertrifft das ganze zweite Quartal - cnbc.com Tesla erwaegt Verkauf des China-Geschaefts - wsj.com Apple-Quartalszahlen im Liveticker - cnbc.com Apple bremst wegen Engpaessen in der Lieferkette - ft.com Microsoft-Quartalszahlen, Azure knackt 100 Milliarden - cnbc.com Groesster Kurssprung der Firmengeschichte - finance.yahoo.com Meta-Aktie faellt nach Zuckerbergs Agenten-Vision - ft.com Amazon erhoeht KI-Investitionen auf 220 Milliarden - ft.com Amazon-Quartalszahlen, AWS waechst 37 Prozent - cnbc.com Big Tech investiert mehr als eine Billion in KI - ft.com Reddit-Quartalszahlen, Umsatz plus 61 Prozent - cnbc.com Robinhood mit Rekordumsatz durch Volatilitaet - marketwatch.com Infantino setzt FIFA-Verbaenden eine Frist von 53 Tagen - telegraph.co.uk Wie man ein Atomkraftwerk in den Iran faelscht - digitaldigging.org LinkedIn fuehrt einen Melde-Button fuer KI-Schrott ein - 404media.co Richterin zerlegt den Pentagon-Fall gegen Anthropic - axios.com Durows Reaktion auf den russischen Haftbefehl - xcancel.com

Welcome to Macro, Micro and Small Cap News with Sharepickers! In today's episode for Friday, 31st July 2026, we review the main market indices, examine the Bank of England's rate decisions alongside Neil Woodford's macroeconomic insights, look at AI security concerns, and highlight 3 promising UK stocks worth researching.

Plus: IBM says new research shows quantum computers can outperform conventional ones. And investors and analysts are awaiting Amazon's latest earnings. Danny Lewis hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Andrew, Ben, and Tom discuss Warsh holding rates at 3.50-3.75% in a divided 6-3 vote with market-implied inflation expectations dropping meaningfully since the last meeting, the BOE also holding at 3.75% in a matching 6-3 split, the emerging Mag-7 split with Microsoft rising 8% on improved AI ROI, better throughput, and Copilot seat growth versus Meta down 9% on longer time-to-value and fuzzy CapEx guidance beyond 2027, the broader takeaway that Mag-7 is now focused on driving AI revenue and CapEx efficiency which is bearish for Nvidia near-term but long-term positive for the industry, a Russian missile leaving a 30-foot crater on the Poland-Ukraine border, Admiral Cooper preparing a 10-14 day punishing air campaign against Iran, and South Korea weighing a short-selling ban.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

*Note: This episode was recorded before news broke of Situational Awareness' unwind, which gives us much better insight into the pace and magnitude of the move in the Korean markets specifically.* Chips, China, and credit. The three forces tearing through the AI trade right now, and we called it last week. In this episode we break down why the bond market cracked first, what widening credit spreads on Nvidia, Meta, and Oracle are actually telling you about default risk, and why the Nasdaq is bleeding while the S&P barely flinches. We walk through Alphabet's first negative free cash flow after twenty years of printing money, the CapEx numbers that keep getting revised upward, and the moment the market stopped rewarding spending and started punishing it. If you have ever wondered how to read a credit spread, we show you the math live. Then we get into the China story that moved markets this week. CXMT went public in the largest mainland Chinese semiconductor IPO on record, oversubscribed 212 times, and the Korean stock exchange took the hit because the KOSPI is essentially a memory-chip index wearing a trench coat. We explain why memory matters in an AI data center, why Samsung, SK Hynix, and Micron controlling 90 percent of the market was the whole moat, and what reports of domestically produced DUV lithography machines would mean for US export controls. We also unpack Nvidia guaranteeing borrowing for a 10-gigawatt OpenAI data center in Ohio, and whether circular financing between chipmakers and model labs is clever structuring or an accounting Ouroboros. Finally, the philosophical hangover. We react to Elon Musk's Economist interview and his claim that money stops mattering within a decade, pressure-test his deflationary argument against MV equals PQ, and ask why every science fiction author who ever imagined artificial superintelligence wrote a horror story. Plus Anthropic's positioning ahead of a possible IPO, the distillation and copyright fight with publishers, the rare books being unbound and shredded to feed training data, and where value actually accrues if models commoditize. Energy and molecules, or something else entirely. Subscribe for weekly deep dives on AI infrastructure, credit markets, semiconductors, and the money moving underneath the entire AI build-out.

Yes. There are strikingly similar themes in play

Sean Emory of Avory & Co. debriefs Meta's July 29, 2026 earnings call, framing four key questions: AI monetization, open vs closed source strategy, enterprise AI, and the CapEx path into 2027 and 2028.Chapters00:00 Meta Earnings Debrief01:14 Show Intro & Disclosures01:55 Revenue & Guide Highlights02:55 AI Monetization Flywheel06:49 Open Versus Closed Models08:46 Enterprise AI On WhatsApp11:01 CapEx Outlook And Bottlenecks13:04 Wrap Up & Key TakeawaysThis discussion is for educational purposes only and should not be considered personalized investment, legal, or tax advice. Views reflect our opinions as of the recording date and may change.

In this episode of Facts vs Feelings, Ryan Detrick, Chief Market Strategist at Carson Group, and Sonu Varghese, Chief Macro Strategist at Carson Group, dig into Apple reclaiming its title as the world's largest company by market cap after sitting out the AI spending race, while hyperscalers like Google, Amazon, and Microsoft pour ever-larger sums into CapEx. They break down record Q2 blended earnings growth of 38% year-over-year, the outsized role investment gains in private holdings like SpaceX and Anthropic played in Google's headline profit beat, andwhy core net income tells a different story. The conversation shifts to the "chip crash" playing out in South Korea, where the KOSPI has fallen more than 30% from its June 22 peak amid margin calls and central bank rate hikes, and what that says about crowded momentum trades and the explosion of leveraged ETF products tied to tech and semis. Ryan and Sonu also cover the rotation into low volatility, financials, and healthcare, why flows into tech remain historically stretched even after the pullback, and preview this week's Fed decision amid unusually high rate-hike odds. They close with apersonal update on Ryan's eye surgery, a shoutout to guest and TrendLabs Founder JC Parets' record-breaking episode, and details on the live 200th episode show in Boston.[Key Takeaways]Apple overtook NVIDIA as the world's largest company by market cap (~$4.9 trillion) after largely sitting out heavy AI CapEx spending, while free cash flow for semiconductor companies surpassed hyperscaler free cash flow for the first time this quarter.Q2 blended S&P 500 earnings growth hit 38% year-over-year, the best pace since Q3 2021, driven largely by tech (+65%), energy (+128%), and communication services (+112%); excluding Google, growth drops to 26%.A large share of Google's reported profit surge came from investment gains in private holdings (SpaceX, Anthropic) rather than core operations, a pattern also inflating net income at Amazon, NVIDIA, and Microsoft.South Korea's KOSPI fell roughly 33% from its June 22nd peak (before a further 10% one-day drop) as margin calls and a Bank of Korea rate hike hit heavily levered chip and momentum trades.Momentum's one-year excess return over the S&P 500 pulled back from the 96th to the 75th percentile relative to the last 40 years, while low volatility stocks are up 8% and financials up 11% since the market's June 2nd peak.Fed rate-hike odds this week sit near their highest pre-meeting level in recent memory, with the committee reportedly divided as inflation, a resilient labor market, and AI/Middle East-driven cost pressures complicate the outlook.Jump to:0:00 - Welcome And Quick Setup0:31 - Apple Reclaims Top Market Cap5:16 - AI Capex Arms Race Reality Check8:35 - Record Margins And Earnings Surge16:44 - South Korea Sparks Chip Crash23:49 - Ryan's Eye Patch Surgery Story29:58 - Why Tech Flows Look Crowded35:28 - Leveraged Products And Margin Call Risk42:40 - Rotation Into Low Vol And Defensives46:57 - Contrarian Thinking Versus Momentum54:41 - Interstellar Detour And Time Talk57:19 - Fed Uncertainty And Rate Hike Odds1:02:16 - Live Boston Show And Final ThanksConnect with Ryan:• LinkedIn: https://www.linkedin.com/in/ryandetrick/• X: https://x.com/RyanDetrickConnect with Sonu:• LinkedIn: https://www.linkedin.com/in/sonu-varghese-phd/• X: https://x.com/sonusvarghese?lang=enQuestions about the show? We'd love to hear from you! factsvsfeelings@carsongroup.com

Microsoft and Meta head into earnings with investors asking whether the massive spending behind artificial intelligence can actually produce the returns needed to justify the cost.Chuck Zodda and Paul Lane break down why hyperscaler CapEx is becoming a bigger concern for markets, how depreciation from trillions in AI infrastructure spending could pressure future profits, and why companies may need enormous new AI revenue just to break even on the buildout. They also discuss Mark Zuckerberg's pushback against AI regulation, the risks of increasingly powerful AI agents, Ford's outlook as buyers keep favoring trucks and SUVs, FIFA's reported effort to attract outside investors, Nike's struggles in China, and why DoorDash's FAA approval for drone delivery raises new questions about technology, jobs, and public safety.

Is Microsoft (MSFT) undervalued after a steep sell-off in recent months? That's the question on many investors' minds heading into earnings after Wednesday's close. Tom White turns to the key support and resistance levels to watch in the stock into and after the Mag 7 company's report. He offers an example options trade for Microsoft. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Kevin Hincks does not see Fed Chair Kevin Warsh and the FOMC raising interest rates for July. He points to recent economic data and consensus data for upcoming prints like core PCE he says tilts toward a pause. Kevin also outlines his expectations for Microsoft (MSFT) and Meta Platforms (META) earnings and why AI ROI will be the point investors watch for the most. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Greg Dukes explains why markets are focused more on CapEx than earnings, highlighting Alphabet's (GOOGL) infrastructure spending and the opportunities it creates across energy, construction, and real estate. He also discusses Fed rate hike expectations and why investors should look beyond hyperscalers to companies benefiting from the investment boom.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Jay Mehta discusses tensions in Mag 7 price action, driven primarily through growing AI CapEx. Investors are investing in other sectors as concerns over AI spending boil over. Jay also highlights what the AI CapEx boom means for the market's forward momentum and breaks down expectations ahead for the Mag 7.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Matt Maher talks about his expectations for Meta Platform's (META) earnings. He says what works most for Meta is their power in advertising, backed by AI implementation behind its advertising tech. Matt also offers his takeaways on the Mag 7 and its aggressive spending on AI infrastructure.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Marley Kayden and Alex Coffey talk about the biggest earnings movers so far this week in Microsoft (MSFT) and Meta Platforms (META). One beat on the top and bottom line while the other missed on EPS, signaling a mixed after-hours trading session as investors weigh if growing CapEx is offering enough AI ROI. Marley and Alex turn to other notable tech earnings from Qualcomm (QCOM), Arm (ARM), and Lam Research (LCRX), along with one of the biggest consumer discretionary names in Starbucks (SBUX). ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Microsoft (MSFT) surpassed wall street's expectations on the top and bottom line, while Meta Platforms (META) and Qualcomm (QCOM) both missed on EPS as investors weigh CapEx and its ability to generate long-term ROI. Marley Kayden takes investors through the biggest earnings after Wednesday's trading session. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

"I would not jump to the alarm bell" when it comes to the AI trade's substantial selling action, says Ted Thatcher, pointing to Alphabet (GOOGL) as a bellwether for the Mag 7 in AI spending and ROI. He expects Microsoft (MSFT), Meta Platforms (META), and Amazon (AMZN) to all raise CapEx for 2026 but also sees these Big Tech giants benefitting from it long-term. Ted turns to how open source AI models like Kimi are changing the way investors see CapEx.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling -https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watch Watch onVizio - https://www.vizio.com/en/watchfreeplus-exploreClassification: Schwab InternalWatch on DistroTV - https://www.distro.tv/live/schwab-network/ Follow us on X –https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetwork Follow us onLinkedIn - https://www.linkedin.com/company/schwab-network

Emily Roland considers the current earnings season a phenomenal one that's being overshadowed by CapEx figures from Mag 7 companies. She says Alphabet (GOOGL), Microsoft (MSFT), Meta Platforms (META) and Amazon (AMZN) need to prove that all their AI spending is offering substantial ROI. As investors continue to digest all the AI volatility, Emily offers ways to diversify your portfolio while still keeping it tied to tech themes. She turns to the Fed and offers her reasoning as to why the committee can hold interest rates.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling -https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watch Watch onVizio - https://www.vizio.com/en/watchfreeplus-exploreClassification: Schwab InternalWatch on DistroTV - https://www.distro.tv/live/schwab-network/ Follow us on X –https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetwork Follow us onLinkedIn - https://www.linkedin.com/company/schwab-network

Mark Brennan, Founder, CEO, and Director of Cerrado Gold Inc (TSX.V: CERT) (OTCQX: CRDOF), joins me to review their Q2 2026 operational metrics at the producing Minera Don Nicolas (MDN) gold mine in Argentina.  We discuss the increased life of mine and MDN due to the acquisition of the adjacent Falcon Properties, and the ongoing 70,000 meter exploration program, both at surface and now with an underground drill rig. Additionally we get an update on the permitting process at the Lagoa Salgada VMS Project in Portugal and ongoing tradeoff studies for the coming Feasibility Study  at the Mont Sorcier Iron Project in Quebec.   Q2 2026 MDN Operating Highlights: Q2 Production of 15,415 vs 11,437 GEO in Q2 2025, 2026 first half Production of 28,257 vs 22,600 GEO in 2025 Heap leach production improved to deliver 9,981 GEO in the quarter Underground development work continued; leading to increased production for H2/26 CIL plant continues to process a blend of stockpile material with an increasing mix of ore from underground operations, resulting in total production of 5,434 GEO in Q2 Acquisition of Falcon Properties has the potential to extend Heap Leach operations based on historical drill results. Combined with the existing exploration program, the acquisition is expected to position the mine to add new mineable material quickly Full year production guidance of 50,000-60,000 GEO maintained   Mark and I review their Minera Don Nicolas producing gold project in Argentina, and the combination of heap leach and underground gold equivalent ounce production for the second quarter. He also highlighted the advantages of the Falcon Properties acquisition, and how it adds years to the existing heap leach mine life, as well as substantial exploration upside.  We discuss the key objectives from the ongoing 70,000 meter drill program will be looking to extend the project mine life in a substantial way and find new high-grade areas, at surface and underground, for future mine sequencing.   On June 17th, the Cerrado gold announced that its wholly owned subsidiary, Redcorp - Empreendimentos Mineiros, Lda. (“Redcorp”) has been granted the injunctive relief it sought in connection with the opinion of the Portuguese Environmental Agency (Agência Portuguesa do Ambiente – “APA”). Granting of the injunction has the effect of suspending the effects of the APA opinion while the legal action commenced by Redcorp to overturn APA's opinion continues to progress. This decision ensures that Redcorp continues to hold its concession contract and PIN classification for the Lagoa Salgada project valid and in good standing.  Mark said the board and management team remain very constructive that a resolution will be found and were pleased that the court agreed with their position.   On July 15th the Company announced that it has elected to extend the completion date of the Bankable Feasibility Study at its Mont Sorcier high grade iron project in Quebec, led by Voyager Metals, a 100% owned subsidiary of Cerrado Gold, to review numerous optimization and trade off opportunities that have been identified during the BFS process as having the potential to materially enhance the overall value of the project.  Key Optimization Focus Areas Include: Enhanced Mine plan to reduce Overall Strip Ratio, Tailing Dam Construction, CAPEX and OPEX review in light of Ongoing Regional Inflation, Trade-off in Concentrate Quality vs Premium Price.   We wrap up discussing the underappreciated current valuation that the company is receiving for the producing MDN mine in Argentina, for both the development-stage Lagoa Salgada and large Net Present Value of the Mont Sorcier Project.     If you have questions for Mark regarding Cerrado Gold, then please email those to me at Shad@kereport.com.   In full disclosure, Shad is a shareholder of Cerrado Gold at the time of this recording, and may choose to buy or sell shares at any time.   Click here to see the latest news from Cerrado Gold.   For more market commentary & interview summaries, subscribe to our Substacks:   The KE Report: https://kereport.substack.com/ Shad's resource market commentary: https://excelsiorprosperity.substack.com/     Investment disclaimer: This content is for informational and educational purposes only and does not constitute investment advice, an offer, or a solicitation to buy or sell any security. Investing in equities and commodities involves risk, including the possible loss of principal. Do your own research and consult a licensed financial advisor before making any investment decisions. Guests and hosts may own shares in companies mentioned.    

Learn more about Astraeus Wealth Management: http://astraeuswealth.com/partner-with-us Guy Adami and Danny Moses discuss rising global yields and stress points in markets, focusing on Japan's weakening yen, deteriorating bond market, and risks around Japan's role as a major US Treasury holder and potential carry-trade unwind, with BOJ and Fed meetings ahead. They note August's tendency toward volatility and how attention may shift from strong earnings to macro concerns as AI-driven CapEx pressures free cash flow at hyperscalers, raising valuation questions and fears of open-source competition. They preview key earnings including Apple (seen as a defensive AI conduit despite a rich valuation), Microsoft, Meta, and major energy firms, which may post strong results but avoid political blowback. They discuss mixed consumer signals from Capital One, AmEx, and retailers, reiterate a constructive longer-term view on gold tied to Fed policy, and share updates on Moses's podcasts, Substack work, and a veterans charity event. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

In this episode of The Circuit, hosts Ben Bajarin and Jay Goldberg review major updates across the semiconductor industry, beginning with AMD's Advancing AI Day where they discuss CEO Lisa Su's performance, the technical details of the Helios rack-scale architecture, and AMD's strategic fit for inference with partners like Anthropic. They next unpack Intel's strong quarterly earnings, pointing out that the company beat revenue and margin estimates while confirming customer traction for its 18A and 14A process nodes, despite mixed market reactions over its conservative CapEx approach. The hosts then analyze the broader semiconductor capital equipment (semicap) sector, arguing that increased CapEx from key fab operators will drive sustained demand for equipment makers. Finally, they cover Texas Instruments' solid quarterly report, noting signs of recovery in automotive and industrial markets along with surging demand for power semiconductors used in AI data centers.

Derek Moore is joined by Mike Snyder and Shane Skinner this week to talk about whether the Mag 7 companies have a free cash flow problem compared to historical payout ratios. Then, they look at the relationship in price action between SpaceX and Tesla. Later, what does the options market say about next week's Apple and Microsoft earnings reports? Oh yea, they touch on Strategy changing how it calculates the preferred STRC Sharp ratio, the US Dollar Index breaking out, oil prices, and much more.   Do Mag 7 companies have a free cash flow problem? Comparing Mag 7 payout ratios to historical levels How rising cap ex is squeezing free cash flow The relationship in price action between SpaceX and Tesla What does the options market imply for next week's Apple earnings? Comparing earnings implied volatility in AAPL vs MSFT Strategy changes how it calculates the preferred STRC Sharpe ratio The US Dollar Index breaks out Where oil prices go from here Lots of dispersion under the surface of the S&P 500   Mentioned in this Episode     Derek Moore's book Broken Pie Chart https://amzn.to/3S8ADNT   Jay Pestrichelli's book Buy and Hedge https://amzn.to/3jQYgMt   Derek's book on public speaking Effortless Public Speaking https://amzn.to/3hL1Mag   Contact Derek derek.moore@zegainvestments.com

In today's Cloud Wars Minute, I look at why playing to win — not playing it safe — is becoming the defining strategy for AI and cloud leaders. Highlights 00:01 — Last week, we talked about Google Cloud's extraordinary Q2, with revenue up 82% to almost $25 billion, backlog up almost 400% to $514 billion. I wanted to talk today a little bit about a challenge that Google Cloud and the other hyperscalers are facing, because this extraordinary growth requires very aggressive investments in CapEx to build out data center capacity. 00:58 — Will these companies continue to have the courage to play to win, as opposed to trying to appease rattled investors? Right after Alphabet reported these numbers, Alphabet also had to say that its CapEx budget for 2026 is going to go up slightly to about $200 billion, while Q2 cash flow was negative $5.9 billion. 01:49 — But it's not a long-term trend here. The market reaction was they hammered Alphabet stock, and its market cap went down $250 billion after Google Cloud reported this. My point is, you've got to keep a clear head in these crazy sorts of times, and I think Alphabet is doing exactly that. Sundar Pichai cited immense opportunities during the Q2 earnings call and talked about the complete AI stack. 02:56 — They believe these investments they're making now are going to pay off with significantly more data center capacity next year. In the short term, Google Cloud is going to tap into some third-party data center capacity, Pichai said, as one of the ways to take care of customers. They're willing to trade that very short-term pain for enormous contracts down the line. 04:09 — It's a wild time here, but definitely, as I've said many times, this is the greatest growth market the world has ever known. These big hyperscaler companies are now faced with challenges that no company has ever had to face before. I applaud the Alphabet leadership for playing to win rather than playing not to lose. That's just not going to cut it in the Cloud Wars. Visit Cloud Wars for more.

What happens when non-technical people at a hackathon build in one day what software startups have been pitching for years?In this episode of KP Unpacked, KP Reddy and Nick unpack why sitting in a hackathon full of non-technical AEC people building working prototypes in eight hours is making software feel uninvestable. Incumbents are building features they've wanted for five years. Nobody needed corporate approval. Nobody needed a startup. They just needed a day and a keyboard. If that's the new baseline, what exactly is a software company selling?The conversation covers why PE firms are overpaying for AEC companies at 14x EBITDA and losing their best people 18 months after close (almost clockwork), why Procore's AI agents announcement landed with a thud, why vibe coding is a rabbit hole that creates individual value but rarely scales to the company, and why token pricing is heading toward the same bundled unlimited model as internet bandwidth in the 90s. KP also reveals his new LinkedIn rule: send a 10-page resume, not a one-page highlight reel. If you control what I see, I can't assess what matters. And a hospital system owner asked Zero RFI to build a real-time team qualification tool because they're tired of getting the B team swapped in mid-project without notice.Key questions answered:Why is software getting harder to invest in after every hackathon?What did non-technical AEC people build in one day that startups have been pitching for years?Why are PE firms losing their best AEC people 18 months after acquisition, almost to the day?What's wrong with paying 14x EBITDA for a firm that doesn't grow like that?Why did Procore's AI agents announcement land with nobody caring?Is vibe coding a rabbit hole or a real productivity tool?Why should you send a 10-page resume instead of a one-page highlight reel?How is KP cross-referencing resumes against his LinkedIn connections using skills files?Why do hospital owners want real-time team qualification tools mid-project?What's the AI diffusion crossing the chasm moment for the broader economy?Why is the Bay Area network effect getting stronger, not weaker?Why are tech company balance sheets suddenly going CapEx heavy for the first time ever?If you're building software for AEC and wondering why incumbents are building your features in-house, a PE firm trying to understand why cultural fit isn't transferring post-acquisition, or a founder debating whether to raise venture or bootstrap, this episode will force you to ask whether the software playbook still applies when anyone can build anything in a day.Listen now.

The yen just hit a 40-year low and Japan is trapped. Whether they hike or freeze, it ends the same way: the pin that pricks our bubble.Tonight's episode is sponsored by NetSuite. For the first time ever you can try NetSuite Next for free. If your revenues are at least in the seven figures, Go to http://netsuite.ai/goldTonight's episode is also sponsored by Rockwell Automation. Download their 11th Annual State of Smart Manufacturing Report at https://rok.auto/sosmInvestors are far too complacent about risks that are now hiding in plain sight. The AI trade cracked this week: Alphabet fell 10% after announcing even higher CapEx, Oracle is down 41% on the year, Meta and Amazon fell, and Microsoft is nearly in a bear market. SpaceX now trades 49% below its post-IPO high with its float set to jump from 5% to 40% by year end, and Tesla dropped 18%, costing Elon Musk nearly $100 billion in a week. Peter Schiff compares the roughly three-quarters of a trillion dollars in annual AI CapEx to the dot-com build-out, where the early favorites went bankrupt and took their vendors down with them.The bigger danger is Japan. The yen fell to a 40-year low against the dollar, the 30-year JGB yield hit an all-time high near 4%, and with debt above 200% of GDP and a policy rate still at just 1%, Japan is trapped. Whether the Bank of Japan finally hikes aggressively or stays timid, the result spills into the United States, potentially forcing the world's largest holder of US Treasuries to dump its $1.1 trillion position. Schiff calls Japan the pin that pricks the far bigger US bubble. Meanwhile the US 30-year yield hit a 20-year high of 5.16% on more than four times the debt of 2006, oil is up 30% in July guaranteeing a hotter CPI, and gold rose on the week even as bonds and stocks fell, with the miners signaling a bottom. He closes on why record-low jobless claims are meaningless in a gig economy and why Trump's new slave-labor tariffs are an unconstitutional tax on Americans.Chapters:00:00 Japan Sparks US Crisis00:41 AI CapEx Reality Check07:51 AI Bubble Parallels13:03 Gold Miners Rebound19:17 Oil Bonds Warning Signs32:16 Japan Debt Rate Trap34:36 Weak Yen Trade Deficits37:22 Japan Creditor Status Slips41:22 Two Japan Crisis Paths44:26 US Vulnerability Dominoes45:21 Unemployment Claims Hype47:20 Why Claims Mislead51:37 New Tariffs Legal Workaround59:03 Wrap Up Subscribe CallFollow @peterschiffX: https://twitter.com/peterschiffInstagram: https://instagram.com/peterschiffTikTok: https://tiktok.com/@peterschiffofficialFacebook: https://facebook.com/peterschiff#PeterSchiffShow #gold #inflationOur Sponsors:* Check out Chilipad and use my code GOLD for a great deal: https://sleep.me* Check out Fast Growing Trees and use my code GOLD for a great deal: https://www.fast-growing-trees.com* Check out Plaud AI and use my code GOLD for a great deal: https://plaud.ai* Check out Quince and use my code quince.com/gold for a great deal: https://www.quince.com* Check out TruDiagnostic and use my code GOLD20 for a great deal: https://www.trudiagnostic.comPrivacy & Opt-Out: https://redcircle.com/privacy

Jordi Visser is a veteran macro investor with 30+ years of experience and the author of the VisserLabs Substack. In this conversation, we break down the AI trade — Chinese open-source models catching up, Google's massive CapEx bet, and the memory shortage bottlenecking it all. We also cover Travis Kalanick's stealth robotics empire, what AGI in three years means for jobs and the Fed, and where Bitcoin, Ethereum, and the Clarity Act go next.======================Arch Public is an agentic trading platform that automates investment strategies across Stocks, Commodities, ETFs and Crypto. Whether you're rotating into AI & Gold, allocating to the S&P 500, or accumulating Bitcoin, Arch Public executes your plan 24/7 without ever taking custody of your assets or funds. Sign up today at https://www.archpublic.com, and start your FREE automated trading strategy! ======================This episode is brought to you by TikTok for Business. If you run a company, your next wave of customers may already be on TikTok. With more than 200 million monthly active users in the U.S. and 51% unique reach, TikTok gives brands access to audiences they can't reach anywhere else. Learn how to turn that reach into growth at TikTok for Business ( https://anthonypompliano.splashthat.com/ )======================Uphold is the easiest way to buy and sell crypto unlike any other platform allowing you to trade in just one step between any supported asset. Check them out at https://www.uphold.com/pomp/ This video includes a paid sponsorship with Uphold. I'm compensated by Uphold for promoting its products and services and may receive commissions from referrals. Terms apply. Not available in all jurisdictions. Digital assets are risky and may result in the total loss of your capital.======================0:00 - Intro0:48 - Chinese open-source AI models & the risk to portfolios12:10 - Google's massive CapEx bet & the odds it pays off16:53 - Anthropic's growth slowdown & the shift to token efficiency19:39 - The memory & compute shortage29:00 - Travis Kalanick's stealth robotics & ghost-kitchen empire40:06 - The Fed & rate policy43:23 - Bitcoin's setup & why the "easy" AI trade is over50:36 - Jordi's prompting method for AI research & upcoming video

Looking at clues from the past, our Global Head of Fixed Income Research Andrew Sheets examines how the recurring themes – from deregulation to volatility – are shaping markets and why every cycle still takes its own path.Read more insights from Morgan Stanley.----- Transcript -----Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. Today, what can Odysseus teach us about investing? It's Friday, July 24th at 2pm in London.Like many of you, this week I saw The Odyssey. The enduring appeal of this story more than 2,700 years after it was composed is a reminder that some themes are universal. Pride, resourcefulness, determination, self-control, or the lack thereof, mattered to both an ancient Greek dinner party and resonate with anybody investing today.But drawing lessons from the past is also tricky. We do not have that much financial history, and markets contain too many variables for the same combination to align twice. Some judgment, art, and dare we say storytelling is always involved in deciding which historical periods best describe the present. Those disclaimers aside, we've argued in our year ahead outlook that 1997 to 1998 and 2005 to 2006 are some of the most useful templates for the current backdrop.That remains our view. They suggest a cycle that has further to run, equities outperforming credit, and a preference to own volatility. Both of these periods were defined by a sharp rise in corporate activity. That is certainly what we're seeing today.We forecast U.S. capital expenditure to rise 23 percent in 2026, and 26 percent in 2027. AI is the biggest driver of this spending but build-outs in energy infrastructure are also playing a role. And increased corporate CapEx is certainly a global story, especially in Asia.Then there's M&A, which also rose significantly in these two past historical periods. As recently as early 2024, global M&A volumes were unusually depressed, some of the lowest levels in over 30 years, adjusted for economic size. But that's no longer the case. And more recently, M&A is currently running up 64 percent relative to a year ago.Important current macroeconomic data also looks somewhat similar to these past two periods. The current levels of U.S. core PCE inflation, the unemployment rate, and the 10-year yield are pretty close to the averages seen in 1997, 1998, 2005, and 2006.And the U.S. 2s10s yield curve, well, it broadly flattened then, and it has broadly been flattening today. A third similarity, maybe less obvious but no less important, is deregulation. Both 1997 and 1998 and 2005 to 2006 saw significant financial deregulation. And we're seeing that again now. From the Basel Endgame to NAIC risk weights to Solvency II changes to savings reforms in Europe, Korea, and elsewhere, the current trend appears to be on a firmly deregulatory path.Even more simply, 1997 and 1998 and 2005 to 2006 provide interesting narrative bookends to two ways that I often hear the current environment being described. The late '90s? Well, that was defined by rising excitement around a transformational new technology – then the internet – and the prospect of a more productive future. Sound familiar? And the mid-2000s? Well, that was defined by a very unequal economy and rising consumer stress – but growth that was still supported by a seemingly inexhaustible investment demand from a rising market force. Then that force was emerging markets. Today, it's AI. Again, somewhat familiar. If these periods serve as a guide, the cycle probably has further to run, and corporate aggression should favor equities over credit.But if we learn anything from the trials of Odysseus, the journey can throw up plenty of surprises along the way. Thank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.

Intel's massive quarter not enough to boost chip stocks. AI spending concerns cropping up in both the equity and the credit markets--and that could be good for Apple. Plus, the bullish options action ahead of the busiest week of this earnings season.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ben discusses SLB rising 5% after beating on broad-based international drilling activity with its Data Center Solutions business on track to exceed $1 billion annualized run rate this year and $2 billion by end of 2027, Intel's after-hours pop fading as CapEx ballooned to over $20 billion for 2026 with 2027 significantly higher despite strong Client and Data Center results, Tenet Healthcare soaring 16% on a massive EPS guidance raise from Medicaid supplemental payments in TX and CA, HCA falling 3% on payer mix headwinds from ACA coverage losses, and NextEra reiterating all guidance through 2035 as power demand accelerates.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

Dr Clarida is a managing director and global economic advisor at PIMCO. Prior to rejoining PIMCO in 2022, he was the firm's global strategic advisor from 2006 to 2018. He served as Vice Chairman of the Board of Governors of the U.S. Federal Reserve System from September 2018 to January 2022. Richard is also Professor of Economics and International Affairs at Columbia University. Before joining PIMCO in 2006, he was Assistant Secretary of the Treasury for Economic Policy, in which he served as chief economic advisor to two U.S. Treasury Secretaries. Earlier in his career, he was with Credit Suisse and Grossman Asset Management. He has 26 years of investment experience and holds a PhD and a master's degree in economics from Harvard University. He received an undergraduate degree with Bronze Tablet Honors from the University of Illinois. In this podcast, we discuss: Clarida's Macro Origin Story Global Economic Resilience The AI Capex Boom Labour's Declining Income Share The "Warsh Fed" Doctrine Revisiting Forward Guidance AI's Impact on Productivity and R-star The Era of Global Fragmentation Optimism for the Next Generation  You can follow Richard on LinkedIn as well as the PIMCO website. 

"The Intel (INTC) story is largely over," argues Michael Robinson, pointing to the stock's stellar surge over recent months as a sign that "easy money" has already been made. Sean O'Hara adds that the AI spending story is real but would not put a big position on Intel due to its growth story taking time. Both panelists discuss other names they like in the AI trade, from AMD Inc. (AMD) to Vertiv (VRT), and how Intel differs itself from the growing CapEx trends across Big Tech after Alphabet (GOOGL) and Tesla (TSLA) raised investor concerns.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

"Nvidia (NVDA) is firing on all cylinders but not getting any of the benefits," says Ray Wang. He says the demand for it and AI are there and has a $280 price target for the stock, pointing to significant earnings growth backing his bullish expectations. Companies like Alphabet (GOOGL) and Tesla (TSLA) raising CapEx add to Ray's thesis that Nvidia has more room to run and eventually benefit software giants like Palantir (PLTR) and ServiceNow (NOW). ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Marley Kayden highlights Alphabet (GOOGL), Tesla (TSLA), and Intel (INTC) earnings this week and explains how CapEx overshadowed top and bottom line beats from two of the three Big Tech firms. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Marley Kayden and Sam Vadas look beyond the first round of Mag 7 earnings and fears of rising CapEx to focus on other headlines catching investor attention to close the week. They talk about the significance of Nvidia (NVDA) CEO Jensen Huang joining X and economic data showing a surprising amount of resiliency. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

A.M. Edition for July 23. Oil futures are rising again today, after Iran-backed Houthi militants claimed attacks on a pair of Saudi tankers in the Red Sea, and as the U.S. surges special-operations forces to the Middle East. Plus, Alphabet and Tesla shares tumble as the big-spending tech giants turn cash-flow negative. WSJ reporters Meghan Brobowsky and Becky Peterson break down the numbers and what to make of Elon Musk's most boring earnings call ever. And the FDA investigates a new outbreak of cyclospora. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Despite growing political resistance, investment in data centers isn't slowing. Ariana Salvatore explains why supply constraints may actually accelerate AI capital spending.Read more insights from Morgan Stanley.----- Transcript -----Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of Public Policy Research at Morgan Stanley. Today, I'll be talking about why we still expect robust AI capital spending in spite of some rising political pushback. It's Thursday, July 23rd at 10am in New York. It should be no surprise to our listeners that data center pushback, a topic that we've been following for some time, has been growing louder. But in 2026, it's accelerated meaningfully. Data we track suggests that an estimated $156 billion of projects were canceled or delayed in 2025. This year alone, in just the first quarter, we've seen almost that same exact number. The opposition is coming from several directions.Communities are raising concerns about rising electricity bills, environmental pressures related to water use, and the local quality of life effects of large-scale construction. But it's also coming from lawmakers across the aisle. State legislatures with both Democratic and Republican lawmakers have been advancing this type of policy.At the same time, we're forecasting a little less than a trillion dollars of AI CapEx this year alone, and we think it's an increasingly important component of the macroeconomic growth outlook.So how do we square that circle? First, and most importantly, we think this is primarily a supply-side risk rather than a demand-side one. We don't expect the backlash to materially reduce projections for compute demand. Instead, it could widen the gap between that demand and the industry's ability to bring new capacity online through things like permitting delays, grid interconnection constraints, and local opposition.Despite that more difficult political and infrastructure environment, our internet team, led by Brian Nowak, remain constructive on AI capital spending. Our broader thematic estimate for total AI CapEX, including the neo cloud providers, stands at approximately $870 billion in 2026, and we actually see risks skewed even higher from here.So why is spending still increasing as the environment for building data centers becomes more challenging? There are a few reasons.First, the AI ecosystem remains compute constrained. The urgency to invest has not diminished. In fact, growing social opposition and political uncertainty ahead of the 2028 presidential election may actually be encouraging hyperscalers to begin projects earlier, which our credit strategists outline as a potential scenario here. A pull forward of demand before the political and execution risk grows even louder.Second, the timelines associated with data center construction have become longer. From groundbreaking to operational launch, projects can now take as long as three years or even more. That gives companies a strong incentive to begin developing future capacity well in advance, even if the political pushback is strong.And third, the underlying demand signal is not slowing. Global weekly token usage, which our analysts view as an important proxy for compute demand, has increased since early January. It's rising and continues to do so throughout the course of this year. So, in short, the pushback is real, but it appears to be reshaping the build-out rather than stopping it. That's why our base case is for a conditional build-out. We think projects are likely to face greater scrutiny, we think projects are likely to face greater scrutiny, longer delays, and more requirements related to environmental impact and community benefits.But ultimately, we still think they cross the finish line. That could mean higher costs, it could mean longer development timelines, and greater geographic dispersion of projects away from the largest existing data center markets. It could also accelerate the shift toward on-site and behind-the-meter power generation. Fuel cells, turbines, and energy storage are becoming increasingly important as operators look for ways to reduce their reliance on these lengthy grid interconnection processes, and that can benefit companies that are able to bring those solutions to the forefront. Meanwhile, our U.S. equity strategy team maintains a relative preference for hyperscalers over semiconductors over the next several months. As you heard our CIO and Chief Equity Strategist Mike Wilson explain yesterday, that's because the team sees the hyperscalers as early in discounting the market's renewed focus on CapEx discipline. Putting it all together, we see the growing pushback against data centers as representing a genuine risk to the pace, cost, and geography of the AI infrastructure build-out. But again, this isn't just a demand story, it's a supply story. And somewhat paradoxically, the scarcity and the uncertainty created by these constraints could actually end up pulling capital spend forward rather than reducing it.Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share thoughts on the market with a friend or colleague today.

Plus: new Google research says AI is assisting workers, not replacing them. And shares in STMicroelectronics fall on new revenue forecast. Imani Moise hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this Dividend Cafe Thursday episode, Brian Szytel recaps a broad market selloff with stocks and bonds down as the Dow fell nearly 600 points, the S&P 500 dropped 1.5%, and the Nasdaq slid 2.4% while the 10-year yield rose about four basis points to 4.7%. He attributes pressure to escalating Middle East tensions after a Houthi attack in the Red Sea, driving oil sharply higher (WTI up 6% near $92 and Brent up 7% above $100), and to disappointing earnings from bellwether tech names Google and Tesla, with Google showing negative free cash flow amid heavy CapEx. He notes markets are only about 4% off highs, cautions that volatility is normal, questions the usefulness of the Shiller CAPE given decades of “overvaluation,” and highlights very strong weekly jobless claims (187, lowest since 1969), which could raise the odds of a Fed hike. 00:00 Market Wrap Overview 00:52 Oil Shock and Rates Rise 01:27 Earnings Hit Tech Leaders 02:49 Volatility and Drawdown Reality 03:36 Shiller CAPE Debate 04:06 Jobs Data and Fed Outlook 04:54 Sign Off and Disclosures Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

In the second part of our economic roundtable, Michael Gapen, Jens Eisenschmidt and Chetan Ahya join Seth Carpenter to discuss how central banks are balancing sticky inflation, resilient growth and regional policy trade-offs.Read more insights from Morgan Stanley.----- Transcript -----Seth Carpenter: Welcome to Thoughts on the Market. I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. And once again today, I am joined by Morgan Stanley's chief regional economists: Michael Gapen, the Chief U.S. Economist, Jens Eisenschmidt, our Chief Europe Economist, and on the other side of the world, Chetna Ahya, our Chief Asia Economist. Yesterday, we talked about what's supporting growth around the world, especially AI spending in the U.S. and some government spending in Europe, and Asia's role in making all of this happen. Today, we're going to try to dig deeper and go into policy. It's Tuesday, July 21st at 10 am in New York Jens Eisenschmidt: And 4pm in Frankfurt. Chetan Ahya: And 10pm in Hong Kong. Seth Carpenter: Since the last time we did this in mid-April, I will say the debate around central banks has probably become more complicated. Global growth has held up, probably better than many people expected. And inflation, which picked up a lot, started to recede. But it has not gone away. And some of the forces helping to shape the economy, the AI spending, government spending, that possible upswing in manufacturing, that could keep demand strong, and it might keep pushing inflation higher. So, the question today is, if growth remains resilient, how much room really do central banks have to navigate? Mike, let me start with you because your call for the Fed here in the U.S. is out of consensus, or at least at odds with where the market is pricing things. We talked about the demand going from AI. You pointed out that imports are actually limiting how much domestic demand there is. So, what is the underlying story for inflation in the U.S.? And what does it mean for the Fed? Michael Gapen: So, our view is that inflation will come down in the U.S. So, we think disinflation will be driven by some payback in energy prices. Some payback from tariffs, which have pushed up goods prices over the last year. And some further diminishment in housing-related inflation, namely shelter. So, we think on a broad-based perspective, inflation has already peaked and will start moving lower. And we think we've seen evidence of this in recent inflation prints. A risk to that, though, is from the demand side of the economy and AI-related inflation in two parts. One, higher software prices, chipflation. So, the pass-through of some of the AI pricing components. Fortunately, here, they're about less than 1 percent of the consumer basket. So, we don't think that there's a great risk, a strong risk, a high risk of AI-related inflation in the consumer bundle. I think the real risk is that maybe we underestimate broad-based demand, animal spirits. And so, you might just see a broad-based increase in inflation from stronger demand. That'll be a little bit harder to see in real times. But our expectation is that inflation moves lower to about 3 percent, by the end of this year and closer to 2.5 percent next year. Seth Carpenter: All right. Thanks, Mike. And in fact, the most recent inflation report that we just got confirms your perspective that inflation should be coming down. And so, I guess the question then remains: What would it take for the Fed to hike this year if inflation has come down like we've seen? Michael Gapen: Well, I think that the answer there is that inflation wouldn't come down in line with our expectations. So, if the view is that energy prices, tariffs, and shelter inflation should provide plenty of offset and bring inflation down, I think the answer is you don't get payback. Explicitly, core goods prices stay elevated. Maybe we get ongoing disruptions in the Middle East that push energy prices higher and create second-round effects. So, I think inflation just lingering at elevated levels could mean the Fed gets brought in to raise rates in September or later this year. We think if they're patient enough, they'll see enough disinflation to keep them on the sidelines. But the risk is disinflation forecast is too optimistic, inflation stays firm, the Fed needs to raise rates. Seth Carpenter: All right, Jens, what about for you and the ECB? They've already raised interest rates once this year. I think you've got a forecast for them raising interest rates again in September. What could make you wrong about that forecast? What's going to make you convinced that you're right about that forecast? And is there a similar tension that the ECB is wrestling with that Mike talked about for the Fed? Jens Eisenschmidt: Yeah. I mean, starting with the last part of your question, I think no doubt, very similar tension. Just that, of course, it's less obvious. It's essentially a nuanced European version instead of the loud American version that we always stereotypically think the world looks like. So, essentially, we have here clearly not an AI boom. That, I mean, there's no question. And we have discussed that yesterday. Still, there is certainly the notion that the world demand is not really weak, and some of this will also arrive in Europe. And so, you have that tension between maybe there's more resilience than we had thought, and so inflation will not come down through to slack as much. And so, we might actually add something here in terms of monetary restrictiveness. Now, the other thing that is often forgotten, even though it's blatantly obvious, the starting point is just different. The ECB is running neutral monetary policy by all accounts. I mean, you could say 2 percent is neutral, and now they are 2.25. But, you know, there are ranges of uncertainty around any estimate. And the latest that they published runs – goes from 1.75 to 2;2.5. So basically, even if they were to increase rates to 2.5 in September, you could go with the microphone around the governing council, and you would probably find a lot of people saying, "Well, this is still a neutral policy." That's probably not the case for the U.S. So, I guess this matters here for that debate too. Seth Carpenter: All right. Yesterday we talked about lots of different things, but for Europe, we brought up fiscal policy. How do you think about fiscal policy and how it affects monetary policy? And so, I'm thinking about two channels. One, how much does the ECB care that if they keep pushing up interest rates, they're going to increase the debt service burden for countries that are already facing high debt costs? And second, is fiscal policy going to be the extra impetus for inflation that forces even more rate hikes from the ECB? Jens Eisenschmidt: I guess it depends on who you ask. Certainly, more concerned members in the governing council that would point to exactly that fiscal stimulus as a reason why interest rates have to be increased further from here. The other answer I would give is – probably for now at least, the view on fiscal policy is really model-based. You look at what type of increase in interest rate gets you essentially more fiscal restraint because there's an increase in interest rate bill and so less spending somewhere else. And that gets you basically less stimulus or less growth, I mean, very roughly speaking. I don't think it's a major concern for now. We haven't reached yet interest rates where this would start to play a role. I guess, again, Europe being fragmented as it is, with all the political risk that's around the corner. Think about the elections in France and Italy and Spain next year. That will very likely find itself expressed in spreads. And so, the higher the interest rates are, the larger the spreads could become. Seth Carpenter: So, for each of you, there's clearly a role for inflation. One of the risks we'll talk about maybe is inflation expectations and how maybe there's a big shift in what's going on with inflation. But Chetan, that brings me to you and Asia, because one economy where there unquestionably has been a fundamental shift in inflation and inflation expectation over the past several years is Japan. The Bank of Japan is on this normalization path where they're raising interest rates. Interest rates had been negative and then zero, and now they're gradually raising things up. Inflation has come back to Japan. Markets are looking at what the Bank of Japan is likely to do. Can you tell us a little bit about what our view is for the Bank of Japan this year and next? And what might make them hike interest rates faster than we think? And is there any risk that in fact they hike interest rates slower than we think? Chetan Ahya: Yeah, Seth. So, we are expecting BoJ to hike twice from here. The first rate hike is coming up in December of this year, and then another one coming up in June of next year. And then we think that, you know, the underlying inflation trend in Japan is not really that strong. So, while market pricing is for about three more rate hikes instead of two that we are building in our base case. And some of the macro investors are even talking about four more rate hikes. We think the underlying inflation trend warrants a caution and BoJ to go slowly than what the market is pricing in and what the macro investors are saying in. And the key part of our framework on thinking about Japan's inflation is that bulk of the explanation to inflation rise in Japan lies in currency moves. And secondarily, you can look at also the other drivers are more from supply side, which is higher energy prices or food prices. Whereas it's not driven so much by demand. To elaborate further on why it is not driven by demand, when you look at Japan's consumption trend, and if you index it to hundred at pre-COVID levels in September [20]19 then it's currently about 101; i.e., that it's just about 1 percent up over the last seven years. So that's a very tepid trend of consumption demand. And therefore, we don't think that BoJ needs to rush into hike in a more aggressive pace going forward. Seth Carpenter: So, there is this fundamental shift, but boy, it's not on a tear, and so the BoJ can take its time. You know, Chetan, it's hard to wrap up a conversation about the global economy without talking about China. I get the sense that there's not a lot going on with monetary policy, but we did just see a soft Q2 GDP print. So, against that backdrop, what should we be expecting in terms of policy? Is there any monetary policy coming? Or is there going to be some fiscal expansion? Or is China just sort of stuck in this lower gear? Chetan Ahya: Yeah, Seth. So, we were also surprised by the soft GDP print. But when you look into the data, actually, it was interestingly doing well on exports. And I mentioned earlier about how the global CapEx trend is helping Asia. It's definitely helping China too. But at the same time, China's domestic demand turned out to be quite weak. And particularly in the areas where we think that the policy response can be providing some help, i.e., infrastructure spend, was also very weak. And therefore, we are expecting that in the back half of the year, you will see the government taking up some fiscal expansion. Not new stimulus announcement, but whatever they had budgeted. They have enough room within that to utilize that budget and actually increase that fiscal spending towards infrastructure. We have about 2 trillion RMB worth of funds available for the government to go ahead and spend in the second half. And then lift that growth trend, which has dipped to 4.3 percent in second quarter to back to 4.6 percent in the back half of the year. Seth Carpenter: You know what? Maybe that's a great place for us to leave it. We've gone around the world again today, but this time focusing much more on policy. In the U.S., the Fed is facing this interesting situation. We think inflation is coming down. The last CPI print went in our favor. And so as a result, our forecast is that the Fed doesn't change policy at all this year. But it's going to come down to the data, and in particular, whether or not Mike and his team are right in terms of where inflation is going. In Europe, the ECB has already raised interest rates once this year. Jens and team are looking for another interest rate hike. The ECB really does seem more sensitive to inflation coming from the energy shock, but there are lots of other crosscurrents that they're paying attention to as well. And then the other major developed market central bank, the Bank of Japan, is on this normalization path. They are in the process of raising interest rates, but Chetan pointed out to us that the growth rate is such that they don't have to be in any sort of hurry, and they can take their time. So, with that, Mike, Jens, Chetan, thank you so much for helping us connect all of these dots. And to the listeners, thank you for listening. If you enjoy the show, please leave us a review wherever you listen. And share Thoughts on the Market with a friend or a colleague today.

AI investment is reshaping the global outlook. In part one of this economic roundtable, our panel explores where the momentum is strongest — and where investment still needs to catch up.Read more insights from Morgan Stanley.----- Transcript -----Seth Carpenter: Welcome to Thoughts on the Market. I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. Michael Gapen: And I'm Michael Gapen, Chief U.S. Economist. Chetan Ahya: And I'm Chetan Ahya, Chief Asia Economist. Jens Eisenschmidt: And I'm Jens Eisenschmidt, Chief Europe Economist. Seth Carpenter: And today is going to be our third quarter economic roundtable taking a wide-angle view on the global economy and all the key forces shaping our outlook and the economy. Seth Carpenter: It's Monday, July 20th at 10am in New York Jens Eisenschmidt: And 4pm in Frankfurt. Chetan Ahya: And 10pm in Hong Kong. Seth Carpenter: Since our last roundtable in April, the global economy has continued to face all sorts of shocks, a mix of resilience and friction. Inflation pressures have not disappeared. Energy and geopolitical risks have come up, they've receded, they've come back, they've receded all over the place But there is one underlying source of momentum that we have to talk about. And that is the AI-driven CapEx cycle. Michael, let me turn to you because the U.S. is a real focal point of all of this. Tell me a little bit about where Morgan Stanley Research is thinking about hyperscaler CapEx. How big it is? And then for you, when you think about the U.S. economy, just how big of a driver is it for what we're looking for in the U.S.? Michael Gapen: Yeah, we continue to revise higher our estimates for hyperscaler and AI-related CapEx in the U.S. economy. We were thinking a little over a trillion for 2027. Now we're more like 1.2 - 1.3 trillion, maybe as high as 1.4 trillion in 2028. So, the level of hyperscaler spending continues to keep rising. The growth rate and its effect on the economy is likely to slow. But as you noted, it's still a major driver of momentum in the U.S. You would look at that headline number and think, "Wow, that's, you know, 3.5 percent or so of GDP. Must be a massive source of momentum for GDP growth." But roughly about 60 percent of that hyperscaler CapEx spending goes to items like computers and peripherals, equipment spending categories that have a very, very high import content. We still get a significant number that AI CapEx is probably contributing around 40 basis points to growth this year. Be a similar-sized amount perhaps next year.So, for an economy that's growing somewhere a little bit above 2 percent right now, maybe closer to 2.5 percent next year, that's a non-trivial amount. We just have to remember it's fueling growth around the world, just not here in the U.S. Seth Carpenter: Yeah, that's a really great point because I have seen some estimates where people say, "Well, if it wasn't for AI CapEx, the U.S. economy wouldn't have grown at all." And that's clearly wrong, as you point out. But U.S. imports are necessarily exports from somewhere else. And, Chetan, if I can pull you into the story then, U.S. firms are buying a lot of AI-related equipment from Asia. What does that mean in your part of the world? And in particular, I'm thinking about Korea, Taiwan, and maybe some other economies in Asia. What's the critical story there? Chetan Ahya: So, for Asia, this has definitely been a big boon. If you look at Asia's exports, they have been booming, and particularly for the ones which are exporting semiconductors to the U.S. They are seeing semiconductor exports growing by 90 percent. And when we go back in time and compare Asia's semiconductor exports, it's very tightly linked to the U.S. IT CapEx. And it's not surprising when Mike Gapen mentions about the imports going up. It's on the other side, helping Asia's exports quite meaningfully. So, so far, we've seen this benefiting Korea, number one, Taiwan, and also Japan. All these three are big beneficiaries of U.S. AI CapEx. And of course, also not just U.S., but the other countries which are doing any little amount of CapEx on AI front, that's also helping these three economies in the region. Seth Carpenter: You've been doing a lot of work, Chetan, recently about how much the story can actually broaden out, that the AI CapEx cycle has really contributed to Asian growth, but it doesn't tell the whole story that there's a broader industrial cycle. Can you give us a little bit of a flavor of that story? Chetan Ahya: That's right, Seth. So, we are actually highlighting that there is a CapEx and industrial super cycle that is underway in Asia, and there are four components to this story. AI and semiconductors CapEx, which we just briefly discussed. Number two is energy. Number three is defense. And number four is industrial supply chain onshoring related CapEx. I know that everybody still thinks that AI is the most important part of this story, but when I give you the numbers and the breakup of that... So, for Asia, AI and semiconductor companies CapEx is about $380 billion in 2026, but energy CapEx is going to be $900 billion. So, this is a far broader story than just AI for Asia. Seth Carpenter: Mike, let me come back to you and to the U.S. then. So, isn't the growth story also broader than that as well domestically? So, what's going on in terms of consumer spending in the U.S., and is there a broader CapEx story in the U.S. as well? Michael Gapen: I would say, is it broader than that? I think maybe you could argue also it's narrower than that. Here's what I mean by that. As I noted AI CapEx contributing about 40 basis points to growth, it's certainly underpinning equity valuations in the U.S. and underpinning strong wealth creation. So about [$]180 trillion in household net worth in the U.S. About [$]55 trillion of that has been created in just the last five years alone, underpinned in part by AI-related spending and optimism about future profitability. That's really supported spending by upper income households. So, I think it's both investment-led and consumer-led, but they're inextricably linked. So, the positive for the U.S. is that it's providing a lot of resilience. The negative component of that is it feels like momentum in the U.S. is narrowly driven. Jens Eisenschmidt: Let me maybe jump in here from Europe to provide some perspective from the other side. So, I think it's a fair summary to say that AI investment is not yet, or maybe will never get there, dominating the business cycle. What we do have instead is an unusually consumption-driven expansion. That has to do not so much with an extraordinary strength of consumption, but more of an absence of other factors. Now, prospectively looking forward, we think the fiscal expansion might help lifting us a little bit. And then it is really the debate how much AI investment can arrive in Europe. For now, I would say it's probably a factor of 20 that separates European investment plans from the plans we know that exist for the U.S. Seth Carpenter: Let me stick with you then in Europe because you brought up fiscal as one of the factors going on here and where it's going… You and your team recently wrote a blue paper talking about what the outlook is for fiscal policy in Europe, and in particular, we had this era of cheap debt. Interest rates in Europe were low, at times negative. It was super easy to borrow. Not as much happened then. There's been a shift towards more fiscal expansion at the same time that interest rates have gone up, causing the cost of debt to go up. Feels like there's a lot of push and pull going on. Can you unpack for us a little bit what was in that paper you wrote, what's going on with fiscal policy in Europe, especially in Germany? And what it might mean over time for Euro-area countries? Jens Eisenschmidt: Yeah, so I think fiscal policy in Europe really is looking at a regime shift. So, there is this very famous, probably in the U.S. even more so than here, notion that the Europeans have built a very comfortable welfare state. And that's true if you just look at the accounting from a GDP perspective. It's close to 50 percent that, you know, budgets are actually extended on welfare spending. And now you have three structural headwinds for any type of fiscal spend. So, one is aging related costs, you mentioned it already. Defense spending has to increase significantly, and the interest rate costs will also rise significantly. All of that means there will be very hard choices to be made. The one thing that actually could help here is growth. Growth is the one thing that's, for now at least, missing, at least in comparison to the U.S. It's probably half what we expect, what the U.S. colleagues think is in stake for the U.S., and a quarter or even less than that of what is there in Asia. So, growth is really the key, the solution, the answer to everything in Europe. More growth than just 1 percent, which is potential, would help solving that fiscal challenge. For now, it looks really, really like an uphill battle. Returning to Germany, it's the one country that has a very good fiscal starting position. They are pushing a lot but they're to some extent pushing a string. So, even with the German huge fiscal package, given that private sector investments so far are absent, doesn't get us a ton of growth. Seth Carpenter: Chetan, maybe I'll come back to you before we close part one of this roundtable. The AI CapEx cycle started with AI, broadened out further. How long do you expect this cycle to last? How durable can it be? And how might it compare to previous CapEx cycles? Chetan Ahya: Yeah, Seth. So, we think this will be a multi-year CapEx cycle. And when we are thinking about the duration of the cycle, there are two things that I would keep in mind. Number one is that most of the drivers that we just discussed – the CapEx on AI, energy, defense, and industrial supply chain onshoring related investments – these are all structural drivers. So, we think these are going to continue for some more time. At this point of time, we have the visibility for this cycle to be lasting for three-four more years. And then the second point of framework that I would keep in mind is that the corporate balance sheets are in a pretty good shape. So, when you are thinking about the leverage in the private sector, you can look at both households and the corporate sector balance sheet. But since the cycle is CapEx driven, we are looking at the corporate balance sheets, and they are in a pretty good shape. Across the region, corporate debt to GDP is below where it was in 2019. Seth Carpenter: Mike, let me, let me wrap up quickly with you. We talked about AI, AI CapEx. For now, that's a very strong demand story. When are we going to see a supply side of things coming from AI? Are you already seeing a big contribution to GDP and growth from productivity coming from AI? Michael Gapen: We are, but not outside of the high-tech sectors, and we're seeing limited, what I'll call labor market restructuring of tasks and occupations beyond high AI-exposed occupations. So right now, everything is still very isolated I think maybe as we get into 2029 and beyond, so as Chetan says, we probably have a three to four-year super cycle here around a build-out phase. Then we might see some of that broader-based diffusion to other non-tech sectors in the economy. Seth Carpenter: All right, Jens, for you, let's wrap up here. So, what is the state of play for the build-out in the CapEx cycle for AI in Europe? Jens Eisenschmidt: Yeah, it's very early stages. As I said before, we really; we connected to all the industry experts or analysts covering the sector and the total plans are a factor of 20 below what we see in the U.S. by just the seven hyperscalers. So, I would say very fragmented, very small, in general. Not only AI. I think the one thing I would be looking at for any type of sign of revival, sign of growth is investment. The second would be investment. And you can guess what the third would be… Investments in the core countries. That's really what we need to see, and we haven't seen much in Germany or France on this front. Seth Carpenter:That's a great place for us to stop today. We talked about the real side of the economy, AI, CapEx, trade. Tomorrow we're going to come back, and we'll talk about how that growth outlook affects inflation. And once you start talking about growth and inflation, you got to talk about policy, and that's where we'll be tomorrow. Mike, Jens, and Chetan, thank you for joining today. And for the listeners, thank you for listening. Be sure to tune in tomorrow for Part 2 of our conversation. And I have to say, if you enjoy this show, please leave us a review wherever you listen, and share Thoughts on the Market with a friend or a colleague today.

Leo Young is the founder and managing partner of Cornell Communities, a private equity real estate firm revitalizing manufactured housing communities across eight states. He studied finance in college, then moved into sales at Tesla to build the communication skills he knew he was missing, working his way up to top regional salesperson before leaving to pursue real estate full time. After earning his real estate license, working in brokerage, and investing passively in apartments, Leo launched his own firm. Cornell Communities acquires and operates middle market mobile home parks, expanding access to affordable housing while delivering risk managed returns to accredited investors.     Make sure to download our free guide, 7 Questions Every Passive Investor Should Ask, here.     Key Takeaways Stack skills deliberately, since finance, sales, and operations compound over a career Vet the operator harder than the pro forma, because execution drives returns Buy in the middle market where institutions with cheaper capital are not competing Underwrite infrastructure first, since older parks carry hidden CapEx risk Create value through expense discipline and rent normalization, not unit renovations     Topics From Finance to Tesla Sales Leo studied finance but could not hold a presentation or speak in front of a room He joined Tesla to fix that weakness and became the top regional salesperson Why He Left a Dream Job for Real Estate Sales income required constant output and did not build lasting wealth A first passive apartment investment and distribution check convinced him to go all in Manufactured Homes vs. Mobile Homes Manufactured housing is the legal term tied to federal HUD construction standards Roughly 20 million Americans live in these communities, across a wide quality range Buying in the Middle Market Institutions and REITs with cheaper capital absorb the top quality assets Leo targets workable properties where his team can execute a clear value add What He Underwrites First Infrastructure leads: water, sewer lines, and roads on parks 50 to 70 years old Purchase price, location, and regulations follow, then his own team bandwidth How He Vets Sponsors as a Limited Partner Most decks oversell the property and undersell the team He asks for case studies and how the sponsor responds when a deal goes wrong Why the Economics Work Residents own their homes, which lowers the operating expense ratio and lifts NOI Heavy land improvement creates more depreciable value in a cost segregation study Lot rents sit at the low end of the housing market, so demand stays strong The Two Main Value Levers Expenses: rebuild vendor contracts and move home and utility costs to residents Rent: normalize lot rents toward market while keeping the value proposition intact Site improvements like roads, fencing, signage, and lighting support resident relations Why Homes Rarely Move Relocating a home can cost $7,000 to $10,000 and risks damage in transit Most residents sell in place and cash in the equity they built Community and Retention Turnover runs near 5%, compared with roughly 50% in apartments Private yards and driveways make the setting closer to a subdivision than a building