Podcasts about 3X

Welcome back to Tom Ferry's Outliers Series—where we unpack the mindset and disciplines of agents doing extraordinary things.   This week, Tom sits down with Joy Lynn, a Salt Lake City agent who started last year with a $250,000 GCI goal… then blew past it so fast she had to keep raising the ceiling. She finished the year at $750,000.   But it wasn't a new tool, a viral strategy, or a lucky market that got her there.   It was a simple rule that's far more powerful than anything else.    In this episode, you'll discover: The gap that's probably costing you hundreds of thousands of dollars A 90-day rule for evaluating new habits, systems, or prospecting methods The non-negotiable morning routine she credits for her entire transformation Why she stopped cold calling – and what she did instead   If you're working hard but keep chasing the next shiny object… this episode will stop you in your tracks.   And as you'll learn from the episode, Joy credits her success to working with a Tom Ferry Coach.    Want proof that you can 3X the highest goal you've ever set in your life?    Schedule a free call with a Tom Ferry consultant to learn more about coaching and if it's right for you:

Success with money isn't just “more numbers on a screen”. It changes how you feel in your body, your relationships, and your life. In this episode, I'm taking you behind the scenes of what actually happens when your portfolio is optimized, and why most women can't imagine these results until they experience them. You'll hear the real, unexpected ripple effects my clients report after we simplify their investments, clean up the mess, and build a portfolio that performs without constant stress. Tune in to learn: The 7 surprising results of a well-managed portfolio Why you have no idea what it feels like if you've never experienced a well-managed, optimized portfolio What's actually on the other side of having your money “dialed” The real behind-the-scenes questions women ask inside my private containers — and the instant clarity they get Why an optimized portfolio is actually quite the opposite of confusing, overwhelming, and stressful.

Coming up on today's Movie Show, Andy and Rachel review  - Scream 7 - When a new Ghostface killer emerges in the town where Sidney Prescott has built a new life, her darkest fears are realized as her daughter becomes the next target. They will also review This is Not a Test, The President's Cake, K-POPS!, and More Than We Ever Imagine.  Andy & Rachel continue their segment Talk'n Oscars with the category for Best  Andy and Rachel will mention the Peacock movie, Threshold -  Documentary about Jessie Diggins, a 3X gold medalist cross country skier who suffered from an eating disorder at the peak of her career. They will also review The Bluff(Prime Video), In the Blink of an Eye(Hulu), and Man on the Run on Prime Video.  In addition, they will look at streaming series like Paradise S2(Hulu), Scrubs(Hulu), Bridgerton S4 Pt.2(Netflix), and Monarch: Legacy of Monsters S2 on Apple TV+.  Here are some honorable mentions:   

Most women assume advisors outperform because they're “experts.” The data inside real portfolios tells a very different story. In this episode, I take you behind the scenes of reviewing real client portfolios - many advisor-managed, ranging from $30K to over $3M, and show you exactly what's working, what's quietly draining wealth, and why it's not only possible, but easy, for women to outperform advisors without a finance degree or prior investing experience.Tune in to learn: Why it's possible to outperform an advisor even if you've never invested on your own The real returns inside advisor-managed portfolios  and how they compare to the market The one critical thing advisor-managed portfolios are missing Why advisor portfolios are designed for complexity, not returns How fees and underperformance compound into millions lost over time

Send a textThe bums return to the rail yard with S6:E0147 — as the top rolls eyes with a post Super Bowl bow tying — and the NFL sleeps until the next campaign starts (and we don't want to hear SHIT about the NFL draft until April Fool's Day at the very earliest); Paddy and heiress visit Welsh-Ryan Arena in Evanston (advice includes wearing your skinny jeans); Sconnie hoops are legit; a Lindsey Vonn update makes “break a leg” a literal reality; and the only other thing to discuss is the Winter Olympics — headlined by curling— and Jake “Everyone Hates You” Paul's fiancé — a Dutch speed skater with assets.  The second half bounces in another infamous Beer review that features River Saint Joe's self-titled “Pilsner” (ABV 4%) — a super light, musty nosed pils that resembles Ultra but tastes better (crowler effect notwithstanding); the Kalshi betting platform will let you bet on Spaulding's booger eating or anything else that floats your boat; a pre-trip Travelogue featuring Eddie's eldest, who's heading to an American city larger than NYC, and 3X larger than metro-Chicago by population; Paddy shares a book review “The Boys in the Light” (by Nina Willner) — a WWII era attention grabber; fan talk from Rude Dude — and Lefty (desperately seeking answers).   Get some, because really, it's all there is until March Madness and MLB baseball get rolling.  No excuses. Recorded on February 12th, 2026 at B.O.M.'s global headquarters ‘East Bunker' in Chicago, IL USA. 

Devora is one of the most influential voices in consumer insights today, shaping how brands — from Netflix to Pepsico, TikTok, and Waymo — understand and influence shopper behavior.As Chief Strategy Officer at Alter Agents, Devora designs research studies to solve the toughest brand challenges — leading 3X brand growth — and is part of an exciting revolution in research called agile neuroscience testing that uses biometrics and AI to reveal subconscious consumer reactions in real time. Shopper insights and strategy have been Devora's passion for 15 years, during which time she has worked with top brands like Snapchat, Activision, Nespresso, Bose, and Schwab. She's also the brains behind the methodology used by Google for their groundbreaking ZMOT research. Whether it's decoding consumer choice, the rise of "shopper promiscuity," or how brands can future-proof their strategies — Devora goes beyond surface-level data to tap into how people buy, why they switch brands, and what companies must do to stay ahead. She has co-authored retail and shopping insights books like Fire in the Zoo and Influencing Shopper Decisions, and her TEDx on the Future of Shopping and Retail has nearly 300K views.Connect with Devora here:https://www.linkedin.com/in/devorarogers/https://www.facebook.com/AlterAgents/mentions/?_rdrhttps://www.instagram.com/alter_agents/?hl=enhttps://alteragents.com/Download our FREE Optimize Your LinkedIn Profile Guide here:https://www.thetimetogrow.com/ecsoptimizeyourprofile

Most entrepreneurs think their funnel is broken when it doesn't convert. But after building funnels for over two decades, I can tell you the truth… ninety-nine percent of the time, the funnel isn't the problem. The problem is we haven't done the work of testing. The difference between a funnel that struggles and one that scales to millions usually comes down to a few small changes - tested intentionally and consistently. In this episode of The Russell Brunson Show, I pull a classic out of my vault: Tested Advertising Methods by John Caples. This is one of the foundational books that shaped modern advertising and completely transformed how I look at marketing. I share the story of my very first split test - changing nothing but the color of a headline - and how that simple tweak increased conversions by nearly 27%, instantly giving me a “raise” without more traffic, more ad spend, or more work. If you've ever wanted to know how to systematically grow your business instead of gambling on guesses, this episode is your blueprint. Key Highlights: ◼️Why 80% of your ad's success depends on the headline - and how to structure it so it pulls people into the first paragraph ◼️The exact split-testing framework I use inside ClickFunnels to “give myself a raise” every single day ◼️The biggest mistake entrepreneurs make when testing (and why testing more than one variable at a time kills your data) ◼️Why specifics dramatically out-convert generalities - and how one precise headline 3X'd our results ◼️How old-school advertisers risked tens of thousands of dollars per test - and what their discipline can teach us today Testing isn't just a tactic… it's a philosophy. It's the difference between hoping your funnel works and knowing it works. When you understand how to test headlines, offers, and pages the right way, you stop guessing and start engineering growth. John Caples mastered this decades ago, and when you adopt that same mindset, you'll realize you're just one split test away from your next breakthrough. ◼️⁠⁠⁠https://russellbrunson.com/notes⁠⁠ ◼️If you've got a product, offer, service… or idea… I'll show you how to sell it (the RIGHT way) Register for my next event →⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://sellingonline.com/podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ◼️Still don't have a funnel? ClickFunnels gives you the exact tools (and templates) to launch TODAY → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://clickfunnels.com/podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

Infrastructure was passé…uncool. Difficult to get dollars from Private Equity and Growth funds, and almost impossible to get a VC fund interested. Now?! Now, it's cool. Infrastructure seems to be having a Renaissance, a full on Rebirth, not just fueled by commercial interests (e.g. advent of AI), but also by industrial policy and geopolitical considerations. In this episode of Tech Deciphered, we explore what's cool in the infrastructure spaces, including mega trends in semiconductors, energy, networking & connectivity, manufacturing Navigation: Intro We're back to building things Why now: the 5 forces behind the renaissance Semiconductors: compute is the new oil Networking & connectivity: digital highways get rebuilt Energy: rebuilding the power stack (not just renewables) Manufacturing: the return of “atoms + bits” Wrap: what it means for startups, incumbents, and investors 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 73 of Tech Deciphered, Infrastructure, the Rebirth or Renaissance. Infrastructure was passé, it wasn’t cool, but all of a sudden now everyone’s talking about network, talking about compute and semiconductors, talking about logistics, talking about energy. What gives? What’s happened? It was impossible in the past to get any funds, venture capital, even, to be honest, some private equity funds or growth funds interested in some of these areas, but now all of a sudden everyone thinks it’s cool. The infrastructure seems to be having a renaissance, a full-on rebirth. In this episode, we will explore in which cool ways the infrastructure spaces are moving and what’s leading to it. We will deep dive into the forces that are leading us to this. We will deep dive into semiconductors, networking and connectivity, energy, manufacturing, and then we’ll wrap up. Bertrand, so infrastructure is cool now. Bertrand Schmitt We're back to building things Yes. I thought software was going to eat the world. I cannot believe it was then, maybe even 15 years ago, from Andreessen, that quote about software eating the world. I guess it’s an eternal balance. Sometimes you go ahead of yourself, you build a lot of software stack, and at some point, you need the hardware to run this software stack, and there is only so much the bits can do in a world of atoms. Nuno Gonçalves Pedro Obviously, we’ve gone through some of this before. I think what we’re going through right now is AI is eating the world, and because AI is eating the world, it’s driving a lot of this infrastructure building that we need. We don’t have enough energy to be consumed by all these big data centers and hyperscalers. We need to be innovative around network as well because of the consumption in terms of network bandwidth that is linked to that consumption as well. In some ways, it’s not software eating the world, AI is eating the world. Because AI is eating the world, we need to rethink everything around infrastructure and infrastructure becoming cool again. Bertrand Schmitt There is something deeper in this. It’s that the past 10, even 15 years were all about SaaS before AI. SaaS, interestingly enough, was very energy-efficient. When I say SaaS, I mean cloud computing at large. What I mean by energy-efficient is that actually cloud computing help make energy use more efficient because instead of companies having their own separate data centers in many locations, sometimes poorly run from an industrial perspective, replace their own privately run data center with data center run by the super scalers, the hyperscalers of the world. These data centers were run much better in terms of how you manage the coolings, the energy efficiency, the rack density, all of this stuff. Actually, the cloud revolution didn’t increase the use of electricity. The cloud revolution was actually a replacement from your private data center to the hyperscaler data center, which was energy efficient. That’s why we didn’t, even if we are always talking about that growth of cloud computing, we were never feeling the pinch in term of electricity. As you say, we say it all changed because with AI, it was not a simple “Replacement” of locally run infrastructure to a hyperscaler run infrastructure. It was truly adding on top of an existing infrastructure, a new computing infrastructure in a way out of nowhere. Not just any computing infrastructure, an energy infrastructure that was really, really voracious in term of energy use. Nuno Gonçalves Pedro There was one other effect. Obviously, we’ve discussed before, we are in a bubble. We won’t go too much into that today. But the previous big bubble in tech, which is in the late ’90s, there was a lot of infrastructure built. We thought the internet was going to take over back then. It didn’t take over immediately, but there was a lot of network connectivity, bandwidth built back in the day. Companies imploded because of that as well, or had to restructure and go in their chapter 11. A lot of the big telco companies had their own issues back then, etc., but a lot of infrastructure was built back then for this advent of the internet, which would then take a long time to come. In some ways, to your point, there was a lot of latent supply that was built that was around that for a while wasn’t used, but then it was. Now it’s been used, and now we need new stuff. That’s why I feel now we’re having the new moment of infrastructure, new moment of moving forward, aligned a little bit with what you just said around cloud computing and the advent of SaaS, but also around the fact that we had a lot of buildup back in the late ’90s, early ’90s, which we’re now still reaping the benefits on in today’s world. Bertrand Schmitt Yeah, that’s actually a great point because what was built in the late ’90s, there was a lot of fibre that was built. Laying out the fibre either across countries, inside countries. This fibre, interestingly enough, you could just change the computing on both sides of the fibre, the routing, the modems, and upgrade the capacity of the fibre. But the fibre was the same in between. The big investment, CapEx investment, was really lying down that fibre, but then you could really upgrade easily. Even if both ends of the fibre were either using very old infrastructure from the ’90s or were actually dark and not being put to use, step by step, it was being put to use, equipment was replaced, and step by step, you could keep using more and more of this fibre. It was a very interesting development, as you say, because it could be expanded over the years, where if we talk about GPUs, use for AI, GPUs, the interesting part is actually it’s totally the opposite. After a few years, it’s useless. Some like Google, will argue that they can depreciate over 5, 6 years, even some GPUs. But at the end of the day, the difference in perf and energy efficiency of the GPUs means that if you are energy constrained, you just want to replace the old one even as young as three-year-old. You have to look at Nvidia increasing spec, generation after generation. It’s pretty insane. It’s usually at least 3X year over year in term of performance. Nuno Gonçalves Pedro At this moment in time, it’s very clear that it’s happening. Why now: the 5 forces behind the renaissance Maybe let’s deep dive into why it’s happening now. What are the key forces around this? We’ve identified, I think, five forces that are particularly vital that lead to the world we’re in right now. One we’ve already talked about, which is AI, the demand shock and everything that’s happened because of AI. Data centers drive power demand, drive grid upgrades, drive innovative ways of getting energy, drive chips, drive networking, drive cooling, drive manufacturing, drive all the things that we’re going to talk in just a bit. One second element that we could probably highlight in terms of the forces that are behind this is obviously where we are in terms of cost curves around technology. Obviously, a lot of things are becoming much cheaper. The simulation of physical behaviours has become a lot more cheap, which in itself, this becomes almost a vicious cycle in of itself, then drives the adoption of more and more AI and stuff. But anyway, the simulation is becoming more and more accessible, so you can do a lot of simulation with digital twins and other things off the real world before you go into the real world. Robotics itself is becoming, obviously, cheaper. Hardware, a lot of the hardware is becoming cheaper. Computer has become cheaper as well. Obviously, there’s a lot of cost curves that have aligned that, and that’s maybe the second force that I would highlight. Obviously, funds are catching up. We’ll leave that a little bit to the end. We’ll do a wrap-up and talk a little bit about the implications to investors. But there’s a lot of capital out there, some capital related to industrial policy, other capital related to private initiative, private equity, growth funds, even venture capital, to be honest, and a few other elements on that. That would be a third force that I would highlight. Bertrand Schmitt Yes. Interestingly enough, in terms of capital use, and we’ll talk more about this, but some firms, if we are talking about energy investment, it was very difficult to invest if you are not investing in green energy. Now I think more and more firms and banks are willing to invest or support different type of energy infrastructure, not just, “Green energy.” That’s an interesting development because at some point it became near impossible to invest more in gas development, in oil development in the US or in most Western countries. At least in the US, this is dramatically changing the framework. Nuno Gonçalves Pedro Maybe to add the two last forces that I think we see behind the renaissance of what’s happening in infrastructure. They go hand in hand. One is the geopolitics of the world right now. Obviously, the world was global flat, and now it’s becoming increasingly siloed, so people are playing it to their own interests. There’s a lot of replication of infrastructure as well because people want to be autonomous, and they want to drive their own ability to serve end consumers, businesses, etc., in terms of data centers and everything else. That ability has led to things like, for example, chips shortage. The fact that there are semiconductors, there are shortages across the board, like memory shortages, where everything is packed up until 2027 of 2028. A lot of the memory that was being produced is already spoken for, which is shocking. There’s obviously generation of supply chain fragilities, obviously, some of it because of policies, for example, in the US with tariffs, etc, security of energy, etc. Then the last force directly linked to the geopolitics is the opposite of it, which is the policy as an accelerant, so to speak, as something that is accelerating development, where because of those silos, individual countries, as part their industrial policy, then want to put capital behind their local ecosystems, their local companies, so that their local companies and their local systems are for sure the winners, or at least, at the very least, serve their own local markets. I think that’s true of a lot of the things we’re seeing, for example, in the US with the Chips Act, for semiconductors, with IGA, IRA, and other elements of what we’ve seen in terms of practices, policies that have been implemented even in Europe, China, and other parts of the world. Bertrand Schmitt Talking about chips shortages, it’s pretty insane what has been happening with memory. Just the past few weeks, I have seen a close to 3X increase in price in memory prices in a matter of weeks. Apparently, it started with a huge order from OpenAI. Apparently, they have tried to corner the memory market. Interestingly enough, it has flat-footed the entire industry, and that includes Google, that includes Microsoft. There are rumours of their teams now having moved to South Korea, so they are closer to the action in terms of memory factories and memory decision-making. There are rumours of execs who got fired because they didn’t prepare for this type of eventuality or didn’t lock in some of the supply chain because that memory was initially for AI, but obviously, it impacts everything because factories making memories, you have to plan years in advance to build memories. You cannot open new lines of manufacturing like this. All factories that are going to open, we know when they are going to open because they’ve been built up for years. There is no extra capacity suddenly. At the very best, you can change a bit your line of production from one type of memory to another type. But that’s probably about it. Nuno Gonçalves Pedro Just to be clear, all these transformations we’re seeing isn’t to say just hardware is back, right? It’s not just hardware. There’s physicality. The buildings are coming back, right? It’s full stack. Software is here. That’s why everything is happening. Policy is here. Finance is here. It’s a little bit like the name of the movie, right? Everything everywhere all at once. Everything’s happening. It was in some ways driven by the upper stacks, by the app layers, by the platform layers. But now we need new infrastructure. We need more infrastructure. We need it very, very quickly. We need it today. We’re already lacking in it. Semiconductors: compute is the new oil Maybe that’s a good segue into the first piece of the whole infrastructure thing that’s driving now the most valuable company in the world, NVIDIA, which is semiconductors. Semiconductors are driving compute. Semis are the foundation of infrastructure as a compute. Everyone needs it for every thing, for every activity, not just for compute, but even for sensors, for actuators, everything else. That’s the beginning of it all. Semiconductor is one of the key pieces around the infrastructure stack that’s being built at scale at this moment in time. Bertrand Schmitt Yes. What’s interesting is that if we look at the market gap of Semis versus software as a service, cloud companies, there has been a widening gap the past year. I forgot the exact numbers, but we were talking about plus 20, 25% for Semis in term of market gap and minus 5, minus 10 for SaaS companies. That’s another trend that’s happening. Why is this happening? One, because semiconductors are core to the AI build-up, you cannot go around without them. But two, it’s also raising a lot of questions about the durability of the SaaS, a software-as-a-service business model. Because if suddenly we have better AI, and that’s all everyone is talking about to justify the investment in AI, that it keeps getting better, and it keeps improving, and it’s going to replace your engineers, your software engineers. Then maybe all of this moat that software companies built up over the years or decades, sometimes, might unravel under the pressure of newly coded, newly built, cheaper alternatives built from the ground up with AI support. It’s not just that, yes, semiconductors are doing great. It’s also as a result of that AI underlying trend that software is doing worse right now. Nuno Gonçalves Pedro At the end of the day, this foundational piece of infrastructure, semiconductor, is obviously getting manifest to many things, fabrication, manufacturing, packaging, materials, equipment. Everything’s being driven, ASML, etc. There are all these different players around the world that are having skyrocket valuations now, it’s because they’re all part of the value chain. Just to be very, very clear, there’s two elements of this that I think are very important for us to remember at this point in time. One, it’s the entire value chains are being shifted. It’s not just the chips that basically lead to computing in the strict sense of it. It’s like chips, for example, that drive, for example, network switching. We’re going to talk about networking a bit, but you need chips to drive better network switching. That’s getting revolutionised as well. For example, we have an investment in that space, a company called the eridu.ai, and they’re revolutionising one of the pieces around that stack. Second part of the puzzle, so obviously, besides the holistic view of the world that’s changing in terms of value change, the second piece of the puzzle is, as we discussed before, there’s industrial policy. We already mentioned the CHIPS Act, which is something, for example, that has been done in the US, which I think is 52 billion in incentives across a variety of things, grants, loans, and other mechanisms to incentivise players to scale capacity quick and to scale capacity locally in the US. One of the effects of that now is obviously we had the TSMC, US expansion with a factory here in the US. We have other levels of expansion going on with Intel, Samsung, and others that are happening as we speak. Again, it’s this two by two. It’s market forces that drive the need for fundamental shifts in the value chain. On the other industrial policy and actual money put forward by states, by governments, by entities that want to revolutionise their own local markets. Bertrand Schmitt Yes. When you talk about networking, it makes me think about what NVIDIA did more than six years ago when they acquired Mellanox. At the time, it was largest acquisition for NVIDIA in 2019, and it was networking for the data center. Not networking across data center, but inside the data center, and basically making sure that your GPUs, the different computers, can talk as fast as possible between each of them. I think that’s one piece of the puzzle that a lot of companies are missing, by the way, about NVIDIA is that they are truly providing full systems. They are not just providing a GPU. Some of their competitors are just providing GPUs. But NVIDIA can provide you the full rack. Now, they move to liquid-cool computing as well. They design their systems with liquid cooling in mind. They have a very different approach in the industry. It’s a systematic system-level approach to how do you optimize your data center. Quite frankly, that’s a bit hard to beat. Nuno Gonçalves Pedro For those listening, you’d be like, this is all very different. Semiconductors, networking, energy, manufacturing, this is all different. Then all of a sudden, as Bertrand is saying, well, there are some players that are acting across the stack. Then you see in the same sentence, you’re talking about nuclear power in Microsoft or nuclear power in Google, and you’re like, what happened? Why are these guys in the same sentence? It’s like they’re tech companies. Why are they talking about energy? It’s the nature of that. These ecosystems need to go hand in hand. The value chains are very deep. For you to actually reap the benefits of more and more, for example, semiconductor availability, you have to have better and better networking connectivity, and you have to have more and more energy at lower and lower costs, and all of that. All these things are intrinsically linked. That’s why you see all these big tech companies working across stack, NVIDIA being a great example of that in trying to create truly a systems approach to the world, as Bertrand was mentioning. Networking & connectivity: digital highways get rebuilt On the networking and connectivity side, as we said, we had a lot of fibre that was put down, etc, but there’s still more build-out needs to be done. 5G in terms of its densification is still happening. We’re now starting to talk, obviously, about 6G. I’m not sure most telcos are very happy about that because they just have been doing all this CapEx and all this deployment into 5G, and now people already started talking about 6G and what’s next. Obviously, data center interconnect is quite important, and all the hubbing that needs to happen around data centers is very, very important. We are seeing a lot movements around connectivity that are particularly important. Network gear and the emergence of players like Broadcom in terms of the semiconductor side of the fence, obviously, Cisco, Juniper, Arista, and others that are very much present in this space. As I said, we made an investment on the semiconductor side of networking as well, realizing that there’s still a lot of bottlenecks happening there. But obviously, the networking and connectivity stack still needs to be built at all levels within the data centers, outside of the data centers in terms of last mile, across the board in terms of fibre. We’re seeing a lot of movements still around the space. It’s what connects everything. At the end of the day, if there’s too much latency in these systems, if the bandwidths are not high enough, then we’re going to have huge bottlenecks that are going to be put at the table by a networking providers. Obviously, that doesn’t help anyone. If there’s a button like anywhere, it doesn’t work. All of this doesn’t work. Bertrand Schmitt Yes. Interestingly enough, I know we said for this episode, we not talk too much about space, but when you talk about 6G, it make me think about, of course, Starlink. That’s really your last mile delivery that’s being built as well. It’s a massive investment. We’re talking about thousands of satellites that are interconnected between each other through laser system. This is changing dramatically how companies can operate, how individuals can operate. For companies, you can have great connectivity from anywhere in the world. For military, it’s the same. For individuals, suddenly, you won’t have dead space, wide zones. This is also a part of changing how we could do things. It’s quite important even in the development of AI because, yes, you can have AI at the edge, but that interconnect to the rest of the system is quite critical. Having that availability of a network link, high-quality network link from anywhere is a great combo. Nuno Gonçalves Pedro Then you start seeing regions of the world that want to differentiate to attract digital nomads by saying, “We have submarine cables that come and hub through us, and therefore, our connectivity is amazing.” I was just in Madeira, and they were talking about that in Portugal. One of the islands of Portugal. We have some Marine cables. You have great connectivity. We’re getting into that discussion where people are like, I don’t care. I mean, I don’t know. I assume I have decent connectivity. People actually care about decent connectivity. This discussion is not just happening at corporate level, at enterprise level? Etc. Even consumers, even people that want to work remotely or be based somewhere else in the world. It’s like, This is important Where is there a great connectivity for me so that I can have access to the services I need? Etc. Everyone becomes aware of everything. We had a cloud flare mishap more recently that the CEO had to jump online and explain deeply, technically and deeply, what happened. Because we’re in their heads. If Cloudflare goes down, there’s a lot of websites that don’t work. All of this, I think, is now becoming du jour rather than just an afterthought. Maybe we’ll think about that in the future. Bertrand Schmitt Totally. I think your life is being changed for network connectivity, so life of individuals, companies. I mean, everything. Look at airlines and ships and cruise ships. Now is the advent of satellite connectivity. It’s dramatically changing our experience. Nuno Gonçalves Pedro Indeed. Energy: rebuilding the power stack (not just renewables) Moving maybe to energy. We’ve talked about energy quite a bit in the past. Maybe we start with the one that we didn’t talk as much, although we did mention it, which was, let’s call it the fossil infrastructure, what’s happening around there. Everyone was saying, it’s all going to be renewables and green. We’ve had a shift of power, geopolitics. Honestly, I the writing was on the wall that we needed a lot more energy creation. It wasn’t either or. We needed other sources to be as efficient as possible. Obviously, we see a lot of work happening around there that many would have thought, Well, all this infrastructure doesn’t matter anymore. Now we’re seeing LNG terminals, pipelines, petrochemical capacity being pushed up, a lot of stuff happening around markets in terms of export, and not only around export, but also around overall distribution and increases and improvements so that there’s less leakage, distribution of energy, etc. In some ways, people say, it’s controversial, but it’s like we don’t have enough energy to spare. We’re already behind, so we need as much as we can. We need to figure out the way to really extract as much as we can from even natural resources, which In many people’s mind, it’s almost like blasphemous to talk about, but it is where we are. Obviously, there’s a lot of renaissance also happening on the fossil infrastructure basis, so to speak. Bertrand Schmitt Personally, I’m ecstatic that there is a renaissance going regarding what is called fossil infrastructure. Oil and gas, it’s critical to humanity well-being. You never had growth of countries without energy growth and nothing else can come close. Nuclear could come close, but it takes decades to deploy. I think it’s great. It’s great for developed economies so that they do better, they can expand faster. It’s great for third-world countries who have no realistic other choice. I really don’t know what happened the past 10, 15 years and why this was suddenly blasphemous. But I’m glad that, strangely, thanks to AI, we are back to a more rational mindset about energy and making sure we get efficient energy where we can. Obviously, nuclear is getting a second act. Nuno Gonçalves Pedro I know you would be. We’ve been talking about for a long time, and you’ve been talking about it in particular for a very long time. Bertrand Schmitt Yes, definitely. It’s been one area of interest of mine for 25 years. I don’t know. I’ve been shocked about what happened in Europe, that willingness destruction of energy infrastructure, especially in Germany. Just a few months ago, they keep destroying on live TV some nuclear station in perfect working condition and replacing them with coal. I’m not sure there is a better definition of insanity at this stage. It looks like it’s only the Germans going that hardcore for some reason, but at least the French have stopped their program of decommissioning. America, it seems to be doing the same, so it’s great. On top of it, there are new generations that could be put to use. The Chinese are building up a very large nuclear reactor program, more than 100 reactors in construction for the next 10 years. I think everybody has to catch up because at some point, this is the most efficient energy solution. Especially if you don’t build crazy constraints around the construction of these nuclear reactors. If we are rational about permits, about energy, about safety, there are great things we could be doing with nuclear. That might be one of the only solution if we want to be competitive, because when energy prices go down like crazy, like in China, they will do once they have reach delivery of their significant build-up of nuclear reactors, we better be ready to have similar options from a cost perspective. Nuno Gonçalves Pedro From the outside, at the very least, nuclear seems to be probably in the energy one of the areas that’s more being innovated at this moment in time. You have startups in the space, you have a lot really money going into it, not just your classic industrial development. That’s very exciting. Moving maybe to the carbonization and what’s happening. The CCUS, and for those who don’t know what it is, carbon capture, utilization, and storage. There’s a lot of stuff happening around that space. That’s the area that deals with the ability to capture CO₂ emissions from industrial sources and/or the atmosphere and preventing their release. There’s a lot of things happening in that space. There’s also a lot of things happening around hydrogen and geothermal and really creating the ability to storage or to store, rather, energy that then can be put back into the grids at the right time. There’s a lot of interesting pieces happening around this. There’s some startup movement in the space. It’s been a long time coming, the reuse of a lot of these industrial sources. Not sure it’s as much on the news as nuclear, and oil and gas, but certainly there’s a lot of exciting things happening there. Bertrand Schmitt I’m a bit more dubious here, but I think geothermal makes sense if it’s available at reasonable price. I don’t think hydrogen technology has proven its value. Concerning carbon capture, I’m not sure how much it’s really going to provide in terms of energy needs, but why not? Nuno Gonçalves Pedro Fuels niche, again, from the outside, we’re not energy experts, but certainly, there are movements in the space. We’ll see what’s happening. One area where there’s definitely a lot of movement is this notion of grid and storage. On the one hand, that transmission needs to be built out. It needs to be better. We’ve had issues of blackouts in the US. We’ve had issues of blackouts all around the world, almost. Portugal as well, for a significant part of the time. The ability to work around transmission lines, transformers, substations, the modernization of some of this infrastructure, and the move forward of it is pretty critical. But at the other end, there’s the edge. Then, on the edge, you have the ability to store. We should have, better mechanisms to store energy that are less leaky in terms of energy storage. Obviously, there’s a lot of movement around that. Some of it driven just by commercial stuff, like Tesla a lot with their storage stuff, etc. Some of it really driven at scale by energy players that have the interest that, for example, some of the storage starts happening closer to the consumption as well. But there’s a lot of exciting things happening in that space, and that is a transformative space. In some ways, the bottleneck of energy is also around transmission and then ultimately the access to energy by homes, by businesses, by industries, etc. Bertrand Schmitt I would say some of the blackout are truly man-made. If I pick on California, for instance. That’s the logical conclusion of the regulatory system in place in California. On one side, you limit price that energy supplier can sell. The utility company can sell, too. On the other side, you force them to decommission the most energy-efficient and least expensive energy source. That means you cap the revenues, you make the cost increase. What is the result? The result is you cannot invest anymore to support a grid and to support transmission. That’s 100% obvious. That’s what happened, at least in many places. The solution is stop crazy regulations that makes no economic sense whatsoever. Then, strangely enough, you can invest again in transmission, in maintenance, and all I love this stuff. Maybe another piece, if we pick in California, if you authorize building construction in areas where fires are easy, that’s also a very costly to support from utility perspective, because then you are creating more risk. You are forced buy the state to connect these new constructions to the grid. You have more maintenance. If it fails, you can create fire. If you create fire, you have to pay billions of fees. I just want to highlight that some of this is not a technological issue, is not per se an investment issue, but it’s simply the result of very bad regulations. I hope that some will learn, and some change will be made so that utilities can do their job better. Nuno Gonçalves Pedro Then last, but not the least, on the energy side, energy is becoming more and more digitally defined in some ways. It’s like the analogy to networks that they’ve become more, and more software defined, where you have, at the edge is things like smart meters. There’s a lot of things you can do around the key elements of the business model, like dynamic pricing and other elements. Demand response, one of the areas that I invested in, I invest in a company called Omconnect that’s now merged with what used to be Google Nest. Where to deploy that ability to do demand response and also pass it to consumers so that consumers can reduce their consumption at times where is the least price effective or the less green or the less good for the energy companies to produce energy. We have other things that are happening, which are interesting. Obviously, we have a lot more electric vehicles in cars, etc. These are also elements of storage. They don’t look like elements of storage, but the car has electricity in it once you charge it. Once it’s charged, what do you do with it? Could you do something else? Like the whole reverse charging piece that we also see now today in mobile devices and other edge devices, so to speak. That also changes the architecture of what we’re seeing around the space. With AI, there’s a lot of elements that change around the value chain. The ability to do forecasting, the ability to have, for example, virtual power plans because of just designated storage out there, etc. Interesting times happening. Not sure all utilities around the world, all energy providers around the world are innovating at the same pace and in the same way. But certainly just looking at the industry and talking to a lot of players that are CEOs of some of these companies. That are leading innovation for some of these companies, there’s definitely a lot more happening now in the last few years than maybe over the last few decades. Very exciting times. Bertrand Schmitt I think there are two interesting points in what you say. Talking about EVs, for instance, a Cybertruck is able to send electricity back to your home if your home is able to receive electricity from that source. Usually, you have some changes to make to the meter system, to your panel. That’s one great way to potentially use your car battery. Another piece of the puzzle is that, strangely enough, most strangely enough, there has been a big push to EV, but at the same time, there has not been a push to provide more electricity. But if you replace cars that use gasoline by electric vehicles that use electricity, you need to deliver more electricity. It doesn’t require a PhD to get that. But, strangely enough, nothing was done. Nuno Gonçalves Pedro Apparently, it does. Bertrand Schmitt I remember that study in France where they say that, if people were all to switch to EV, we will need 10 more nuclear reactors just on the way from Paris to Nice to the Côte d’Azur, the French Rivière, in order to provide electricity to the cars going there during the summer vacation. But I mean, guess what? No nuclear plant is being built along the way. Good luck charging your vehicles. I think that’s another limit that has been happening to the grid is more electric vehicles that require charging when the related infrastructure has not been upgraded to support more. Actually, it has quite the opposite. In many cases, we had situation of nuclear reactors closing down, so other facilities closing down. Obviously, the end result is an increase in price of electricity, at least in some states and countries that have not sold that fully out. Nuno Gonçalves Pedro Manufacturing: the return of “atoms + bits” Moving to manufacturing and what’s happening around manufacturing, manufacturing technology. There’s maybe the case to be made that manufacturing is getting replatformed, right? It’s getting redefined. Some of it is very obvious, and it’s already been ongoing for a couple of decades, which is the advent of and more and more either robotic augmented factories or just fully roboticized factories, where there’s very little presence of human beings. There’s elements of that. There’s the element of software definition on top of it, like simulation. A lot of automation is going on. A lot of AI has been applied to some lines in terms of vision, safety. We have an investment in a company called Sauter Analytics that is very focused on that from the perspective of employees and when they’re still humans in the loop, so to speak, and the ability to really figure out when people are at risk and other elements of what’s happening occurring from that. But there’s more than that. There’s a little bit of a renaissance in and of itself. Factories are, initially, if we go back a couple of decades ago, factories were, and manufacturing was very much defined from the setup. Now it’s difficult to innovate, it’s difficult to shift the line, it’s difficult to change how things are done in the line. With the advent of new factories that have less legacy, that have more flexible systems, not only in terms of software, but also in terms of hardware and robotics, it allows us to, for example, change and shift lines much more easily to different functions, which will hopefully, over time, not only reduce dramatically the cost of production. But also increase dramatically the yield, it increases dramatically the production itself. A lot of cool stuff happening in that space. Bertrand Schmitt It’s exciting to see that. One thing this current administration in the US has been betting on is not just hoping for construction renaissance. Especially on the factory side, up of factories, but their mindset was two things. One, should I force more companies to build locally because it would be cheaper? Two, increase output and supply of energy so that running factories here in the US would be cheaper than anywhere else. Maybe not cheaper than China, but certainly we get is cheaper than Europe. But three, it’s also the belief that thanks to AI, we will be able to have more efficient factories. There is always that question, do Americans to still keep making clothes, for instance, in factories. That used to be the case maybe 50 years ago, but this move to China, this move to Bangladesh, this move to different places. That’s not the goal. But it can make sense that indeed there is ability, thanks to robots and AI, to have more automated factories, and these factories could be run more efficiently, and as a result, it would be priced-competitive, even if run in the US. When you want to think about it, that has been, for instance, the South Korean playbook. More automated factories, robotics, all of this, because that was the only way to compete against China, which has a near infinite or used to have a near infinite supply of cheaper labour. I think that all of this combined can make a lot of sense. In a way, it’s probably creating a perfect storm. Maybe another piece of the puzzle this administration has been working on pretty hard is simplifying all the permitting process. Because a big chunk of the problem is that if your permitting is very complex, very expensive, what take two years to build become four years, five years, 10 years. The investment mass is not the same in that situation. I think that’s a very important part of the puzzle. It’s use this opportunity to reduce regulatory state, make sure that things are more efficient. Also, things are less at risk of bribery and fraud because all these regulations, there might be ways around. I think it’s quite critical to really be careful about this. Maybe last piece of the puzzle is the way accounting works. There are new rules now in 2026 in the US where you can fully depreciate your CapEx much faster than before. That’s a big win for manufacturing in the US. Suddenly, you can depreciate much faster some of your CapEx investment in manufacturing. Nuno Gonçalves Pedro Just going back to a point you made and then moving it forward, even China, with being now probably the country in the world with the highest rate of innovation and take up of industrial robots. Because of demographic issues a little bit what led Japan the first place to be one of the real big innovators around robots in general. The fact that demographics, you’re having an aging population, less and less children. How are you going to replace all these people? Moving that into big winners, who becomes a big winner in a space where manufacturing is fundamentally changing? Obviously, there’s the big four of robots, which is ABB, FANUC, KUKA, and Yaskawa. Epson, I think, is now in there, although it’s not considered one of the big four. Kawasaki, Denso, Universal Robots. There’s a really big robotics, industrial robotic companies in the space from different origins, FANUC and Yaskawa, and Epson from Japan, KUKA from Germany, ABB from Switzerland, Sweden. A lot of now emerging companies from China, and what’s happening in that space is quite interesting. On the other hand, also, other winners will include players that will be integrators that will build some of the rest of the infrastructure that goes into manufacturing, the Siemens of the world, the Schneider’s, the Rockwell’s that will lead to fundamental industrial automation. Some big winners in there that whose names are well known, so probably not a huge amount of surprises there. There’s movements. As I said, we’re still going to see the big Chinese players emerging in the world. There are startups that are innovating around a lot of the edges that are significant in this space. We’ll see if this is a space that will just be continued to be dominated by the big foreign robotics and by a couple of others and by the big integrators or not. Bertrand Schmitt I think you are right to remind about China because China has been moving very fast in robotics. Some Chinese companies are world-class in their use of robotics. You have this strange mix of some older industries where robotics might not be so much put to use and typically state-owned, versus some private companies, typically some tech companies that are reconverting into hardware in some situation. That went all in terms of robotics use and their demonstrations, an example of what’s happening in China. Definitely, the Chinese are not resting. Everyone smart enough is playing that game from the Americans, the Chinese, Japanese, the South Koreans. Nuno Gonçalves Pedro Exciting things are manufacturing, and maybe to bring it all together, what does it mean for all the big players out there? If we talk with startups and talk about startups, we didn’t mention a ton of startups today, right? Maybe incumbent wind across the board. But on a more serious note, we did mention a few. For example, in nuclear energy, there’s a lot of startups that have been, some of them, incredibly well-funded at this moment in time. Wrap: what it means for startups, incumbents, and investors There might be some big disruptions that will come out of startups, for example, in that space. On the chipset side, we talked about the big gorillas, the NVIDIAs, AMDs, Intel, etc., of the world. But we didn’t quite talk about the fact that there’s a lot of innovation, again, happening on the edges with new players going after very large niches, be it in networking and switching. Be it in compute and other areas that will need different, more specialized solutions. Potentially in terms of compute or in terms of semiconductor deployments. I think there’s still some opportunities there, maybe not to be the winner takes all thing, but certainly around a lot of very significant niches that might grow very fast. Manufacturing, we mentioned the same. Some of the incumbents seem to be in the driving seat. We’ll see what happens if some startups will come in and take some of the momentum there, probably less likely. There are spaces where the value chains are very tightly built around the OEMs and then the suppliers overall, classically the tier one suppliers across value chains. Maybe there is some startup investment play. We certainly have played in the couple of the spaces. I mentioned already some of them today, but this is maybe where the incumbents have it all to lose. It’s more for them to lose rather than for the startups to win just because of the scale of what needs to be done and what needs to be deployed. Bertrand Schmitt I know. That’s interesting point. I think some players in energy production, for instance, are moving very fast and behaving not only like startups. Usually, it’s independent energy suppliers who are not kept by too much regulations that get moved faster. Utility companies, as we just discussed, have more constraints. I would like to say that if you take semiconductor space, there has been quite a lot of startup activities way more than usual, and there have been some incredible success. Just a few weeks ago, Rock got more or less acquired. Now, you have to play games. It’s not an outright acquisition, but $20 billion for an IP licensing agreement that’s close to an acquisition. That’s an incredible success for a company. Started maybe 10 years ago. You have another Cerebras, one of the competitor valued, I believe, quite a lot in similar range. I think there is definitely some activity. It’s definitely a different game compared to your software startup in terms of investment. But as we have seen with AI in general, the need for investment might be larger these days. Yes, it might be either traditional players if they can move fast enough, to be frank, because some of them, when you have decades of being run as a slow-moving company, it’s hard to change things. At the same time, it looks like VCs are getting bigger. Wall Street is getting more ready to finance some of these companies. I think there will be opportunities for startups, but definitely different types of startups in terms of profile. Nuno Gonçalves Pedro Exactly. From an investor standpoint, I think on the VC side, at least our core belief is that it’s more niche. It’s more around big niches that need to be fundamentally disrupted or solutions that require fundamental interoperability and integration where the incumbents have no motivation to do it. Things that are a little bit more either packaging on the semiconductor side or other elements of actual interoperability. Even at the software layer side that feeds into infrastructure. If you’re a growth investor, a private equity investor, there’s other plays that are available to you. A lot of these projects need to be funded and need to be scaled. Now we’re seeing projects being funded even for a very large, we mentioned it in one of the previous episodes, for a very large tech companies. When Meta, for example, is going to the market to get funding for data centers, etc. There’s projects to be funded there because just the quantum and scale of some of these projects, either because of financial interest for specifically the tech companies or for other reasons, but they need to be funded by the market. There’s other place right now, certainly if you’re a larger private equity growth investor, and you want to come into the market and do projects. Even public-private financing is now available for a lot of things. Definitely, there’s a lot of things emanating that require a lot of funding, even for large-scale projects. Which means the advent of some of these projects and where realization is hopefully more of a given than in other circumstances, because there’s actual commercial capital behind it and private capital behind it to fuel it as well, not just industrial policy and money from governments. Bertrand Schmitt There was this quite incredible stat. I guess everyone heard about that incredible growth in GDP in Q3 in the US at 4.4%. Apparently, half of that growth, so around 2.2% point, has been coming from AI and related infrastructure investment. That’s pretty massive. Half of your GDP growth coming from something that was not there three years ago or there, but not at this intensity of investment. That’s the numbers we are talking about. I’m hearing that there is a good chance that in 2026, we’re talking about five, even potentially 6% GDP growth. Again, half of it potentially coming from AI and all the related infrastructure growth that’s coming with AI. As a conclusion for this episode on infrastructure, as we just said, it’s not just AI, it’s a whole stack, and it’s manufacturing in general as well. Definitely in the US, in China, there is a lot going on. As we have seen, computing needs connectivity, networks, need power, energy and grid, and all of this needs production capacity and manufacturing. Manufacturing can benefit from AI as well. That way the loop is fully going back on itself. Infrastructure is the next big thing. It’s an opportunity, probably more for incumbents, but certainly, as usual, with such big growth opportunities for startups as well. Thank you, Nuno. Nuno Gonçalves Pedro Thank you, Bertrand.

Falling in love with investing isn't about avoiding volatility. It's about changing how you relate to it.In this episode, I take you behind the scenes of a client message, a private conversation with one of my closest friends after the 35% silver crash, and the single driver behind my investing philosophy,  the reason I never worry about the market and continue to make so much money investing.Tune in to learn:Why I never worry about the marketHow to fall in love with turbulent times instead of fearing themThe reframe on investing that nobody else will share with youThe single driver of my investing philosophy + why it's not found in any finance textbookWhat a 35% silver crash looks like when you're not operating from fear

In this episode of the Dyslexic Entrepreneur podcast, Stephen Martin shares valuable insights for aspiring entrepreneurs looking to start a side hustle. He emphasizes the importance of understanding that projects often take longer than expected, particularly for dyslexic individuals. Stephen discusses the significance of passion in choosing a business venture and the necessity of learning to delegate tasks to avoid burnout. He encourages listeners to embrace the entrepreneurial journey and learn through experience, while also highlighting the common challenges faced by dyslexic entrepreneurs.TakeawaysIt always takes longer than you think.3X the timeframe for projects.Stick with your projects longer.Choose a business you are passionate about.Don't just pursue money; find joy in the process.Learn to delegate tasks effectively.Dyslexic entrepreneurs often excel at starting projects.Reality can be challenging; prepare for it.The way to learn is through hands-on experience.Avoid building a tiring business.Side hustle, dyslexic entrepreneur, business tips, entrepreneurship, passion, time management, delegation, startup advice, entrepreneurial journey, success strategies, ADHD, adults with dyslexia, support for adults.Join the clubrightbrainresetters.comGet 20% off your first orderaddednutrition.comIf you want to find out more visit:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠truthaboutdyslexia.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our Facebook Group⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠facebook.com/groups/adultdyslexia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Gold and silver just experienced a sharp crash, and for a lot of women, it triggered confusion, fear, and “what do I do now?”.In this episode, I slow everything down and give you the context you actually need to understand what just happened, why commodities move the way they do, and how to think about this kind of volatility without panicking or making reactive decisions. Tune in to learn:What a commodity actually isWhat just happened with gold, silver, and other commoditiesWhy commodities can drop fast—even when nothing is “wrong”Why people buy commodities in the first placeHow to understand this kind of volatility without losing your mind

Three friends graduated high school together. Same opportunities, same starting point. Fifteen years later, one makes $85,000 working overtime (exhausted), one makes $115,000 through specialization (better income, still trapped by time), and one makes $105,000 total with $30,000 coming from assets that earn while she sleeps.The difference isn't intelligence or work ethic. It's leverage.Most Canadians are stuck trading time for money. Statistics Canada reports that over 30% of workers have taken on side gigs just to survive. We work more hours for the same money because we don't understand the three fundamental paths to earning more.Path One: Work more hours (1X leverage, hard ceiling). Path Two: Increase your skills through education and specialization (3X leverage, higher income but still time-dependent). Path Three: Buy assets that work for you (infinite leverage, no ceiling).This episode combines Naval Ravikant's principles on leverage with Robert Kiyosaki's Rich Dad Poor Dad framework—but for regular people, not just entrepreneurs. You don't need to own a business. You can start with $50.Learn how to calculate your "Freedom Number," open your first investment account, and begin building wealth through Canadian index funds, real estate, and digital assets. Discover why only 3% of Canadians achieve financial independence, and how you can join them.Golden Hour Challenge: Create your Personal Leverage Map and take your first concrete step toward Path Three income.Stop renting out your time. Start buying assets.Connect with Chris Cooper:Website - https://businessisgood.com/

Piano music courtesy of Harpeth Presbyterian Church used with permission. However, I rarely listen to her podcast because I can read it online faster. Did you know that if you are an Apple user and work in Pages (Apple's word processing program). Seri will tell you how long it takes to read something or speak the content. For most of us, reading is 3X faster. In the big book of podcasting that I never got around to writing — It pays to read your script aloud — this avoids all those nasty little glitches & stumbles that come with our speech.

I made a 632% return in 299 days on a leveraged ETF, but I sold early and paid higher taxes. Why? In this video, I break down my trade from $95 to $695, explain how 3X leveraged ETFs work, and show you the math behind my decision to take short-term capital gains rather than wait for long-term rates. I share three key lessons about tax planning, leverage, and knowing when to lock in profits. Whether you're new to trading or experienced, you'll learn why sometimes paying more in taxes is actually the smart move. [Link to YouTube Video]⁠⁠⁠⁠⁠⁠Dapper Dividends Recommendation Tracker Spreadsheet⁠⁠⁠⁠⁠⁠⁠⁠Check out my current portfolio on

Generating alpha in "boring" businesses often outperforms chasing the latest tech trend. In this solo episode of Mechanics of Money, Sam Silverman breaks down the Private Equity Roll-Up Strategy, specifically how consolidating fragmented industries like commercial paving can generate returns of 3X to 8X.We look past the unglamorous nature of asphalt to reveal the financial mechanics of buying cash-flowing assets at low multiples and exiting at institutional valuations.In this episode, we cover:The Math of Arbitrage: How to buy small operators at 3-4X earnings and exit to institutional buyers at 8X or higher, turning $20M of invested capital into a $56M exit.The "Paving Thesis": Why the $110 billion Bipartisan Infrastructure Law and the essential nature of road maintenance make this a recession-resistant asset class.Economic Moats: Why high equipment costs and supply chain relationships create barriers to entry that protect investor value from new competition.The "Silver Tsunami": How the retirement of baby boomer business owners, over half of the industry, is creating a massive acquisition opportunity for well-capitalized buyers.Execution Risk: The reality of integration failure, culture clashes, and why "simple to explain" does not mean "easy to execute".Links & Resources:Newsletter: Join the Mechanics of Money weekly deep dive: https://www.mechanicsofmoney.coInvest: Invest with Silverman Capital: https://silvermancapital.coAbout the Host: Sam Silverman is the Founder of Silverman Capital, a private equity and real estate investment firm. Mechanics of Money is the audio playbook for high-net-worth individuals moving from "High Earner" to "Sophisticated Allocator."

Risk and returns are often treated like complex financial theory, but they actually follow simple rules that most people are never taught.In this episode, I break down how risk and returns really work, why “risk-free high returns” are impossible, how to spot scams instantly, and why extreme returns don't belong in your long-term plan, so you can invest with clarity, confidence, and strategy instead of guesswork.Tune in to learn:How risk and returns actually work without you having to be an economist or go get a finance degreeThe counterintuitive thing you must know about understanding risk and how it relates to your portfolioHow to spot a scam immediatelyWhy you can't build extreme returns into your long-term projectionsWhat to do instead

Roger Rosmus, Founder, CEO, & Director of Goliath Resources (TSX.V: GOT) (OTCQB: GOTRF), joins us live from the AME Roundup to highlight the positive investor sentiment after attending 3 back-to-back Vancouver resource conferences.  Additionally, we dig into recent news released on the final 70 gold-only drill hole assays returned from last year's program, the pending assays for 110 multi-element results on all the 2025 drill holes, along with a number of corporate initiatives around not doing a share consolidation, on buying back the NSR from 3% down to 2%, and on fast-tracking its ownership in the Golddigger Property located in the Golden Triangle, B.C. that hosts the high-grade Surebet gold discovery from 49% to 100%.   Drill hole GD-25-319 intersected 19.13 g/t Au over 6.10 meters, including 22.86 g/t Au over 5.10 meters, including 29.09 g/t Au over 4.00 meters in quartz-sulphide veins, part of the Golden Gate Zone Assays are still pending for 110 drill holes from 2025 for multi-element gold equivalent (AuEq) results. These results will be released in the near future once all assays have been received, compiled and interpreted. 100% of the drill holes completed to date, have all intersected gold mineralization clearly demonstrating the remarkable continuity, grades, and widths in 5 Main Gold-Rich Zones comprising 46 mineralized lodes that remain open for expansion. Of the holes drilled during the 2025 campaign, 83 out of 110 holes (or 76%) contained visible gold to the naked eye (VG-NE). The fully funded 2026 drill program will be mainly focused on expanding the 5 Main Mineralized Zones. Data compilation and interpretation is underway which will be used to vector in on the indicated Motherlode causative intrusive source to this extensive high grade gold system with widespread VG-NE.     As part of the transaction to J2 Syndicate Holdings Ltd. to acquire 100% ownership in the Golddigger Property, that Goliath is now set to publish a Maiden Resource Estimate (MRE) on the Golddigger Property before June, 1 2030 and on every 3 year anniversary of June 1,2030 thereafter vs. the prior requirement, in the original agreement, to publish the MRE by June 1, 2027.    Roger outlines the Company's rationale that it makes far more sense to keep expanding the mineralization with aggressive exploration programs, versus trying to pin down the MRE at this stage. He provides both positive examples of companies that have taken this route, versus the negative examples of companies that rushed to put out a MRE, only to fence themselves in with regards to valuations and market perceptions.   Wrapping up, we reviewed that in December cornerstone investors Rob McEwen and Crescat Capital increased their stakes in the company, as well as discussing the optionality that Goliath has with regards to their equity holdings of McEwen Inc shares which have essentially gone up about 3X since acquiring them.  In addition to future conversion of warrants from Rob McEwen, the (MUX) shares provide the option for more capital inflows into the company treasury to fund more exploration.   If you have any questions for Roger about Goliath Resources, then please email us at Fleck@kereport.com or Shad@kereport.com.     In full disclosure, Shad is a shareholder of Goliath Resources at the time of this recording and may choose to buy or sell shares at any time.   Click here to follow the latest news from Goliath Resources   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.

Work with Jimmy & the Vreeland Capital Team to build a 20-Unit Portfolio that will get you the equivalent of a retirement account 3X faster with a third of the capital. Visit https://tinyurl.com/mainstreetpatriot... In this episode of The Real Estate Fast Pass, hosts Jimmy Vreeland and Susie Vreeland break down a headline making the rounds in the mortgage world and use it as a springboard to explain what actually moves 30-year mortgage rates. Jimmy unpacks how Fannie Mae and Freddie Mac support lending liquidity through mortgage-backed securities, why mortgage rates tend to track the 10-year Treasury, and what happens when big institutional buyers step in (or step out) of the bond market. From there, they zoom out to the bigger truth: even if rates dip, affordability doesn't magically fix itself when the real constraint is housing supply—lower rates can just pour gasoline on demand and push prices higher. If you're trying to build long-term wealth, this conversation will help you tune out the noise, understand the system, and focus on the repeatable move: lock in smart, stable assets, use leverage responsibly, and keep stacking 30-year fixed “boats in the water” while everyone else is chasing headlines. About Jimmy Vreeland Jimmy graduated from the United States Military Academy at West Point, spent 5 years as an Army Ranger, and deployed three times twice to Iraq and once to Afghanistan. On his last deployment, he read Rich Dad Poor Dad by Robert Kiyosaki which led him down the path of real estate investing. As his own portfolio grew, eventually he started a real estate investing business.  Since 2018 his team at Vreeland Capital has supplied over 100 houses a year to high performing, passive investors who want to work with his team and his team is now managing over 800 houses. Get in touch with Jimmy and his team at www.jimmyvreeland.com/getstartedinrealestate More about Jimmy Website: www.jimmyvreeland.com Linkedin: www.linkedin.com/in/jimmy-vreeland Instagram: www.instagram.com/jimmyvreeland Facebook: www.facebook.com/JimmyVreeland Youtube: www.youtube.com/@JimmyVreelandC >>>>>>Get free access to the private Ranger Real Estate facebook group

Today on the show, we have Matthew Tharp, CEO of Hunter.io, the all-in-one email outreach platform used by over 4 million people to identify prospects and run cold email campaigns. Previously, Matthew was VP of Worldwide Retention at LogMeIn, where he owned NRR across nine products—giving him a rare masterclass in retention challenges at different stages and scales.In this episode, we uncover why retention isn't a problem you solve when growth stalls—it's DNA you build from day one. Matthew shares the paradox of his career: building a company with 95%+ annual retention that got acquired, versus joining a high-growth PLG business with churn issues that needed solving before scaling further.We explore why over-indexing on either growth or retention creates problems, how to identify the usage patterns that predict churn in the first three weeks, and why every company that tries to fix retention late struggles. The lesson: balance from the beginning beats transformation later.We also discuss how Hunter achieved 3X growth this year by going back to basics—running a rigorous ICP analysis, choosing battles they could win instead of markets where competitors were spending $100M, and layering new customer segments without creating product bloat.Finally, we dig into cold outreach data: why email lists under 100 people dramatically outperform larger ones, why shorter emails force the clarity that drives replies, and how constraints—not scale—are the real performance lever in outbound.As always, I'd love to hear from you. You can email me directly at andrew@churn.fm, and don't forget to follow us on X.Churn FM is sponsored by Vitally, the all-in-one Customer Success Platform.

Success shame is one of the most invisible forces holding women back from wealth.When women hit big milestones, start making big money, or begin holding wealth at higher levels, something unexpected often happens, we shrink, minimize, or subconsciously sabotage our success.In this episode, I unpack where success shame comes from, why it exists, and how the subconscious programming behind it creates invisible glass ceilings in women's wealth.Tune in to learn:How to eliminate success shameThe unusual phenomenon that happens when women hit big milestones or start making big moneyWhere success shame comes from and why it occursThe sneaky subconscious programming that keeps invisible glass ceilings of wealth in place for women

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This week i discuss Corey Holcomb going to far in his clap back to Anton Daniels and more thoughts from the aftermath. Then I turn my attention to the up tick of racist videos and The Red Crew parting in Miami. I discuss how social media has giving people balls to do things that normally a sane person would not do or even say in public. Then i discuss Druski's Mega-Church skit and the reaction from it. Last up , I discuss our Dictator And chief trip to Europe and his obsession with Greenland . 3X nominated for an AVN Award for Podcast of the year Please Vote herehttps://avn.com/awards/voting/favorite-adult-podcast(Please go Vote)Sponsored ByPassDat Apparel ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.teepublic.com/user/the-inhaling-potnas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SmokeKind THCA ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://smokekind.com/?ref=bobbie_lucas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sara Jay's CBDsUse Promo Code: BOBBIE To receive 10% off your order⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://sarajaycbd.com/

I'm pulling back the curtain on exactly how I'm planning to 3X my audience, demand, and sales in 2026. I'm also getting real about what I'm stopping, what I'm changing, and what I'm doubling down on. I'll be honest, 2025 was a year of reflection for me. We officially crossed multiple 7 figures in the agency (which is wild to celebrate), but I also realized my growth has been slow and sustainable. And while that's not a bad thing, I'm ready to challenge myself to grow faster without burning out or 3xing my workload. I'm sharing the exact metrics I'm tracking: Audience quality over follower count Demand through conversations and inquiries  Sales through actual completed purchases.  And I'm breaking down what I learned didn't work: inconsistent posting, trying to speak to everyone, creating too much offer-specific content, and spending more time refining deliverables than my positioning. But here's what I'm doing instead: making short form content non-negotiable, getting crystal clear on my thing in the industry, focusing on my corner of the internet instead of going mainstream viral, and selling through better positioning—not better offers. I'm also announcing something I haven't shared anywhere else yet: we're going through a major brand reinvention. I'm talking new aesthetics, new website, new positioning, maybe even a new name. Because I know that what got me here won't get me to where I want to go. Connect with me:  Website Join our email list! Instagram Pinterest Become the celebrity of your niche and learn how to turn your marketing into a campaign that actually sells out. In the Campaign Crash Course™, you'll learn how to build anticipation and sell your offers with the same strategy behind brands like SKIMS, Poppi, and Rhode — all in just 60 minutes. https://highflierpowerhouse.com/course Get creative support to turn your content into sales before, during and after your launches. From content classes to learn new campaign marketing skills, to custom designed assets completely done for you, we've thought of it all inside Sales Studio. Join today: https://highflierpowerhouse.com/retainer  Triple your audience, demand and sales with a 90-day marketing reinvention designed to position you as the #1 choice in your industry and change the way you show up online. Apply for The Industry's Choice https://highflierpowerhouse.com/industrys-choice   

Think You Know Silver? Take the Quiz and Uncover What You Never Learned in School! https://www.rethinkingthedollar.com/silver-iq/Silver price yen surge, silver parabolic chart, fiat currency collapseThe price of silver in Japanese yen just went vertical; tripling in under 12 months. This isn't a meme coin, penny stock, or hype-driven asset. It's physical silver, exploding against a G7 fiat currency.What's happening in Japan isn't isolated; it's a flashing red warning for the global financial system. In this video, we break down the dramatic 3X surge in silver priced in yen, the collapse of confidence in long-term bonds, and the systemic cracks forming in fiat currencies worldwide.As Japanese Government Bond (JGB) yields rise, capital is repatriating fast. A move that threatens carry trades and stresses global liquidity. When trust in “paper” erodes, real assets like silver take the lead. And this time, it's not about fear... it's about monetary policy inevitability.✅ Too Expensive for Silver? Think Again. Stack smart and secure your future now: https://bit.ly/Shop4Silver✅ Gold That Fits in Your Wallet? Discover stackable ¼ grain gold cards for real-world barter: https://minigoldbars.com✅ Turn Paper into Power. Get spendable 24K GoldNotes before the next reset: http://buygoldnotes.com✅ The Gold-Backed Bank Is Here. Open your free silver & gold account now: https://bit.ly/GoldSilverBanking✅ Stack Silver, Earn Weekly. Build wealth on autopilot with QuickSilver: http://mysilverteam.com✅ Final Crypto Boom Incoming? Trade, buy & sell with full control on Crypto.com: https://crypto.com/app/jw2btwdxa7DISCLAIMER: The financial and political opinions expressed in this video are those of the guest and not necessarily of "RTD." Views expressed in this video should not be relied on for making investment decisions or tax advice and do not constitute personalized investment advice. The information shared is for the sole purpose of education and entertainment only.

Hello and Welcome to the DX Corner for your weekly Dose of DX. I'm Bill, AJ8B.The following DX information comes from Bernie, W3UR, editor of the DailyDX, the WeeklyDX, and the How's DX column in QST. If you would like a free 2-week trial of the DailyDX, your only source of real-time DX information, just drop me a note at thedxmentor@gmail.comKP5 - Desecheo Island – On Monday, KP5/NP3VI began operations from Desecheo Island, the first time KP5 has been on the air since 2009. NP4G, Otis, and several other team members were on the island setting up the antennas, stations and solar panels. We have an update from the KP5 team - Desecheo DXpedition 2026 Update“Please be patient—this is only Day Two of a planned 30-day activation. This DXpedition represents a completely new operating concept designed specifically for environmentally sensitive, highly regulated, and restricted locations. As with any first-of-its-kind effort, refinements are ongoing. Our team leaders, along with electrical and software engineers, are actively fine-tuning operating schedules, band and mode selection, RF power levels, and overall system performance to optimize results as conditions evolve. Due to Desecheo's geographic location, North American stations are currently dominating propagation. That said, our operators have been explicitly instructed to listen for DX stations and to give them priority whenever possible in order to broaden log coverage. We ask for your patience as we work through the pileups and strive to put as many stations as possible into the log.A primary goal of this DXpedition is to deliver as many ATNOs as we can worldwide. Thank you for your understanding, your support, and your cooperation.Good luck in the pileups—and we'll see you in the log!73,Steve N2AJMedia Officer & PilotDesecheo DXpedition 20265H - Tanzania - Dr. Charles, NK8O, is QRV from Tanzania as 5H3DX until February 9 with limited radio activity due to other commitments. He plans to operate mainly 20 to 10 meters, possibly 6 meters if conditions allow, using simple antennas. A more extensive operation is expected in April, and he is exploring remote operation, though limited Internet access is currently a challenge.VP0/H - South Shetland Islands - LZ0A (LZ1AAW), Ivo, continues his activity from the Bulgarian Antarctic Base on Livingston Island. He is QRV in his spare time and has been reported on FT8 on 20 and 15 meters. QSL via LZ1KDP.3X, Guinea - Herman, YB3GIH, is QRV from Boff as 3X/YB3GIH and plans to remain there until about June while working on a contract. His station setup includes an ICOM IC-718 at 100W and a homebrew vertical antenna. He is operating on 20 and 15M SSB. QSL options are eQSL, Club Log, and LoTW.TY - Benin - Gerard's, F5NVF, flight to Benin was delayed by snow in Paris, but he plans to be active on CW as TY5GG later this week where he is scheduled until April 6.PJ2 - Curacao – Jeff, K8ND, operating as PJ2ND, is QRV in Curaáao and will stay until January 30. He will participate in the CQWW 160 Meter CW Contest as PJ2T later in the month and will be active on the bands as PJ2ND until then. QSL for PJ2ND goes via K8ND or Logbook of the World, and for PJ2T via KU9C or Logbook of the World.6W – Senegal – Rudi, 6W/DB1RUL, is QRV and will continue to January 20. He plans to upload the log to LoTW. Other routes are the bureau to his home callsign, or direct with 2 USD. 4S - Sri Lanka – Peter, 4S7KKG, says everything is the same as in previous years. "I'll be here in 4S until the end of March!" His other call is DC0KK. QSL direct or bureau through Club Log OQRS or, he calls it "direct-direct," with SASE and 2 USD to his German home QTH. Until next week, this is Bill, AJ8B saying 73 and thanks to my XYL Karen for her love and support. I Hope to hear you in the pileups! Have a great DX week!

本期节目由专业生发品牌达霏欣赞助播出！达霏欣24年专研生发，男女分治双浓度，男性女性均有选择，止脱生发认准达霏欣，买达霏欣就上京东APP！【专属购买链接】→https://3.cn/1-0USzdCj【5%浓度购买人群建议】男性首选，适合中重度脱发人群，清爽不油腻。【2%浓度购买人群建议】女性首选，适合防脱及头皮敏感人群，温和不刺激。节目提要：1. 2025车企成绩单出炉，是稳中向好还是危机四伏2. 补贴退坡+产能压力，2026车市为何是等等党的狂欢？3. 合资品牌断臂求生，本土团队掌权能否逆转颓势？4. 理想陷入转型困境、零跑逆袭狂飙，新势力2026格局将如何改写？5. 油车性价比回归，新能源与燃油车该如何抉择？6. 车企内部定价逻辑大揭秘，今年买车怎样才能不吃亏？【本期高光】Part 1 2025车市全景：巨头排位赛与数字游戏00:01:23 2025年车企成绩单发布，2026年买车时机成焦点：年前还是年后？00:02:09 车企晒成绩单就像高考：「清华北大」一抓一大把，最差也是「985」！00:04:21 「宇宙第一」比亚迪狂卖460.24万辆，距离500万目标仅一步之遥。00:06:01 上汽踩下销量刹车，450.75万辆屈居第二：销量与盈利的权衡艺术。00:11:21 长安汽车卖出291.3万辆，距300万「王者门槛」仅差一口气！00:18:42 东风集团增长率仅0.01%：一个「1‱」级别的「原地踏步」艺术。00:19:39 广汽集团销量下滑14.06%，成为主流车企中唯一两位数退步的「逆流者」。Part 2 新能源暗战与品牌突围记00:06:49 吉利的秘密武器：新能源车迅猛增长，302.46万辆背后的野心。00:09:58 极氪9X站稳四五十万价位，国产高端车真的成了？00:12:33 长安新能源矩阵：起源41.1万领跑，深蓝33.3万，阿维塔12万。00:14:28 奇瑞的销量支柱与高端之困：瑞虎系列卖170.09万，星途仅12.04万。00:16:23 智界品牌定位模糊，消费者为何买单？9.05万辆销量需拭目以待。00:33:00 长城汽车2025目标400万，实际完成132.37万：完成率仅33.09%的「高大上」目标。00:39:34 新势力亮点：小鹏销量42.94万同比增126%，市场下沉策略奏效。Part 3 合资阵痛与权力游戏00:24:31 丰田放下身段赢市场：赛纳靠优惠成霸主，低姿态策略见效。00:25:43 广汽丰田铂智3X成爆款，75.6万辆销量撑起广汽半边天。00:52:51 合资品牌降价潮：丰田、大众老款销量反超新款，价格低到怀疑质量？00:53:14 合资车企利润大滑坡：曾加价爆款车型利润下滑近90%！00:58:58 权力大转移：丰田放权本土研发铂智3X成功，日产N7决策失误销量下滑。01:00:41 合资品牌在华生存法则：不用中国技术和团队？那就是等死。01:01:59 豪华车的两难：奔驰、保时捷回归油车，新能源市场盈利难。Part 4 消费者博弈与未来悬念00:03:52 消费者与车企的博弈：扛住不买车，车价可能降5000甚至15000！00:31:26 网约车市场饱和冲击显现：埃安销量大跌超20%，全年仅卖29.01万辆。00:48:24 2026年补贴退坡在即，新能源与燃油车之间，消费者冷静抉择。01:08:18 比亚迪价格战新动向：老汉老唐退役，新汉新唐价格下调，顶配冲刺30万。【本期主播】三刀：自称“别人研究车，而我研究人”的汽车KOL。2006年从事汽车销售，2013年成立播客工作室，靠一支麦克风从播客做到抖音、B站，小红书、微博等平台。节目里既聊车，也聊人间冷暖，刀友们口中的“老大哥”。抖音丨快手丨小红书丨视频号：三刀侃车汽车之家丨懂车帝丨bliblli丨公众号丨喜马拉雅丨小宇宙：百车全说微博：百车全说三刀欢迎在苹果播客、小宇宙、喜马拉雅、网易云音乐、qq音乐、蜻蜓FM、微博音频、微信视频号搜索【百车全说】，马上订阅节目，不错过每次更新。加入听友社群，微信号：46415254想与三刀1对1交流，扫码加入知识星球：

Work with Jimmy & the Vreeland Capital Team to build a 20-Unit Portfolio that will get you the equivalent of a retirement account 3X faster with a third of the capital. Visit https://tinyurl.com/mainstreetpatriot... In this episode of The Real Estate Fast Pass, hosts Jimmy Vreeland and Susie Vreeland break down what 2026 really looks like for real estate investors—without the hype, fear, or coastal noise. Drawing directly from insights shared inside the latest Collective Genius meeting, Jimmy unpacks why the market has entered what he calls the Great Stall: a period of steady pricing, persistent housing shortages, and slower—but far more predictable—growth. While headlines focus on interest rates and dramatic market swings, the reality on the ground tells a very different story, especially in Midwest rental markets. Jimmy and Susie explain why new construction sitting on the market doesn't mean a crash is coming, how a multi-million-unit housing shortage continues to support long-term rental demand, and why the average first-time homebuyer age hitting 40 years old is a massive signal for landlords. They also share why builders are unlikely to solve the inventory problem anytime soon, why tenants are staying longer, and why stable, “boring” deals may offer the clearest path to consistent wealth in 2026 and beyond. If you're looking to invest with confidence—not headlines—this episode lays out exactly why slow, steady, and repeatable real estate might be the smartest move you can make. About Jimmy Vreeland Jimmy graduated from the United States Military Academy at West Point, spent 5 years as an Army Ranger, and deployed three times twice to Iraq and once to Afghanistan. On his last deployment, he read Rich Dad Poor Dad by Robert Kiyosaki which led him down the path of real estate investing. As his own portfolio grew, eventually he started a real estate investing business.  Since 2018 his team at Vreeland Capital has supplied over 100 houses a year to high performing, passive investors who want to work with his team and his team is now managing over 800 houses. Get in touch with Jimmy and his team at www.jimmyvreeland.com/getstartedinrealestate More about Jimmy Website: www.jimmyvreeland.com Linkedin: www.linkedin.com/in/jimmy-vreeland Instagram: www.instagram.com/jimmyvreeland Facebook: www.facebook.com/JimmyVreeland Youtube: www.youtube.com/@JimmyVreelandC >>>>>>Get free access to the private Ranger Real Estate facebook group

This episode is for women in their 40s, 50s, or 60s who are wondering if it's too late to invest.The real question isn't whether you started early enough — it's whether your money is compounding now. Tune in to learn:Why the real question isn't “did I start early enough?” — it's “is my money compounding or not?”The one thing you don't realize about how much time you actually still haveThe difference between how a 40-year-old invests vs a 20-year-oldHow money that isn't compounding is silently shrinkingWhat actually happens when you get your money invested — even in your 40s, 50s, or 60sHow to make the most of the time you do have left using real numbers

This episode i discuss ICE and the recent shootings that has happened. I give my thoughts and I discuss why previous Presidents didn't do what Trump is doing with ICE. I discuss how Minnesota shooting happened around the same time as George Floyd and in Minnesota. I discuss the cover up and more. Then I take a deep look at Trumps start of this year from the capturing of the Venezuela President to the so call stimulus checks that might come. 3X nominated for an AVN Award for Podcast of the year Please Vote herehttps://avn.com/awards/voting/favorite-adult-podcast(Please go Vote)Sponsored ByPassDat Apparel ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.teepublic.com/user/the-inhaling-potnas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SmokeKind THCA ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://smokekind.com/?ref=bobbie_lucas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sara Jay's CBDsUse Promo Code: BOBBIE To receive 10% off your order⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://sarajaycbd.com/

Yosef Lee is a full-time litigation attorney based in New York who pivoted into multifamily real estate investing to gain greater control over his time and legacy. Driven by his desire to be more present for his two daughters, Yosef began his investing journey in 2019, joining mastermind communities and building a network from scratch. Since then, he has become a general partner in 17 syndications, participated in 5+ joint ventures, and successfully exited multiple deals—including a 3X equity multiple from his first investment. He now shares his journey to help others take purposeful action, emphasizing relationships, self-education, and long-term vision.     Make sure to download our free guide, 7 Questions Every Passive Investor Should Ask, here.     Key Takeaways Join the right masterminds and network consistently to accelerate your learning and deal flow. Learn the language of multifamily investing before pitching yourself or underwriting deals. Focus on people first, trustworthy partnerships are more important than proximity in out-of-state investing. Multifamily value-add deals are often won through rent increases, not just renovations. Being honest about where you are in your journey builds authentic trust with your network.     Topics From Legal to Legacy Yosef shares how his role as a litigation attorney conflicted with his values as a father. Realized that financial success wasn't enough without freedom of time, place, and occurrence ("TPO"). Accidental Discovery of Multifamily Found BiggerPockets in 2019 and stumbled into multifamily after exploring other investment options. Chose multifamily for its scalability and team-based structure. First Deal Breakdown: 44 Units in Kansas Partnered with others through a mastermind group to buy off-market. Pushed rents by $150–$200 and executed a cash-out refinance before ultimately selling for 3X returns. The Power of Masterminds and Community Did 200+ Zoom calls in 2020 to build relationships. Contrasts 80% of people who said "don't join" masterminds vs. the 20% who helped him scale. Emphasizes that education is free, but access to the right people is worth paying for. Authentic Branding and Thought Leadership Recalls a 2019 comment from John Casmon that gave him the confidence to start showing up online, even before his first deal. Encourages investors to be real about where they are and build in public.    

What if the difference between AI mediocrity and breakthrough isn't the tool—it's how you architect your approach? Carter Jensen from The Uncommon Business joins the crew to reveal why most people are stuck "button pushing" while others are unlocking 3X productivity gains. This isn't theory; it's the frontline reality of businesses transforming workflows with the right AI architecture. If you're tired of surface-level AI hype and ready for actionable intelligence on integrating AI into security, compliance, and everyday business operations, this episode delivers. Whether you're Blockbuster or Netflix is up to you.

We are officially in 2026, and this is one of the most important episodes I've ever recorded.I'm recapping what actually happened in the markets in 2025 and sharing how I'm thinking about investing in 2026 - especially as AI continues to transform everything.Tune in to learn:What actually happened in the markets in 2025, including key stats and winnersWhy 2026 is going to be a huge year for investorsThe impact of AI and why it's going to be transformationalHow to win as a woman investor in the next few yearsHow to approach investing in 2026 without fear or guesswork

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

Happy New Year and I come with reflecting on the past five years of my podcast and what i have coming for the New Year. I recap The Premium Smoke Fest on Loyalfans and more. Then I discuss the outrage from Dr. Cheyenne Bryant dress during the Jezzy Birthday party and if it's fair criticism . Then i talk politics and the MAGA spit. I then talk about Adam 22 Fighting Jason Luv, Doechii and Adin Ross and Natalie Nunn's interview with Cam Newton and I answer the question of should men take a Baddie seriously. 3X nominated for an AVN Award for Podcast of the year Please Vote herehttps://avn.com/awards/voting/favorite-adult-podcast(Please go Vote)Sponsored ByPassDat Apparel ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.teepublic.com/user/the-inhaling-potnas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SmokeKind THCA ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://smokekind.com/?ref=bobbie_lucas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sara Jay's CBDsUse Promo Code: BOBBIE To receive 10% off your order⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://sarajaycbd.com/

Thomas Collins explains house hacking, multifamily investing, and how to make your assets pay for your lifestyle—starting with your first property.Full DescriptionIn this episode of RealDealChat, Jack Hoss sits down with Thomas Collins, founder of Shift Rich Academy, to break down one of the most powerful entry points into real estate investing: house hacking.Thomas shares how a single conversation at his day job sparked a mindset shift that led him from renting at $1,400/month to owning a duplex where his tenants paid nearly the entire mortgage. He explains why multifamily properties (especially duplexes, triplexes, and fourplexes) are the smartest first move for new investors—and how FHA loans make it possible with just 3.5% down.The conversation goes deep into:House hacking vs flips (and why HGTV gets it wrong)Establishing your buy box when you plan to live in the propertyUsing short-term rentals to dramatically increase cash flowCostly mistakes with tenants, licenses, and partnershipsWhy systems, virtual assistants, and AI prevent real estate from becoming another jobHow confidence explodes after the first dealWhy waiting for “perfect” kills momentumThis episode is packed with real-world lessons, beginner clarity, and systems thinking for anyone sitting on the sidelines.

End chaos in your firm—300+ peers use this framework. Free video here: https://www.businessofarchitecture.com/framework In this episode of Business of Architecture, Rion Willard sits down with Camila Brugger, founder of WorldTeams—the company quietly transforming how architecture firms grow. Camila shares her personal journey from witnessing the chaos of her parents' small practice to building a 650-person global team that serves over 200 firms. Her story is raw, energizing, and full of practical insight for any architect tired of doing it all alone. You'll hear how remote talent can unlock growth, freedom, and surprising loyalty—even if you've tried outsourcing before and failed. Camila reveals the mindset shifts and systems that make remote work actually work. And she doesn't hold back on the tough lessons that helped her scale without burning out. In this episode, you'll discover… The silent hiring mistake most architects are still making—and how it's costing them thousands. What one architecture firm owner did to 3X their team without opening a single job ad. Why your dream lifestyle might be just one mindset shift away. To learn more about Camila, visit her website: https://worldteams.com/

>>My new book, The Wicked Smart Golf Fitness Formula, is live to help you master golf fitness for longer drives, more energy, and avoiding injuries.  If you want to hit the ball farther, avoid injuries, gain stamina, and play your best golf for decades—not just next season—The Wicked Smart Golf Fitness Formula is your roadmap. Written by +2 handicap golfer, 3X author, fitness enthusiast, and performance coach Michael Leonard, this book takes a no-nonsense approach to golf fitness. No gimmicks. No complicated science. Just a proven formula built from years of training, interviewing experts, and applying real results on the course. Whether you feel out of shape, inconsistent late in the round, or stuck at the same distance year after year, improving your fitness is a reliable way to transform your golf game—without changing your swing. Inside, you'll learn how to train like a golfer, move like an athlete, and build a body that supports powerful, consistent golf. What You'll Discover Inside.... Pillar I: Strength Training Build functional strength that translates directly to the golf swing. Learn how to train smarter—not harder—with golf-specific exercises, simple routines, and power-building strategies. Pillar II: Cardio for Golfers Develop endurance that lasts all 18 holes (and beyond). Improve energy, boost conditioning, lower fatigue, and finish rounds strong with cardio methods designed specifically for golf. Pillar III: Peak Performance Habits Unlock mobility, flexibility, recovery, and sleep tactics to help you move pain-free and feel your best. Learn how to warm up properly, prevent injury, and create a body built for long-term performance. Pillar IV: Nail Your Nutrition Fuel your body for power, focus, and consistency. Discover simple golf nutrition strategies, hydration tips, smart supplementation, and easy habits that improve performance on and off the course. Pillar V: Speed Training Learn how to safely increase swing speed using proven drills, overspeed training concepts, and power-building routines. Add yards to your driver without sacrificing accuracy. You'll Also Learn How To: Improve daily energy, focus, and recovery Reduce stiffness, pain, and overuse injuries Add 10–20+ yards through proper speed training Create a personalized golf fitness plan you'll actually follow Build consistency by upgrading sleep and recovery routines Eat for performance, energy, and fat loss—without rigid diets Build golf workouts that increase strength, stamina, and mobility You don't need to train like a bodybuilder. You don't need hours in the gym. And you definitely don't need to overhaul your swing. You need a simple, sustainable fitness system built for golfers. One that helps you hit it farther, feel better, and play your best—season after season. With a proper golf fitness plan, you'll learn how to train for life. Whether you're 18, 38, or 58, this system will help you build golf strength for longer drives, avoid injuries, and have more energy than ever. Whether you're a scratch player or just getting started, The Wicked Smart Golf Fitness Formula gives you everything you need to become a stronger, faster, healthier, more consistent golfer. Stop wasting time in the gym or pounding the pavement. Start training like an athlete so you can play golf for decades to come.

In this episode, I'm sharing the six signs your advisor is f*cking you based on what I've seen behind the scenes after reviewing countless client portfolios.I'm breaking down why so many women end up confused, doubting themselves, and missing the one number that actually tells you whether your investments are underperforming. Tune in to learn:The six clear signs your advisor is actually costing you moneyWhy you end up feeling confused or like an idiot after talking to most advisorsThe one number you absolutely must know about your investmentsHow to tell if your portfolio is underperforming the averageWhat's really happening behind the scenes when advisors avoid clear answers

In this episode, I'm breaking down the biggest counter-intuitive mistakes that even seasoned investors make,  mistakes I've made myself.These are the things people believe will make them money, but actually cost them millions over time. Tune in to learn:The biggest mistakes that smart, experienced investors makeWhy what feels sophisticated often loses money in the long runThe three L's of the advisory industryA juicy and unusual take on cryptoTwo common investing strategies you've been taught and why doing the opposite makes more money

Work with Jimmy & the Vreeland Capital Team to build a 20-Unit Portfolio that will get you the equivalent of a retirement account 3X faster with a third of the capital. Visit https://tinyurl.com/mainstreetpatriot... In this episode of The Real Estate Fast Pass, hosts Jimmy Vreeland and Susie Vreeland step away from the usual tactical breakdowns to explore how shifting tax policy, inflation, and government money printing are quietly reshaping where capital flows in the U.S. Without debating politics, they focus on what investors actually need to understand: policy changes create real microeconomic consequences, and those consequences directly impact real estate opportunity, competition, and long-term returns. Jimmy and Susie connect the dots between inflation, asset ownership, and wealth preservation—explaining why capital tends to migrate out of high-tax, high-friction environments and into markets where it can move more freely. They break down why owning hard assets matters in an expanding money supply, how being on the sidelines carries more risk than most people realize, and why stable Midwest markets can offer a quieter, more disciplined path to long-term growth than overcrowded “hot” destinations. If you're a high-income earner feeling squeezed by moving goalposts, this episode will help you think strategically about protecting what you've earned and positioning your portfolio for durable, inflation-resistant wealth. About Jimmy Vreeland Jimmy graduated from the United States Military Academy at West Point, spent 5 years as an Army Ranger, and deployed three times twice to Iraq and once to Afghanistan. On his last deployment, he read Rich Dad Poor Dad by Robert Kiyosaki which led him down the path of real estate investing. As his own portfolio grew, eventually he started a real estate investing business.  Since 2018 his team at Vreeland Capital has supplied over 100 houses a year to high performing, passive investors who want to work with his team and his team is now managing over 800 houses. Get in touch with Jimmy and his team at www.jimmyvreeland.com/getstartedinrealestate More about Jimmy Website: www.jimmyvreeland.com Linkedin: www.linkedin.com/in/jimmy-vreeland Instagram: www.instagram.com/jimmyvreeland Facebook: www.facebook.com/JimmyVreeland Youtube: www.youtube.com/@JimmyVreelandC >>>>>>Get free access to the private Ranger Real Estate facebook group

We have Mike Monaghan on the show today and covering the “Birth of an ETF.” He’s going to talk about the Founders ETF and its new launch. We’re also going to talk a little bit about what it takes to get an ETF up and running. From a compliance perspective, remember, there’s no guarantee of future performance. https://youtu.be/o-m3PYHKXqk?si=qBaHkJpUt7xgdpjG Transcript of “The Birth of an ETF” 00:00 The Founders ETF Frazer Rice (00:00.986)Welcome back, Mike. Michael Monaghan (00:02.616)Frazer, it’s great to be back. Frazer Rice (00:04.4)You are at an interesting point in time right now. You’re about to start up Founders ETF and I think you’re about to get trading authorization to get going. Maybe tell us a little bit about the process to set up an ETF. Then we’ll dive into the strategy a little bit. Michael (00:21.25)Yeah, absolutely right. We should start trading on the SIBO Thursday, so two days from now. And we’ve launched our first fund, the Founders 100, that owns the 100 best founder-led companies. I’d be happy to go through some of the process that it takes to set up an ETF. Frazer Rice (00:40.014)Love it. ETFs are the main way to go now in terms of getting an inveestment cvhicle up and running. What has your experience been around? The Popularity of the ETF Structure Michael (00:52.014)Yeah, so ETFs have become the primary investment vehicle for a few reasons. Let’s outline those reasons. Then we can go through some of the steps that it takes to set up an ETF. So on the advantage side of an ETF, they’re typically a bit lower cost than traditional mutual fund products. Importantly, they’re tax advantaged. So there’s no gains or losses that occur during the normal ETF growth phase. Everything that happens within the ETF is done with what’s called an authorized participant. So you do exchanges. And so there’s no capital gains that are assigned to the investors. As long as they hold the ETF, a tax trigger only occurs when they actually sell the ETF. Finally, it’s a great way to get exposure to the market. So whether you want to own a broad market index, one of the legacy indexes, or a vehicle like ours. That gives you in one single trade, rather than having to guess who’s going to win. Is Nvidia going to win or Palantir who’s going to win? You can own a hundred of the best winners in the market in one single stock ticker. In our case, FFF. Frazer Rice (02:07.364)So let’s dive into that theme a little bit. As you said, it’s the top hundred founder led companies. First and foremost, public I assume, private, you’re not diving in those waters. Public vs Private Michael (02:20.59)Correct. So these are the hundred best publicly traded founder led stocks. And we generally fish from the 200 largest founder led publicly traded stocks. So a lot of these are names and founders that are very well recognized. Whether it’s Elon at Tesla or a Mark at Metta, Larry at Oracle, Rich Fairbanks at Capital One. These are all very well known founders. They’re great entrepreneurs who are leading highly scalable, very high performing publicly traded stocks. 02:53 Understanding Founder-Led Companies Frazer Rice (02:53.914)So let’s define founder a little bit. Obviously we have sort of the cult of personality around high-end CEOs. It sounds like you’re identifying companies that have been founded. The people who are running them not only founded them, but they scaled them. They have now gotten them to a level of maturity. That’s different from the typical public company that we find in the S &P 500. Definition of Founder Michael (03:19.104)Yeah. So first let’s define a founder. Then let’s talk about why we think the founder led companies outperform a traditional S&P company. We define the founder as being a chief executive leader. It could be chief executive officer, could be chief technology officer. Sometimes that say a scientific or medical company, would be the chief scientific or chief medical officer. And that person conceived and founded the company, took it from zero to one. It’s their imprint that has guided it over its 10 or 20 or 30 year period. That’s taken it from a small private company to a venture backed company to a large publicly traded company. And so the idea being the person that founded it continues to run it to this day. We talk about the fact that we own an Nvidia that Jensen still runs. But we don’t own Intel. We own Meta because Mark still runs it, but we don’t own Google. We own Dell computer because Michael Dell still runs it. But we don’t own Apple. We own Capital One because Rich Fairbank still runs it, but we don’t own American Express. Investment Process Frazer Rice (04:25.86)Got it. So lots of things to get into here. How does it a company get on your radar screen? And then ultimately, how does it get off of it? Michael (04:35.806)Great question. the getting on the screen is fairly mechanical. We look at the 200 largest by market capitalization founder led stocks. So we look at all U.S. listed. So it could be listed on the New York Stock Exchange or NASDAQ, but it has to be U.S. listed. We then look at the 200 largest. And from there, we select the 100 best using a quantitative factor model. So I’m have a Sanford Bernstein background and so do some of the folks here. And so for folks who are familiar with Bernstein’s research, we use a Bernstein factor model to pick the best, the hundred best names out of the 200 largest. That’s how they get on our radar. And to get off is quite simple if they retire. So if a CEO announces he’s retiring, per the prospectus, we have 90 days to sell the stock. once we, so for example, Mr. Buffett recently stepped down from Berkshire Hathaway. And so we sell Berkshire Hathaway on his announcement and no longer own the stock. Frazer Rice (05:38.0)things like corporate mergers or divestitures or maybe even a reclassification of stock where the founder stays on in some capacity but their decision making has been reduced. How do you analyze that? 05:54 The Investment Strategy Behind the ETF Michael (05:54.326)Yeah, so there is some human overlay judgment calls here and the founder has to be an executive officer leading the company. So they can’t just run a division. They can’t just be chairman of the board. They have to be the executive in charge of running the company. Frazer Rice (06:14.0)And if for, I guess one of the exits possibly would be if, and I don’t know if this is even possible, but if NVIDIA were to take over Meta and there isn’t room for Jensen and Mark in the same suite, how do you analyze something like that? Michael (06:34.253)So in the business combinations where you have two founder-led companies or a non-founder-led company swallowed up by a founder-led company, as long as an original founder remains, it remains in the portfolio. So we’ve had some stocks that had, say, three to four co-founders. And as long as one of those co-founder remains, it remains in the portfolio. Voting Shares Frazer Rice (06:58.352)So one of the things that’s a bee in my bonnet is the concept of having shares where, in a sense, they’re super majority or voting components and then shareholders that have less decision making authority to act as a check and balance around the company. Is that something you’re not really that worried about or is it something that may be a factor that’s important later on? Michael (07:24.525)So we actually think that’s one of the opportunities that this exists. Like one of the things that we haven’t talked about yet is why is all this alpha there? Why is this uncaptured alpha there for us to go get? And we think historically in the past, active money managers have sometimes shied away from these founder led companies because to your point, Frazier, oftentimes the founder has managed to have super voting control, 10 to one shares, 101 shares. So they completely control the company. And some of these larger active money management complexes have said, well, we as the shareholder, we need to be able to have a vote and we’re going to underown these stocks. We have the opposite view. We think these founders are special. So we think that by the time a Mark or a Elon has driven their company into the public markets, they’ve showed that they know how to set the vision, ruthlessly execute and generate value for the shareholders. Concerns? And so we’re not concerned by super voting structures. Oftentimes those are the stocks that we want to own because it’s the founder that’s in control and setting the direction of the business and generating high returns for the shareholders. We view it as you either believe in them and you own the stock or you don’t believe in them and sell the stock. We’re not interested in other people’s getting on the board and monkeying with the decisions of the founders. Frazer Rice (08:30.255)Is this it? What is it about the founders, especially for those that go from zero to one, then to scale, and then to shepherding a mature business? What makes them better and what drives the alpha that you’re trying to seek? In terms of putting together a portfolio of these types of companies? 09:01 The Importance of Founders in Business Michael (09:02.891)Yeah, so the great ones tend to be a bit irreverent. They tend to be highly visionary. They tend to be charismatic communicators and relentless in their execution ability. They’ve got a great ability to pivot if a change needs to be made. And rthe moral authority to set a tone to generate very high rates of return. We see it sort of over and over and over in these founder led companies. And if you look at some of the studies that we’ve done. There’s a study that Bain Capital, Bain had done years ago in combination with Harvard Business Review, founder led companies tend to outperform non-founder led companies in say the S &P 500 by 3X. So it’s this personality type of high vision and high execution tends to drive outsize returns. And it’s a bit of a self-selecting process. What makes Founders Unique? If you think about it by the time any of these founders that we own or talk about have got to the public market. They first had to identify an opportunity to go after. They had to develop a great product by listening to their customers. And they’ve shown that they can scale all the way from a series A round, B, C, D, all the way investing and generating high rates of return in the private markets. Transitions of Founders to Executives They get to the public markets, continue to do that. And now you get a little bit of an effect of a echo of that, of now all of sudden you’re in the public markets. If you get enough scale, you have this highly effective business. Now you’re getting relatively cheap capital that you’re feeding into your business through the public markets. And now you continue to grow. Frazer Rice (10:42.096)Just to summarize at least what I’m hearing is that they’ve gotten to the point of becoming public. They’ve been able to say no to losing control in exchange for either putting some liquidity back in their pocket or otherwise moving on. And so they’ve almost ratified their vision and message and they keep going. And by the fact that they’re public, there’s enough liquidity for everyone else out there in terms of their investments. So it ends up being a win-win. Michael (11:11.157)I think so. That’s what we see. Frazer Rice (11:13.316)So one thing that I’ve been sort of reading about and thinking about is the concept that the number of public companies is becoming less, well, it’s decreasing, and that many people are able to stay private for longer. Do you worry that your universe is going to get too small to provide sort of a canvas for your ideas here? 12:02 Market Trends and Future Outlook Michael (11:37.549)Let’s talk about three phases of that. We don’t, we actually see the data showing that there’s more and more opportunities within founder led. So let’s look at history and then let’s move to the future. So historically, probably about the time you and I joined the securities business, they would actually take the, to your point, they would take the founder, they would kick out this charismatic founder. They would put in some mid-level proctor or GE middle level manager to be the you know, the suit in the room to take the company public. And that was sort of in the late nineties and people figured out that wasn’t such a good idea. So if you actually look at the chart, there’s more and more founders staying and leading their public, their, their publicly traded companies. That’s number one. Number two. Yes. We have seen some companies stay private, obviously Stripe, SpaceX, but we are now seeing, for example, SpaceX coming to the public markets. Eli is talking about coming next year. so we, we haven’t seen it so far impact the pool with which we can fish in. And as I mentioned, that’s what we saw historically. Public Markets and the Future In the future, think, Frazer, I think we’re going to start to see a conversion of public and private markets, meaning these private mega cap companies have liquidity. And I think that you’ll see more and more ability to trade those stocks almost in public liquidity. So I think these two markets are converging. So I think that Not only do we have plenty of founders in the traditional public markets, I think that the liquidity and the big privates is going to converge to a public market style shortly anyway. Frazer Rice (13:13.232)You’re in a curious time as far as launching an ETF around this concept. I know a lot of people are wary of Mag-7 and ultra valuations and issues related to that. How do you respond to that concept that a lot of the growth has taken place in seven, maybe seven out of the hundred that you’ve chosen? Debunking the Mag-7 (to the Mag-3) Michael (13:33.356)Yeah, so that’s a misconception. We see Mike Saylor get on TV and wave his arms around it, but it’s not really true. First of all, what’s interesting, if you tear apart the Mag-7, it’s actually the Mag-3. The outperformance in the Mag-7 has come from Meta, Tesla, and NVIDIA. So it’s not just the Mag-7, it’s a founder led. And now you say, well, that’s a small sample set. Let’s look at a bigger sample set. So if you look at the NASDAQ 100, for example, It’s actually the 20 founder led companies have driven most of the outperformance over the last 25 years. And what I’m about to tell you about the S &P 500 probably won’t surprise you. It’s the 37 founder led companies that have driven most of the outperforming the S &P 500. So the outperformance is coming from founders, not from any specific part of the market. And one of the things that we think is great about this ETF is to avoid concentration. 14:50 Risk Management I know you’re really familiar with the concept of active share and that’s how different you are than the S &P 500. We have an 85 % active share to the S &P 500. So if you own the founders 100 ETF, you have much different exposure to the market than say the S &P 500. And so we think it helps reduce some of that concentration. We’ve done some things to make sure that we are diversified. First of all, we do own 100 stocks. Diversification So really good diversification across that. And then number two, while we run a market weight portfolio, we cap. No stock can be bigger than 7 % of the portfolio, so we don’t get out of balance at any point. So we think that we mitigate some of those concentration risks and we allow people to invest in innovation without being over concentrated to any one name, say the MAG-7, for example. So we think that we’re giving our investors really good exposure to innovation through the founders, but not exposing them to pre-existing market concentrations. And then finally remind everyone It’s not the MAG-7, it’s not the NASDAQ-100, it’s not the S &P-500, it’s the founders within each of these are what are driving the outsized performance in those analytical groups. Frazer Rice (15:36.218)So from a diversification standpoint, obviously not everything in one name, the 7 % cap you described, do you have sector concentration guidelines as well? Michael (15:45.749)We don’t have sector concentration guidelines, but if you look at the nature of the portfolio, we were fairly well diversified. We’re slightly overweight tech and financials versus say the S &P, but we own healthcare stocks, own consumer stocks, we own energy stocks. So we’re giving you a broad exposure to the market. Leverage Frazer Rice (16:05.924)Let’s talk about leverage for a second. I know a lot of people are trying to juice returns by piggybacking off of other people’s money on that front. Does that have a place in your ETF? Michael (16:17.004)So there’s no leverage in the ETF. We sort of believe in get rich the slow way. I like to tell people that it’s very hard to make money in the stock market over the short term, but it’s not particularly difficult over the very long term. think Mr. Munger and Mr. Buffett used to talk about this. the idea being, leverage can impact you in times that are not favorable. So we believe in just owning the stocks unlevered, let them compound over very long periods of time. And we think that by doing that, we and our shareholder, we think our shareholders can generate wealth over very long periods of time. Taxes Frazer Rice (16:54.98)So tax efficiency, the concept of holding period, does that play into your process at all? Michael (17:04.316)So remember within the ETF, as long as you’re managing your trading properly within the ETF, there’s no tax implications inside of it for your shareholders. Your shareholders only would be impacted at selling. So assuming they hold the stocks for over a year, any gains would be long-term capital gains treatment. Frazer Rice (17:27.024)And when you’re describing the investor profile that you’re looking to attract here, who is this for? Michael (17:35.916)Yeah, so the person that, you we really think it’s appropriate for you if you have a five year or more holding period and you want to have long-term capital appreciation. You know, if your goal is to be exposed to the best minds and public securities, that’s the founder led companies, and you want to compound your wealth over a very long period of time and have a high probability of outperforming the traditional broad market indexes, this ETF is designed for you. 17:59 Investor Profile and ETF Positioning Frazer Rice (18:04.705)And as you’re sort of outlining that profile and for those people who are trying to figure out where this fits in from an equity allocation perspective, you’re in charge in many ways of the spoke of a hub and spoke component of people are really sort of looking at indexes as the base of their equity portfolio. What are you looking for? What kind of benchmarks do you sort of measure yourself against? Michael (18:35.007)Yeah, so we think this is absolutely a core holding. So if you’re looking to build out you or your client’s portfolio, we think this should sit at the core. It is on the growth side, so it’s core growth. We think that it is a one-for-one replacement for, the NASDAQ 100. Or, for example, somebody holding the triple Qs. We think this is a better holding than the triple Qs. So we benchmark ourselves against them and against the S &P 500. Ee look at beating those two broad market indexes, generating better risk return for our investors. Frazer Rice (19:13.019)For those listeners that are out there and want to find out more, what’s the best way that they can either get a hold of you or maybe even better, do you have a ticker symbol ready that people can discover? FFF and Contact Information Michael (19:25.215)Yeah, absolutely. So the ticker is FFF. So that’s the FFF ETF that we’ll trade on. And investors can find that at their favorite brokerage firm, whether they’re Schwab customers, Interactive Brokers customers, Fidelity customers, trades under one ticker, just like a stock. Frazer Rice (19:44.365)And let’s take, we have a few minutes to go here, which is great. Your experience in terms of establishing the ETF, maybe a couple of some of the touch points when you went from vision to execution here, what was the process? Michael (20:00.106)Yeah, so ETF has a few basic processes that are regulated under the 1940 Securities Act. And so a lot of those rules are set up to protect the end investors. So for example, the securities live within a trust. So we set up our own trust. Some people use a mingled trust. We thought it was better for our end investors to have our own trust that we set up that has an independent trust board that oversees to make sure that we’re executing our strategies as we’ve outlined in the prospectus to make sure that we’re Doing the best we can for our investors. You’ve got to set that up There’s a few firms that do the plumbing for the for the ETFs would say US Bank is probably the largest player. So US Bank provides our our fund custody and fund administration and then there’s just a few other vendors in the space that sort of help with all the plumbing to make sure that the ETF runs smoothly. So it’s probably a six month process if you stay really focused to get all of that set up. 20:58 Navigating the ETF Launch Process Frazer Rice (21:03.313)You get that set up, how do you approach the Schwabs and the Fidelitys and the other platforms to make sure that people can access, buy, sell, whatever they want to do with your ETF? Michael (21:14.347)Yeah, that’s a great question. So the online brokerages typically put you on the platform as soon as you’re listed on a major US exchange. So you’ve got to get listed on NASDAQ, NYSE or CIBO. We chose CIBO. So again, on the traditional online brokers, you’re there day one. And then the big wire houses, JP Morgan, Goldman, Morgan Stanley, BAML, they typically have a few hurdles that you’ve got to get through, whether it’s daily trading liquidity assets under management. And over time, as you run the wickets through their process, you’re added to those platforms. Macro Issues? Frazer Rice (21:48.721)We live in a political age and a time when there’s just chaos everywhere, different types of rules in order to allocate capital. If you’re an investor trying to guess what’s happening politically, et cetera, that are difficult, you must be positive as far as the environment for founders to find success in this country and beyond. Is there anything that you’re looking for to make sure that those conditions hold? Michael (22:18.225)Yeah, we don’t really look at the macro or political backgrounds. think over very long periods of time, U.S. innovation outperforms. so we sort of we think that, again, one of the great things with investing in founders is they keep adapting as the background changes behind them. So we think over very long periods of time, the U.S. has great economic growth. And for those people that have worried about little blips along the way, we think the founders are the absolute best at mitigating those blips. Frazer Rice (22:48.334)I like to say you bet against America at your own peril and it sounds like from a founder perspective it’s still a great place for them to locate their businesses and grow them here. Michael (23:01.042)Absolutely. 23:50 Final Thoughts and Contact Information Frazer Rice (23:02.971)Just to reiterate, FFF is the ticker symbol for people to find it. any other contact points for people to find you if they’re interested in what you’re putting together. Michael (23:15.613)Yeah, so we have a great website at FounderETFs.com. can go check out there or anyone’s happy to email me, just michael at FounderETFs.com. Happy to chat with anyone who has interest about the portfolio, the strategy, or what we’re building. Frazer Rice (23:32.197)Well, great to have you back on, Mike. Thank you for putting up with my attempt at looking like Steve Jobs. It’s 25 degrees in New York here, and I am the stupid one who’s not in California or somewhere warm. appreciate you taking the time to be on and talking about your new product. Michael (23:48.011)Yeah, it was great to be on here. Really a huge fan of your podcast and just the level of guests that you’re able to interview and help educate your viewers. Frazer Rice (23:56.849)Mike, thanks for being on. Michael (23:59.061)Thanks a lot, Frazer. https://www.amazon.com/Wealth-Actually-Intelligent-Decision-Making-1-ebook/dp/B07FPQJJQT/ Previously with Mike Monaghan ETF EDUCATION ARTICLES ON ETF.COM

Would you like access to our advanced agency training for FREE? https://www.agencymastery360.com/training Have you ever felt like enterprise clients were running your agency instead of the other way around? Buried in endless proposals no one reads, forced into rushed timelines, or watching your margins shrink every time a project drags out? Today's featured guest opens up about how he broke out of that exhausting cycle. Instead of over-delivering just to keep big clients happy, and seeing little return, he made the bold decision to focus on smaller, more committed clients who were ultimately more profitable and easier to build long-term relationships with. He'll share what he learned about sustainable growth, including why productizing your services sounds great in theory but can actually become counterproductive when it only happens externally. He'll also explain the sales shift that changed everything: offering a low-risk, "foot-in-the-door" engagement that qualifies prospects, builds trust, and creates a smooth path into deeper service offerings. Charlie Clark is the founder of Minty Digital, a boutique SEO agency focused on travel and lifestyle brands that originally launched in Barcelona and now operates from London. In this conversation, he'll break down the mindset, systems, and strategy needed to stop chasing validation from big brands and instead build a business where profitability, alignment, and respect come first. In this episode, we'll discuss: Why mid-market clients deliver higher profits than enterprise. How internal productization increases efficiency by 3X. How clear pricing transforms the sales cycle. How AI forced a new level of adaptability in SEO agencies. Subscribe Apple | Spotify | iHeart Radio Sponsors and Resources This episode is brought to you by Wix Studio: If you're leveling up your team and your client experience, your site builder should keep up too. That's why successful agencies use Wix Studio — built to adapt the way your agency does: AI-powered site mapping, responsive design, flexible workflows, and scalable CMS tools so you spend less on plugins and more on growth. Ready to design faster and smarter? Go to wix.com/studio to get started. From Struggling Freelancer to Sustainable Agency Growth After a short stint in an agency at age 22, Charlie tried to go solo before realizing he didn't yet know how to grow a business. He assumed he could do it on his own and quickly learned he wasn't ready yet. Instead of quitting, he got a job as a Digital Marketing Manager, where he could make mistakes, learn operations, and understand what actually works inside a business. Moving to Barcelona created the perfect environment for momentum. His one-month stay turned into ten years after he landed several clients within weeks. His first retainer was €500 a month, which he laughs about now, but he admits it took years before he learned how to price correctly and move away from low-margin retainers. Those early years were full of trial and error, but the big breakthrough was realizing that charging more wasn't always the key to profit. Charging smarter was. Real Profit Lives in the Middle, Not the Top One of the strongest lessons Charlie learned was that bigger retainers did not equal bigger profit. Working with enterprise clients, he saw they could easily squeeze margins, the team would end up over-delivering, and on top of it all, payment terms were a nightmare. After years, he realized these clients often cost the agency money when the team over-delivered just to keep them happy. By contrast, the clients who had been with him since the early days, the ones paying between three and six thousand per month, were the most profitable and the most loyal. They bought the same deliverables. They stayed for years. And they matched the agency's internal processes beautifully. Once he realized this, he moved to intentionally pursuing that sweet spot. Not the five figure monthly retainers or the cut rate ones. The predictable, operationally aligned middle where the team can deliver consistently and profitably. For Charlie, this changed everything from sales cycle speed to team alignment to lifetime value. Internal Productization: The System that 3X Efficiency Many agencies think productization means selling rigid packages that make you look less strategic. Charlie took the opposite approach. Internally, his team adopted highly productized systems, templates, and SOPs. They knew exactly what to do for a three thousand dollar client versus a six thousand dollar one, and how much effort each one required. Externally, the offer still looked consultative and customized. Clients saw a broad range of what could be included, but the delivery stayed tight behind the scenes. This improved profitability, gave the team clarity, and dramatically sped up onboarding. The biggest win? It eliminated the "start from scratch every time" problem that slows agencies down and kills margins. How Clear Pricing Transforms the Sales Cycle Before productization, Charlie would spend hours on proposals that often got ghosted. Once he added transparent pricing, clear expectations, and prequalification to the website, the right clients were self-selecting before the call even happened. By the time he spoke with them, they understood the price and the structure. Now he closes clients on the call or even through a single WhatsApp message. This is the power of clarity. It shortens cycles, reduces friction, and saves enormous amounts of time for a lean team. However, transparent pricing attracts budget mismatches, so Jason recommends removing pricing from agency's websites and switching to triage calls and the Foot-In-The-Door model. At the end of the day, there are a thousand ways to create a better sales process. What matters is that it filters, qualifies, and positions you as the advisor. Why a Paid Discovery Offer Builds Trust and Prevents Ghosting Both Charlie and Jason agree that a small, paid upfront engagement solves the biggest challenge in agency sales. Trust. SEO agencies in particular fight an uphill battle here. The barrier to entry is low. There are thousands of one-person shops. Many prospects have been burned before. A small paid engagement builds confidence, shows value quickly, and prevents ghosting. The Foot-in-the-Door offer should be simple, done live with the client, and designed to build the relationship. Not overloaded with deliverables that overwhelm the client and make them feel uneducated. When done right, it leads naturally into a larger project and then a retainer. Charlie's Kickstart product functions the same way. For eight hundred dollars, clients get quick wins and clarity. It works because it gives prospects a safe way to test the relationship and because it positions the agency as a trusted advisor instead of a vendor chasing a proposal. How AI Forced a New Level of Adaptability in SEO Agencies Charlie admitted that two years ago he felt bored with SEO. Then AI exploded. Search interfaces changed. Clicks shifted. And suddenly the industry was moving faster than ever. For many agencies, this uncertainty created fear. For Charlie, it sparked energy. He leaned back in, started speaking at events, ran experiments on AI search, and brought a fresh curiosity back to himself and his team. He described the past year as a sink-or-swim moment for agencies. The ones who coasted struggled. The ones who adapted thrived. Lean teams with solid systems could move faster and deliver more value. In his words, being nimble is now a competitive advantage. Figuring out AI reignited his passion in the business but it was far too much to tackle alone. This is why agency owners should have a community to lean on to try to figure out changes in the industry. Your network will determine your speed of growth. Agency owners who surround themselves with peers sharing what works and what fails will survive the next wave of industry change. The ones who go alone will struggle. Do You Want to Transform Your Agency from a Liability to an Asset? Looking to dig deeper into your agency's potential? Check out our Agency Blueprint. Designed for agency owners like you, our Agency Blueprint helps you uncover growth opportunities, tackle obstacles, and craft a customized blueprint for your agency's success

I haven't done the math, but imagine if your parents gave you $5000 annually until you're 18, their company matched it with $2500. That's $135,000. If that compounds 3X, then you have roughly $400,000 to start life. Use it for education, a business, to buy your first home, or just keep investing it.I heard that it becomes an IRA, if it's not used for education. So perhaps you can't use it right away, but take that money out to 65 and you get astounding numbers.I ran the numbers and a nominal return on that money would yield an inflation-adjusted $7M in today's dollars. Every child would theoretically retires a multi-millionaire.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to the Conscious Millionaire Show. 3X each week - M / W / F   Become an Ultra-Performer - Entrepreneurs. Experts, Professionals - Committed to Becoming Top-1% of Performers. Rev $250K to $50M? Sign up for complimentary Breakout Session. Find out your #1 block keeping you from scaling faster and discuss if an Ultra-Performer Program working directly with JV is right for you.     Schedule Your Breakthough Session     Join Host JV Crum III, with 2 exits and over 75M revenues in his companies, he is the Ultra-Performer Coach for 6- to 8-figure owners ready to join the top 1% of Ultra-Performers.   Season 12 of the award-winning Conscious Millionaire Show. World's #1 conscious business and performance podcast for foundeers and entrepreneurs who want to become Ultra-Performers.    Access Conscious Millionaire Show     Millions of Listeners in 190 countries. Inc Magazine "Top 13 Business Podcasts" with over 3,000 episodes and 100 million listeners world-wde. Listen 3X a week.

Work with Jimmy & the Vreeland Capital Team to build a 20-Unit Portfolio that will get you the equivalent of a retirement account 3X faster with a third of the capital. Visit https://tinyurl.com/mainstreetpatriot... In this episode of The Real Estate Fast Pass Podcast, hosts Jimmy Vreeland and Susie Vreeland tackle one of the biggest hurdles for high-income earners who want real estate without a second job: “How do I buy a house I've never seen in a city I've never visited—and actually sleep at night?” Jimmy and Susie explain why walking a property can sometimes increase anxiety and lead to emotional decision-making, especially for first-time or out-of-state buyers. Instead, they break down the repeatable “boring on purpose” system that protects both your money and your peace of mind—complete with detailed photo documentation, standardized scopes of work, and a clear inspection process focused on the big-ticket mechanics like roof, HVAC, and plumbing. They also reveal how their underwriting builds certainty through a triangulation of value—using internal analysis, third-party appraisal input, and agent insights to determine after-repair value—so buyers aren't guessing or relying on vibes. You'll hear how transparency, systems, and proven reps remove decision fatigue, why true wealth comes from rinse-and-repeat execution (not one-off “fun” projects), and how boring Midwest rentals can be the most powerful path to building real, long-term passive wealth. If you want the returns of real estate without the constant attention, this episode will show you how to invest confidently from anywhere. About Jimmy Vreeland Jimmy graduated from the United States Military Academy at West Point, spent 5 years as an Army Ranger, and deployed three times twice to Iraq and once to Afghanistan. On his last deployment, he read Rich Dad Poor Dad by Robert Kiyosaki which led him down the path of real estate investing. As his own portfolio grew, eventually he started a real estate investing business.  Since 2018 his team at Vreeland Capital has supplied over 100 houses a year to high performing, passive investors who want to work with his team and his team is now managing over 800 houses. Get in touch with Jimmy and his team at www.jimmyvreeland.com/getstartedinrealestate More about Jimmy Website: www.jimmyvreeland.com Linkedin: www.linkedin.com/in/jimmy-vreeland Instagram: www.instagram.com/jimmyvreeland Facebook: www.facebook.com/JimmyVreeland Youtube: www.youtube.com/@JimmyVreelandC >>>>>>Get free access to the private Ranger Real Estate facebook group

Jonathan Goldman is an internationally acknowledged pioneer in the field of sound healing. He is an award winning author, Grammy nominated musician and work renowned teacher.     Welcome to the Conscious Millionaire Show. 3X each week - M / W / F   Become an Ultra-Performer - Entrepreneurs Committed to The Top-1%. Revenues $250K to $50M? Sign up for complimentary Breakout Session with JV. Find out your #1 block keeping you from scaling faster, profiting more, and making your greatest impact.      Schedule Your Breakthough Session     Join Host JV Crum III, with 2 exits and over 75M revenues in his companies, he is the Ultra-Performer Coach for 6- to 8-figure owners ready to join the top 1% of Ultra-Performers.   Season 12 of the award-winning Conscious Millionaire Show. World's #1 conscious business and performance podcast for foundeers and entrepreneurs who want to become Ultra-Performers.    Access Conscious Millionaire Show     Millions of Listeners in 190 countries. Inc Magazine "Top 13 Business Podcasts" with over 3,000 episodes and 100 million listeners world-wde. Listen 3X a week.

  Seth Levine, is co-founder of Foundry, a VC firm in Boulder, Co with several billion under management. He is the co-author of the book, Capital Evolution.      Welcome to the Conscious Millionaire Show. 3X each week - M / W / F   Become an Ultra-Performer - Entrepreneurs. Experts, Professionals - Committed to Becoming Top-1% of Performers. Rev $250K to $50M? Sign up for complimentary Breakout Session. Find out your #1 block keeping you from scaling faster and discuss if an Ultra-Performer Program working directly with JV is right for you.     Schedule Your Breakthough Session     Join Host JV Crum III, with 2 exits and over 75M revenues in his companies, he is the Ultra-Performer Coach for 6- to 8-figure owners ready to join the top 1% of Ultra-Performers.   Season 12 of the award-winning Conscious Millionaire Show. World's #1 conscious business and performance podcast for foundeers and entrepreneurs who want to become Ultra-Performers.    Access Conscious Millionaire Show     Millions of Listeners in 190 countries. Inc Magazine "Top 13 Business Podcasts" with over 3,000 episodes and 100 million listeners world-wde. Listen 3X a week.

It's time to draw your 2026 marketing plan, and we want to help you maximize profits with Meta ads. We're offering you 30 monthly deliverables,10 ad types, media buying, and access to Tier 11's Data Suite before the year ends.Claim your Creative Diversification Package now at: https://www.tiereleven.com/cd Are you trying to squeeze every last bit out of Meta, Google, and TikTok but still missing a piece of the growth puzzle? What if one of the most powerful performance channels is hiding in plain sight on your TV? That's exactly the concept we dig into today as we explore the opportunities inside connected TV and why it's becoming a must-have in every marketer's media mix.We're joined by Vibhor Kapoor, Chief Business Officer at NextRoll, to unpack how CTV has evolved from a “big brand awareness play” into a precision-targeted, full-funnel performance channel. We get into CPM shifts, attribution clarity, identity graphs, retargeting flows, and the wild amount of audience-level data available inside modern streaming platforms. Plus, we talk about how repurposed social creatives, not $200K production shoots, are already driving 2–3X stronger ROAS for brands and B2B companies. As you build your 2026 plan, this conversation will redefine multi-channel advertising and where the smartest budget shifts are happening. In This Episode:- What NextRoll does now: the evolution- How NextRoll handles attribution on social platforms- Why AI-driven budget optimization is necessary- What is CTV and why is it exploding?- CPM shifts in CTV targeting - Targeting and retargeting CTV ads across devices - Real-world CTV results- What marketers must know for 2026Mentioned in the Episode:AdRoll / NextRoll CTV case studies Listen to This Episode on Your Favorite Podcast Channel:Follow and listen on Apple: https://podcasts.apple.com/us/podcast/perpetual-traffic/id1022441491 Follow and listen on Spotify:https://open.spotify.com/show/59lhtIWHw1XXsRmT5HBAuK Subscribe and watch on YouTube: https://www.youtube.com/@perpetual_traffic?sub_confirmation=1We Appreciate Your Support!Visit our website: https://perpetualtraffic.com/ Follow us on X: https://x.com/perpetualtraf Connect with Vibhor Kapoor:Website: https://www.adroll.com/ LinkedIn: https://www.linkedin.com/in/vibhor Connect with Ralph Burns: LinkedIn - https://www.linkedin.com/in/ralphburns Instagram -

Welcome to the Conscious Millionaire Show. 3X each week - M / W / F   Become an Ultra-Performer - Entrepreneurs Committed to The Top-1%. Revenues $250K to $50M? Sign up for complimentary Breakout Session with JV. Find out your #1 block keeping you from scaling faster, profiting more, and making your greatest impact.      Schedule Your Breakthough Session     Join Host JV Crum III, with 2 exits and over 75M revenues in his companies, he is the Ultra-Performer Coach for 6- to 8-figure owners ready to join the top 1% of Ultra-Performers.   Season 12 of the award-winning Conscious Millionaire Show. World's #1 conscious business and performance podcast for foundeers and entrepreneurs who want to become Ultra-Performers.    Access Conscious Millionaire Show     Millions of Listeners in 190 countries. Inc Magazine "Top 13 Business Podcasts" with over 3,000 episodes and 100 million listeners world-wde. Listen 3X a week.

I am not a big fan of using split shot but after this interview with Dom Swentoskey [36:41] of the Troutbitten blog and podcast, I'm going to use it a lot more. Dom's method of using split shot is simple and convenient, and he teaches us about placement, adding shot, and removing shot easily—and how to keep it from sliding on your tippet without placing it above a knot. Whether you fish nymphs or streamers, Dom has some great suggestions on using split shot properly. In the Fly Box this week, we have some great tips and questions form listeners, including: Is a 10-foot, 4-weight fly rod a good all-around rod for fishing in New England? If I have a floating and full sinking line for bass fishing, would an intermediate line be the next one to try for largemouth bass? Why aren't there more resources like books on fly fishing for largemouth bass? How many different floating fly lines do you have at home? When you are taking a trip, how many floating lines do you take? I have been steelhead fishing in Alaska with a tight line presentation. In what situations would an indicator be beneficial? I don't quite understand why we would take food out of a fish's mouth by using a throat pump. How can you justify this? Will egg flies work in Colorado? Is it possible to shoot line with a bow-and-arrow cast? Are there any saltwater barbless hooks? Why do spawning shrimp patterns always have the egg cluster tied near the head? A tip on using small magnets to hold hooks at the fly-tying bench A tip on using a pool noodle to hold larger saltwater flies after tying them Is an 8-pound bass leader the same as a 3X leader? A tip from a listener on how to alleviate shoulder pain when fly fishing.