Podcasts about Equinix

SpaceX, Amazone, Nvidia của Mỹ, Softbank của Nhật, Brookfield của Canada, hay MGX của Các Tiểu Vương Quốc Ả Râp Thống Nhất.. đầu tư hàng chục tỷ đô la vào Pháp để xây dựng các trung tâm xử lý dữ liệu data center. Đâu là những lợi thế của Pháp trong mắt các nhà đầu tư công nghệ ? Phúc hay họa khi trở thành « sân sau » của những nhà cung cấp dịch vụ trong thời đại digital ? 80 % các dịch vụ tin học tại châu Âu đều phụ thuộc vào các nhà cung cấp ở Mỹ : Các data center cho phép Paris tự chủ về công nghệ kỹ thuật số với Hoa Kỳ ? Tại hội nghị kêu gọi đầu tư nước ngoài vào Pháp Choose France hôm 01/06/2026, Pháp đã nhận được hơn 90 tỷ euro : Phần lớn trong số hơn 70 dự án tập trung vào lĩnh vực trí tuệ nhân tạo và công nghiệp số. Bất chấp rất nhiều khó khăn về địa chính trị hiện tại, trong 7 năm liên tiếp, Pháp vẫn là địa điểm đầu tư hấp dẫn tại châu Âu trong mắt các nhà đầu tư quốc tế, theo thăm dò của cơ quan tư vấn EY. Trước ngày khai mạc hội nghị tập đoàn Nhật Bản Softbank đã thông báo đầu tư 45 tỷ euro tại Pháp từ nay đến năm 2031 để xây dựng các trung tâm xử lý dữ liệu data center. Nhìn xa hơn nữa Softbank cam kết đầu tư đến 75 tỷ tại Pháp để « xây dựng cơ sở hạ tầng phục vụ công nghệ AI ». Sáng lập viên tập đoàn Nhật Bản này, Masayoshi Son kể lại ông đã gặp tổng thống Macron tại Tokyo hai tháng trước đây. Khi đó nguyên thủ Pháp đã hỏi nhà đầu tư này liệu có thể nhanh chóng thông báo một chương trình đầu tư vào Pháp hay không và ông câu trả lời của chủ nhân Softbank là có. Masayoshi Son giải thích với báo chí Paris là đã quyết định đầu tư 75 tỷ euro để phát triển trí tuệ nhân tạo bởi nhân loại đang « bước vào thời đại của AI. Những quốc gia có cơ sở hạ tầng cần thiết cho lĩnh vực còn rất mới mẻ này sẽ nắm giữ một vai trò then chốt trong tương lai cả về công nghiệp lẫn công nghệ và đối với nhân loại ». 352 data center sử dụng 3,2 % tiêu thụ điện trên toàn quốc Tổng thống Macron từ khi lên cầm quyền năm 2017 luôn xem thu hút vốn đầu tư nước ngoài là một ưu tiên. Ông đã chín lần chủ trì hội nghị Choose France tại lâu đài Versailles, ngoại ô Paris, với những khách mời sáng giá nhất trong giới tài chính, công nghiệp của Hoa Kỳ, Nhật Bản, châu Âu và trước chiến tranh Ukraina cũng đã có không ít các doanh nhân Nga tham dự. Riêng năm nay, trí tuệ nhân tạo, data center, công nghệ digital … đặc biệt được quan tâm. Tháng 2/2025 với hội nghị « hành động vì AI » Emmanuel Macron đã nuôi tham vọng, với 67 triệu dân, Pháp cũng có thể trở thành một mắt xích quan trọng trong thời đại công nghệ số, và là một « trung tâm quốc tế » về AI. Sức hút các data center của Pháp   Hiện tại Pháp đã có 350 data center và mục tiêu xây dựng thêm 60 trung tâm khác nữa trên toàn quốc trong 10 năm nữa. Đó sẽ là những trung tâm có công suất ngày càng lớn cho phép Paris giải quyết cùng lúc hai vấn đề : giảm mức độ phụ thuộc vào các cơ sở hạ tầng công nghệ số của Hoa Kỳ, và đẩy mạnh các hoạt động kinh tế của Pháp. Trên con đường chinh phục các nhà đầu tư trong lĩnh vực digital, Pháp có nhiều lợi thế, mà đầu tiên hết là khả năng sản xuất điện, như giải thích của François Monnier, tổng biên tập tuần báo tài chính Investir : « Điện khí hóa là một điểm mạnh của châu Âu mà đứng đầu bảng là Pháp. Hơn nữa đây là một lĩnh vực đang phát triển rất mạnh. Để đáp ứng nhu cầu của các trung tâm quản lý dữ liệu data center thì Pháp có nhiều tập đoàn lớn rất có uy tín như là như Schneider Electric chuyên cung cấp các dịch vụ, tìm kiếm những  giải pháp năng lượng đáp ứng nhu cầu cho các tập đoàn trong lĩnh vực công nghệ số … hay là Legrand chuyên cung cấp các thiết bị điện hay tập đoàn năng lượng. Lĩnh vực trí tuệ  nhân tạo chiếm 25 % doanh thu của Legrand. Còn Axens hiện diện trong nhiều lĩnh vực từ lọc dầu, hóa dầu xử lý năng lượng tái tạo, xử lý nước ».  Antoine Fournier, chủ tịch cơ quan tư vấn Thésée DataCenter ghi nhận : Các khoản đầu tư lẽ ra đổ về những quốc gia khác ở châu Âu, như Đức hay Hà Lan đang chuyển hướng về Pháp, bởi Pháp có một vị trí địa lý « trung tâm ». Điện hạt nhân Theo lời Régis Casstané tổng giám đốc chi nhánh tại Pháp của tập đoàn Equinix (nhà cung cấp thiết bị cho các data center lớn nhất thế giới), từ 2022 giá năng lượng của Pháp rẻ hơn so với một số quốc gia khác (như là Đức phải phụ thuộc vào khí đốt của Nga), Pháp lại có các nhà máy điện hạt nhân, lá chủ bài của Pháp trong mắt các nhà đầu tư AI và data center. Bởi vì trung tâm xử lý dữ liệu là một « nguồn tiêu thụ năng lượng vô cùng lớn », cần có nhiều điện để vận hành. …   Theo thẩm định của cơ quan quốc gia Pháp về năng lượng và môi trường ADEM, tiêu thụ điện của 350 data center sẽ tương đương với mức tiêu thụ của « hơn một chục thành phố với trên 100 ngàn dân » và đó là những « cực hút đến 3 % nguồn điện trên toàn quốc ». Đáng quan ngại hơn nữa là các trung tâm data đó ngày càng lớn « ngày càng cần nhiều năng lượng để hoạt động, do vậy trong hơn 10 năm nữa, nhu cầu về điện dành riêng cho các trung tâm này sẽ tăng lên gấp 4 lần so với hiện nay ». Năm 2025, điều trần trước một ủy ban tại Hạ Viện, Eric Schmidt cựu chủ tịch tổng giám đốc Google từng kêu gọi các dân biểu Mỹ đầu tư vào cơ sở hạ tầng điện lực : « Chúng ta cần nhất hiện nay là điện và nhu cầu rất, rất lớn. Nếu quý vị cho phép nói thẳng, thì tôi xin thưa rằng, chúng tôi mong đợi Hạ Viện phát triển điện lực, bất kỳ dưới hình thức nào, đó có thể là năng lượng tái tạo, là nhiệt điện. Miễn làm sao chúng ta có khả năng đáp ứng nhu cầu tiêu thụ và phải nhanh chóng đạt được mục đích này » Điều đó cho thấy vì sao mà các « ông lớn » trong thế giới công nghệ của Mỹ đua nhau chạy sang Pháp đầu tư. Gilles Babinet, tác giả cuốn « Green AI » NXB Odile Jacob nói đến một lá chủ bài của Pháp mà cả Mỹ lẫn Trung Quốc hai siêu cường kinh tế thế giới và cũng là những bên tiên phong trong lĩnh vực công nghệ số và trí tuệ nhân tạo đều không có được : «  Không một quốc gia nào trên thế giới có 15 giga Watt mà đấy lại là điện phi carbone. Mỹ không có khả năng này, Trung Quốc cũng không. Cho nên tất cả các nhà đầu tư trên thế giới cùng nhòm ngó khối lượng điện này của Pháp ». Gauthier Roussilhe, chuyên gia về môi trường digital điều hành công ty tư vấn Hubblo, Paris, do vậy cảnh báo trước nguy cơ các ông vua công nghệ kỹ thuật số và AI trên thế giới đang muốn biến Pháp thành « sân sau » của riêng mình : « Tất cả những thông cáo rầm rộ về các chương trình đầu tư rất lớn về công nghệ số tại Pháp, trước hết là nhằm bảo đảm rằng, hiện tại và trong tương lai, các tập đoàn này có thể tận dụng tiềm lực về điện lực của nước Pháp (…) Những trung tâm xử lý dữ liệu càng lớn bao nhiêu, thì càng cho thấy trong lượng khổng lồ của các doanh nghiệp Mỹ. Softbank thực ra đã bỏ vốn đầu tư và hiện diện trong rất nhiều các tập đoàn công nghệ cao và nhất là trong các tập đoàn của Mỹ. Hệ quả hiển nhiên kèm theo là khâu quản lý các data center sau này sẽ thuộc về phía Hoa Kỳ ». Điều này dường như đã được cả sáng lập viên lẫn giám đốc tài chính tập đoàn Softbank nhìn nhận. Ông Masayoshi Son nói rõ : « Softbank bảo đảm khâu tài chính cho các dự án. Khách hàng của chúng tôi là những hyperscalers, tức là những ông không lồ trong lĩnh vực công nghệ đám mây, như là Amazon, Microsoft hay Google ». Về phần Nahoko Hoshino, giám đốc tài chính Softbank, bà chỉ hờ hững khi nhắc đến các đối tác châu Âu trong lúc đã tiến hành đàm phán với những khách hàng nặng ký là Amazon, Microsoft hay Google, Open AI. Về câu hỏi, đánh cược vào những data center có cho phép nước Pháp vực dậy kinh tế và mang lại công việc làm cho người dân hay không, giới trong ngành đồng loạt trả lời là không. Một quan chức tại thành phố La Courneuve so sánh : Với một diện tích tương đương, mở một nhà máy công nghiệp chế tạo trực thăng Eurocopter, cho phép  tạo thêm 700 công việc làm, nhưng với một trung tâm xử lý dữ liệu thì chỉ cần tuyển dụng 36 nhân viên.

AI conversations are everywhere, but what stood out to me in my chat with Harmeen Mehta from Equinix at Google Cloud Next '26 was how grounded their approach is. They did not start with big external announcements. They started inside.Harmeen shared a simple idea. If AI is going to change how a company works, it has to show up in how employees work first. Not as a side experiment, but as part of daily workflows. That shift is what moved AI from a pilot to something core to the business.At Equinix, AI is not sitting on the edges. It is being used to remove real friction from day-to-day work. Helping teams move faster, reduce repetitive tasks, and focus on higher value problems. That is where the impact starts to become real.But what stood out even more was how they approached trust.Employee hesitation is real. Questions around accuracy, reliability, and job impact come up quickly. Instead of ignoring that, they leaned into it. Clear use cases, transparency, and gradual rollout made a big difference in adoption.The biggest takeaway from this conversation was simple.Do not try to scale AI before you make it work internally.If your own teams are not using it, trusting it, and seeing value from it, scaling it across the business will not work.And looking ahead, the shift is already happening. Not years from now, but right now. AI is starting to change how work gets done inside enterprises, one workflow at a time.#data #ai #equinix #security #googlecloudnext #api #google #theravitshow

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

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

In der heutigen Folge sprechen die Finanzjournalisten Daniel Eckert und Holger Zschäpitz über den Jensen-Huang-Effekt an der Wall Street, gute Zahlen bei Dax-Konzernen und KI-Fantasie bei Ford. Außerdem geht es um Merck KGaA, E.on, Infineon, Allianz, Deutsche Telekom, T-Mobile US, Verbio, Jenoptik, Aixtron, Suss Microtec, Nvidia, Tesla, Micron Technology, Apple, Qualcomm, Cisco, Cerebras, Ford, Nebius, Microsoft, Alphabet, Amazon, Meta, Oracle, Equinix, Digital Realty, GlobalWafers, Soitec, CoreWeave, Morgan Stanley, iShares Core MSCI World ETF (WKN: A0RPWH), Xtrackers MSCI World ETF (WKN: A1XB5U), SPDR MSCI World ETF (WKN: A2N6CW). Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und - ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Régis Castagé, directeur général d'Equinix France, dévoile 20 ans de l'histoire des datacenters, de l'Internet balbutiant à l'explosion de l'IA.L'interview exclusive de Régis Castagé, dans un fascinant exercice "back to the future". De l'arrivée d'Internet en 1998 à la révolution de l'IA en 2026, explorez comment Equinix est devenu leader mondial des datacenters neutres et interconnectés. Découvrez les défis, les innovations et les perspectives pour les datacenters en France : souveraineté, énergie, résilience face aux géants du cloud et de l'IA. Et comprendre pourquoi les datacenters sont les infrastructures critiques du digital !LinkedIn - https://www.linkedin.com/company/datacenter-magazineSite web - https://dcmag.fr/Youtube - https://www.youtube.com/channel/UCCtW-B5vCepFyjbAfCpa5_QHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

A lot of AI projects look great in a pilot. Very few make it to production. At NVIDIA GTC, I sat down with Glenn Dekhayser from Equinix to understand why. And the answer is not what most people expect. It is not just about models. It is about everything around them. We talked about why so much enterprise data is still unusable for AI.- Siloed systems- Lack of structure- No clear path from data to deploymentAnd then there is infrastructure. Because scaling AI is not just about training a model once. It is about running it reliably, securely, and close to where the data lives. That is where most projects break. One idea that stood out. Thinking of the data center as an “AI factory.” Not just storage. Not just compute. But a system designed to continuously turn data into outcomes. And that changes how enterprises need to plan.From day one. If you are serious about AI, this is the shift. From experiments to infrastructure.More conversations coming from GTC.#data #ai #equinix #NVIDIAGTC #theravitshow

Equinix, Inc., the world's digital infrastructure company®, has announced the availability of Equinix Fabric Intelligence, an AI-native operational layer to manage network infrastructure. Fabric Intelligence enables enterprises to deploy AI-powered networking across their operations, a shift from legacy software-defined networking design to simplify the complexities of today's AI workflows. Powering the Equinix Distributed AI Hub, Fabric Intelligence introduces smart automation for deploying, optimizing and maintaining global infrastructure, giving organisations a more resilient, efficient and adaptive backbone for their AI workloads. "The whole concept of AI is to make processes faster, and manual processes for network monitoring and management are difficult, if not impossible, to scale effectively," said Jim Frey, Principal Analyst at Omdia. "Our research shows 93% of organizations agree that network automation will be essential for keeping pace with future change, and 88% also agree that AI itself will be required for effective network automation. With Fabric Intelligence, Equinix is providing enterprises the AI-driven control plane for deploying, activating, and managing multi-cloud networking, to help them meet the scale and automation needs of the distributed AI era." AI thrives in dynamic, connected environments, but many enterprises rely on slow, rigid legacy network architectures that were never designed for the speed and complexity of today's intelligence systems. As AI adoption continues to accelerate, traditional network operations teams are struggling to keep up. Manual workflows can create bottlenecks, long deployment cycles hamper growth, and visibility gaps compound the challenge. AI demands real-time, adaptive networking—driving a shift to AI-assisted network operations that interpret telemetry and respond dynamically. The result is a widening gap between the speed of AI and the networks expected to support it. Fabric Intelligence automates how AI workloads connect and operate across clouds, data centres and edge environments. It provides organisations with a smarter way to manage the complexity of AI by automating how their connections are set up, adjusted and maintained across these distributed environments. As a result, distributed systems run reliably without constant manual effort, freeing teams to focus on strategic business priorities, such as building new AI capabilities and scaling operations. "All enterprises are focused on leveraging AI to transform their business, but most lack the infrastructure needed to deploy it at scale in ways that drive their growth," said Jon Lin, Chief Business Officer at Equinix. "As agentic AI matures and inferencing applications proliferate across the enterprise, networking infrastructure needs to be faster and more flexible than ever before.Fabric Intelligence turns infrastructure from a constraint to a competitive advantage by enabling our customers to spend less time managing complexity and more time moving their business forward." Fabric Intelligence provides a suite of AI-native solutions enabling enterprises to design, deploy and manage their infrastructure using intuitive tools like natural language, automated agentic workflows and powerful predictive insights. Combined with Equinix's global infrastructure of 280 high-performance data centers in 77 metros around the world, Equinix is helping to accelerate enterprise adoption of AI tools and next-generation infrastructure. Earlier this year, Equinix also joined the Agentic AI Foundation (AAIF), the open foundation driving the transparent and collaborative evolution of agentic AI, as a Gold member. This commitment will help build an open, secure and infrastructure-ready foundation for the global autonomous economy. Fabric Intelligence, part of the Equinix Fabric® portfolio with more than 4,400 customers worldwide, is made up of the following components: Fabric Super Agent An AI superagent that helps customers au...

In der heutigen Folge sprechen die Finanzjournalisten Philipp Vetter und Holger Zschäpitz über neue Disruptionsangst bei Software-Aktien, einen möglichen neuen Spirituosen-Giganten und einen Rauswurf aus dem SDax. Außerdem geht es um Lam Research, Marvell, Intel, Broadcom, Snowflake, Cloudflare, Servicenow, Palantir, Autodesk, Workday, SAP, Brown-Forman, Diageo, Gerresheimer, Shelly Group, OpenAI, CoreScientific, Coreweave, Terawulf, Equinix, Digital Realty, Meta, Rheinmetall, Siemens Energy, Novo Nordisk, Apple, Allianz, Bayer, Deutsche Telekom, Droneshield, Paypal, Micron Technology, Deutsche Bank. Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und - ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

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

Kabbalah Astrology and the Week Ahead Hosted on Acast. See acast.com/privacy for more information.

Alissa Coram and Ken Shreve 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

In this episode of Great Business Minds, João Marques Lima speaks with Krupal Raval, Executive Vice President and Chief Strategy Officer at CyrusOne, about the forces reshaping global digital infrastructure. With a career spanning leadership roles at Digital Realty, Equinix, and now CyrusOne, Raval has witnessed - and helped shape - the industry's evolution from post-dot-com consolidation to today's AI-driven capital supercycle. The conversation explores how the sector has transformed into what Raval describes as a “land of giants” - an environment where access to capital, power and long-term partnerships increasingly determines who can compete at scale.   The AI inflection point According to Raval, the scale of change now underway is without precedent. While traditional cloud infrastructure is forecast to nearly double in capacity over the coming years, AI-driven workloads are projected to expand at multiples of that. The implications are profound: Data centre design is shifting toward higher density and advanced cooling architectures. Capital requirements are moving from billions to trillions. Power strategy is becoming as critical as real estate strategy. At CyrusOne, this has translated into a “power-first” approach - securing long-term energy partnerships and investing in net-new generation capacity to support future growth responsibly.   Capital, competition and discipline The episode also examines the structural shift from public to private ownership across the sector, and what that means for long-term value creation. Backed by KKR and BlackRock, CyrusOne operates with a multi-decade investment horizon - a model Raval argues is particularly well-suited to infrastructure cycles of this magnitude. He discusses: Why the industry requires patient capital The risks of overextension amid record demand How to avoid “building snowflakes” in an era of rapid technological change The importance of maintaining operational discipline during hypergrowth   Power, community and long-term responsibility Beyond gigawatts and growth projections, Raval emphasises the importance of trust - with employees, customers and communities alike. From energy partnerships in Texas to workforce development initiatives and local engagement programmes, the conversation highlights the delicate balance operators must strike: enabling exponential compute growth without triggering community backlash or grid instability. As AI accelerates discovery across healthcare, materials science and climate research, Raval argues that digital infrastructure is no longer a background utility - it is becoming central to global competitiveness and societal advancement. “In ten years,” he says, “we could see a hundred years of discovery.”   Leadership in a hypergrowth cycle The discussion closes on leadership - the role of humility in hiring, the importance of working with people you trust, and the necessity of passion in sustaining performance through relentless growth cycles. Quoting Steve Jobs, Raval reflects on a philosophy that has shaped his career: “You don't hire smart people to tell them what to do. You hire smart people so they can tell you what to do.”  

New analysis from Wood Mackenzie shows that 220 gigawatts of additional power demand from data centers is in the pipeline in the US, and 183 GW of that is already backed by firm commercial commitments. That is a huge amount to add in just a few years: it's equal to about 22% of US peak demand in 2025. The big question is whether the US electricity industry going to be able to meet that additional demand. And if so, how?On the second day of ACORE's 2026 Policy Forum in Washington, host Ed Crooks talks to industry leaders and experts about the answers to those questions. First he talks to Wood Mackenzie's Anna Shpitsberg, who is global head of power and renewables research. She breaks down the numbers on electricity demand from new data centers, and discusses some of the implications for the industry.Next up is someone whose role is right at the heart of the data center boom. Arthur Haubenstock is senior counsel at Equinix, which is one of the world's largest developers, owners and operators. He talks about what data centers actually need in terms of electricity supply, and gives his perspective on some of the controversies currently raging around the industry.A key issue for him is how data center developers can benefit local communities by cutting their electricity bills and strengthening the stability of the grid. He talks about the reality behind popular ideas such as BYOP (bring your own power) and BYONCE (bring your own new clean energy). And he explains why data centers often cannot be flexible loads on the grid, the constraints on backup generation, and why power grids matter.Ray Long, President and CEO of ACORE, then joins the show to talk about his key takeaways from the event. He says the AI-driven data center boom is creating great opportunities for all kinds of energy, including renewables and other low-carbon technologies. But progress is being slowed by three critical challenges: permitting delays, trade policy uncertainty, and regulatory bottlenecks.With electricity demand surging, he says, tackling those policy barriers is essential. Governments and the power industry need to find ways to stop electricity bills soaring and the grid becoming unstable, while enabling the infrastructure buildout required for AI. Finally, Ed talks to three entrepreneurs who are leading startup companies that aim to build the energy industry of the future. Kimberly Johnston of NextGen Energy, Saxon Metzger of Polaris Ecosystems, and Ebony Seymour of Ellement Group, explain the problems in energy that they are taking on, and talk about what they need to accelerate their growth.This episode is brought to you by ACORE, the nonpartisan nonprofit organization uniquely operating at the intersection of energy affordability, reliability, and clean energy deployment. ACORE is focused on strengthening the electric grid and driving clean energy investment that delivers for the American people. ACORE's membership includes industry leaders across the clean energy economy. Nearly 80% of the booming utility-scale domestic clean energy growth was financed, developed, owned, equipped, or contracted by ACORE members.  Visit www.acore.org to learn more about ACORE's work and upcoming events, like the ACORE Finance Forum on May 12-13 in New York City. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

US equity markets are lower, with S&P down 0.2%, following mixed performance on Thursday. Bonds firmer. US 10-year benchmark down 1 bp at 4%. Gilts 2 bps lower at 4.3%. Bund eases to 2.7%. Dollar softer versus European majors, little changed versus yen. Oil up. Gold flat. Industrial metals higher. Bitcoin weaker. UK politics likely to get some attention after Greens won the Gorton and Denton by-election in greater Manchester, with Reform coming second. Further reports highlighting the likelihood of a very lowkey fiscal update from Chancellor Reeves next Tuesday, as she seeks to end cycle of policy speculation. Update from the UK National Audit Office showed HMRC collected extra £16B from biggest firms last year via a more hands-on approach.Companies Mentioned: Warner Bros. Discovery, Partners Group Holding, CPPIB, Equinix, Alphabet, Meta

Equinix, Inc. (Nasdaq: EQIX), the world's digital infrastructure company®, in the presence of An Taoiseach, Micheál Martin, has announced that it is committing USD $350 million, up to a landmark USD $700 million, to support the construction of a 150,000 sq. ft. advanced manufacturing facility by Hanley Energy. The new facility, located in Dundalk, Co. Louth, will serve as a global hub for manufacturing specialized power equipment essential for Equinix's high performance data centres and AI-driven workloads. The deal covers an initial 5-year period, extendable to 10 years, with a minimum of USD $70 million from Equinix annually, underscoring its long-term commitment to Ireland and the country's role in Equinix's global operations. The partnership with Hanley Energy, which was recently acquired by the American multinational manufacturing company, Jabil, will create hundreds of new roles. Hiring for the Hanley factory has already commenced for an initial 200 engineers and technicians, focused on precision engineering, quality assurance, and lean manufacturing. Apprenticeship and training programs will also be introduced to build future-ready talent in the Louth region. By co-locating production under one roof, Equinix expects to achieve 10–15% faster lead times compared to traditional procurement methods. The facility will manufacture low-voltage switchgear, Power Distribution Units (PDUs), and Remote Power Panels (RPPs), all critical components for reliable and efficient power distribution in data centres worldwide. The building of the new facility, by Hanley Energy, will prioritise low-carbon materials and efficient construction practices. The facility will feature a temperature-controlled testing laboratory – the only one of its kind in Ireland or the UK – which will enable equipment to undergo rigorous endurance and environmental tests. Taoiseach Micheál Martin said: "This significant announcement reinforces Ireland's position as a leader in digital infrastructure and advanced manufacturing. The creation of hundreds of skilled jobs and the introduction of world-class facilities in Dundalk is a major boost for the region and for our national economy." Adaire Fox-Martin, CEO and President, Equinix, said: "This investment builds upon Equinix's longtime presence in Ireland and reflects the strategically important role the country plays in the global technology ecosystem. Our expansion in Dundalk further strengthens our ability to meet growing customer demand while creating local jobs and supporting the community." Peter Lantry, Managing Director, Equinix Ireland, said: "This is a huge win for Ireland and the Louth region – highlighting the world class engineering talent that Ireland continues to develop. By securing our supply chain and investing in local manufacturing, we're not only accelerating delivery but also creating hundreds of high-skilled jobs. Importantly, we remain committed to Ireland, continuing to invest and grow our presence here. This reinforces our long-term presence and ensures we can meet the growing demand for digital infrastructure worldwide." Hanley Energy delivers seamless integration from design to manufacturing under one roof, backed by proven expertise in engineering and testing. The new state-of-the-art facility includes Ireland and the UK's only independent temperature rise test lab certified by Intertek as an Enhanced Level 3 SATELLITE Customer Testing Facility. This capability ensures compliance and performance at the highest global standards. John O'Driscoll, CEO, Hanley Energy, said: "Partnering with Equinix on this transformative project highlights the strength of Irish engineering and innovation. Our advanced testing facilities and expertise will ensure that the equipment produced here meets the highest global standards, supporting data centres worldwide." Michael Lohan, CEO, IDA Ireland, said: "Today's announcement by Equinix demonstrates Ireland's continued attractiveness as a location for ...

It's not just Nvidia anymore. We will discuss the earnings from companies like ARM and Broadcom, analyzing the shift toward "Custom Silicon" designed specifically for distinct AI models.Today's Stocks & Topics: Tenet Healthcare Corporation (THC), CMOC Group Limited (CMCLF), Market Wrap, Banco Bilbao Vizcaya Argentaria, S.A. (BBVA), Cloudflare, Inc. (NET), Semiconductors: The "Custom Chip" War, How to Rollover a 401k, Dollar General Corporation (DG), Alphabet Inc. (GOOG), Housing Market, Equinix, Inc. (EQIX).Our Sponsors:* Check out Anthropic: https://claude.ai/invest* Check out Quince: https://quince.com/INVESTAdvertising Inquiries: https://redcircle.com/brands

Send a textEvery business transformation begins with human transformation.This week on The UpLevel Podcast, join us for a powerful conversation as we welcome Jenna Paterson, Talent Development leader at Equinix, to explore what conscious leadership really looks like in practice, especially in an era defined by AI acceleration and rapid change.From a childhood shaped by bullying and resilience to leading groundbreaking leadership programs at Equinix, Jenna shares how her origin story became the foundation of her leadership philosophy. She takes us inside Ground Break, a transformational leadership experience built on the truth that systems change only when people change.If you care about building human-centered organizations, cultivating conscious leaders, and strengthening culture from the inside out, this episode is for you.In This Episode:Why right relationship starts with self-leadershipHow defining moments shape leadership identityWhy emotional intelligence remains essential in an AI-driven workplaceThe connection between nervous systems and organizational cultureThe paradox leaders must hold in complex timesWhy transformation efforts fail when they ignore the human systemThe powerful ROI of psychological safety and presenceThree essential questions every leader must ask themselvesAbout Jenna:Jenna is a dedicated Talent Development professional based in the UK, currently shaping leadership development  and cultural transformation at Equinix. With a rich background in learning and leadership development; spanning roles in digital infrastructure and luxury retail sectors, she brings both breadth and depth to her craft.As a certified emotional intelligence practitioner, strengths coach and experienced facilitator, Jenna blends evidence-based frameworks with an empathetic, human-centred approach. She's passionate about crafting immersive learning experiences, and brings a thoughtful, systems-oriented approach to development.Her passion lies in cultivating inclusive, psychologically safe environments where people can grow, reflect, and develop conscious leadership. Jenna believes leadership starts with self-leadership. By staying curious, practicing empathy, and showing up authentically, she builds meaningful relationships that create trust and unlock growth; for herself and those around her.LinkedIn: linkedin.com/in/jenna-elizabeth-paterson-assoc-cipd-b1a78a54

APAC stocks traded higher but with some of the gains in the region capped after the weak handover from the US and with the NFP report on the horizon, while participants also digested earnings and data in thinned conditions, with Japanese markets shut for a holiday.Ukrainian President Zelensky plans spring elections alongside a referendum on the peace deal after a US push.US President Trump said he might send a second carrier to strike Iran if talks fail and stated that "Either we will make a deal or we will have to do something very tough like last time".European equity futures indicate a quiet cash market open with Euro Stoxx 50 futures +0.1% after the cash market finished with losses of 0.2% on Tuesday.Looking ahead, highlights include ECB Wage Tracker, US NFP (Jan), Japanese PPI (Jan), BoC Minutes (Jan), OPEC MOMR. Speakers include ECB's Cipollone & Schnabel, Fed's Schmid, Bowman & Hammack. Supply from Germany & US. Earnings from T-Mobile, McDonalds, AppLovin, Equinix, Motorola Solutions, Hilton, Kraft Heinz, TotalEnergies, Michelin.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

China is reportedly considering probing wine from France; could consider launching anti-dumping duty to French wine, and potentially take counter measures against the EU if it adopt duties.European bourses are trading on the backfoot; FTSE 100 outperforms on the back of firmer commodity prices; US equity futures mixed.DXY slightly lower heading into US NFP, JPY continues to gain, AUD bid after RBA's Hauser said inflation is "too high".Fixed income rangebound; Bunds little moved following tepid auction.Crude edges higher as Trump mulls sending another carrier near Iran; Gold rangebound; Base metals rise, led by nickel prices following an cut in output from the world's largest mine.Looking ahead, highlights include US NFP (Jan), Japanese PPI (Jan), BoC Minutes (Jan), OPEC MOMR. Speakers include ECB's Schnabel, Fed's Schmid, Bowman & Hammack. Supply from the US. Earnings from T-Mobile, McDonalds, AppLovin, Equinix, Motorola Solutions, Hilton and Kraft Heinz.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Send us a textIn this episode of the Inside Data Centre Podcast, Andy Davis is joined by Matt Brown, Senior Director of Design Delivery at Equinix, and Andrew Higgins, Senior Director of Global Master Planning and Sustainability at Equinix.They explore how Equinix builds and sustains high-performance teams across global design and construction. Matt and Andrew share career journeys that span engineering, consultancy, leadership, and international project delivery, and they break down how culture, collaboration, talent development, and sustainability shape the way Equinix operates at scale.Key Topics:How diverse backgrounds strengthen global design and construction teamsCreating alignment across regions, time zones, and culturesMentorship, coaching, and career mobility inside EquinixLeadership behaviours that drive autonomy, trust, and well-beingWhy innovation and sustainability motivate high-performing teamsWhat makes Equinix's culture distinct in a competitive industryTune in for a candid look at how Equinix nurtures people, builds leadership, and delivers global projects at pace.Support the showThe Inside Data Centre Podcast is recorded in partnership with DataX Connect, a specialist data centre recruitment company based in the UK. They operate on a global scale to place passionate individuals at the heart of leading data centre companies. To learn more about Andy Davis and the rest of the DataX team, click here: DataX Connect

529 Bryant St. in Palo Alto, California, is home to a key landmark in tech history. Now owned and operated as a data center by Equinix, the building has been a networking hub to a variety of firms, including the earliest telephone switch board operators and early internet firms like Alta Vista. Marketplace's Meghan McCarty Carino visited the data center to learn more.

529 Bryant St. in Palo Alto, California, is home to a key landmark in tech history. Now owned and operated as a data center by Equinix, the building has been a networking hub to a variety of firms, including the earliest telephone switch board operators and early internet firms like Alta Vista. Marketplace's Meghan McCarty Carino visited the data center to learn more.

Are you an AI skeptic or an enthusiast? Ethan and Drew sit down with Igor Tarasenko, Senior Director of Product Software Architecture and Engineering at Equinix, to break down the reality of AI in the network. In this sponsored episode, Tarasenko discusses why APIs are the new CLI, the critical need for observability in AI,... Read more »

Are you an AI skeptic or an enthusiast? Ethan and Drew sit down with Igor Tarasenko, Senior Director of Product Software Architecture and Engineering at Equinix, to break down the reality of AI in the network. In this sponsored episode, Tarasenko discusses why APIs are the new CLI, the critical need for observability in AI,... Read more »

Are you an AI skeptic or an enthusiast? Ethan and Drew sit down with Igor Tarasenko, Senior Director of Product Software Architecture and Engineering at Equinix, to break down the reality of AI in the network. In this sponsored episode, Tarasenko discusses why APIs are the new CLI, the critical need for observability in AI,... Read more »

Vivemos um momento em que a inteligência artificial, a computação em nuvem, as redes de alta velocidade, o sensoriamento de ambientes e o processamento de grandes volumes de dados ampliam a capacidade de inovação de empresas e governos. Ao mesmo tempo, esses avanços elevam a demanda por energia, infraestrutura e recursos naturais. O Start Eldorado desta semana traz a terceira parte de mais um evento da série Conexões - Construindo o Futuro com Inovação e Tecnologias Sustentáveis - que reuniu convidados que trouxeram suas visões sobre quais caminhos precisam ser construídos em datacenters, cidades inteligentes e suas políticas públicas, nas estratégias corporativas e operadoras para sustentar a nova era da IA, dados e dispositivos de forma eficiente e responsável. O apresentador Daniel Gonzales recebeu Eduardo Zago, presidente Latam da Equinix; Mário Rachid, diretor executivo da Claro Empresas; Hingo Hammes, prefeito de Petrópolis (RJ); e José Renato Gonçalves, presidente da NEC no Brasil. O Start vai ao ar todas as quartas-feiras, às 21h, na Rádio Eldorado FM (107,3 para toda a Grande SP), app, site e assistentes de voz. See omnystudio.com/listener for privacy information.

How difficult will it be to train and build an AI Agent that has expertise in a given domain? Will it happen in the next year, or 3 years or 10 years? And who will benefit in the marketplace from his evolution? SHOW: 984SHOW TRANSCRIPT: The Cloudcast #984 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[Mailtrap] Try Mailtrap for free[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTESOpenAI looks to train their models to replace junior bankersWHAT WOULD BE THE STAGES OF AN EXPERT AGENT?Train it on a set of standard knowledge (e.g. Masters of Accounting, Auditing, International Tax)Train it on a set of well-defined case studies, to provide industry contextTrain it on a set of adjacent case studies and other domains (business, law, specific industries)How to train corner cases?How to train gray areas like ethics, morality, or cost-benefit analysis? Who is motivated to train these experts? What would the cost of these experts be? Can it be similar to a human, or need to be a fraction, or a premium? Is there a way to build memory (e.g. experience) without disclosing client information? Is there a way to build shareable knowledge between agents for reinforcement training/learning?FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Vivemos um momento em que a inteligência artificial, a computação em nuvem, as redes de alta velocidade, o sensoriamento de ambientes e o processamento de grandes volumes de dados ampliam a capacidade de inovação de empresas e governos. Ao mesmo tempo, esses avanços elevam a demanda por energia, infraestrutura e recursos naturais. O Start Eldorado desta semana traz a segunda parte de mais um evento da série Conexões - Construindo o Futuro com Inovação e Tecnologias Sustentáveis - que reuniu convidados que trouxeram suas visões sobre quais caminhos precisam ser construídos em datacenters, cidades inteligentes e suas políticas públicas, nas estratégias corporativas e operadoras para sustentar a nova era da IA, dados e dispositivos de forma eficiente e responsável. O apresentador Daniel Gonzales recebeu Eduardo Zago, presidente Latam da Equinix; Mário Rachid, diretor executivo da Claro Empresas; Hingo Hammes, prefeito de Petrópolis (RJ); e José Renato Gonçalves, presidente da NEC no Brasil. O Start vai ao ar todas as quartas-feiras, às 21h, na Rádio Eldorado FM (107,3 para toda a Grande SP), app, site e assistentes de voz. See omnystudio.com/listener for privacy information.

Dan McConnell (Senior VP Product Management at @HitachiVantara) talks about how Enterprise infrastructure is having to evolve to keep up with the data challenges of AI. SHOW: 983SHOW TRANSCRIPT: The Cloudcast #983 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[Mailtrap] Try Mailtrap for freeSHOW NOTES:Hitachi Ventara (website)Topic 1 - Welcome to the show. Tell us about your background.Topic 2 - How is AI changing the way enterprises need to think about infrastructure?Topic 3 - What makes AI workloads so different from traditional workloads? How is AI exposing the limitations of broken cloud environments?Topic 4 - Where are organizations feeling the most pressure? What are the biggest misconceptions enterprises have about preparing for AI?Topic 5 - What problems come from managing separate systems? What is the biggest driving factor toward unified data platforms?Topic 6 - How is Hitachi Vantara helping customers handle the growing infrastructure demands created by AI?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

It's been nearly a decade since the last evolution of the PaaS platform, but AI has the potential to reshape and evolve this value concept. Let's explore that's possible. SHOW: 982SHOW TRANSCRIPT: The Cloudcast #982 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[Mailtrap] Try Mailtrap for free[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTESVercel v0Building Web Apps with just English and AI (Acquired podcast, Feb 2025)Vercel on The Cloudcast (2024)Vercel on The Cloudcast (2021)8 tools to build your own PaaS (2025)IS PAAS READY TO TAKE THE NEXT STEP? Where could PaaS evolve to now?Can new PaaS services abstract the developer, and just focus on business logic and business ideas? What languages or design patterns would be mandated? (web only, mobile only, web + mobile? )Can we template “best practices” enough to be reliable?Can we template compliances needed to handle financial transactions, customer data, etc.?Can troubleshooting become an automated service?Where was PaaS in the past? (Heroku, Google AppEngine, Cloud Foundry, Kubernetes)Language specificCloud specificAbstracting the infrastructure and securityFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Vivemos um momento em que a inteligência artificial, a computação em nuvem, as redes de alta velocidade, o sensoriamento de ambientes e o processamento de grandes volumes de dados ampliam a capacidade de inovação de empresas e governos. Ao mesmo tempo, esses avanços elevam a demanda por energia, infraestrutura e recursos naturais. O Start Eldorado desta semana traz a primeira parte de mais um evento da série Conexões - Construindo o Futuro com Inovação e Tecnologias Sustentáveis -, que reuniu convidados que trouxeram suas visões sobre quais caminhos precisam ser construídos em datacenters, cidades inteligentes e suas políticas públicas, nas estratégias corporativas e operadoras para sustentar a nova era da IA, dados e dispositivos de forma eficiente e responsável. O apresentador Daniel Gonzales recebeu Eduardo Zago, presidente Latam da Equinix; Mário Rachid, diretor executivo da Claro Empresas; Hingo Hammes, prefeito de Petrópolis (RJ); e José Renato Gonçalves, presidente da NEC no Brasil. O Start vai ao ar todas as quartas-feiras, às 21h, na Rádio Eldorado FM (107,3 para toda a Grande SP), app, site e assistentes de voz. See omnystudio.com/listener for privacy information.

Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss the top stories in Cloud and AI from November 2025.SHOW: 981SHOW TRANSCRIPT: The Cloudcast #981 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST:  "CLOUDCAST BASICS"SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[Mailtrap] Try Mailtrap for freeSHOW NOTES:Link to November 2025 News and ArticlesFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

As the Thanksgiving weekend passes, let's look at some technology things that we're thankful for and excited about. SHOW: 980SHOW TRANSCRIPT: The Cloudcast #980 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[Mailtrap] Try Mailtrap for free[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTESTHINGS TO BE THANKFUL FOR:Thankful for healthHappy Birthday to ChatGPTThe feature on an iPhone that remembers the code sent in an SMS messageRole playing, brain storming, scenario planning on ChatGPT/GeminiTools like Vercel that let non-programmers use AI to build apps (the future of PaaS)Tools like NotebookLM making complex stuff like LLM training, RAG patterns,  True competition in the cloud market as differentiation emergesThe beginnings of alternatives to NVIDIA in the AI HW Acceleration marketThe evolution of wearables like fitness trackers, or AirPods (better sound quality, noise cancelling, language translation, etc.) FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Evan Kaplan (@EvanKaplan, CEO @InfluxDB) talks about Physical AI and the evolving and emerging technologies required to bring AI to physical locations and activities. SHOW: 979SHOW TRANSCRIPT: The Cloudcast #979 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Mailtrap] Try Mailtrap for freeSHOW NOTES:InfluxData homepageEvan on The Cloudcast #394SpaceNews article on Time Series and AI in SpaceTime Series is critical to Physical AITopic 1 - Welcome back to the show, Evan. Give everyone a brief introduction.Topic 2 - We last spoke in 2019, and our goal with that show was to give everyone an introduction to time series databases. There's a link in the show notes for those who want to go back and get a refresher. But, if folks aren't up to speed, give everyone a quick definition of time series and its impacts in recent yearsTopic 3 - First, we need to discuss Physical AI. What is Physical AI, and how is it different from, say, GenAI or Agentic AI? It seems that AI in the mainstream equates LLMs with AI, but that isn't correct. We are talking about deterministic AI, not probabilistic solutions. Can you explain to everyone the difference and why it matters?Topic 4 - Why is the concept of time series so crucial to Physical AI? Additionally, you provided a great analogy comparing time series data collection to low-resolution and high-resolution images. Can you explain to everyone why this is so important?Topic 5 - Let's talk about some use cases. How and where does this intersection of Physical AI and time series impact organizations the most? Is this specific to certain industries (robotics, aerospace, IoT, etc.) or specific collection mechanisms (telemetry, sensor data, etc.)Topic 6 - Are we shifting with AI to a state that is less reactive and more proactive with an active intelligence?Topic 7 - What kind of demands do real-time, modern workflows and data streaming place on the infrastructure? When I think of time series, I think of real-time data, which means ultra-low latency and processing near the source, among other things. FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Once again NVIDIA had a record earnings quarter (Q3FY26), but the strength of their on-going success will be dependent on many factors that may or may not be within their control. Let's explore those broader factors.SHOW: 978SHOW TRANSCRIPT: The Cloudcast #978 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Mailtrap] Try Mailtrap for free[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:NVIDIA Earnings (Q3FY2026 - November 2025)WHAT WILL BE THE NEW METRICS AND MILESTONES TO TRACK?Customer Revenues (e.g. CoreWeave, OpenAI)“Alternatives” Revenues (e.g. Google/TPUs, AMD, China, etc.)Customer Success Stories (%ROI, Business Differentiation, Business Acceleration)Growth of Data Centers (e.g. buildouts, zoning approvals, etc.)Electricity Buildouts (e.g. nuclear, coal, alternative, regulatory changes, municipality adoption)Accounting Deep-Dives into NVIDIA (not fraud, but days receivables, inventory buybacks, etc.)$500B in back orders (Oracle, Microsoft, OpenAI, GrokAI)FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

SHOW: 975Rohan Sathe, CEO and Co-Founder of Nightfall AI, discusses the rise of Shadow AI, where employees unknowingly leak sensitive corporate data through generative AI tools like ChatGPT. We explore how Nightfall's AI-native approach transforms autonomous systems to defend against AI-powered data exfiltration across SaaS apps, endpoints, and browsers. SHOW TRANSCRIPT: The Cloudcast #975 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[Mailtrap] Try Mailtrap for free[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:Sunday Perspective touches on Shadow AINightfall websiteTopic 1 - Welcome to the show, Rohan. Give everyone a brief introduction, including your time at Uber Eats.Topic 2 - How do you define Shadow AI? We hear Shadow AI compared to Shadow IT back at the start of cloud. However, this looks different because everyone's learning curve is much smaller. For Shadow IT to happen, you had to know IT (servers, storage, etc.). Is this the correct way to think about the problem?Topic 3 - How big is the Shadow AI problem today?Topic 4 - Normally, data leaks would be discovered by traditional DLP (data loss prevention) tools. In my experience, those tools have been cumbersome and clunky, and you often face the classic trade-off between user productivity and security, as well as the need to lock down access. How has this mindset evolved in the era of AI? Topic 5 - What happens when AI-powered attacks meet AI-powered defense?Topic 6 - Let's talk about the technical architecture. How does Nightfall actually work across SaaS apps, endpoints, browsers, and AI tools?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Is the current level of AI funding and investment rational or irrational? Is it possible that it's both at the same time? Let's look at some numbers and the thought process behind them.SHOW: 976SHOW TRANSCRIPT: The Cloudcast #976 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:A whole bunch of AI-related statsSam Altman on BG2 podcastDO WE HAVE ANY IDEA HOW TO MEASURE THE IMPACT OF AI?How much is one model better than another (e.g. Gemini vs. CoPilot)?How much improvement should a software developer get?How much improvement should a knowledge worker get?How much cost savings should a chatbot provide?How long should it take to make a model understand a company's data?How many workers can a company displace with AI?OpenAI in 2030 - 26 gigawatts could power between 3.7 million to 17.3 million modern GPU serversOpenAI in 2035 - 50 gigawatts could power between 37 million to 173 million modern GPU serversFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

SHOW: 975SHOW TRANSCRIPT: The Cloudcast #975 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:LaunchAny websiteapicoach.io (note)Lessons learned after a decade of API StrategyLatest book: “Principles of Web API Design: Delivering Value with APIs and Microservices (Addison-Wesley under the Vaughn Vernon signature series)Upcoming report on AI-Assisted API DesignJames on The Cloudcast #153James on The Cloudcast #435Topic 1 - Welcome back to the show James. It's hard to believe it's been 11 years since we last spoke on the show! Give everyone a brief introduction.Topic 2 - To say we've come a long way with APIs as an industry is an understatement. But let's set the table for everyone. In your interactions with Enterprise customers, what trends or standards are currently top of mind? Topic 3 - You wrote an article (link in show notes) titled Lessons after a Decade of API Strategy. What struck me from the article was the combination of technology, business, and even culture, all of which have to come together. When talking to Enterprises these days, have we moved past understanding what APIs are and straight to solving problems with APIs?Topic 4 - What are the most common use cases you see today? API transformation? API sprawl and consolidation/documentation? Integration of SaaS/3rd party services?Topic 5 - No conversation would be complete without a discussion about AI and AI's impact on API's. I view this in several different ways. AI is creating APIs, and AI is being consumed through APIs. How do you think about AI and its impact? What's changed and what has stayed the same?Topic 6 - Second aspect to the AI topic, where and how does security fit into this intersection of AI and APIs?Topic 7 - If anyone is interested, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

In the history of technology era changes, things tend to change slowly at first, and then quickly all at once. We've now reached the beginning of the all-at-once era of AI change. SHOW: 974SHOW TRANSCRIPT: The Cloudcast #974 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:The Benefit of Bubbles (Stratechery)Cube Research - Energy needs of AIAre we building AI for Progress or Power? (Prof G Markets)Amazon earnings call (Oct 2025) transcriptMicrosoft Azure earnings call (Oct 2025) transcriptWHAT WAS THE LEADING AI TREND FOR EACH YEAR?2022 - What is ChatGPT2023 - What can GenAI do? (Jobs, Skills, etc.)2024 - Alternatives to OpenAI and NVIDIA, Where is Google?, Where is AWS?2025 - Open source in AI, AI Regulations, AI Partnerships, Deep Seek, AI Agents, AI Funding, AI Power Challenges, AI Gov't Involvement2026 - ???FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss the top stories in Cloud and AI from October 2025.SHOW: 973SHOW TRANSCRIPT: The Cloudcast #973 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST:  "CLOUDCAST BASICS"SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:Link to October 2025 News and ArticlesFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Patterns in technology tend to eventually repeat themselves (or at least rhyme), and Amazon is beginning to repeat some of the patterns of Cisco from the early 2000s SHOW: 972SHOW TRANSCRIPT: The Cloudcast #972 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:Staying nimble and continuing to strengthen our organization (Amazon, Oct 2025)Strengthening our culture and teams (Amazon, Sept 2024)Amazon Leadership PrinciplesWHERE ARE THE BIGGEST AI RISKS?Significant changes from one industry era to the next (Internet to SaaS, Cloud to AI)Once dominant position in a market now appears less dominant (Worth more than next 20 competitors; 7yr head start)Guiding cultural principles well-known within the company - start adding more and more to themEmployee motivation driving by stock price as much as work activitiesCulture evolves and erodes over time as the business changes and evolves - it's nearly impossible to reset the company cultureCompanies can rebound from being lost in the woods, but it's very, very difficultDeath by 1000 cuts in a virtuous cycle - employees are constantly more worried about job status than project status - the perception of who gets to make mistakes and who is responsible for mistakesFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Soham Mazumdar, CEO and Co-Founder of WisdomAI, discusses how organizations can break free from the "drowning in data but starving for insights" paradox that plagues modern enterprises. We explore his journey from Google's TeraGoogle project to co-founding and scaling Rubrik through its $5.6 billion IPO, and why he left that success to build an agentic AI approach to Business Intelligence (BI) that transforms how businesses extract value from their data investments.SHOW: 971SHOW TRANSCRIPT: The Cloudcast #963 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:WisdomAI websiteTopic 1 - Welcome to the show, Soham. We overlapped briefly at Rubrik. Give everyone a quick introduction and tell everyone a bit about your time at Google prior to RubrikTopic 2 - You helped scale Rubrik from inception to a $5.6 billion IPO in 2024. What was the "aha moment" that made you leave that success to tackle the enterprise data analytics problem with WisdomAI?Topic 3 - Let's define the core problem. Organizations invest heavily in modern data platforms - Snowflake, Databricks, etc. - but there is the term "drowning in data but starving for insights." What's broken in the traditional BI stack that prevents business users from getting answers?Topic 4 - How do agentic AI and BI fit together? WisdomAI introduces the concept of "Knowledge Fabric" and agentic data insights. Break this down for us - how does this fundamentally differ from traditional dashboards and BI tools?Topic 5 - One of the biggest challenges with GenAI in enterprise settings is hallucination. You've emphasized that WisdomAI separates GenAI from answer generation. How does your approach tackle this critical trust issue?Topic 6 - Let's talk about data integration complexity. Your platform works with both structured and unstructured data - Snowflake, BigQuery, Redshift, but also Excel, PDFs, PowerPoints. How do you handle this "dirty" data reality that most enterprises face?Topic 6a - With so much data, how do most organizations get started? What's a typical use case for adoption?Topic 7 - If anyone is interested, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

AI infrastructure conversations tend to be dominated by GPUs, data center buildouts, and power and water consumption. But networks also play a crucial role, whether to support huge file transfers to a compute cluster, stream telemetry from edge locations to feed AI pipelines, or provide high-speed, low latency connectivity for AI agents. On today's Tech... Read more »

Why are 95% of AI projects struggling to create positive ROI, according to a recent MIT study? Do businesses need help getting those first few AI projects started and driven to success?SHOW: 970SHOW TRANSCRIPT: The Cloudcast #970 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:MIT Study - The State of AI in Business (2025)The Information - Interview with Vihod Khosla (Oct 2025)WHERE ARE THE BIGGEST AI RISKS?Why are Businesses struggling with AI projects in 2025?What are simple steps to make AI projects successful?FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

In-depth review of M5 iPad Pro, we debate who should get M5 MacBook Pro, OpenAI finally released it's AI browser Atlas but falls short in areas, iPhone Air demand is scant, and Ai customer support is the worst.------------------------------Send Us a Voice MemoWe want to hear from you! Send us a voice memo that may get played on the show! Click here to submit. ------------------------------Bonus Episode: Stephen quit his job. Listen here!------------------------------Sponsored by:CleanMyMac - Get Tidy Today! Try 7 days free and use my code PRIMARYTECH for 20% off at clnmy.com/PrimaryTechnologyInterconnected: Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. We're diving deep into the systems behind AI, automation, quantum, and beyond. Listen wherever you get your podcasts, and on YouTube here.------------------------------Show Notes via EmailSign up to get exactly one email per week from the Primary Tech guys with the full episode show notes for your perusal. Click here to subscribe.------------------------------Watch on YouTube!Subscribe and watch our weekly episodes plus bonus clips at: https://youtu.be/ZuHmc0gDKe8------------------------------Join the CommunityDiscuss new episodes, start your own conversation, and join the Primary Tech community here: social.primarytech.fm------------------------------Support the showGet ad-free versions of the show plus exclusive bonus episodes every week! Subscribe directly in Apple Podcasts or here if you want chapters: primarytech.memberful.com/join------------------------------Reach out:Stephen's YouTube Channel@stephenrobles on ThreadsStephen on BlueskyStephen on Mastodon@stephenrobles on XJason's Inc.com Articles@jasonaten on Threads@JasonAten on XJason on BlueskyJason on Mastodon------------------------------We would also appreciate a 5-star rating and review in Apple Podcasts and SpotifyPodcast artwork with help from Basic Apple Guy.Those interested in sponsoring the show can reach out to us at: podcast@primarytech.fm------------------------------Links from the showiPad Team Interview - YouTubeApple's F1 Deal Includes Something I Didn't Expect. It Makes Perfect SenseApple is the exclusive new broadcast partner for Formula 1® in the U.S. - AppleM5 iPad Pro Review: Finally, Truly, Pro - YouTubeM5 MacBook Pro vs MacBook Air: Which is Right for You? - YouTubeThe M5 MacBook Pro Is Apple's Most Underrated Product in YearsM5 Apple Vision Pro | Heavier, Faster and Better! - YouTubeApple Confirms Vision Pro is Not Eligible for Trade-In - MacRumorsChatGPT - AtlasAtlas vs Comet: Begun the AI Browser Wars Have - YouTubeApple confirms it pulled controversial dating apps Tea and TeaOnHer from the App Store | TechCrunchMeta kicking ChatGPT out of WhatsApp for 50M users - 9to5MacApple's iPhone Air Experiment Fails as Supply Chain Cuts Production by 80% - MacRumorsTyler Stalman's iPhone Air Camera ReviewiOS 26.1 beta 4 adds new setting to tone down Liquid Glass transparency - 9to5MacAn AWS Outage Took Down Snapchat, Fortnite, and ChatGPT, and Showed How Fragile Everything Really IsFedEx's Use of AI Chatbots Is the Worst Thing a Company Could Do to Its Customers (00:00) - Intro (02:51) - iPad Team Interview (03:51) - Apple Gets F1 Rights (08:51) - M5 iPad Pro Review (17:05) - Apple Polishing Cloths (20:57) - M5 MacBook Pro DEBATE (41:15) - Sponsor: CleanMyMac (43:20) - Sponsor: Interconnected (44:32) - M5 Apple Vision Pro (53:15) - OpenAI Atlas Browser (01:01:01) - Amazon Ai Products (01:05:46) - Tea App Removed (01:06:20) - WhatsApp Removes ChatGPT (01:07:20) - iPhone Air "Demand" (01:12:58) - iOS 26.1 Liquid Glass Toggle (01:14:03) - AWS Outage (01:15:53) - Listener Voicemail (01:20:25) - Ai Customer Support

Monzy Merza (@monzymerza, CEO/Founder @Crogl) talks about build a next-generation Enterprise SOC by leveraging AI to stay ahead of Cybersecurity threats.SHOW: 969SHOW TRANSCRIPT: The Cloudcast #969 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:Crogl websiteTechCrunch articleForbes ArticleIntellyx ArticleLast WatchDog ArticleTopic 1 - Welcome to the show, Monzy. Give everyone a brief introduction and tell us about your unique journey from government research to Splunk to Databricks to founding Crogl.Topic 2 - Let's start with the current state of cybersecurity and AI. We're seeing headlines about AI being the top cybersecurity concern for 2025, even overtaking ransomware. From your perspective, what's driving this shift and why should organizations be paying attention to the intersection of cybersecurity and AI?Topic 3 - You've described Crogl as an "Iron Man suit" for security analysts. That's a compelling metaphor. Can you break down what you mean by that and how your approach differs from the traditional "reduce alerts" mentality that most vendors have been pushing?Topic 4 - Let's talk about your "knowledge engine" and what you call an “AI for the Enterprise SOC”. You're using compound AI systems with LLMs, smaller models, and knowledge graphs. This sounds quite different from vendors who are just "bolting on" LLMs to existing tools. Walk us through this architectural decision and why it matters.Topic 5 - The cybersecurity industry is experiencing massive alert fatigue - 4,500 alerts per day, with analysts only able to investigate 8-25 of them. Your philosophy is "every alert should be analyzed" rather than filtering them out. That seems counterintuitive to what the market has been doing. How does your autonomous investigation approach actually work in practice?Topic 6 - Where do you see this evolution heading, and what are the implications for SOC teams and security practitioners? Are we heading toward fully autonomous SOCs?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodI

What happens when the biggest technology trend is built on economics that don't make any sense when viewed end-to-end? Eventually, some aspect has to change, but which one? SHOW: 968SHOW TRANSCRIPT: The Cloudcast #968 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTES:Tokens are getting more expensiveNvidia Earnings, Moats and China, Nvidia vs. the AI LabsOracle Assures Investors on AI Cloud Margins as It Struggles to Profit From Older Nvidia ChipsWHERE ARE THE BIGGEST AI RISKS?Jensen Huang (NVIDIA CEO) is convinced that the future will be built on always having the most advanced GPUs and Frontier Models. Oracle is telling investors that they will significantly increase profit margins between now and 2028-30, although they have low margins now and they don't make high margins on older chipsThe economics of AI (reasoning models, high#s of AI tokens) is in contrast to the established SaaS business model.Right now everything AI-associated has a high valuation, but these forces are working against each other. Which one is going to give first? FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Animesh Koratana - CEO and Founder of PlayerZero discusses how agentic AI is transforming software quality assurance through predictive code simulation, and how teams can shift from reactive debugging to proactive problem prevention in the era of AI-generated code.SHOW: 967SHOW TRANSCRIPT: The Cloudcast #967 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:PlayerZero websiteTopic 1 - Welcome to the show Animesh. Tell us about your background and your involvement in.Topic 2 - Let's start with the core problem you're solving. What is "predictive software quality" and why is this becoming critical now, especially in the era of AI-generated code?Topic 3 - How does agentic code simulation work, and what makes it different from traditional testing approaches?Topic 4 - This feels like it democratizes software quality beyond just engineering teams. How does PlayerZero work across different roles - developers, QA, product managers, and support teams?Topic 5 - Integration and workflow - how does PlayerZero fit into existing CI/CD pipelines and development workflows? What does the implementation look likeTopic 6 - Let's talk about scale and complexity. How does PlayerZero handle large, distributed systems with microservices, databases, and complex architecturesTopic 7 - If someone out there is interested and wants to get started, what is the best place to started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Danny and Katie look at California's new AI safety law, OpenAI's in-chat checkout, and the rise of AI “actors”, and how it all depends on one thing – data centres, the hidden plumbing of the internet. They're joined by Adaire Fox-Martin, CEO of Equinix, which runs the giant facilities where the world's data lives, to explain how the company is racing to keep up with demand and why it plans to add as much capacity in the next five years as it did in the past 27. But with soaring energy use and limited space, can the industry keep pace?Image: Jack Taylor/The Times Hosted on Acast. See acast.com/privacy for more information.