Podcasts about ai saas

What does it take for a professional snowboarder, six X Games appearances, gold and silver medals, fifteen years on tour, to land in real estate? For Dan Brisse, the answer was watching the guys five and ten years ahead of him lose their houses, their cars, and worse.That was the wake-up call. While most of his peers spent every pay raise on the biggest house they could buy, Dan was reading books and buying apartments. By the time his snowboarding career ended, he had 70-some units already producing passive income.Today Dan co-leads Granite Towers Equity Group, a 3,300-unit multifamily portfolio focused on Dallas-Fort Worth, Nashville, and select Minnesota submarkets. The playbook is disciplined: newer assets (1985 and up), 140 to 300 units, $20 to $40 million purchase price, 95%+ occupancy submarkets with real pent-up demand. Eighty-twenty split with LPs. Sixty-five to seventy-five percent loan-to-value, fixed rate, non-recourse. CapEx raised liquid up front, so the bank cannot force a bad spend.What you'll hear:The chairlift moment that reframed every financial decision Dan has made sinceWhy 78% of pro athletes are broke within three years of retirement and the side-hustle move he made to avoid joining themThe Cleburne, Texas case study: $6.75M acquisition, $2.1M LP raise, full-cycle returnsA second case study where interior upgrades and water conservation drove a 1.8x equity multiple in two yearsWhy Dan believes we are in a generational buying window with multifamily trading at 30-40% discounts versus 2021-22 peaksHis sharpest definition of wealth, and why "rich" and "wealthy" aren't the same thingLearn more about Granite Towers Equity Group: https://www.granitetowersequitygroup.com/contact-usElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors.

JB Thibodeaux grew up in Acres Homes in Houston and is a third-generation carpenter and concrete specialist who turned that craft into a vertically-integrated real estate development business. His firms, J.B. Thibodeaux Homes & Properties, CCB Construction LLC, and CCB General Contractors LLC, have led over $100 million in projects across mass development, pocket development, and affordable housing infill. This year, the Houston market crowned him the "Duplex King."In this episode, Ed and JB unpack a contrarian lesson the real estate education industry rarely teaches: the difference between the real estate hustle and the real estate business. Hustle gets you doors. Business gets you a sustainable enterprise. Most operators only ever learn the first one.JB walks through the Houston duplex pricing math that vindicated his long conviction on the market, from "praying to get $350" on a 2,500 square foot duplex to selling at $479, almost $500,000. He explains his concept-to-keys pocket-development model, Houston's no-zoning-but-deed-restrictions quirk, the 60/40 build-to-sell vs. build-to-hold split for his client base, and why vertical integration (owning the GC) is a margin lever most flippers underestimate.In this episode:Pocket development inside a major metro: the frameworkThe duplex math that vindicated a Houston long-holdHouston's "no zoning" reality: where the leverage actually livesWhy "the gurus teach you doors, not business"House hacking as the duplex exit strategyGenerational construction knowledge as competitive moatMayor Sylvester Turner naming February 20 "James 'JB' Thibodeaux Day" (2019)If you're a flipper, wholesaler, or operator trying to build a business and not just a deal pipeline, this is the episode.This week's book: The Miracle Morning by Hal ElrodGuest: JB Thibodeaux, Founder & Managing Partner, J.B. Thibodeaux Homes & PropertiesWebsite: jbthibodeaux.comLinkedIn: linkedin.com/in/james-thibodeaux-8a98b51b7Instagram: @jbthibodeauxhomesElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Most people don't fail at sales because they're “bad at sales.” They fail because fear hijacks their brain at the exact moment they need clarity. That's why I loved talking with Maggie Pounds, co-founder of Salescoach Pro, an AI-powered sales training platform that helps sales reps practice real conversations before they ever get on the phone with a real customer. If you've ever frozen, rambled, or felt your heart rate spike during an objection, you'll recognize what she describes: even simulated role play can feel shockingly real, and that realism is what builds true confidence. Maggie shares her unconventional path from urban farming and farmers markets to building an AI product alongside her husband, plus how a simple Thanksgiving dinner conversation with her sister sparked the core idea. We get into what makes effective sales coaching work, why repeated reps matter more than generic advice, and how tailored feedback, scoring, and structured scenarios can improve onboarding and sales enablement without draining a manager's time. We also talk about the practical question everyone asks: why not just use ChatGPT, and what a purpose-built voice role play experience does differently. Beyond the product, Maggie brings a refreshing perspective on AI hype and fear, especially for people who worry they're “behind.” She argues for curiosity, moving slowly, and using technology as a tool to enhance human skill, not replace it. We close with a candid look at intentional living, homeschooling, presence, and giving yourself permission to pause projects in different life chapters, plus what “to be bolder” really means when you listen to yourself and take the next step. If this conversation helps you rethink confidence, sales practice, or building a business that supports your real life, subscribe, share the episode with a friend, and leave a review so more listeners can find the show.

Dan Ihara has sold over $1 billion of real estate, moved 1,600+ units, and completed 400+ 1031 exchanges—without failing a single one.And he built the whole thing around a niche almost no operator talks about: senior-care real estate.In this episode, Dan and Ed get into the demographic inevitability that drives his business (“should we all be blessed to live long enough, we're going to need some level of care”), why 1031 exchanges fail for everyone else but never for him, and his definition of success that has nothing to do with the billion-dollar number on his resumé.What you'll hear:The senior-care thesis: why aging boomers are an inevitability, not a trend, and the RE play sitting on top of itHow Dan closed 400 1031 exchanges without a single failure—the process, the team, the disciplineWhy “success is doing what I want, when I want, with whom I want” is the only definition that matters once you have the numbersThe conversation every operator should be having with their family—and almost none areAbout Dan Ihara: 20-year real estate veteran. Billion-dollar producer. Built a specialty practice serving seniors transitioning out of long-held real estate, primarily through 1031 exchanges.Subscribe to Real Estate Underground for weekly conversations with operators who've been through the cycle and lived to talk about it.Elevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors.

夏日暑假快到了，提早規劃下一檔出國旅遊吧！ 在國外上網也不用再那麼麻煩了 由 NordVPN 所推出的 Saily eSIM 服務，真的方便好用 下載 Saily eSIM 的 APP，在裡面購買並且啟用，eSIM 就可以開始作用 超過 200 個地區可使用，還可以幫你追蹤網路使用量 立刻點及專屬連結下載 Saily APP，並在結帳時使用優惠代碼 [miula]，立即享有專屬 eSIM 方案 85 折優惠！ M觀點 X Saily eSIM - https://saily.com/miula --- EP304. 川習會的結果分析、馬斯克會放棄 Grok 嗎、AI 公司為何狂殺 SaaS？ | M觀點 --- (00:40) EP304 預告 (03:28) 業配時間：Saily eSIM (06:10) 閒聊話題：借錢給朋友 & 台美股回檔 (12:00) 第一個話題：川習會的結果分析 (52:09) 第二個話題：馬斯克會放棄 Grok 嗎 (1:10:28) 第三個話題：AI 公司為何狂殺 SaaS？ --- M觀點資訊 --- 科技巨頭解碼: https://bit.ly/3koflbU M觀點 Telegram - https://t.me/miulaviewpoint M觀點 IG - https://www.instagram.com/miulaviewpoint/ M觀點Podcast - https://bit.ly/34fV7so M報: https://bit.ly/345gBbA M觀點YouTube頻道訂閱 https://bit.ly/2nxHnp9 M觀點粉絲團 https://www.facebook.com/miulaperspective/ 任何合作邀約請洽 miula@outlook.com -- Hosting provided by SoundOn

Most multifamily operators treat internet as a tenant problem or a cost line they grumble about. Adam Bell, Founder and CEO of Internet Subway, says they're leaving real money on the table.Adam runs a modern ISP focused exclusively on apartment communities. His company delivers fiber-to-the-unit (FTTU) in bulk to property owners, who pass it to residents as an included utility and capture the rent spread. Industry data shows 5 to 15% NOI uplift on well-executed bulk deployments. The math alone is worth the conversation.But the bigger reframe is the one Ed pulls out of him halfway through: the fiber itself is an asset. Billions are flowing into fiber networks nationally. Property owners are in a rare position to own a portion of that infrastructure, a 30-year asset hiding in their own buildings.In this episode:The bulk internet math on a value-add multifamily deal, and how an owner should actually underwrite itWhy "managed WiFi" gets oversold and what to insist on insteadBulk internet versus experience, and why the difference matters more than the priceFiber as a 30-year asset class that scales in capacity, not maintenanceThe contrarian take from his industry piece "Managed Wi-Fi isn't all it's cracked up to be"If you're an operator, syndicator, or value-add multifamily investor, this is the episode that turns a cost line into a revenue line and reframes the building itself as a fiber asset.This week's recommended book: The Confident Mind by Nate ZinzerGuest: Adam Bell, Founder and CEO, Internet SubwayWebsite: internetsubway.comLinkedIn: linkedin.com/in/adamlloydbElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors.

Links & Socials: Product Tranquility: https://producttranquility.com Dan Balcauski on LinkedIn: search “Dan Balcauski” SaaS CEO Pricing Scorecard (free): producttranquility.com Edit your podcasts like a pro:https://get.descript.com/mrzy10nwivuqJoin me as a guest or start your podcast journey:https://www.joinpodmatch.com/nickkuhne Timestamps: 00:00 – Volcano chat & intro 01:30 – Is pricing art or science? 04:45 – Why founders delay pricing decisions 05:30 – ChatGPT's pricing evolution explained 09:30 – Why freemium can actually work for OpenAI 12:30 – Customer Lifetime Value (CLV) reality check 16:45 – Pricing brand-new AI SaaS products 20:00 – You must earn the right to monetise 23:30 – Microsoft Copilot pricing lesson 26:30 – Where to find Dan & the free scorecard Connect with me on:All my linksBecome a guestSign up for RiversideGet Descript #DigitalMarketing #Branding #PersonalBranding #MarketingInsights #SocialMediaStrategy Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailBrandon Cobb's business model is simple: take farmland, get it approved for housing, and sell it to national home builders like Lennar and DR Horton. The builders have no choice but to buy. They're publicly traded. Plan A is buy land and build houses. Plan B is buy more land and build more houses. There is no Plan C that doesn't involve buying land.Brandon returns to Real Estate Underground to break down how HPG Capital creates new housing neighborhoods in Nashville, why he stopped building homes but kept developing land, and how he structures deals that pay investors 18% preferred returns with no personal guarantees.What you'll learn:- The three phases of land development and how to profit from each one without ever building a house- How to de-risk a deal by lining up your buyer and getting city approval before spending real money- Why "big puzzle pieces next to small puzzle pieces" on a GIS map is the buy signal- How he gets 20% deposits from national builders, released day one, to fund development- Why he ditched vertical integration after hitting $22 million in annual development- The biggest mistake he made with strategic partners and how to avoid it- His "Body, Being, Balance, Business" framework for measuring successBooks mentioned: The Surrender Experiment and The Untethered Soul by Michael SingerLearn land development: learnlanddevelopment.com (free 8-hour course)Invest with HPG Capital: hbgcapital.net/waitlistElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Amanda Hendrix helped scale a healthcare staffing firm from $2.2M to $11M in a single year during the COVID boom. Then the market normalized. Bill rates dropped from $145 to $110 in thirty days. Vendors got cut. Recruiter morale cracked. Agencies that hired aggressively into the surge got squeezed out one by one. This episode is the post-mortem you don't usually get from someone who lived both sides of it. Now Head of Growth at Ember Hiring, an AI SaaS platform built for healthcare staffing, Amanda works with nearly 20 agencies across the industry. What she's seeing inside those firms is the most direct AI warning Benjamin has put on the show. A year ago, the line in AI engineering circles was that staffing agencies who didn't adopt AI would be out of business in five years. The current timeline she's hearing? Eighteen months. That's the operational reality being traded between agency operators while consolidation accelerates underneath them. One of her clients merged five agencies into one then acquired four more. She predicts the 400 to 500 active travel nursing agencies operating today will collapse to roughly fifty within ten years. The conversation moves through what actually scaled the firm in year three, what habits from hyper-growth quietly became liabilities once the market tightened, and the leadership decisions that kept the company alive when bill rates collapsed. Amanda is candid about cutting salaries, choosing not to over-hire during the boom, and the moment she realized her former mentor's "don't throw people at the problem" advice had saved the company. She also walks through what the recruiter desk needs to look like over the next eighteen months: AI-funneled pre-qualified leads in one bucket, an active book of business in another, submitted candidates in the third — with the recruiter's job becoming pure relationship work and oversight of AI agents handling the rest. She explains why managing 40 to 50 candidates is no longer enough, and why 100-plus is the new floor. Amanda drops a stat that should change how every healthcare recruiter thinks about flow: less than ten percent of candidates who apply actually get the role they applied for, because the job is filled before the submission packet reaches the client. Speed is preparation. Consistency beats charisma. The recruiters who treat themselves like entrepreneurs are the ones who'll still be standing in five years. If you run an agency, lead a team, or bill a desk in any vertical of recruiting, this is the episode to forward to anyone on your team who still thinks AI is optional. This episode of The Elite Recruiter Podcast is brought to you by Atlas, the AI-first recruitment platform built to eliminate admin and turn every candidate conversation into pipeline. Atlas captures every conversation automatically, then lets you query your entire database with MagicSearch — ask "who mentioned wanting a four-day week" or "who's open to relocating next year" and get answers instantly. No keyword guessing. No digging through old notes. Atlas customers report over 40% EBITDA growth and over 80% increase in monthly billings after rollout. Unlock your exclusive listener offer at https://recruitwithatlas.com

Send us Fan MailBen Reinberg has done over $1 billion in commercial real estate transactions. He didn't get there chasing returns. He got there by treating CRE as what it actually is: a hard asset that produces cash flow, not a bet.Ben is the Founder and CEO of Alliance CGC, where he's built one of the most respected portfolios in the country, with deep focus on net-leased properties and medical office buildings.In this episode, Ben and Ed cover:- Why medical office is recession-resistant in any market (the "human body never goes out of style" rule)- The over-leverage mistake that taught Ben the real lesson of commercial real estate- Cash flow, tax efficiency, and long-term security as the only three goals worth chasing- How to navigate one of the most chaotic markets in decades- The difference between treating real estate as a commodity vs. a hard asset- What old-school CRE investors do that the new wave gets wrongConnect with Ben: benreinberg.comReal Estate Underground is hosted by Ed Mathews of Clark St Capital.Elevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

＜目次＞(0:00) Notion Labs Japan 西氏の自己紹介、Notionの日本展開(7:28) SaaS is Deadとは？(9:37) NotionのAI活用(10:35) ソフトウェアはどこまでコピーできるのか？(14:02) AIによる開発スピードの変化(15:47) SaaSとAIに対する予算の変動(18:45) AI時代の中でのNotionのビジネスモデルの進化(21:06) NotionでのAIエージェント活用事例(21:58) 人間 vs AIエージェント向けのサービス(23:33) コンテキストの重要性(26:56) 仕事場のOSになるための設計(30:44) SaaS業界はプラットフォーム化が加速するのか(33:36) 複数モデルを対応する理由、新モデルの対応スピードの大変さ(35:29) 西氏のAI情報キャッチアップ方法(35:54) パーソナライズとマルチプレイヤー化されたソフトウェア(39:15) 宮武さんの予想：半分の既存SaaSサービスが無くなる(40:10) 株式市場の評価：AI売上比率とAI売上成長(41:53) Notionの今後の日本の取り組みNotion | カスタムエージェントの構築や、すべてのアプリを横断する情報検索、面倒な作業の自動化を行えるAIワークスペースなど、チームはより多くの作業をスピーディにこなせるツール。https://www.notion.com/ja西 勝清氏 (@katsu2488)https://x.com/katsu2488＜About Off Topic＞Podcast:Apple - https://apple.co/2UZCQwzSpotify - https://spoti.fi/2JakzKmOff Topic Clubhttps://note.com/offtopic/membershipX - https://twitter.com/OffTopicJP草野ミキ:https://twitter.com/mikikusanohttps://www.instagram.com/mikikusano宮武テツロー: https://twitter.com/tmiyatake1

Get our free Claude Cowork Workflow: https://clickhubspot.com/detf Ep. 419 How do you publish 250 pieces of content per week with zero employees? Kipp and Sabrina Ramonov of Blotato, dive into the exact AI-powered content workflow that built a 2 million-person audience—all as a solo creator. Learn more on building repeatable brand voice skills with AI, automating content creation from raw images, and managing an entire social media calendar through smart connectors and tools. Sabrina Ramonov is on a mission to teach 1 million people AI. She's the solo founder of Blotato.com, an AI SaaS app for creators and entrepreneurs to go viral on multiple social platforms like me (0 to 500k+ in 6 months solo). Mentions Sabrina Ramonov https://www.youtube.com/@sabrina_ramonov Blotato https://www.blotato.com/ Claude Cowork https://support.claude.com/en/articles/13345190-get-started-with-claude-cowork Airtable https://www.airtable.com/ Canva https://www.canva.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

Send us Fan MailClay Lehman spent 20 years in real estate, starting at Arthur Andersen, running the Ocala controller desk for Pulte Homes, and eventually building Lehman's Strategic Partners to help agents grow their businesses. He runs an AI Facebook group with members in 65 countries and now spends most of his time helping real estate pros turn AI from a shiny object into a revenue tool. If you're still using AI to write listing descriptions and calling it a day, this one will push your thinking.In this episode you'll learn:- Why Clay calls obscurity, not competition, the biggest threat to any real estate business- How the "eliminate, automate, delegate" framework turns documented processes into leverage- Which AI tools Clay actually uses daily, and how Gemini, Claude, and ChatGPT scored in his blind writing test- How Ed built an AI voice agent that gets a lead on the phone in 45 seconds, across five languages- Why the Harvard 391% rule makes speed-to-lead the single biggest edge in real estate today- How solo operators and small teams can look and act like companies many times their sizeResources Mentioned: Google NotebookLM for deal prep and process documentation. Gamma for instant presentations. ylopo for lead nurture automation.Takeaway: AI is a force multiplier, not a crutch. It levels the playing field on access, but experience and wisdom still do the heavy lifting. Use it as the spotter at the gym, not the guy lifting the weight.Elevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

In this episode of Dropshipping Talks, Mario explores 5 AI-powered business ideas you can start right now to grow your online income. Join us as we break down each opportunity, how it works, and how you can get started fast using simple tools and smart execution. What You'll Learn: AI Dropshipping & Print on Demand: How to launch product-based businesses with minimal setup and faster turnaround. AI UGC & Content Repurposing: Ways to create and monetize content without starting from scratch. AI SaaS & Automation: How to build scalable systems that generate income beyond one-time sales. Why Listen? This episode is a must-listen if you're looking for practical, beginner-friendly ways to start making money online using AI without overcomplicating the process.⭐ Start Your

Trump spontantwittrar, och AI-frossan rycker fram från sektor till sektor – hur agerar en aktiv investerare i kaoset? Carl Gustafsson, medgrundare och småbolagsförvaltare på Protean, berättar om sin investeringsfilosofi och hur han förhåller sig till AI som hot eller möjlighet för fondens portföljbolag. Vi pratar om Vitec, Storytel, Acast och Plejd. Plus: vad kostar det egentligen att starta ett fondbolag? I veckans avsnitt medverkar: Jacob Bursell – Monopol MediaJohan Isaksson – InvesterareLars Jörnow – Medgrundare, EQT VenturesCarl Gustafsson – Medgrundare och förvaltare, Protean TIDSSTÄMPLAR 00:00:00 – Intro: Trump-tweets, Iran-effekter och börsoro – hur förhåller sig en aktiv förvaltare? 00:02:00 – Hur Protean agerar i kaos: Snabba beslut, liten fond och köplägen i volatilitet 00:06:00 – AI-hotet mot mjukvarubolagen: Sektor för sektor – från juridik till OTA-bolag 00:08:00 – Hur man tänker kring AI som investerare: Vinnare, förlorare och allt däremellan 00:12:00 – Smart Optics och Vitec: En tydlig hårdvaruvinnare och ett kontroversiellt bottenfiske 00:16:00 – Investeringsfilosofin: 57% rätt räcker – om du tjänar dubbelt mot vad du förlorar 00:18:00 – Likviditet i småbolag: Varför Protean satt ett tak på fyra miljarder 00:20:00 – Att starta ett fondbolag: FI-ansökan, 2–3 miljoner i juridikkostnad och ett år av byråkrati 00:24:00 – Från 350 miljoner till 7 miljarder: Hur man bygger förtroende som ny aktör 00:26:00 – ESG eller inte: Varför Protean valt bort ESG-profilen – och hur vinden vänt internationellt 00:30:00 – Owner-operator-modellen: Inga kommittéer, 0,5%-testpositioner och snabba pivoter 00:32:00 – Småbolagsdöden: Pandemi-euforin, utflöden och bottenfiskemöjligheterna som uppstår 00:38:00 – Vitec som case study: AI-förlorare eller köpläge? FCF Yield från 2–3% till 7% 00:42:00 – AI på Proteans kontor: Globalfond som använder AI för att screena 40 japanska aktier 00:46:00 – Kritisk vs utbytbar mjukvara: Bloomberg till 250 000 per år – eller något man bygger själv 00:52:00 – Rapportperioden Q1: Soufflé-reaktioner och varför utflöden driver kraftiga kursrörelser 00:56:00 – Plejd: Vad händer när ett bolag passerar en miljard euro i market cap? 01:00:00 – ACAST: Säljbolag, programmatisk annonsering och en 50/50-delning som kanske inte håller 01:04:00 – Storytel: Turnaround, Spotify-hotet som kom och gick – och Sune-katalogens kraft 01:10:00 – Poddkatalogen vs bokkatalogen: Vad är tidlöst och vad är flyktigt? 01:16:00 – Enshittification och konsolidering: Vad händer med konsumenterna när spelarna konvergerar? 01:18:00 – Avrundning: Fyra år som utmanare och varför Protean vägrar bli etablissemang OM PODDEN Marknaden består av Jacob Bursell, Hampus Brodén, Viktor Fritzén, Johan Isaksson, Lars Jörnow och Petter Hjertstedt. Twitter/X: https://x.com/Marknaden_podd Kommentera och ge feedback – vi vill höra vad ni tycker! Mejla: jacob@monopolmedia.se #protean #carlgustafsson #småbolag #fondförvaltning #aktier #aiinvesteringar #vitec #storytel #acast #plejd #smartoptics #esg #fondstart #bottenfiske #småbolagsdöden #trump #marknaden #podcast #svenska

Send us Fan MailJoel Kraut co-founded BRRRR Loans after losing $4.2 million in the 2008 crash. He had 144 properties at over 100% leverage when the market turned. Five tenants called the same day to say they couldn't pay rent.Today he runs one of the country's fastest-growing private lending shops, and he sees the DSCR lending industry doubling in size over the next three years.In this episode, Joel and Ed break down:- Why investors are rotating from Texas and Florida to Ohio, Kentucky, and the Midwest- How DSCR loans actually work (and why 1.0 coverage isn't enough)- The real math on leverage: why the smartest investors stay at 60% LTV or below- Why "relationship capital" matters more than real estate knowledge- What happened when Joel wired money on March 5, 2020 and the market froze- His $99 training modules vs. the $35K guru trap- The best advice he ever got: "Post that in 5 years. Shut up. Go back to work."Joel currently reads: Buy Back Your Time by Dan MartellConnect with Joel: brrrr.comReal Estate Underground is hosted by Ed Mathews of Clark St Capital.Elevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Send us Fan MailLadislas Maurice left a corporate career at Nestle to spend the last nine years investing in real estate across emerging and frontier markets around the world. He buys apartments in Nairobi for $65,000, sits on land in Nicaragua, and flips properties in Montenegro. His approach is the opposite of what most US investors are used to: no leverage, no perfect data, and no rushing.In this episode, Ed and Ladislas talk about:Why emerging market real estate is actually a harder asset than US multifamily (and arguably less risky)How he evaluates a new country before putting money in (the Uzbekistan stock exchange story is worth the listen alone)Managing property across dozens of countries with local property managers (and why you always need a backup)The case for putting a small percentage of your portfolio outside the USHow $200K in a Panama bank or $400K in Turkish real estate can buy generational citizenship and a Plan B for your familyThe Ivory Coast deal that went wrong and why you should never skip the buyer's agentHis father's best advice: "You don't have to answer every question"Learn more about Ladislas at The Wandering Investor and on YouTube.Elevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Send us Fan MailTyler Vinson has spent 25 years in investment real estate, from duplexes and flips to multifamily, commercial, and Class A storage. Now he's building the infrastructure to bring real estate into the digital age as the founder of RE Tokens, one of only about 10 companies in the US with an SEC-registered digital broker dealer ATS license, and the only one focused exclusively on real estate.In this episode, Tyler breaks down what real estate tokenization actually is, how it enhances (not replaces) traditional syndication, and why it matters for both GPs and LPs. He explains how restricted shares can become tradeable digital assets, how non-accredited investors can participate after 12 months under Rule 144, and what the secondary marketplace looks like in practice. He also shares the personal story that became his mission: a friend from high school who wanted to invest but didn't qualify.We also get into the lessons Tyler learned from being over-leveraged during the 2008 financial crisis, his disciplined approach to education, and why the future of real estate investing is undeniably digital.What you'll learn in this episode:- What real estate tokenization is and how it works (the gold brick to gold coins analogy)- How tokenization creates a pathway to liquidity for traditionally illiquid LP positions- The SEC compliance framework: Rule 144, ATS marketplaces, and why registration matters- How non-accredited investors can access deals that were previously off limits- Why over-leverage is the #1 risk in real estate and how Tyler rebuilt after 2008Book on Tyler's nightstand: The Power of the Subconscious Mind by Dr. Joseph MurphyConnect with Tyler Vinson and RE Tokens:- Website: retokens.com (free Quick Start Guide to Real Estate Tokenization)- LinkedIn: Tyler Vinson / RE Tokens- YouTube: RE TokensElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Send us Fan MailAshley Garner grew up swinging a hammer on student rentals near West Virginia University. Decades later, he jumped from a 35-unit portfolio to a $21 million, 196-unit acquisition in North Carolina. In this episode, he breaks down what that leap taught him about conservative underwriting, why his dad's "cash is king" advice saved his business more than once, and how he manages a growing portfolio with a core team of three.What you'll learn:- Why Ashley underwrites for what a property does right now, not what it could do- How flat rent growth and rising expenses are squeezing multifamily operators and what to do about it- The KPIs he tracks every Monday to keep 200+ units running- Why he's evolving from B-/C value-add to larger markets with better financing- The mindset shift that makes raising capital feel like offering an opportunity instead of asking for money- Why "if you need to raise $10 million, go ahead and raise $11"Ashley is currently raising capital for Bryn Mawr Village, a 196-unit property in Jacksonville, NC being refinanced to a HUD 223(f) loan. Learn more at abgmultifamily.com.This Week's Book: Think and Grow Rich by Napoleon HillElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

In this episode, Vinícius Chacon, a marketing lead who helped launch an AI SaaS platform, joins Louise Liu to share a behind-the-scenes look at the realities of AI product launches—from skyrocketing costs and pricing challenges to attracting the wrong audience—and the pivot that turned it around.For more information, check out Vinícius Chacon's article: I Was the Marketing Guy Behind a 2-Million-User AI Platform. Here's What I Learned.All rights reserved. © Product Marketing Hive.

In this episode, Vinícius Chacon, a marketing lead who helped launch an AI SaaS platform, joins Louise Liu to share a behind-the-scenes look at the realities of AI product launches—from skyrocketing costs and pricing challenges to attracting the wrong audience—and the pivot that turned it around.For more information, check out Vinícius Chacon's article: I Was the Marketing Guy Behind a 2-Million-User AI Platform. Here's What I Learned.All rights reserved. © Product Marketing Hive.

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.

Send a textNeil Henderson is a general partner and Director of Investor Relations at Nomad Capital, a Wilmington, NC-based private equity firm with a twist on self-storage: they buy vacant big-box retail buildings and convert them into climate-controlled storage facilities.The numbers behind their model are hard to ignore. Ground-up self-storage construction runs $120-130 per square foot and takes nearly three years. Nomad's conversions come in at $60-65 per square foot, acquisition to occupancy in 12-14 months. Half the cost, a third of the time.In this episode, we get into:- Why old Kmarts and strip malls are the perfect conversion targets- How vertical integration keeps construction costs at cost-plus-12% vs the industry standard 25%- Their current deal: a 171,000 sq ft strip mall in Rocky Mount, NC for $6M with seller financing- Why 2026 loan maturities could create a wave of distressed self-storage opportunities- The Sam Zell principle that guides every acquisition: buy below replacement cost- Neil's Las Vegas condo in 2005 and what it taught him about buying when everyone else is greedyLearn more about Nomad Capital at nomadcapital.usBook recommendation: "How to Break Up with Your Phone" By Catherine PriceElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Would you like access to our advanced agency training for FREE? https://www.agencymastery360.com/training How are you protecting yourself from the real risk of owner burnout? Agency owners often burn out because they built a business that depends entirely on them. Today's featured guest is a former agency owner turned AI SaaS founder. He'll unpack what really caused his agency collapse, what he learned from it, and how he rebuilt from a completely different role. Austin Armstrong is the owner of Syllaby, a tool for social media marketing that helps users create their very own realistic digital clone to personalize their marketing efforts, allowing them to forge a deeper connection with their audience. Austin spent over a decade in the agency world, working his way up from intern to running an agency before launching his own. For a while, it worked, until the cracks appeared. His agency was built around organic marketing and heavily centered on his personal brand. High months meant hiring fast. Low months meant wondering if payroll would clear. When a few large clients (that accounted for about 60% of monthly revenue) churned, the instability became unbearable. So Austin made his tech pivot and moved to starting Syllaby, which also came with a role pivot. More recently, he just released his first book Virality and is the co-founder of the upcoming AI marketing World conference. In this episode, we'll discuss: From agency failure to early AI adopter Why the founder bottleneck is emotional The founder evolution model AI exposes weaknesses Subscribe Apple | Spotify | iHeart Radio Sponsors and Resources This episode is brought to you by Wix Studio: If you're leveling up your team and your client experience, your site builder should keep up too. That's why successful agencies use Wix Studio — built to adapt the way your agency does: AI-powered site mapping, responsive design, flexible workflows, and scalable CMS tools so you spend less on plugins and more on growth. Ready to design faster and smarter? Go to wix.com/studio to get started. Making the Decision to Be an Early Adopter When he started Syllaby, Austin could already see the writing on the wall with AI. He was already not happy navigating the agency world, so the question was, "Do I want to place a bet as an early adopter of this technology? Potentially cannibalizing my own agency?" He spoke with several clients and business owners and came to the conclusion that most people hire an agency because they know they need to create content to be relevant, but didn't know how to pick the right topics, and in many cases didn't want to be on camera. They needed help staying consistent and accountable. Some of them don't even have the money to hire an agency, but still have a message and an expertise to share. So Austin started to look for ways to automate those processes using AI. The Founder Bottleneck Is Emotional Before It's Operational The emotional weight of the unraveling of Austin's agency was real. Nightmares about client complaints. Constant vigilance. Inability to disconnect. Eventually, he decided to make a bet on AI and launched Syllaby, an AI-powered content platform designed to automate much of what agencies manually execute, from topic discovery to scripting to publishing. Now, looking back, he sees his agency's failure came from several mistakes. It wasn't bad marketing or lack of demand. It was structural dependency. The agency relied on: His personal brand His client relationships His decision-making His emotional capacity When large clients churned, revenue collapsed because concentration risk hadn't been designed out of the model. When delivery required nuance, he couldn't step away because "he stirred the pot." This is the Operator trap. The Founder Evolution Model Most founders believe they own an agency. In reality, the agency owns them. What is supposed to happen as your agency evolves is that your role in it evolves as follows: Operator → Manager → Architect → CEO → Owner At the Operator level: Sales depends on you. Delivery depends on you. Escalations go to you. Pricing goes through you. And when you focus on one area, another suffers. Systems Create Freedom But They Also Create Identity Shifts As the owner, being needed feels good and letting go feels disorienting. Austin acknowledged this tension. In his agency, clients wanted him. Even with SOPs, some work required nuance. Some of it was ego. Some of it was positioning. Some of it was hiring the wrong people in the wrong seats. Having learned his lesson, things look very different in his SaaS company, where he can rely on strong partners, defined ownership, AI-supported workflows, and clear decision rights. Now he can disappear for two weeks, go skiing with family, speak at events, and the business doesn't break. AI Exposes Weakness All over the industry owners agree that AI isn't replacing strong agencies. It's exposing weak ones. At Syllaby, Austin has integrated AI so much is hard to think where he DOESN'T use it. He automates what many agencies sell manually: SEO-based topic discovery Script generation Video creation Scheduling and publishing For smaller businesses, this lowers the barrier to entry. For agencies, it creates leverage. Which tool are owners using? This varies from time to time. What you should be doing is testing them all out to see which ones work better for you, as well as creating a brief with all the information you'll need in case you decide to migrate to a different tool. Jason calls this his "AI Operating Brief", a master document loaded with: Company positioning Customer data Success stories CRM insights Transcripts Strategic principles Once embedded into AI tools, it eliminates repetitive context-setting and removes founder bottlenecks. Austin does something similar with what he calls his "Austin Codex", years of content, frameworks, and intellectual property housed inside AI models. The result is institutional memory without constant founder involvement. Time Audits Reveal the Hidden Ceiling Austin is a big fan of the full-time audit exercise: For one to two weeks, document: Every task Start and end times Whether it's mandatory or optional Your enjoyment level The dollar value of your time The outcome is uncomfortable. Once you're done, you'll see which $10 tasks eating $1,000/hour time, the emotional drain disguised as "important work", and the distractions masquerading as urgency. He outsourced email management, calendar coordination, travel booking — all consolidated into a daily executive summary delivered where he actually spends time. Not because he can't do it, but because he shouldn't. The bigger lesson: you don't scale an agency… you outgrow your role. Do You Want to Transform Your Agency from a Liability to an Asset? Looking to dig deeper into your agency's potential? Check out our Agency Blueprint. Designed for agency owners like you, our Agency Blueprint helps you uncover growth opportunities, tackle obstacles, and craft a customized blueprint for your agency's success.

Send a textJoe Fairless built Ashcroft Capital into one of the most recognized multifamily syndicators in the country — $2B+ in assets, properties across the Sunbelt, and a vertically integrated management company. But he started with $30K, student loans, and an apartment in New York where one paycheck covered rent and the other covered everything else.In this episode, Joe gets candid about the deals that didn't work, the market conditions that are keeping multifamily investors in a holding pattern, and the one acquisition strategy most operators are completely ignoring right now: going direct to the lender.Here's what we cover:Why Fort Worth and Orlando are Ashcroft's two highest-conviction markets heading into late 2026How Joe acquired a property for less than the outstanding debt — and what it took to get thereThe lender relationship play that gives you first look at off-market distressed deals (even if you don't have your own management company)Where the supply/demand shift is — and why Q3 2026 is the number operators keep landing onJoe's personal 3.5% math: out of 140 LP deals across 50+ operators, what's actually gone to zeroThe fixed vs. floating rate lesson that still stingsHow Joe defines success — and it has nothing to do with deal countJoe also shares his three bucket list goals for the year. One involves a fifth grader with a 2040 chess rating. That's all we're saying.This week's book: The Road Less Traveled by M. Scott PeckElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Send a textIn this episode, Ed welcomes Michael Pouliot of Carbon Real Estate Investments, a vertically integrated private equity firm operating workforce housing apartments across the Southeast. Pouliot explains Carbon's buy box: 100–300 unit, older vintage (1970s–1990s) properties in strong school districts and stable submarkets, targeting families and raising rents about 20% through substantial CapEx that prioritizes deferred maintenance alongside unit upgrades. They talk about navigating Sunbelt challenges like insurance and taxes by avoiding high-risk areas, staying conservative in underwriting, and emphasizing strong entry pricing. Pouliot shares a bullish view that the next 12–18 months are a strong buying window as the market works through distress, debt maturities, and oversupply absorption, with more constructive sentiment and capital expected around 2027–2028. He outlines Carbon's strategy for 2026: keep buying with fixed-rate, low-leverage debt, hold long-term, and offer investor liquidity via recapitalizations rather than selling assets. The conversation also covers regional scaling for operational efficiency, selective adoption of AI tools (voice/chat agents, SOP knowledge bases, automation) to augment staff, and Pouliot's perspective on purpose, mentorship, lifestyle trade-offs versus Wall Street, and how he defines success. Pouliot closes by directing viewers to investwithcarbon.com for Carbon's weekly newsletter and content.00:00 Cycle Outlook 2027-202800:11 Show Intro and Mission00:52 Welcome and Subscribe01:42 Meet Carbon Real Estate02:44 Insurance and Tax Headwinds05:07 Buy Box and Resident Avatar07:01 Why Stable Markets Win08:34 Distress Deals and Assumable Debt12:29 Oversupply and Absorption Math14:58 Strategy for 202618:41 Vertical Integration and CapEx20:32 Tech and AI in Property Ops14:23 AI Ops Automation23:28 Human Touch Investing24:31 Real Estate Tech Lag25:19 Deal Junkie Purpose26:23 Paranoia Prevents Errors28:26 Wall Street What Ifs33:38 Learning Diet Books35:56 Defining Success Seasons38:19 Life Outside Real Estate41:05 Where To Follow CarbonThis week's book: How Countries Go Broke by Ray DalioElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Building software is supposed to take years of coding, endless stress, and a long grind to profitability. Omar wanted to test that belief. After a decade running WebinarNinja, he set out to answer one bold question: can you build a real SaaS product in just 7 days using nothing but AI? In this episode, Omar shares his experiment to create a fully functional, ready‑to‑sell app powered entirely by AI. This is a very special kind of episode. You'll get to follow along as the process unfolded day by day, something that's never been done before on the show. Omar walks through the planning, the tools he used, the testing, and the problems he ran into along the way. You'll hear what worked, what didn't, and why clarity and focus matter more than speed. It's an inside look at an experiment designed to give you both inspiration and practical takeaways. Hit play at the top of the page and experience Omar's 7‑day AI SaaS experiment. The lessons inside could reshape how you think about building your next software idea. MBA2749 Can You Build A Profitable SaaS In 7 Days With Just AI? My Experiment With Proof!See Nicky AI in action - watch the demo on YouTube now! Guest CollaboratorChris Ashby - Telescope.design Founder of Telescope, guiding AI‑driven startups with impactful design and strategy. Tools Mentioned Leap OpenAI Stripe GitHub Cursor Wispr Flow Mux ChatGPT Windsurf Lovable Watch the episodes on YouTube: https://lm.fm/GgRPPHiSUBSCRIBEYouTube | Apple Podcast | Spotify | Podcast Feed Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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Send a textThis week, Ed welcomes Hawaii-based real estate developer and educator Fuzzy Jardine to Real Estate Underground. Fuzzy shares his background growing up in Hawaii, getting into trouble with drugs and alcohol, going to prison, and using that time to educate himself with books like Rich Dad Poor Dad. After struggling to find work as an ex-con, he took multiple jobs, then invested $26,000 in real estate education after hearing about Fortune Builders, and learned to find deals through strategies like bandit signs and Craigslist ads. He explains how taking action led him from deal-finding to partnering with a local developer and eventually building 100+ affordable homes for local families on a more rural island, typically priced around $300K–$425K. Fuzzy talks about “The Pono Way,” emphasizing respectful, ethical investing, illustrated by a deal where a distressed homeowner was helped with housing, a car, and additional funds while the investors still profited. He also describes co-founding the Hui Mastermind with Asha Smith, including webinars, bus tours showing the full build process, meetups, and master classes teaching how to get started in real estate and fund deals without traditional bank financing. In a lightning round, Fuzzy says family is his main purpose, shares advice about being on time and owning mistakes quickly, reflects on saying yes too often and taking responsibility for a project headed toward a loss, and names motivators he follows on YouTube and podcasts. Check out Fuzzy's book, “Out of Paradise: How to Build Wealth Investing in Real Estate the Pono Way,” and shares where to find him online: fuzzyjardine.com, huimastermind.com, Instagram @hifuzzy, and YouTube “Investing in Hawaii.”00:00 Take Action Mindset00:11 Show Intro and Opportunity00:52 Meet Fuzzy Jardine01:54 From Prison to Real Estate05:58 Why Building Homes08:33 Finding Deals and First Partner10:03 Working Three Jobs to Learn12:30 The Pono Way Ethics15:58 Hui Mastermind Origins19:15 Lightning Round Purpose20:28 Mentors and Hard Lessons23:21 Books and Writing His Own24:43 Defining Success and Fun26:40 Where to Find Fuzzy27:24 Final Thanks and Call to ActionThis week's book: Rich Dad, Poor Dad by Robert KiyosakiElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

春節轉眼過去了，當大家拿了壓歲錢和紅包，肯定心裡最關心的就是金融市場的可能變化？我們今天來關注兩則金融市場的新話題，首先，一月份開始的軟體股票下殺，象徵的是什麼？還有，最近的華爾街熱門話題 預測市場 又該怎麼解讀？其實說白了，一個和人工智慧有關聯，一個和政經情勢發展緊密相連，我們一一來幫大家解讀。 一， 進入二月份，美國股市開始呈現兩極化震盪走勢，S&P 500 多數成份股上漲，但受到大型科技股和軟體股慘遭血洗的拖累，三大指數集體回落。投資人日益擔憂人工智慧（AI）非常可能威脅傳統軟體業者的核心業務，引發軟體股創下自去年 4 月以來最大單日跌幅。 事實上，自從Anthropic 於 一月12 日發布全新的 AI 工具後，市場對軟體業恐遭顛覆的恐懼就一發不可收拾，整體而言，摩根士丹利追蹤的一籃子SaaS股票，今年以來已下跌 15%。 但2月7日，但在情緒最恐慌的時刻，黃仁勳 卻公開反駁：「AI 取代軟體，是世界上最不合邏輯的事情。」AI 沒有殺死軟體，只是逼軟體「進化」，這也讓市場重新思考這波下殺，究竟是「產業被顛覆」，還是「估值與商業模式被迫重估」？對投資人而言，恐慌背後，是否反而隱藏著結構性機會？ 二， 很難想像，今年一月中，《大賣空》原型之一摩西（Danny Moses）認為，投資人需要密切關注預測市場，因為這可能會帶來一些投資上的洞悉和機會。根據《Business Insider》報導，現今的賭注已經不限於運動或天氣，連虛擬貨幣價格甚至 Labubu 的價值都能下注。 現在，ETF 發行商正在將這些概念金融化，轉變為易於取得且受監管的產品。Roundhill 率先啟動了這一趨勢，申請了與總統、參議院和眾議院選舉結果掛鉤的 ETF，利用互換協議（swaps）或直接持有「事件合約」來獲得曝險。Bloomberg指出這是更廣泛的「萬物 ETF 化」（ETF-ization of everything）趨勢的一部分，強調此類產品如何將預測市場資產證券化並開闢新的投資渠道。 不過，繼一月份，麻州法官宣布將對預測市場平台 Kalshi 發出初步禁令，要求其停止在該州提供體育相關事件合約。 2 月 17 日，內華達州博彩控制委員會與州檢察長正式起訴 Kalshi，指控其平台上的體育事件合約實質上是無照體育博彩，違反州法規。我們應該怎麼看待這波預測市場風潮？ Powered by Firstory Hosting

In this Dialogue episode of The Synopsis, we discuss the recent sell off of software names, Constellation Software, and introduce the idea of "Point of Monetization" to analyze why Dating Apps are such a bad business.  Five Minute Money Newsletter Free Sign Up Watch the ServiceNow Video here, or the Constellation Software Video Here, the Adobe Video Here.  ~*~ You can also get a free trial to AlphaSense to read 200k+ expert calls through this link.  ~*~ For full access to all of our updates and in-depth research reports become a Speedwell Member here. Please reach out to info@speedwellresearch.com if you need help getting us to become an approved research vendor in order to expense it. -*-*-*-*-*-*-*-*-*-*-*-*-*-*- Show Notes (0:00)  — Intro (3:48)  — ServiceNow Business Overview (13:00)  — Risks to ServiceNow (21:32)  — AI Control Tower (24:14)  — Competitive Dynamics (32:10)  — Valuation (35:18)  — Mature Margin Diatribe (42:39)  — AI Risk Rebuttal (46:16)  — ServiceNow Blue Sky Scenario (47:38)  —  Intuit Disrupted? (50:46)  — Will Margins Collapse for SaaS? (52:54)  — Difference Between SMB vs Enterprise SaaS -*-*-*-*-*-*-*-*-*-*-*-*-*-*- For full access to all of our updates and in-depth research reports, become a Speedwell Member here. Please reach out to info@speedwellresearch.com if you need help getting us to become an approved research vendor in order to expense it. *-*-*- Follow Us: Twitter: @Speedwell_LLC Threads: @speedwell_research Email us at info@speedwellresearch.com for any questions, comments, or feedback. -*-*-*-*-*-*-*-*-*-*- Disclaimer Nothing in this podcast is investment advice nor should be construed as such. Contributors to the podcast may own securities discussed. Furthermore, accounts contributors advise on may also have positions in companies discussed. This may change without notice. Please see our full disclaimers here:  https://speedwellresearch.com/disclaimer/

週一天下零時差關注以下財經大事： 一、美國最高法院宣判對等關稅違法，川普援引其他法條繼續徵收關稅，接下來會發生什麼事？ 二、AI代理會不會取代軟體服務（SaaS）？這禮拜Salesforce的最新財報可見端倪。 三、Google、亞馬遜資本支出飆高、股價跌，對輝達會有什麼影響？ 文：郭家宏 、辜樹仁 製作團隊：李洛梅、張雅媛 ＊閱讀零時差，點這看全文

בפרק השני בסדרה שבה אנחנו מדברים על איך AI משנה ומעצב מחדש את מאנדיי, דריה ורטהיים מארחת את רועי מן וערן זינמן, המייסדים והמנכ"לים של החברה, לשיחה על נקודת המפנה הדרמטית ביותר בעולם התוכנה מאז המצאת המחשב האישי. אם ב-20 השנים האחרונות תוכנות שימשו בעיקר ככלי לתיעוד עבודה שנעשתה בחוץ, היום ה-AI מאפשר לתוכנה לעבור לקדמת הבמה ולבצע את העבודה בעצמה. ״From managing the work to doing the work". בפרק נדבר על מה המשמעות של להטמיע באמת AI בחברה שלנו, לפי שלושה עקרונות מפתח - החלפת בני אדם, העצמת עובדים ליכולות של 10X, והנגשה של משימות ומומחיות שעד כה עובדים לא היו יכולים לעשות בעצמם - כך שכל אדם יכול ליצור קוד, לעצב, או ליצור מוזיקה ללא ידע מוקדם. נדבר גם על מה הוא לא AI, ולמה לפזר AI Dust מעל המוצר שלנו מבלי לשנות את ליבת הערך שלו זה פשוט לא מספיק. רועי וערן מסבירים את ההבנה שהציפייה של הלקוחות היא שונה לחלוטין כיום, עד כדי כך שכל דבר שהוא פחות מחסכון של תהליך מחשבה וביצוע הוא פשוט לא מספיק. כתבו לנו בתגובות מה חשבתם על הפרק. עקבו אחרינו בלינקדאין: https://www.linkedin.com/company/26500813/admin/page-posts/published/ עקבו אחרינו באינסטגרם: https://www.instagram.com/startupforstartup/ רוצים להתחבר למשקיעים ויזמים? הירשמו לפלטפורמת החיבורים באתר שלנו - https://www.startupforstartup.com/networks/See omnystudio.com/listener for privacy information.

Send a textMelissa Nash spent $10K on a fully renovated $190K property with a tenant already in place.Cash flows $200/month.Sounds boring until you realize: $10K all-in. Someone else is buying her a house. And she never left California.This week on Real Estate Underground: How a self-described "lazy investor" built a portfolio across five markets while coaching 2,000+ investors to do the same. No flying out to properties. No managing contractors. No second job disguised as passive income.What you'll learn:The questions that separate good property managers from disasters (hint: it's not about their fees)Why Melissa went from flips and BRRRs back to turnkey investing—and why she's never looking backHow to vet teams 2,000 miles away when you can't be on-siteThe $99K property that changed everything (and why fear almost killed the deal)Property managers as your best acquisition source—if you ask the right questionWhy this conversation matters:Melissa made every mistake you're worried about making. Trusted the wrong contractors. Lost money on flips. Froze on deals for years. Then figured out a system that works.If you're analyzing deals but not pulling the trigger—she's been there. If you're wondering how to invest out of state without getting burned—she learned it the hard way so you don't have to.We talk systems, spreadsheets, and the "snowball payoff" strategy she swears she'll start using someday (she won't—because she loves buying deals too much).For operators who:Want to invest out of state but don't know who to trustAre tired of real estate being a second jobNeed a framework for vetting property managersWant to hear from someone who's helped 2,000 people actually do thisNo hype. No theory. Just 11 years of buying properties she's never seen—and making it work.Hit subscribe. Leave a comment. Tell us what you're working on.Real Estate Underground. Where operators talk to operators about what actually works.Check out Melissa's free community at: hellomelissanash.comThis week's book: The Creature from Jekyll Island: A Second Look at the Federal Reserve by G. Edward Griffin Elevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Dagens ämnen: 0:00 Intro 4:11 Truecaller 9:45 Karnov och AI/SaaS 17:26 Small och Mid Cap 25:06 Ska man köpa dippen? 31:41 Index 35:45 EG7 och småbolag 39:28 Veckans Fill or Kill   www.instagram.com/fillorkillpodden Tack RoboMarkets! http://gorobo.pro/2aue @RoboMarketsSE

注目ニュースのコメント欄とNewsPicksの最新オリジナルコンテンツなどを紹介するAI音声番組。◇ニュースキャッチアップ◇カカオの代わりに“ヒマワリ”の種　「チョコか？」100万個ヒットのワケhttps://npx.me/s/8eJOUTYJ◇オリジナルコンテンツ◇【絶句】2026年、大手4社で「100兆円投資」がヤバいhttps://npx.me/s/or0EkpHGAIでSaaSが作れる時代の「不都合な真実」https://npx.me/s/ekwhwZO8【五輪も炎上中】「SNS私刑」に加担する人たちの正体https://npx.me/s/7beviWJ7【自問】そのスマホ、本当に「仕事」だけのため？https://npx.me/s/SO7fAEXxアンジェラ・アキからの手紙 〜拝啓 大切なあなたと私へ〜https://npx.me/s/yTXdlOcW※このAI音声番組はNewsPicksが実験的に運用しています。 内容の正確性や品質には十分配慮しておりますが、もしお気づきの点がありましたら、 下記リンクからご連絡ください。https://newspicks.zendesk.com/hc/ja/requests/new

＜ヘッドライン＞総選挙、自民党が316議席確保し単独で定数の3分の2を上回る歴史的勝利　高市総理大臣「国民から政策転換をなんとしてもやり抜いていけという力強い形で背中を押してもらった」「党一丸となって歯をくいしばって国民の皆さまとの約束を実現していく」／国の借金、昨年末時点で1342兆1720億円と1年間で24兆5355億円増え過去最大 予算の財源不足を埋める新規国債の発行で残高膨らむ／米1月雇用統計、非農業部門就業者数が前月比13万人増え「5万〜7万人増」との市場予想を大幅に上回る　「米雇用の失速不安はやや和らいだ」との見方増える　一部大企業によるレイオフ計画が今後の懸念材料／米NY連銀・四半期報告「昨年10〜12月期の米国の家計債務総額は18兆7800億ドルと1年前より4.1%増加し、7〜9月に続き過去最大更新」　住宅ローンと学生ローンで延滞の増加傾向目立つ／米AI開発新興アンソロピック「エヌビディアやマイクロソフト、投資ファンドから300億ドルの出資を受けた」「法人向けAIの提供が拡大し年換算の売上高が2兆円規模に急増した」　AIが業務ソフトの事業モデルを崩す「SaaSの死」の震源として株式市場で注目を集める　直近1年間の売上高・企業価値増加率、オープンAIを上回る／米WSJ「オープンAIが安全対策幹部の一人を性差別を理由に解雇したが本人は否定。幹部はオープンAIが対話型AI・ChatGPTで性的会話を解禁する計画について社内で反対していた」　オープンAI、ChatGPTで今年「エロティカ」と呼ぶコンテンツを解禁する方針／ビットコイン、価格下落続き昨年10月につけた最高値12万6000ドル台の半値近い水準で推移　機関投資家による関連ETFからの資金引き揚げ相次ぐ、暗号資産を保有する企業の株価にも打撃 ＜ポイント＞　（１） 高市自民一強の課題（２） 今週のマーケット（３） ｢SaaSの死｣は起こるか？ ＜ここ／これを見てきた＞東京上野・不忍の池の筏

Send a textMastering Short-Term Rentals with Data-Driven Real Estate Strategies - Featuring Kenny Bedwell from STR InsightsIn this insightful episode of the Real Estate Underground podcast, host Ed Mathews welcomes Kenny Bedwell from STR Insights to discuss the intricacies of short-term rental investments. Kenny shares his journey from a data analyst at Citibank to a successful real estate investor specializing in short-term rentals. He emphasizes the importance of choosing the right markets, investing in amenities, and focusing on the guest experience to succeed in the competitive short-term rental space. Kenny also highlights his strategies for managing properties remotely, leveraging local resources, and the value of balancing work and family life. This episode is a must-listen for anyone looking to optimize their investments in the short-term rental market.00:00 Introduction and Podcast Overview01:19 Guest Introduction: Kenny Bedwell from STR Insights01:45 Kenny's Background and Real Estate Journey02:19 The Shift to Short-Term Rentals02:45 Navigating Regulations and Diversifying Investments04:00 Understanding the Short-Term Rental Market09:17 Creating Unique Guest Experiences15:50 Managing Short-Term Rentals Across Multiple States20:07 Managing Property Operations20:57 Human Capital and Property Management23:35 Personal Drive and Motivation25:09 Valuable Advice and Lessons Learned32:41 Defining Success and Personal Growth34:46 Hobbies and Family Life37:25 Connecting with Kenny BedwellThis Week's Book: The Pumpkin Plan: A Simple Strategy to Grow a Remarkable Business in Any Field (Entrepreneurship Simplified) - By Mike MichalowiczElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

Send a textWhat if you could turn a 10-hour due diligence process into 20 minutes of review?Alberto Rizzoli, CEO of V7 Labs, reveals how AI workflow automation is eliminating the "purgatory" of real estate paperwork, and why operators who master it now will 10x their competition within a year.In this episode, you'll discover:⚡ The "napkin test" for identifying which processes to automate first (hint: weekly tasks taking 1+ hours)⚡ Why AI fails at big, open-ended requests, and the simple fix that makes it reliable⚡ How to abstract 1,000 leases without adding headcount as you scale⚡ A free lease abstraction tool you can test today at scanmylease.comAbout Alberto Rizzoli: Computer visionary turned founder, Alberto built V7 Labs into a platform processing hundreds of billions in transactions. His mission? Make work actually matter by eliminating everything that doesn't.This Week's Book: Do the Work: Overcome Resistance and Get Out of Your Own Way - by Steven PressfieldElevista - Speed as a Service™Elevista Connect is the first AI-powered lead conversion system built for real estate investors. Heads up: If you find this week's book intriguing and you buy using our link, we receive a small commission that helps support the show. Thank you!

In this episode, Alex Theuma and Mark Walker, CEO of Nue, discuss how AI is accelerating the pace of change in SaaS and the knock-on effect this is having on pricing and monetisation. Mark explains how Nue has become a critical part of the infrastructure powering many of the world's fastest-growing AI-native and scaled SaaS companies, including OpenAI, Anthropic, and Jasper. Drawing on learnings from these companies, he unpacks how usage-based models, committed spend contracts, and rapid product experimentation are replacing traditional SaaS playbooks. Alex and Mark also reflect on life as an entrepreneur, scaling teams, managing stress, and the need to embrace constant change. - Why AI has disrupted product development cycles and changed how SaaS companies create value. - How faster product iteration is forcing new pricing and monetisation models. - The rise of committed spend, consumption-based contracts and experimentation at scale. - Why you should build revenue systems for the company you want to become, not the one you are today. - How AI-native startups and scaled SaaS companies are converging on the same challenges. - Why speed across product, systems and execution is now the ultimate competitive advantage.       Check out the other ways SaaStock is helping SaaS founders move their business forward: 

saas.unbound is a podcast for and about founders who are working on scaling inspiring products that people love, brought to you by https://saas.group/, a serial acquirer of B2B SaaS companies. In episode #4 of season 6, Anna nadeina talks with Edward Barrow, Co-Founder & CEO @ Cloud Capital, a Cloud Cost Management platform helping finance (CFOs) and engineering teams control, forecast, and optimize cloud spending, especially on AWS, by bridging financial and technical visibility.----------- Episode's Chapters -----------1:37 — Ed's Journey: From Bootstrap to VC-Backed SaaS6:48 — Post-Acquisition Life: M&A and Private Equity9:16 — Cloud Spend Benchmarks: 10-20% of Revenue12:10 — The Commitment Trap: Saving Money vs Taking Risk15:39 — AI's Impact on Cloud Economics20:11 — How Cloud Capital Works: Financial Risk Transfer29:28 — Building AI-Native: Wisdom Plus Automation34:01 — Partnership Strategy: Bottom-Up vs Top-Down41:49 — 20 Years of Lessons: Running at Brick Walls43:55 — Founder Hack: Walking MeetingsEd - https://www.linkedin.com/in/ebarrow/ Cloud Capital - https://www.cloudcapital.co/ Subscribe to our channel to be the first to see the interviews that we publish - https://www.youtube.com/@saas-groupStay up to date:Twitter: https://twitter.com/SaaS_groupLinkedIn: https://www.linkedin.com/company/14790796

The SaaS Revolution Show with Alex Theuma and Kady Srinivasan, CMO at Freshworks and former CMO at You.com. Kady shares how she rebuilt the You.com GTM strategy from the ground up after multiple pivots. Rather than trying to fix broken SaaS playbooks, she replaced them with a multithreaded marketing model that 10x'd MQLs and grew ACV by 86% in just two quarters. Alex and Kady discuss: - Why traditional GTM playbooks break down in the AI era - What multithreaded marketing actually looks like in practice - How to structure marketing teams for ownership and speed - The role of prompt marketers and AI-native workflows - Using AI as an execution accelerator, not a strategy shortcut - The differences between selling to AI natives and AI laggards - The reality of operating with a complex, multi-tool GTM stack       Check out the other ways SaaStock is helping SaaS founders move their business forward: 

Every year I do a video on predictions for the year. Last year I predicted that Alibaba would have a record year, and it did. I predicted that we'd see a resurgence in luxury ecommerce brands and that AI Saas companies would become a thing, and both did. This year? I'm predicting that the Democrats win back the male vote, that we'll see a big pullback in stocks due to the AI bubble crash, and I talk about my three top stock picks for the year. If you want to start your road to $1m, head to https://capitalism.com/playbook-yt What are your predictions? Drop them in the comments. Timestamps: 0:00 - My predictions for 2026 1:00 - Reflecting on last years predictions 7:51 - We'll see a 20-25% pullback in the economy 10:32 - Agentic commerce becomes a thing 12:29 - We enter an ecommerce "springtime" 13:36 - Community becomes the new email list 15:18 - Pinterest becomes a big player 17:02 - Politics and the Democrats 19:00 - My top stock picks for the year 23:22 - Summary

SaaStr 836: The Step-By-Step Playbook for Building AI-Powered GTM Teams with Personio's CRO Philip Lacor, CRO of Personio, shares his company's journey to building an AI-powered go-to-market motion, including 5 critical lessons learned and 4 real-world use cases delivering measurable results. In this podcast, Philip breaks down: ✅ The 5 lessons for AI transformation: top-down + bottom-up motion, cross-functional teams, prioritization frameworks, building AI culture, and combining great stack with context  ✅ How to build AI-powered workflows that actually work (not just more tools to test)  ✅ Real use cases: Win/loss analysis, expansion SDR assistants, intent scoring, and AI chat ✅ Why their expansion SDRs went from 2 hours of research per day to 15 minutes while doubling pipeline per rep ✅ The truth about AI ROI: where it shows up and how long it takes  ✅ How to get your team excited about AI (not scared of it) Philip doesn't hold back on what's working, what's failed, and what questions they still haven't answered. If you're a CRO, founder, or GTM leader trying to figure out how to actually implement AI beyond the hype, this is the playbook.  --------------------- This episode is Sponsored in part by HappyFox: Imagine having AI agents for every support task — one that triages tickets, another that catches duplicates, one that spots churn risks. That'd be pretty amazing, right? HappyFox just made it real with Autopilot. These pre-built AI agents deploy in about 60 seconds and run for as low as 2 cents per successful action. All of it sits inside the HappyFox omnichannel, AI-first support stack — Chatbot, Copilot, and Autopilot working as one. Check them out at happyfox.com/saastr   ---------------------   Hey everybody, the biggest B2B + AI event of the year will be back - SaaStr AI in the SF Bay Area, aka the SaaStr Annual, will be back in May 2026.    With 68% VP-level and above, 36% CEOs and founders and a growing 25% AI-first professional, this is the very best of the best S-tier attendees and decision makers that come to SaaStr each year.     But here's the reality, folks: the longer you wait, the higher ticket prices can get. Early bird tickets are available now, but once they're gone, you'll pay hundreds more so don't wait.    Lock in your spot today by going to podcast.saastrannual.com to get my exclusive discount SaaStr AI SF 2026. We'll see you there. --------------------- More from SaaStr: https://www.saastr.com

This is a conversation about augmenting, not automating, creativity.Leslie Walsh is the Head of Strategy at RYA, the AI SaaS provider that's creating innovative campaigns for name brands. Leslie's experience as an advertising veteran includes leading brand and digital strategy for some of the world's biggest brands like MINI Cooper, Nestle, Oscar Mayer and AT&T. Leslie is a graduate of the University of Georgia.

Join Chris Corrado, CEO Americas and President at Squirro, in an in-depth conversation with Gary Fowler as they explore one of the most urgent challenges in AI today: trust. Discover how enterprises can deploy generative and agentic AI responsibly, securely, and at scale while maintaining transparency and protecting business integrity.

Chapters: Rafeh Qazi (Co-founder, Poppy.AI) joins us to share what he's learned during is journey from a struggling immigrant to building a $50M AI startup. After a car accident left his mother injured and his family with no money to pay for surgery, Rafeh channeled his frustration into a relentless drive to succeed. In this episode, Rafeh breaks down his rise to fame teaching millions to code, and his pivoted from service-based businesses to building Poppy.AI, the insights he gained from Alex Hormozi, and why he believes software is the ultimate asset for building wealth. Join us to discover the strategies behind scaling an AI business, why "product-founder fit" matters more than market fit, and how to turn massive failures into an 8-figure valuation. Chapters: 0:00 - Intro  02:50 - Motivation and The Car Accident That Changed Rafeh's Life  05:26 - Learning to Code to Survive 07:04 - Dropping Out of College to Teach  13:05 - Launching Courses that Made of $150K 22:49 - The Lawsuit, Losing Everything, and Meeting Alex Hormozi 31:28 - Why He Built Poppy.AI (SaaS vs. Services)  37:07 - Using Poppy.AI to Generate THOUSANDS for Our Clients 48:00 - AI Copywriting vs Humans 53:04 - Scaling with Affiliates and Hitting $500k Per Month 59:43 - Valuation and Raising Capital 1:05:35 - Talent Vs Money (Which Bottleneck is a Harder Problem?)

Kent Bye—host of the Voices of VR podcast and one of XR's most prolific journalists with over 1,680 published interviews—joins Charlie and Ted for a wide ranging conversation on the state of immersive storytelling, the ethics of AI, and why XR's future might be less about consumer headsets and more about embodied presence and human connection. Kent's decade-long commitment to documenting artists, creators, and developers at the ground level offers a counterpoint to hype-driven tech coverage, revealing the messy, vital ecosystem sustaining VR through festival circuits, location-based entertainment, and government-funded experimental projects that rarely make headlines.The conversation opens with Jeff Bezos's new AI robotics company Prometheus, Amazon's one-to-one human-robot workforce parity, and the implications of industrial AI automation. Ted shares his recent appearance on cinematographer Roger Deakins's podcast, where they discussed AI as a creative tool rather than a threat—a perspective Kent echoes when discussing artists who use AI to critique AI's "colonizing force." Kent explains his philosophy of "boots on the ground" journalism inspired by Knight Ridder's Iraq War reporting, focusing on developers and creators closest to the work rather than corporate press releases.Kent reveals why he's been lukewarm on smart glasses despite industry excitement—monocular displays give him headaches, his prescription is too strong for current hardware, and most importantly, there's no compelling narrative content yet. He contrasts this with VR's rich immersive storytelling at festivals like Venice Immersive, Sundance New Frontier, IDFA DocLab, and Tribeca, where government-funded European projects push the medium's boundaries in ways U.S. startups can't afford to explore. The discussion touches on Meta's Ray-Ban AI glasses, the impracticality of Meta's neural band input, and why Snap's developer platform remains the most interesting AR ecosystem despite limited consumer traction.Guest HighlightsPublished 1,682 VR interviews with 1,000+ unpublished; focused on artists, creators, and developers over corporate narratives.Covers 30+ hours of immersive content per festival at Venice, Sundance, IDFA DocLab—documenting ephemeral art that may never distribute widely.Started in 2014 after buying Oculus DK1; began by capturing oral history at Silicon Valley VR Conference's first gathering.Background as F-22 Raptor radar systems engineer turned documentary filmmaker—blends hardcore technical knowledge with artistic sensibility.Advocates for XR as antidote to smartphone addiction—technologies that foster embodied presence rather than infinite distraction.News HighlightsJeff Bezos launches Prometheus AI robotics company—focusing on industrial applications where enterprise adoption will drive innovation faster than consumer markets.Amazon hits one-to-one human-robot workforce parity—roughly 1 million humans, 1 million robots, with plans to shed 100K+ workers over five years.Warner Brothers settles with AI music company Udio—following Axel Springer, AP, and Fox licensing deals as New York Times litigation drags on.Enterprise AI startups raise massive rounds—Stut (collections automation, $29.5M from Andreessen), Albatross (real-time personalization, $12.5M), signaling vertical-specific AI SaaS wave.HaptX acquired by Ohio manufacturer—haptic glove company pivots to industrial training applications after years targeting consumer VR.Thanks to our sponsors Zappar and VitureNew episodes every Tuesday. Hosted on Acast. See acast.com/privacy for more information.