Podcasts about plg

Play Episode Listen Later May 28, 2026 60:31

Enterprise sales breaks down when teams confuse activity with progress, champions with coaches, or product interest with business urgency. Stuart Gwynn, a top-performing enterprise seller at MongoDB, joins John Kaplan and John McMahon to unpack what separates disciplined enterprise execution from deal chasing. Drawing from his path from SDR at Pure Storage to closing the largest deal in MongoDB history, Stuart explains why discovery is the foundation of value-based selling, how to test whether a champion will actually sell internally, and why large deals require multiple stakeholders, rigorous qualification, and a team operating around a shared account vision. He also shares how elite individual contributors lead without formal management titles, where AI is already changing buyer expectations, and why process only works when it is paired with judgment. Stuart Gwynn is an enterprise sales leader at MongoDB who has exceeded goal every year since joining the company in 2019. Before MongoDB, he spent seven years at Pure Storage, rising from SDR to named account rep and finishing as one of the company's top performers before moving into strategic enterprise selling. Connect with Stuart: LinkedIn Episodes mentioned: The Discipline Behind Scaling from PLG to Enterprise with Sahir Azam Why Sales Execution Wins in an AI-First World with Brian McCarthy, President of Global Revenue and Field Operations at Cursor Key takeaways from this episode: 00:00 – What it really takes to combine a rigorous value framework with the human judgment required to scale enterprise selling. 02:42 – Why discovery becomes the moment where real pain, executive relevance, and budget-worthy outcomes either surface or disappear. 07:59 – What leaders often overlook about the trust required before customers will quantify the true cost of a problem. 11:28 – Why champion identification quietly determines whether a deal has internal momentum or only surface-level support. 21:35 – The mistake many sellers make when pipeline pressure pushes them toward activity instead of disciplined qualification. 18:50 – A look inside the preparation habits that help enterprise teams align before high-stakes customer conversations. 56:25 – Why many leaders get top-talent management wrong by applying the same operating rhythm to every rep. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

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小人物上籃-霹靂鍵盤#229 下周要道歉還是要慶祝？PLG季後賽+籃協改選選前之夜 feat .布里 (2026/05/25)

The Recruiting Brainfood Podcast

Play Episode Listen Later May 26, 2026 121:15

連楊柯維奇都認不出來、貝納師都叫不出來，是有多久沒有討論PLG了啦！在PLG終於進入季後賽、勇士與獵鷹前兩戰戰成1:1平手、Game 3開打前夕，這集節目討論了獵鷹如何從前陣子比較鬆散的狀態再度繃緊神經，以及勇士在古德溫主導進攻外，曾祥鈞與莫巴耶的表現與定位問題，還有第一輪季後賽的結果預測。嗯…這種預測，又是個下周要道歉的節奏？與職籃季後賽同時間要登場的，還有籃協5/31的理監事改選。Roy邀請將挑戰個人理事名額的布里，上節目聊聊從理事長、理事、到監事改選對協會的意義，以及布里期待未來籃協能更致力推動的六大方向。本來是造勢大會，怎麼後來變成辯論答詢？！期待藉由這次討論，小人物聽眾們也一起對攸關台籃發展的各個議題，有更多不同的思考，同時也預祝持續用心督籃的布里，能夠獲得足夠會員投票的支持，下周從布里升等為布理事！最後，不論是正火熱開打中的TPBL Finals，還是接力登場的PLG Finals，霹靂鍵盤每場冠軍賽後的即時討論podcast，也會優先與贊助會員分享，歡迎小人物們一起享受接下來三周的台籃球季大結局！成為

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Brainfood Live On Air - Ep388 - Introducing Next Generation AI Recruitment Agents - Juicebox Live Demo

Play Episode Listen Later May 26, 2026 57:02

INTRODUCING NEXT GENERATION AI RECRUITING AGENTS Juicebox have been one of the most impressive businesses in the AI recruiting space. Now with 6,000 customers, they are ready to announce a step change in how Juicebox works for recruiters to keep a competitive edge. Juicebox agents now REMEMBER AND SELF CORRECT. When a search isn't surfacing the right people, Agents reflect on their reasoning and proactively edit their own criteria instead of waiting for you to bring it up. THEY ASK QUESTIONS When requirements conflict or something doesn't make sense, the Agent clarifies. For example: "Would you prefer candidates with PLG or demo-led growth experience or both, but with a smaller candidate pool?" THEY ADAPT TO CHANGING REQS Recalibrate by directly chatting to your Agent. For example, you can say: "Add GEO/SEO to the search" and the Agent adjusts and remembers context from your previous conversations. LASTLY, THEY HANDLE OUTREACH Agent writes multi-step sequences in your team's voice, with company selling points and per-candidate personalization, at scale. CEO David Paffenholz with live demo - register here (click on the green button 'save my space') and follow the channel here for updates.

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Giving Agents Computers — Ivan Burazin, Daytona

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later May 21, 2026 70:27

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

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The SaaS Growth Playbook: PLG, Self-Service & Activation | Sanjay Sarathy | 390

SaaS Fuel

Play Episode Listen Later May 21, 2026 49:32

If your SaaS product delivers genuine value fast, growth takes care of itself. That's the core thesis Sanjay Sarathy has spent 8+ years proving at Cloudinary, where he oversees a self-service business representing nearly a third of the company's revenue across 11,000+ paying customers in 150+ countries — without feet on the ground in most of them.In this episode, Sanjay breaks down what product-led growth actually looks like when it's executed well: not just free trials and clever onboarding flows, but building such a frictionless, valuable experience that developers naturally tell other developers. He shares why Cloudinary invested in technical support before marketing, how they redefined "activation" to mean real value (not just uploading a file), why discoverability is a non-negotiable pillar of their growth strategy, and how they're now rethinking the developer experience for a world where AI agents and LLMs are writing the code.This is a masterclass in developer-led PLG from someone who has lived it at scale.Key Takeaways4:07 — The Growth Levers Have Changed SEO, outbound, and paid are still valid, but word of mouth (especially in developer communities), AEO, and agentic discoverability have become powerful new growth engines — when they're earned as a byproduct of value, not engineered as a primary goal.8:28 — Why PLG Before Enterprise Cloudinary was built by developers for developers. They started with self-service because that's what their founding team would have wanted. Only after PLG proved itself did enterprise customers come knocking — and it was far easier to layer on security, SLAs, and support than to bolt on a product that developers already loved.13:46 — Great Product Isn't Enough Without Distribution Cloudinary is in 150 countries with no boots on the ground in most of them. SEO, developer relations, and a docs site that functions as a discovery engine are what made global reach possible. Distribution and product must go hand-in-hand.15:36 — Discoverability Is a Strategy, Not a Tactic "Discoverability" is a recurring internal theme at Cloudinary — constantly asking how to ensure the right people, in the right context, can find and experience the product's value.16:03 — The Cannibalization Trap Cloudinary made the mistake of launching a new product without considering its impact on existing products — and cannibalized their own business. They now use a two-track product strategy: "mature" products with full go-to-market support, and "invest" products being validated for product-market fit before scaling.19:24 — Invest in Support Before Marketing One of Cloudinary's earliest and most impactful decisions: invest heavily in technical support first. Happy, successful developers become word-of-mouth advocates. That bet paid off across an entire community.21:06 — Developer Experience in the Age of AI Tooling Developer experience today means meeting developers where they work — VS Code, Cursor, Claude, Windsurf. Cloudinary built a VS Code extension and is working to minimize hallucinations by giving LLMs accurate, context-rich instructions for using Cloudinary correctly.24:03 — Redefining Activation Uploading a file to Cloudinary is not activation. Doing something with that file — transforming it, tagging it, delivering it — is activation. Reframing their metric around genuine value changed how they prioritized onboarding.33:25 — The Seven-Day Activation Window Data shows clearly: if users don't activate within the first 7 days, a second surge doesn't come. Most activation happens in the first 4–5 days. This insight shapes everything about how Cloudinary approaches onboarding urgency.27:01 — Speak Use Cases, Not Features "We have automated image optimization" means nothing. "Your images are 40% lighter and you'll save X on bandwidth" means everything. The language of outcomes and use cases is what drives adoption and expansion.36:39 — Pricing Must Communicate Value Cloudinary's self-service pricing has remained largely flat for years while the product has added enormous capability — intentionally improving the value/price ratio over time. They also offer pay-as-you-go flexibility for seasonal businesses.44:28 — The 90-Day PLG Focus: Build Trust For founders building a PLG motion right now, Sanjay's single most important recommendation: engender trust. Do what you say. Follow up when you say you will. Make your product deliver on its promise. Trust is the flywheel.Tweetable Quotes"We never set out to get word of mouth. We set out to create value. Word of mouth was the byproduct." — Sanjay Sarathy"If your product genuinely helps people win, growth becomes a natural byproduct." — Sanjay Sarathy"Distribution is equally as important as the product itself. You can have a great product and go nowhere." — Sanjay Sarathy"Discoverability isn't a campaign. It's a strategy." — Sanjay Sarathy"Uploading a file isn't activation. Doing something valuable with it is." — Sanjay Sarathy"If a developer doesn't activate in the first seven days, don't expect another surge. It won't come." — Sanjay Sarathy"Stop talking about your features. Start talking in the language of your customer's use cases." — Sanjay Sarathy"We're okay with free users who are actively using the product. They pay us back in word of mouth." — Sanjay Sarathy"In a PLG motion, trust is the flywheel. Without it, everything else breaks down." — Sanjay Sarathy"We fell in love with our own capabilities and forgot that customers don't care. Use cases are what drive adoption." — Sanjay SarathySaaS Leadership Lessons1. Build Distribution Like You Build Product Cloudinary reaches 150+ countries without sales reps in most of them — through SEO, developer relations, documentation, and community. Great products disappear without intentional distribution. Your discoverability strategy is a growth strategy.2. Earn Word of Mouth — Don't Engineer It The moment you prioritize getting word of mouth over generating it as a byproduct of genuine value, you've lost the plot. Build something that makes people win, then step back and let them talk. The data will tell you if it's working.3. Start Narrow, Validate, Then Scale Cloudinary's "invest vs. scale" product framework exists because they once cannibalized their own product line by expanding without rigor. Validate product-market fit in a controlled way before committing the full go-to-market machine. Repeatability before scale.4. Redefine Your Activation Metrics Around Real Value Ask yourself: is the action we're measuring actually a moment of value, or just a moment of presence? Cloudinary stopped counting uploads and started counting transformations. The metric you optimize shapes the product you build.5. Invest in Customer Success Before You Think You Need To Cloudinary prioritized technical support ahead of marketing in their early days. Counter-intuitive — and it was exactly right. Successful users become advocates. That investment compounded for years through word of mouth and developer trust.6. Speak the Language Your Customer Thinks In "Automated image optimization via F-Auto" is internal language. "Your images are 40% lighter and your site is faster" is customer language. The translation layer between what your product does and what your customer achieves is where adoption lives or dies. Build that bridge deliberately.Guest Resourcessanjay@cloudinary.comwww.cloudinary.comhttps://www.linkedin.com/in/sanjaysarathy/https://x.com/guffnuffEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

trust ai strategy growth speak invest seo saas playbook reframing distribution counter activation validate sanjay cursor self service vs code plg slas developer experience aeo windsurf repeatability big pond cloudinary

小人物上籃-霹靂鍵盤#228 要賣票還是賣國豪？TPBL季後賽 + 台籃非官方年度獎項票選Part 2 feat. 籃球伙 Allen （2028/05/18)）

powered group b plg firstory

Play Episode Listen Later May 19, 2026 173:44

經過漫長的 7 個月，PLG 例行賽終於在上週完成，領航猿也終於領到上季的冠軍戒指了！但是在重啟 PLG 討論之前，正如火如荼進行中的 TPBL 季後賽，才是現在籃球迷眾所矚目的焦點吧。(笑)TPBL 季後賽 Group A，上週跟國王道歉後，這週又要道歉了！國王外線火力太驚人，即使小高（高錦瑋）瘋狂輸出也擋不住！雲豹出局後，休賽季需要哪些調整與補強？Group B 方面，攻城獅無法複製首戰贏球的劇本，本季驚奇之旅就此止步。季後誰該留誰該換？國豪該簽嗎？晉級總冠軍賽的夢想家與新北國王，兩支勁旅集人氣、實力與經驗於一身，各有哪些贏球的關鍵重點？（⚠️ 重大預告：霹靂鍵盤在總冠軍賽期間，將再次推出「每場賽後的即時 Podcast」！本企劃為小人物會員專屬限定福利。歡迎想要在第一時間聽到總冠軍賽最前線深度分析的聽眾，加入我們的節目贊助會員行列，詳細入會資訊請點選節目資訊欄或首頁連結！）上週討論了一半的【2026台籃非官方年度獎項票選】，本週三位主持人＋籃球伙 Allen 繼續揭曉！在兩職籃聯盟合併票選的平行宇宙中，年度第一隊、年度第二隊、最佳進步獎、世界盃 10 人名單、2000年後出生的明日之星、年度總經理、年度啦啦隊，以及「最佳經營」與「最需要加油」的球團，超過兩百位投票者的結果，跟你想的一樣嗎？下週，PLG 季後賽的討論也要加入戰局，5 月底的台籃大賽不斷，歡迎鎖定每週霹靂鍵盤！成為

Why Your Sales Rep Needs to Be an Architect, Not Just a Closer with Adam Carr

Make It Happen Mondays - B2B Sales Talk with John Barrows

Play Episode Listen Later May 18, 2026 50:52

The sales playbook is being rewritten and product-led growth is at the center of it.In this episode, John sits down with Adam Carr, CRO at Apollo.io, to dig into how PLG has evolved, what AI is doing to the sales rep's role, and why the best sellers today need to think less like closers and more like go-to-market architects. From hiring smarter to building SDR programs that actually stick, this one's packed with real talk from two people who've lived it.If you're in sales leadership, trying to figure out how to build and develop a team in this new world, this episode will give you a framework to work with.Want to level up your team before the market moves on without you? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll Learn:Why people don't buy from people they like but from people they trustHow to hire intentionally and slow down when everyone's pushing you to scale fastThe difference between PLG, product-led sales, and sales-led growthWhat product signals actually matter when deciding when to bring in a sales repThe three skills every modern sales rep needs to stay relevant in an AI-first worldAdam Carr is the CRO of Apollo, where he leads the company's go-to-market strategy and revenue growth initiatives. Since joining in 2025, he has focused on scaling a modern GTM engine that blends product-led and sales-led growth, aligning sales, marketing, product, and customer success into a unified customer journey. Under his leadership, Apollo continues to support millions of users and hundreds of thousands of businesses in accelerating growth through a scalable, customer-centric platform.LinkedIn: https://www.linkedin.com/in/adamhcarr/John Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution—helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows:LinkedIn: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/TikTok: https://www.tiktok.com/@johnmbarrowsCheck out John's Membership: https://go.jbarrows.com/Join John's Newsletter: https://www.jbarrows.com/newsletter

amazon tiktok ai closer apollo newsletter architects membership salesforce cro make it happen gtm sdr sales reps okta plg trusthow growthwhat john barrows jb sales adam carr

The Mutiny Pivot: Why Jaleh Rezaei Shut Down an 8-Figure SaaS to Go All-In on AI

Play Episode Listen Later May 15, 2026 49:57

In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jaleh Rezaei, co-founder and CEO of Mutiny, to unpack one of the boldest founder moves you'll hear this year. After building Mutiny into an eight-figure ARR SaaS company, Jaleh made the rare decision to shut down most of the original business and rebuild around AI agents. She shares why trying to run both a traditional SaaS company and an AI-native company at the same time created constant friction, slowed the team down, and made it impossible to move at the pace the market demanded. Jaleh walks through how she made the call, what gave her confidence to follow through, and what the first 90 days of the pivot actually looked like. That includes shrinking the team, moving to a smaller in-person setup, carefully migrating customers, and rebuilding company culture around speed, customer obsession, and founder-level context. The conversation also dives into why Mutiny shifted from sales-led to product-led growth, how self-serve products expose weaknesses faster, and why “showing” value beats explaining it, especially in AI. Jaleh also shares her view on what still counts as defensible in AI, why experience generation and analytics matter more than basic data movement, and how she personally uses AI across recruiting, meeting prep, and writing support. It's a candid look at conviction, timing, and what it really takes to rebuild for the next wave. Key Highlights: 01:41 - Why She Left 8-Figure ARR Behind Jaleh explains why combining a SaaS business with an AI-native business created roadmap, pricing, and execution conflicts that made a harder pivot inevitable. 05:01 - The Gut Check Behind a High-Stakes Pivot How she built conviction for a risky decision, what made “moving as fast as possible” the real north star, and the advice she gives founders facing the same choice. 11:13 - Reframing the Pivot as Mission, Not Failure Why walking away from a successful product did not feel like giving up, and how first-principles thinking helped her reconnect the company to its original vision. 15:05 - The First 90 Days of the Transition A behind-the-scenes look at shrinking the team, getting back to a small in-person setup, and creating the conditions needed to find product-market fit again. 17:01 - How Mutiny Migrated Customers Gracefully The detailed playbook for protecting customer trust during the transition, from partner selection and pricing negotiations to white-glove migration support. 23:03 - Building a Team for Startup Intensity Again How Jaleh thought about team size, in-office culture, and the level of intensity required to compete in the current AI market. 25:58 - What Founders Must Stop Delegating Pre-PMF Why founders need direct exposure to customer calls, onboarding, pricing conversations, and product friction if they want to move fast and make better decisions. 32:12 - Why the New Mutiny Had to Be Product-Led Jaleh shares why self-serve makes products better, how AI products benefit from instant hands-on proof, and why PLG also improved the sales-led motion. 40:22 - What a Real AI Moat Looks Like Her take on defensibility in AI, why simple data workflows will get commoditized, and why Mutiny is focused on experience generation, analytics, and self-improving systems. 45:15 - Jaleh's Highest-Leverage AI Workflows The practical ways she uses AI today across recruiting, meeting prep, and writing optimization, plus why she still believes strong writing needs a human point of view. Resources:

ceo ai mission building team figure pivot saas reframing mutiny key highlights plg wes bush jaleh esben friis jensen

Season 4, #6- Building Reputation with Purpose

The Founders Sandbox

Play Episode Listen Later May 14, 2026 47:17

In this episode of the Founder's Sandbox, Brenda McCabe sits down with growth advisor and author Vanessa Golsby to explore what it really takes to scale private equity-backed SaaS companies. Vanessa shares the story behind her new book, The $100M Push: The Four Decisions PE-Backed SaaS CEOs Make to Deliver Growth in 100 Days, and reveals the four critical decisions CEOs must lead to build scalable, resilient growth: defining the ideal customer profile, aligning go-to-market execution, making strategic investment decisions, and creating long-term operational accountability. Drawing from her experience advising more than 100 middle-market software companies and serving as an operating partner in private equity, Vanessa offers an inside look at how investors think, why commercial alignment matters, and how CEOs can create predictable growth through disciplined execution. The conversation also explores the role of generative AI in modern go-to-market strategy, the importance of reputation and purpose-driven leadership, and the entrepreneurial leap Vanessa took to launch her own advisory firm. This episode is packed with practical insights for founders, SaaS executives, and growth leaders looking to scale with clarity, confidence, and purpose. You can find out more about Vanessa at: https://www.linkedin.com/in/vanessa-goolsby/ https://www.linkedin.com/in/vanessa-goolsby https://vanessagoolsby.com/ Or order her book at: https://www.amazon.com/100M-Push-Decisions-PE-Backed-Deliver/dp/1963549309 Transcript: 00:04 Welcome back to the Founder's Sandbox. I am Brenda McCabe, your host. Now in the fourth season, the Founder's Sandbox is a podcast that gathers business owners, founders, professional service providers. 00:31 and corporate directors. And we all are working towards the same mission, which is building scalable, resilient, purpose-driven companies to build a better world. We do this with underpinning, with great corporate governance, and really working with the founders to build that resilience and scalability. My guest, um join me here in what I like to consider a fun sandbox. 00:55 And this month, my guest, I'm actually delighted to invite Vanessa Golsby. Vanessa's joining me from, is it Dallas? Dallas, that's right. Dallas, Texas. So um more here, but thank you Vanessa for joining me on the Founder's Sandbox. And I wanna give a brief introduction to why Vanessa's here today. There's multiple um boxes that she checks, largely Vanessa. 01:22 has her own firm. She is a growth advisor who specializes in scaling private equity back middle market software companies. And it's an interesting time and that space that I'm certain we're going to get to a question here in a minute about the impact of generative AI and all those models out there and the effect on software businesses. You're a seven-year veteran as an operating partner. 01:48 in two private equity firms and portfolio SaaS CEOs. She has helped more than 100 middle market software companies drive growth, execute go-to-market companies, go-to-market, pardon me, turnarounds, and deliver investor returns through sharper commercial execution. That's all in the commercial execution, isn't it, Vanessa? That's right. Yeah. And prior to advising, she was a former operator leading product and commercial. 02:16 teams for 18 years at brands like Travelocity and Financial Times, which I didn't know that when we first were talking. I hadn't realized when we had our first conversations of your corporate experience with Travelocity and Financial Times. So you brought a lot of that corporate kind of know-how into the private equity world and you actually started your own firm. it four months back? 02:44 October, October of 2025. My goodness. So you're not even into your first year. I know. So, and, and, uh, you are an author. So your book, um, so I don't know when you found the time, Vanessa, but your book, the 100 million push the four decisions PE backed as SAS CEOs make to deliver growth. And a hundred days is out. 03:13 Matter of fact, this last week and we're in the third week of April, it uh hit bestseller, right? That's right. Amazon. Yeah. And in that book, we'll get into it. You distill the framework that you've developed. I don't know when, while setting up your own firm, but you developed over decades in the trenches, codifying the sequence behind the big four decisions. 03:40 that enable CEOs to scale with speed, clarity, and confidence. So welcome to the Founder Sandbox. Great. Thanks for having me. Happy to be here. Well, I always like to start with uh my guests to really talk about your origin story. And I think what's very appropriate for today's uh episode is what drove you to actually write a book, right? 04:09 because it distills both your professional as well as um this new tool that you got out there in the market. Yeah, you know, I never thought I would set out to write a book, if I'm being honest. I had, I'd spent, at this point, I'd spent probably about five years as an operating partner, so as a growth advisor for PE firms. And so in that role, I had been 04:38 pretty well practiced at writing best practices. So I understood how to codify a framework and explain it, you know, in long form, basically. But I never had dreams of being like a full author, like writing a book is totally different than writing a best practice. uh But a really strange thing happened about five years into my career as an operating partner. So I'd had about 18 years, as you mentioned, like in the trenches, like a tactical, and then about five years as an advisor. 05:06 And um over the course of those five years, I had developed for myself this framework because when I moved to the firm that I was at at that point, I was having to work on about 10 software companies at a time. And it's really difficult to show results uh efficiently when you're having to focus on so many different companies who have different industries and different sizes and different needs. And so I created this framework just so I could work at scale. 05:35 And uh I had been running it probably about three years at this point when I needed to go back and take a look at some of my case studies. So I wanted to collect case studies. And luckily, because I was still at the firm, I was able to get access to actual data from these companies that had been running the framework. And oftentimes what happens, because I focus on middle market software, there's a sales cycle. So oftentimes what happens 06:04 is we'll run through this framework and we'll see immediate results by way of pipeline and maybe bookings depending on the sales cycle time. But oftentimes we don't see the actual bookings and revenue results until a quarter or two after, depending on what it is that we're selling. So this was really the first time that I had really paused and like done, if anybody here has had to do a case study or fact finding exercise for a PE firm, know like what a... 06:32 slog it is to have to like go look through all this data. I like found the time, I prioritized it. And what I found was, I mean, there was no surprises in terms of like when we wrapped up our, usually my engagements, I try not to be there longer than 90 days. So it's either a 30 day, 60 day or 90 day plan that we run through. It's pretty tight ah in terms of how we manage through it. So by the end of our... 06:57 I have a sense of some results, like whether it's pipeline or early bookings. have some walking away knowing that we've seen some lift, but this was the first time I'd been able to go back like a couple of years to see like, what about those first companies that ran through it? And I'll tell you, Brenda, I fell out of my chair. I was like, I cannot believe the consistency. You can see in the data, like the trajectory, the upward trajectory from when we started working on the framework and then where they were today. And 07:27 At that, that was like the first seed. Like that was like a Thursday. And I was like, I don't know what to do with this information, but I have this information. Oh my gosh, this works. can't believe it. Right. And I really had to sit with that. And over the course of like two or three weeks, a few other things kind of happened that led me to the path of writing a book. Um, and one of those is I was listening to a podcast. I'm an avid podcast listener. 07:54 And I was catching up on April Dunford. She wrote a book on positioning. Obviously awesome. It's a great book for positioning. And I was going to have to run a positioning workshop. And so I was like, oh, let me like get into my head back into the game on messaging. So I just like queued up like the latest podcast I could find from her and then went on a run. And then I was like a captive audience. I went on this run. It turns out the podcast I had queued up was not about positioning. It was about her journey as an author and writing her book. 08:23 So I spent an hour listening and getting really inspired. And when I came back from that run, I thought, you know what? I have to tell the people, there is a way to consistently build and scale companies when they're going from, my framework is very from 10 to that first 100 million. And so that was really the inspiration for me. then it's just been a journey from there. 08:52 We'll get to it, but you uh codified um when you had those aha moments, right? You went back and looked at the cohorts of the companies that you had been working with, right? 30, 60, 90 day framework, for lack of another word. Can you share what are those four things that enterprise SaaS CEOs do? 09:18 Sure, so my framework is an order of operations. So everything that happens at the beginning has like downstream implications on the other activities. And originally when I created this order of operations, I hadn't high leveled it in terms of four decisions. I did that for the book because I wanted to write the book for CEOs. CEOs are such a, especially going to the first hundred million. CEOs. 09:45 have to have their hand on the strategic wheel of commercial growth. not yet mature, they haven't yet matured out of that. There is a place over a hundred where you can start to delegate more of the idea of commercial strategy to like a, you know, top tier executive CRO, for example. But when you're working on the path, especially if you're PE-backed to a hundred, you really need to stay involved. And that had, I had noticed that that core ingredient oftentimes was 10:15 one of the gaps I was inadvertently closing when I was working with these companies. And so because of that, I wrote the book for CEOs. And since I was writing it for CEOs, I was like, oh, I need to go one level higher than my traditional order of operations, which is very like activity sequenced and like talk about more of like, what is like, what is strategy? Strategy is making a decision and committing to it. So what are the four decisions that a CEO needs to direct and commit to have their team commit to in order to see this growth? 10:44 And those four decisions kind of tell the story of growth from up to the first hundred million. Frankly, it's kind of the same above a hundred, except the last decision actually becomes the first decision over a hundred. But anyway, that's right. So four decisions that CEOs that you were saying that are 10 and get to and to get in order to get to a hundred million, they have to be really continuously involved. 11:13 in the growth of the company. They cannot delegate until they reach that um upper level. They don't necessarily need to direct or be boots on the ground in these areas. But when they make these decisions and they guide their teams and champion these decisions, what happens as a byproduct of this is they inadvertently align their business in a way that is 11:43 successful for commercial strategy. So for example, I'll just walk through the decisions quickly to give you an example of how this works. um So the first decision, I high level it as the ideal customer profile or the ICP, which is just another way of saying who are we going to target? And my bit, my specialization is being PE backed. So part of what CEOs and companies hire me for is certainly the pattern recognition of working on over a hundred software engagements. 12:13 but also that sort of behind the scenes view of what the investor is expecting. you know, bringing that idea. When your PE backed, once that investment round closes, are inadvertent, not inadvertently, you are inherently um signing up to expand and grow either within your market, into an adjacent market, or in some other capacity. And just by that definition, you need to, 12:41 understand who your target is going to be, who your best buyer is going to look like for this next round of growth. So it's generally, this is such a major trigger event, this idea of becoming um PE backed, that it's generally a signal for CEOs to say, okay, now let's take a look and see if our existing customer today is going to get us to where we need to be in five years. Because that's five year journey is what you've signed up to take on essentially. So the first 13:10 The first decision is that ICP decision. Once we have an understanding of who we're going to target, then we focus, especially with the commercial side, we focus on how are we going to turn those targets into opportunities, right? So in software, it very much goes from like lead to opportunity to closed one deal, right? So that's what I mean when I say opportunities and or pipeline opening. And this idea of how do we turn targets into opportunities? I high level this decision as the SLA. 13:40 which is a pretty common service level agreement. in this framework, it covers about five or six very specific decisions that your sales, marketing, channel partner and CS teams need to align around to ensure that the build of their lead management system and how they're qualifying those leads to become opportunities is sufficient enough to have some predictability. like you have some confidence that when you put a dollar out, 14:10 into a marketing campaign, it's going to convert into pipeline, really, right? And then ideally into bookings from there. And so that's the second decision. the first one, who do we target? ICP decision. The second, how do we turn those targets into opportunities? The SLA decision. Once you reach... 14:29 Once you have the confidence and some predictability flowing through, now you're ready to make a more strategic decision. And these last two decisions are really where the CEO not just champions, but takes an active role in the decision making. The next one is the contribution decision. So this is now that we know who we're going to target and we understand and have confidence that when we target those buyers, they are going to turn into customers. The next question is where do we invest? 14:57 to go get more of those targets. So who's going to contribute to our revenue number? How much are we going to put into channel partners? How much are we going to invest into marketing? How much are we investing into outbound? How much are we investing into PLG or a self-serve motion, right? How much is new? How much is expansion? And in this decision, we start to bring the CFO in to take more of a governance posture around commercial. So we give the CEO more context around 15:26 Some of the horse trading that typically happens in a silo between the teams. We now have those kinds of conversations around investment decisions and headcount and budgets all together in a room. I run this like a workshop, but all together in a room. And the book teaches the CFO and the CEO how to run this on their own. Excellent. for kind of the terminology that I would use and correct me if I'm wrong, it's kind of capital allocation. So a bit more rigor. 15:56 is brought in with this discipline of budgeting, right? You're talking about contribution decisions, So it's budgeting, capital allocation, and um bringing another uh kind of the controller of the purse strings, the CFO. That's right. Right? And jointly with the CEO are posturing and actually sprinkling it down to their direct reports, I suspect. 16:25 Right. Well, we so the way that I teach contribution modeling is everyone needs to be in the room. No one function, not the CFO, not the CEO, not the CRO can make these decisions for the entire commercial team who is actually going to need to. Yes, it is a budget allocation exercise, but actually that's the second step. The first step, it's a goal setting exercise. oh We break down. 16:53 Each of those pipeline sources has different stages, which we just got very deep on in our SLA decision. So we understand what those stages are called. We understand how long we expect somebody to stick in those stages. We understand what those conversion rates are through those stages. And now that we have some sense of those inputs, we basically enabled ourselves to sign up for a number. So now we can look at marketing and we can say, oh 17:22 If you're gonna sign up for a million dollars in pipeline this year, that means at this selling price, you're gonna drive this many deals, right? At this conversion rate, at this close rate, this means you need to have this many opportunities and that this conversion rate from lead to opportunity, you need to drive this many leads. Can you drive this many leads? And the marketing person's like, that's a lot of leads. I don't know if I can drive that many leads, right? 17:48 And if they hesitate and they say like, can't realistically get that many, we look around the room and we say, okay, who else can drive more leads? Let's look at channel partners. Now we do the same thing from referral to meetings booked to, know, et cetera, et cetera down the So it's very like, it's very precise in terms of setting goals at the funnel stages, but not to become that, like we're not expecting frankly, to get a bullseye out of this workshop. What we're doing is we're kind of snapping the chalk line to say, 18:17 Okay, this is what we think we can go do. And now we're gonna meet with the CFO leading, we're gonna meet every two weeks or every month, and we're gonna see how we're doing. Are we driving this many leads for marketing? Are we getting this many referrals from channel partners? Are we booking this many meetings through the BDRs? And if the answer is no, then we look around the room. Where else can we do it this month? So we have something we can react to in real time, and rather than showing up to the board meeting and saying like, yeah, it was kind of a miss, but I think we have some ideas for next quarter. 18:46 Like this puts everyone in a position now to become far more reactive to what's happening in real time uh as a group, as like a singular one team. And what about the fourth? Yeah, so the fourth decision. And again, this decision is fourth when you're going to 100 million. But if you were above 200 million or as you like progress to like four up to a billion, this actually can become sometimes the first decision. 19:14 when you kind of need to work your way to this point um for when you're going to 100 million, especially after the contribution decision, that contribution. Yeah. Cause that's going to surface a lot of ahas for teams. Like oftentimes you're like, Oh, actually we need to break into a new market. We're saturated or, my gosh, you know, like we need a, you know, too many, we need a ton more reps or actually we don't need more new sale reps. What we need is expansion reps and really need more there. So 19:43 Like in that contribution conversation, you really surface so many of your growth levers that you're prepared for the fourth decision. So the fourth decision is now that we know who we're going to target and we know with confidence how we're going to turn those targets into opportunities. And we understand where we're going to investigate more of those targets. Now we talk about how are we going to do this over the long term? So how are we going to do this not just this year, but for the hold period? So for five years. 20:10 And so this decision I high level as the OKRs, which is an industry term. I didn't come up with that, but it stands for objectives and key results. And it's essentially gives the CEO like almost like a project management framework for long-term planning. um And you really can't necessarily jump to number four if you're going up to that hundred day plan without having these first three decisions at least somewhat cemented or somewhat committed to. 20:39 um Otherwise, what ends up happening is your OKRs are, you know, have like 25 things you're going to try and go tackle. So you kind of like, kind of, you know, by just by um the effort of making these first three decisions, you've already like started to prioritize for your team where the important levers are that you're going to focus on. 21:01 Thank you. I wanted to ask you by publishing this book, are you putting yourself out of business? That's a good question. A grow-to-market advisor, The enterprise SaaS sector that's under a lot of pressure right now with the dinner to bay eye. So let's take the two questions. Let's take them apart. And I'm being a bit. It's a great question. I asked myself that question. Yeah. 21:29 Yeah, my publisher asked this too. Why put it out there? You're putting yourself out of business or no? Yeah. Well, you know, the way I, there's a couple of answers to this, a couple of dimensions to this. The first is, you know, a lot of the motivation to write this book was to get the word out. Like when I saw the consistency and how well the results sustain when companies run through this framework, I was like, Oh my. 21:56 Why aren't we telling all of the CEOs that there's a way to go do this? Like we know these activities, it's things like territory planning and quota setting and SLAs. like, know, people know that activities that need to happen, but the unlock here is the sequence, like it's important to do them in order and that they're done altogether, which is the role of the CEO, right? Is to ensure that the right people are in the room when you're making these decisions and everything's like. 22:24 That's the those are the connectors right is are the those are the interlocks are the decisions the activations happen You know within the function so I? Was passionate like we talk about purpose the reason I was excited to be on this podcast is because this is very purposeful for me It felt like holy cow Look what I discovered under the pyramid I got to tell the people like there's an easier way to do this We don't have to bang our head against the wall to try and figure this out the hard way so 22:53 In that way, it didn't really feel like an option to necessarily hide it. ah And then the other side of me thought about it in terms of like changing the oil in my car. Like, I know that I can change the oil in my car. It's not a difficult, complex process. Like, it's very straightforward. But do I want to do, do I want to like get in coveralls and crawl underneath my car, like find the little lackey thing? No, I don't want to do any of that. I would far rather just bring someone in. 23:22 take the guesswork out, have it done, have it done correctly the first time, and leverage someone else's expertise in case they find something that I wasn't expecting. ah So I feel like I'm still bring, like when people leverage me to run through this, I'm still bringing a lot of value that you're not gonna necessarily get out of the book. mean, people, CEOs and firms hire me because of the pattern recognition and because I've seen these things play out enough times across different industries. 23:51 uh But I don't want to be a holdup. Like, please, if you are able to do it, then I welcome, I encourage you please to go run these plays yourself. And I try to give a lot of, it's very structured. This book is, the structure of this book was really difficult to come up with. It probably took me the longest amount of time, honestly. But I wrote it in a way that a CEO could read it quickly, because I know they don't want to read too many things. They are very busy. um 24:18 And so like they could digest it quickly and they could hand it off because that's kind of their role is to say like, I'm going to now equip my leaders to go do this and do it successfully. And they still have a role to play. But again, they don't have to be like in the trenches. Right. And without um seeing the book right now, I sound and Kendall on audibles or Kendall, um are there like exercises? Are there, is it like a handbook or is it um I'm a CEO? I 24:48 read your book um and I want to contact you. Do I to come in and maybe do some seminars? How does that work? Because this is a marketing tool as well. Yeah, yes. mean, of course I this book can be just a step by step guide for CEOs and their teams if they want to take it that way. So I tried to write it dimensionally. So the first dimension is 25:13 It equips the CEOs to understand, like the first two chapters are really around what is the investor expecting of you? Basically it's like, here's a little bit of the behind the scenes. Yeah, that was intriguing for me when we first spoke of it. Yeah, you've been in that room. Yeah, like I've been in it. Yeah, exactly. like, you know, one of the things that, again, like a lot of things happened in this like two or three week time period when I was kind of coming to the conclusion that I was going to write this book. And one of them was I was in a board. 25:44 meeting and there was a CEO advisor also in this board meeting and I could see the CEO advisor was um giving great advice based on their singular experience but the truth is is their experience was so unique to them that it would be really difficult it'd be like saying like 26:07 Yeah, just, once you press post, it's gonna go viral. It's like, let's not over promise here, you know, what's realistic. And that really hit me to say like, oh, this is a unique perspective. Like I'm not necessarily an investor and I'm not a CEO. it's been years since I've like managed a commercial team or been a GM, but I have... 26:34 I've flown all of those altitudes and I've been an observer in all of those rooms so many times that like the patterns, you just can't deny the patterns. um So yeah, I'll stop there. I'll pause there. So you do the reveal, right? So for any CEOs of enterprise, um SAS companies, this is a must read, right? Because you're doing the real deal. What is actually happening in the boardrooms of those private equity? uh 27:05 partners right that are yes looking at their portfolio companies yes yes thank you yes so i start with like you need to equip yourself with understanding what is expected of you when you took this investment which isn't frankly always talked about like it's not always revealed to the CEO ah so that's the first step and then it is a step-by-step guide so like there are the four decisions and then within each decision 27:33 I show them the book is structured to show them, tell them what the decision is, give them some case studies of other companies who have solved it, give them some red flags that say like, look, this is a really helpful book if you just closed your investment and you need to run like a, they call it a hundred day plan of like, you're going to deploy a lot of that, those investment dollars very quickly in order to like try to get traction on growth. So this is, I wrote it in that framework just because it is naturally 28:00 predisposed to running in like a 90 day plan framework anyway. um But it's also one that oftentimes in a hold period, you're going to hit some kind of plateau, right? It's very rare to like knock a home run out of the park right out of the gate. And so I also, so like in that, in that first part, so like each part, each decision has a part. So there's like a part for, there's like a four chapters on ICP, four chapters on SLA, four chapters on contribution. 28:26 The first chapter tells you, like gives you the red flags to look for if this is an issue, tells you what the investor is expecting, tells you your role and how you can direct the team, tells you when you need to maybe outsource, like what's the things you should absolutely do and the things that are kind of like nice to haves. Then the next chapter goes into how do you make this decision? And each of these decisions, the way that my approach is, 28:53 Um, is I like to do like 50 % gut and like 50 % data. So I always start my engagements with like surfacing from your internal experts already. Like a lot of times your C-suite lieutenants. Yeah. They like, I get called in for audits. Like that's like oftentimes I'm brought in initially for an audit of some kind. And in that audit, it's like a 360 commercial audit. And in that audit, I have like a week that I just cap off and I talk to anyone that you'll let me talk to. 29:23 And they're telling me the problems. like, this is really like, we've known this is very rare for people to like, I have no idea. They know what they did to get here. And so we start with the gut. And so in this framework for the book, the gut is surfaced through workshops. I'm a huge advocate of workshops. think, you know, honestly, my time with Vista really beat this into me, like the importance and the value of workshops, because not only is it a great place to surface everyone altogether, but it's 29:52 early adoption. Like when your voice is heard and you could challenge something in the room, when the decision is being made, you're far more likely to adopt it when we get to the final output. So I'm a huge fan of workshops. So each of these has a workshop. And this is a lot by and large when I'm training, when I'm teaching the CEOs, it's like, this is what you need to get out of the workshop. This is agendas. You can, have all of my agendas are up for download. Like you can download the agenda. You can run through it yourself. And this is who needs to be. 30:21 Yeah, like I want this to be helpful. That's the whole point is like it's supposed to be taking the guesswork out for the CEOs. uh And then you need to there's a data validation. Like, yeah, everyone's got gut. But then we do need like we are going to make some commitments here. So exactly. Yeah. So we need to like in each of these have different places that you go and source that data to validate. uh 30:43 So that's how we make the decision. Then I go through how you execute the decision. And for CEOs, this is almost like the TLDR. It's like, give you like, look, these are the steps that they're go through. Then in each of these chapters, I go far more into detail. This is what you're gonna go tell, like this is what your management team is gonna go do. And this is what good is gonna look like. So you're not done with this step until you've seen these five things come out of this exercise, essentially. 31:07 And then finally, each of these parts, so we've got like, what is the decision? How do we execute the decision? I'm sorry, how do we make the decision? How do we execute the decision? And then how do we measure the decision? And this goes back to how your growth story. So a CEO's role is not just to understand, right, our long-term objectives that may be surfaced in our investment thesis, right? Those are the first two chapters. It's not just coordinating the execution and setting the priorities and resourcing your team, right? Those are the four decisions. 31:37 But you also need to tell that story and you need to tell it in a way that makes you show well, that makes your company show well, and that makes you more attractive, frankly, at your next round of investment. so, yeah, externally telling exactly. So as well as internally. that's right. So that was really long winded, but that's basically the structure. It goes pretty far into detail, but I do. 32:02 high level for CEOs, like you can skip this part, just give it to your zero. So, so the book is out and um you started as you went rogue yourself and said, I'm working for myself and yeah, that's right. And um what happened is you've got some of your clients that had seen your, your work in prior years and, have taken you on as their advisor. 32:31 Why are they taking you on? it around your, are you scalable or your purpose? I mean, you're wanting to give back. So yeah, tell me. And you shared a little bit when we were talking before the podcast about you got a call from a client that you had from many, many years ago. Yeah. Yeah. I, you know, when I was deciding to go out on my own, it was really scary, right? Because I had, I never really even, I, I had been motivated to write the book. 33:00 And that was almost as far as my thinking had gone. And then at that point, the book was supposed to come out. Originally, the book was supposed to come out in January and we could have a whole other podcast about writing a book. so originally it was kind of, I knew like internally, I was like, gosh, by October, I was like, I need to make a decision. Like, what am I going to do? Am I staying? Am I going? Am I doing something else? And so I reached out to every person that, that I, you know, had some sort of like respected conversation, like a respected relationship with. 33:29 over the course of my career. And I basically asked him like, what do I do? What would you do? And I'm really lucky because at this point, I had been an advisor for about seven years, you know, with really established firms and the folks that I had worked with, that knew me, knew what I could do, had since gone on to a million other firms. So like my network on the firm side was pretty large. 33:59 And in those conversations, there was just inevitably a conversation that ended with like, look, if you go, I'll give you your first client right now. And so I was like, well, there you go. Close the door, a window, let's go. That was how it went. Yeah, so you reached out to your network, which is super powerful. Yeah, it really was. And it was honestly, I had surfaced my network throughout kind of writing the book because 34:27 You know, one of the things I think that is unique about my situation versus some of the other authors who have written fantastic, and I'm an avid business book reader, Fantastic Frameworks, is that my perspective is from the operating partner's point of view. And I am, yeah, it's very like, and so I'm really lucky because I, as I mentioned, like a lot of the folks that I have worked with over the years are now at so many different firms. 34:57 And so as I was writing this book, I would send out surveys to people and just say, Hey, just like gut check, do you see this too? Are you seeing this? Like when I wrote a whole chapter on like the value creation plan and you know, the value creation plan is one of those things that people talk about. Like it's this like standardized formal process, but it's wildly different, like firm to firm, like it's so totally different. And I just wanted to uh get a better sense of how these different firms of these different sizes were actually running their value creation plans. 35:26 And that's just impossible for me to do by myself. Like I need my network for that. So this whole process has been really great. And just like also bringing together some of my work friends that I hadn't been able to really, or I hadn't like, you know, kept up with as well as I should have. And so now I feel like my network is just like really thriving and humming. And I feel so much closer to like these people now than I have in a long time. So it's been really beautiful in that way. 35:54 Thanks for sharing. know, I want to ask you how has, well, your frameworks be at all affected in your opinion by the generative AI and how it's taken quite a bit of value out of the stock market. So now it's back up, right? So let's, so was, are you isolated from that effect? Your, your, your, your, just your, your frameworks. 36:22 Yeah, you know it's funny I wrote this book so I've done a lot around writing best practices for AI for go-to-market teams so I was pretty what by the time I wrote this book I had a lot of already like pretty packed research and thinking around AI and what it could do and what it couldn't do. I of course how could I you know I wrote this book almost two years ago now like 36:46 has really changed the game and just some of the new models that have come out. We knew that they were gonna be pretty revolutionary, but it was hard to be very specific. But I did, in the book, I have a very specific point of view on how AI can ah make what you do more effective, more scalable, where can use what you are bringing to the table and... uh 37:12 The word is escaping me, which is ironic scale, basically what you could do. And so that's my approach to AI and it's still my approach to AI. So I don't see AI as a competitor. I see it as an accelerator, really. And so I'll take account scoring as a great example. So in this idea of 37:38 these four decisions, one of the activities that you inevitably will need to do, it's under the ICP decision. So once you have an understanding of who you're going to target, you want to then score the accounts that are in your database to say like, is this a tier one, is this a tier two, is this a tier three, is this a tier four, and we're not gonna like, they're actually gonna churn too fast for them to even be worth that selling to. And so you're building out this account scoring model. Now, there are platforms that can just do this for you. 38:06 and they're just like, look at your data and they're like, great, we're gonna do this for you. But those platforms don't know your growth plan. They don't understand like what your investment thesis is. They don't understand that you have a very concentrated point in time where you're going to make, you know, a 30 % CAGR, you know, you've got like big, big goals. You're not just trying to do status quo every year. And so it's in that same kind of vein, like the human still needs to drive and be the director of... 38:33 where the AI is going to execute. um But AI is a fantastic accelerator. I'm excited. I love partnering with AI. It's not perfect. I think of it as almost like an MBA intern, like whip smart, smarter than I will ever be. But you can't totally take your hands off the wheel. You're like, there's context. That's great analogy. Oh my goodness, that's hilarious. It is true. um 39:03 AI. particularly like the perplexity model because it's on top of all of them for uh writing and preparing some of the work I do with my clients. So it becomes my companion is what I call it. Right? Yeah. Oh yeah. Definitely. Excellent. Well, I'd like to give you an opportunity to share how my listeners can reach out to you. Oh, sure. They'll be in your notes. Vanessa. Carry on. Okay. Great. 39:32 So I have a website Vanessa ghouls be calm I'm also on LinkedIn both ways You know are pretty easy ways to just you can look at my calendar and schedule time if you're interested Often time like my most most of the ways that I get brought into engagements is There is some kind of trigger event where the CEO or the PE firm Says like we need we need some 39:59 things, some kind of audit, some kind of assessment, some kind of strategy, some kind of like, what are our growth levers, right, to get us to whatever the next thing is. It's generally a two to four week audit. em And as I mentioned earlier, it combines interviews with your team with I have like a list of artifacts that we start off with. It's, I don't want to say it's like diligence, because it's not like diligence. But it is a pretty thorough 40:25 uh So you get sales, marketing, customer success, channel partners, digital, all of that. uh And oftentimes CEOs will have like a specific need on top of that. you know, I've got one where I just did one where it was like, we want to see, you know, we know we just got our investment came through and we kind of need to set our hundred day plan. So where should we go? You know, what are the foundations we need to build and fortify for this next round? uh We have one. 40:53 One other trigger that's pretty common is on the back of maybe M &A, where you have like two go-to-market teams that need to integrate together. Yeah, they like will bring me into sales. How are we gonna do that? Yeah. Or they have done that and maybe they're still not quite hitting that like expansion number that was originally conceptualized. um And then, yeah. And then the third, which is, I mean, it's like the... 41:21 the least positive, but honestly, the most exciting for me is, you you're like an a mid hold plateau. You're like, gosh, you know, I had one just last month where it was like, they hit this $30 million ceiling and they for like three years have thrown every spaghetti they could at the wall and just could not get past this ceiling. And, um, and so like the audit can, it's very focused and like trying to get to whatever the objective is, but it's, it's holistic because my whole, my whole shtick, right. Is that like, 41:51 It's no one team. It's like all of the teams kind of have to interlock in a line together. Yeah. Yeah. Quite revealing. Excellent. Those are excellent use cases. Um, and we'll put this in the show notes as well as your website and Vanessa. Um, let's come back to the sandbox. I do like to do a round of just questions about three words and what is the meaning for you. Um, and each of my guests comes up with their own um interpretation, their own meaning. it's 42:19 So what does resilience mean to you, Vanessa? Yeah, think resilience means being internally motivated. There's a drive that is not necessarily anchored or reactive to anything that's happening externally. uh For some reason, you just can't let it go. 42:47 How about scalable? What's scalable? Oh, wow. I mean, spent so many years uh writing about being scalable. Yeah, you know, it's funny when I think about being scalable, you know, it actually initially comes to mind as like growing pains, like this idea of growing pains. uh And I'm just now kicking myself for not reading the prep questions closer. We're going to rip a little bit, but. 43:15 But yes, being scalable is having that resilience through the growing pains, knowing, right, having like some kind of faith that at the end it's gonna be bigger, better, probably bigger than you even really could even have imagined or maybe even in a direction that might not have been initially planned. Excellent, excellent. Yes, and I also wanna just, I think. 43:43 you know, we're back to the title of the episode, is, um, and which is building purpose, building reputation with purpose. And you were adamant about that. So what does purpose mean? And maybe you'll bring into, know, what, what is building reputation with purpose for you? know, I, um, 44:11 It's funny, I feel like it really goes back to this resilience question, but it's so much of it just comes down to acting with kind of like, like I work with companies that have like cultural values, right? And they're like, oh, or Patrick Lindsay only has a great one, like the heat, likes to say, you know, hire people that are hungry, humble and smart, right? So like, you have your like keywords, your brand words, your value words. And I think for me, 44:40 um over the years, my purpose has been to act with integrity and grace and curiosity. And, um and that's something that I don't think about logically, right in life. But I try to bring that kind of inspiration to the teams that I'm working with. And it's a lot of the reason why I wrote the book was to say like, 45:10 Look, there is a way. You don't have to follow every single thing that's in this book. But if you get stuck, isn't it helpful to have a guide, like a troubleshooting guide to say like, oh, let me just go to the index here. I'm a little stuck on territories. I'm going to get over it. And that's the spirit that I try to bring to everything that I do, which is, yeah, we can solve any problem. Like any problem is solvable. And guess what? Execution problems are the easiest thing to solve. So like, 45:40 Let's have some fun and we can, we can, there's a way to do it basically. Right. Excellent. Thank you. And last question, did you have fun in the sandbox today? I had so much fun. This was great. You know, honestly, I didn't really know how this, like I do enough of these podcasts now and it's so usually anchored on the framework and like, you know, the execution and like, you know, very tactical. 46:07 And so this was just a really, this was like a breath of fresh air because we got to talk a little bit about the human side of it, which I find really motivating. It is. And I do recall you were really set on building you and you it's your reputation. Do you have Vanessa Goldsby that has gotten to you, gotten you where you are today and by giving back and providing that, you know, writing that book and then, you know, serendipity, you decide, Oh my gosh, I'm going to go out on my own. So it's, your reputation. 46:35 that has been built with purpose. I want to thank you for joining me here in the Founders Sandbox. To my listeners, if you like this episode with Vanessa Goldsby, sign up for the month release of the Founders Sandbox where I have guests that are Founders, business owners, service providers like Vanessa, um and board directors who build with strong governance, resilient, scalable, and purpose-driven companies. 47:03 So signing off for this month. Thank you very much. Thank you, Brenda.

小人物上籃-霹靂鍵盤#226 窩腰驗牌！季初預測大翻車，特攻國王誰先放暑假？TPBL季後賽前瞻！(2026/05/04)

Play Episode Listen Later May 5, 2026 125:07

法國賭神皮爾.卡松：窩腰驗牌！－小人物聽眾們還記得這個梗嗎？總之經過27周纏鬥，TPBL例行賽在上周末結束，本周節目開錄前，Roy挖出季初三人跟小鐵一起預測的排名，一隊一隊的驗(檢)牌(視)我們原本對該隊的期待、以及打完36場例行賽後的實際排名。包括許多頻道都已經一段時間跳過的戰神與海神，我們也老老實實的聊了兩隊面對的課題、季後需要的調整。當然，眼前的TPBL季後賽才是本周主菜！節目上架時不知道結束沒的第四種子挑戰賽，雖然三人一致看好某一隊晉級，但對於戰況的預期卻很不相同，歡迎小人物們搭配現實世界的挑戰賽，聽聽看哪位主持人的發言會被打臉。針對周末登場的Group A雲豹vs第四種子、Group B夢想家vs攻城獅，三人對於晉級Finals的球隊預測就不太一樣了！整季都是領先姿態的雲豹，能在季後賽繼續發揮特色球風嗎？越打越有風采的夢想家，能夠專注的打出水準嗎？攻城獅的驚奇之旅，走到終點了嗎？且聽Kong精簡的賽前分析。最後，本週當然也討論了「新竹領航猿」的戰況，同時2026台籃非官方年度獎項票選也持續進行中，歡迎投下你心目中兩聯盟合併票選的最佳得獎者！

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182: The Ecosystem-Led Growth Playbook: How Clay Built a Community That Sells (w/Yash Tekriwal @ Clay)

Ground Up

Play Episode Listen Later Apr 29, 2026 49:50

Databox is an easy-to-use Analytics Platform for growing businesses. We make it easy to centralize and view your entire company's marketing, sales, revenue, and product data in one place, so you always know how you're performing. Learn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreMost SaaS companies build a sales team first and a community second — if ever. Clay did the opposite. In this episode, Yash Tekriwal, Head of Ecosystem Growth at Clay, walks through the full ecosystem-led growth playbook that took Clay from nine people in a Williamsburg apartment to nearly 370 — without a traditional sales motion. From a certification program that actually proves competency, to 84 Clay Club chapters around the world, to IRL workshops where attribution is nearly impossible to measure but the results are undeniable, this is the blueprint for building a community that compounds into revenue.In this episode, you'll learn:The six subgroups of Clay's ecosystem and the specific metrics that govern each oneWhy Clay's certification program looks nothing like any credentialing process you've seen — and why that's the whole pointHow IRL workshops influence deal velocity and contract size even when you can't perfectly attribute them to revenueWhy Clay tracks two types of data — hard financial metrics and "the feeling" — and why both matter equallyHow the reverse demo / PLG motion became the product extension of Clay's ecosystem philosophyWhy GTM leaders who can't tie community to pipeline are asking the wrong question • • The honest truth about ecosystem ROI: indirect, unattributable, and absolutely worth it

Why Sales Execution Wins in an AI-First World with Brian McCarthy, President of Global Revenue and Field Operations at Cursor

Play Episode Listen Later Apr 24, 2026 55:12

PLG can create explosive growth, but it can also mask fundamental gaps in execution, capacity, and long-term durability. As AI-native companies scale at unprecedented speed, revenue leaders face a new tension: how to convert bottom-up adoption into enterprise value without breaking the system that fueled growth. Brian McCarthy joins to unpack how Cursor is navigating this shift, why sales execution becomes the moat in a world of swappable technology, and what it takes to build a go-to-market machine that keeps pace with innovation while deepening customer trust. Brian McCarthy is President of Global Revenue and Field Operations at Cursor and former CRO at Rubrik, where he helped scale the company from $118M to $1.5B in ARR. He is known for building high-performance revenue organizations and execution-focused cultures in complex enterprise environments. Connect with Brian: LinkedIn Resources mentioned: Ep. 71 - What the Best Sales Leaders Do with Brian McCarthy All In Podcast with Chamath, Jason, Sacks & Friedberg Key takeaways from this episode: 03:30 – Why great leaders know when to step away, and how building a successor is the true test of an execution machine 09:50 – What to look for in a once-in-a-career opportunity, and why timing matters more than brand or hype 17:03 – How PLG success created a capacity crisis, and why too much demand can degrade customer experience 29:29 – The decision to radically reduce account load, and how focus enables better selling and better buying experiences 32:16 – Why champions, not features, drive revenue, and how to intentionally build them across the organization 38:19 – The required balance between bottom-up adoption and top-down value selling in technical markets 41:31 – Why “clock speed” is the defining trait of modern sellers, and how enablement must fuel continuous learning 49:09 – The shift from tools to AI factories, and what it means for the future of software development and selling 52:01 – Why culture, trust, and human relationships remain the durable moat in a world of rapidly changing technology Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

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Built on a Crisis: Jeff Wang on Winning Enterprise AI Coding with Windsurf

SaaS Talkâ„¢ with the Metrics Brothers - Strategies, Insights, & Metrics for B2B SaaS Executive Leaders

Play Episode Listen Later Apr 24, 2026 36:17

When Jeff Wang stepped into the CEO role at Windsurf, it was not part of some long-term succession plan. It happened in the middle of a full-blown crisis. In this episode of the ProductLed Podcast, Wes Bush and Esben Friis-Jensen sit down with Jeff to unpack the wild chain of events that followed the collapsed OpenAI acquisition, the founders leaving for Google, and the intense 72-hour window Jeff had to help save the company and protect 250 jobs. He shares how Windsurf navigated that moment, how the Cognition deal came together, and what it has been like leading one of the most closely watched teams in AI coding ever since. Jeff also gets into what made Windsurf so strategically valuable in the first place, from shipping early breakthroughs in autocomplete, chat, context engineering, and agent workflows, to building one of the first generally available coding agents on the market. Beyond the origin story, the conversation goes deep on go-to-market strategy, why free products worked early on, how token economics changed the game, and why enterprise AI adoption takes far more than handing teams a tool. They also explore Windsurf 2.0, the shift toward managing multiple agents at once, how Jeff uses AI in his own CEO workflows, and why founders need to obsess over painful problems, customer conversations, and product-market fit instead of flashy demos. Key Highlights: 00:00 - The 72-Hour Crisis That Changed Everything Jeff shares the short version of the OpenAI, Google, and Cognition saga, and what it was like stepping into the CEO role during a company-defining emergency. 01:40 - Why Big Tech Wanted the Windsurf Team A look at the execution speed, product breakthroughs, and agent innovations that made Windsurf one of the most valuable teams in AI coding. 04:10 - The Future of Coding Is Multi-Agent Jeff explains why developers are moving from one-on-one AI assistance to managing many agents at once, and how Windsurf 2.0 is built for that shift. 08:54 - How Free Became Their Growth Wedge From free autocomplete to on-prem enterprise deals, Jeff walks through Windsurf's early PLG motion and how it created awareness and pipeline. 13:10 - The Hard Truth About AI Pricing A candid discussion on token costs, self-serve subsidies, pricing pressure, and why raising prices can reveal whether you truly have product-market fit. 16:13 - Why Enterprise AI Sales Are Top-Down Jeff shares how Windsurf sells into large companies by focusing on transformation, adoption, security, and measurable outcomes instead of seat counts. 20:51 - What It Takes to Drive Real AI Adoption Why playbooks, training, and solving a meaningful first use case matter more than just rolling out a shiny new tool to an engineering team. 24:40 - Jeff's AI Workflows as CEO Jeff reveals how he uses AI and custom playbooks for go-to-market research, outreach preparation, and spotting product trends before opening dashboards. 32:32 - Jeff's Advice for Every Product Founder Build around painful problems, talk to hundreds of prospects, and learn to enjoy rejection because that is often where the real insight comes from. Resources:

ceo ai google future advice crisis winning built openai wang coding cognition key highlights what it takes plg enterprise ai windsurf wes bush esben friis jensen

The State of B2B Go-to-Market in 2026: The ICONIQ Findings

Play Episode Listen Later Apr 22, 2026 27:12

Dave "CAC" Kellogg and Ray "Growth" Rike discuss the ICONIQ 2026 State of GTM Report, a 32-page benchmark study based on a January 2026 survey of 155+ B2B SaaS executives across CROs, CEOs, and RevOps leaders. The pair digs into what the data says about how high-growth companies go to market differently, how usage-based pricing is reshaping sales compensation, and where AI in the GTM stack is actually delivering results versus falling short.Topics CoveredGTM Motion Mix: Top-Down vs. Bottom-Up vs. Hybrid. The data shows roughly 60% of companies use a hybrid motion, but high-growth companies skew more toward bottom-up and PLG. Ray and Dave unpack the ICONIQ "variable growth bar" definition and what the motion mix signals about the source of growth.Channel and Partnership Revenue Is Bigger Than Expected. ICONIQ reports channel partnerships representing 27-31% of revenue for high-growth companies. That is well above the 11-15% Ray typically sees in comparable reports. Dave calls it the long-awaited comeback of channel in SaaS, and both hosts flag the near-absence of self-serve as a surprise.Quota Setting and Commission Structures in a Usage-Based World. For the first time in a major GTM benchmark, ICONIQ covers how companies set quotas and structure commissions in a consumption and outcome-based pricing environment. 30% of respondents use forecasted consumption to set quota. Commission payout timing is split across four models, signaling how unsettled the go-to-market compensation playbook remains.Clawbacks Are Back. With usage-based and prepaid consumption models on the rise, 45-50% of companies now have clawback provisions in sales compensation. Ray and Dave discuss why clawbacks are a morale killer for sales teams and what the smarter alternative looks like in practice.POC and Free Trial Conversion Rates. POC-to-paid conversion improved from 36% to 50% year over year. Ray and Dave discuss resource allocation for proof-of-concepts, including dedicated versus shared solution architects, and raise the question of where forward-deployed engineers fit into the picture.AI in GTM: Where It Is and Isn't Working. Lead gen and call transcription top the adoption charts, but AI-driven forecasting sits at only 38%. Ray flags the gap between AI-native and traditional SaaS companies in GTM AI adoption. Dave points to slide 30 as a reality check: pipeline efficiency and unit economics are not yet showing meaningful improvement from AI investment.If you are responsible for GTM strategy, sales compensation, or measuring the ROI of AI investments, this episode gives you a practical lens on one of the best benchmark reports published in 2026. Ray and Dave go beyond summarizing the slides. Dave and Ray flag caveats in the methodology, challenge the data where it warrants scrutiny, and connect the findings to real-world operating decisions on quota design, commission structures, channel strategy, and AI adoption. If you only have time for one GTM benchmark deep-dive this year, this is the episode to start with.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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小人物上籃-霹靂鍵盤#224 宇宙夢自己就能攻頂，不需要球迷亂衝場助陣！

Play Episode Listen Later Apr 21, 2026 126:29

從1勝4敗開局，一路北伐的夢想家終於在例行賽剩最後兩周的此刻，成功攻頂，而且全員健康！這周節目討論了夢想家近況出色的原因，而無論原因有多少個，都不包括衝動闖進場的球迷行為－霹靂鍵盤呼籲即使在競爭排名的關鍵時刻，還是要感性生活，理性看球。在夢想家攻頂的同時，打得並不差的攻城獅狀況又如何？領先大半季、被擠到第二名的雲豹該擔心嗎？後面還有近況越來越好的阿巴西與特攻在虎視眈眈，季後賽排位似乎得打到最後一刻才能確定。TPBL熱度升高的同時，PLG獵鷹卻陷入太久沒比賽、全隊缺少磨合的困境！在例行賽最後的主場週連敗兩場的獵鷹，有哪些問題需要克服？還來得及嗎？這周霹靂鍵盤也迎來Roy的歸隊，上週我們聽到舊金山小人物Max的台籃觀賽體驗，那Roy在舊金山與洛杉磯的NBA觀賽經驗又是如何？有什麼台籃其實做得不錯、以及該學習的地方？最後，一年一度的「台籃非官方年度獎項票選」又快要展開了，今年霹靂鍵盤繼續與籃球伙合作，在兩聯盟合併票選的平行宇宙中設計獎項，歡迎小人物聽眾們留意節目與社群上即將發布的票選資訊！成為

nba fantasy powered curry plg firstory

Revolutionizing Hiring: Unveiling the AI-Powered Future of Recruitment

The SaaS CFO

Play Episode Listen Later Apr 16, 2026 25:59

Kristen Beck, CEO and co-founder of Elly, shares her career path from being Trello's first revenue hire through leadership roles at Atlassian, Shogun, and Typeform, and explains why she returned to startups to build an AI-native recruiting platform. Elly focuses on capturing and learning from unstructured conversational data across hiring—from kickoff through interviews and debriefs—to help teams make better decisions, reduce time spent, and reveal patterns in successful hires. Beck outlines Elly's two main customer segments: high-volume employers (including manufacturing and home healthcare) and fast-growing startups, and describes how AI enables insights beyond basic efficiency gains. She discusses Elly's founding timeline, seed fundraising to $8M (Atomic incubation and Sorenson-led round), PLG-led growth via word of mouth, a free ATS with paid AI features using subscription-plus-usage pricing, and key metrics like K-factor and module adoption, with Elly headquartered in New York and a 10-person team. Show Notes: 00:00 Welcome and Intro 00:08 Kristen Beck Career Path 02:07 What Ellie Does 03:01 Why Build Ellie 04:44 Ideal Customer Profiles 06:08 How AI Changes Recruiting 08:57 Founding Story and MVP 09:41 Seed Round and Investors 10:35 Why Raise vs Bootstrap 11:49 Fundraising Lessons in Crowded Markets 14:20 Go To Market and PLG 18:42 Pricing and Usage Model 20:39 Key Metrics and Virality 22:19 Team and 2026 Priorities 23:56 AI Makes Distribution Matter 25:31 Where to Learn More Links: SaaS Fundraising Stories: https://www.thesaasnews.com/news/elly-raises-8-million-in-funding Kristen Habacht's LinkedIn: https://www.linkedin.com/in/kristen-habacht-80288780/ Elly's LinkedIn: https://www.linkedin.com/company/ellyai/ Elly's Website: https://www.elly.ai/ To learn more about Ben check out the links below: Subscribe to Ben's daily metrics newsletter: https://saasmetricsschool.beehiiv.com/subscribe Subscribe to Ben's SaaS newsletter: https://mailchi.mp/df1db6bf8bca/the-saas-cfo-sign-up-landing-page SaaS Metrics courses here: https://www.thesaasacademy.com/ Join Ben's SaaS community here: https://www.thesaasacademy.com/offers/ivNjwYDx/checkout Follow Ben on LinkedIn: https://www.linkedin.com/in/benrmurray

How to Turn Customers into Brand Advocates and Drive Word-of-Mouth Growth | Ken Rapp | 379

SaaS Fuel

Play Episode Listen Later Apr 14, 2026 46:24

Most SaaS founders obsess over acquisition — but what happens after the sale is where loyalty is either built or silently lost. In this episode, Jeff Mains sits down with Ken Rapp, CEO and co-founder of Blue Stream, to explore the largely overlooked post-purchase experience and why it may be the biggest growth lever hiding in plain sight.Ken shares the story behind Blue Stream — born from a cracked guitar that nobody warned him to care for — and how that personal frustration became a mission to help brands stay connected to customers from "doorstep to delight." He breaks down the Activate → Engage → Care framework, explains the phenomenon of "ghost churn," and reveals how a 5:1 ratio of education to commercial messaging builds the kind of trust that turns first-time buyers into lifelong advocates and brand champions.Whether you're running a subscription SaaS business or a physical product brand, this episode reframes post-sale not as an afterthought — but as the next true frontier of growth.Key Takeaways[0:49] — Jeff frames the core problem: companies pour resources into getting the "yes," then go silent — leaving customers to figure it out alone.[2:17] — Ken tells the origin story: a cracked acoustic guitar in a New England winter that nobody warned him to humidify — the spark that created Blue Stream.[4:56] — Ken introduces the concept of the "connected consumer" — bridging the gap from when a product lands on the doorstep to when it becomes a habit.[6:34] — Jeff asks what made Ken identify post-sale as the next frontier; Ken explains his "unmet needs" philosophy — solve real problems no one else has solved yet.[7:49] — "Doorstep to Delight" defined: the entire journey from package arrival through unboxing, usage, and habit formation.[13:54] — The ghost churn problem: over 50% of customers don't return after the first purchase, even when companies invest heavily in acquisition incentives.[15:00] — The 5:1 ratio: five educational/caring messages before any commercial ask — and 90%+ of consumers stay on product journeys once started.[16:01] — Blue Stream sees 30% improvements in retention across all clients — and the metric is directly measurable in dollars saved or earned.[17:46] — The Activate → Engage → Care framework explained: 30 days (activation/unboxing), 30–90 days (skill and usage engagement), then ongoing maintenance/care.[19:03] — The 30-day checkpoint: 70% of customers who aren't thriving want to succeed — they just needed someone to ask. 93% of at-risk customers re-engage when proactively reached out to.[22:03] — For SaaS PLG founders: a better activate phase isn't a welcome email — it's automated conversation.[23:50] — AI with guardrails: load only your product content into the "vault" so consumers get safe, brand-accurate answers — not hallucinated internet results.[28:33] — Subscription vs. LTV lens: churn reduction for subscriptions; cross-sell and upsell for high-ticket products. Both show ~30% improvement.[31:54] — Jeff's insight: "Recurring revenue is not recurring relevance." You have to earn the subscription every single month.[32:33] — Zero party data: knowing why customers bought unlocks superior marketing segmentation and dramatically lowers CAC.[36:12] — The second "why": don't just know what they bought — know why they bought it. That insight unlocks everything.[40:26] — Polly introduced: Blue Stream's AI product advisor that drafts 30/90/360-day journeys in minutes using data from Blue Stream's data lake + your brand content.[45:22] — Freemium launch: up to 100 consumers/month free — so any brand can experience post-purchase product advising at no cost.[46:08] — Ken's one action for SaaS founders this week: visit bluestream.ai's blog — resources on personalization and retention strategies are free and immediately actionable.Tweetable Quotes"Customers don't churn because of price — they churn because somewhere along the way, the magic wore off and nobody noticed." — Jeff Mains"The product lands on your doorstep and that's when you're kind of left on your own. That's the moment we decided to own." — Ken Rapp"Ghost churn is real — over 50% of customers don't come back for a second purchase. You're filling a leaky bucket every single time." — Ken Rapp"A 5-to-1 ratio: five educational conversations before you ever ask for a cross-sell, upsell, or repeat sale. That's how you build trust." — Ken Rapp"Recurring revenue is NOT recurring relevance. You have to earn that subscription month after month after month." — Jeff Mains"Don't stop at the first 'why.' Go one layer deeper. That's what unlocks everything." — Ken Rapp"90% of consumers who started a post-purchase product journey are still on them — years later. Because it's a trusting relationship." — Ken Rapp"We saw 93% of at-risk customers — ones rating the product a 1, 2, or 3 — re-engage when we reached out proactively. They wanted to succeed." — Ken RappSaaS Leadership Lessons1. The real sale starts at delivery — not conversion. Most SaaS teams celebrate at "won." Ken's framework reframes that moment as the beginning of the customer relationship, not the end. If your onboarding stops at a welcome email, you're missing the moment customers decide whether to stay forever or ghost you quietly.2. Ghost churn is the enemy you can't see. More than 50% of customers won't repurchase without post-sale engagement — and most never tell you why. SaaS leaders must instrument the post-activation experience the same way they instrument the funnel. What you don't measure, you can't fix.3. Education earns permission. Commerce burns it. Ken's 5:1 rule — five value-adding, educational touchpoints before any commercial ask — is a masterclass in trust-building at scale. SaaS founders who lead with selling lose the relationship. Those who lead with helping earn it.4. Conversation beats automation — but conversation can scale. The activation phase for PLG isn't about sequences and tutorials. It's about proactive, personalized dialogue: "Why did you buy this? What problem are you solving? How can we help you succeed?" AI with guardrails makes this scalable without sacrificing the human feel.5. Ask the second "why" — always. Knowing a customer bought your product tells you almost nothing. Knowing why they bought — what lifestyle goal, pain, or aspiration drove them — unlocks segmentation, expansion, and churn prediction. Zero party data collected through post-sale conversations is more valuable than any third-party data you'll ever buy.6. Recurring revenue must be re-earned, not assumed. As Jeff put it: recurring revenue is not recurring relevance. SaaS leaders who treat subscription revenue as locked-in are building on sand. The ones who treat each billing cycle as an opportunity to re-deliver value are building real retention — and real enterprise value.Guest Resourcesken@blustream.ioblustream.iohttps://www.linkedin.com/in/ken-rapp-b922766/Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

From 265 Million Users to Enterprise Gold Standard: How Canva Scales GTM Without Losing Its Soul

The aSaaSins Podcast

Play Episode Listen Later Apr 10, 2026 20:20

Justin sits down with Jessica Chiew, Global Head of GTM Strategy and Operations at Canva, to unpack what it really takes to evolve from a beloved PLG product into a full-fledged enterprise sales machine without breaking what made it great.Jessica shares how Canva navigates the complexity of multiple handoffs across a PLG-to-SLG journey, why time to value is everyone's job (not just CS), and how she's thinking about marrying product usage data, CRM structure, and conversational intelligence into something actually actionable. Plus, both Justin and Jessica geek out on the AI and vibe coding moment we're all living through and why it changes everything for revenue teams.Chapters[00:00] Intro & Jessica's Path to Canva Justin welcomes Jessica and she traces her journey from Melbourne to San Francisco, through Asana, and into Canva's B2B buildout.[02:57] Who Owns Onboarding and Time to Value? Jessica breaks down Canva's take on ownership across PLG and enterprise motions — and why success in the first hundred days is a team sport, not a CS problem.[05:57] Moving Upmarket: What Changes (and What Doesn't) From solo users with credit cards to C-suite transformations with change management — how Canva's onboarding approach shifts dramatically as deal size grows.[09:40] Using PLG Signals in an Enterprise World The Asana flashback: how rich end-user data and human relationship context can finally live in the same place, and what that unlocks for enterprise sellers.[11:47] The Tech Stack Behind the Motion CRM alone isn't enough. Jessica walks through how Canva combines structured CRM data, product usage signals, and a full conversational intelligence database to build a real picture of account health.[14:00] AI, Agents, and the Vibe Coding Moment Justin shares his own revelation using AI agents for GTM workflows, and Jessica drops that she recently vibe coded a weekly forecast interface. Neither of them is going back.[16:21] What's Ahead for Canva GTM in 2026 Canva Create in LA, platform updates Jessica can't fully share yet, and the team's focus on becoming the gold standard for enterprise go-to-market.

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Why Governance Is the New Growth Lever in Modern GTM, with Adrian Rosenkranz, CRO at Webflow

The RevOps Review

Play Episode Listen Later Apr 10, 2026 25:41

Webflow CRO Adrian Rosenkranz breaks down how go-to-market teams are evolving in an AI-first world. From unifying PLG and enterprise motions to treating governance as a driver of growth, this episode explores how to design revenue systems that scale, without sacrificing control.

ai modern governance lever webflow new growth plg rosenkranz

小人物上籃-霹靂鍵盤#222 南華BCL首戰惜敗，領航猿與國王穩進六強嗎？ feat. 台籃補習班首席助教Coach Sean

Play Episode Listen Later Apr 7, 2026 79:34

Roy在舊金山看Stephen Curry與Kevin Durant的這周，正好是BCL East開打的日子，也正好小人物之友Sean香港進修空檔跟Kong一起在修頓場館看南華體育會vs烏蘭巴托野馬的BCL首戰，就這麼剛好，那一定得來聊聊2026 BCL吧！領航猿與國王再次代表台灣籃球參戰BCL East，同在B組的對手就是Kong與Sean現場觀賽的南華與野馬，4/6這一場糾纏到最後才分勝負的精采比賽，分別反映出兩隊的什麼特點？南華在球賽中場還搞退休儀式？領航猿與國王從小組賽晉級六強賽的機會有多大？且聽Kong與Sean的戰況回報與解析。焦點回到台籃，PLG重返台中，周日上演勇士在第四節被領航猿大逆轉！發生了什麼事？勇士能夠在本周與領航猿的例行賽最後交手調整回來嗎？TPBL戰況依然膠著，特攻雖然在愚人節被戰神翻盤，但周末兩戰贏得精彩、也再次擠進前四名，內馬的好表現能帶來更多變陣想像嗎？有機會進一步挑戰攻城獅佔據的第三名嗎？而剩下賽程較多、把排名主動權握在手中的攻城獅，近期又有哪些值得注意的球員？最後，夢想家雖然連勝中斷，但高柏鎧回歸、還有時間磨合陣容，這次能否帶著更好的狀態進軍季後賽？Roy外出取材的霹靂鍵盤，時長變短但內容沒有打折，歡迎小人物聽眾收聽、一起追蹤台籃動態、也別忘記要調整自己的Fantasy Basketball陣容！成為

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How Usage Signals Redefine the Sales Motion with Dan Fougere

The Marketing Millennials

Play Episode Listen Later Apr 5, 2026 6:58

Today, we're revisiting a segment from our episode on Product-Led Growth and modern sales playbooks with Dan Fougere. Dan is the former Chief Revenue Officer at Datadog and former Head of Global Sales at Medallia, now advising high-growth startups. In this clip, Dan breaks down why traditional sales playbooks fail in PLG environments, and how leaders need to shift toward usage-based signals and first principles thinking. He explains how buyer engagement now starts inside the product, what those signals actually look like, and how sales teams should adapt their timing, messaging, and motion accordingly. Dan Fougere is the former Chief Revenue Officer at Datadog and former Head of Global Sales at Medallia, now advising high-growth companies on scaling modern revenue models. Connect with Dan: LinkedIn Get the Force Management framework for building sales motions that align to how modern buyers evaluate and adopt products: The Predictable Revenue Framework: Guide for Leaders Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

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How To Build A Winning Marketing Playbook with Kyle Coleman, Global VP of Marketing at ClickUp | Ep. 406

Play Episode Listen Later Apr 3, 2026 44:38

What does it take to create $100 million in incremental pipeline in a single year? Kyle Coleman, Global VP of Marketing at ClickUp, unpacks his mission to help the company reach $1B in ARR and why “normal f***ing sucks” might be the best company value he's ever worked under. From his start as an SDR to becoming a two-time CMO, Kyle shares lessons on category design, uniting sales and marketing, and creating demand in a saturated AI-hyped world. Plus, what's ClickUp's “No Lead Left Behind” initiative all about? Kyle breaks it down, along with how to productize a horizontal platform, why brand awareness makes or breaks regional sales success, and how to build strategic messaging that resonates. Whether you're scaling a PLG motion or trying to land 7-figure enterprise deals, this is the episode for you. Wrike brings structure, visibility, and accountability to work, so companies can make better business decisions, improve efficiency, and reduce risk. Learn more at wrike.com/tmm⁠ Follow Kyle: LinkedIn: https://www.linkedin.com/in/kyletcoleman/ Follow Daniel: LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing/ Sign up for The Marketing Millennials newsletter: www.workweek.com/brand/the-marketing-millennials Daniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit: www.workweek.com

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#190: Building Faster with AI-Powered Product Demos That Convert - Joseph Lee

Practical Founders Podcast

Play Episode Listen Later Apr 3, 2026 61:47

Joseph Lee is the co-founder and CEO of Supademo, a fast-growing SaaS company solving a common pain: quickly creating new product demos. In just two and a half years, they built a modern, AI-powered solution that dramatically simplifies how teams showcase software. Supademo has reached $3M ARR in 2.5 years and is growing more than 100% annually with a freemium model. The product enables teams to create interactive, annotated, and even translated demos in minutes instead of days or weeks. The freemium model, reverse trial onboarding, and viral product loops have driven strong PLG growth, while enterprise demand is now emerging as a second growth engine. Joseph is a second-time founder with global experience from Korea to Vancouver to New York. He's raised a small amount of capital but is focused on practical execution. His approach reflects tghe broader shift of using AI to solve real workflow bottlenecks and grow efficiently without heavy funding. Key Takeaways Speed Wins - Reducing demo creation from weeks to minutes unlocks more usage, faster iteration, and better customer understanding Do The Work - Early traction came from building demos for prospects manually, removing friction and proving value instantly Reverse Trials - With free plans drive high conversion by letting users experience full value before choosing a plan PLG + Enterprise - Bottom-up growth creates stability, while enterprise deals add larger revenue but less predictability Constant Reinvention - Product-market fit is temporary in AI—founders must ship fast, iterate weekly, and stay paranoid Quote from Joseph Lee, Co-founder and CEO of Supademo "There's no bread and butter GTM channel that is going to work permanently into the future. And the biggest learning that I took away was product market fit nowadays has a finite stamp when it comes to a period of time that it's valid for. "You have to constantly reinvent yourself and be paranoid, because the market is changing, new competition is coming, and the dynamics are changing. You can't rest on your laurels, you got to be constantly innovating, like at a faster pace than ever before. "Our team competitive advantage is the ability to move quickly and ship quickly. It's combining gut based on our intel and context of the industry and tribal knowledge with some data to act faster than anyone else. Not analysis paralysis or having everything planned out. Just shipping something that may be imperfect, but using that as leverage to learn quickly and iterate quickly." Links Joseph Lee on LinkedIn Supademo on LinkedIn Supademo website Podcast Sponsor – LaunchBay LaunchBay helps B2B software companies automate client onboarding and implementation so customers activate faster and everyone stays aligned. If your onboarding includes data collection, setup steps, approvals, training, or any level of customization, LaunchBay replaces the messy mix of emails, spreadsheets, and meetings with a clear, all-in-one onboarding system. Teams use LaunchBay to onboard clients faster, stay on top of follow-ups automatically, and deliver a smoother experience, without hiring more people or adding more tools. Visit launchbay.com/practical and get 25% off your first 3 months on any LaunchBay plan. The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding. A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

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The Discipline Behind Scaling from PLG to Enterprise with Sahir Azam

Play Episode Listen Later Apr 2, 2026 67:21

High-growth companies demand constant reinvention, yet most leaders underestimate how deeply roles, go-to-market models, and buyer behavior evolve over time. This episode explores what it actually takes to adapt at that level, from navigating internal resistance to aligning product and sales with how customers truly buy. Sahir Azam brings a rare operator-to-investor perspective, unpacking the realities of PLG to enterprise transitions, the cultural discipline required to scale sales, and how AI is reshaping both software and the sales function itself. The conversation also challenges common assumptions around SaaS models, tooling, and where value will accrue as AI infrastructure matures. Sahir Azam is a Partner at Index Ventures investing in AI infrastructure, and former Chief Product Officer at MongoDB where he led the Atlas transformation into a multi-billion-dollar platform. He brings a rare operator's perspective on building go-to-market discipline, scaling sales culture, and navigating the product-distribution balance that separates winners from founders who fail. Connect with Sahir: Index Ventures LinkedIn Get the Force Management framework for navigating product-go-to-market fit and building the sales discipline that separates scaling companies from those that fail: The Predictable Revenue Framework: Guide for Leaders Key takeaways from this episode: 00:00 – How Sahir Azam went from building MongoDB Atlas into a multi-billion-dollar platform to investing in the infrastructure shaping AI's next wave 06:24 – The secret to driving change inside a company before trying to win in the market 10:10 – What PLG and enterprise sales actually have in common when you design around the buyer 12:18 – What it's really like to move upmarket and why most companies underestimate the cultural shift required 23:50 – Sahir Azam's unexpected perspective on technical founders who struggle to scale 41:12 – A peek into where real value in AI is being built and why infrastructure is the leverage point 01:02:00 – What you can do right now to stay relevant as AI reshapes how top sellers operate Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

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小人物上籃-霹靂鍵盤#221 攻城獅向前突刺！TPBL季後賽資格爭奪戰分析 feat. 小鐵 03/23/2026

The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch

Play Episode Listen Later Mar 31, 2026 138:42

「部隊起床！」在MC志彥宏亮的一聲令下，本來要走去球隊商店逛逛的Roy跟小枚不由得肅然起敬，差點要開始摺棉被XD 3/29周日，兩位主持人重訪久違的攻城獅主場「Yes Sir獅紫軍」主題周，Roy看一場還不夠，賽後續攤洋基主場、自己搞一個新竹觀賽double header！這周節目就從兩場觀賽心得展開－很久沒打(?)PLG的領航猿，不只一度被洋基逆轉，連吹判尺度也不太適應？！Roy明明人在現場，為什麼看不到卡總爆走？難道只顧著看睦那京跟球迷互動？還有前一場獵鷹新開箱的洋將飛鬥士，體格真的不像籃球員，差點拿下單場大三元的他，會為獵鷹帶來多少幫助？話題回到TPBL，攻城獅除了認真玩軍旅元素、主題周內容超豐富，球場上也打出越來越好的聲勢。目前排名第三的獅紫軍，還有往上爬、取得季後賽第一輪主場優勢的機會嗎？Roy邀請小人物上籃金牌顧問小鐵，討論前五名隊伍的晉級機會、可能排名，還有剩下例行賽程的關鍵對手。這是夢想家成功登頂的一季嗎？季前最被看好的特攻能避免打挑戰賽嗎？且聽節目一隊一隊分析討論。最後，雖然戰神與海神在季後賽之爭有點落隊，但是在上週新推出的TPBL Fantasy Basketball遊戲中，兩隊球員仍然是要角！小人物聽眾們也有加入聯賽嗎？歡迎聽聽Roy、Kong、小枚組織球隊的想法，也歡迎分享你的選人邏輯！成為

plg

20Sales: Inside Figma's $1BN ARR Revenue Machine | Why We Do Not Have Customer Success or SDRs | Why I Do Not Believe in Sales Quotas with Shaunt Voskanian, CRO @ Figma

Play Episode Listen Later Mar 21, 2026 62:50

Shaunt Voskanian is the CRO @ Figma, where he has scaled the sales machine to over $1BN in ARR and over 400 people. Prior to Figma, Shaunt was Senior VP of Global Sales at Datadog where he scaled the revenue org to $1BN in ARR. AGENDA: 04:33 - Are Great Sales Leaders Born or Trained? 06:55 - In a world of PLG, is sales less important than ever? 11:51 - Why does Shaunt not believe in traditional customer success teams? 14:31 - Does the role of the SDR survive in two years' time? 19:19 - When is the right time for sales to intercept in a PLG motion? 21:43 - How to Set Sales Quotas in a PLG AI Sales World? 31:19 - How has what you look for in sales hires changed over time? 42:54 - How do you judge sales performance if not on quota? 54:49 - Quick fire: Outdated sales tactic, What Role Dies, Best Sales AI Tool

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The Evolution of Product-Led Growth: PLG x AI

Play Episode Listen Later Mar 21, 2026 15:39

In this episode, Wes breaks down how PLG is evolving and why the fastest-growing AI companies are still using it, just with a completely different playbook. The old model was about reducing friction. The new model is about doing the work for the user. It starts with Shutterstock, a company that had PLG nailed for years. But once AI image generators arrived, everything changed. Users no longer wanted to browse and compare endless options. They wanted to type what they needed and get the result instantly. That same shift is now reshaping software everywhere. You'll also hear examples like Google Slides vs. Gamma, Stack Overflow vs. Cursor, and Westlaw vs. Harvey, where AI-native products are not just easier to use. They are taking on more of the actual work. The episode also breaks down the three versions of PLG. PLG 1.0 is built for builders. PLG 2.0 is powered by AI and built for editors. PLG 3.0 goes even further, with agents completing work on the user's behalf. As products move through these stages, time to value drops and market potential grows. If you are building a product-led company, this episode will challenge how you think about growth, user expectations, and what it takes to win in an AI-first market. Key Highlights: 0:00 - Why PLG is evolving 0:19 - The Shutterstock example 1:24 - From reducing friction to doing the work 1:32 - Google Slides vs. Gamma 2:23 - Stack Overflow vs. Cursor 2:39 - Westlaw vs. Harvey 3:23 - The three versions of PLG 4:32 - What defines PLG 2.0 5:24 - How AI expands TAM 7:53 - What PLG 3.0 looks like 11:03 - Which version are you building for? Resources: Shutterstock: https://www.shutterstock.com Gamma: https://gamma.app Cursor: https://www.cursor.com Harvey: https://www.harvey.ai Westlaw: https://legal.thomsonreuters.com/en/products/westlaw

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#280 - Kupić Maca czy ogarnąć AI?

Luźno Przy Kawie

Play Episode Listen Later Mar 20, 2026 66:09

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小人物上籃-霹靂鍵盤#219 猿班人馬與補強之王，誰能闖過東超六強！ feat.台籃補習班首席助教Sean、民視體育中心主播暐喆

SDR Game - Sales Development Podcast

Play Episode Listen Later Mar 17, 2026 141:30

經過本土聯賽大半季的熱身（？），領航猿挑戰東超冠軍之旅終於來到澳門。在六強賽開打之際，節目邀請台籃補習班首席助教Sean & 民視體育中心主播暐喆，來聊聊賽事展望。代表台籃出賽的除了領航猿，還有東補戴維斯、西補Sina Vahedi的國王，兩隊選擇不一樣的備戰路線，面對日韓強敵各有多少勝算？他們的對手首爾SK騎士與宇都宮皇者各有哪些必須嚴防的重點？如果晉級四強賽，對手將是台灣球迷也很熟悉的東京電擊與琉球黃金國王，他們的近況又如何？焦點回到台籃，連敗給領航猿跟洋基的勇士，差點落到與第四名沒有勝差的低谷，還好《突破極限的信念》帶著周桂羽突破極限、加上近況極佳的陳又瑋，才讓勇士在本季例行賽主場終戰，不至於再以輸球收場。看書行情能撐多久？勇士想奮起還需要什麼？另兩地賽況，近期同樣火熱的夢想家，少了馬建豪又讓高柏鎧休息，但打出了越來越像季後賽模式的好球，他們做對了什麼？而攻城獅在註冊大限前不只補了馬力，還有令人驚喜的徐小龍，他們帶給球隊哪些幫助？接下來有不少主場賽事的獅紫軍，能繼續提升排名嗎？歡迎小人物聽眾跟著節目一起追蹤進入球季後段、各隊開始為季後賽備戰的每一場台籃賽事！成為

plg

OK28: How to Scale Outbound From 5% to 46% With Human SDRs, No Standups, and an AI-Native Operating System - Jonathan Chemouny, GTM at ElevenLabs

Play Episode Listen Later Mar 17, 2026 52:41

⁠⁠⁠⁠

How Slido Cut Support Tickets by 70% With AI ft. Jo Massie (Slido)

Unchurned

Play Episode Listen Later Mar 13, 2026 18:41

Ready to churn less and win more?

community ai lessons advice digital mvp hiring coo tickets preview cisco using ai massie plg slido

Diagnosis and Management of Plasminogen Deficiency

Rare Disease Discussions

Play Episode Listen Later Mar 12, 2026 9:28

Amol Sura, MD, Foster Center for Ocular Immunology, Dept of Ophthalmology, Duke University, Durham, North Carolina, discusses the diagnosis and management of plasminogen deficiency, a rare condition in which the eyes are first affected, but it manifests in mucous membranes throughout the body.“The eyes are a window into the health of the entire body,” said Dr. Sura, and that is precisely the case for patients with plasminogen deficiency type 1 (PLGD-1). For this rare, inherited condition, the first symptom observed is the growth of ligneous or wood-like lesions on the surface of the eye or eyelid (in about 80% of patients, first seen at ages 9–10 months). It is often characterized as ligneous conjunctivitis. If left untreated, PLGD-1 may result in severe—even life-threatening—complications, affecting mucous membranes in many other areas of the body, including the respiratory tract, central nervous system, gingiva, and genitourinary tract. Vision loss, tooth loss, and other serious outcomes have been reported. In this homozygous condition involving the PLG gene, the plasminogen protein is defective; it cannot effectively perform its intended function of breaking down fibrin. The result is the formation of fibrin clots appearing as ligneous growths or pseudomembranes. Once removed, these lesions reappear, and this should tip off the physician (most often an ophthalmologist) that something systemic is the cause.The diagnosis of PLGD-1 is straightforward: A blood test showing decreased plasminogen activity levels is confirmatory. A genetic test is not necessary. In the past, management of PLGD-1 was through the use of off-label therapies, including heparin eyedrops, immunologics, and plasma infusions. A form of intravenous human plasminogen was approved by the U.S. Food and Drug Administration in 2021, and this is considered the standard of care today. With this therapy, plasminogen plasma levels can be restored, lowering the risk of new fibrin clots throughout the body. For newly diagnosed patients, the best advice for caregivers is to establish a relationship with a hematologist, who will serve as the principal care provider. Although a multidisciplinary care team is required (e.g., ophthalmology, obstetrics/gynecology, dentistry), the need for other specific providers will depend on the other organs or sites affected. Dr. Sura believes that PLDG-1 is underdiagnosed, but with increasing awareness, and the availability of effective treatment, diagnosis and management of these patients can be improved today.For more information on PLGD-1, visit https://checkrare.com/plasminogen-deficiency-fibrin-accumulation-and-its-effects-on-patients-2/

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小人物上籃-霹靂鍵盤#218 籃球棒球雙殺韓國！WBC 狂熱與 TPBL 註冊大限的終極盤點 03/09/2026

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Mar 11, 2026 123:14

經過WBC預賽高潮迭起的一周，相信小人物聽眾們也跟三位主持人一樣，既為Team Taiwan小將們喝采、也為他們感到揪心吧！即使無緣前進邁阿密，擊敗韓國的精采一戰，還是這屆WBC非常令人難忘的記憶。雖然運動迷肯定得聊棒球，但霹靂鍵盤仍然是台灣籃球節目，而這周我們還是有看籃球的！ PLG，勇士挾著「三月宇宙邦」的氣勢、洋基靠著禁區壓倒的態勢，先後擊敗獵鷹，突然陷入連敗的獵鷹除了少翟蒙而戰力大減，還面對哪些課題？ TPBL，領頭的雲豹也在主場連吞兩敗，即使克羅馬狀態極佳也沒有搞定戰局，他們有什麼需要擔心的地方？同時間第2名到第5名球隊的相互糾纏，場場勝負都影響排名，更不用說排名落後的戰神與海神顯然也沒放棄，都讓例行賽後半段依然充滿變數。本周的理性會客室，隨著3/9的TPBL註冊大限來到，7隊也決定了本季最終名單，哪些選擇令人意外？哪位壓線加盟的洋將能為戰局帶來衝擊？最後，「註冊大限」存在的用意與意義是什麼？這個突如其來的提問，有標準答案嗎？延後或拿掉會更好嗎？歡迎聽眾分享自己的想法！成為

plg firstory

Cursor's Third Era: Cloud Agents

Play Episode Listen Later Mar 6, 2026 66:39

All speakers are announced at AIE EU, schedule coming soon. Join us there or in Miami with the renowned organizers of React Miami! Singapore CFP also open!We've called this out a few times over in AINews, but the overwhelming consensus in the Valley is that “the IDE is Dead”. In November it was just a gut feeling, but now we actually have data: even at the canonical “VSCode Fork” company, people are officially using more agents than tab autocomplete (the first wave of AI coding):Cursor has launched cloud agents for a few months now, and this specific launch is around Computer Use, which has come a long way since we first talked with Anthropic about it in 2024, and which Jonas productized as Autotab:We also take the opportunity to do a live demo, talk about slash commands and subagents, and the future of continual learning and personalized coding models, something that Sam previously worked on at New Computer. (The fact that both of these folks are top tier CEOs of their own startups that have now joined the insane talent density gathering at Cursor should also not be overlooked).Full Episode on YouTube!please like and subscribe!Timestamps00:00 Agentic Code Experiments00:53 Why Cloud Agents Matter02:08 Testing First Pillar03:36 Video Reviews Second Pillar04:29 Remote Control Third Pillar06:17 Meta Demos and Bug Repro13:36 Slash Commands and MCPs18:19 From Tab to Team Workflow31:41 Minimal Web UI Philosophy32:40 Why No File Editor34:38 Full Stack Cursor Debate36:34 Model Choice and Auto Routing38:34 Parallel Agents and Best Of N41:41 Subagents and Context Management44:48 Grind Mode and Throughput Future01:00:24 Cloud Agent Onboarding and MemoryTranscriptEP 77 - CURSOR - Audio version[00:00:00]Agentic Code ExperimentsSamantha: This is another experiment that we ran last year and didn't decide to ship at that time, but may come back to LM Judge, but one that was also agentic and could write code. So it wasn't just picking but also taking the learnings from two models or and models that it was looking at and writing a new diff.And what we found was that there were strengths to using models from different model providers as the base level of this process. Basically you could get almost like a synergistic output that was better than having a very unified like bottom model tier.Jonas: We think that over the coming months, the big unlock is not going to be one person with a model getting more done, like the water flowing faster and we'll be making the pipe much wider and so paralyzing more, whether that's swarms of agents or parallel agents, both of those are things that contribute to getting much more done in the same amount of time.Why Cloud Agents Matterswyx: This week, one of the biggest launches that Cursor's ever done is cloud agents. I think you, you had [00:01:00] cloud agents before, but this was like, you give cursor a computer, right? Yeah. So it's just basically they bought auto tab and then they repackaged it. Is that what's going on, or,Jonas: that's a big part of it.Yeah. Cloud agents already ran in their own computers, but they were sort of site reading code. Yeah. And those computers were not, they were like blank VMs typically that were not set up for the Devrel X for whatever repo the agents working on. One of the things that we talk about is if you put yourself in the model shoes and you were seeing tokens stream by and all you could do was cite read code and spit out tokens and hope that you had done the right thing,swyx: no chanceJonas: I'd be so bad.Like you obviously you need to run the code. And so that I think also is probably not that contrarian of a take, but no one has done that yet. And so giving the model the tools to onboard itself and then use full computer use end-to-end pixels in coordinates out and have the cloud computer with different apps in it is the big unlock that we've seen internally in terms of use usage of this going from, oh, we use it for little copy changes [00:02:00] to no.We're really like driving new features with this kind of new type of entech workflow. Alright, let's see it. Cool.Live Demo TourJonas: So this is what it looks like in cursor.com/agents. So this is one I kicked off a while ago. So on the left hand side is the chat. Very classic sort of agentic thing. The big new thing here is that the agent will test its changes.So you can see here it worked for half an hour. That is because it not only took time to write the tokens of code, it also took time to test them end to end. So it started Devrel servers iterate when needed. And so that's one part of it is like model works for longer and doesn't come back with a, I tried some things pr, but a I tested at pr that's ready for your review.One of the other intuition pumps we use there is if a human gave you a PR asked you to review it and you hadn't, they hadn't tested it, you'd also be annoyed because you'd be like, only ask me for a review once it's actually ready. So that's what we've done withTesting Defaults and Controlsswyx: simple question I wanted to gather out front.Some prs are way smaller, [00:03:00] like just copy change. Does it always do the video or is it sometimes,Jonas: Sometimes.swyx: Okay. So what's the judgment?Jonas: The model does it? So we we do some default prompting with sort. What types of changes to test? There's a slash command that people can do called slash no test, where if you do that, the model will not test,swyx: but the default is test.Jonas: The default is to be calibrated. So we tell it don't test, very simple copy changes, but test like more complex things. And then users can also write their agents.md and specify like this type of, if you're editing this subpart of my mono repo, never tested ‘cause that won't work or whatever.Videos and Remote ControlJonas: So pillar one is the model actually testing Pillar two is the model coming back with a video of what it did.We have found that in this new world where agents can end-to-end, write much more code, reviewing the code is one of these new bottlenecks that crop up. And so reviewing a video is not a substitute for reviewing code, but it is an entry point that is much, much easier to start with than glancing at [00:04:00] some giant diff.And so typically you kick one off you, it's done you come back and the first thing that you would do is watch this video. So this is a, video of it. In this case I wanted a tool tip over this button. And so it went and showed me what that looks like in, in this video that I think here, it actually used a gallery.So sometimes it will build storybook type galleries where you can see like that component in action. And so that's pillar two is like these demo videos of what it built. And then pillar number three is I have full remote control access to this vm. So I can go heat in here. I can hover things, I can type, I have full control.And same thing for the terminal. I have full access. And so that is also really useful because sometimes the video is like all you need to see. And oftentimes by the way, the video's not perfect, the video will show you, is this worth either merging immediately or oftentimes is this worth iterating with to get it to that final stage where I am ready to merge in.So I can go through some other examples where the first video [00:05:00] wasn't perfect, but it gave me confidence that we were on the right track and two or three follow-ups later, it was good to go. And then I also have full access here where some things you just wanna play around with. You wanna get a feel for what is this and there's no substitute to a live preview.And the VNC kind of VM remote access gives you that.swyx: Amazing What, sorry? What is VN. AndJonas: just the remote desktop. Remote desktop. Yeah.swyx: Sam, any other details that you always wanna call out?Samantha: Yeah, for me the videos have been super helpful. I would say, especially in cases where a common problem for me with agents and cloud agents beforehand was almost like under specification in my requests where our plan mode and going really back and forth and getting detailed implementation spec is a way to reduce the risk of under specification, but then similar to how human communication breaks down over time, I feel like you have this risk where it's okay, when I pull down, go to the triple of pulling down and like running this branch locally, I'm gonna see that, like I said, this should be a toggle and you have a checkbox and like, why didn't you get that detail?And having the video up front just [00:06:00] has that makes that alignment like you're talking about a shared artifact with the agent. Very clear, which has been just super helpful for me.Jonas: I can quickly run through some other Yes. Examples.Meta Agents and More DemosJonas: So this is a very front end heavy one. So one question I wasswyx: gonna say, is this only for frontJonas: end?Exactly. One question you might have is this only for front end? So this is another example where the thing I wanted it to implement was a better error message for saving secrets. So the cloud agents support adding secrets, that's part of what it needs to access certain systems. Part of onboarding that is giving access.This is cloud is working onswyx: cloud agents. Yes.Jonas: So this is a fun thing isSamantha: it can get super meta. ItJonas: can get super meta, it can start its own cloud agents, it can talk to its own cloud agents. Sometimes it's hard to wrap your mind around that. We have disabled, it's cloud agents starting more cloud agents. So we currently disallow that.Someday you might. Someday we might. Someday we might. So this actually was mostly a backend change in terms of the error handling here, where if the [00:07:00] secret is far too large, it would oh, this is actually really cool. Wow. That's the Devrel tools. That's the Devrel tools. So if the secret is far too large, we.Allow secrets above a certain size. We have a size limit on them. And the error message there was really bad. It was just some generic failed to save message. So I was like, Hey, we wanted an error message. So first cool thing it did here, zero prompting on how to test this. Instead of typing out the, like a character 5,000 times to hit the limit, it opens Devrel tools, writes js, or to paste into the input 5,000 characters of the letter A and then hit save, closes the Devrel tools, hit save and gets this new gets the new error message.So that looks like the video actually cut off, but here you can see the, here you can see the screenshot of the of the error message. What, so that is like frontend backend end-to-end feature to, to get that,swyx: yeah.Jonas: Andswyx: And you just need a full vm, full computer run everything.Okay. Yeah.Jonas: Yeah. So we've had versions of this. This is one of the auto tab lessons where we started that in 2022. [00:08:00] No, in 2023. And at the time it was like browser use, DOM, like all these different things. And I think we ended up very sort of a GI pilled in the sense that just give the model pixels, give it a box, a brain in a box is what you want and you want to remove limitations around context and capabilities such that the bottleneck should be the intelligence.And given how smart models are today, that's a very far out bottleneck. And so giving it its full VM and having it be onboarded with Devrel X set up like a human would is just been for us internally a really big step change in capability.swyx: Yeah I would say, let's call it a year ago the models weren't even good enough to do any of this stuff.SoSamantha: even six months ago. Yeah.swyx: So yeah what people have told me is like round about Sonder four fire is when this started being good enough to just automate fully by pixel.Jonas: Yeah, I think it's always a question of when is good enough. I think we found in particular with Opus 4 5, 4, 6, and Codex five three, that those were additional step [00:09:00] changes in the autonomy grade capabilities of the model to just.Go off and figure out the details and come back when it's done.swyx: I wanna appreciate a couple details. One 10 Stack Router. I see it. Yeah. I'm a big fan. Do you know any, I have to name the 10 Stack.Jonas: No.swyx: This just a random lore. Some buddy Sue Tanner. My and then the other thing if you switch back to the video.Jonas: Yeah.swyx: I wanna shout out this thing. Probably Sam did it. I don't knowJonas: the chapters.swyx: What is this called? Yeah, this is called Chapters. Yeah. It's like a Vimeo thing. I don't know. But it's so nice the design details, like the, and obviously a company called Cursor has to have a beautiful cursorSamantha: and it isswyx: the cursor.Samantha: Cursor.swyx: You see it branded? It's the cursor. Cursor, yeah. Okay, cool. And then I was like, I complained to Evan. I was like, okay, but you guys branded everything but the wallpaper. And he was like, no, that's a cursor wallpaper. I was like, what?Samantha: Yeah. Rio picked the wallpaper, I think. Yeah. The video.That's probably Alexi and yeah, a few others on the team with the chapters on the video. Matthew Frederico. There's been a lot of teamwork on this. It's a huge effort.swyx: I just, I like design details.Samantha: Yeah.swyx: And and then when you download it adds like a little cursor. Kind of TikTok clip. [00:10:00] Yes. Yes.So it's to make it really obvious is from Cursor,Jonas: we did the TikTok branding at the end. This was actually in our launch video. Alexi demoed the cloud agent that built that feature. Which was funny because that was an instance where one of the things that's been a consequence of having these videos is we use best of event where you run head to head different models on the same prompt.We use that a lot more because one of the complications with doing that before was you'd run four models and they would come back with some giant diff, like 700 lines of code times four. It's what are you gonna do? You're gonna review all that's horrible. But if you come back with four 22nd videos, yeah, I'll watch four 22nd videos.And then even if none of them is perfect, you can figure out like, which one of those do you want to iterate with, to get it over the line. Yeah. And so that's really been really fun.Bug Repro WorkflowJonas: Here's another example. That's we found really cool, which is we've actually turned since into a slash command as well slash [00:11:00] repro, where for bugs in particular, the model of having full access to the to its own vm, it can first reproduce the bug, make a video of the bug reproducing, fix the bug, make a video of the bug being fixed, like doing the same pattern workflow with obviously the bug not reproducing.And that has been the single category that has gone from like these types of bugs, really hard to reproduce and pick two tons of time locally, even if you try a cloud agent on it. Are you confident it actually fixed it to when this happens? You'll merge it in 90 seconds or something like that.So this is an example where, let me see if this is the broken one or the, okay, this is the fixed one. Okay. So we had a bug on cursor.com/agents where if you would attach images where remove them. Then still submit your prompt. They would actually still get attached to the prompt. Okay. And so here you can see Cursor is using, its full desktop by the way.This is one of the cases where if you just do, browse [00:12:00] use type stuff, you'll have a bad time. ‘cause now it needs to upload files. Like it just uses its native file viewer to do that. And so you can see here it's uploading files. It's going to submit a prompt and then it will go and open up. So this is the meta, this is cursor agent, prompting cursor agent inside its own environment.And so you can see here bug, there's five images attached, whereas when it's submitted, it only had one image.swyx: I see. Yeah. But you gotta enable that if you're gonna use cur agent inside cur.Jonas: Exactly. And so here, this is then the after video where it went, it does the same thing. It attaches images, removes, some of them hit send.And you can see here, once this agent is up, only one of the images is left in the attachments. Yeah.swyx: Beautiful.Jonas: Okay. So easy merge.swyx: So yeah. When does it choose to do this? Because this is an extra step.Jonas: Yes. I think I've not done a great job yet of calibrating the model on when to reproduce these things.Yeah. Sometimes it will do it of its own accord. Yeah. We've been conservative where we try to have it only do it when it's [00:13:00] quite sure because it does add some amount of time to how long it takes it to work on it. But we also have added things like the slash repro command where you can just do, fix this bug slash repro and then it will know that it should first make you a video of it actually finding and making sure it can reproduce the bug.swyx: Yeah. Yeah. One sort of ML topic this ties into is reward hacking, where while you write test that you update only pass. So first write test, it shows me it fails, then make you test pass, which is a classic like red green.Jonas: Yep.swyx: LikeJonas: A-T-D-D-T-D-Dswyx: thing.No, very cool. Was that the last demo? Is thereJonas: Yeah.Anything I missed on the demos or points that you think? I think thatSamantha: covers it well. Yeah.swyx: Cool. Before we stop the screen share, can you gimme like a, just a tour of the slash commands ‘cause I so God ready. Huh, what? What are the good ones?Samantha: Yeah, we wanna increase discoverability around this too.I think that'll be like a future thing we work on. Yeah. But there's definitely a lot of good stuff nowJonas: we have a lot of internal ones that I think will not be that interesting. Here's an internal one that I've made. I don't know if anyone else at Cursor uses this one. Fix bb.Samantha: I've never heard of it.Jonas: Yeah.[00:14:00]Fix Bug Bot. So this is a thing that we want to integrate more tightly on. So you made it forswyx: yourself.Jonas: I made this for myself. It's actually available to everyone in the team, but yeah, no one knows about it. But yeah, there will be Bug bot comments and so Bug Bot has a lot of cool things. We actually just launched Bug Bot Auto Fix, where you can click a button and or change a setting and it will automatically fix its own things, and that works great in a bunch of cases.There are some cases where having the context of the original agent that created the PR is really helpful for fixing the bugs, because it might be like, oh, the bug here is that this, is a regression and actually you meant to do something more like that. And so having the original prompt and all of the context of the agent that worked on it, and so here I could just do, fix or we used to be able to do fixed PB and it would do that.No test is another one that we've had. Slash repro is in here. We mentioned that one.Samantha: One of my favorites is cloud agent diagnosis. This is one that makes heavy use of the Datadog MCP. Okay. And I [00:15:00] think Nick and David on our team wrote, and basically if there is a problem with a cloud agent we'll spin up a bunch of subs.Like a singleswyx: instance.Samantha: Yeah. We'll take the ideas and argument and spin up a bunch of subagents using the Datadog MCP to explore the logs and find like all of the problems that could have happened with that. It takes the debugging time, like from potentially you can do quick stuff quickly with the Datadog ui, but it takes it down to, again, like a single agent call as opposed to trolling through logs yourself.Jonas: You should also talk about the stuff we've done with transcripts.Samantha: Yes. Also so basically we've also done some things internally. There'll be some versions of this as we ship publicly soon, where you can spit up an agent and give it access to another agent's transcript to either basically debug something that happened.So act as an external debugger. I see. Or continue the conversation. Almost like forking it.swyx: A transcript includes all the chain of thought for the 11 minutes here. 45 minutes there.Samantha: Yeah. That way. Exactly. So basically acting as a like secondary agent that debugs the first, so we've started to push more andswyx: they're all the same [00:16:00] code.It is just the different prompts, but the sa the same.Samantha: Yeah. So basically same cloud agent infrastructure and then same harness. And then like when we do things like include, there's some extra infrastructure that goes into piping in like an external transcript if we include it as an attachment.But for things like the cloud agent diagnosis, that's mostly just using the Datadog MCP. ‘Cause we also launched CPS along with along with this cloud agent launch, launch support for cloud agent cps.swyx: Oh, that was drawn out.Jonas: We won't, we'll be doing a bigger marketing moment for it next week, but, and you can now use CPS andswyx: People will listen to it as well.Yeah,Jonas: they'llSamantha: be ahead of the third. They'll be ahead. And I would I actually don't know if the Datadog CP is like publicly available yet. I realize this not sure beta testing it, but it's been one of my favorites to use. Soswyx: I think that one's interesting for Datadog. ‘cause Datadog wants to own that site.Interesting with Bits. I don't know if you've tried bits.Samantha: I haven't tried bits.swyx: Yeah.Jonas: That's their cloud agentswyx: product. Yeah. Yeah. They want to be like we own your logs and give us our, some part of the, [00:17:00] self-healing software that everyone wants. Yeah. But obviously Cursor has a strong opinion on coding agents and you, you like taking away from the which like obviously you're going to do, and not every company's like Cursor, but it's interesting if you're a Datadog, like what do you do here?Do you expose your logs to FDP and let other people do it? Or do you try to own that it because it's extra business for you? Yeah. It's like an interesting one.Samantha: It's a good question. All I know is that I love the Datadog MCP,Jonas: And yeah, it is gonna be no, no surprise that people like will demand it, right?Samantha: Yeah.swyx: It's, it's like anysystemswyx: of record company like this, it's like how much do you give away? Cool. I think that's that for the sort of cloud agents tour. Cool. And we just talk about like cloud agents have been when did Kirsten loves cloud agents? Do you know, in JuneJonas: last year.swyx: June last year. So it's been slowly develop the thing you did, like a bunch of, like Michael did a post where himself, where he like showed this chart of like ages overtaking tap. And I'm like, wow, this is like the biggest transition in code.Jonas: Yeah.swyx: Like in, in [00:18:00] like the last,Jonas: yeah. I think that kind of got turned out.Yeah. I think it's a very interest,swyx: not at all. I think it's been highlighted by our friend Andre Kati today.Jonas: Okay.swyx: Talk more about it. What does it mean? Yeah. Is I just got given like the cursor tab key.Jonas: Yes. Yes.swyx: That's that'sSamantha: cool.swyx: I know, but it's gonna be like put in a museum.Jonas: It is.Samantha: I have to say I haven't used tab a little bit myself.Jonas: Yeah. I think that what it looks like to code with AI code generally creates software, even if you want to go higher level. Is changing very rapidly. No, not a hot take, but I think from our vendor's point at Cursor, I think one of the things that is probably underappreciated from the outside is that we are extremely self-aware about that fact and Kerscher, got its start in phase one, era one of like tab and auto complete.And that was really useful in its time. But a lot of people start looking at text files and editing code, like we call it hand coding. Now when you like type out the actual letters, it'sswyx: oh that's cute.Jonas: Yeah.swyx: Oh that's cute.Jonas: You're so boomer. So boomer. [00:19:00] And so that I think has been a slowly accelerating and now in the last few months, rapidly accelerating shift.And we think that's going to happen again with the next thing where the, I think some of the pains around tab of it's great, but I actually just want to give more to the agent and I don't want to do one tab at a time. I want to just give it a task and it goes off and does a larger unit of work and I can.Lean back a little bit more and operate at that higher level of abstraction that's going to happen again, where it goes from agents handing you back diffs and you're like in the weeds and giving it, 32nd to three minute tasks, to, you're giving it, three minute to 30 minute to three hour tasks and you're getting back videos and trying out previews rather than immediately looking at diffs every single time.swyx: Yeah. Anything to add?Samantha: One other shift that I've noticed as our cloud agents have really taken off internally has been a shift from primarily individually driven development to almost this collaborative nature of development for us, slack is actually almost like a development on [00:20:00] Id basically.So Iswyx: like maybe don't even build a custom ui, like maybe that's like a debugging thing, but actually it's that.Samantha: I feel like, yeah, there's still so much to left to explore there, but basically for us, like Slack is where a lot of development happens. Like we will have these issue channels or just like this product discussion channels where people are always at cursing and that kicks off a cloud agent.And for us at least, we have team follow-ups enabled. So if Jonas kicks off at Cursor in a thread, I can follow up with it and add more context. And so it turns into almost like a discussion service where people can like collaborate on ui. Oftentimes I will kick off an investigation and then sometimes I even ask it to get blame and then tag people who should be brought in. ‘cause it can tag people in Slack and then other people will comeswyx: in, can tag other people who are not involved in conversation. Yes. Can just do at Jonas if say, was talking to,Samantha: yeah.swyx: That's cool. You should, you guys should make a big good deal outta that.Samantha: I know. It's a lot to, I feel like there's a lot more to do with our slack surface area to show people externally. But yeah, basically like it [00:21:00] can bring other people in and then other people can also contribute to that thread and you can end up with a PR again, with the artifacts visible and then people can be like, okay, cool, we can merge this.So for us it's like the ID is almost like moving into Slack in some ways as well.swyx: I have the same experience with, but it's not developers, it's me. Designer salespeople.Samantha: Yeah.swyx: So me on like technical marketing, vision, designer on design and then salespeople on here's the legal source of what we agreed on.And then they all just collaborate and correct. The agents,Jonas: I think that we found when these threads is. The work that is left, that the humans are discussing in these threads is the nugget of what is actually interesting and relevant. It's not the boring details of where does this if statement go?It's do we wanna ship this? Is this the right ux? Is this the right form factor? Yeah. How do we make this more obvious to the user? It's like those really interesting kind of higher order questions that are so easy to collaborate with and leave the implementation to the cloud agent.Samantha: Totally. And no more discussion of am I gonna do this? Are you [00:22:00] gonna do this cursor's doing it? You just have to decide. You like it.swyx: Sometimes the, I don't know if there's a, this probably, you guys probably figured this out already, but since I, you need like a mute button. So like cursor, like we're going to take this offline, but still online.But like we need to talk among the humans first. Before you like could stop responding to everything.Jonas: Yeah. This is a design decision where currently cursor won't chime in unless you explicitly add Mention it. Yeah. Yeah.Samantha: So it's not always listening.Yeah.Jonas: I can see all the intermediate messages.swyx: Have you done the recursive, can cursor add another cursor or spawn another cursor?Samantha: Oh,Jonas: we've done some versions of this.swyx: Because, ‘cause it can add humans.Jonas: Yes. One of the other things we've been working on that's like an implication of generating the code is so easy is getting it to production is still harder than it should be.And broadly, you solve one bottleneck and three new ones pop up. Yeah. And so one of the new bottlenecks is getting into production and we have a like joke internally where you'll be talking about some feature and someone says, I have a PR for that. Which is it's so easy [00:23:00] to get to, I a PR for that, but it's hard still relatively to get from I a PR for that to, I'm confident and ready to merge this.And so I think that over the coming weeks and months, that's a thing that we think a lot about is how do we scale up compute to that pipeline of getting things from a first draft An agent did.swyx: Isn't that what Merge isn't know what graphite's for, likeJonas: graphite is a big part of that. The cloud agent testingswyx: Is it fully integrated or still different companiesJonas: working on I think we'll have more to share there in the future, but the goal is to have great end-to-end experience where Cursor doesn't just help you generate code tokens, it helps you create software end-to-end.And so review is a big part of that, that I think especially as models have gotten much better at writing code, generating code, we've felt that relatively crop up more,swyx: sorry this is completely unplanned, but like there I have people arguing one to you need ai. To review ai and then there is another approach, thought school of thought where it's no, [00:24:00] reviews are dead.Like just show me the video. It's it like,Samantha: yeah. I feel again, for me, the video is often like alignment and then I often still wanna go through a code review process.swyx: Like still look at the files andSamantha: everything. Yeah. There's a spectrum of course. Like the video, if it's really well done and it does like fully like test everything, you can feel pretty competent, but it's still helpful to, to look at the code.I make hep pay a lot of attention to bug bot. I feel like Bug Bot has been a great really highly adopted internally. We often like, won't we tell people like, don't leave bug bot comments unaddressed. ‘cause we have such high confidence in it. So people always address their bug bot comments.Jonas: Once you've had two cases where you merged something and then you went back later, there was a bug in it, you merged, you went back later and you were like, ah, bug Bot had found that I should have listened to Bug Bot.Once that happens two or three times, you learn to wait for bug bot.Samantha: Yeah. So I think for us there's like that code level review where like it's looking at the actual code and then there's like the like feature level review where you're looking at the features. There's like a whole number of different like areas.There'll probably eventually be things like performance level review, security [00:25:00] review, things like that where it's like more more different aspects of how this feature might affect your code base that you want to potentially leverage an agent to help with.Jonas: And some of those like bug bot will be synchronous and you'll typically want to wait on before you merge.But I think another thing that we're starting to see is. As with cloud agents, you scale up this parallelism and how much code you generate. 10 person startups become, need the Devrel X and pipelines that a 10,000 person company used to need. And that looks like a lot of the things I think that 10,000 person companies invented in order to get that volume of software to production safely.So that's things like, release frequently or release slowly, have different stages where you release, have checkpoints, automated ways of detecting regressions. And so I think we're gonna need stacks merg stack diffs merge queues. Exactly. A lot of those things are going to be importantswyx: forward with.I think the majority of people still don't know what stack stacks are. And I like, I have many friends in Facebook and like I, I'm pretty friendly with graphite. I've just, [00:26:00] I've never needed it ‘cause I don't work on that larger team and it's just like democratization of no, only here's what we've already worked out at very large scale and here's how you can, it benefits you too.Like I think to me, one of the beautiful things about GitHub is that. It's actually useful to me as an individual solo developer, even though it's like actually collaboration software.Jonas: Yep.swyx: And I don't think a lot of Devrel tools have figured that out yet. That transition from like large down to small.Jonas: Yeah. Kers is probably an inverse story.swyx: This is small down toJonas: Yeah. Where historically Kers share, part of why we grew so quickly was anyone on the team could pick it up and in fact people would pick it up, on the weekend for their side project and then bring it into work. ‘cause they loved using it so much.swyx: Yeah.Jonas: And I think a thing that we've started working on a lot more, not us specifically, but as a company and other folks at Cursor, is making it really great for teams and making it the, the 10th person that starts using Cursor in a team. Is immediately set up with things like, we launched Marketplace recently so other people can [00:27:00] configure what CPS and skills like plugins.So skills and cps, other people can configure that. So that my cursor is ready to go and set up. Sam loves the Datadog, MCP and Slack, MCP you've also been using a lot butSamantha: also pre-launch, but I feel like it's so good.Jonas: Yeah, my cursor should be configured if Sam feels strongly that's just amazing and required.swyx: Is it automatically shared or you have to go and.Jonas: It depends on the MCP. So some are obviously off per user. Yeah. And so Sam can't off my cursor with my Slack MCP, but some are team off and those can be set up by admins.swyx: Yeah. Yeah. That's cool. Yeah, I think, we had a man on the pod when cursor was five people, and like everyone was like, okay, what's the thing?And then it's usually something teams and org and enterprise, but it's actually working. But like usually at that stage when you're five, when you're just a vs. Code fork it's like how do you get there? Yeah. Will people pay for this? People do pay for it.Jonas: Yeah. And I think for cloud agents, we expect.[00:28:00]To have similar kind of PLG things where I think off the bat we've seen a lot of adoption with kind of smaller teams where the code bases are not quite as complex to set up. Yes. If you need some insane docker layer caching thing for builds not to take two hours, that's going to take a little bit longer for us to be able to support that kind of infrastructure.Whereas if you have front end backend, like one click agents can install everything that they need themselves.swyx: This is a good chance for me to just ask some technical sort of check the box questions. Can I choose the size of the vm?Jonas: Not yet. We are planning on adding that. Weswyx: have, this is obviously you want like LXXL, whatever, right?Like it's like the Amazon like sort menu.Jonas: Yes, exactly. We'll add that.swyx: Yeah. In some ways you have to basically become like a EC2, almost like you rent a box.Jonas: You rent a box. Yes. We talk a lot about brain in a box. Yeah. So cursor, we want to be a brain in a box,swyx: but is the mental model different? Is it more serverless?Is it more persistent? Is. Something else.Samantha: We want it to be a bit persistent. The desktop should be [00:29:00] something you can return to af even after some days. Like maybe you go back, they're like still thinking about a feature for some period of time. So theswyx: full like sus like suspend the memory and bring it back and then keep going.Samantha: Exactly.swyx: That's an interesting one because what I actually do want, like from a manna and open crawl, whatever, is like I want to be able to log in with my credentials to the thing, but not actually store it in any like secret store, whatever. ‘cause it's like this is the, my most sensitive stuff.Yeah. This is like my email, whatever. And just have it like, persist to the image. I don't know how it was hood, but like to rehydrate and then just keep going from there. But I don't think a lot of infra works that way. A lot of it's stateless where like you save it to a docker image and then it's only whatever you can describe in a Docker file and that's it.That's the only thing you can cl multiple times in parallel.Jonas: Yeah. We have a bunch of different ways of setting them up. So there's a dockerfile based approach. The main default way is actually snapshottingswyx: like a Linux vmJonas: like vm, right? You run a bunch of install commands and then you snapshot more or less the file system.And so that gets you set up for everything [00:30:00] that you would want to bring a new VM up from that template basically.swyx: Yeah.Jonas: And that's a bit distinct from what Sam was talking about with the hibernating and re rehydrating where that is a full memory snapshot as well. So there, if I had like the browser open to a specific page and we bring that back, that page will still be there.swyx: Was there any discussion internally and just building this stuff about every time you shoot a video it's actually you show a little bit of the desktop and the browser and it's not necessary if you just show the browser. If, if you know you're just demoing a front end application.Why not just show the browser, right? Like it Yeah,Samantha: we do have some panning and zooming. Yeah. Like it can decide that when it's actually recording and cutting the video to highlight different things. I think we've played around with different ways of segmenting it and yeah. There's been some different revs on it for sure.Jonas: Yeah. I think one of the interesting things is the version that you see now in cursor.com actually is like half of what we had at peak where we decided to unshift or unshipped quite a few things. So two of the interesting things to talk about, one is directly an answer to your [00:31:00] question where we had native browser that you would have locally, it was basically an iframe that via port forwarding could load the URL could talk to local host in the vm.So that gets you basically, so inswyx: your machine's browser,likeJonas: in your local browser? Yeah. You would go to local host 4,000 and that would get forwarded to local host 4,000 in the VM via port forward. We unshift that like atswyx: Eng Rock.Jonas: Like an Eng Rock. Exactly. We unshift that because we felt that the remote desktop was sufficiently low latency and more general purpose.So we build Cursor web, but we also build Cursor desktop. And so it's really useful to be able to have the full spectrum of things. And even for Cursor Web, as you saw in one of the examples, the agent was uploading files and like I couldn't upload files and open the file viewer if I only had access to the browser.And we've thought a lot about, this might seem funny coming from Cursor where we started as this, vs. Code Fork and I think inherited a lot of amazing things, but also a lot [00:32:00] of legacy UI from VS Code.Minimal Web UI SurfacesJonas: And so with the web UI we wanted to be very intentional about keeping that very minimal and exposing the right sum of set of primitive sort of app surfaces we call them, that are shared features of that cloud.Environment that you and the agent both use. So agent uses desktop and controls it. I can use desktop and controlled agent runs terminal commands. I can run terminal commands. So that's how our philosophy around it. The other thing that is maybe interesting to talk about that we unshipped is and we may, both of these things we may reship and decide at some point in the future that we've changed our minds on the trade offs or gotten it to a point where, putswyx: it out there.Let users tell you they want it. Exactly. Alright, fine.Why No File EditorJonas: So one of the other things is actually a files app. And so we used to have the ability at one point during the process of testing this internally to see next to, I had GID desktop and terminal on the right hand side of the tab there earlier to also have a files app where you could see and edit files.And we actually felt that in some [00:33:00] ways, by restricting and limiting what you could do there, people would naturally leave more to the agent and fall into this new pattern of delegating, which we thought was really valuable. And there's currently no way in Cursor web to edit these files.swyx: Yeah. Except you like open up the PR and go into GitHub and do the thing.Jonas: Yeah.swyx: Which is annoying.Jonas: Just tell the agent,swyx: I have criticized open AI for this. Because Open AI is Codex app doesn't have a file editor, like it has file viewer, but isn't a file editor.Jonas: Do you use the file viewer a lot?swyx: No. I understand, but like sometimes I want it, the one way to do it is like freaking going to no, they have a open in cursor button or open an antigravity or, opening whatever and people pointed that.So I was, I was part of the early testers group people pointed that and they were like, this is like a design smell. It's like you actually want a VS. Code fork that has all these things, but also a file editor. And they were like, no, just trust us.Jonas: Yeah. I think we as Cursor will want to, as a product, offer the [00:34:00] whole spectrum and so you want to be able to.Work at really high levels of abstraction and double click and see the lowest level. That's important. But I also think that like you won't be doing that in Slack. And so there are surfaces and ways of interacting where in some cases limiting the UX capabilities makes for a cleaner experience that's more simple and drives people into these new patterns where even locally we kicked off joking about this.People like don't really edit files, hand code anymore. And so we want to build for where that's going and not where it's beenswyx: a lot of cool stuff. And Okay. I have a couple more.Full Stack Hosting Debateswyx: So observations about the design elements about these things. One of the things that I'm always thinking about is cursor and other peers of cursor start from like the Devrel tools and work their way towards cloud agents.Other people, like the lovable and bolts of the world start with here's like the vibe code. Full cloud thing. They were already cloud edges before anyone else cloud edges and we will give you the full deploy platform. So we own the whole loop. We own all the infrastructure, we own, we, we have the logs, we have the the live site, [00:35:00] whatever.And you can do that cycle cursor doesn't own that cycle even today. You don't have the versal, you don't have the, you whatever deploy infrastructure that, that you're gonna have, which gives you powers because anyone can use it. And any enterprise who, whatever you infra, I don't care. But then also gives you limitations as to how much you can actually fully debug end to end.I guess I'm just putting out there that like is there a future where there's like full stack cursor where like cursor apps.com where like I host my cursor site this, which is basically a verse clone, right? I don't know.Jonas: I think that's a interesting question to be asking, and I think like the logic that you laid out for how you would get there is logic that I largely agree with.swyx: Yeah. Yeah.Jonas: I think right now we're really focused on what we see as the next big bottleneck and because things like the Datadog MCP exist, yeah. I don't think that the best way we can help our customers ship more software. Is by building a hosting solution right now,swyx: by the way, these are things I've actually discussed with some of the companies I just named.Jonas: Yeah, for sure. Right now, just this big bottleneck is getting the code out there and also [00:36:00] unlike a lovable in the bolt, we focus much more on existing software. And the zero to one greenfield is just a very different problem. Imagine going to a Shopify and convincing them to deploy on your deployment solution.That's very different and I think will take much longer to see how that works. May never happen relative to, oh, it's like a zero to one app.swyx: I'll say. It's tempting because look like 50% of your apps are versal, superb base tailwind react it's the stack. It's what everyone does.So I it's kinda interesting.Jonas: Yeah.Model Choice and Auto Routingswyx: The other thing is the model select dying. Right now in cloud agents, it's stuck down, bottom left. Sure it's Codex High today, but do I care if it's suddenly switched to Opus? Probably not.Samantha: We definitely wanna give people a choice across models because I feel like it, the meta change is very frequently.I was a big like Opus 4.5 Maximalist, and when codex 5.3 came out, I hard, hard switch. So that's all I use now.swyx: Yeah. Agreed. I don't know if, but basically like when I use it in Slack, [00:37:00] right? Cursor does a very good job of exposing yeah. Cursors. If people go use it, here's the model we're using.Yeah. Here's how you switch if you want. But otherwise it's like extracted away, which is like beautiful because then you actually, you should decide.Jonas: Yeah, I think we want to be doing more with defaults.swyx: Yeah.Jonas: Where we can suggest things to people. A thing that we have in the editor, the desktop app is auto, which will route your request and do things there.So I think we will want to do something like that for cloud agents as well. We haven't done it yet. And so I think. We have both people like Sam, who are very savvy and want know exactly what model they want, and we also have people that want us to pick the best model for them because we have amazing people like Sam and we, we are the experts.Yeah. We have both the traffic and the internal taste and experience to know what we think is best.swyx: Yeah. I have this ongoing pieces of agent lab versus model lab. And to me, cursor and other companies are example of an agent lab that is, building a new playbook that is different from a model lab where it's like very GP heavy Olo.So obviously has a research [00:38:00] team. And my thesis is like you just, every agent lab is going to have a router because you're going to be asked like, what's what. I don't keep up to every day. I'm not a Sam, I don't keep up every day for using you as sample the arm arbitrator of taste. Put me on CRI Auto.Is it free? It's not free.Jonas: Auto's not free, but there's different pricing tiers. Yeah.swyx: Put me on Chris. You decide from me based on all the other people you know better than me. And I think every agent lab should basically end up doing this because that actually gives you extra power because you like people stop carrying or having loyalty with one lab.Jonas: Yeah.Best Of N and Model CouncilsJonas: Two other maybe interesting things that I don't know how much they're on your radar are one the best event thing we mentioned where running different models head to head is actually quite interesting becauseswyx: which exists in cursor.Jonas: That exists in cur ID and web. So the problem is where do you run them?swyx: Okay.Jonas: And so I, I can share my screen if that's interesting. Yeahinteresting.swyx: Yeah. Yeah. Obviously parallel agents, very popal.Jonas: Yes, exactly. Parallel agentsswyx: in you mind. Are they the same thing? Best event and parallel agents? I don't want to [00:39:00] put words in your mouth.Jonas: Best event is a subset of parallel agents where they're running on the same prompt.That would be my answer. So this is what that looks like. And so here in this dropdown picker, I can just select multiple models.swyx: Yeah.Jonas: And now if I do a prompt, I'm going to do something silly. I am running these five models.swyx: Okay. This is this fake clone, of course. The 2.0 yeah.Jonas: Yes, exactly. But they're running so the cursor 2.0, you can do desktop or cloud.So this is cloud specifically where the benefit over work trees is that they have their own VMs and can run commands and won't try to kill ports that the other one is running. Which are some of the pains. These are allswyx: called work trees?Jonas: No, these are all cloud agents with their own VMs.swyx: Okay. ButJonas: When you do it locally, sometimes people do work trees and that's been the main way that people have set out parallel so far.I've gotta say.swyx: That's so confusing for folks.Jonas: Yeah.swyx: No one knows what work trees are.Jonas: Exactly. I think we're phasing out work trees.swyx: Really.Jonas: Yeah.swyx: Okay.Samantha: But yeah. And one other thing I would say though on the multimodel choice, [00:40:00] so this is another experiment that we ran last year and the decide to ship at that time but may come back to, and there was an interesting learning that's relevant for, these different model providers. It was something that would run a bunch of best of ends but then synthesize and basically run like a synthesizer layer of models. And that was other agents that would take LM Judge, but one that was also agentic and could write code. So it wasn't just picking but also taking the learnings from two models or, and models that it was looking at and writing a new diff.And what we found was that at the time at least, there were strengths to using models from different model providers as the base level of this process. Like basically you could get almost like a synergistic output that was better than having a very unified, like bottom model tier. So it was really interesting ‘cause it's like potentially, even though even in the future when you have like maybe one model as ahead of the other for a little bit, there could be some benefit from having like multiple top tier models involved in like a [00:41:00] model swarm or whatever agent Swarm that you're doing, that they each have strengths and weaknesses.Yeah.Jonas: Andre called this the council, right?Samantha: Yeah, exactly. We actually, oh, that's another internal command we have that Ian wrote slash council. Oh, and they some, yeah.swyx: Yes. This idea is in various forms everywhere. And I think for me, like for me, the productization of it, you guys have done yeah, like this is very flexible, but.If I were to add another Yeah, what your thing is on here it would be too much. I what, let's say,Samantha: Ideally it's all, it's something that the user can just choose and it all happens under the hood in a way where like you just get the benefit of that process at the end and better output basically, but don't have to get too lost in the complexity of judging along the way.Jonas: Okay.Subagents for ContextJonas: Another thing on the many agents, on different parallel agents that's interesting is an idea that's been around for a while as well that has started working recently is subagents. And so this is one other way to get agents of the different prompts and different goals and different models, [00:42:00] different vintages to work together.Collaborate and delegate.swyx: Yeah. I'm very like I like one of my, I always looking for this is the year of the blah, right? Yeah. I think one of the things on the blahs is subs. I think this is of but I haven't used them in cursor. Are they fully formed or how do I honestly like an intro because do I form them from new every time?Do I have fixed subagents? How are they different for slash commands? There's all these like really basic questions that no one stops to answer for people because everyone's just like too busy launching. We have toSamantha: honestly, you could, you can see them in cursor now if you just say spin up like 50 subagents to, so cursor definesswyx: what Subagents.Yeah.Samantha: Yeah. So basically I think I shouldn't speak for the whole subagents team. This is like a different team that's been working on this, but our thesis or thing that we saw internally is that like they're great for context management for kind of long running threads, or if you're trying to just throw more compute at something.We have strongly used, almost like a generic task interface where then the main agent can define [00:43:00] like what goes into the subagent. So if I say explore my code base, it might decide to spin up an explore subagent and or might decide to spin up five explore subagent.swyx: But I don't get to set what those subagent are, right?It's all defined by a model.Samantha: I think. I actually would have to refresh myself on the sub agent interface.Jonas: There are some built-in ones like the explore subagent is free pre-built. But you can also instruct the model to use other subagents and then it will. And one other example of a built-in subagent is I actually just kicked one off in cursor and I can show you what that looks like.swyx: Yes. Because I tried to do this in pure prompt space.Jonas: So this is the desktop app? Yeah. Yeah. And that'sswyx: all you need to do, right? Yeah.Jonas: That's all you need to do. So I said use a sub agent to explore and I think, yeah, so I can even click in and see what the subagent is working on here. It ran some fine command and this is a composer under the hood.Even though my main model is Opus, it does smart routing to take, like in this instance the explorer sort of requires reading a ton of things. And so a faster model is really useful to get an [00:44:00] answer quickly, but that this is what subagent look like. And I think we wanted to do a lot more to expose hooks and ways for people to configure these.Another example of a cus sort of builtin subagent is the computer use subagent in the cloud agents, where we found that those trajectories can be long and involve a lot of images obviously, and execution of some testing verification task. We wanted to use that models that are particularly good at that.So that's one reason to use subagents. And then the other reason to use subagents is we want contexts to be summarized reduced down at a subagent level. That's a really neat boundary at which to compress that rollout and testing into a final message that agent writes that then gets passed into the parent rather than having to do some global compaction or something like that.swyx: Awesome. Cool. While we're in the subagents conversation, I can't do a cursor conversation and not talk about listen stuff. What is that? What is what? He built a browser. He built an os. Yes. And he [00:45:00] experimented with a lot of different architectures and basically ended up reinventing the software engineer org chart.This is all cool, but what's your take? What's, is there any hole behind the side? The scenes stories about that kind of, that whole adventure.Samantha: Some of those experiments have found their way into a feature that's available in cloud agents now, the long running agent mode internally, we call it grind mode.And I think there's like some hint of grind mode accessible in the picker today. ‘cause you can do choose grind until done. And so that was really the result of experiments that Wilson started in this vein where he I think the Ralph Wigga loop was like floating around at the time, but it was something he also independently found and he was experimenting with.And that was what led to this product surface.swyx: And it is just simple idea of have criteria for completion and do not. Until you complete,Samantha: there's a bit more complexity as well in, in our implementation. Like there's a specific, you have to start out by aligning and there's like a planning stage where it will work with you and it will not get like start grind execution mode until it's decided that the [00:46:00] plan is amenable to both of you.Basically,swyx: I refuse to work until you make me happy.Jonas: We found that it's really important where people would give like very underspecified prompt and then expect it to come back with magic. And if it's gonna go off and work for three minutes, that's one thing. When it's gonna go off and work for three days, probably should spend like a few hours upfront making sure that you have communicated what you actually want.swyx: Yeah. And just to like really drive from the point. We really mean three days that No, noJonas: human. Oh yeah. We've had three day months innovation whatsoever.Samantha: I don't know what the record is, but there's been a long time with the grantsJonas: and so the thing that is available in cursor. The long running agent is if you wanna think about it, very abstractly that is like one worker node.Whereas what built the browser is a society of workers and planners and different agents collaborating. Because we started building the browser with one worker node at the time, that was just the agent. And it became one worker node when we realized that the throughput of the system was not where it needed to be [00:47:00] to get something as large of a scale as the browser done.swyx: Yeah.Jonas: And so this has also become a really big mental model for us with cloud, cloud agents is there's the classic engineering latency throughput trade-offs. And so you know, the code is water flowing through a pipe. The, we think that over the coming months, the big unlock is not going to be one person with a model getting more done, like the water flowing faster and we'll be making the pipe much wider and so ing more, whether that's swarms of agents or parallel agents, both of those are things that contribute to getting.Much more done in the same amount of time, but any one of those tasks doesn't necessarily need to get done that quickly. And throughput is this really big thing where if you see the system of a hundred concurrent agents outputting thousands of tokens a second, you can't go back like that.Just you see a glimpse of the future where obviously there are many caveats. Like no one is using this browser. IRL. There's like a bunch of things not quite right yet, but we are going to get to systems that produce real production [00:48:00] code at the scale much sooner than people think. And it forces you to think what even happens to production systems. Like we've broken our GitHub actions recently because we have so many agents like producing and pushing code that like CICD is just overloaded. ‘cause suddenly it's like effectively weg grew, cursor's growing very quickly anyway, but you grow head count, 10 x when people run 10 x as many agents.And so a lot of these systems, exactly, a lot of these systems will need to adapt.swyx: It also reminds me, we, we all, the three of us live in the app layer, but if you talk to the researchers who are doing RL infrastructure, it's the same thing. It's like all these parallel rollouts and scheduling them and making sure as much throughput as possible goes through them.Yeah, it's the same thing.Jonas: We were talking briefly before we started recording. You were mentioning memory chips and some of the shortages there. The other thing that I think is just like hard to wrap your head around the scale of the system that was building the browser, the concurrency there.If Sam and I both have a system like that running for us, [00:49:00] shipping our software. The amount of inference that we're going to need per developer is just really mind-boggling. And that makes, sometimes when I think about that, I think that even with, the most optimistic projections for what we're going to need in terms of buildout, our underestimating, the extent to which these swarm systems can like churn at scale to produce code that is valuable to the economy.And,swyx: yeah, you can cut this if it's sensitive, but I was just Do you have estimates of how much your token consumption is?Jonas: Like per developer?swyx: Yeah. Or yourself. I don't need like comfy average. I just curious. ISamantha: feel like I, for a while I wasn't an admin on the usage dashboard, so I like wasn't able to actually see, but it was a,swyx: mine has gone up.Samantha: Oh yeah.swyx: But I thinkSamantha: it's in terms of how much work I'm doing, it's more like I have no worries about developers losing their jobs, at least in the near term. ‘cause I feel like that's a more broad discussion.swyx: Yeah. Yeah. You went there. I didn't go, I wasn't going there.I was just like how much more are you using?Samantha: There's so much stuff to be built. And so I feel like I'm basically just [00:50:00] trying to constantly I have more ambitions than I did before. Yes. Personally. Yes. So can't speak to the broader thing. But for me it's like I'm busier than ever before.I'm using more tokens and I am also doing more things.Jonas: Yeah. Yeah. I don't have the stats for myself, but I think broadly a thing that we've seen, that we expect to continue is J'S paradox. Whereswyx: you can't do it in our podcast without seeingJonas: it. Exactly. We've done it. Now we can wrap. We've done, we said the words.Phase one tab auto complete people paid like 20 bucks a month. And that was great. Phase two where you were iterating with these local models. Today people pay like hundreds of dollars a month. I think as we think about these highly parallel kind of agents running off for a long times in their own VM system, we are already at that point where people will be spending thousands of dollars a month per human, and I think potentially tens of thousands and beyond, where it's not like we are greedy for like capturing more money, but what happens is just individuals get that much more leverage.And if one person can do as much as 10 people, yeah. That tool that allows ‘em to do that is going to be tremendously valuable [00:51:00] and worth investing in and taking the best thing that exists.swyx: One more question on just the cursor in general and then open-ended for you guys to plug whatever you wanna put.How is Cursor hiring these days?Samantha: What do you mean by how?swyx: So obviously lead code is dead. Oh,Samantha: okay.swyx: Everyone says work trial. Different people have different levels of adoption of agents. Some people can really adopt can be much more productive. But other people, you just need to give them a little bit of time.And sometimes they've never lived in a token rich place like cursor.And once you live in a token rich place, you're you just work differently. But you need to have done that. And a lot of people anyway, it was just open-ended. Like how has agentic engineering, agentic coding changed your opinions on hiring?Is there any like broad like insights? Yeah.Jonas: Basically I'm asking this for other people, right? Yeah, totally. Totally. To hear Sam's opinion, we haven't talked about this the two of us. I think that we don't see necessarily being great at the latest thing with AI coding as a prerequisite.I do think that's a sign that people are keeping up and [00:52:00] curious and willing to upscale themselves in what's happening because. As we were talking about the last three months, the game has completely changed. It's like what I do all day is very different.swyx: Like it's my job and I can't,Jonas: Yeah, totally.I do think that still as Sam was saying, the fundamentals remain important in the current age and being able to go and double click down. And models today do still have weaknesses where if you let them run for too long without cleaning up and refactoring, the coke will get sloppy and there'll be bad abstractions.And so you still do need humans that like have built systems before, no good patterns when they see them and know where to steer things.Samantha: I would agree with that. I would say again, cursor also operates very quickly and leveraging ag agentic engineering is probably one reason why that's possible in this current moment.I think in the past it was just like people coding quickly and now there's like people who use agents to move faster as well. So it's part of our process will always look for we'll select for kind of that ability to make good decisions quickly and move well in this environment.And so I think being able to [00:53:00] figure out how to use agents to help you do that is an important part of it too.swyx: Yeah. Okay. The fork in the road, either predictions for the end of the year, if you have any, or PUDs.Jonas: Evictions are not going to go well.Samantha: I know it's hard.swyx: They're so hard. Get it wrong.It's okay. Just, yeah.Jonas: One other plug that may be interesting that I feel like we touched on but haven't talked a ton about is a thing that the kind of these new interfaces and this parallelism enables is the ability to hop back and forth between threads really quickly. And so a thing that we have,swyx: you wanna show something or,Jonas: yeah, I can show something.A thing that we have felt with local agents is this pain around contact switching. And you have one agent that went off and did some work and another agent that, that did something else. And so here by having, I just have three tabs open, let's say, but I can very quickly, hop in here.This is an example I showed earlier, but the actual workflow here I think is really different in a way that may not be obvious, where, I start t

How Netlify Became the Obvious Choice in their Market

ai business marketing entrepreneurship startups b2b newsletter motion crm sms sanity devops product marketing calendly growth hacking plg hardison

Play Episode Listen Later Mar 6, 2026 57:49

Chris Bach, founder of Netlify, joins Wes Bush and Esben Friis-Jensen to break down how Netlify became a default choice in modern web development. Chris shares how Netlify started as a bet on a new web architecture that moved beyond monolithic applications, and why bottom-up adoption through developers was not optional, but the only viable go-to-market path. They dig into what many founders skip: building a clear worldview of how the market is evolving, then reverse-engineering what needs to exist for that future to become real. Chris explains how this approach shaped Netlify's early product decisions, its ecosystem strategy, and the narrative that helped attract users, partners, and investors. The conversation also tackles a common founder dilemma: product-led vs. sales-led. Chris offers a simple filter, if you cannot deliver a “magic moment” quickly for an individual user, PLG may be the wrong motion. He also argues that trying to do both sales-led and product-led at the same time often leads to doing neither well. Finally, Chris shares how his investing approach grew out of ecosystem-building, why learning requires asking “stupid” questions, and how he now thinks about the next wave: agents as the new “user,” and the infrastructure required to support them. Key Highlights 00:00 – Why Netlify Became the “Obvious Choice” Wes introduces Chris and tees up the core theme: building a compelling worldview and executing it until the market sees your product as the default. 00:00:59 – Netlify's Mission: Escape the Monolith Chris explains Netlify's original bet on a new web architecture and why early enterprise use cases were limited without a supporting ecosystem. 00:03:34 – When PLG Works: Start With the “Magic Moment” A practical filter for founders: if an individual user cannot quickly experience value, PLG may be a mismatch. 00:07:31 – Pick a Motion First: Hybrid Comes Later Chris warns against trying to do sales-led and product-led at the same time, especially with limited startup resources. 00:11:17 – The Worldview Advantage: Context Before Product How Netlify spent serious time mapping where the web was headed, then reverse-engineered what they needed to build first. 00:15:41 – Storytelling That Wins: Small Story vs. Big Story Why messaging must change depending on the audience, and how Netlify avoided being boxed in as “just hosting.” 00:25:17 – Category Creation: Why Jamstack MatteredChris shares how coining “Jamstack” worked because it benefited the whole ecosystem, not just Netlify's marketing.00:29:08 – Ecosystem Fuel: Directories, OSS, and Deploy PreviewsTactics that helped win developer mindshare, including community resources and making open source easy to deploy.00:32:31 – The First 20: Targeting Influential Early AdoptersNetlify's early focus was literally a list of 20 key people, then expanding in concentric circles from there.00:35:34 – The Next Shift: Agents, Dynamic Web, and AXChris outlines his view of an AI-generated, on-the-fly web and why “agent experience” becomes a critical product frontier. Resources

ai market oss plg netlify jamstack obvious choice wes bush esben friis jensen

How to Build a Product Marketing Motion That Works (with Jeff Hardison)

DGMG Radio

Play Episode Listen Later Mar 5, 2026 59:08

#335 | Jeff Hardison, now VP of Product Marketing at Sanity, joined Dave when he was running product marketing at Calendly to break down what the product marketing role should actually look like inside a B2B company. They get into how Jeff structured his team to serve both a PLG motion and an enterprise sales team at the same time, why he hires for specialization instead of making everyone a generalist, and how he thinks about measuring a function that touches almost every team in the company. Jeff also shares his take on positioning and messaging, how to run product launches that actually rally the company, and the two interview questions he uses to figure out if someone will be happy in a product marketing role. Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:AirOps - The content engineering platform that helps marketers create and maintain high-quality, on-brand content that wins AI search. Go to airops.com/exitfive to start creating content that reflects your expertise, stays true to your brand, and is engineered for performance across human and AI discovery.Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive. Convertr - The enterprise lead data management platform that sits between your lead sources and your CRM, automatically validating, enriching, and standardizing every lead before it touches your systems. Check them out at convertr.io/exitfive.Compound Growth Marketing - A full-funnel demand generation agency that helps high-growth cybersecurity, DevOps, and enterprise software companies drive more pipeline through AI SEO, paid media, and go-to-market engineering. Visit compoundgrowthmarketing.com and tell them Dave sent you.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Why organic referrals drive 80% of Clockwise's growth after a decade of marketing experiments | Matt Martin

Category Visionaries

Play Episode Listen Later Feb 27, 2026 26:01

Clockwise is pioneering intelligent time management for knowledge workers, addressing the fundamental constraint that limits all knowledge work organizations: how teams allocate their most finite resource. Founded in 2016, the company has spent a decade solving the problem of calendar inefficiency and meeting overload that fragments productive time. In a recent episode of BUILDERS, we sat down with Matt Martin, Co-Founder & CEO of Clockwise, to learn about the company's journey from a three-year build cycle to serving major software organizations through a product-led growth motion, the strategic decisions behind targeting software engineers as their wedge market, and why the time management problem remains largely unsolved despite being obvious to anyone who's worked in a large organization.Topics DiscussedWhy time remains the primary economic constraint in knowledge work despite a decade of tooling evolutionThe three-year pre-launch build period and deliberate four-year path to monetizationTargeting software engineers as the wedge: ROI clarity in heads-down time versus meeting-heavy rolesThe graveyard of calendar productivity startups: UI-focused plays, consumer pivots, and buyer/user misalignmentTransitioning from pure PLG to blended motion with enterprise inbound and pilot programsThe stubborn reality of organic growth: why referrals dominate despite extensive channel experimentationBuilding toward AI-powered personalized time agents that embrace individual complexity//Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co//Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

ceo ai europe marketing growth co founders drive experiments roi decade b2b founded organic ui builders front lines referrals 10m plg whispered matt martin clockwise 100m arr

EP56: The busy founder's guide to activation

In Demand: How to Grow Your SaaS to $100K MRR

Play Episode Listen Later Feb 24, 2026 56:05

Activation is the most overlooked growth lever in SaaS, especially for PLG-focused companies. While founders obsess over acquisition, pricing, and retention, they often overlook low-hanging fruit with activation. In this episode of In Demand, Asia and Kim break down what activation actually is, why most teams misunderstand it, and how to improve it using a clear, repeatable process. Asia shares why pop-ups and walkthroughs are not a strategy, why survivor bias is distorting your view of product performance, and how as few as three to five UX interviews can unlock growth. If you have a free trial, self-serve motion, or product-led growth model, this episode walks through a practical framework to improve activation. Got a question you'd like Asia to unpack on the podcast? Record a voicemail here. Links: DemandMaven https://www.userinterviews.com/ Respondent.io Amplitude Mixpanel Chapters (00:01:00) - Why activation is often an overlooked growth lever in PLG SaaS.(00:04:05) - What activation actually means and how it connects acquisition and retention.(00:11:00) - Why pop-ups, overlays, and onboarding walkthroughs aren't working as well anymore.(00:14:00) - What good trial-to-paid benchmarks look like and why most bootstrappers leave money on the table.(00:19:45) - The process of improving activation, starting with step one, UX interviews with qualified strangers.(00:28:05) - What to pay attention to when doing UX interviews.(00:30:55) - The three levers to improve UX: cognitive overload, uncertainty, and limited attention.(00:36:50) - What steps to take after making initial improvements.(00:42:00) - How to think about later-stage activation.(00:52:45) - Activation starting from your homepage.

founders guide record busy saas ux activation plg respondent in demand

506: Positioning as a Growth Lever

Renegade Thinkers Unite: #2 Podcast for CMOs & B2B Marketers

Play Episode Listen Later Feb 20, 2026 51:02

Feature-and-function decks aren't winning anymore. In this episode of Renegade Marketers Unite, Drew sits down with Bob Wright (Firebrick) to break down how B2B CMOs can use positioning to drive growth, shorten sales cycles, and stand out in crowded markets. They unpack why product-first stories fail, how to get to "one voice" across the company, and what it really means to own a key business problem that buyers care about. In this episode: The three biggest positioning mistakes: product-first thinking, misalignment, and no owned problem Creating urgency when "do nothing" is the real competitor Why "why you, why now" matters more than "how it works" When and how to rethink positioning after PLG, acquisitions, or expansion How to stand out in a world of AI sameness Building positions that sales actually uses If your messaging is drifting into "blah blah blah" territory, this episode will help you reset around problems, not products. For full show notes and transcripts, visit https://renegademarketing.com/podcasts/ To learn more about CMO Huddles, visit https://cmohuddles.com/

ai growth building feature positioning lever plg cmo huddles

小人物上籃-霹靂鍵盤#214 飛沖繩的猿迷是真愛、去蒙古的史東是真壞feat. Luphan陸大 02/09/2026

google meet plg firstory sugo

Play Episode Listen Later Feb 11, 2026 128:36

過年回家很無聊？親戚聊完就只剩滑手機？打開 SUGO 用「附近」功能，直接找附近的人聊天、揪局！不用左右滑、等配對，上線就能開聊！隨時有人陪你過年不孤單✨快點擊連結下載

94 - How modern SaaS teams build scalable growth systems - With Alex Laventer

B2B SaaS Marketing Snacks

Play Episode Listen Later Feb 11, 2026 49:05

Are you actually growing your product, or just stacking signups that never turn into usage?A lot of teams get stuck there. More registrations feel good, but it's not the same as real usage, paid adoption, and a pipeline you can trust. And now with AI in the mix, it's easy to create more activity without getting more signal.In this episode of B2B SaaS Marketing Snacks, hosts Stijn Hendrikse and Brian Grav bring on their first guest, Alex Laventer.Alex has spent years in growth roles in B2B SaaS, including leading growth at DataStax and now leading go-to-market work on an AI agent product at IBM.The conversation gets practical fast, what “growth” really means, and how teams split (or combine) growth marketing and product growth.You'll walk away with a clearer way to measure growth, how to set up tracking you can rely on, and where AI can help (and where it tends to distract), including lead scoring and workflow automation.In this episode, you'll learn:Why signups mislead growth conversationsWhere teams lose signal without trackingHow PQLs connect product and marketingPerspective on sales assist with PLGExample: AI-assisted lead scoring workflows By the end, you'll know what to measure, what to ignore, and what to fix next so “growth” stops being a vague label and starts being a real operating system. Resources shared in this episode:BSMS 88 - Why founders overestimate PLG, and what VCs should check before investingBSMS 23 - Product led growth vs. sales led growthThe Foundation of a Successful SaaS GTM (Go-to-Market) Strategy T2D3 CMO MasterclassSubmit and vote on our podcast topicsABOUT B2B SAAS MARKETING SNACKSSince 2020, The B2B SaaS Marketing Snacks Podcast has offered software company founders, investors and leadership a fresh source of insights into building a complete and efficient engine for growth.Meet our Marketing Snacks Podcast Hosts: Stijn Hendrikse: Author of T2D3 Masterclass & Book, Founder of KalungiAs a serial entrepreneur and marketing leader, Stijn has contributed to the success of 20+ startups as a C-level executive, including Chief Revenue Officer of Acumatica, CEO of MightyCall, a SaaS contact center solution, and leading the initial global Go-to-Market for Atera, a B2B SaaS Unicorn. Before focusing on startups, Stijn led global SMB Marketing and B2B Product Marketing for Microsoft's Office platform.Brian Graf: CEO of KalungiAs CEO of Kalungi, Brian provides high-level strategy, tactical execution, and business leadership expertise to drive long-term growth for B2B SaaS. Brian has successfully led clients in all aspects of marketing growth, from positioning and messaging to event support, product announcements, and channel-spend optimizations, generating qualified leads and brand awareness for clients while prioritizing ROI. Before Kalungi, Brian worked in television advertising, specializing in business intelligence and campaign optimization, and earned his MBA at the University of Washington's Foster School of Business with a focus in finance and marketing. Visit Kalungi.com to learn more about growing your B2B SaaS company.

ceo university founders ai business washington growth office market microsoft modern mba product roi ibm saas vcs chief revenue officer scalable b2b saas stijn product led growth plg datastax foster school bs ms atera smb marketing acumatica

How AskElephant achieved 400% growth with zero marketing spend | Woody Klemetson

Category Visionaries

Play Episode Listen Later Feb 11, 2026 27:10

Woody Klemetson scaled sales from 100 people at Divi to 350 at Bill.com post-acquisition, then walked away to build something harder: infrastructure for hybrid AI-human revenue teams. At AskElephant, he's tackling the problem that every revenue leader faces but few can articulate—how to actually implement AI in revenue operations when your systems weren't built for it. With zero marketing spend, AskElephant hit 400% growth through pure referral motion and converts 85% of pilots to production (versus single digits industry-wide). Woody breaks down why most "AI-ready" companies aren't, how to structure pilots that actually ship, and what it takes to hire sellers who orchestrate agents instead of relying on armies of support staff. Topics Discussed: Post-acquisition culture collision: the cost of moving too fast versus too slow Why "AI readiness" is usually one person at a company, not the organization The 27-agent CRM system that delivers 5% forecast accuracy without human input Revenue outcome systems as category evolution: solving for predictability across disconnected tools Pilot-first GTM that converts at 85% by starting with one-minute-per-day wins Partner-led distribution through consultants evolving from slideware to implementation Hiring ops-minded sellers who code: over half of non-engineers using Cursor daily The PLG expansion coming in 2025 and why traditional demand gen is getting tested alongside door-to-door GTM Lessons For B2B Founders: Culture integration requires explicit deceleration early: Woody's team assumed Bill.com wanted their aggressive startup velocity immediately post-acquisition. They didn't slow down to map cultural differences, causing "whiplash" across 350 people. The specific mistake: not creating space to understand Bill's processes before challenging them. Even when acquired for your approach, the first 90 days should be listening and mapping, not executing. Only after understanding their system can you effectively challenge and merge cultures. This applies whether you're acquiring or being acquired—the cultural work is non-negotiable and front-loaded. Diagnose AI readiness by system documentation, not enthusiasm: Most companies think they're AI-ready because leadership wants AI. Reality check: if your teams haven't documented their systems and processes, AI has nothing to learn from. AskElephant starts some customers with basic dictation—not because it's revolutionary, but because it's the prerequisite to anything meaningful. The diagnostic question: "Walk us through your current customer journey." If the answer is "we have sales stages," you're not ready for automation. You need documented systems before AI can execute them. Start by having AI observe and document before it acts. Build agents incrementally to compound context: AskElephant runs 27 different CRM agents that collectively deliver 5% forecast accuracy. This wasn't built in one sprint—it took 40 hours of training and context-building. Each agent handles a specific job: contact creation, data enrichment, ICP scoring, churn monitoring, stage updates. The misconception founders have: AI should work perfectly from the first prompt. The reality: you build agents brick by brick, each one learning from the previous context layer. This is why their forecasting works—because 27 agents watching different signals together create accuracy that one "smart" agent can't. Pilot conversion at scale requires deliberately small scope: Single-digit pilot-to-production rates happen because teams scope too big. AskElephant's 85% conversion comes from "dream big, implement small." First pilot: automated CRM notes. Then: notes humans wish they'd written. Then: automated field updates. Each step saves minutes, builds trust, proves value. Woody's framework: if you're not saving one minute per person per day in your first pilot, you've scoped wrong. The goal isn't to wow with ambition—it's to ship something that works perfectly, then expand from proven trust. Their customers average 27 hours saved per week per person, but none started there. Revenue outcome systems emerge from tool sprawl failure: Every revenue leader uses 15-20 disconnected tools trying to make revenue predictable. The category insight isn't "operating systems"—it's that companies care about outcomes, not operations. AskElephant's positioning: we focus on the outcome (predictable revenue), not just the operating infrastructure. This distinction matters because it shifts the conversation from technical plumbing to business results. When creating categories, find the frame that makes the buyer's problem visceral and your solution inevitable, even if you're solving similar problems as others in the space. Partner-led GTM turns consultants into distribution: AskElephant's entire growth came through partners: Salesforce/HubSpot consultants becoming AI strategists, sales coaches extending from training to implementation. The unlock: these partners needed a way to deliver lasting value beyond slideware. Previously, a coach would train your team and leave. Now they implement AI systems that hold teams accountable to the training, creating longer engagements and better outcomes. For founders: identify services providers whose business model gets dramatically better by incorporating your product. They become your sales force because you make them more valuable to their clients. Hire for orchestration capability, not pure sales skill: Over half of AskElephant's non-engineering team uses Cursor daily. Woody hires "ops-minded" and "tech-minded" sellers who can manage AI agents alongside human work. The old model: silver-tongued seller + solutions engineer + 27 support people. The new model: one seller orchestrating 27 AI agents. These reps don't build lists, don't create SOWs, don't write product scopes, don't need SEs for demos. But they still need human connection skills—listening, curiosity, presence. The hiring filter: can this person think in systems and implement technical solutions while maintaining high-touch relationships? If they can't code enough to orchestrate agents, they can't scale in this environment. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

ai europe marketing growth reality walk partner single pilot hiring hire b2b revenue crm woody ses front lines 10m achieved icp gtm cursor divi plg whispered sows 100m arr

Dune: B2B Marketing Lessons on Finding Value in Unpopular Places with Madhav Bhandari, Head of Marketing at Storylane

Remarkable Marketing

Play Episode Listen Later Feb 3, 2026 46:26

Some of the most powerful ideas in marketing don't come from marketing at all. They come from stories that refuse to play it safe.That's the lesson of Dune, the sci-fi epic once considered unfilmable and now one of the most successful franchises of the decade. In this episode, we break down its marketing lessons with the help of our special guest Madhav Bhandari, Head of Marketing at Storylane.Together, we explore what B2B marketers can learn from world-building, pattern interruptions, and betting on emerging talent.About our guest, Madhav BhandariMadhav Bhandari is the Head of Marketing at Storylane. He's a a B2B marketer with 12+ years of experience helping startups grow from scrappy beginnings ($2M+ ARR) to category leadership ($20M+ ARR and beyond). Madhav built lean, high-performing marketing engines across both PLG / sales-led companies. His strength and philosophy is doing marketing that stands out. I focus on work that drives action and ties directly to pipeline.Madhav has helped many scale-ups grow beyond $10M ARR, either as a full-time leader or a hands-on advisor. I love taking on this challenge.What B2B Companies Can Learn From Dune:Show the product, don't narrate it. Madhav's first lesson from Dune is about restraint. The film works because it removes exposition and lets the audience experience the world firsthand. He draws a direct parallel to B2B marketing, saying, “ You've seen the B2B website homepages that are just full of jargon. And I think now is the time to actually show the product.” Too many B2B teams rely on jargon, stock imagery, and abstract claims, forcing buyers to imagine value. The takeaway is simple: remove the guesswork. Interactive demos, real visuals, and tangible experiences outperform explanations every time. If buyers have to imagine what your product does, you've already added friction.Go where the work is unpopular but important. In Dune, the most valuable resource in the universe lives in the most unremarkable place. Madhav says, “ Unpopular but important projects, that's where the largest customer growth lies.” In marketing, that means resisting the pull of flashy homepage redesigns and brand exercises when the real leverage sits deeper, product pages, conversion paths, and messy parts of the funnel no one wants to own. If everyone wants to work on it, it's probably already optimized. The real upside lives where attention is scarce.Bet on emerging voices, not just famous ones. Dune didn't rely on a single A-list star to succeed, and Madhav has seen the same dynamic play out in B2B. His experience is clear: “ anytime I've gone with… a very popular influencer… that I interviewed, those episodes the way I thought they would perform, didn't really perform that well. Bu what's funny is that the people that are relatively unpopular but have done incredible work are the episodes that did fantastic.” Big names feel safe, but they're expensive and often underdeliver. Audiences respond more to sharp thinking and real experience than borrowed fame. In B2B, the fastest way to build trust is to help your audience discover someone worth listening to, before everyone else does.Quote“ Today, in our world, sameness is risky… The worst that could happen … is it's gonna perform the same as if you would've not done that, and the best case scenario is it's just gonna do insanely well.” Time Stamps[01:03] Meet Madhav Bhandari, Head of Marketing at Storylane01:08 Why Dune?01:51 Role of Head of Marketing at Storylane02:37 Breaking Down Dune10:53 B2B Marketing Takeaways from Dune25:18 Influencer Campaign Strategies28:28 The Power of Brand Awareness31:12 Storylane's Marketing Strategy35:08 Creative Marketing Examples38:37 Content Strategy and Founder Branding45:25 Final Thoughts and TakeawaysLinksConnect with Madhav on LinkedInLearn more about StorylaneAbout Remarkable!Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both nonfiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Head of Production). Remarkable was produced this week by Jess Avellino, mixed by Scott Goodrich, and our theme song is “Solomon” by FALAK. Create something remarkable. Rise above the noise. 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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