Podcasts about Twilio

Tired vs. Wired: $4 Trillion in IPOs Coming, $100B in M&A, and Why the SaaSpocalypse is Over The public markets spent the last twelve months telling you B2B software was finished. Stocks down 60 to 70 percent. PE firms buying nobody. For the first time in history, software trading at a discount to the S&P 500. And at the exact same moment, Anthropic is projecting $50 billion in revenue, Cursor is getting acquired for $60 billion, and SpaceX, Anthropic, OpenAI, and Databricks are about to generate more market value than every other IPO since 2000 combined. Both things are true - and which one defines your next 18 months depends entirely on one question: are you tired or are you wired? In this episode, SaaStr CEO and Founder Jason Lemkin calls the market as he sees it, names who is winning and who is pretending, and makes the case that the Cambrian explosion in B2B is just getting started. You'll learn: Why the SaaSpocalypse was never about B2B dying - it was about pre-AI software dying - and what the Palantir, Twilio, and Atlassian re-acceleration stories actually tell you The four categories every B2B company falls into right now, and why category four founders need to stop pretending the recovery is coming on its own Why vibe coding your CRM is dead as a concept, and what "putting deals on your calendar" actually means as a product strategy Why your biggest near-term competitive edge might be two days of engineering work - making your API agent-friendly before your competitors do What SaaStr's own journey from 20 humans to 3 humans and 21 agents teaches you about consistency as the only real cheat code in agents This is for you if: Your growth has slowed and you are not sure whether it is a market problem or a you problem - this session will help you figure out which You are a founder or exec who has been in the "AI is coming" conversation for a year but has not yet seen it show up in your revenue You want the unfiltered version of where B2B is headed in the next 18 months, including the parts most people are too polite to say out loud

Fusion has been “ten years away” for decades — but one corner of the field just crossed a line that changes the conversation. In December 2022, Lawrence Livermore National Lab's National Ignition Facility achieved ignition: a self-sustaining fusion reaction that produced net energy. And they've repeated it.So what happens when you take the only fusion approach that's proven to work, and focus less on new physics… and more on building the industrial supply chain to do it again and again, cheaply and reliably? You get a field trip!In part one of a two-part field trip to Livermore, California, Molly visits Inertia Energy's “House of Fusion” to meet two of the company's co-founders:Jeff Lawson (yes, that Jeff Lawson — founder of Twilio and majority owner of The Onion) on the business case for commercializing ignition, and why Inertia thinks the economics are finally ready.Mike Dunn, former Lawrence Livermore power-plant designer and Stanford professor, on what it takes to turn a lab breakthrough into a power plant — from a gigawatt-scale “engine” that can follow renewables on the grid, to building a million precision fuel targets a day.We talk about:What “ignition” actually means — and why it's different from “fusion someday”Why Inertia is starting with the only physics regime that's been proven to produce net fusion energyThe two big bottlenecks: high-power diode lasers and mass-manufactured fusion targetsHow scaling semiconductor manufacturing could drive laser costs down (and why “1,000x” matters)What a fusion target is: a tiny fuel capsule inside a miniature “oven” (and why lead beats gold for economics)Why a fusion plant looks more like a high-RPM engine than a one-off experiment — and how that changes everythingPotential early markets beyond electricity: high-temperature process heat for steel, cement, and fertilizerWhat it looks like to build a fusion company in Silicon Valley: Apple/Waymo-style process engineers, high-end metrology, and a Nerf gun used as a stand-in for high-speed target trackingThunderdome. Yes, really.Links:Inertia Fusion: https://inertia.com/Everybody in the Pool: https://www.everybodyinthepool.com/Subscribe to the Everybody in the Pool newsletter: https://www.mollywood.co/Become a member for the ad-free version of the show: https://everybodyinthepool.supercast.com/Join our Discord! https://discord.gg/2EsDhwQC2z Hosted on Acast. See acast.com/privacy for more information.

Join Tyler Wells, Co-founder and CTO of BrainGrid, for a forward-looking discussion on how artificial intelligence is rewriting the rules of product development. Boasting over 25 years of distributed systems engineering—including a foundational tenure at Skype building Facebook's first video-calling engine and 7+ years directing Video and global SRE at Twilio—Tyler has built infra where structural failure was not an option. In this episode, we explore why the traditional constraints of software engineering—headcount, timelines, and budgets—are dissolving, leaving a brand-new bottleneck at the front of the innovation cycle: human imagination.

Today, we're joined by Jeff Lawson - co-founder of Twilio and now founder of Inertia, a fusion energy company commercialising the Lawrence Livermore fusion breakthrough (the first experiment to produce more energy from fusion than it consumed.)Cameron McLain talks to Jeff about why he thinks the barriers to fusion are manufacturing problems, not physics problems, what a 10-15 year timeline to grid energy actually looks like, and why he thinks SaaS is heading for a structural reckoning.He speaks about:Why the Lawrence Livermore breakthrough proved the physics works.Why the two commercial barriers are cost problems, not technical problems. For almost 100 years, fusion was '3 decades away' because nobody knew if it could work. But now it can work, the question is commercialisation.Why fusion and solar will win together. In 50 years, Jeff expects the grid to run on two sources: solar-plus-battery and fusion. How to make Agile work in hardware (and why the Gantt chart is a lie!)Why SaaS has an innovator's dilemma in the AI age.Why infrastructure companies win when the world is building.Why storytelling runs through everything: fundraising, hiring, selling.Building a purpose driven company? Read more about Giant Ventures at www.Giant.vc.Music credits: Bubble King written and produced by Cameron McLain and Stevan Cablayan aka Vector_XING.Please note: The content of this podcast is for informational and entertainment purposes only. It should not be considered financial, legal, or investment advice. Always consult a licensed professional before making any investment decisions.

Join Angel Cisneros, Founder and CEO of Saptiva AI, for a deep dive into the structural mechanics of building tech ecosystems that endure. In 2007, two years before WhatsApp launched, Angel co-founded Quiubas Mobile, converting a lean, bootstrapped messaging social network into the dominant underlying telecommunications backbone for all of Latin America. WhatsApp itself became his first Silicon Valley client, relying completely on the layer he built. Now, following Quiubas' high-profile acquisition by Twilio, Angel is executing the exact same playbook for the artificial intelligence era. In this episode, we explore why raw silicon and generic LLM models are commodities, and why the ultimate moat belongs to the orchestration and control plane.

While the market chased AI names, communications software stocks like Twilio (TWLO) and Bandwidth (BAND) quietly re-accelerated. Here's what the financials actually show — and whether these forgotten names deserve a spot in your portfolio.CSI breaks down two API-layer software companies left for dead after the pandemic era that are now showing signs of fundamental re-acceleration. We analyze quarterly revenue trends, operating profit trajectory, and free cash flow for both — including the key distinction between Twilio's headline revenue and organic revenue (stripping out carrier pass-through fees).We also cover Bandwidth's emerging relationship with Salesforce as a voice-powered AI agent infrastructure provider, and what that means for future revenue growth.Plus, we address the macro question investors are asking: if enterprises pull back on AI token spending, does that actually send them back to SaaS vendors? We break down both sides of that thesis.This is a fundamentals-first look at an under-covered corner of the software market — no hype, just the numbers.

AI can generate content at scale — but it can't replace good taste, clear judgment, or emotional connection. In the season 7 premiere of Real Creative Leadership, Adam Morgan explores why human creativity is becoming more valuable in the age of AI.

E-commerce reporter Ann Gehan talks with TITV Host Akash Pasricha about OpenAI's rapid push into conversion-oriented ads ahead of its anticipated IPO. We also talk with Modal Labs CEO Erik Bernhardsson about the company's new $4.65 billion valuation and infrastructure roadmap, and we get into Twilio's stock performance and voice AI agent risks with Financial Analysis Columnist Anita Ramaswamy.Articles discussed on this episode: https://www.theinformation.com/articles/twilios-ai-boost-double-edged-swordhttps://www.theinformation.com/articles/openais-next-ad-move-going-small-scale-bigSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - OpenAI Scales Conversion Ads and Targets Small Businesses08:26 - Modal Labs CEO Erik Bernhardsson on $4.6B AI Infra Roadmap16:10 - GitHub's Maintenance Issues and the Need for AI Native Code Tools25:19 - Twilio Defies the SaaS Apocalypse on Voice AI Hype29:26 - Usage-Based Pricing Risks & Telecom Cost Pressures on Twilio

Alissa Coram and Ed Carson walk through Tuesday's market action and discuss key stocks to watch in Stock Market Today. Learn more about your ad choices. Visit megaphone.fm/adchoices

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

I've always admired Jeff Lawson. I vividly remember the first time he tried to explain Twilio to me. He still had hair back then. I will admit that it took me a few of these sessions to understand Twilio, but I knew Jeff was brilliant. Not only did Jeff build an amazing company with a strong culture, but he also created an entirely new segment in communications. After 16 years of running Twilio, including an IPO, Jeff hung up his coding gloves. The next stop, surprising (at first) was The Onion. He shares the story in this interview. Spoiler: The Onion is doing well and back in print. Moving from a multi-billion-dollar comms powerhouse to a satire publication may not seem intuitive. Poetic is a better description as he transitioned from messaging to the message. One can respect Lawson, Twilio, and The Onion — it's a happy journey. I have always considered satire as a critical form of storytelling. It is one of the hardest forms of comedy because it requires so much awareness. The Onion is an important source of truth, which is more than Jeff would say about CRM. Speaking of CRM, Lawson's views of CRM haven't changed. I asked him if his views have changed since SIgnal 2022. Where he declared CRM a "$69 billion failure." Spoiler: No. For decades, CRM giants promised a dream of unified customer data, but Lawson says the evidence speaks for itself. Lawson shares how Salesforce, born as a B2B deals tracker, was shoehorned into a broader B2C CRM tool with disastrous results. He says companies pay through the nose for the privilege of having their data held hostage. He is now watching Salesforce pivot to a #Headless model, similar to what Twilio built years ago. Lawson is very suspicious of how the CRM industry will deliver headless. He doubts it will be the disruptive alternative that's needed. Our conversation covers a lot of ground, including the future of human agents, the never-launched Billio service, consumption-based pricing, the speed and height of the current hype cycle, council AI, and even fusion energy. As we navigate an era defined by decision fatigue and algorithmic noise, we are left with Lawson's central provocation: Is your relationship with technology actually serving your well-being, or are you just paying through the nose for hype that fails to deliver?

Atlassian spent three years connecting 150 billion organizational objects before the results appeared: 44% more accurate AI answers, 48% fewer tokens, a coding agent that reviewed 2 billion lines of code in two minutes. That's the proof enterprises are pointing to when they argue that context graphs are the unlock. What the benchmark obscures is the order of operations — the graph had to exist before any of those numbers were possible.The reorganization bet is running in parallel, and it's moving faster than the infrastructure. Airbnb's CHRO is converting documentation to markdown, building skills libraries, mining meeting recordings before institutional memory disappears — five structural prerequisites before the first agent goes live. Meta is posting $26.8 billion in Q1 profit, laying off 8,000 people, and reporting “horrifically, historically low” employee morale. Both are restructuring around AI. Only one is sequencing it correctly.In AI customer experience, Twilio is working against a Qualtrics finding that 1 in 5 AI interactions delivers zero benefit. Rikki Singh's diagnosis is precise: the orchestration layer is there, but it's running without the context layer underneath it. FAQ automation with better packaging is still FAQ automation. The unlock is real — but only when all three pieces are in place, in order. The knowledge worker playbook in this edition addresses the fourth variable: what happens to the people whose roles disappear when the gathering does.Rikki Singh leads product innovation at Twilio — what the company is calling its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the foundational research on what makes a great PM. The Qualtrics 2026 CX Trends Report found nearly 1 in 5 consumers who used AI customer service saw zero benefit — the baseline she is working against.* Why AI CX is still FAQ automation with better packaging* Why AI spend is as unpredictable as AI upside* The wrapper that makes AI feel like it thinks* Vitamins vs painkillers: the product sense filter* How to protect long-horizon bets inside a big company* Why the brand — not the vendor — owns AI failureListen: Spotify | Apple PodcastsJamil Valliani leads AI product at Atlassian, where he has spent three years building the Teamwork Graph across 300,000 companies. Recorded live at Team ‘26 in Anaheim, where Atlassian demonstrated what connecting 150 billion organizational objects produces: 44% more accurate AI answers using 48% fewer tokens, and a coding agent that reviewed 2 billion lines of code in 2 minutes.* Why your team spends 80% on gathering, not deciding* The adoption pattern that turns skeptics into converts* How to build trust with AI one small task at a time* Why giving AI less data often gets you a better answer* How leaders stop waiting for Friday status reports* From 2 ideas to 10: the creative unlock nobody explains“You didn't hire your team to write reports. You hired them to advance the business forward.” — Jamil VallianiListen: Spotify | Apple PodcastsNo one is measuring ROI & fewer understand knowledge graphsWe attended Atlassian Team ‘26 in Anaheim to cover the Teamwork Graph and what knowledge graphs actually mean for the future of work. Key learnings:* Everyone is in such a rush to increase adoption numbers that no one cares to measure ROI, only velocity* In the rush to adopt, many orgs are discovering dozens of agents built by individuals that are unsanctioned and eating up tokens* While there's excitement about announcements about getting access to more context, few understand what to do with the context that's currently available to them today

FREE DOWNLOAD: How to Set Up the Hermes Agent → https://value.8figureagency.co/hermes Ready to become an AI-native agency? Book a call at 8figureagency.coGuest: Ben Fisher, founder of Skinny and Bald. Hampton member. Been coding since fifth grade. Has been CEO twice, CTO three times. Describes himself as 60% product, 40% engineer.The pull: Ben is one of the sharpest guys in the Hampton AI channel. This is his second time on the pod. Jordan came with real questions about funnels, databases, and skills — Ben answered live, then pulled up his actual meeting-processing skill on screen share.What we coveredThe build vs. buy question. Jordan's giveaway funnel pulls comments → emails → form fills → booked calls across LinkedIn and X, six campaigns a week. His partner said use GoHighLevel. Ben's framing: build custom with Claude when you control the maintenance, use a tool when it solves 80%+ without workarounds. Texting is the exception — Twilio's regulatory rabbit hole can eat days even with Claude Code.Databases and custom funnels. Jordan wants the funnel experience to mirror what the user clicked — landing page copy, follow-up sequence, everything. Ben's example: he still uses Kit.com for his newsletter, but layered custom API logic on top. He didn't rebuild Kit. He enriched it.The thing that separates real builders from vibe coders. “What distinguishes really effective builders really comes down to workflow.” Same models. Same Claude. Different results because of how people work. Ben's non-negotiable: test-driven development. Plan first. Write the tests. Then build. Otherwise Claude tells you it shipped something that doesn't exist.The “your friend Ben is absolutely correct” story. Hampton buddy building a chief of staff agent in Slack and WhatsApp. Asked Claude if it was secure. Claude said yes. Ben listed four gaps. Buddy pasted Ben's message into Claude. Claude wrote back: “Your friend Ben is absolutely correct. We don't do this, this, this, and this.” Lesson: you cannot ask the AI to verify the AI.Claude Skills, real talk. Skills are mostly plain English text files the AI reads. They get highly personal fast — Ben said his public repo of skills is becoming less useful to others because the nuances are his. Best move for most people: use someone else's skill as a reference, have Claude analyze how it works, then build your own flavor.Ben's content-from-meetings workflow (live demo). Fireflies records every call → transcripts get stored as markdown files in a local folder → a Claude skill called process meeting notes runs on demand, pulls the last 3 days, formats each meeting in EOS Level 10 format (clear accountability, agreements, action items), and routes to-dos to the right project repo. Why local files instead of remote Fireflies calls? Speed. His second brain reads disk faster than it makes API calls across nine months of transcripts.Writing in your voice with AI. Ben studied journalism and advertising. He uses Claude as a sparring partner first, last-mile editor second. Reference for anyone serious about this: every.to publishes their full editorial AI process, including how to build an anti-AI style guide. Ben also actively removes em-dashes from his AI output now because they've become the tell.Markdown files as the convention. .md is what the AI world runs on. Pound signs for headers, asterisks for bold. Doesn't really matter if you use .txt or .docx — but markdown gives the AI hierarchy it can parse.Tools and references mentionedFireflies, Claude Code, N8N, Zapier, Kit.com, GoHighLevel, Twilio, Obsidian, every.to, Superpowers (Claude skill harness), Hampton, EOS Level 10 format, Ruben's “How AI” Substack.Where to find Benskinnyandbald.com — consulting offersdearben.ai — Ben's AI podcast where execs submit questions and he answers live with screen share, plus his newsletterReady to build an AI-native agency that runs on systems, not scrambling?8 Figure Agency helps seven-figure agency owners install the agents, automations, and AI workflows that turn your team into a 10x operation. Done-for-you implementation starting at $2K/month.Book your call: 8figureagency.covalue.8figureagency.cohermesThis playbook shows you the exact stack. Install instructions, the 30-day roadmap, the five daily prompts that turn your agent into a second brain, and eight use cases pulled straight from agencies doing it right now.8figureagency.coAI Solutions for Marketing Agencies | 8figure agencyOptimize your marketing agency with our AI solutions. Join 1,000+ agencies and scale your revenue today!every.toEveryEvery — The only subscription you need to stay at the edge of AI. Ideas, apps, and training from practitioners who build with AI daily.http://every.to/skinnyandbald.comBen FisherI help companies figure out where AI fits — and then build it.

As companies shift from one-way customer notifications to AI-powered, personalized conversations at scale, developers need advanced communications infrastructure to build omnichannel digital messages. Twilio — which powers B2C SMS, two-factor authentication, customer alerts and reminders alongside other digital interactions — has positioned itself as critical infrastructure for the AI era. Growth is accelerating and new products are poised to offer an added lift to revenue. In this episode of the Tech Disruptors podcast, CEO Khozema Shipchandler joins Bloomberg Intelligence senior telecom analyst John Butler to discuss Twilio's turnaround, its new Conversations suite, digital messaging tools, and the rising importance of identity, governance and observability amid the rise of AI agents. They also explore voice and self-serve trends, carrier fees, competition and investment priorities.

Atlassian connected its AI agents to a richer layer of company knowledge (documents, projects, goals, people) and measured a 44% improvement in answer accuracy using 48% fewer resources. Same models. Different information. Brian Armstrong restructured Coinbase the same week: 14% headcount cut, five management layers maximum. When AI can surface what previously required institutional memory and senior tenure, the organizational layers built around that knowledge become harder to justify.The visible shift gets covered in tech headlines. What gets lost in the announcement energy: none of this works if the company hasn't decided what it wants AI to do.The more widespread barrier is upstream of governance. Most executives approving AI budgets are working through the aftermath of pilots that underdelivered, first-generation deployments that didn't survive contact with their actual data, and early model results that left skepticism the current tools have since substantially outrun. That trust deficit — organizations evaluating new AI investment based on experiences two generations old — is where enterprise AI projects most commonly stall. Shadow AI governance and deployment intent are real risks, but they're downstream of that harder problem. There is no closing the capability gap inside an organization that is quietly waiting for the next deployment to fail too.John Willis co-wrote The DevOps Handbook because software teams were shipping code fast without feedback loops or governance. He sees the same pattern repeating with AI — and he spent five decades documenting what happens when the gap between vendor promises and operational reality gets this wide.* Why shadow AI is more dangerous than an outright ban* Why throughput without governance means instability at scale* Why governance creates flow instead of stopping it* Why most teams have ML evaluation tools when they need audit trails* Why even a five-person startup needs digitally signed records of agent decisions* What AI winters teach us about where we actually are nowListen: Spotify | Apple PodcastsRikki Singh leads product innovation at Twilio — what the company calls its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the definitive research on what makes a great PM. The Qualtrics 2026 CX Trends Report found nearly 1 in 5 consumers who used AI customer service saw zero benefit. That number is the benchmark she is working against.* Why most AI CX is still FAQ automation with better packaging* Why the LLM wrapper creates false confidence — the model generates strings, it is not thinking* Vitamins vs painkillers: how to parse what customers don't say out loud* How to protect long-horizon bets inside a public company* Why the brand owns the accountability when AI gets a high-stakes interaction wrongListen: Spotify | Apple Podcasts

Khozema Shipchandler, CEO of Twilio (TWLO), talks with Marley Kayden about the company's latest earnings and ways it backs the growth story. Shares jumped more than 30% since the report. Khozema says Twilio has a lot of space to capitalize on the AI trade, especially in the agentic AI space as more companies use the tech. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

My guest today is Tyler Wells, co-founder of Brain Grid.Tyler recounts 25+ years in software, from an early IBM XT to work across military communications, startups, Skype/Microsoft, and seven and a half years at Twilio building video and SRE organizations, before founding Propel Data (which didn't find product-market fit) and then Brain Grid. He describes an experiment-driven approach to building high-performance systems by defining hypotheses, creating a “steel thread” MVP, and prioritizing observability for 2:00 AM incidents. He discusses how AI coding shifts focus from typing code to architecture, documentation, critical thinking, and red-teaming plans, while warning that agents need guidance on separation of concerns and DRY to avoid refactor side effects. Brain Grid emerged from using Cursor agents during Propel's wind-down and aims to generate detailed specs, acceptance criteria, and validation loops so agents implement features reliably, with attention to token efficiency. He also covers co-founder traits, chaos engineering, compliance challenges for solopreneurs, career advice, and staying grounded through exercise, cooking, and family.Tyler Wells is the Co-founder and CTO at BrainGrid, BrainGrid is one of the first platforms built specifically to replace the missing product management role in AI-native software development.He is currently building BrainGrid — helping engineering teams ship faster with AI-assisted requirements breakdown and task management. We're focused on bridging the gap between product ideas and implementation-ready work.His Background: He has spent 25+ years building systems where failure isn't an option—from satellite communications at Hughes Space to real-time video at global scale. I led the team that built Facebook's first video calling feature powered by Skype, then spent 7+ years at Twilio building their Video Platform (WebRTC) and leading SRE/Observability across the company.

AGENDA: 00:00 – Mag Seven Earnings: The "Super Bowl" of Tech Results 04:45 – Google's Cloud Explosion & The AI Search "Disruption" That Never Came 15:53 – Microsoft's $190B Bet: Is AI the Only Thing Keeping Growth Flat? 21:59 – Meta's $150B Future Bet vs. Wall Street's Need for Spreadsheets 28:50 – Palantir's Home Run: Why Big Companies Spend Big Money on AI 38:43 – Apple's Quiet Consistency & The Stealth Inflation of Memory Chips 41:11 – The SaaS Apocalypse Over? Atlassian and Twilio Lead the Re-acceleration 50:50 – Anthropic's $50B Raise & The Math Behind Token vs. Salary Spend 01:05:59 – Sierra's $15B Valuation: Replacing the $400B Customer Service Labor Market 01:13:39 – Musk vs. Altman Trial: Statute of Limitations, Standing, & Private Diaries 01:17:42 – The End of Managers? Brian Armstrong & The Rise of the "Individual Contributor"    

Inbal Shani (CPO and Head of R&D @ Twilio) deconstructs the transformation of the R&D org at Twilio! We explore the shift from a GM-led model to a unified platform strategy and “why structure must always follow strategy.” Inbal shares her framework for moving from output-focused metrics to input goals, prioritizing “time-to-value,” and the nuances of measuring AI products. We discuss using "R&D roadshows" as a strategic company transformation tool and why engineering leaders must master product positioning. We also dive into mental models for future-proofing your business, from "working backwards" to solve customer problems, to embedding systems thinking into the DNA of your engineering team, and critical questions to identify and optimize decisions around your company's moat.    ABOUT INBAL SHANI As Chief Product Officer, Inbal leads Twilio's R&D organization, encompassing product, engineering, and R&D operations. She is dedicated to driving platform-wide innovation, empowering customers, and delivering transformative, customer-focused solutions.   Unblocked: The context engine your coding agents are missing. Give your coding agents the context your best engineers have. Your agents can read code, but they don't know how your team works. Rules and MCPs give access to information but not understanding. That's why you still have to tell them where to look and what to look for. Unblocked gives your agents the history, conventions, and decisions behind your code so they generate mergeable output without the back and forth. It automatically surfaces the right context for every task, so agents stay on track without the set up tax or the correction loops. getunblocked.com/elc   SHOW NOTES: Catalysts for Twilio's R&D transformation and the shift away from organizational silos (2:49) Strategy Drives Structure: The lightbulb moment at a strategy offsite that demanded structural change to execute vision (5:14) Why structure must follow strategy and creating a "change-constant" culture (7:23) Implementing the “working backwards” methodology and the internal power of the PRFAQ (13:52) Tactical ways to filter customer signals and find real unmet problems versus feature requests (16:35) Shifting from output-focused goals to input goals and prioritizing "Time to Value" (18:35) Using weekly product reviews to align metrics with qualitative customer feedback (21:34) Measuring AI Products: Why AI products require behavior-based measurement instead of traditional binary testing (23:24) Building security by design with layered protection for AI-generated code environments (26:09) Mental models for future-proofing your business by acting as a "fortune teller" for needs (28:45) The R&D Roadshow: Enabling the entire company on new ways of working through storytelling (32:28) Why engineering leaders must master product positioning to bridge the gap to market (38:33) Relatable storytelling: Explaining Twilio's value to your parents to sharpen your pitch (41:47) Rapid Fire Questions (43:14)   LINKS AND RESOURCES How Minds Change: The Surprising Science of Belief, Opinion, and Persuasion - In this lively journey through human psychology, bestselling author and creator of the You Are Not So Smart podcast David McRaney investigates how minds change--and how to change minds.   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey everyone, Alex here

What happens when your next customer isn't even human? Chris Koehler is the CMO of Twilio, and his 2026 marketing plan lasted exactly one month before AI agents forced him to rethink everything. In this conversation, Chris gets real about why planning horizons have collapsed from years to weeks, what happens when AI agents — not humans — are doing the buying, and why brand might matter MORE in an AI-driven world, not less. Chris Koehler is the Chief Marketing Officer at Twilio, a $5B+ customer engagement platform. Previously, he led marketing at Box and helped build the analytics engine at Adobe.   Key takeaways: • Why using AI to speed up bad processes is the wrong move — you need to reimagine them entirely • The agent buying continuum: SEO → AEO → human-to-agent → agent-to-agent • Why brand awareness becomes MORE critical when agents do the discovery • The 'frozen food vs pizza' framework for the future of software • How to stop feeling paralyzed: the quadrant exercise he uses to prioritize AI initiatives   Follow Chris on LinkedIn: linkedin.com/in/ckoehler/ Learn more: twilio.com   Chapters: 00:00 Meet Chris Koehler, CMO of Twilio 01:44 The Healthy Tension Between IT and Marketing 02:44 Planning in Weeks, Not Quarters 04:40 The Rise of Asynchronous AI Work 07:22 Unlearning 30 Years of Marketing 11:37 Frozen Food vs Pizza: The Future of Software 14:07 Content Scarcity to Content Abundance 15:51 Could AI Agents Fix the Privacy Problem? 17:11 The Agent Buying Continuum 22:12 The Tsunami Most People Can't See 24:40 How to Get Embedded in LLM Recommendations 26:33 Does Brand Matter When Agents Are Buying? 31:01 Measuring Success When You Can't Track Anything 33:12 The AI Agent Webinar Experiment 36:44 What Events Look Like in 2029 42:48 How Chris Curates AI Intel Daily 46:22 Advice for Paralyzed CMOs 49:01 Lightning Round ----Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

How are brands supposed to deliver AI-powered customer experiences when their data is scattered across systems that were never designed to work together? In this episode, I sit down with Peter Bell, VP EMEA Marketing at Twilio, to unpack one of the most important AI topics that still does not get enough attention outside technical circles, Model Context Protocol, or MCP. While many conversations about AI remain stuck on model hype, chatbots, and the latest product launch, Peter brings the discussion back to something far more practical. If businesses want AI to deliver real outcomes in customer service, marketing, and brand engagement, they first need a reliable way to connect large language models to the right data, in the right systems, with the right controls in place. That is why this conversation matters. Peter explains how MCP could become one of the biggest unlocks for enterprise AI by creating a standard way for LLMs to access information across fragmented tools like CRM platforms, marketing systems, and other business applications. Instead of forcing every company to build custom integrations from scratch, MCP creates a more consistent path for connecting models to the context they need. For me, that is where this episode really earns its place, because it moves the AI conversation away from vague ambition and toward the plumbing that actually makes useful AI possible. We also talk about why first-party data remains so important, especially as businesses try to create customer experiences that feel seamless, personal, and trustworthy. Peter makes the point that public models may be useful for general knowledge, but brands cannot rely on generic internet-trained systems to solve precise business problems. If you want AI to support travel bookings, customer service, or commerce journeys, you need specific data, strong governance, and a much clearer understanding of the problem you are trying to solve. That sounds obvious, but it is still where many AI projects fall apart. Another part of our conversation focuses on trust, which feels especially relevant right now. From scams and impersonation to consumer fatigue and poor automation, brands are under pressure to move faster without losing credibility. Peter shares how Twilio is thinking about branded calling, RCS, conversational AI, and voice experiences that feel modern without becoming intrusive or robotic. We also discuss why too many companies still automate too broadly, too quickly, without defining the actual use case first. What I enjoyed most here was Peter's balanced view. He is optimistic about where AI is heading, but he is also realistic about the work still required to get there. This is not a conversation about AI magic. It is about data access, governance, trust, brand experience, and the standards that may quietly shape the next phase of AI adoption far more than the flashy headlines. So if you have been hearing more people mention MCP and wondering why it matters, or if you are trying to understand what needs to happen before enterprise AI can move from promise to practical value, this episode will give you plenty to think about. Is Model Context Protocol the missing layer that finally helps AI connect with the real world of business data?

Season 5, Episode 14: The Phone Call Is the LeadIn this episode of the Pest Control Marketing Domination Podcast, Casey Lewis breaks down one of the most overlooked growth tools in the pest control industry: your telephone system.A lot of pest control companies spend money on websites, SEO, Google Ads, Local Services Ads, and reviews, but still lose good leads because the phone is not answered quickly, routed properly, or backed up the right way. When a homeowner or business owner has a pest problem, speed matters. The company that answers first and handles the call well often wins the sale.This episode covers the importance of speed to lead, why every pest control business needs a clear process for answering and transferring calls, and how to make sure callers get to the right person fast. Casey also discusses the difference between using a simple cell phone, a traditional office line, and more advanced VOIP systems such as RingCentral, GoTo Connect, Comcast Business Voice, Voice for Pest, and other modern phone solutions. He also explains why owning and controlling your business phone numbers is critical so you do not create unnecessary headaches later when it is time to port numbers or switch providers.A major focus of this episode is HighLevel's phone system and IVR capabilities, including how HighLevel can use Twilio-based infrastructure and LeadConnector phone tools to manage inbound calls, routing, transfers, voicemail, and AI-powered overflow. Casey also explains the role of AI receptionists and why AI works best as an overflow, after-hours, weekend, and holiday backup, not as a replacement for a great live voice when a real customer needs help now.If you want to improve conversions, reduce missed opportunities, and build a better customer experience, this episode will help you think through the right structure for your phone system and your front-end communication process.Connect with Casey Lewis / Rhino Pest Control MarketingWebsite: https://rhinopestcontrolmarketing.com/Apple Podcasts: https://podcasts.apple.com/us/podcast/pest-control-marketing-domination-podcast/id1636764782Spotify: https://open.spotify.com/show/5MahEvV0KIKHZoq9DDSgIPFacebook: https://www.facebook.com/rhinopestcontrolmarketing/LinkedIn: https://www.linkedin.com/in/caseylewis1/Contact:Casey LewisRhino Pest Control Marketingcasey@rhinopros.com(925) 464-8383

In this episode of Engineering Enablement, Jesse Adametz joins Abi Noda, this time to host. Together, they explore how AI is showing up across the SDLC, not just in code generation, and how it is shifting bottlenecks across the development process. They unpack what “AI readiness” actually means in practice, and why it often comes down to developer experience fundamentals like documentation, environments, and feedback loops.They also discuss why enablement matters more than tool choice, how teams are thinking about measuring ROI, and what changes as background agents become more common. Finally, they explore how the role of the engineer may evolve, the open questions teams are still grappling with, and the challenges of non-engineers contributing to codebases.Where to find Jesse Adametz: • LinkedIn: https://www.linkedin.com/in/jesseadametz • X: https://x.com/jesseadametz • Website: https://www.jesseadametz.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro(02:12) Where AI is showing up across the SDLC(05:53) AI readiness and its link to developer experience(08:23) Why enablement, education, and experimentation matter more than tool choice(13:05) The case for a dedicated enablement team(14:50) Measuring AI ROI: challenges and tradeoffs(19:46) Background agents and token spend(24:12) Measuring agent output with PR throughput(26:58) How the engineer role might change(31:01) Specs and documentation in the age of AI(33:11) Non-engineers writing code(35:30) What's changing in the SDLC and open questionsReferenced:• Measuring AI code assistants and agents• Lessons from Twilio's multi-year platform consolidation• The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win• How Claude remembers your project - Claude Code Docs• specIsJustCode : r/ProgrammerHumor

In 2023, stepping into the CFO role at Twilio, Aidan Viggiano faced a defining reality: “the first hard call was a layoff,” she tells us. The company had surged during the pandemic as digital communications accelerated, but by mid-2022, growth slowed while profitability lagged. “We can't be slowing in growth and not be profitable and not generating cash,” she explains, describing the moment that forced a fundamental shift in strategy.Over the next six months, Twilio reduced its workforce by about 40%, she tells us—a decision that tested not just financial discipline but leadership resolve. For Viggiano, the experience underscored a core principle: communication is as critical as the decision itself. “The importance of over communication…being transparent… and treating everybody with humanity,” she tells us, became central to how she navigated the transition.This moment reflects a broader leadership mindset shaped by aligning growth with accountability. The pivot from rapid expansion to balanced performance required not only cost action but a cultural reset—one grounded in clarity, trust, and execution.At the same time, Viggiano continues to frame Twilio's value through its role as a communications infrastructure provider, powering interactions between businesses and consumers at scale. From authentication codes to real-time customer engagement, the company's reach is often invisible but essential.For Viggiano, the lesson is clear: finance leadership is not only about numbers—it is about guiding organizations through inflection points with transparency, discipline, and humanity.

Inbal Shani dropped her PhD for a job offer she never expected to recieve, and it set off a career spanning Amazon, AWS, Microsoft, GitHub, and now Twilio, where she serves as CPO and Head of R&D. In this episode, Inbal explains how curiosity is really a leadership trait, why introverts can be the most connected leaders in the room, and how the future of product management is being rewritten.References:AWS https://aws.amazon.com/Github: https://github.com/Inbal Shani: https://www.linkedin.com/in/inbalshani/Microsoft: https://www.microsoft.com/Technion: https://www.technion.ac.il/en/Tel Aviv University: https://english.tau.ac.il/Twilio: https://www.twilio.com/Uber: https://www.uber.com/Timestamps:00:00 Introduction01:47 Inbal's childhood: introversion and learning to build06:00 Being one of six women in aerospace engineering07:06 Fostering a strong belief in yourself09:36 Why Inbal dropped out of her PhD13:27 Learning to lead others as an introvert15:24 Why curiosity is a core leadership trait16:45 Two books that shaped Inbal's career18:27 What determines a great team member22:40 Empathy is a prerequistie to success26:36 How Inbal made her career choices28:31 Advice to younger women navigating their career31:14 Inbal's journey to CPO at Twilio34:04 The future of product management39:42 Could the app era be over already?

Tyler Wells has spent over 25 years in software development — with stops at Skype, Microsoft, Twilio, and multiple startups along the way. Now he's channeling all of that experience into BrainGrid, a platform designed to solve one of AI's biggest frustrations: turning a messy idea in your head into working, deployable software.In this episode, Tyler breaks down why simply having access to Claude Code or Cursor doesn't automatically make anyone a developer, why context is king when working with AI agents, and what he and his co-founder discovered that became the foundation for BrainGrid.In this episode:Why non-technical founders still face real barriers even with powerful AI coding tools availableHow BrainGrid acts as an AI-powered product manager — asking the right questions to turn your idea into a detailed software specificationThe origin story of BrainGrid and the "aha moment" that started it allWhy Anthropic's models consistently outperform the competition in their evaluationsThe biggest daily challenges of building a product at the speed AI now makes possibleWhat we're getting right — and dangerously wrong — about AIConnect with Tyler Wells & BrainGrid: braingrid.ai & LinkedIn

Zineb Bhaby, AI Lead at the Norwegian Refugee Council, introduces NRC's CLEAR (Crisis Learning, Early-warning, Anticipation, and Response) initiative and discusses the critical necessity of data collaboration in the humanitarian sector with Humanitarian AI Today producer Brent Phillips. The CLEAR initiative is a three-year project supported by Twilio that is designed to build a digital "backbone" for humanitarian cooperation that the humanitarian community can collectively maintain and evolve. Zineb stresses that CLEAR's goal is bring together humanitarian, academic and private sector partners through a consortium to integrate diverse data sources into unified early warning and early action systems, leveraging artificial intelligence and predictive analytics to transform how humanitarian organizations detect, prepare for and respond to crises. Discussing CLEAR and challenges associated with the collection and use of data by aid organizations and the imperative to do better, Zineb nevertheless emphasizes that strict data governance remains a priority to protect the safety and sensitivity of information regarding vulnerable populations. By prioritizing an agile, safety preserving, open-source approach that bridges the gap between available information and field response, the initiative seeks to create a more resilient and unified technological foundation for the entire humanitarian ecosystem.

On this special segment of The Full Ratchet, the following Investors are featured: Ethan Austin of Outside VC Arianna Simpson of Andreessen Horowitz Navin Chaddha of Mayfield Each investor highlights a situation where they decided not to invest, why they passed, and how it played out. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

AGENDA: 04:14 Anthropic's $30B Raise at $380B 06:18 Why SaaS Stocks Keep Getting Crushed 18:15 Wall Street's New Religion: AI Replaces Headcount  22:42 The Bear Case for Shopify: What Could Go Wrong? 31:51 Replit and Lovable are Proof Figma Missed Out: Figma; Buy or Sell?  48:42 Stripe Raises at $140BN: Is Stripe Wildly Overvalued or Adyen Undervalued?  54:36 OpenAI Buys OpenClaw 01:06:28 Thrive's $10B Growth Fund 01:09:10 Arif Janmohamed Leaves Lightspeed for New Firm 01:17:12 Workday's Founder Returns as CEO: Will it Work?  01:20:34 Which Founder Returns Next: HubSpot, Twilio, Gitlab? 01:24:03 Is Monday.com a Screaming Buy? 01:28:25 Jason and Harry Bet $200,000  

Stevie Case is the CRO of Vanta, the trust management platform serving everyone from founders to Fortune 100 CISOs. A former pro-video gamer who stumbled into sales through a mentor's bet, Stevie has built one of the most unconventional paths to the C-suite in tech. In this episode, she unpacks why early revenue hires fail, what separates a true CRO from a VP of Sales, and why she believes fewer than 10% of current CROs will thrive by 2028. In today's episode, we discuss: Why early revenue hires fail What a top 1% CRO actually does The scaling mistake Stevie made by copying Twilio's playbook at Vanta Why Vanta remains 100% sales-led at every segment AI vs. humans in go-to-market References: Cursor: https://cursor.sh/ Gong: https://www.gong.io/ Salesforce: https://www.salesforce.com/ Twilio: https://www.twilio.com/ Vanta: https://www.vanta.com/ Where to find Stevie: LinkedIn: https://www.linkedin.com/in/steviecase/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Why early revenue hires fail 02:23 Who to hire at $5M in revenue 04:16 Coin-operated sellers vs. long-term builders 05:57 What excellence looks like in the CRO role 07:44 Metrics, confidence, and velocity 12:04 Should CROs lead sales? 14:39 From shy seller to revenue leader 16:36 Learning to scale at Twilio 17:44 "There is no CRO playbook" 19:58 Stevie's scaling mistake at Vanta 22:16 Why Vanta stays 100% sales-led 23:16 The value of planning 24-26 months ahead 29:54 When trusting intuition was the wrong call 30:49 Do humans still have a place in the future of GTM? 33:33 Stevie's leadership non-negotiables 36:36 The myth of hiring for industry expertise 40:00 What stays centralized in a 600-person company 47:09 The hidden leverage of a customer's first 30 days 53:42 Why the CRO role will face enormous changes by 2028 58:42 What leaders must do now to stay relevant 01:02:30 Unpacking the CEO-CRO dynamic

The CEOs of Medtronic and eToro join the show to break down quarterly results. eToro seeing crypto volumes plunge along with the price of the asset but investors liking what they heard in terms of outlook. Then, the CEO of Twilio on how his company is using AI and why certain companies are being caught up in the software sell-off. Plus, a new quantum company going public on the NYSE. The CEO of ‘Infleqtion' joins the join with the stock jumping in early trade. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today we tackle the noise surrounding the AI movement with Paul Klein, CEO and Founder of Browserbase. With a career spanning early-stage Twilio to raising $70 million in under two years for his own infrastructure startup, Paul brings much-needed critical thinking to the "AI bubble" debate. We explore the bridge between old-world sales principles and modern, developer-first GTM strategies. Paul breaks down why Product-Led Growth (PLG) should be viewed as a pipeline engine rather than just a revenue machine and explains the power of the "Logo Flywheel" in creating executive FOMO.

In this episode of Builders Wanted, we're joined by Ann Rich, Senior Director of Design at Adobe. Kailey and Ann dive into the intricate world of product design where empathy drives innovation. They discuss the challenges and strategies in leading design at scale, how Adobe builds trust in the era of generative AI, and the importance of cross-functional collaboration. Ann shares insights on inclusive design, co-innovation with customers, and the evolving role of designers in creating user-centric and technologically advanced solutions.-------------------Key Takeaways:Successful AI-era design requires deep technical understanding alongside creative craft—designers must know the models and technology behind their interfaces to bridge human needs with AI capabilities.Speed and adaptability are essential as market paradigms can shift between conception and launch, requiring experimentation, customer co-innovation, and iterative validation over traditional research cycles.Design leadership gains influence by grounding decisions in data and user needs rather than aesthetic opinion, transforming design into a strategic driver in executive and engineering conversations.-------------------“ [Design] is really changing from a two-way model of communication and interaction to a three-way or more discussion. That's really thinking about it being a human, the interface they're working on, and then all of the things happening behind the scenes. In order for someone to be successful with what you're designing, designers have to start understanding the technology behind it. Because in order to deliver on the use case, you actually have to understand the technology and it will change the interface.” – Ann Rich-------------------Episode Timestamps:‍*(01:50) - Ann's mission at Adobe as a design leader‍*(08:15) - How trust factors into Adobe's design process‍*(16:53) - Ann's approach to inclusive design‍*(25:08) - What design teams should stop doing‍*(31:12) - A recent project that made a measurable difference for users‍*(39:06) - Ann's advice for designers looking to elevate their voice-------------------Links:Read Ann's Article How to Adapt Your Design Practice for the Age of Generative TechnologyConnect with Ann on LinkedInConnect with Kailey on LinkedInLearn more about Caspian Studios-------------------SponsorBuilders Wanted is brought to you by Twilio – the Customer Engagement Platform that helps builders turn real-time data into meaningful customer experiences. More than 320,000 businesses trust Twilio to transform signals into connections—and connections into revenue. Ready to build what's next? Learn more at twilio.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Recorded live at NRF 2026: Retail's Big Show, this episode of the RETHINK Retail Podcast features Michelle James, Vice President at CTIA, in conversation with Melissa Blassingame, RVP of Partner Alliances at Twilio. As consumers increasingly ignore unknown calls, this episode explores how Branded Calling ID in retail is helping brands rebuild trust and improve voice engagement. - Why trust in voice communications is critical for modern retail - How Branded Calling ID improves answer rates and customer experience - Real-world retail use cases, including delivery and customer support - How industry standards and collaboration are restoring trust in voice

After building products at Microsoft (Xbox, Surface), a gaming startup acquired by Disney, Twilio, and Box, Vanessa Larco joined NEA where she led seed investments in Greenlight (debit card for kids), Majuri (C2C jewelry), and Limitless (acquired by Meta). She served on Robinhood's board for five and a half years through IPO and the GameStop crisis. In this conversation, Vanessa breaks down the specific traits that separate top 1% founders from the rest, why venture capital is experiencing structural chaos from simultaneous mega-fund expansion and generational transition, and why technical founders who deeply understand consumer behavior change represent the next wave of breakout companies. Topics Discussed: How customer-focused decision-making at Robinhood during GameStop contradicted public perception The specific paradox great founders must balance: maniacal focus versus recruiting ability Why venture is simultaneously dealing with fund size chaos and generational leadership transition The decision framework for staying in venture versus returning to operating Why consumer is radically underinvested despite users' demonstrated willingness to pay for "magical" experiences How AI tools create internet-scale behavior change by synthesizing information rather than just accessing it The authentic voice problem in VC personal branding and platform-specific challenges GTM Lessons For B2B Founders: Great founders possess maniacal focus on the right problems, not all problems: Vanessa describes exceptional founders as having an "insatiability" where "they pick the thing and they can focus on the thing and not get distracted by anything else and be maniacal about it." This isn't generic persistence—it's the ability to identify which specific problem deserves obsessive attention while ignoring everything else. Employees often push back ("we have these other fires"), but top founders maintain "one track" focus. The implementation challenge: most founders spread maniacal energy across too many initiatives. The best founders are "obsessive compulsive about how they build" on 1-2 things maximum, then deliberately de-prioritize everything else, even when it feels irresponsible. Incentive structure misalignment creates unwinnable scenarios: During GameStop, Robinhood faced retail traders whose incentives were fundamentally incompatible with traditional market participants. As Vanessa notes, "if your team and your company is bound by a certain set of incentives and you're up against someone with a very different set of incentives, that never really ends well." The Wall Street Bets mantra—"we can stay irrational longer than they can stay solvent"—explicitly weaponized this mismatch. For founders: map not just competitor strategies but their underlying incentive structures. Are they optimizing for growth, profitability, strategic acquirer appeal, or something else? When your incentives conflict with a market participant's (customer, partner, regulator, competitor), you cannot win through superior execution alone—you need structural repositioning. Technical founders who ship faster capture AI-era market position: Vanessa specifically seeks "technical founders with an eye for consumer behavior change" because "speed is really important in this era." This isn't about being first to market—it's about iteration velocity. When foundational models improve every few months and user expectations evolve weekly, the team that can "deliver on it faster than anyone else" compounds advantages. Non-technical founders add product/sales/fundraising cycles between insight and deployment. Technical founders collapse these cycles, testing behavioral hypotheses in days rather than quarters. In markets where "what's possible" changes monthly, this velocity differential determines who owns category definition. Behavior change wedges beat feature superiority: Vanessa looks for founders who understand "how this new technology is changing how people behave and changing what people expect of their tools" and can identify "what need can I fulfill better because I can build this thing that couldn't be built before." The critical insight: users don't adopt based on capability—they adopt when technology enables a behavior they already want but couldn't execute. She emphasizes products that are "radically faster, radically cheaper, radically easier" (not 10% better) and founders who understand "how they'll wedge into behaviors." Implementation framework: don't ask "what can this technology do?" Ask "what behavior is currently blocked by cost/speed/complexity that this technology removes the blocker for?" Category creation happens post-problem-solving, not pre-launch: Discussing Robinhood's positioning, Vanessa reveals how the team "stayed focused" on enabling "people to continue participating in the markets" rather than defending an abstract category. The company focused on structural problems (settlement times, capital requirements) rather than category messaging. For founders: solve the acute problem your customer articulates, even if it seems tactically narrow. Category definition emerges after you've solved related problems for enough customers that the pattern becomes obvious. Premature category creation forces you to defend an abstract positioning rather than deepen specific problem-solving. Personal brand building only works at the intersection of authenticity and utility: Vanessa admits "I can't find my authentic voice on Twitter to save my life" and her successful posts are "when I'm on an airplane and it's delayed by like over an hour and I'm angry." Meanwhile, "video and audio, way more my comfort zone" but requires "discipline that I don't think I yet possess." The lesson for founders: audience building helps ("people then know what you are, what you stand for... it helps establish trust faster, it helps people find you") but forced authenticity backfires. Better to own one channel where your natural communication style works than maintain mediocre presence across all platforms. LinkedIn for thoughtful analysis, Twitter for real-time reaction, podcasts for deep conversation—pick the format that doesn't require you to perform. // 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

(0:00) Intro(1:45) About the podcast sponsor: The American College of Governance Counsel(2:31) Start of interview(3:04) Jeff's origin story. Began career in investment banking at First Boston before transitioning to a 25-year run as CFO across media companies (King World, Nielsen) and tech (DoubleClick, Oracle).(7:16) Transitioning to Bessemer Venture Partners.(8:40) Focusing on his board career and audit committee member. ValueClick, Priceline (Booking Holdings).(11:06) Growth in Public vs. Private Markets(12:49) The State of European Entrepreneurial Ecosystem(13:41) The Role of BVP CFO Council(15:31) Understanding California and Silicon Valley's Unique Culture(18:44) AI's impact on the CFO role(20:54) Dynamics Between CEOs and CFOs(23:12) CFOs in Startups vs. Public Companies "We've observed that about 5% of the headcount of any co' at any size is in the finance dpt.")(25:25) CFOs as Board Members(27:35) Board decisions on CEO hiring and firing. "The CEO's role is to articulate an effective strategy, to hire a great team, and then to execute that strategy well using that great team." "If over five years the CEO has never changed their mind based on board input, you have the wrong board."(30:36) On effective Board Composition(32:41) Navigating Shareholder Activism, including his experience at Twilio(37:35) The Debate: Stay Private or Go Public. "There are three ownership structures: public companies, PE-owned companies (where PE controls CEO), and founder-controlled private companies" "I think you're going to see quite a few companies stay private forever or for decades."(39:30) Preparing for the Future of Venture Capital (41:13) Optimizing Board Meeting Content. "Effective boards: 2/3 of time on未made decisions. Ineffective boards: show and tell." "Best-run companies: CEO encourages board members to meet with executives outside board meetings."(45:50) Books that have greatly influenced his life:The Snowball: Warren Buffett and the Business of Life by Alice Shroeder (2008)My Early Life by Winston Churchill (1930) How to Talk So Kids Will Listen & Listen So Kids Will Talk by Adele Faber and Elaine Mazlish (1980)(47:07) His mentors (50:50) Quotes that he thinks of often or lives his life by "You want to live your life to have a seamless web of deserved trust" by Charlie Munger(53:15) An unusual habit or an absurd thing that he loves. Reading adventure stories from G.H. Henty(54:01) The living person he most admires: Warren BuffettJeff Epstein is an operating partner of Bessemer Venture Partners where he leads BVP's CFO Council. He is a former CFO of Oracle and currently serves on the boards of Autodesk, AvePoint, Okta, and Twilio (previously at Kaiser Permanente and Booking Holdings). You can follow Evan on social media at:X: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

In this episode of Run the Numbers, CJ sits down with Chris Brubaker, SVP of Finance at Postscript, who's helped build the finance function from the ground up. Chris shares how he partners with sales through deal desks, sets pricing guardrails, and makes sure finance helps close deals instead of slowing them down. They dig into his hands-on approach to automation using AI with limited engineering resources, how Postscript's metrics evolved as the company scaled, when to trust internal data over benchmarks, and where teams get tripped up. Plus, a private jet accounting story—because of course.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS:Chris on LinkedIn: https://www.linkedin.com/in/wchrisbrubaker/Postscript: https://postscript.io/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:So You're Looking for a “Strategic” CFO? Bloomerang's Steve Isom on What That Really Meanshttps://youtu.be/cgHOtvG1CesThe IPO Playbook: Expert Advice from Lee Kirkpatrick, Twilio's Former CFOhttps://youtu.be/PTKAUD7PSWUThe CFO Case for Probabilistic Forecasting With AI | Bruno Annicqhttps://youtu.be/Dl8nDZPJMpE—TIMESTAMPS:00:00:00 Preview and Intro00:02:22 Sponsors — Rillet | Tabs | Abacum00:06:55 Interview Begins00:07:36 First Finance Hire and Early Scale at Postscript00:09:02 Usage-Based Margins, COGS, and the Twilio Parallel00:10:31 Partnering With Sales and Building Deal Desk00:13:16 Pricing Guardrails, Payback, and Deal Economics00:15:35 How Deal Desk Evolves Over Time00:16:01 Sponsors — Brex | Metronome | RightRev00:19:44 Making Finance a Deal-Closing Partner00:20:44 Automating Deal Desk With a Slack Bot00:23:48 How Technical Finance Leaders Need to Be00:25:17 Automating Without Engineering Help00:27:12 Why Human Touch Still Matters in SaaS00:27:53 Postscript's Finance Tech Stack00:28:30 ERP Migration and Month-End Efficiency00:29:42 The Reality of Continuous Close00:30:34 First Real AI Wins in Accounting00:31:18 Experimenting With AI Forecasting00:33:32 Metrics That Matter: Usage as a Leading Indicator00:35:49 How Metrics Evolve as the Company Scales00:37:41 Understanding the Product in a Usage-Based Model00:39:27 Micro-Seasonality and Forecasting Volatility00:42:21 How to Use Benchmarks Without Misusing Them00:43:50 Long-Ass Lightning Round: A Costly Modeling Mistake00:45:45 Advice to a Younger Finance Leader00:47:05 The Private Jet Accounting Story00:49:11 Credits#RunTheNumbersPodcast #FinanceLeadership #DealDesk #UsageBasedSaaS #AIinFinance This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

Hubble Network is redefining what's possible in satellite connectivity by connecting standard Bluetooth chips to satellites over 500 kilometers away using advanced antenna arrays and digital beamforming. Founded in 2021 by Alex Haro (co-founder of Life360, which IPO'd in 2019 and grew to 80+ million monthly active users) and Ben Longmier (whose previous company's protocol became Amazon Sidewalk after acquisition), Hubble has launched seven operational satellites via SpaceX and is serving enterprise customers across intermodal logistics, off-grid construction, and outdoor recreation. In a recent episode of BUILDERS, I sat down with Alex to explore how Hubble is building the infrastructure layer for global IoT—positioning as the "T-Mobile of space" rather than competing in device markets. Topics Discussed: The technical architecture behind connecting Bluetooth to satellites: lowering bit rates, optimizing modulation, and deploying hundreds of antennas for digital beamforming SpaceX's rideshare program mechanics and what it actually takes to book satellite launches as a startup Why Hubble deliberately chose to be network infrastructure rather than building hardware for specific verticals The psychology barrier of overcoming Bluetooth's short-range association—even among experienced RF engineers from Google, Amazon, and Starlink Strategic focus decisions when facing unlimited market opportunity across construction, agriculture, mining, logistics, and defense Transparent pricing as a developer-first GTM strategy versus traditional enterprise carrier sales models The transition from Life360's consumer hardware exploration to founding a satellite networking company GTM Lessons For B2B Founders: Choose your competitive layer strategically—infrastructure scales differently than applications: Hubble explicitly positioned as network infrastructure, not a device manufacturer. Alex stated: "We're not focused on building the hardware or devices. We very much view ourselves as a networking company." This allows enterprise customers to integrate Hubble connectivity into their existing devices with just a software change to the Bluetooth chip. The result: each B2B customer can deploy hundreds or thousands of devices to their end users, creating exponential reach. For founders building horizontal technology, consider whether competing at the infrastructure layer—even if less immediately tangible—creates superior unit economics and market leverage versus building full-stack solutions. Developer-first positioning requires operational commitment, not just marketing: Hubble's pricing transparency wasn't a marketing tactic—Alex described it as "hardcore to our ethos" because their goal is connecting billions of devices. They explicitly modeled after Twilio and Stripe rather than Verizon or AT&T, making it possible for engineers to validate unit economics independently and start free trials without sales conversations. This wasn't debated internally because both co-founders and the early team aligned on this approach. For infrastructure companies targeting massive scale, half-measures on developer experience will fail—the entire go-to-market motion must support self-service validation and transparent economics. Constraint forces clarity—unlimited TAM demands disciplined ICP filtering: Despite viable use cases across construction, oil and gas, mining, agriculture, supply chain, and defense, Alex emphasized: "In the early stages, focus is the most important thing. Every hour matters and being able to focus matters quite a bit and defocusing yourself can really hurt." Hubble's "sexy hook of Bluetooth to space" generates inbound interest across industries, creating constant pressure to expand. Their active debate centers on which industry leaders are "solving important use cases" with existing customer bases of "hundreds, if not thousands of customers." For founders with horizontal technology, resist opportunistic deals—filter aggressively for partners who provide concentrated distribution rather than one-off deployments. Physical demonstration collapses credibility timelines for counterintuitive technology: Hubble faced skepticism even from sophisticated RF engineers because of hardwired associations between Bluetooth and short range. Alex noted: "Some of the investors that joined our A or B, they passed on our seed and A because they thought, well, I believe in Alex, but is this really physically possible?" Post-launch with working satellites, the conversation shifted from "is this possible?" to commercial terms. The lesson isn't just "show don't tell"—it's that for technically improbable innovations, rushing to demonstrable proof compresses months of explanation into minutes of validation. Founders should potentially sacrifice feature breadth to reach a single, undeniable proof point faster. Operational domain expertise reveals infrastructure gaps others can't see: Alex spent years as CTO of Life360 attempting to build connected hardware for families—smart pet collars, GPS watches for kids, fall detectors—but existing networks had "super short battery life, very bulky, no global coverage, way too expensive." He invested in Ben's previous mesh network company and became a close advisor before co-founding Hubble. The insight wasn't theoretical—it came from failing repeatedly to solve the problem with existing infrastructure. Founders should treat operational frustrations in previous roles as proprietary market intelligence: you've already paid the learning cost that competitors will need years to acquire. // 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

The AI boom isn't a level playing field—and most startups are running a race they're set up to lose. In this episode, Dr. Manu Kumar explains why the real winners of this AI wave are the incumbents who already control distribution and customers, not the scrappy upstarts.Dr. Manu Kumar is the founder of K9 Ventures and an early investor in companies like Lyft, Twilio, Lucidchart, Carta, Auth0, and Everlaw, with over 15 years backing more than 50 early-stage startups. Drawing from his experience as a founder, PhD in Human-Computer Interaction, and solo GP, Manu breaks down why this AI cycle is structurally different from past tech shifts—and what that means for founders, operators, and VCs.In this conversation, Manu argues that the biggest moat in AI today isn't the model, the data, or the tech—it's distribution. Companies like Google and Microsoft already have massive customer bases and control the channels where AI products are discovered and adopted, which tilts the game heavily in their favor. He explains how this changes the calculus for AI startups, what kinds of products still have a shot, and why some founders should stop pretending they're competing on a fair field.You'll also hear Manu's philosophy on founder success: why he optimizes for grit, “insane perseverance in the face of complete resistance,” and technical founders who can actually build the product themselves. He shares how he evaluates early-stage teams at the two-person-and-an-idea stage, why gut instinct still matters when there's no data, and how to think about market size when the category doesn't really exist yet.If you're building in AI, investing in AI, or just trying to understand where this wave is really headed, this episode gives a brutally honest look at who has the power—and what founders can still do about it.

In this episode of Builders Wanted, we're joined by Michelle Suzuki, Chief Marketing Officer at Commerce. Kailey and Michelle delve into the impact of agentic commerce, the evolution of AI in customer engagement, and strategies for maintaining consistency and relevance in marketing. Michelle also shares insights on the challenges and opportunities in rebranding and driving data-driven marketing.-------------------Key Takeaways:Embracing change and leveraging data-driven insights are essential for marketers to stay relevant and effective in a rapidly evolving commerce landscape.The most successful marketing strategies combine creative brand-building with rigorous data analysis, ensuring that emotional connection and measurable outcomes drive growth.Truly understanding your audience and meeting them where they are enables organizations to deliver more personalized, impactful experiences.-------------------“ The front end and the back end, it's sort of like that brand and demand element is how do you make this holistic ecosystem that is really productive for the experience and really driving what that looks like as you put together your overall strategy.  It's so important to think holistically about what it is that you're meaning to deliver and incorporating all of those elements together so that there aren't jagged, hard edges between them. But it's all one entire ecosystem that presents something that is more comfortable and relative to what it is that the user is hoping to experience with you.” – Michelle Suzuki-------------------Episode Timestamps:‍*(01:56) - What being a builder means to Michelle ‍*(06:07) - The shift most critical for brands right now‍*(13:05) - Bridging the gap between data-driven and creative marketing‍*(27:30) - Lessons from rebranding‍*(33:04) - Building teams for speed and effectiveness‍*(35:13) - Quick hits-------------------Links:Connect with Michelle on LinkedInConnect with Kailey on LinkedInLearn more about Caspian Studios-------------------SponsorBuilders Wanted is brought to you by Twilio – the Customer Engagement Platform that helps builders turn real-time data into meaningful customer experiences. More than 320,000 businesses trust Twilio to transform signals into connections—and connections into revenue. Ready to build what's next? Learn more at twilio.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this special episode of Builders Wanted, recorded live from Twilio Transform in New York City, we're joined by Rikki Singh, Twilio's VP of R&D for Emerging Technologies. Rikki explores groundbreaking advancements in AI, security, and communications, touching on the evolution of technology and customer expectations as we approach 2026. The conversation delves into the role of AI in software engineering, the importance of trust and privacy by design, changes in customer engagement, and the future of agentic workflows.-------------------Key Takeaways:Building robust systems and prioritizing speed empowers organizations to drive innovation rapidly while maintaining high standards of quality.Reliable, well-structured data and clearly defined, measurable objectives are critical for achieving success in AI and analytics initiatives.The most impactful product enhancements stem from actively listening to customers, understanding their challenges, and reimagining features as needed.-------------------“ The fact that we want to give you contextual memory that is able to capture communication, that matters. Because that's where you're expressing your satisfaction, your happiness, your joys. So how do we take that and then use that to help you rather than microsegment you on demographics and target you? I think that's the positive pivot I hope we make as this technology allows for that.” – Rikki Singh-------------------Episode Timestamps:‍*(01:48) - What excites Rikki heading into 2026‍*(02:54) - What feels different about today compared to a year ago‍*(07:14) - Themes shaping the next 12 months for builders‍*(19:43) - What's evolving fastest: the tech stack, the buyer, or the org chart?‍*(27:50) - What builders underestimate about AI and where it's going‍*(43:36) - Quick hits-------------------Links:Connect with Rikki on LinkedInConnect with Kailey on LinkedInLearn more about Caspian Studios-------------------SponsorBuilders Wanted is brought to you by Twilio – the Customer Engagement Platform that helps builders turn real-time data into meaningful customer experiences. More than 320,000 businesses trust Twilio to transform signals into connections—and connections into revenue. Ready to build what's next? Learn more at twilio.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

How do you make sense of an industry that is changing at a pace few predicted, especially with SIGNAL London still fresh in our minds and Twilio unveiling the next stage of its vision for customer engagement? That question sits at the heart of today's conversation with Peter Bell, VP of Marketing for EMEA at Twilio, who joined me to unpack what the past year has taught both companies and consumers about AI's role in shaping modern experiences. Peter begins by grounding everything in a single, striking shift. Only a year ago, AI-powered search barely registered in global traffic. Today it accounts for around a fifth of all searches. That leap signals a broader behavioral shift as consumers move instinctively toward conversational interfaces, which, in turn, leaves brands with a clear message. The clock has moved on. AI is no longer a nice-to-have. It is a direct response to how people now choose to discover, question, and buy. Our conversation turns to the gap between customer expectations and the experiences they receive. Peter discusses why brands often struggle to integrate channels, data, and AI coherently. He explains how first party data has become the anchor for any serious AI strategy, why generic public models cannot solve brand-specific tasks, and why the most successful teams start with simple, tightly scoped problems. A password reset may not sound glamorous, yet it is the kind of focused use case that teaches teams how to govern data, automate safely, and build confidence in the process. We also spend time on branded calling, RCS, and the evolution of voice. Peter breaks down what modern messaging now looks like and why trust sits at the center of every interaction. His explanation of Conversational Relay shows why natural voice exchanges finally feel within reach after years of frustration with rigid IVR systems. The thread running through all of this is clear. Consumers want speed and clarity, but they want reassurance too, and brands need to honor both sides of that equation. Later in the conversation, Peter makes one of the episode's most compelling points. Brand visibility has become harder, not easier, because much of the early research now occurs within AI tools. Buyers form opinions long before they speak with a sales rep. That shift explains why so many B2B companies are returning to high-impact brand channels, whether that is F1 sponsorships or other standout moments that keep them in the initial consideration set. We close with the topic that Peter believes will define the next stage of enterprise AI. Model Context Protocol. MCP has emerged as a quiet breakthrough, enabling LLMs to access data across CRM systems, files, and other software through a standard protocol. This removes one of the biggest blockers in AI projects: the practical challenge of connecting disparate data to a model built for a specific purpose. As Peter puts it, MCP gives companies a realistic way to make the special-purpose models that deliver reliable ROI. It is a wide-ranging conversation shaped by SIGNAL London's announcements, the evolving customer journey, and a year in which AI moved from curiosity to expectation. I would love to know what part stood out most to you. Are you seeing the same shifts Peter describes in your own business, and how are you preparing for the year ahead? Useful Links Interact with the Inside the Conversational AI Revolution report. Learn more about the Signal event Connect with Peter Bell, VP of Marketing for EMEA at Twilio. Tech Talks Daily is sponsored by Denodo

Today's episode of the Punk CX podcast is with Sharath Keshava Narayana, CEO and Co-Founder of Sanas, which provides a real-time speech understanding platform with accent translation, noise cancellation and now real-time speech translation technology. Sharath and I talk about the challenges with current translation methods, what they are doing with regards to real-time speech translation, the possibilities that this type of technology offers for customer service and experience and the exciting fact that Douglas Adams' Babelfish application/device that featured in his book The Hitchhiker's Guide to the Galaxy is getting much closer. We also finish off with Sharath's best advice, his Punk CX brand and his very own good news story. This interview follows on from my recent interview – Twilio's secret sauce and CarFinance247's road to success – Interviews from Twilio SIGNAL London 2025 – and is number 566 in the series of interviews with authors and business leaders who are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees.

On the podcast, I talk with Greg about knowing when to pivot, why most consumer apps shouldn't raise VC, and why making free trials optional outperformed making them the default.Top Takeaways:

In this episode of Builders Wanted, we're joined by Sumeet Arora, Chief Product Officer at Teradata. Sumeet shares his insights on the importance of speed and innovation in the fields of data analytics and AI, emphasizing how Teradata delivers impactful business results by transforming complex data challenges into actionable solutions. The discussion dives into product leadership principles, the balance between speed and reliability, and the evolving landscape of analytics.-------------------Key Takeaways:Building strong systems and focusing on velocity enables organizations to innovate quickly without sacrificing quality.Trustworthy, well-modeled data and clear, measurable outcomes are essential for successful AI and analytics.The best product improvements come from listening to customers, obsessing over their problems, and being willing to rethink or remove features.-------------------“ I think it's equally important for people in my role to not just build a great product, but also build it fast. It has to be fast and excellent, both. And doing things faster in this era means that you have to also treat velocity as a product itself.  It's almost like setting up the right system and then great things come out.” – Sumeet Arora-------------------Episode Timestamps:‍*(02:06) - Defining the mission of a builder ‍*(03:12) - Velocity as a product ‍*(07:51) - The shift to invisible, frictionless analytics ‍*(23:04) - Lessons from product failures ‍*(34:28) - Quick hits-------------------Links:Connect with Sumeet on LinkedInConnect with Kailey on LinkedInLearn more about Caspian Studios-------------------SponsorBuilders Wanted is brought to you by Twilio – the Customer Engagement Platform that helps builders turn real-time data into meaningful customer experiences. More than 320,000 businesses trust Twilio to transform signals into connections—and connections into revenue. Ready to build what's next? Learn more at twilio.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this week's episode, Joe Fairless interviews Jeffrey Brogger. Jeffrey shares practical ways to revive “dead” contacts using AI voice agents that hold natural two-way conversations and surface real buying signals. He explains where these tools outperform blast emails and basic SMS, why latency and compliance matter, and how he builds lower-cost, highly customizable stacks with VAPI, Twilio and modern LLM voices. The discussion closes with an omnichannel playbook that uses ads, email, website activity and AI automations to turn quiet databases into qualified call-backs. Jeffrey Brogger Current role: AI Strategist, Growth Architect, Founder of JJB Industries Based in: Huntington Beach, California Say hi to them at: https://jjbind.com/ Alternative Fund IV is closing soon and SMK is giving Best Ever listeners exclusive access to their Founders' Shares, typically offered only to early investors. Visit smkcap.com/bec to learn more and download the full fund summary. Join us at Best Ever Conference 2026! Find more info at: https://www.besteverconference.com/  Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at⁠ ⁠⁠⁠www.bestevercommunity.com⁠⁠ Podcast production done by⁠ ⁠Outlier Audio⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's episode, I'm talking with Andrew Wilhelms, VP of Talent Management at Databricks and a seasoned leader with a wealth of experience from organizations like Twilio and Tesla. In t his conversation, we explore the evolving world of talent development and break down the difference between building individual capabilities and managing organizational systems, and why understanding both is crucial in today's dynamic business landscape.Andrew also shares insights from his unique journey, including how his philosophy background shaped his approach to leadership, what he believes actually transforms good teams into high-performing ones, and why the next wave of talent will require us all—no matter our title—to start thinking (and leading) like executives. They also dig into the impact of AI on both work and leadership, the importance of designing a positive employee experience, and practical ways to move beyond “knowing” to actually “doing” when it comes to developing great leaders.Key Notes and topics we cover in this episode:The Nature of LeadershipPreparing Future LeadersChallenges in Corporate LeadershipTalent Management vs. Talent DevelopmentThe Next Paradigm Shift in Talent: AIAI and Tools in Talent DevelopmentThe “Brickster Experience” at DatabricksPsychological Safety and Growth MindsetManager and Leadership Development ModelsCareer Development PhilosophyLessons Learned & ReflectionsTalent Development TrendsRecommended ResourcesCareer Advice for Talent ProfessionalsThis episode is also sponsored by LearnIt, which is offering a FREE trial of their TeamPass membership for you and up to 20 team members of your team. Check it out here.Connect with Andy here: Website | LinkedInConnect with Andrew Wilhelms here: LinkedInOrder my new book, Own Your Brand, Own Your Career on AmazonAnd my first book, Own Your Career Own Your Life, is on Amazon as well.

Are you building your customer data strategy around your goals, or are your goals constrained by your data platform?Agility in today's marketing technology landscape isn't just about speed—it's about flexibility. Brands need data architectures that adapt to their needs, not the other way around. And with the right approach, that agility can fuel personalization, better customer outcomes, and real business value.Today we're going to talk about composable customer data platforms and how AI is enhancing decision-making to increase customer lifetime value.To help me discuss this topic, I'd like to welcome Tejas Monahar, co-CEO and co-Founder of Hightouch. About Tejas Manohar Tejas Manohar is the cofounder/co-CEO of Hightouch. Prior to founding Hightouch, Tejas was an early engineer at Segment, the leading company in the Customer Data Platform (CDP) space that was acquired by Twilio for $3.2B. At Segment, Tejas realized that many of the challenges of building a best-in-class CDP would be better solved on top of the data warehouse and a modern data stack and hence, he founded Hightouch. When Tejas isn't thinking about data, he likes running and playing competitive table tennis. Tejas Manohar on LinkedIn: https://www.linkedin.com/in/tejasmanohar/ Resources Hightouch: https://www.hightouch.com https://www.hightouch.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brandsDon't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150" Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

Are you building your customer data strategy around your goals, or are your goals constrained by your data platform? Agility in today's marketing technology landscape isn't just about speed—it's about flexibility. Brands need data architectures that adapt to their needs, not the other way around. And with the right approach, that agility can fuel personalization, better customer outcomes, and real business value. Today we're going to talk about composable customer data platforms and how AI is enhancing decision-making to increase customer lifetime value.To help me discuss this topic, I'd like to welcome Tejas Monahar, co-CEO and co-Founder of Hightouch. About Tejas Manohar Tejas Manohar is the cofounder/co-CEO of Hightouch. Prior to founding Hightouch, Tejas was an early engineer at Segment, the leading company in the Customer Data Platform (CDP) space that was acquired by Twilio for $3.2B. At Segment, Tejas realized that many of the challenges of building a best-in-class CDP would be better solved on top of the data warehouse and a modern data stack and hence, he founded Hightouch. When Tejas isn't thinking about data, he likes running and playing competitive table tennis. Tejas Manohar on LinkedIn: https://www.linkedin.com/in/tejasmanohar/ Resources Hightouch: https://www.hightouch.com https://www.hightouch.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brandsDon't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150" Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company