Podcasts about 20x

Partner with Jay: https://www.jayschwedelson.com/contactㅤPre-order Jay Schwedelson's new book, Stupider People Have Done It (out June 9, 2026).All net proceeds are donated to The V Foundation for Cancer Research, let's kick cancer's butt: https://www.amazon.com/Stupider-People-Have-Done-Marketing/dp/1637635206ㅤSubscribe to Jay's newsletter for weekly marketing tips and tactics: https://www.jayschwedelson.com/newsletterㅤRegister for Eventastic (FREE + VIRTUAL!) https://www.eventastic.comㅤRegister for GuruConference (FREE + VIRTUAL!) https://www.guruconference.comㅤConnect with Jay on LinkedIn: https://www.linkedin.com/in/schwedelson/Check out Jay's YouTube channel: https://www.youtube.com/@schwedelsonCheck out Jay's Instagram: https://www.instagram.com/jayschwedelson/Ask Jay anything: https://www.jayschwedelson.com/askㅤLeave a comment and follow the show, it really helps us out!ㅤMASSIVE thank you to our Sponsor, CallRail!CallRail is the AI-powered lead intelligence platform that helps marketers prove exactly what's driving results. With CallRail, you can connect every call, text, chat, and form submission directly to the campaign that generated it so you finally know what's working and where to double down.Plus, with built-in AI conversation intelligence, CallRail analyzes your customer conversations, captures leads 24/7, and gives you deeper insights into what your prospects actually care about.If you're tired of guessing about your marketing ROI and want real data behind your campaigns, CallRail has you covered.Start a Free Trial Here: https://www.callrail.com/dothisㅤPaying for an AI tool and quietly wondering whether you're getting what they promised? That nagging feeling just turned into a class action lawsuit, and Jay Schwedelson thinks it's the first of many in a world where nobody can actually measure what "usage" even means. He also makes a surprisingly convincing case for why dropping the work-email requirement on your forms is a win and not a leak, with detours through LinkedIn GIFs, a Netflix rom-com, and a bestseller list nobody saw coming.ㅤBest Moments:(00:35) LinkedIn is rolling out GIFs in comments, and why that matters more than it sounds(02:45) The Claude Max buyer who expected 20X more usage and found he was getting closer to 6X(02:56) Why "am I actually getting the AI usage I'm paying for" is about to become a recurring legal fight(04:00) Year over year, 27% fewer companies are forcing a work email on their forms(04:54) The case for letting job seekers use a personal email, and why your brand wins when they get hired(08:16) "Stupider People Have Done It" lands at #87 on the USA Today list, with $130,000 raised for cancer research

Most women think the answer to making more money is working harder, longer, or squeezing more into an already packed schedule. In this episode, I break down why that is one of the biggest wealth traps I see, and how the real game is learning to create more output with the exact same input. I'm sharing the identity shift that helped me turn a $54K cash week into over $1M in long-term wealth, why Tony Robbins earns hundreds of millions in the same 24 hours we all have, and how to stop optimizing for cash… and start optimizing for true wealth. Tune in to learn: The one reason people like Tony Robbins make millions with the exact same hours you have The difference between optimizing for cash versus optimizing for wealth The identity shift that can help you create 20X more wealth with the exact same time and money you have right now What leverage actually is + how to create more output for the same input in your business, money, and life Why working harder is not what creates the biggest financial results + what high-net-worth women do instead

How to Make £80,000 in ONE DAY? My Top 5 Life-Changing Books!In this episode, I'm sharing the 5 most powerful books that completely shifted my mindset, built my property empire, and even helped me generate £80,000 in a single day. If you feel stuck in the corporate layout or want to scale your UK property investment journey, these books are your blueprint for success.In this deep dive, you will learn the specific lessons from each book that can transform your financial future:Rich Dad Poor Dad: How to stop working for money and start making money work for you by understanding assets vs. liabilities.Multiple Streams of Income: Why relying on one strategy is slow and how to diversify within the UK property market using BRRR and Serviced Accommodation.Millionaire Success Habits: The daily routines and mindset shifts that separate the wealthy from the rest.Influence: The Psychology of Persuasion: The critical skill of communicating with investors and partners to get them to say "Yes."$100M Offers: The secret to crafting property deals and business offers so good that people feel stupid saying no.Investing in your personal development is the highest return on investment you will ever get. These aren't just books; they are the tools I used to 20X my business revenue.About Rahim Bah: Rahim Bah is a public speaker, entrepreneur, property investor, property educator, business mentor, and content creator. The Rahim Bah Channel is focused on educating people to invest in UK property, personal development, business, and how to become an entrepreneur. Whether you're a young entrepreneur, property entrepreneur, have a business idea, or are just thinking about how to start a business, you'll get the value and business motivation you need to succeed from these episodes.

The SaaS multiples run was long, but it had to come to an end. Or Had it? Navigation: Intro Setting The Scene The Roots — This Didn’t Happen Overnight The Structural Thesis — Why This Isn’t Just A Sell-Off The Private Market Fallout The Bull Case — Is The Market Wrong? Separating The Wheat From The Chaff — Who Survives? Wrap-Up & Key Takeaways 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 Introduction Nuno Goncalves PedroWelcome to Episode 75 of Tech DECIPHERED, the SaaS Apocalypse: Why AI Breaks or has Broken or Broke the Software Business Model. In today’s episode, we will talk about what’s been going on in SaaS. SaaS, also known as Software as a Service, as a sector, has just had its worst month since the 2008 financial crisis. Give or take, around 1 trillion in software stock market cap has evaporated this year, and it was triggered in many ways by the rise of a lot of the things we’re seeing, in particular, agentic AI. We’ll talk about it later.One of the key triggers seems to have been the launch of Claude or Claude Cowork. There’s a lot of fears that the model that is taken as SaaS to be the darling of investors, both VCs, private equity funds, and also retail investors, has now evaporated. The sweetheart industry no longer works. Bertrand, what happened to SaaS? What’s happening? Bertrand SchmittSetting The SceneWe are in the middle of what some are calling the SaaSpocalypse. I think that was a coined term early this year. It’s pretty bad. We are recording that March 13th. Definitely January, February of this year, 2026, were really terrible. There is no question about it. Strangely enough, since the start of the war with Iran, there has been a small rebound, so we will see how it goes. But also to give some context, we are still not worse than what happened in 2022. We are still in a better place so far. I would say the difference, there is clearly a focus in terms of SaaS versus tech in general for that down term. Nuno Goncalves PedroWe’ve seen obviously a lot of things happening, right? A lot of announcements. The iShares expanded Tech-Software ETF down 25% year-to-date. Everyone seems to be running into panic, JPMorgan, Goldman Sachs. Basically, Jefferies, I think, as you said, originally termed this the SaaSpocalypse. But definitely, it seems like everyone’s trying to sell stock and saying, “Hey, SaaS is going to die.” We’ve seen a lot of interesting elements to this, we’ll talk about it later, around AI eats software. Software eats the world. AI now eats software. I guess AI eats the world.But the reality is, we’ll discuss it later in the episode, it might be just a lot of stuff that’s reacting to what’s actually happening in the market, that there was a couple of misses in terms of numbers, that the growth of some of the key SaaS players that are driving a lot of the public stock wasn’t that great recently. That adding to some launches like we mentioned, the Claude Cowork launch, et cetera, has led people to say, “Hey, maybe some entire spaces of SaaS don’t make much sense going forward.” Bertrand SchmittActually, I don’t know if you noticed, but I think it was yesterday, it was announced that the CEO of Adobe just resigned. I was shocked how bad they managed the transition to AI. I guess it’s one of the first victims of what has been happening. From my perspective, and I will go deeper, but there is a bit of an overreaction. Claude is amazing as a tool, but the launch of Claude Cowork, a few plugins decimating the market, I think that’s an overreaction in the sense that many of these SaaS companies will be able to actually benefit from AI as well. Or some of the new AI tools really, really depend on the existence of an underlying SaaS layer that’s controlling some processes, some data. So I think we have to be careful about the extremes.At the same time, what is true, the growth rate has been going down for SaaS. If you look in the 2021 to these days, we move maybe from 30-11%, 12% average growth rate. It’s a dramatic difference in growth rate, and you cannot keep the same valuation when your growth rate has been divided by three. I mean, that’s just not possible.I think that there might be some overreaction about what company like Claude can truly achieve. At the same time, the reality is there that while SaaS companies are usually relatively strong companies, the growth rate has diminished, and as a result, so should the valuation.The Roots — This Didn’t Happen OvernightBut maybe we can move deeper about what happened the past 2 years about SaaS. Nuno Goncalves PedroIndeed. Some things going back as much as 2024 when Salesforce had its worst trading day. By then, in 2 decades, and went down by 20% on a rare revenue miss. So some early people, a lot of analysts, see this as an early warning of what was to come. Late last year, a huge shift as the different labs of a bunch of different players started launching agentic solutions, which in some ways started eating into a lot of the functionality, not just of vertical SaaS, but also of horizontal SaaS. As a distinction for some of our listeners who are not familiar with that distinction, vertical SaaS is normally SaaS that’s very specific to a specific industry or sub-industry or specific arena, whereas horizontal SaaS is normally SaaS that doesn’t require much adaptation to work across industries. A good example of that might be HR management systems.But basically, because of some of the early developments in those labs and a lot of the solutions that we started seeing around agentic tools, the market started being less positive on SaaS players and trying to readjust it. Those are the historic moments, 2024, 2025. Then all of a sudden, we see the growth rates of SaaS companies coming down, because obviously this doesn’t only have manifestations in the public equity markets. This has manifestations in clients.People, at this moment in time, we’ll talk about it later, are reconsidering their options. They’re like, “Why should I have a SaaS tool? Should I buy it from another player? Should I have a more holistic solution or an integration with Claude, for example? Should I develop in-house?” We’ll talk at length on what’s in customers’ minds, but customers started changing their views and stop buying some solutions that were out there from the large players that are public equities today. Bertrand SchmittYeah, it’s clear that there has been also just overall industry-wide tendency to try to cut on the SaaS subscriptions. Maybe there was too much interest buying too many software solutions, not rationalizing enough, not being careful about the spend. It makes sense that this has hurt overall SaaS growth rate. At the same time, there has been a transfer from IT spending from SaaS tools to AI, so we create a smaller budget for buying SaaS software.But going back, when you look at the change in revenue multiples, it’s crazy. In 2021, we were close to 20X EV, enterprise value to revenues. Now we are talking about 6-7X entering 2026, and we will see later on it does crunch even more. Right now, we are at 4X revenues. So from 20 to 6 to 4, and that’s the lowest in terms of multiples since 2016. That’s 10 years ago. P/E multiple for what multiples also comprise from close to 40 to close to 20.Talking about Adobe, Adobe trades at 5-year average of 30X, now at 12X. No wonder the CEO resigned. I don’t want to be mean, but I think it’s clear some CEO were very strong leading their companies into a SaaS paradigm, but were not as strong leading their company to a new AI paradigm. I think the markets are going to be brutal. If you are good at showing that you can transition to AI, you’re an important piece of the puzzle for AI, that’s one thing. But if the markets believe your products have not kept up, then it’s truly big trouble.I mean, they are not the only one. Intuit 34% decline in a month. Atlassian, minus 35 in a week. ServiceNow also down a third. They are not the only one, but definitely companies have to show some proof of either the lack of vulnerability in an AI world or their capacity to really move strong to a brand-new AI world. Nuno Goncalves PedroThe Structural Thesis — Why This Isn’t Just A Sell-OffWhat are the structural issues? Why wasn’t this just a sell-off? Why is this structurally a problem? The first thing is really around monetization and business model. SaaS 1.0 or 2.0, however we want to call it, was based on seat-based licensing. Seat-based licensing was the notion that with more employees and more users on the platform, there would be more revenue for the SaaS company. Very simple, very clear, very lucrative.Now, obviously, AI agents don’t occupy seats. An agent can do the work of 10 people, can do the work of 20 people, 30 people, 100 people, whatever it is. Therefore, if I’m a company, and I’m using agents, and not necessarily a human user, I’m not going to buy 10 licenses for the work of 10. I have one license, and it’s used by an agent that basically has access to that tool. That’s the first issue. The first issue is that the seat-based pricing, assuming humans, assuming a certain degree of productivity, et cetera, all of a sudden is under stress. Bertrand SchmittMaybe to highlight some point, not every SaaS company was focused on per-seat pricing. Me, when I led App Annie, we didn’t have a per-seat licensing or pricing at all, so we were focused on value-based pricing. But that’s true that around us, we have seen that quite a lot of your typical SaaS business was run on a per-seat pricing. Anytime there is a market downturn, you pay a dear price for your per-seat pricing. On top of it, these days, as you said, we have AI. In an AI world, the per-seat pricing model breaks down. Nuno Goncalves PedroIndeed. Now people are asking for other kinds of pricing schema, right? Either flat pricing based on certain usage patterns or, for example, outcome-based pricing. So depending on the outcome of what I’m trying to achieve, is it a booking of a sales call, is it something else? Whatever it is, I pay for that. But I do not pay for seats because that doesn’t work anymore.There have been a lot of movements around these licensing agreements and these basic elements. Some have actually now tried to create agentic licensing agreements. It’s like, “Okay, I have licensing agreements now for your agents, not for your end users.” It used to be end user licensing agreements. It’s now agentic licensing agreements. Obviously, there’s a shift.Part of the shift is, I believe people want to be in a measurement scale that is different. They don’t want just to pay for a seat. They want to pay for either specific outcomes that are very clearly measurable or have flat fees across the board on a variety of things. I think we’ll see the emergence of a couple of these business models and these monetization models more significantly. I do think we’re still to see some innovation around some of these monetization models, which will occur over the next probably few years as people are getting used to it. Okay, now it makes more sense for me to pay by this rather than by that.Again, because it’s a disruption, we’re still getting and nailing down what effectively the new monetization models and business models will look like for some of these players, but it still will be served as a service. We’ll come back to that later as well. Agents can do a lot of stuff and whatever, but it’s like agents and AI are software. AI is software, whatever you want to call it. AI is software at its base and its profound meaning and what it does, et cetera. Bertrand SchmittSeat-based pricing, usage-based pricing, yes, it’s too simple. Yes, it has its flaw. But at the same time, when the industry started, it made a lot of sense. That’s easy to manage, easy to control, at least from the SaaS company perspective. But definitely now that the industry is maturing, I can see that rise and the benefit and value of moving to an outcome-based pricing or to a value-based pricing. What I like with that also, it’s more truly win-win for both sides, for the SaaS companies as well as for the customer of the SaaS company. If you are more win-win, more aligned, I think it’s a better situation, more frictionless. I think it would be a big change.Another interesting piece of the puzzle, obviously, of all the changes we’re seeing is that one of the best assumptions in SaaS was you have 80% to 90% gross margin. If you are below 80%, there were serious questions coming your way in terms of what’s wrong with your business model as a SaaS business. Below 80% was blinking yellow light, below 70, blinking red lights. But now, it’s very different because AI-native companies, you’re expecting more a 50-60% gross margin.Obviously, if you’re SaaS companies, you better move fast to more AI-native tools and services. That will impact your margin. When you decrease so much your margins, of course, it will impact your valuation. There is no other way around that. You cannot value the same way a 90% gross margin business and a 50% gross margin business. That’s simply not reasonable. I think that one is part of the change and part of a different way to value companies. It’s very reasonable. Nuno Goncalves PedroThe first two structural issues is, one, obviously the per-seat pricing piece is potentially dying or at least becoming less pervasive in the market, added to these emerging pricing and monetization models that we just discussed, value-based, outcome-based, some usage-based pricing, some hybrid models that are also out there with some base subscriptions and then other kinds of things and tiers on top of it, either usage or outcome-based.The third big structural shift that we are seeing is, and I already alluded to it earlier, this notion of build-versus-buy. In the past, I think the market went fully into buy. In some ways, even beyond the, “I will buy one” solution that solves all the problems, we went into best in class. We went to unbundled buying: I’ll buy the best solutions for what I need in my corporation and enterprise needs.Now we’re getting a shift back into building: I’ll build my own stuff. I think a lot of it is relating to two things. One, there’s coding agents out there like Claude Code, Codex from OpenAI, and a bunch of other coding agents that have emerged. There’s a lot of solutions out there, like we mentioned already, Claude Cowork, that really managed to have agentic solutions into workflows that are deeply embedded into some of the enterprises.At the end of the day, I think there’s a lot more of this notion of, I have all my data in-house. I want to really leverage all the data I have. I don’t want to just use a third-party solution that has generic data. I want to use my data set, I want to use my stuff, and I want to basically fit that into ongoing improvements in terms of workflow.The other piece, I think, what’s happening with IT departments in some large corporations that’s leading to this build mindset rather than this buy mindset is also the notion of maybe we have too many people. How do we really express our productivity if we don’t have solutions that are at the core of our processes? If we have solutions at the core of the processes that we develop ourselves or that we develop in partnership with integrators, et cetera, but using some of these new AI platforms, we also have more visibility on the people that we can let go.Now, I know this is quite negative, but I think this has also been leading to all the layoffs that we’ve been seeing across industries recently, where people are like, “Well, I can just extract productivity.” We’ve seen some of those very visible ones. We were talking about Amazon and what’s happening at Amazon with the layoffs recently. A significant amount of layoffs recently announced.Then some other issues on the other side where apparently the junior engineers that were still working on stuff using Claude and other tools that they were using internally started breaking platforms and breaking systems. Anyway, definitely there’s a lot of that going into this build mindset. I want to have control. I want to make sure I understand where the productivity enhancements are, and that will give me more visibility on the people that I need to keep and the people that I need to let go. Bertrand SchmittI’m not so convinced about this part of the puzzle. I think that for many, AI is a convenient demand, but I’m more thinking that some companies, Amazon included, Microsoft, truly, truly over-hired in 2020, 2021. Yes, they scaled back a bit, 2022, 2023. But I don’t think they ever scaled back to what was reasonable given their needs. So it’s quite convenient to say, “No, it’s not management mistake of efficiency, it’s something new AI, and we have to adjust to that.”What I believe is true, however, is that you cannot fund both at the same time in the sense of you cannot finance an over-bloated workforce, and two, significant extremely large AI investment. At some point, these companies were faced with a choice, and they took a reasonable decision on this to be more efficient with their workforce.But personally, I think that actually the ability to do so much more with AI will make more companies think more about their teams and building things because when suddenly your engineers can be way more efficient, can build way more, the value increases. So you could argue that there is an opportunity for companies to deliver more, and as a result, I can see if you’re a good engineer, then there will be opportunities to build more value, potentially across more companies.So we might see a shift where you have more growth in software-related jobs outside the core top 10 bigger software companies, but growing more widely across your typical S&P 500 and even SMBs who could never afford to really deliver value with typical software engineering. But now suddenly, software engineering equipped with AI can be more dramatic in terms of value for them. Nuno Goncalves PedroI agree this is a scapegoat. I agreed that there’s a lot of posturing as well. If someone can lay off a significant percentage of their… It’s almost like the percentage of people you can lay off becomes your new pattern as a CEO, your new, “Basically, I’m saying right now to the market, I can cut…” I mean, Block, I think, cut off 40% of their workforce.At this point in time, seems a bit dehumanized. I think the tech companies are the worst cases, in particular because AI also does disrupt them a lot in their own processes internally. But it feels to me right now, it’s a little bit this one-upmanship of, “Okay, I can lay off more people than you can, kind of thing.” It’s precisely all the fears that a lot of people have around AI. It’s like you’re dehumanizing work. It’s like at the end of the day, people are still needed to work, et cetera. Bertrand SchmittBut I think Block might be one of these companies that completely over-hired over the past few years and never took the pill to reoptimize the business. Nuno Goncalves PedroI think we mentioned it at a previous episode that there was an estimate at some point in time that… For example, even Google had more than double the number of engineers they needed at any given point in time. So obviously, they did hoard engineering resources in other capacities. But at this point in time, it feels a little bit like up to you since being a software engineer right now is a kiss of death kind of thing. Which is weird because at the same time, we are seeing tremendous reallocation of capital overall in the industry towards infrastructure and platforms, where hyperscalers are at 660-690 billion in infrastructure CapEx for this year alone, and 75% of that being AI, where we are seeing a lot of movements around how do I budget accordingly if I’m a corporation.To your point, I think you made that point earlier, Bertrand, how if I’m the CIO of a company, do I allocate my resources more clearly, in particular, if I’m taking into account that I need to spend more money on AI and AI tooling and AI platforms. Obviously, at the end of the day, the CFOs are still there, and the CFOs are basically saying, “Hey, guys, we went into an unbundled world. We had all these agreements with all these people. I want more concentration.” At the same time, the CEO is telling me we need AI, “So whatever it is, you guys tell me what it is, but we can’t increase our budget for this stuff. We need to decrease it, and there needs to be AI in it.” Obviously, there’s a lot of reallocation also at a micro level within the corporate world. Bertrand SchmittYes, you cannot say it will be more built versus buy. At the same time, we are going to need less engineers to do the build. You see what I mean? Even with AI helping you, building which still cost you more, require more software engineering than just a buy decision. For me, what’s interesting is that not so many of these stories can be true at the same time. You require a next workforce, but at the same time, you’re going to rebuild your whole software stack from zero just because of the AI God that you just brought in from cloud. This is not reasonable, simply not reasonable. Nuno Goncalves PedroI think the thesis is that your top engineer is I think, in particular, the more senior engineers, can now do the job of 10. Therefore, what I am switching in terms of cost, I’m not saying I’m agreeing with the thesis, but the thesis is that. What I’m reallocating in terms of budget is, I’m reallocating towards spend at infrastructure platform level, on tokens, et cetera. That’s basically, I think, the thesis of what we’re seeing happening right now. Bertrand SchmittYes, but if you were just, quote, unquote, buying software, you’re not building software. You didn’t need software engineering to just buy software. Your software engineer that becomes as valuable as 10, yeah, but you had zero if you were just buying software. You see what I mean? Nuno Goncalves PedroNo, IT departments have always had engineers, the larger corporations. Yeah, for sure. Bertrand SchmittIt’s a very different game if you are moving from buying to building. It’s my point, I guess. Nuno Goncalves PedroIt is. Just to be clear, Bertrand, this whole build-versus-buy, the build is going to be done with a lot of use of outsourcing and a lot of use of service providers and a lot of use of integrators, et cetera. This whole bullshit of build-versus-buy, in effect, it’s a misnomer because at the same time, you’re going to have to hire, to your point, you’re going to have to hire companies, et cetera, to help you do this. It’s not magically that you can do it off the existing IT departments that you have. Bertrand SchmittExactly. The question will also be, is your first priority of business to rebuild Salesforce from scratch so that it better fits your internal need as a corporation because you have rebuilt from scratch with AI? I don’t think so. That for me is total overhyped bullshit. Klarna was big on that, this is total BS, quite frankly. Not only it didn’t work, but it makes zero business sense. Zero business sense. You’re not going to rebuild a CRM just for the fun of it while your software engineering could be focused on your core value proposition as a business. If you’re a company just starting, you have processes from scratch, you still don’t have solution, yeah, maybe you could consider that.But even then, is it really your priority versus building your core value proposition? For me, that’s a big question. But what I would expect, however, is that this overall trend mindset and stuff is going to keep the pressure on two software companies in terms of reducing tiers of cost, in terms of delivering more value, in terms of being more aligned to the business, and in terms of overall growth rates that are simply not the same as they used to be. Nuno Goncalves PedroBefore maybe we move to another topic, I think it’s clear, we’ll come back to that later, that there are a lot of overblown elements in this. You can never disregard a couple of very, very core elements. A lot of these software companies have very deep tooling into significant enterprise customers. You can’t just rebuild it from scratch yourself to your point. Not only does it make sense, but you can’t. It would take you years to do it. Good luck to you.Secondly, they have also distribution. They are pervasive in the market. They have sales forces. They have people that are selling out there. They have go-to-market teams. Again, we’ll talk about that in maybe one of our penultimate sections today. But maybe to move forward, we talked a lot about the public equity markets and how there’s been a reckoning by institutional and retail investors, et cetera.The Private Market FalloutBut also there’s been a private market fallout. The first one is very obvious to understand. Private equity firms loaded themselves with SaaS. Some even went after roll-up strategies in SaaS, like bringing a bunch of companies together and trying to attack a market and really getting a significant part of that. Software accounts for roughly 25% of the private credit market, which is incredible. Just that’s private credit alone, significant again. They’re loaded with a bunch of companies that have nowhere to go. They can’t IPO, nobody else is interested in buying them unless it’s for a huge write-off or write-down. That’s the first problem right now that we’re seeing in this fallout, which is the private equity market itself. Not only the buyout market, but also we saw a lot of growth funds loading themselves with private equity stock, with a rather SaaS stock, private SaaS stock.Right now, there’s nowhere for that to go. They’re stuck between rock and a hard place with a lot of solutions that are not growing at the rates they were growing before, with a public market that’s not really interesting right now to IPO in, because as we were mentioning earlier, the multiples have gone downhill dramatically, so it’s not interesting. Basically, it’s a chicken-and-egg issue. I would love to sell this now, but I can’t because I have awful market. I can’t IPO it either, so what do I do with all these assets? That’s the first issue here. Bertrand SchmittIt’s clear that you have to be pretty delusional to think that what’s happening in the software public markets is not impacting the private markets. We don’t know why it will be in six months. In six months, it could keep getting worse in the public markets. Six months, at some point, maybe there is a recognition it went too far in terms of adjustment. It’s always tough. But at the same time, you have to be prudent. For sure, what it means is that if I’m a private equity investor in a SaaS business, you have to be a very, very, very special SaaS company to get more financing these days at good terms.Sometimes it’s a very simple math. If you fundraise at 20X, even 10X, how do you go to get to another round of financing if now your multiples are at 4X? That simply makes absolutely no sense whatsoever. Or you need to have grown into your valuation enough that it’s not crazy anymore. If you raise at 20X, and now you’re in 4X multiple, then you need to have grown 5X in your revenues so that you simply stay at the same valuation, or maybe you have to accept a different valuation. But again, quite frankly, the tough part would be convincing investors that it make any sense to put money in a SaaS business. Nuno Goncalves PedroJust to rub it in, just to make it even worse, the secondary market, which was a great market for exits or partial liquidations, et cetera, is demanding now huge discounts. There’s no way I’m going to buy into a stock if it’s not growing at the same pace. I’m like, “I’m sorry.” I will buy your stock at a significant discount. In some cases, it might be what would be a lesser price per share than your last round or your last two rounds. Not just, I want a discount on what you think you’re worth, but it’s like, I want a discount on your last round.Because there’s liquidity issues also in some parts of the market, we were talking just about the private equity firms, some of these deals will go through. If all of this wasn’t quite enough, we have what’s happening in venture capital, which is very close to my heart, of course, because that’s where I play. If you come to me, it’s like I’m a SaaS player immediately off the game. I’m like, “Really? You’re a SaaS, tell me more.” I was just talking to a player recently, SaaS play, there was nothing around AI in their pitch.It’s not just because you have AI in your pitch that I’m going to give you money, clear, but if you’re doing a SaaS play and there’s no AI in your pitch, I’m like, “Am I missing something?” If it looks very classic, I’m like, “Oh.” There’s been a huge, huge reduction in confidence in the VC space in investing in SaaS. There’s a tremendous hyper focus on AI, and in AI investing, AI apps, platforms, infrastructure by most VC firms at this moment in time. And so at this point in time, if you’re a non-AI SaaS player trying to raise money, where’s your AI play? I think that’s the question you’re going to get. It’s going to be very difficult to raise, very difficult to raise. Bertrand SchmittI agree with you. Myself, I saw that SaaS startups with absolutely no AI in their deck, and I was so shocked. I was like, “Guys, where are you living? Are you living in a parallel universe? Are you living under a rock? What’s going on?” Then they are like, “Yeah, but we’re preparing something like that, I come back and prepare.”But even then, as you say, it’s not just leaving AI in your deck. It’s what are your proof points? What have you delivered? How do you make sure that it’s truly differentiator? And how does it make sense versus a pure AI native companies? How are you going to find the new cloud tools that are going to get out in a few weeks and more or ChatGPT or whatever? You have to have a very different proof point. There is nothing new in the past. It’s how are you going to survive against Google? How are you going to survive against Salesforce? How are you going to survive against Microsoft? So nothing is new.Software universe is changing. There’s always that big guys that can destroy you in a matter of weeks. So the question is more, how are you going to be smart enough not to be killed too easily and to find your way in a space that’s probably moving faster than ever? That is probably the difference is that it’s weeks after weeks, you have big change. I’m pretty sure it didn’t happen in that space before because I’ve seen there, I’ve seen that, and it’s moving faster than ever. But it’s nothing new that there is this big company potentially destroying your business. You have to be smart.I feel in some ways, maybe it’s the 2020s, but people stopped being smart, quite frankly. They just raised easy at very large valuation and think that you just do something sometimes pretty basic in terms of software development and that’s good enough. Your GTM is traditional, and you think you made it, and you deserve some investment. I think you must have seen some of this. I have seen a lot of this. In some ways, it’s good. The market is becoming more discerning. Nuno Goncalves PedroThe Bull Case — Is The Market Wrong?But is the market wrong? Maybe shifting to that, at least my perspective is it’s wrong. It’s not fully wrong, but it’s wrong. There’s a right sizing of multiples, but maybe 4X is not the right multiple either. This whole 20X on actuals and 40X on forward stuff didn’t make any sense. There is an argumentation to say that the market is oversold. All the banks have come forward. Goldman Sachs, JPMorgan, Jeffries, Morgan Stanley. Everyone’s come forward and said there’s been definitely, Bank of America, whatever, there’s been an overselling of stock, a dramatic overselling of stock. There’s been a panic that wasn’t warranted. The price has gone down too dramatically for some of these key players.I think part of it, in some ways, is what we were alluding to earlier, the fact that some of these players have built really important stacks that are fitting their customers in a significant on core processes. You can’t just rip it off and put something new. Magically, it will work. It will be around building things around it rather than building things that replace it. Will there be over the long term potential disruption of some of these players around CRM and other solutions? For sure, we’ll see it.But definitely, some of the existing players, public companies that are large, are here to stay, and they themselves will buy into these markets. They’ll acquire positions into other service providers into toolmakers, into other platforms that allow them to be fully AI-enabled and to make their platforms more AI-enabled. I do think there was a huge amount of overselling. The second thing we already alluded to as well as go-to-market. If I’m selling something to someone, there’s a salesperson involved or there are a couple of salespeople involved, they’re not going anywhere. So in some ways, that relationship building with CIOs, with their teams, with procurement teams, all of that is still there.And a lot of the large SaaS players have been doing this for decades. So they have the surface of attack and go-to-market that will take a long time to build for even some of these startups that are disrupting, so to speak, the market. My view is there has been too much panic and the modes of the large players that are already public, in some cases, haven’t been considered at all. Bertrand SchmittThere’s definitely some truth in that. Another piece of the puzzle is that if SaaS is not growing as fast as it used to be, it’s still growing. Many companies are still very good cash generation machines. Many of these companies are moving to AI full speed, improving their tools, changing how you can search their data, how you can leverage their data. They are very close to the data, so they know best how to deliver value on this data. They can integrate existing AI tools. There are a lot of ways for them to capture part of the value that native AI companies are claiming they will get. I think it’s definitely going to, and we’ll talk more later on. I think there will be a question around how do you differentiate the best SaaS companies from the worst SaaS companies in that context.But maybe I just felt we moved a bit quickly on one big event that’s shaping the software industry, it’s the current crash in private credit. Do you have some thoughts about that? Because what’s happening there is pretty crazy, to be frank. Nuno Goncalves PedroYeah, we’ve seen a lot of these players like KKR and Apollo getting slaughtered. Basically, Blue Owl, TPG, Ares, KKR all fell double this in one day on private credit exposure fears. Overall, Apollo has fell 7% as the date of as we were recording BlackRock, 5%. These guys were walking on water and all of a sudden, there was like, “What happened?” And what happened was private credit exposure. A lot of the concerns in the market is private credit is super sexy, and for those who don’t understand what it means is I’m giving credit to a private company in exchange for something, either warrants in the company or revenue sharing in the future, or I’ll get your revenues in advance from you, or I’ll take, whatever it is. There’s over exposure.There’s this potential logic that all these guys are scaling, all the companies that they give private credit to are scaling. And now there are concerns that there might be some dramatic credit in the market, that some of these companies are actually going to die, they’re going to implode, or they’re not going to really fulfill their covenants in their private credit agreements. Bertrand SchmittIt was hidden in plain sight, but that some of these private credit funds at 25, 35% exposure to software, IT, and SaaS, so a huge chunk in an industry where you bet on the long term revenues and cash flow to pay back your loans, while at the same time there is a discovery that this business may be at risk in the next three, five years or even one year because of AI.I think that was the first big chink in the armor that suddenly the creditworthiness of these companies might not have been evaluated properly. But two, it looks like there is also fraud that has been happening. I was reading stories how three, four people, accounting companies, were valuing and estimating loans for hundreds of SaaS business. Good luck, this is crazy. It looks like there is another layer to that story. Nuno Goncalves PedroWhen there are industries building a lot of wealth or apparent wealth that’s coming a little bit from out of nowhere, the likelihood that there’s fraud and things that were not properly done is, it sadly increases dramatically or exponentially. I think we’re seeing just maybe the first effects of that. Bertrand SchmittI was reading, for instance, that one of these big funds was no haircut across the portfolio, ever seen value that was 100%, whatever. One quarter after that, one of their clients going out of business and they lost everything. In three months, you move from no haircut to 100% haircut, decent enough part of your portfolio. This is crazy for a credit business. Nuno Goncalves PedroIt’s ostrich syndrome. You just put your head under the ground, and you’re like, “Hey, whatever.” I don’t know. Bertrand SchmittYeah, it’s zero mark-to-market in an industry that should be relatively conservative. This is private credit. This is not VC, this is not startup, this is not equity, this is credit, so pretty scary. Another piece was like, some of them were supposedly senior on the debt, but they were not so senior after all, this is insane. You claim seniority, but you don’t have it.My point, I think what’s happening in private credit is maybe it all started with that what’s going on, a lot of software exposure. It’s risky because of AI, but the more investor dig into it, that’s when they started to realize that maybe there is more than just that software issue. I guess, all of this is going to be an issue for software business because if suddenly you cannot get loans anymore or the loans you add, you have to pay them back or when it’s time to pay them off, you cannot renew the loan. There is nobody else to turn yourself to get another loan to replace it. That’s not going to be fun and that’s going to impact your growth rates. That could potentially also even be worse than that, be dramatic for your own business survival. Nuno Goncalves PedroMaybe now switching back to the positive part for the bull case. We think the market’s wrong, not fully, but wrong. The other side is still things move on. We’ve also had the same issues in credits in several industries in the past when markets imploded and credit came back. In some cases, it took a while. In other cases, it came back relatively quickly. One great analogy on making a bull case on why all of this stock that was sold was oversold, there’s too much stock being sold on SaaS and at prices that don’t make any sense is an analogy, precisely, for example, with retail. Amazon was going to destroy everyone their mother in 2010, and it did not. It was going to destroy Walmart. Walmart passed the $1 trillion market cap. Bertrand SchmittNot too bad. Nuno Goncalves PedroSo what happened? They adapted. They had huge advantages. They had huge advantages in terms of their customer base, presence, relationship with their suppliers, with the offerings they had, et cetera. They had huge advantages of economies of scale, and they leverage those advantages. And those advantages ultimately materialized in tremendous increase in revenue, tremendous increase in market capital as well.Amazon has done really well as well. It’s not like Amazon didn’t do well. Again, I think this notion, people sometimes have this difficulty in separating the notion of disruption from the notion of replacement. Disruption doesn’t mean necessarily full replacement. You can disrupt industries, disrupt players in that industry, and still those players will exist 10, 20 years later, and they’ll be much bigger because they adapted. The ones that don’t adapt may be killed.But the disruption doesn’t necessarily mean replacement or killing. It means just that effectively the rules of the game, the business model, which we already talked about, monetization models, the way that capital flows in that industry, et cetera, all of that shifts. It doesn’t mean that necessarily the existing players are not going to exist tomorrow. In some cases, they will exist and they’ll be even stronger tomorrow. Bertrand SchmittI think what’s happening is truly a disruption of the SaaS business model, of the SaaS valuations, of the SaaS analysis, because now you need a new prism to analyze it. What are the markets doing in the meantime? They are just dumping it, waiting for, “Okay, how do we look at it in a different way? Who are going to be the winners and the losers?” For now, we don’t care, they’re all losers. But I think that the next piece of the puzzle for us in this episode, but for the market is, how are we going to separate the wheat from the chaff? Who is going to survive? Who is going to more than just survive? Who is going to thrive in that new industry. Nuno Goncalves PedroThere I feel the ones that survive, there’s a couple of obvious ones we can go into. Two that immediately come to my mind are data infrastructure, the Snowflakes, Databricks of the world, because this is the underpinning of everything that’s happening around AI. I don’t see the data infrastructure fundamentally shifting right now. It might in the future, but right now I don’t see it fundamentally shift. Those guys have, if anything, tailwinds rather than headwinds.Then the other one that’s very obvious to me is cybersecurity, where I think AI is very additive to it rather than just necessarily replacing everything that exists. In some ways, that already been used for a while, certainly by the top players. Definitely, those are two immediate categories and areas that come to mind that have maybe more headwinds and tailwinds where really AI is adding rather than subtracting to it. Bertrand SchmittNo, I totally agree with you concerning data infrastructure, cybersecurity. You could argue if you take cybersecurity, that with the rise of AI attacks, with AI making it easier than ever to generate attacks, you better build up your security. Nuno Goncalves PedroWith AI? No, but you have to have AI on your side defending as well. The only way to defend AI is AI. Bertrand SchmittThat’s my point. Your cybersecurity vendors will become AI-enabled, will leverage AI at scale in order to defend you, else they won’t be able to defend you, just quite frankly. Nuno Goncalves PedroCorrect. Bertrand SchmittThat’s part of the game. Data infrastructure, no questions. Again, I don’t think you want to redo your infrastructure with brand-new tools, brand-new stuff is the current tools are working great and doing the job. Maybe another piece of the puzzle is that vertical SaaS, domain-specific tools, healthcare, manufacturing, if you have proprietary data, regulatory modes, it will be much harder for AI to disrupt quickly. If you are not disrupted quickly, you have more time to readjust your business model, to adjust your business model, to leverage AI to improve your business model.Again, of course, some companies, we have seen with Adobe, for instance, have not proven great skills at adjusting to AI. Not everyone is going to get out as a winner. I think some categories have better chance to actually not just survive, but potentially thrive. Another piece are systems of record. If you are holding proprietary non-scrapable data that AI needs to function, that you have deep switching costs protecting you, you are not going to disappear right away. I think you will probably survive. If you are smart enough, you might be able to even adjust and leverage AI.But I can see some might just stick to their revenues and hold companies hostage and might not innovate a lot. I guess we’ll do well on the short run, but on the medium to long I would definitely more worried. Nuno Goncalves PedroOne point I would like to make is at the end of the day, there’s more than that. The algorithmic methodologies you should use for specific industries, for specific verticals, for specific use cases could vary. We’re still very early in a lot of the application of some of these AI methodologies. We’re not early in the development of the research around them. They’ve been around for decades, but the application of them is still relatively early. I think that’s one of the advantages why vertical SaaS companies and vertical SaaS solutions right now might have an advantage, because the domain in which you’re operating, even algorithmically, is actually different, and you need to really right purpose it for those environments and for those domains.For me, that’s an important point to make. It’s not just any vertical SaaS. I think vertical SaaS, where there’s algorithmic distinctiveness, definitely has a shot at it. Other might not. We just saw a lot of discussions around legal tech and how legal tech got slaughtered with the launch of Claude Cowork, for example. Definitely, it will depend a little bit on the verticals. Bertrand SchmittTake the legal side. There has been some interesting decision recently where basically, if you use AI for legal advice, then this data, this discussion is not privileged. You are at big risk of discovery. There is a lot of issues that if you are working with real lawyers, will not be there. Your data is not discoverable, your discussion stay private, so it cannot be used against you. I think companies have to be very careful and very worried about how some of these tools are being used because it’s creating new risk. Some of these tools are not going to get privileged in the coming few months, I don’t think so.You could argue most of these companies in the first place claim a right to access your data and leverage it. I think that even in legal, it would be interesting to see how it evolved. AI will be able to claim some privilege at some point? Maybe, I don’t know. But on the short run, I can imagine how the legal profession, for instance, will not let it happen too quickly, and how you have to be very careful. It’s great to move fast, but you have to be careful with what is it that you are getting into. Nuno Goncalves PedroLet me guess, the last company you’re going to say or the last type of companies that you’re going to say are like the survive, thrive are AI-first or AI-native companies. Is that correct? Bertrand SchmittYeah, I guess. Yes. They are going to be less disrupted by AI, given that they’re already AI native. Nuno Goncalves PedroThey are AI. Bertrand SchmittWe are going into another territory. Even if you are AI-native, are you going to still get killed by Claude because you don’t have enough technology or ChatGPT because you don’t have enough technology? You are just that basic rapper around another AI tools. Here my perspective and what I share more and more with some entrepreneurs is you have to be careful if you are just an AI native company, but ultimately you are a very AI light in the sense that, yes, you are a native, but you are just reusing other LLMs and stuff, and you have not built any proprietary tech or moat with your data or in your industry. That’s going to be trouble. That’s going to be trouble.I’m not sure the market discriminated well enough at this stage, but I think there will be quickly some premium around, have you built a real technology mode? Are you really in such a situation that you are not going to get killed by a Claude or ChatGPT in a few weeks? I think there will be some discrimination that’s going to happen. Ai native won’t be enough to save you, basically. Nuno Goncalves PedroI think there’s one thing. One is what you’re saying. Is there fundamental technology differentiation and/or product differentiation that will sustain itself as a moat? The second thing is, even if it’s an AI app at a higher level, the reality is the guys that are in the market today, the OpenAIs, the Googles, the Anthropics, etc., they’re not going to address all use cases. There are places where some use cases will still exist. We saw that in the mobile app economy.In some of these use cases, you’d be like, why hasn’t, for example, Apple addressed the need for this kind of solution, whatever, and maybe it took them a decade to do it. Then, when they did it, they almost killed the market. But you have some of these AI apps that I think will still be in the market that will emerge and will address use cases that for some time, for some reason, OpenAI, Anthropic, etc., won’t go after. To Bertrand’s point, and I think importantly, if you’re an entrepreneur, if you’re writing on a very specific use case, and there’s seemingly a high likelihood that any of these players are going to address at some point, you’re not in a sustainable place. You’re not going to be around very long. Bertrand SchmittOr you have to take that initial leadership position and transform it into a deeper technology mode, a business mode. You have to leverage that first mover advantage, maybe, to something deeper than that, something more defensible. Maybe you pivot also in term of industry. You started in industry A, but you realize industry B is really the good one. You have to really optimize your way and not take anything for granted. Nuno Goncalves PedroBertrand, do you remember when it’s like every release of iOS and whatever, we were like, what industry is Apple going to kill now? What are they integrating? There was a period of time where it was literally like every big release, every major release, the yearly one, you’d be like, what industry are they going to kill now? Bertrand SchmittTotally. Totally. I think the same is happening. Definitely, we say AI, but I think some players have been smart enough to zigzag around that onslaught from Apple, from Google. But some will stay put. We think it’s not going to happen to them. Yes, they got into trouble pretty quickly. I think also what we have seen is that a lot of value could be from players who are simply more neutral and independent vis-à-vis a platform. If you need someone in the middle, your three or four mobile platform, or now your three or four LLMs or AI platforms, there might be value you can extract because companies are not… That’s another piece of the puzzle.You don’t want to just depend on Claude. You don’t know in three months, ChatGPT has a better model. You will want to make sure that whatever you are running can adjust to a change of LLM providers, for instance, or tool providers. I think, for instance, one position could be that mutual player, the one gives you the ability to adjust quickly to different technical AI development. We will see. But I think there are different strategies you can go through to make sure you end up not being killed, and that will require smart entrepreneurs. Nuno Goncalves PedroSeparating The Wheat From The Chaff — Who Survives?We talked about who survives, who doesn’t survive. Let me start with one. Or where I think will be categories that will be incredibly under attack, so a lot of players, I think, will disappear or will become very, very small. One obvious for me is anything that relates to the small, medium business markets, so very SMB-focused SaaS, a lot of regional SaaS stuff that has emerged, copycatting in certain markets because the larger players didn’t want to expand in some of those markets.I think a lot of that stuff gets just replaced because a lot of the SMB markets are price sensitive. A lot of these markets are also best effort-driven. It’s like it doesn’t need to be perfect, it just needs to do the basic stuff. Therefore, I see that market as a market that’s going to get, in all honesty, over the next 3-5 years, slaughtered. It’s not going to be rapid death, but some of them are just going to be totally replaced. Bertrand SchmittI agree with you. If you don’t have a big enough moat, if it’s very shallow, if your clients are moving quickly, you can easily switch based on a small price difference. That’s definitely trouble. Nuno Goncalves PedroI’ll let an anecdote just so people I don’t understand. Because people say, but these regional SaaS solutions normally because of their specificities to the markets and stuff like that, whatever. I literally drafted the other day an agreement, a semi-agreement relating to Portuguese law on Claude in Portuguese, from Portugal, not Brazil and Portuguese. It drafted an agreement from scratch based on my prompting, and it took into account specificities of the Portuguese legal system and taxation. Guys, it’s like, this is a freaking consumer tool. Localization of what? The tax regime and whatever? Who gives a shit? It’s like, again, I think that’s the market that definitely will get a pretty significant beating. Bertrand SchmittAnother market for me, we talk about Adobe, but content creation tools. Here, I think there is a dramatic shift in how you use them. Before you use another Photoshop to replace something in a picture, change a slightly picture stuff. Now, you just say, hey, remove this guy from the picture. Hey, replace. Hey, create that picture from scratch. I have five photo IDs, put these guys in context, put them in your meeting room, and go for it. This is such transformational versus how you used to work before that I think some of this industry is getting destroyed.There will be simply no point of using these tools anymore because something else is just 10X better. That is not even a question. You could argue there is still a niche of professionals doing stuff in an always because it guarantees a bit more higher quality or this or that. Sure. But overall, this is getting disrupted big time and the much bigger business might be totally new and totally AI native. Nuno Goncalves PedroI will do a parochial comment. We have two investments in the content creation space, one more on the marketing side and the other one more on the hardcore content creation side. They’re both AI from inception, so they’re both AI native. One of them is called LetsEnhance, the other one is called blaze.ai. I feel it’s true that there’s going to be a lot of replacement of some of the content creation tools in certain markets like consumer and prosumer, driven by the Nano Bananas of the world and all that stuff.But on the top end and in enterprise and all that stuff, we feel that AI native content creation tools are there to be. It’s actually one of the areas of what I would call use cases or AI apps/platforms where I feel being AI native will give you an advantage. Just being a cross-cut play around the market being Anthropic or OpenAI, whatever, actually won’t solve the problem for some of the markets that need to be served in. Bertrand SchmittMakes sense. I agree with you. Maybe more quickly, some point solutions, relatively high risk. If you have a single function tool, then could be easily replaced potentially by an AI agent. We already talk about it. If you are too SMB-focused, that’s not the best segment of the market, typically. Maybe you can have a single test to check if that company is at risk. If you were to replace that tool, can a $20 a month AI agent do this task? If switch it cost are low, then maybe that’s not a good business opportunity. Maybe you should not invest, or you should sell the stock.Again, maybe you have to focus more on regulated niches, hardware dependent, critical private data, solutions where there is already outcome or value-based pricing in place. You have to put some rules and analysis to help you understand, is this business at risk of significant disruption or not? Not all business are the same. As an investor, that might mean that there would be some good opportunities. SaaS businesses that are going to emerge even stronger right now are at a cheap discount. Nuno Goncalves PedroAbsolutely. I think at the end of the day, certain basic workflow tools that are out there to simplify CRM, some very basic ERP modules, anything that’s very, very simple in terms of if this then that, all those tools are also going to be slaughtered relatively soon, sadly. If you’re in that space, maybe time, as Bertrand was saying earlier, to pivot, to go after some fundamental differentiation, or to do something else. You want to conclude, Bertrand? Bertrand SchmittConclusionSure. I guess we could see that from a trade perspective, from an investor perspective. I think it’s creating quite genuinely some opportunities. Some stocks are in the bargain, some of those are value traps, so you better get your investment skills in order. PE, private credit, definitely a lot of risk, not just from AI, I think from basic fraud as well.Secondary market, as you just say, it’s not an easy one. It’s a canary in the coal mine. I think you will agree, but this is before getting between AI native versus everything else these days, especially if you are more early stage. A more established business, it’s a different thing. But right now, just starting a regular SaaS company, that’s a tough one. From an investor perspective, you need to pivot as fast as you can from seed-based pricing, hybrid, outcome-based, value-based pricing. You have to do the move quickly. You don’t want to be pushed when it’s too late.Build-versus-buy is real, and that will only accelerate as coding agents mature. Vertical specialization, proprietary data are strong moat. They were before as well, so it’s nothing new. But I think the importance of having a true moat is more critical than ever. Lots of companies have received investment with not enough moat, and that’s the one getting destroyed in the private and public market. If you have strong matrix, there is a question of when is a good time to exit? I don’t know if the relations will ever come back. I think it truly depends as well on your business, a strategic fit with acquisition opportunities.Anecdotally, I have seen some businesses who look at exit opportunities and now are finding attractive options. It’s not all that dark, I would say. Maybe to answer to the question, do we have a SaaS apocalypse? Yes and no. Some companies are going to end badly, some companies are going to emerge stronger. I think that’s it for today. Thank you, Nino. Nuno Goncalves PedroThank you, Bertrand.

X: @billyeargin @ileaderssummit @americasrt1776 @NatashaSrdoc @JoelAnandUSA @supertalk @JTitMVirginia Join America's Roundtable radio co-hosts Natasha Srdoc and Joel Anand Samy with Bill Yeargin, one of America's top CEOs who took an iconic American boat manufacturing company which was experiencing financial difficulties. By transforming the corporate culture at Correct Craft, Bill took Correct Craft with revenues of $40 million in 2009 and reached its goal of becoming a billion-dollar enterprise in 2023. During Yeargin's tenure, Correct Craft grew by over 20X and won many awards, including Florida's Manufacturer of the Year and the boating industry's Most Innovative Company. It also became an influential voice in the boating industry as well as in Washington, DC. In highlighting the new book titled "Mindset Matters" which he co-authored with Zach Hutcheson, CFO of Correct Craft, Bill Yeargin shares his insights and experiences over the past 20 years at the helm of Correct Craft. The company played a pivotal role in World War II when the leadership of the company in 1945 heeded the call of General Eisenhower who needed over 400 boats in the winter to move over 15,000 US soldiers in the perilous crossing of Germany's River Rhine. The company was then producing less than 20 boats per month, yet did the impossible in what National Geographic called the "Miracle Production" when Correct Craft built over 400 boats in less than 30 days while keeping the Sabbath. The unique story of Correct Craft over the past 101 years reminds us all of the creativity and ingenuity of Americans fueling innovation and achieving ground-breaking results. About Bill Yeargin: Bill Yeargin is a thought leader, CEO, board member, global traveler (110 countries), innovator, and culture evangelist. He has authored six books including the best sellers Education of a CEO and Faith Leap. Bill has shared leadership insights in innumerable articles and columns for over three decades and has been a popular speaker at hundreds of events on six continents. The company Bill leads as CEO, Correct Craft, is a 100-year-old company with global operations. Correct Craft's subsidiaries include multiple boat brands, engine brands, water sports parks, and entities devoted solely to vertical integration and innovation. The company has manufacturing facilities across the U.S. and distributes into about 70 countries. Under Bill's leadership, Correct Craft has developed a unique culture of “Making Life Better.” They have won all their industry's major awards and were recognized as Florida's “Manufacturer of the Year.” Correct Craft has also been recognized as the boating industry's “Most Innovative Company.” A passionate lifelong learner, Bill has earned a bachelor's degree in accounting and an MBA. He has also completed post-graduate studies at Harvard, Stanford, Wharton, MIT, and the London School of Economics. Bill is a certified public accountant and certified Lean Six Sigma black belt. In addition, he is certified in both Myers-Briggs Type Indicator and DISC. Palm Beach State College recognized Bill as an outstanding alum with its Emerald Torch Award. Nova Southeastern University awarded Bill a doctorate of humane letters in recognition of his “contribution to the lives of others and the betterment of humanity.” Bill served on numerous for-profit and non-profit boards and earned a certificate in corporate governance from both Columbia University and Cornell University. He also earned both a certificate in Risk Governance and Qualified Risk Director® credential from the DCRO Risk Governance Institute. Bill currently serves on multiple boards and is board chair of the National Marine Manufacturers Association (NMMA). Bill actively represents his industry on both national and state issues. He served both the Obama and Trump administrations on cabinet-level advisory councils and has been invited to the White House nine times by three different presidents. Bill was appointed by Florida's governor to serve on the University of Central Florida board of trustees. Bill has been recognized with many of the marine industry's top awards including Boating Industry's “Mover and Shaker of the Year.” Florida Trend magazine has recognized Bill as one of “Florida's Most Influential Business Leaders” and he is an Orlando Business Journal “CEO of the Year.” The governor of Florida also presented Bill with the “Governor's Business Ambassador Medal.” About Correct Craft: Celebrating 100 years of excellence in the marine industry, Correct Craft is a Florida-based company with global operations. Focused on “Making Life Better,” the Correct Craft family includes Nautique, Centurion, Supreme, Bass Cat, Yar-Craft, SeaArk, Parker, and Revel boat companies, Pleasurecraft Engine Group, Indmar Marine Engines, Velvet Drive Transmissions, Ingenity Electric, Mach Connections, Merritt Precision, Osmosis, Watershed Innovation, and Aktion Parks. For more information, please visit www.correctcraft.com. americasrt.com https://ileaderssummit.org/ | https://jerusalemleaderssummit.com/ America's Roundtable on Apple Podcasts: https://podcasts.apple.com/us/podcast/americas-roundtable/id1518878472 X: @billyeargin @ileaderssummit @americasrt1776 @NatashaSrdoc @JoelAnandUSA @supertalk @JTitMVirginia America's Roundtable is co-hosted by Natasha Srdoc and Joel Anand Samy, co-founders of International Leaders Summit and the Jerusalem Leaders Summit. America's Roundtable radio program focuses on America's economy, healthcare reform, rule of law, security and trade, and its strategic partnership with rule of law nations around the world. The radio program features high-ranking US administration officials, cabinet members, members of Congress, state government officials, distinguished diplomats, business and media leaders and influential thinkers from around the world. Tune into America's Roundtable Radio program from Washington, DC via live streaming on Saturday mornings via 68 radio stations at 7:30 A.M. (ET) on Lanser Broadcasting Corporation covering the Michigan and the Midwest market, and at 7:30 A.M. (CT) on SuperTalk Mississippi — SuperTalk.FM reaching listeners in every county within the State of Mississippi, and neighboring states in the South including Alabama, Arkansas, Louisiana and Tennessee. Tune into WTON in Central Virginia on Sunday mornings at 9:30 A.M. (ET). 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Most affiliate programs grow by inches. This one grew by miles. In today's episode, I'm breaking down how we grew affiliate revenue 20X in 48 months. No fluff. No "one weird trick". Just the exact moves that changed everything: what we focused on first, what we stopped doing, the leverage points that mattered most, and a few bets that didn't pay off. If you want a clear playbook for scaling an affiliate program without burning trust, relationships, or your sanity... this is it. Links Mentioned in this Episode   Affiliate Training Templates and Scripts Promo Strategy Call Template Resources Page Guide

Hey dear subscriber, Alex here from W&B, let me catch you up! This week started with Anthropic releasing /fast mode for Opus 4.6, continued with ByteDance reality-shattering video model called SeeDance 2.0, and then the open weights folks pulled up! Z.ai releasing GLM-5, a 744B top ranking coder beast, and then today MiniMax dropping a heavily RL'd MiniMax M2.5, showing 80.2% on SWE-bench, nearly beating Opus 4.6! I've interviewed Lou from Z.AI and Olive from MiniMax on the show today back to back btw, very interesting conversations, starting after TL;DR!So while the OpenSource models were catching up to frontier, OpenAI and Google both dropped breaking news (again, during the show), with Gemini 3 Deep Think shattering the ArcAGI 2 (84.6%) and Humanity's Last Exam (48% w/o tools)... Just an absolute beast of a model update, and OpenAI launched their Cerebras collaboration, with GPT 5.3 Codex Spark, supposedly running at over 1000 tokens per second (but not as smart) Also, crazy week for us at W&B as we scrambled to host GLM-5 at day of release, and are working on dropping Kimi K2.5 and MiniMax both on our inference service! As always, all show notes in the end, let's DIVE IN! ThursdAI - AI is speeding up, don't get left behind! Sub and I'll keep you up to date with a weekly catch upOpen Source LLMsZ.ai launches GLM-5 - #1 open-weights coder with 744B parameters (X, HF, W&B inference)The breakaway open-source model of the week is undeniably GLM-5 from Z.ai (formerly known to many of us as Zhipu AI). We were honored to have Lou, the Head of DevRel at Z.ai, join us live on the show at 1:00 AM Shanghai time to break down this monster of a release.GLM-5 is massive, not something you run at home (hey, that's what W&B inference is for!) but it's absolutely a model that's worth thinking about if your company has on prem requirements and can't share code with OpenAI or Anthropic. They jumped from 355B in GLM4.5 and expanded their pre-training data to a whopping 28.5T tokens to get these results. But Lou explained that it's not only about data, they adopted DeepSeeks sparse attention (DSA) to help preserve deep reasoning over long contexts (this one has 200K)Lou summed up the generational leap from version 4.5 to 5 perfectly in four words: “Bigger, faster, better, and cheaper.” I dunno about faster, this may be one of those models that you hand off more difficult tasks to, but definitely cheaper, with $1 input/$3.20 output per 1M tokens on W&B! While the evaluations are ongoing, the one interesting tid-bit from Artificial Analysis was, this model scores the lowest on their hallucination rate bench! Think about this for a second, this model is neck-in-neck with Opus 4.5, and if Anthropic didn't release Opus 4.6 just last week, this would be an open weights model that rivals Opus! One of the best models the western foundational labs with all their investments has out there. Absolutely insane times. MiniMax drops M2.5 - 80.2% on SWE-bench verified with just 10B active parameters (X, Blog)Just as we wrapped up our conversation with Lou, MiniMax dropped their release (though not weights yet, we're waiting ⏰) and then Olive Song, a senior RL researcher on the team, joined the pod, and she was an absolute wealth of knowledge! Olive shared that they achieved an unbelievable 80.2% on SWE-Bench Verified. Digest this for a second: a 10B active parameter open-source model is directly trading blows with Claude Opus 4.6 (80.8%) on the one of the hardest real-world software engineering benchmark we currently have. While being alex checks notes ... 20X cheaper and much faster to run? Apparently their fast version gets up to 100 tokens/s. Olive shared the “not so secret” sauce behind this punch-above-its-weight performance. The massive leap in intelligence comes entirely from their highly decoupled Reinforcement Learning framework called “Forge.” They heavily optimized not just for correct answers, but for the end-to-end time of task performing. In the era of bloated reasoning models that spit out ten thousand “thinking” tokens before writing a line of code, MiniMax trained their model across thousands of diverse environments to use fewer tools, think more efficiently, and execute plans faster. As Olive noted, less time waiting and fewer tools called means less money spent by the user. (as confirmed by @swyx at the Windsurf leaderboard, developers often prefer fast but good enough models) I really enjoyed the interview with Olive, really recommend you listen to the whole conversation starting at 00:26:15. Kudos MiniMax on the release (and I'll keep you updated when we add this model to our inference service) Big Labs and breaking newsThere's a reason the show is called ThursdAI, and today this reason is more clear than ever, AI biggest updates happen on a Thursday, often live during the show. This happened 2 times last week and 3 times today, first with MiniMax and then with both Google and OpenAI! Google previews Gemini 3 Deep Think, top reasoning intelligence SOTA Arc AGI 2 at 84% & SOTA HLE 48.4% (X , Blog)I literally went

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

I audited my 2025 year looking for lessons learned, relearned, and unlearned. Here's a big one I'm relearning: the difference between someone good on your team versus someone great isn't 10-20% better—it's 10-20X in productivity, output, and impact. I really mean this. I came from the corporate world where I had big budgets and could hire A-players, but when I went out on my own with tighter budgets, I developed a bad habit: hiring cheaper people thinking I could get it all done. I'd hire two or three mediocre people instead of one A-player focused on the most important thing. What happened? Failed prioritization. Mediocre people increased noise, required constant oversight, and diluted my time instead of extending capacity. I was micromanaging and fixing instead of building. This past year I went back to my roots: only accept A-players, which forced me to prioritize ruthlessly. The business accelerated dramatically. This episode breaks down my number one recommendation for hiring A-players: treat it like video production—spend way more time on pre-production and strategy to dramatically reduce post-production work. Instead of jumping to a job post and taking "good enough," spend time defining what success really looks like, who would crush it (beyond resume bullets), and what systems screen people in or out. It feels slower up front but there's no comparison in speed to full output and caliber of people you stack on the team.//Welcome to Repeatable Revenue, hosted by strategic growth advisor , Ray J. Green.About Ray:→ Former Managing Director of National Small & Midsize Business at the U.S. Chamber of Commerce, where he doubled revenue per sale in fundraising, led the first increase in SMB membership, co-built a national Mid-Market sales channel, and more.→ Former CEO operator for several investor groups where he led turnarounds of recently acquired small businesses.→ Current founder of MSP Sales Partners, where we currently help IT companies scale sales: www.MSPSalesPartners.com→ Current Sales & Sales Management Expert in Residence at the world's largest IT business mastermind.→ Current Managing Partner of Repeatable Revenue Ventures, where we scale B2B companies we have equity in: www.RayJGreen.com//Follow Ray on:YouTube | LinkedIn | Facebook | Twitter | Instagram

What does 2026 hold for indie authors and the publishing industry? I give my thoughts on trends and predictions for the year ahead. In the intro, Quitting the right stuff; how to edit your author business in 2026; Is SubStack Good for Indie Authors?; Business for Authors webinars. If you'd like to join my community and support the show every month, you'll get access to my growing list of Patron videos and audio on all aspects of the author business — for the price of a black coffee (or two) a month. Join us at Patreon.com/thecreativepenn. Joanna Penn writes non-fiction for authors and is an award-winning, New York Times and USA Today bestselling thriller author as J.F. Penn. She's also an award-winning podcaster, creative entrepreneur, and international professional speaker. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. (1) More indie authors will sell direct through Shopify, Kickstarter, and local in-person events (2) AI-powered search will start to shift elements of book discoverability (3) The start of Agentic Commerce (4) AI-assisted audiobook narration will go mainstream (5) AI-assisted translation will start to take off beyond the early adopters (6) AI video becomes ubiquitous. ‘Live selling' becomes the next trend in social sales. (7) AI will create, run, and optimise ads without the need for human intervention (8) 1000 True Fans becomes more important than ever You can find all my books as J.F. Penn and Joanna Penn on your favourite online store in all the usual formats, or order from your local library or bookstore. You can also buy direct from me at CreativePennBooks.com and JFPennBooks.com. I'm not really active on social media, but you can always see my photos at Instagram @jfpennauthor. 2026 Trends and Predictions for Indie Authors and Book Publishing (1) More indie authors will sell direct through Shopify, Kickstarter, and local in-person events — and more companies like BookVault will offer even more beautiful physical books and products to support this. This trend will not be a surprise to most of you! Selling direct has been a trend for the last few years, but in 2026, it will continue to grow as a way that independent authors become even more independent. The recent Written Word Media survey from Dec 2025 noted that 30% of authors surveyed are selling direct already and 30% say they plan to start in 2026. Among authors earning over $10,000 per month, roughly half sell direct. In my opinion, selling direct is an advanced author strategy, meaning that you have multiple books and you understand book marketing and have an email list already or some guaranteed way to reach readers. In fact, Kindlepreneur reports that 66% of authors selling direct have more than 5 books, and 46% have more than 10 books. Of course, you can start with the something small, like a table at a local event with a limited number of books for sale, but if you want to consistently sell direct for years to come, you need to consider all the business aspects. Selling direct is not a silver bullet. It's much harder work to sell direct than it is to just upload an ebook to Amazon, whether you choose a Kickstarter campaign, or Shopify/Payhip or other online stores, or regular in-person sales at events/conferences/fairs. You need a business mindset and business practices, for example, you need to pay upfront for setup as well as ongoing management, and bulk printing in some cases. You need to manage taxes and cashflow. You need to be a lot more proactive about marketing, as you won't sell anything if you don't bring readers to your books/products. But selling direct also brings advantages. It sets you apart from the bulk of digital only authors who still only upload ebooks to Amazon, or maybe add a print on demand book, and in an era of AI rapid creation, that number is growing all the time. If you sell direct, you get your customer data and you can reach those customers next time, through your email list. If you don't know who bought your books and don't have a guaranteed way to reach them, you will more easily be disrupted when things change — and they always change eventually. Kindlepreneur notes that “45% of the successful direct selling authors had over 1,000 subscribers on their email lists,” with “a clear, positive correlation between email list size and monthly direct sales income — with authors having an email list of over 15,000 subscribers earning 20X more than authors with email lists under 100 subscribers.” Selling direct means faster money, sometimes the same day or the same week in many cases, or a few weeks after a campaign finishes, as with Kickstarter. And remember, you don't have to sell all your formats directly. You can keep your ebooks in KU, do whatever you like with audiobooks, and just have premium print products direct, or start with a very basic Kickstarter campaign, or a table at a local fair. Lots more tips for Shopify and Kickstarter at https://www.thecreativepenn.com/selldirectresources/ I also recommend the Novel Marketing Podcast on The Shopify Trap: Why authors keep losing money as it is a great counterpoint to my positive endorsement of selling direct on Shopify! Among other things, Thomas notes that a fixed monthly fee for a store doesn't match how most authors make money from books which is more in spikes, the complexity and hassle eats time and can cost more money if you pay for help, and it can reduce sales on Amazon and weaken your ranking. Basically, if you haven't figured out marketing direct to your store, it can hurt you.All true for some authors, for some genres, and for some people's lifestyle. But for authors who don't want to be on the hamster wheel of the Amazon algorithm and who want more diversity and control in income, as well as the incredible creative benefits of what you can do selling direct, then I would say, consider your options in 2025, even if that is trying out a low-financial-goal Kickstarter campaign, or selling some print books at a local fair. Interestingly, traditional publishers are also experimenting with direct sales. Kate Elton, the new CEO of Harper Collins notes in The Bookseller's 2026 trend article, “we are seeing global success with responsive, reader-driven publishing, subscription boxes and TikTok Shop and – crucially – developing strategies that are founded on a comprehensive understanding of the reader.” She also notes, “AI enables us to dramatically change the way we interact with and grow audiences. The opportunities are genuinely exciting – finding new ways to help readers discover books they will love, innovating in the ways we market and reach audiences, building new channels and adapting to new methods of consuming content.” (2) AI-powered search will start to shift elements of book discoverability From LinkedIn's 2026 Big Ideas: “Generative engine optimization (GEO) is set to replace search engine optimization (SEO) as the way brands get discovered in the year ahead. As consumers turn to AI chatbots, agentic workflows and answer engines, appearing prominently in generative outputs will matter more than ranking in search engines.” Google has been rolling out AI Mode with its AI Overviews and is beginning to push it within Google.com itself in some countries, which means the start of a fundamental change in how people discover content online. I first posted about GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) in 2023, and it's going to change how readers find books. For years, we've talked about the long tail of search. Now, with AI-powered search, that tail is getting even longer and more nuanced. AI can understand complex, conversational queries that traditional search engines struggled with. Someone might ask, “What's a good thriller set in a small town with a female protagonist who's a journalist investigating a cold case?” and get highly specific recommendations. This means your book metadata, your website content, and your online presence need to be more detailed and conversational. AI search engines understand context in ways that go far beyond simple keywords. The authors who win in this new landscape will be those who create rich, authentic content about their books and themselves, not just promotional copy. As economist Tyler Cowen has said, “Consider the AIs as part of your audience. Because they are already reading your words and listening to your voice.” We're in the ‘organic' traffic phase right now, where these AI engines are surfacing content for ‘free,' but paid ads are inevitably on the way, and even rumoured to be coming this year to ChatGPT. By the end of 2026, I expect some authors and publishers to be paying for AI traffic, rather than blocking and protesting them. For now, I recommend checking that your author name/s and your books are surfaced when you search on ChatGPT.com as well as Google.com AI Mode (powered by Gemini). You want to make sure your work comes up in some way. I found that Joanna Penn and J.F. Penn searches brought up my Shopify stores, my website, podcast, Instagram, LinkedIn, and even my Patreon page, but did not bring up links to Amazon. If you only have an author presence on Amazon, does it appear in AI search at all? Do you need to improve anything about what the AI search brings up? Traditional publishers are also looking at this, with PublishersWeekly doing webinars on various aspects of AI in early 2026, including sessions on GEO and how book sales are changing, AI agents, and book marketing. In a 2026 predictions article on The Bookseller, the CEO of Bloomsbury Publishing noted, “The boundaries of artificial intelligence will become clearer, enabling publishers to harness its benefits while seeking to safeguard the intellectual property rights of authors, illustrators and publishers.” “AI will be deeply embedded in our workflows, automating tasks such as metadata tagging, freeing teams to focus on creativity and strategy. Challenges will persist. Generative AI threatens traditional web traffic and ad revenue models, making metadata optimisation and SEO critical for visibility as we adjust to this new reality online.” (3) The start of Agentic Commerce AI researches what you want to buy and may even buy on your behalf. Plus, I predict that Amazon does a commerce deal with OpenAI for shopping within ChatGPT by the end of 2026. In September 2025, ChatGPT launched Instant Checkout and the Agentic Commerce Protocol, which will enable bots to buy on websites in the background if authorised by the human with the credit card. VISA is getting on board with this, so is PayPal, with no doubt more payment options to come. In the USA, ChatGPT Plus, Pro, and Free users can now buy directly from US Etsy sellers inside the chat interface, with over a million Shopify merchants coming soon. Shopify and OpenAI have also announced a partnership to bring commerce to ChatGPT. I am insanely excited about this as it could represent the first time we have been able to more easily find and surface books in a much more nuanced way than the 7 keywords and 3 categories we have relied on for so long! I've been using ChatGPT for at least the last year to find fiction and non-fiction books as I find the Amazon interface is ‘polluted' by ads. I've discovered fascinating books from authors I've never heard of, most in very long tail areas. For example, Slashed Beauties by A. Rushby, recommended by ChatGPT as I am interested in medical anatomy and anatomical Venuses, and The Macabre by Kosoko Jackson, recommended as I like art history and the supernatural. I don't think I would have found either of these within a nuanced discussion with ChatGPT. Even without these direct purchase integrations, ChatGPT now has Shopping Research, which I have found links directly to my Shopify store when I search for my books specifically. Walmart has partnered with OpenAI to create AI-first shopping experiences, and you have to wonder what Amazon might be doing? In Nov 2025, Amazon signed a “strategic partnership” with OpenAI, and even though it's focused on the technical side of AI, those two companies in a room together might also be working on other plans … I'm calling it for 2026. I think Amazon will sign a commerce agreement with OpenAI sometime before the end of the year. This will enable at least recommendation and shopping links into Amazon stores (presumably using an OpenAI affiliate link), or perhaps even Instant Checkout with ChatGPT for Amazon. It will also enable a new marketing angle, especially if paid ads arrive in ChatGPT, perhaps even integrating with Amazon Ads in some way as part of any possible agreement, since ads are such a good revenue stream for Amazon anyway. The line between discovery, engagement, and purchase is collapsing. Someone could be having a conversation with an AI about what to read next, and within that same conversation, purchase a bookwithout ever leaving the chat interface. This already happens within TikTok and social commerce clearly works for many authors. It's possible that the next development for book discoverability and sales might be within AI chats. This will likely stratify the already fragmented book eco-system even more. Some readers will continue to live only within the Amazon ecosystem and (maybe) use their Rufus chatbot to buy, and others will be much wider in their exploration of how to find and discover books (and other products and services). If you haven't tried it yet, try ChatGPT.com Shopping Research for a book. You can do this on the free tier. Use the drop down in the main chat box and select Shopping Research. It doesn't have to be for your book. It can be any book or product, for example, our microwave died just before Christmas so I used it to find a new one. But do a really nuanced search with multiple requirements. Go far beyond what you would search for on Amazon. In the results, notice that (at the time of writing) it does not generally link to Amazon, but to independent sites and stores. As above, I think this will change by the end of 2026, as some kind of commerce deal with Amazon seems inevitable. (4) AI-assisted audiobook narration will go mainstream I've been talking about AI narration of audiobooks since 2019, and over the years, I've tried various different options. In 2025, the technology reached a level of emotional nuance that made it much easier to create satisfying fiction audio as well as non-fiction. It also super-charges accessibility, making audio available in more languages and more accents than ever before. Of course, human narration remains the gold standard, but the cost makes it prohibitive for many authors, and indeed many small traditional publishers, for all books. If it costs $2000 – $10,000 to create an audiobook, you have to sell a lot to make a profit, and the dominance of subscription models have made it harder to recoup the costs. Famous narrators and voice artists who have an audience may still be worth investing in, as well as premium production, but require an even higher upfront cost and therefore higher sales and streams in return. AI voice/audio models are continuing to improve, and even as this goes out, there are rumours on TechCrunch that OpenAI's new device, designed by Jony Ive who designed the iPhone, will be audio first and OpenAI are improving their voice models even more in preparation for that launch. In 2026, I think AI-narrated audio will go mainstream with far-reaching adoption across publishing and the indie author world in many different languages and accents. This will mean a further stratification of audiobooks, with high quality, high production, high cost human narrated audio for a small percentage of books, and then mass market, affordable AI-narrated audio for the rest. AI-narrated audiobooks will make audio ubiquitous, and just as (almost) every print book has an ebook format, in 2026, they will also have an audio format. I straddle both these worlds, as I am still a human audiobook narrator for my own work. I human-narrated Successful Self-Publishing Fourth Edition (free audiobook) and The Buried and the Drowned, my short story collection. I also use AI narration for some books. ElevenLabs remains my preferred service and in 2025, I used my J.F. Penn voice clone for Death Valley and also Blood Vintage, while using a male voice for Catacomb. I clearly label my AI-narration in the sales description and also on the cover, which I think is important, although it is not always required by the various services. You can distribute ElevenLabs narrated audiobooks on Spotify, Kobo Writing Life, YouTube, ElevenReader, and of course your own store if you use Shopify with Bookfunnel. There are many other services springing up all the time, so make sure you check the rights you have over the finished audio, as well as where you can sell and distribute the final files. If they are just using ElevenLabs models in the back-end, then why not just do that directly? (Most services will be using someone's model in the back-end, since most companies do not train their own models.) Of course, you can use Amazon's own narration. While Amazon originally launched Audible audiobooks with Virtual Voice (AVV) in November 2023, it was rolled out to more authors and territories in 2025. If your book is eligible, the option to create an audiobook will appear on your KDP dashboard. With just a few clicks, you can create an audiobook from a range of voices and accents, and publish it on Amazon and Audible. However, the files are not yours. They are exclusive to Amazon and you cannot use them on other platforms or sell them direct yourself. But they are also free, so of course, many authors, especially those in KU, will use this option. I have done some for my mum's sweet romance books as Penny Appleton and I will likely use them for my books in translation when the option becomes available. Traditional publishers are experimenting with AI-assisted audiobook narration as well. MacMillan is selling digital audiobooks read by AI directly on their store. PublishersWeekly reports that PRH Audio “has experimented with artificial voice in specific instances, such as entrepreneur Ely Callaway's posthumous memoir The Unconquerable Game,” when an “authorized voice replica” was created for the audiobook. The article also notes that PRH Audio “embrace artificial intelligence across business operations—my entire department [PRH Audio] is using AI for business applications.” And while indie authors can't use AI voices on ACX right now, Audible have over 100 voices available to selected publishing partnerships, as reported by The Guardian with “two options for publishers wishing to make use of the technology: “Audible-managed” production, or “self-service” whereby publishers produce their own audiobooks with the help of Audible's AI technology.” In 2026, it's likely that more traditional publishers — as well as indie authors — will get their backlist into audio with AI narration. (5) AI-assisted translation will start to take off beyond the early adopters Over the years, I've done translation deals with traditional publishers in different languages (German, French, Spanish, Korean, Italian) for some fiction and non-fiction books. But of course, to get these kinds of deals, you have to be proactive about pitching, or work with an agent for foreign rights only, and those are few and far between! There are also lots of languages and territories worldwide, and most deals are for the bigger markets, leaving a LOT of blue water for books in translation, even if you have licensed some of the bigger markets. I did my first partially AI-translated books in 2019 when I used Deepl.com for the first draft and then worked with a German editor to do 3 non-fiction books in German. While the first draft was cheap, the editing was pretty expensive, so I stopped after only doing a couple. I have made the money back now, but it took years. In 2025, AI Translation began to take off with ScribeShadow, GlobeScribe.ai, and more recently, in November 2025, Kindle Translate boosting the number of translated books available. Kindle Translate is (currently) only available to US authors for English into Spanish and also German into English, but in 2026, this will likely roll out to more languages and more authors, making it easier than ever to produce translations for free. Of course, once again, the gold standard is human translation, or at least human-edited translations, but the cost is prohibitive even just for proof-reading, and if there is a cheap or even free option, like Kindle Translate, then of course, authors are going to try it. If the translation gets bad reviews, they can just un-publish. There are many anecdotal stories of indie success in 2025 with AI-translated genre fiction sales (in series) in under-served markets like Italian, French, and Spanish, as well as more mainstream adoption in German. I was around in the Kindle gold-rush days of 2009-2012 and the AI-translation energy right now feels like that. There are hardly any Kindle ebooks in many of these languages compared to how many there are in English, so inevitably, the rush is on to fill the void, especially in genres that are under-served by traditional publishers in those markets. Yes, some of these AI translated books will be ‘AI-slop,' but readers are not stupid. Those books will get bad reviews and thus will sink to the bottom of the store, never to be seen again. The AI translation models are also improving rapidly, and Amazon's Kindle Translate may improve faster than most, for books specifically, since they will be able to get feedback in terms of page reads. Amazon is also a major investor in Anthropic, which makes Claude.ai, widely considered the best quality for creative writing and translation, so it's likely that is used somewhere in the mix. Some traditional publishers are also experimenting with AI-assisted translation, with Harlequin France reportedly using AI translation and human proofreaders, as reported by the European Council of Literary Translators' Associations in December 2025. Academic publisher Taylor and Francis is also using AI for book translation, noting: “Following a program of rigorous testing, Taylor & Francis has announced plans to use AI translation tools to publish books that would otherwise be unavailable to English-language readers, bringing the latest knowledge to a vastly expanded readership.” “Until now, the time and resources required to translate books has meant that the majority remained accessible only to those who could read them in the original language. Books that were translated often only became available after a significant delay. Today, with the development of sophisticated AI translation tools, it has become possible to make these important texts available to a broad readership at speed, without compromising on accuracy.” (6) AI video becomes ubiquitous. ‘Live selling' becomes the next trend in social sales. In 2025, short form AI-generated video became very high quality. OpenAI released Sora 2, and YouTube announced new Shorts creation tools with Veo 3, which you can also use directly within Gemini. There are tons of different AI video apps now, including those within the social media sites themselves. There is more video than ever and it's much easier to create. I am not a fan of short form video! I don't make it and I don't consume it, but I do love making book trailers for my Kickstarter campaigns and for adding to my book pages and using on social media. I made a trailer for The Buried and the Drowned using Midjourney for images and then animation of those images, and Canva to put them together along with ElevenLabs to generate the music. But despite the AI tools getting so much easier to use, you still have to prompt them with exactly what you want. I can't just upload my book and say, “Make a book trailer,” or “Make a short film.” This may change with generative video ads, which are likely to become more common in 2026, as video turns specifically commercial. Video ads may even be generated specifically for the user, with an audience of one, maybe even holding your book in their hands (using something like Cameos on Sora), in the same way that some AI-powered clothing stores do virtual try-ons. This might also up-end the way we discover and buy things, as the AI for eCommerce and Amazon Sellers newsletter says about OpenAI's Sora app, “OpenAI isn't just trying to build a TikTok competitor. They're building a complete reimagining of how we discover and buy things …” “The combination of ChatGPT's research capabilities and Sora's potential for emotional manipulation—I mean, “engagement”—could create something we've never seen before: an AI ecosystem that might eventually guide you through every type of purchase, from the most considered to the most impulsive.” In 2026, there will be A LOT more AI-generated video, but that also leads to the human trend of more live video. While you can use an AI avatar that looks and sounds like you using tools like HeyGen or Synthesia, live video has all the imperfect human elements that make it stand-out, plus the scarcity element which leads to the purchase decision within a countdown period. Live video is nothing new in terms of brand building and content in general, but it seems that live events primarily for direct sales might be a thing in 2026. Kim Kardashian hosted Kimsmas Live in December 2025 with a 45 minute live shopping event with special guests, described as entertainment but designed to be a sales extravaganza. Indie authors are doing a similar thing on TikTok with their books, so this is a trend to watch in 2026, especially if you feel that live selling might fit with your personality and author business goals. It's certainly not for everyone, but I suspect it will suit a different kind of creator to those who prefer ‘no face' video, or no video at all! On other aspects of the human side of social media, Adam Mosseri the CEO of Instagram put a post on Threads called Authenticity after Abundance. He said, “Everything that made creators matter—the ability to be real, to connect, to have a voice that couldn't be faked—is now suddenly accessible to anyone with the right tools.” “Deepfakes are getting better and better. AI is generating photographs and videos indistinguishable from captured media. The feeds are starting to fill up with synthetic everything. And in that world, here's what I think happens.Creators matter more.” It's a long article so just to pick a few things from it: “We like to talk about “AI slop,” but there is a lot of amazing AI content … we are going to start to see more and more realistic AI content.” I've talked to my Patreon Community about this ‘tsunami of excellence' as these tools are just getting better and better and the word ‘slop' can also be applied to purely human output, too. If you think that AI content is ‘worse' than wholly human content, in 2026, you are wrong. It is now very very good, especially in the hands of people who can drive the AI tools. Back to Adam's post: “Authenticity is fast becoming a scarce resource, …The creators who succeed will be those who figure out how to maintain their authenticity [even when it can be simulated] …” “The bar is going to shift from “can you create?” to “can you make something that only you could create?” He talks about how the personal content on Instagram now is: “unpolished; it's blurry photos and shaky videos of people's daily experiences … flattering imagery is cheap to produce and boring to consume. People want content that feels real… Savvy creators are going to lean into explicitly unproduced and unflattering images of themselves. In a world where everything can be perfected, imperfection becomes a signal. Rawness isn't just aesthetic preference anymore—it's proof. It's defensive. A way of saying: this is real because it's imperfect.” While I partially love this, and I really hope it's true, as in I hope we don't need to look good for the camera anymore I would also challenge Adam on this, because pretty much every woman I know on social media has been sent sexual messages, and/or told they are ugly and/or fat when posting anything unflattering. I've certainly had both even for the same content, but I don't expect Adam has been the target for such posting! But I get his point. He goes on:“Labeling content as authentic or AI-generated is only part of the solution though. We, as an industry, are going to need to surface much more context about not only the media on our platforms, but the accounts that are sharing it in order for people to be able to make informed decisions about what to believe. Where is the account? When was it created? What else have they posted?” This is exactly what I've been saying for a while under my double down on being human focus. I use my Instagram @jfpennauthor as evidence of humanity, not as a sales channel. You can do both of course, but increasingly, you need to make sure your accounts at places have longevity and trust, even by the platforms themselves. Adam finishes: “In a world of infinite abundance and infinite doubt, the creators who can maintain trust and signal authenticity—by being real, transparent, and consistent—will stand out.” For other marketing trends for 2026, I recommend publicist Kathleen Schmidt's SubStack which is mostly focused on traditional publishing but still interesting for indies. In her 2026 article, she notes: “We have reached a social media saturation point where going viral can be meaningless and should not be the goal; authenticity and creativity should. She also says, “In-person events are important again,” and, “Social media marketing takes a nosedive… we have reached a saturation point … What publishers must figure out is how to make their social media campaigns stand out. If they remain somewhat uninspired, the money spent on social ads won't convert into book sales.” I think this is part of the rise of live selling as above, which can stand out above more ‘produced' videos. Kathleen also talks about AI usage. “AI can help lighten the burden of publicity and marketing.” “A lot of AI tools are coming to market to lessen the load: they can write pitches, create media lists for you, send pitches for you, and more. I know the industry is grappling with all things AI, but some of these tools are huge time savers and may help a book more than hurt it.” On that note … (7) AI will create, run, and optimise ads without the need for human intervention Many authors will be very happy about this as marketing is often the bane of our author business lives! As I noted in my 2026 goals, I would love to outsource more marketing tasks to AI. I want an “AI book marketing assistant” where I can upload a book and specify a budget and say, ‘Go market this,' then the AI will action the marketing, without me having to cobble together workflows between systems. Of course, it will present plans for me to approve but it will do the work itself on the various platforms and monitor and optimize things for me. I really hope 2026 is the year this becomes possible, because we are on the edge of it already in some areas. Amazon Ads launched a new agentic AI tool in September 2025 that creates professional-quality ads. I've also been working with Claude in Chrome browser to help me analyse my Amazon Ad data and suggest which keywords/products to turn off and what to put more budget into. I'll do a Patreon video on that soon. Meta announced it will enable AI ad creation by the end of 2026 for Facebook and Instagram. For authors who find ad creation overwhelming or time-consuming, this could be a game-changer. Of course, you will still need a budget! (8) 1000 True Fans becomes more important than ever Lots of authors and publishers are moaning about the difficulty of reaching readers in an era of ‘AI slop' but there is no shortage of excellent content created by humans, or humans using AI tools. As ever, our competition is less about other authors, or even authors using AI-assisted creation, we're competing against everything else that jostles for people's attention, and the volume of that is also growing exponentially. I've never been a fan of rapid release, and have said for years that you can't keep up with the pace of the machines. So play a different game. As Kevin Kelly wrote in 2008, If you have 1000 true fans, (also known as super fans), “you can make a living — if you are content to make a living but not a fortune.” [Kevin Kelly was on this show in 2023 talking about Excellent Advice for Living.] Many authors and the publishing industry are stuck in the old model of aiming to sell huge volumes of books at a low profit margin to a massive number of readers, many of them releasing ever faster to try and keep the algorithms moving. But the maths can work for the smaller audience of more invested readers and fans. If you only make $2 profit on an ebook, you need to sell 500 ebooks to make $1000, and then do it again next month. Or you can have a small community like my patreon.com/thecreativepenn where people pay $2 (or more) a month, so even a small revenue per person results in a better outcome over the year, as it is consistent monthly income with no advertising. But what if you could make $20 profit per book? That is entirely possible if you're producing high quality hardbacks on Kickstarter, or bundle deals of audiobooks, or whole series of ebooks. You would only need to sell to 50 people to make $1000. What about $100 profit per sale, which you can do with a small course or live event? You only need 10 people to make $1000, and this in-person focus also amplifies trust and fosters human connection. I've found the intimacy of my live Patreon Office Hours and also my webinars have been rewarding personally, but also financially, and are far more memorable — and potentially transformative — than a pre-recorded video or even another book. From the LinkedIn 2026 Big Ideas article: “In an AI-optimized world, intentional human connection will become the ultimate luxury.” The 1000 True Fans model is about serving a smaller, more personal audience with higher value products (and maybe services if that's your thing). As ever, its about niche and where you fit in the long long long long long tail. It's also about trust. Because there is definitely a shortage of that in so many areas, and as Adam Mosseri of Instagram has said, trust will be increasingly important. Trust takes time to build, but if you focus on serving your audience consistently, and delivering a high quality, and being authentic, this emerges as part of being human. In an echo of what happened when online commerce first took off, we are back to talking about trust. Back in 2010, I read Trust Agents: by Julien Smith and Chris Brogan, which clearly needs a comeback. There was a 10th anniversary edition published in 2020, so that's worth a read/listen. Chris Brogan was also on this show in 2017 when we talked about finding and serving your niche for the long term. That interview is still relevant, here's a quick excerpt, where I have (lightly edited) his response to my question on this topic back in 2017: Jo: The principle of know, like, and trust, why is that still important or perhaps even more important these days? Chris: There are a few things that at play there, Joanna. One is that the same tools that make it so easy for any of us to start and run a business also allow certain elements to decide whether or not they want to do something dubious. And with all new technologies that come, you know, there's nothing unique about these new technologies. In the 1800s, anyone could put anything in a bottle and sell it to you and say, this is gonna cure everything. Cancer — gone. And the bottle could have nothing in. You know, it could be Kool-Aid. And so, the idea of trying to understand what's behind the business though, one beautiful thing that's come is that we can see in much more dimensions who we're dealing with. We can understand better who's the face behind the brand. I really want people to try their best to be a lot clearer on what they stand for or what they say. And I don't really mean a tagline. I mean, humans don't really talk like that. They don't throw some sentence out as often as they can that you remember them for that phrase. But I would say that, we have so many media available to us — the plural of mediums — where we can be more of ourselves. And I think that there's a great opportunity to share the ‘you' behind the scenes, and some people get immediately terrified about this, ‘Ah, the last thing I want is for people to know more about me,' but I think we have such an opportunity. We have such an opportunity to voice our thoughts on something, to talk about the story that goes behind the product. We were all raised on overly produced material, but I think we don't want that anymore. We really want clarity, brevity, simplicity. We want the ability for what we feel is connection and then access. And so I think it's vital that we connect and show people our accessibility, not so that they can pester us with strange questions, but more so that you can say, this person stands with their product and their service and this person believes these things, and I feel something when I hear them and I wanna be part of that.” That's from Chris Brogan's interview here in 2017, and he is still blogging and speaking at writing at ChrisBrogan.com and I'm going to re-listen to the audiobook of Trust Agents again myself as I think it's more relevant than ever. The original quote comes from Bob Burg in his 1994 book, Endless Referrals, “All things being equal, people will do business with, and refer business to, those people they know, like and trust.” That still applies, and absolutely fits with the 1000 True Fans model of aiming to serve a smaller audience. As Kevin Kelly says in 1000 True Fans, “Instead of trying to reach the narrow and unlikely peaks of platinum bestseller hits, blockbusters, and celebrity status, you can aim for direct connection with a thousand true fans.” “On your way, no matter how many fans you actually succeed in gaining, you'll be surrounded not by faddish infatuation, but by genuine and true appreciation. It's a much saner destiny to hope for. And you are much more likely to actually arrive there.” In 2026, I hope that more authors (including me!) let go of ego goals and vanity metrics like ranking, gross sales (income before you take away costs), subscribers, followers, and likes, and consider important business numbers like profit (which is the money you have after costs like marketing are taken out), as well as number of true fans — and also lifestyle elements like number of weekends off, or days spent enjoying life and not just working! OK, that's my list of trends and predictions for 2026. Let me know what you think in the comments. Do you agree? Am I wrong? What have I missed? The post 2026 Trends And Predictions For Indie Authors And The Book Publishing Industry with Joanna Penn first appeared on The Creative Penn.

The Hot Stove is heating up and there's plenty to unpack on Episode 46. We break down the Mets' latest moves, diving into the signings of Jorge Polanco and Luke Weaver—what they signal about roster construction, lineup balance, and David Stearns' broader plan. Is a true power bat still missing, and where could the Mets realistically find it?We also zoom out to the league-wide chaos, reacting to a starting pitching market that's gone completely off the rails and what it means for the Mets' options moving forward. Plus, we dig into renewed Mets–Padres rumors, the possibility of the payroll coming down, and how ownership's approach could shape the rest of the offseason.To top it all off, we react to a stunner on the international front as Munetaka Murakami signs with the White Sox—what happened, why the Mets weren't in it, and what it says about their international strategy.All that and more as we try to make sense of a wild, unpredictable Hot Stove.Had a few technical difficulties along the way so bare with us!Follow on INSTAGRAM, YOUTUBE & X: @cupofmetsSubscribe on SPOTIFY, APPLE or wherever you get your favorite podcasts!Download The SeatGeek App! Use Code: "CUPOFMETS" at first purchase to get $20.00 off!Download The ProphetX App! Use Code: "CUPOFMETS" at signup to get up to 20X in Bonus Cash, matching up to $100!

Ever felt overwhelmed by relentless growth, leadership friction, or the challenge of building teams that actually scale? What if you could gain proven, insider strategies for multiplying operations by 20X while keeping chaos at bay?In this bold episode, Cameron Herold sits down with Harrison Crum, Chief Operating Officer of Ally Waste, to unpack the rarely-told story behind scaling a national waste-services brand—now operating in 40 states, with over 1,500 employees and a mission to dominate a niche few understand.They dig deep on developmental leadership, acquisition integration, ruthless prioritization, and using tech and AI for surprising advantage. If you want to dodge burnout, outpace competitors, and solve execution pain now, don't wait—this conversation reveals real advantages you won't find anywhere else.Timestamped Highlights[00:00] – Harrison spills how sales intelligence and regional structure turn cold prospects into loyal clients[02:31] – Why the “doorstep to dumpster” model wins in multi-family and what luxury tenants secretly value[04:33] – The ugly side of apartment junk and how subscription junk removal flips the profit script[07:03] – Ally Waste's national play: how to dominate fragmented markets and win big contracts[09:29] – Commercial expansion temptations: the real use cases for “waste leveling” in strip malls[13:58] – Navigating hauler relationships, unions, and the anti-mafia garbage wars in New York & New Jersey[16:51] – How 20X growth nearly broke the company—and the relentless focus that turned chaos into margin[21:43] – Acquisitions decoded: finding the right people, fixing culture, and building tech that actually scales[26:04] – Are robots or AI coming for waste? Harrison's thrilling vision for how tech could flip the industry[32:01] – The Ally Way: promoting leaders from within, tough-core values, and intentional developmentAbout the GuestHarrison Crum is the Chief Operating Officer of Ally Waste, a fast-growing, multi-state waste services provider specializing in multifamily and commercial property solutions. With deep experience in Fortune 500 and private sector operations—including past roles at Republic Services and Ford—he's known for scaling Ally's operations by 20X in four years, championing high-retention business models, and building game-changing technology for dirty jobs. Harrison is a seasoned leader in acquisition integration, organizational development, and culture-driven execution.

Criterion breaks down year-end acquisition numbers, highlights stock-market bubble indicators, and lays out a practical commercial real estate strategy to survive a potential 2026–2027 correction. Time Stamps: 0:00 – Introduction 1:30 – Year-end update: $72M acquired + $21M equity raised 2:35 – Growth story: 2019 first deal to “20X” scale + investor base expansion 4:27 – Why talk about a potential 2026–2027 market correction 6:12 – Index run-up: S&P / Dow / NASDAQ context and “bubble” risk framing 8:47 – Valuation red flags: S&P PE ratios vs. 1929 / 2001 comps 9:47 – Buffett Indicator explained (market cap vs. GDP) 10:55 – “Magnificent 7” concentration + elevated PE multiples 12:40 – Awareness over prediction: risk management mindset 13:08 – Macro pressure: national debt + interest cost discussion 15:19 – If stocks crash: what happens to real estate values + inflation response 16:39 – CRE in a downturn: tenant risk, vacancy, and cash reserves 17:25 – Rates drop = refinance opportunity; CRE vs. stocks volatility 18:42 – Why higher-cap buys help: breathing room on cash flow 19:14 – Crash playbook: buy discounted assets, avoid forced sales, keep operating 19:47 – “Don't wait for perfect”: buy through every season Visit TheCriterionFund.com for more information commercialrealestate #commercialrealestateinvesting #cre #realestateinvesting #investing #passiveincome #wealthbuilding #financialfreedom #realestatepodcast #investoreducation #stripcenters #retailrealestate #neighborhoodcenters #caprate #cashoncash #dealmaking #capitalraising #privateequityrealestate #marketcycle #recessionproof #riskmanagement #economicoutlook #interestrates #refinance #valueadd #assetmanagement #tenantmix #vacancy #portfolio #multifamilyinvesting stockmarket #sp500 #nasdaq #dowjones #buffettindicator #priceratios #peratio #magnificentseven #marketcorrection #marketcrash #macro #inflation #deficit #nationaldebt #economy #investingtips #wealthstrategy #longterminvesting #buythedip

Today I'm sharing what three real coaches did differently to increase their prices by $2,000, make 20X their investment back in less than two weeks, and transform their entire business confidence in just a few weeks. These aren't hypothetical stories. These are real coaches who went from playing small and discounting prices to confidently charging premium rates and delivering predictable results. In this case study episode, you'll hear exactly what happened when three different coaches stopped second-guessing their expertise and created frameworks that backed up every promise they made to clients. Plus: Details about the Framework Builder Lab live cohort starting October 22 - the lowest price this container will ever be for eight weeks of live coaching, real-time accountability, and a complete framework. Your framework isn't just how you help clients - it's how you build the confidence to do everything you're capable of. Resources mentioned: Framework Builder Lab: https://amanda-walker.com/lab/ Free coaching questions: amanda-walker.com/questions Instagram: @awalkmyway

Could Bitcoin hit $130K this weekend?! The charts are lighting up, and momentum is building fast. Meanwhile, several altcoins are showing setups that could deliver 20X gains in the next move. LBank Promo - https://www.lbank.com/activity/bonuspro/100M-EN11-BonusPro?

Could Bitcoin hit $130K this weekend?! The charts are lighting up, and momentum is building fast. Meanwhile, several altcoins are showing setups that could deliver 20X gains in the next move. LBank Promo - https://www.lbank.com/activity/bonuspro/100M-EN11-BonusPro?

Episode Summary: Ryan Lang and Brook Bishop dive deep into a record-breaking $81 million book launch, breaking down the strategic elements that made it successful and why most coaches are approaching their business growth completely out of sequence. They reveal how three years of patient trust-building led to one explosive day and what coaches can learn about building systems, relationships, and offers that actually work.Key Takeaways:• (02:47) The donation-based offer strategy: How selling 200 books for $5,998 creates multiple revenue streams while building massive databases through lead capture• (08:32) Why execution isn't the hard part: The real heavy lifting happens 90 days to 6 months before launch through relationship building and affiliate partnerships• (10:44) The $4 million ad spend revelation: How investing over $4 million in ads for a 20X return proves the importance of knowing your numbers and having systems that can handle scale• (18:51) Creating what people actually want: The difference between thinking inwardly ("this would be cool") versus understanding true market demand and building offers people will actually buy• (26:25) Trust as the ultimate currency: How three years of adding value without selling built the foundation for massive revenue generation• (32:33) The sequence problem: Why copying tactics without proper foundation is like switching flour and frosting in a cake recipe - you'll get a very different outcomeNotable Quotes:• "He plays chess while most people are playing checkers. This was 100% orchestrated - he didn't just orchestrate this like a couple months ago." - Ryan Lang (22:31)• "The most important currency these days is trust. And Alex has done an incredible job of just adding value, adding value, adding value." - Brook Bishop (26:25)• "This is not an $81 million day. This is three years of building to this moment." - Brook Bishop (28:02)• "When you create strong systems and structures out of simple pieces, you become nimble, you become scalable, and you become practically bulletproof." - Ryan Lang (31:38)• "You might have all the right ingredients, but if you change up the sequence of when you use those ingredients... you're gonna get a very, very different outcome." - Brook Bishop (32:33)Resources Mentioned:"$100 Million Offers" book and strategic framework "$100 Million Leads" book and methodology “$100 Million Money Models” YouTube LaunchDan Kennedy's "MIFGE" marketing concept Are you building your business in the right sequence, or are you trying to leapfrog to expert-level tactics before mastering the fundamentals? Take an honest look at where you are in your business journey - startup, stability, success, or mastery - and make sure your strategies match your stage. The strongest businesses aren't built overnight; they're built through patient, systematic execution over time.Connect with Empire Partners: Ready to build a coaching business that scales systematically? Subscribe to The Coaching Equation Podcast for weekly insights on building profitable, mission-driven coaching businesses. Leave us a review and share this episode with a coach who needs to hear about proper business sequencing.

Mi sistema de Contenido en Notion

In this episode I'm joined by Art Shectman, founder and CEO of Elephant Ventures, a global innovation firm helping companies unlock the full potential of AI transformation. But we don't just talk tech: we dive into the human side of building and leading a business.Art shares how he intentionally designed a company culture built on dependability, psychological safety, and joyful work. We talk about how his team successfully adopted a compressed four-day workweek, and what it really takes to implement major organizational shifts like AI adoption without sparking panic or burnout.You'll walk away with a better understanding of how to structure your team for accountability (without toxicity), why empowering employees beats micromanaging every time, and how to lead transformation with honesty, clarity, and how to get buy-in from the ground up.If you're thinking about hiring across borders, piloting a 4-day week, or embracing AI in your business you don't want to miss this one. What you'll hear in this episode:[1:40] How Elephant Ventures helps companies get 20X results from AI - not just 20%[3:29] Why Art started hiring internationally in 2008[4:12] The early struggles of global hiring (and why EORs are game-changing now)[7:17] Why founders must prioritize their “joyfulness battery”[10:17] Building culture intentionally: dependability, kindness, and the value pyramid[14:02] Holding people accountable without being toxic[18:17] How Elephant Ventures rolled out a compressed 4-day workweek[22:15] Lessons from testing and scaling flexible work[24:10] Coaching teams through AI transformation without fear[26:29] Creating psychological safety in practice [30:06] Why vulnerability and consistency matter more than charisma[32:19] Where to find Art's thought leadership on AI and transformationResources and links:Connect with Art on LinkedIn: Art ShectmanExplore Elephant Ventures' AI resources: Elevate.ElephantVentures.comConnect with me on LinkedIn: Jackie KochDownload my free HR + Hiring Essentials Playbook: peopleprinciples.co

The future of coding. We cover multiplying engineering output, vibe coding bottlenecks, agents as reviewer, AI roll-ups, and the future of developing software. Merrill Lutsky is co-founder and CEO of Graphite, bringing AI-acceleration and automation to code review. Founded in 2020 out of New York, Graphite has become a key part of the developer ecosystem — as more code is generated with AI, they enable developers to scale the evaluation, testing, and review process before it is released. A growing bottleneck that has become incredibly important. The startup has raised over $70M from leading VC’s such Accel, A16Z, Menlo as well as a receiving a strategic investment from model provider Anthropic. Last year Graphite grew its revenue 20X and is trusted by over 45,000 developers at top engineering organizations such as Shopify and Figma. His second startup, Merrill has helped develop and manage software products for high output engineering companies such as Square, Oscar Insurance, and SelfMade. He holds a degree in Applies Math and Economics from Harvard. Sign up for new podcasts and our newsletter, and email me on danieldarling@focal.vcSee omnystudio.com/listener for privacy information.

Bronson Hill is a leading expert in passive income and wealth psychology, known for his ability to break down the habits and strategies of the ultra-wealthy. As the founder and CEO of Bronson Equity and host of the Mailbox Money Show, Bronson has interviewed over 2,500 millionaires, uncovering the mindsets and investment tactics that separate everyday earners from those who achieve true financial freedom. A former medical sales professional, Bronson walked away from a high-paying career to pursue passive income, scaling his wealth 20X in just a few years. He's a general partner in 2,500 multifamily units worth over $250M and has personally raised over $45M for real estate and private equity deals. Bronson's unique perspective comes from both his humble beginnings and his relentless focus on mindset, making him a sought-after keynote speaker and author. On this episode we talk about: – The mindset shift that helped Bronson 20X his net worth – Lessons from interviewing over 2,500 millionaires – Why “wealth-worthiness” is the hidden key to financial success – How the ultra-wealthy invest differently (and why “alternative assets” aren't so alternative) – Actionable strategies to build confidence and take your first steps toward passive income Top 3 Takeaways 1. Mindset is everything: The right psychology and self-talk are foundational to building wealth. Most people stay broke because they never develop a sense of “wealth-worthiness”—the belief that they deserve and are capable of financial success. 2. Surround yourself with the right people: Who you spend time with determines your financial future. Learning from mentors and modeling the habits of successful investors can accelerate your growth. 3. Take action despite fear: Confidence isn't about being fearless—it's about acting in the face of fear. Start small, celebrate wins, and use setbacks as data for growth. Notable Quotes “Confidence isn't something you're born with—it's something you build. Small wins, big lessons, and relentless action—love this breakdown.” “Make yourself valuable to valuable people.” “Being wealthy is not about having money or not having money. It's the habits and the person you become.” Connect with Bronson Hill: BronsonEquity.com Instagram: @bronsondavidhill Mailbox Money Show: Available on all major podcast platforms

China retaliates to Trump's retaliation to China's tit-for-tat response to tariffs; downward pressure continues as markets trying to recalibrate with a moving tariff target; there are cracks appearing in the treasury market from the basis trade; what will the Fed do? Lance explains the latest gyrations in markets and the sharp rise in bond yields. Basis trade explained; appears to be at 20X leverage: Is someone getting margin calls and liquidating? Tariffs are not the issue here; will the Fed step in? Wall Mart reports earnings, but cannot provide guidance until tariff trouble is resolved. How much money does it take to move markets one-point? Slowing economy = less demand = high unemployment. It's going to get worse before it gets better. There is an alarming trend of 401k early withdrawals; "leakage" has been a problem for a while. Lance and Danny examine the eruption in the bond market; don't over react. The VIX has spiked, but is moderating. What will the Fed do? The best tariff trade is the Roth conversion. SEG-1: Tit-for-tat on Tariffs SEG-2: What is the Basis Trade? SEG-3: It's Going to Get Worse Before It Gets Better SEG-4: Eruption in the Bond Market Hosted by RIA Advisors Chief Investment Strategist Lance Roberts, CIO, w Senior Financial Advisor Danny Ratliff, CFP Produced by Brent Clanton, Executive Producer ------- Watch today's full show video here: https://www.youtube.com/watch?v=oo_zfOuvXVw&list=PLVT8LcWPeAugpcGzM8hHyEP11lE87RYPe&index=1&t=2705s ------- Articles mentioned in this report: "Stupidity And The 5-Laws Not To Follow" https://realinvestmentadvice.com/resources/blog/stupidity-and-the-5-laws-not-to-follow/ "Corporate Yield Spreads Start To Widen" https://realinvestmentadvice.com/resources/blog/daily-market-commentary/ "The Market Crash – Hope In The Fear" https://realinvestmentadvice.com/resources/blog/the-market-crash-a-set-up-for-a-rally/ "The “Liberation Day” Tariffs Crash The Market" https://realinvestmentadvice.com/resources/blog/the-liberation-day-tariffs-crash-the-market/ ------- The latest installment of our new feature, Before the Bell, "Yields Belie Market Instability," is here: https://www.youtube.com/watch?v=pnT6n_NY_tY&list=PLwNgo56zE4RAbkqxgdj-8GOvjZTp9_Zlz&index=1 ------- Our previous show is here: "Markets Rally; What's Next?" https://www.youtube.com/watch?v=aLv00PZtAHM&list=PLVT8LcWPeAugpcGzM8hHyEP11lE87RYPe&index=1 ------- Get more info & commentary: https://realinvestmentadvice.com/newsletter/ -------- SUBSCRIBE to The Real Investment Show here: http://www.youtube.com/c/TheRealInvestmentShow -------- Visit our Site: https://www.realinvestmentadvice.com Contact Us: 1-855-RIA-PLAN -------- Subscribe to SimpleVisor: https://www.simplevisor.com/register-new -------- Connect with us on social: https://twitter.com/RealInvAdvice https://twitter.com/LanceRoberts https://www.facebook.com/RealInvestmentAdvice/ https://www.linkedin.com/in/realinvestmentadvice/ #LiquidityCrisis #MarketVolatility #FinancialStress #CreditCrunch #FedWatch #BondYields #MarketInstability #BasisTrade #ETF #Liquidity #StockMarketRally #MarketOutlook2025 #InvestingInsights #FinanceNews #EconomicTrends #MarketCrash2025 #FearAndFinance #InvestingInUncertainty #FinancialCrisis #HopeInTheCrash #EarningsImpact #TariffEffect #MarketAnalysis #InvestorInsight #TradeWar2025 #MarketBottom #TariffWar #BondMarket #DownsideRisk #Tariffs #MarketLows #InvestingAdvice #Money #Investing

China retaliates to Trump's retaliation to China's tit-for-tat response to tariffs; downward pressure continues as markets trying to recalibrate with a moving tariff target; there are cracks appearing in the treasury market from the basis trade; what will the Fed do? Lance explains the latest gyrations in markets and the sharp rise in bond yields. Basis trade explained; appears to be at 20X leverage: Is someone getting margin calls and liquidating? Tariffs are not the issue here; will the Fed step in? Wall Mart reports earnings, but cannot provide guidance until tariff trouble is resolved. How much money does it take to move markets one-point? Slowing economy = less demand = high unemployment. It's going to get worse before it gets better. There is an alarming trend of 401k early withdrawals; "leakage" has been a problem for a while. Lance and Danny examine the eruption in the bond market; don't over react. The VIX has spiked, but is moderating. What will the Fed do? The best tariff trade is the Roth conversion. SEG-1: Tit-for-tat on Tariffs SEG-2: What is the Basis Trade? SEG-3: It's Going to Get Worse Before It Gets Better SEG-4: Eruption in the Bond Market Hosted by RIA Advisors Chief Investment Strategist Lance Roberts, CIO, w Senior Financial Advisor Danny Ratliff, CFP Produced by Brent Clanton, Executive Producer ------- Watch today's full show video here: https://www.youtube.com/watch?v=oo_zfOuvXVw&list=PLVT8LcWPeAugpcGzM8hHyEP11lE87RYPe&index=1&t=2705s ------- Articles mentioned in this report: "Stupidity And The 5-Laws Not To Follow" https://realinvestmentadvice.com/resources/blog/stupidity-and-the-5-laws-not-to-follow/ "Corporate Yield Spreads Start To Widen" https://realinvestmentadvice.com/resources/blog/daily-market-commentary/ "The Market Crash – Hope In The Fear" https://realinvestmentadvice.com/resources/blog/the-market-crash-a-set-up-for-a-rally/ "The “Liberation Day” Tariffs Crash The Market" https://realinvestmentadvice.com/resources/blog/the-liberation-day-tariffs-crash-the-market/ ------- The latest installment of our new feature, Before the Bell, "Yields Belie Market Instability," is here:  https://www.youtube.com/watch?v=pnT6n_NY_tY&list=PLwNgo56zE4RAbkqxgdj-8GOvjZTp9_Zlz&index=1 ------- Our previous show is here: "Markets Rally; What's Next?" https://www.youtube.com/watch?v=aLv00PZtAHM&list=PLVT8LcWPeAugpcGzM8hHyEP11lE87RYPe&index=1 ------- Get more info & commentary:  https://realinvestmentadvice.com/newsletter/ -------- SUBSCRIBE to The Real Investment Show here: http://www.youtube.com/c/TheRealInvestmentShow -------- Visit our Site: https://www.realinvestmentadvice.com Contact Us: 1-855-RIA-PLAN -------- Subscribe to SimpleVisor: https://www.simplevisor.com/register-new -------- Connect with us on social: https://twitter.com/RealInvAdvice https://twitter.com/LanceRoberts https://www.facebook.com/RealInvestmentAdvice/ https://www.linkedin.com/in/realinvestmentadvice/ #LiquidityCrisis #MarketVolatility #FinancialStress #CreditCrunch #FedWatch #BondYields #MarketInstability #BasisTrade #ETF #Liquidity #StockMarketRally #MarketOutlook2025 #InvestingInsights #FinanceNews #EconomicTrends #MarketCrash2025 #FearAndFinance #InvestingInUncertainty #FinancialCrisis #HopeInTheCrash #EarningsImpact #TariffEffect #MarketAnalysis #InvestorInsight #TradeWar2025 #MarketBottom #TariffWar #BondMarket #DownsideRisk #Tariffs #MarketLows #InvestingAdvice #Money #Investing

Hoy conversé con Sebastián Barrios, Director de Tecnología de Mercado Libre (MELI), la empresa de tecnología más grande de Latinoamérica. Antes de MELI, fundó Yaxi, la primera app de transporte privado en México que luego vendió a Cabify, donde fue CTO por 4 años. -En Startupeable hacemos más gracias a  Notion, la plataforma todo en uno para organizar tu startup.Centraliza documentos, tareas y bases de datos en un solo lugar: el cerebro digital de tu negocio, ahora con IA para agilizar tu trabajo.Nos aliamos con Notion para regalarte 3 meses gratis del plan Plus y acceso ilimitado a su IA.

Hoy conversé con Stefan Moeller, cofundador y CEO de Klar, uno de los bancos digitales más grandes en México con +3M usuarios. Klar factura más de $200M por año y logra operar 10 veces más eficiente que los bancos tradicionales. A la fecha, ha levantado $167M de inversionistas como General Atlantic, Prosus Ventures y Santander.-En Startupeable hacemos más gracias a  Notion, la plataforma todo en uno para organizar tu startup.Centraliza documentos, tareas y bases de datos en un solo lugar: el cerebro digital de tu negocio, ahora con IA para agilizar tu trabajo.Nos aliamos con Notion para regalarte 3 meses gratis del plan Plus y acceso ilimitado a su IA.

#300 In the world of marketing, there are countless strategies and tactics at your disposal, each promising a path to success. In this episode, we dive into a unique and highly effective approach: leveraging quizzes to grow your business!  I recently chatted with my good friend and fellow entrepreneur, Kirsten Tyrrel, about her secret sauce for making quizzes go viral on platforms like TikTok, Instagram, and Pinterest.  But first, you might be thinking, why quizzes? According to Riddle.com, quizzes are more than 20X (or 2,000%) better than pop-up email collection forms…and get 400% more engagement (time on site) than other email opt-in sites. Need we say more?  So if you're wondering how to create a successful marketing questionnaire, you're in luck. Kirsten broke it down for us, making it easier than ever to craft that killer marketing quiz. We've also created a super cool article that tells you all the exciting stuff from this episode. You definitely don't want to miss it, so give it a look right here! (Original Air Date - 10/26/23) What we discuss with Kirsten: + Introduction and Importance of Quizzes in Business + Kirsten Tyrell's Entrepreneurial Journey + The Power of Quiz Funnel Marketing + The Process of Creating a Quiz Funnel + The Impact of Quiz Funnels on Business Growth + Balancing Entrepreneurship and Motherhood + Final Thoughts and Contact Information Resources from this episode: Blog Post: 16 Tips for Using Quizzes to Grow Your Business! Sign up for our FREE Business Course - over 300+ new business ideas, also includes the 7 Phases of a business, so you know where you are now and where you need to go next. You'll also get 7 of the most popular marketing strategies and 31 stay-on-track hacks that successful millionaires follow to grow and automate their businesses. Go to https://www.millionaireuniversity.com/training. And follow us on: Instagram Facebook Tik Tok Youtube Twitter To get exclusive offers mentioned in this episode and to support the show, visit millionaireuniversity.com/sponsors. Want to hear from more incredible entrepreneurs? Check out all of our interviews here! Learn more about your ad choices. Visit megaphone.fm/adchoices

How much do software businesses sell for in the seven, eight, or even nine-figure range? This episode of the Buying Online Businesses Podcast dives into what it takes to sell or buy a SaaS company. Host Jaryd Krause speaks with Diamond Innabi, an expert in buying and selling software businesses. With over 10 years of experience, she has helped close successful deals in industries like energy, government, education, real estate, and technology. Diamond understands the challenges that come with selling a software company, from making sure sellers get full credit for their recurring revenue to explaining complex technology to buyers. She also knows how to position businesses with high customer concentration and other tricky factors in a way that attracts the right buyers. In this episode, listeners will learn: ✅ How long it takes to prepare a business for sale (sometimes up to two years!)✅ What needs to happen before a business is ready to sell✅ How software companies in the $15M - $150M range are valued✅ The biggest mistakes that cause deals to fall apart—and how to avoid them✅ Key negotiation strategies to get the best deal✅ How to assess a business’s strengths and weaknesses before making an offer Since joining SCG in 2014, Diamond has helped grow the team, mentor new investment bankers, and improve processes that lead to better deals. She has a strong track record of getting results and often helps clients secure deals beyond their expectations. Tune in now to discover what it takes to successfully buy or sell a SaaS company.

Ladies, let's be real—almost nothing about Chalene is natural, and that's exactly the way she likes it!  From DIY hair extensions and face tape to jawline hacks and the best self-tanners, Chalene is spilling every beauty secret she swears by. Want to add volume to fine hair, smooth out your skin, or master the art DIY lashes? Consider this your ultimate beauty cheat sheet! No gatekeeping—just real talk about all the little tricks that make a big difference.

Super Bowl 59 Preview: Chiefs vs. Eagles Super Bowl LIX is here! Can the Chiefs make history with a three-peat, or will the Eagles get their redemption? Hosts Brad Fowler and Alex Higdon deliver an extensive breakdown of key matchups, X-factors, and betting picks for the big game. We analyze the Chiefs' run defense, Philly's ground attack, Mahomes and Hurts under pressure, the Eagles' dominant D-line, and the coaching matchups. Plus, we've got multiple prop bet picks and our final predictions—can Saquon Barkley and the Philly defense be the difference-maker, or do Mahomes and Andy Reid find a way yet again? Don't miss this in-depth Super Bowl preview! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Building wealth isn't just about what you do—it's about how you think. And the ultra-wealthy know that financial success starts in the mind.That's exactly what today's guest, Bronson Hill, discovered after interviewing over 2,500 millionaires. Through those conversations, he uncovered the habits, mindsets, and investment strategies that separate everyday earners from those who achieve true financial freedom.A former medical sales professional, Bronson walked away from a high-paying career to pursue passive income, scaling his wealth 20X in just a few years. Bronson is a general partner in 2,500 multifamily units worth over $250M, and has personally raised over $45M for real estate and private equity deals.In this episode, we dive deep into the psychology of wealth, why who you surround yourself with determines your financial future, and the hidden investment strategies the ultra-wealthy use to build lasting fortunes.In this episode, you'll learn:✅ The mindset shift that helped Bronson 20X his net worth—and the surprising reason most people stay broke.✅ How 2,500 millionaires built their wealth—and the biggest investing mistakes to avoid.✅ The investment strategy ultra-wealthy investors use to outperform the stock market.✅ The hidden “Wealth-Worthiness” belief that silently holds most people back from financial freedom.Show Notes: LifestyleInvestor.com/224Tax Strategy MasterclassIf you're interested in learning more about Tax Strategy and how YOU can apply 28 of the best, most effective strategies right away, check out our BRAND NEW Tax Strategy Masterclass: www.lifestyleinvestor.com/taxStrategy Session For a limited time, my team is hosting free, personalized consultation calls to learn more about your goals and determine which of our courses or masterminds will get you to the next level. To book your free session, visit LifestyleInvestor.com/consultationThe Lifestyle Investor InsiderJoin The Lifestyle Investor Insider, our brand new AI - curated newsletter - FREE for all podcast listeners for a limited time: www.lifestyleinvestor.com/insiderRate & ReviewIf you enjoyed today's episode of The Lifestyle Investor, hit the subscribe button on Apple Podcasts, Spotify, or wherever you listen, so future episodes are automatically downloaded directly to your device. You can also help by providing an honest rating & review.Connect with Justin DonaldFacebookYouTubeInstagramLinkedInTwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Exclusive Interview with Former NFL Executive Jack Easterby Jack Easterby served as the Executive Vice President of Football Operations for the Houston Texans and was instrumental in shaping their organization. Before that, he played a key role with the New England Patriots from 2013 to 2018, where he served in leadership, and character-coaching to help foster a winning culture. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Ohio State's Dominance, Lamar's Legacy, Did the Lions' Super Bowl Window Close? Ohio State caps off an incredible CFP run with a dominant National Championship win. We break down their historic playoff journey, the expanded format, and what's next for Notre Dame. In the NFL, we recap the Divisional Round games, dive into Lamar Jackson's playoff struggles, the Chiefs' officiating controversies, and whether the Lions' Super Bowl window just slammed shut. Plus, Jayden Daniels' historic rise and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

NFL Wild Card Recap, College Football Playoff Semifinals Reaction, and Deion Sanders to the Dallas Cowboys Rumors In this episode, we dive into NFL Wild Card Weekend surprises, discuss rumors of Deion Sanders as a potential Dallas Cowboys coach, and react to the latest College Football Playoff action. Plus, we break down Mike McCarthy's future, analyze standout player performances, debate coaching decisions, and explore the quarterback dilemmas facing teams like the Steelers and Vikings. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Lions Dominate Vikings, Jerod Mayo Fired, Week 18 and CFP Quarterfinals Recap We recap NFL Week 18 and the College Football Playoff quarterfinals. Highlights include the Lions dominating the Vikings, Jerod Mayo's firing in New England, and playoff shakeups. We break down Burrow's big year, Mike Evans' record-setting season, and Aaron Rodgers' 500 TD milestone. On the college side, we cover Ohio State's blowout win, Notre Dame's upset over Georgia, and the ASU vs. Texas thriller. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Eagles the NFL's Best Team? Travis Hunter's Historic Heisman, and Belichick's Shocking Move to CFB The Eagles dominate the Steelers—are they the best team in the NFL? Josh Allen proves he's unstoppable, and the Broncos rally to stay hot. Chiefs offensive issues continue-Is Mahomes still elite? Plus, Bill Belichick shocks the football world—will he succeed in college football?—and Travis Hunter wins the Heisman. Where does he rank among the all-time greats? Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

NFL Week 14 Reactions: Bills' Troubles, Vikings' QB Dilemma, and CFP Format Controversy In this episode, we dive into NFL Week 14 reactions: Are the Bills in trouble after another flat performance? Do the Rams have a chance to surge late in the season? Will the Steelers re-sign Russell Wilson this offseason? We discuss the Vikings' quarterback situation, the College Football Playoff controversy, and why the new 12-team playoff format already needs an overhaul. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Eagles Soar, Chiefs Flaws, Bills Dominate, and CFB Championship Picks This week, we dive into the Eagles' dominance as legit Super Bowl contenders, fueled by their physicality and a lights-out defense under Vic Fangio. The Ravens show signs of regression, we discuss the Chiefs' flaws and why Brad thinks they are the most unimpressive 11-1 team in NFL history. Meanwhile, the Bills crush the 49ers in a snowy disaster, solidifying their spot as the AFC's best. We break down playoff seeding, Brad calls out Kyler Murray, we discuss the Steelers' ceiling and is Joe Burrow being wasted in Cincinnati? Plus, we make our picks for the College Football Championship games and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Eagles Soar, Chiefs Flaws, Bills Dominate, and CFB Championship Picks This week, we dive into the Eagles' dominance as legit Super Bowl contenders, fueled by their physicality and a lights-out defense under Vic Fangio. The Ravens show signs of regression, we discuss the Chiefs' flaws, Mahomes' regression, and why Brad calls them the most unimpressive 11-1 team in NFL history. Meanwhile, the Bills crush the 49ers in a snowy disaster, solidifying their spot as the AFC's best. We break down playoff seeding, Brad calls out how overrated Kyler Murray is, and we discuss the Steelers' ceiling, Joe Burrow's brilliance being wasted in Cincinnati. Plus, we make our picks for the College Football Championship games and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

NFL Power Shifts, Eagles Dominate, and CFP Chaos Brad Fowler and Alex Higdon dive into a packed episode of Pint Glass Football, tackling the biggest NFL and college football storylines of the week. Are the Eagles and Chiefs as good as their records suggest? How good are the Vikings?Did the Giants quit on Brian Daboll? Plus, we break down the Caleb Williams hype, the 49ers' struggles with injuries, and how much do we trust the Kansas City Chiefs after a close win to the Panthers? On the college side, seven Top 20 teams fell on Saturday, shaking up the CFP picture. What does it mean for teams like Georgia, Ohio State, and surprise risers in the ACC? And could Arizona State be the best team in the Big 12? Brad and Alex discuss it and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Can Colorado Make the CFP? Bills Dominate KC, Lamar's Steelers Struggles, and More! Hosts Brad Fowler and Alex Higdon break down another packed week of NFL and college football action! Why can't Lamar Jackson solve the Steelers? Are the Bills the team to beat after a dominant win over the Chiefs? Plus, Colorado's run toward the CFP continues, and Bo Nix shines while Jayden Daniels cools off. The guys also dive into Georgia's resurgence, Oregon's gritty escape in Madison, and Shanahan's challenges in San Francisco. They also recap Alex's red-hot betting picks from last week and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Lions Find a Way, Steelers Contenders? Colorado's CFP Path We break down the Lions' gritty win, question if the Steelers are true contenders, and discuss the Chiefs' shaky season despite their perfect record. We also analyze the Bears' offensive struggles, pump the brakes on Washington, and explore the surprising parity in college football with Ole Miss' upset over Georgia and Colorado's comeback against Texas Tech, and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Lions Stomp Packers, Bills Dominate Dolphins, USC in Freefall & More! In this episode, hosts Brad Fowler and Alex Higdon break down the key takeaways from Week 9 in the NFL and a few marquee college football games. They discuss the Eagles' narrow escape against Jacksonville, questioning Coach Sirianni, the Cowboys' struggles with Dak's performance this year after his huge contract extension. Detroit's dominance over Green Bay, Tua's limitations compared to Josh Allen, and highlight standout performances from the Arizona Cardinals, LA Chargers, and Rams. Shifting to college football, they react to Ohio State's win over Penn State, Oregon's impressive road win over Michigan, and USC's collapse under Lincoln Riley, and could Deion Sanders and Colorado sneak into the CFP? Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Cowboys' Struggles Persist, Rodgers' Jets Collapse, CFP Shake-Up & More! Cowboys' season looks bleak as Dak Prescott's struggles, a porous defense, and weak run game raise big questions about their playoff hopes. We recap a wild Sunday slate with six nail-biter finishes, impressive rookie QB showings, and the Bills rolling to 6-2 with Josh Allen's MVP-level play. Plus, we dive into the Lions' claim as the NFL's best team, are the Falcons and Steelers contenders, and predictions for the College Football Playoff. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Chiefs Win Ugly, Jared Goff for MVP? Texas QB Controversy, Lincoln Riley on the Hot Seat & More In this episode, Brad and Alex break how the Kansas City Chiefs' defense has taken the lead while Patrick Mahomes has struggled to match his usual brilliance. The guys also dive into Brock Purdy's underwhelming performance, Jared Goff's dominance over Minnesota, and the continued woes of the New York Jets, Amari Cooper's impact on the Bills, Alabama's playoff chances, Georgia's defensive showcase, and the growing QB controversy at Texas. Finally, they touch on the rise of Indiana and Lincoln Riley on the hot seat at USC. Tune in for expert insights, bold takes, and in-depth analysis from PGF! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Cowboys Routed, Jets Alive?, Packers Roll, Oregon's BIG win & More! In this episode, hosts Brad Fowler and Alex Higdon break down the biggest headlines from the NFL and college football. The Jets trade for Davante Adams, but will it be enough to save their season? The Bills add Amari Cooper to bolster their offense. Brad and Alex discuss the Cowboys' embarrassing 47-9 loss to the Lions and why Dallas' decision to extend Dak Prescott may have been a costly mistake. Plus, Caleb Williams shines in Chicago while Trevor Lawrence continues to struggle in Jacksonville, and Jordan Love and the Packers dominate the Cardinals. Washington's impressive effort in a loss to Baltimore, Dan Lanning's "controversial" decision in Oregon's huge win over Ohio State, Texas' dominance over Oklahoma, Arizona State's big upset over Utah and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Aaron Rodgers is Cooked, Cowboys Comeback, 49ers Problems, Upset Saturday & More Brad and Alex break down a wild week in the NFL and college football. They discuss why Aaron Rodgers is "cooked," Dak Prescott's clutch Cowboys comeback, the potential game of the year Ravens vs. Bengals, Deshaun Watson's downfall, and analyze how the 49ers' late-game collapse could signal bigger issues, and Sean McDermott's poor clock management. Plus, an "Upset Saturday" in college football as Alabama, Tennessee, and USC all stumble, while Michigan's magic fades. Tune in for more on NFL's rookie QBs, college football chaos, and more! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Eagles Get Stomped, Jets Embarrassed, Ravens Statement Win, Alabama is Back & More In this episode, Brad and Alex break down the Ravens' dominant win over the undefeated Bills, highlighting Derrick Henry's massive game and Baltimore's defensive turnaround. They discuss whether the Bills should be concerned and question if the Saints' hot start was a mirage after a sloppy loss. The guys also cover the Bucs' win over the Eagles, Jalen Hurts' struggles, and the Browns' nightmare situation with Deshaun Watson. They discuss Jayden Daniels's hot start and Brad attacks Kyler Murray in Arizona's latest loss. They talk Jets' struggles with Aaron Rodgers, Alabama/Georgia's wild showdown, Travis Hunter's Heisman case, and a controversial Miami win over Virginia Tech. Listen in for all the top NFL and college football insights! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Is Jalen Hurts Overrated? Cowboys' Struggles, Sam Darnold for MVP, Arch Manning's Debut & More In this episode, Brad and Alex dive into NFL Week 3 and college football Week 4, including the Eagles winning an ugly game, but Brad questions Jalen Hurts' ability, calling him the most overrated player in the NFL. They discuss Caleb Williams and the Bears' dysfunctional coaching, and Sam Darnold's resurgence with the Vikings, leaving the team with a big decision at the QB position. The guys also cover KC continuing to win despite Patrick Mahomes' dip in play, the Cowboys' issues, and Pittsburgh's surprising start led by a dominant defense. Shifting to college football, they react to Colorado's thrilling OT win, Arch Manning's debut, USC's first Big Ten test, and Utah's statement win in the Big 12. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

NFL Week 3/CFB Week 4 Preview & Betting Picks Hosts Brad Fowler and Alex Higdon preview the biggest matchups of NFL Week 3 and College Football Week 4, breaking down key games and sharing their top betting picks. Get expert insights, game analysis, and winning strategies for the weekend's action! Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr

Bryce Young Benched, It's Over for Tua, Saints Smash Cowboys, Travis Hunter is the Heisman & More In this episode, Brad Fowler and Alex Higdon break down the biggest storylines in football. From Bryce Young getting benched and questions surrounding Tua Tagovailoa's future, to the Saints' statement win over the Cowboys. They also dive into Travis Hunter's Heisman-worthy performance, Alabama's dominance, and Arch Manning's lights-out game for Texas. Plus, they discuss the ongoing struggles of rookie QBs, Kentucky's near-upset of Georgia, FSU's surprising struggles, and recap NFL Week 2 and CFB Week 3 action. Connect with UsGet exclusive articles and subscribe to our FREE newsletter at Pint Glass Football. Subscribe to our YouTube channel for exclusive video content: Pint Glass Football on YouTube. Sponsors: Underdog FantasyUnderdog Fantasy is the easiest place to play fantasy sports! Win up to 20X your money in a single night. Sign up today and use promo code PGF to get your Special Pick + First Time Deposit offer up to $250 in bonus cash! SeatGeekSeatGeek offers the best seats at the best prices. Never worry about overpaying for tickets again. Each ticket has a 0 to 10 score so you know if you're getting a good deal! Download the SeatGeek app and enter code PGFPOD for $20 off your first ticket purchase. BetAlyticsBetAlytics is a predictive sports betting software platform that helps you win more single bets and parlays. Take back the advantage from the sportsbooks. Get 25% off any package with promo code PGF. Visit BetAlytics to learn more. Bettor EdgeBettor Edge is a sports betting platform that lets you create your own betting lines and prices for real money betting positions with no sportsbook fees. Use promo code PGF to receive a FREE $20 on your first order at bettoredge.com. ZencastrZencastr is the ultimate all-in-one podcasting platform. Record, edit, distribute, and monetize all from one place. Use our special link Zencastr and code PGFP to save 30% off your first three months of Zencastr Professional. #madeonzencastr