Podcasts about Satya

The 7 Principles of Successful Partnering in the Age of AI Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/Check Out UPX:https://theultimatepartner.com/experience/ In this engaging session, Vince Menzione reflects on his extensive career transitioning from direct enterprise sales to building massive channel ecosystems, while unveiling the seven core operating principles essential for modern partnering. Highlighting tectonic industry shifts—from the PC and Cloud eras to the current AI revolution—Vince explains how traditional playbooks are becoming obsolete and why adopting a growth mindset, modeled by leaders like Satya Nadella, is critical for survival. He delves into the rising importance of hyperscaler marketplaces and co-selling, urging leaders to cultivate adaptability (AQ), emotional intelligence (EQ), and mutual trust to thrive in this rapidly changing tech landscape. https://youtu.be/5n8dqiamnmE Key Takeaways Traditional industry playbooks are outdated almost immediately due to the rapid acceleration of AI and market changes. Implementing a “growth mindset” is a foundational operating principle that can transform corporate culture and drive massive valuation increases. Executive commitment and clarity of vision are mandatory for aligning an entire organization around successful partnering. Building a strong brand story and maintaining a maniacal focus on OKRs turns strategic vision into executed results. The technology landscape has experienced massive tectonic shifts from the PC era to the Cloud, Mobile, and now AI, requiring high adaptability (AQ). Mutual trust remains the non-negotiable foundation for any successful professional relationship or partnership. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Vince Menzione, growth mindset, Satya Nadella, channel building, tech ecosystem, tectonic shifts, AI revolution, co-selling strategies, hyperscaler marketplaces, organizational alignment, executive commitment, OKRs execution, AQ strategy, mutual trust, B2B technology Transcript [00:00:00] Vince Menzione: Because I think we’re all paralyzed by AI and all the changes that are going on in our world, and playbooks are no longer good because they’re outdated the week after they come out. [00:00:12] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. What a list. Oh my gosh. I gotta tell you, I was just going back this morning and, and looking to see first of all the number, the sheer number is incredible. [00:00:36] Vince Menzione: But look at, look at all these top executives. These are, these are like market movers. The game changers. These are people that are doing more in our world, in our ecosystem than most others. And we are very fortunate to have the representation from these organizations. From these leaders in the room, and we try to curate an event that is more than a, a sales pitch. [00:01:00] Vince Menzione: We’re, in fact, we, we’re not a sales pitch. We’re all about, you know, helping you achieve more. And we try to frame that around operating principles. So, uh, a little bit of a roadmap lately. I mean, this started out like how did we get here in like, maybe five spots along the way. But, uh, for those of you who don’t know me and my background, and I’ve had an incredible career, I’ve been very blessed. [00:01:20] Vince Menzione: I did a startup that we grew from 6 million to 125 million. Went public on the Toronto Exchange. I’m still friends with the CEO, by the way. Helped, helped him grow and exit that company. Uh, I then followed one of the leaders there to go do a turnaround with Golden Gate Capital, and we took that and that’s where I built my first channel. [00:01:37] Vince Menzione: I went from doing enterprise sales as a direct seller, direct sales leader, VP to then going to building a channel. During nine 11, uh, this company was selling rugged notebook computers. Our biggest competitor was not a US company, and I spent a lot of time on Capitol Hill. I met with several congressmen and senators at a time when people did that, and they talked to each other. [00:01:58] Vince Menzione: And, uh, I built a channel. I got its a GSA schedule, and I understood. So I understood intuitively, even from that point in my career, how to move, how to shift from direct selling to building a channel, building a business around that. We became the growth engine of the company. One of my partners was one of the largest defense contractors, general Dynamics. [00:02:19] Vince Menzione: They had the big contract if you were selling to the US Army. And I knocked down the door basically and said, you got a partner with us. And that’s how we got the relationship established. And they wound up buying us for like 10 x what Golden Gate Capital had had spun us out for. And then Microsoft recruited me. [00:02:36] Vince Menzione: And for almost 10 years I was the GM of public sector partner strategy. And so I was, I was there and we’ll talk about Satya and other things, but I was there when we started the cloud. I was there when we pivoted the business from the old model and working with OEMs and trying to, to do things a different way to the cloud and co-selling and things like that. [00:02:56] Vince Menzione: And, uh, had a great experience. And then when I left I was like, oh, I’m just gonna go work for another big tech company. I started a podcast. I had a friend who said, you should do a podcast on partnering. You know a lot about this more than you probably think you do. And almost 10 years ago, I started a podcast in a spare bedroom. [00:03:13] Vince Menzione: And you know, it, it was, it built a following and there’s a lot of work, by the way, people, a lot of people do podcasts today. It was a lot of work for those of you. I congratulate anybody doing that. Uh, I went back inside for two years because I felt like I needed to go back into a big corporate environment. [00:03:29] Vince Menzione: And then I left during COVID and I learned a lot being at a big corporation about how hard it was to partner. Like it’s still hard. I don’t know how many people in the room feel this way. I know, I know the numbers are much better and Jay will talk through the numbers, but it’s not easy and a lot of organizations don’t understand it. [00:03:47] Vince Menzione: And that’s what we talk about here and we try to help people to achieve more and how to, how to get that mindset in the right place. But anyway, so. We started, we started doing the podcast after COVID, it took off. We did an event. Uh, there’s actually four of the five people that did partner. We called it Partner Mastermind. [00:04:06] Vince Menzione: We did an event about four years ago, uh, separately. And that led to Ultimate Partner. And it’s a long, the long history in the last four years of 10 events, like it’s been an incredible blast. And I want to thank each of you for being along this, this incredible ride with us as we continue to grow and expand. [00:04:24] Vince Menzione: We’ve been doubling every year for the last four years and um, I feel very blessed to be part of this. So I did wanna spend a minute with you on this. I don’t like the drain this slide, but I do wanna identify what I believe are seven operating principles of what makes successful partnering. And you know, you might say there’s eight, you might say there are other things I think about principles as opposed to tactics. [00:04:50] Vince Menzione: Tactics are transactional. They’re temporary and a point in time, and it’s how you respond and react to a situation. Principles are things you take with you, and that’s what we hope to do at Ultimate Partner. Take those things with you and then, then apply some of the things to the tactics that we need to have. [00:05:06] Vince Menzione: And so we talk about growth mindset. Uh, you know, depending on where you stand about Microsoft, these days, when this guy came in, stock was $36 a share. Okay. It’s in the four hundreds now. It was up to over 500 not long ago. He applied a different mindset. The first three things he did, Le got a copy of Carol Dweck’s book about mindset. [00:05:28] Vince Menzione: Growth mindset versus fixed mindset. Uh, he brought in Dr. Michael Vet, who’s a leading sports psychologist, like in, in the industry, who was the Seattle Seahawks sports psychologist. Mike’s been a podcast guest of mine. I’ve been to his studio. Um, and then he, we, he, he changed, he, he brought down, he took down the walls of the way Microsoft operated because leaders fought with each other. [00:05:51] Vince Menzione: They competed with each other for resources, for monetization, for everything. And he changed the mindset. Nobody’s a perfect CEO, but if I was to say to you who I think the best CEO of the last 10 years were, I’d give it to Saja Nadella, but it’s about mindset. It’s about changing or having the right mindset and applying that growth mindset to a successful partner. [00:06:12] Vince Menzione: Executive commitment, I talked about that. Other organizational will go nameless, but if you don’t, you can have the CEO down to the selling floor. Everyone needs to speak partnering, like in order to get it right in an organization. The whole company, the resources, the investments, the alignment, all has to align around partnering. [00:06:32] Vince Menzione: Executive commitment is incredible. Tony Saan took a small MSP to a half a billion dollar exit, took them to go, uh, Google Partner of the Year, seven straight years in a row. I think they’re eight this year. Uh, but Tony’s a good friend of mine. He is also been a guest on the podcast and, uh, somebody I’ve admired and worked with. [00:06:50] Vince Menzione: This is Dr. Michael Dravet. We talk about clarity, like once you get your mindset, once you get executive commitment, you then need to determine like how, what’s the vision? How do we drive success together? You need to turn, you need to know internally how to go do that. Then you lock arms with another organization and then you apply it to that partnership. [00:07:10] Vince Menzione: So that’s incredibly critical. Then, then you gotta do everything right? Like I always kid around about my days at Microsoft, we’d have these incredible meetings with leaders. They’d come meet with us at partner conference. I would literally go back to back for several days in the room. Slide deck after slide deck. [00:07:27] Vince Menzione: We’re high fiving at the end. [00:07:29] Vince Menzione: We’re gonna go do it [00:07:31] Vince Menzione: six months later. Crickets. Nothing happens, right? This happens a lot in partnering. Unfortunately, like we, we set up the right situation. We line everybody. We’re gonna go execute, we’re gonna drive results. You have to apply maniacal, focus, OKRs, everything to everything you do. [00:07:48] Vince Menzione: You need to apply. And by the way, you’re gonna hear from a lot of leaders here that do this type of work. So this is incredibly, uh, critical to success, brand and story. Like I wanna work with Microsoft. There’s gonna be probably 40 plus Microsoft leaders in the room, some of ’em sitting here and around the room. [00:08:06] Vince Menzione: How do you do that? Right? This is Ducks Raymond S. Good friend of mine at Point. I knew at point when they were just starting out. Scott Sackett is here. He’ll be up on stage. Uh, this man was expert on brand and story. Learn from people that are successful, how to be successful yourself, if you wanna be a top partner, if you wanna grow your business, whether you’re working with Microsoft, Google, Amazon, or any of the other partners in this room. [00:08:30] Vince Menzione: You need to be very clear about your brand, articulate it well, and drive a story against that. And that’s really super critical for success. And then once we do all those things, we start driving a flywheel of success. Aaron Feiger and some of the other people in the room, Reese Barry, are gonna be talking about how they do that. [00:08:47] Vince Menzione: They will help these organizations be successful. Pick putting that stake in the ground and driving it. And then what happens is after you drive this incredible success, what does my partner do? My tech giant, the company I’ve been working with, they go change everything. The market changes, the dynamics change. [00:09:05] Vince Menzione: This thing in November of 2022 called AI Happens, Chad, GBT hits the market. How do I respond and react to that? I need to be adaptable. I need to drive an AQ strategy on top of my EQ and iq, and we’ll talk more about that. So these are the operating principles, and we lay it out as a, as a diagram. And by the way, you see mutual trust. [00:09:26] Vince Menzione: Trust has to be in every room without trust, you have no partnerships, without trust, you have no business success. Like you can get buy in business, you can get buy in life, but trust is foundational. And I was very blessed to have that like grain ingrained in me as a young boy. Uh, so that’s our, that’s our operating principles. [00:09:48] Vince Menzione: Um, I’m working on a book right now. It’s almost done though. We’re, we’re talk, we’ll talk about that more, but that’s, that’ll be in the book. Um, and then we’ve been talking about tectonic shifts and I don’t know who said it first, Jay or, or me, but I know who you said it in the studio several years ago. [00:10:04] Vince Menzione: Jay’s been in our, our Boca studio many, many times. But we’ve been talking about tectonic shifts and Oh my gosh, right? So think about, I want everybody to think about this for a second. If you’ve been around tech for a while. We’ve gone through several, like these 10 year phases, the PC era, the cloud era, the well, the cloud. [00:10:23] Vince Menzione: We had client server, pc, client server, we had cloud, we had mobile, and now we hit ai. Those eras all took a period of time, right? They didn’t happen overnight. Like there was a trend like five, six years, seven years, maybe eight years, and then COVID happened, and I believe that COVID was the acceleration point because. [00:10:44] Vince Menzione: We were all forced to do things we didn’t do before. People went out and bought PCs that didn’t have them. Kids had to learn from home. Healthcare was administered tele telehealth, we didn’t do telehealth before. We had like 5% of the population to telehealth before that, uh, our work environment changed, right? [00:11:02] Vince Menzione: We were doing Zoom calls or teams calls back when I was at Microsoft Days, but the world started doing it. Our life started to change. That’s why being in the room places like this is so important. And so that really has accelerated everything. And this, you know, all these things have been accelerating over time and these are significant shifts. [00:11:22] Vince Menzione: We have the three leaders of the three marketplace organizations coming on stage here. Uh, the three hyperscalers, because marketplace went from, we were talking about it like, this is really cool. You need to go do it. A few years ago. So Microsoft lowering the rates on it, and then everything changed and then everybody started accelerating and it became the fungible token. [00:11:43] Vince Menzione: ’cause we used to, we used to partner, we used to take spreadsheets and put ’em up against each other and try to figure out deals and fax copies of deals that came in and say, we want credit for this one. And then Marketplace became a way to create a fun non fungible token. And really drive your success. [00:11:59] Vince Menzione: And so we have all the leaders that are running marketplaces in this room, by the way. So this is gonna be like the most incredible rich conversation. Co-selling. Co-selling is a, you know, a non-starter day. You have to co-sell it. People, we used to do vendor channel, which means I had somebody selling my stuff that’s not happening anymore. [00:12:19] Vince Menzione: And Jay, we’ll talk about the seven seats at the table. But this is all, these are all the things that have been changing. And of course, ai. I think that we are sitting here and I, I, I’ll share, and I’m stressing this, like this is, you need to be in this room because you’re gonna hear from leaders about what the next steps are. [00:12:35] Vince Menzione: ’cause I think we’re all paralyzed by AI and all the changes that are going on in our world and playbooks are no longer good because they’re outdated the week after they come out. So I need to, I need to follow this in real time. I think this is super important that you do, and it’s why we exist and it’s why this time is like no other. [00:12:53] Vince Menzione: I think, you know, we said maybe a generation, maybe it’s a lifetime in terms of the shifts that we’re seeing. So I, I kind of started here and I wanted to end here, uh, just because the light doesn’t go out. That’s what it’s all about. And this is it. This is it for me, right? This is my, my last run. I’m not gonna go work for a company after this. [00:13:16] Vince Menzione: I’m not gonna go into become a consultant. And I want this truly to be like special. And I want you to all feel like you’re part, you are part of it, and however much you wanna lean in and be part of it in the future, we want to grow this in the right way. I, I feel that we have an a unique opportunity. [00:13:34] Vince Menzione: Because we’re not a vendor, we’re not selling anything. I feel like we’re a platform. We’re that we’re that lighthouse and others can come in that are experts and I feel like more and more of ’em are showing up. And you know, the PDG guys did a great job today and others in the room and people that have been friends and supporting us for for years as on that sponsor slide. [00:13:56] Vince Menzione: And so we just want to continued this journey with each of you. Um, and so I want your feedback on what we’re doing. I want, I love your support. I love your passion. I love the fact that you’re still here in the room talking with, with or being here, listening to me today. Um, this is, that lighthouse is, you can see these pictures. [00:14:15] Vince Menzione: These are all family photos. Um, we go to that lighthouse, not because it’s a lighthouse, but uh, it happens to be like a landmark in our town. And, uh, it’s kind of cool. And actually the re Joe Namath has owns the restaurant across from the lighthouse, so we, we’ve got to see him a couple of times, which is kind of cool. [00:14:34] Vince Menzione: But I, I, I, I was posting this lighthouse when I started the podcast. And I was, yeah. ’cause that’s where I live and it’s my hometown. And I think about Dakota Rings and I think about other things. But, um, this is what matters. This is what matters is helping others. And we all are gonna need each other in this world because AI is gonna change our lives. [00:15:00] Vince Menzione: And dramatically it’s, I I think this is a once in a lifetime thing. But I think having people that you trust and being in the room with others where you can learn and grow and adapt, adaptability is so important. So, um, analog is the new digital as my, my good friend Gary V now says. And I think there’s this huge opportunity around what we do as ultimate partner to help everybody reach their pinnacle to everybody. [00:15:26] Vince Menzione: Be the ultimate partner. And I want to thank you for coming. I want your, thank you for your support, friendship, love. And, uh, you’re just an incredible group. Thank you. [00:15:41] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.

בעידן של שפע מידע ופייק ניוז, קשה מאוד לדעת מה באמת נכון. אבל האם שמתם לב שה"פייק ניוז" הגדול ביותר קורה דווקא בתוך ההכרה שלנו?בהרצאה זו נצלול אל תוך עולם היוגה סוטרה של פטנג'לי ונחקור את מושג ה"סאטיה" (Satya) - תרגול האמת. נבין מדוע אמת אינה רק "עובדה יבשה", כיצד ההכרה והאגו שלנו משקרים לנו כדי להצדיק פחדים וצורך בצדקנות, ואיך ארבע השאלות של הדלאי לאמה יכולות לשנות לחלוטין את מערכות היחסים שלנו עם עצמנו ועם הילדים שלנו.תרגול אמת הוא תרגול מתמשך של ניקוי ההכרה והדעות שלנו כדי לפעול מתוך מודעות וצלילות, ולא מתוך אוטומט הישרדותי.

Takeaway: This episode dives into the psychology of pain, the emotional patterns that shape our relationships, and why integrity is the missing ingredient in modern dating. Serena Sterling, PsyD brings a rare blend of clinical depth, personal conviction, and entrepreneurial courage to the conversation.Serena Sterling, PsyD is a doctor of psychology, author, and founder of Satya, a dating app built around behavioral integrity and real connection. Her background in mind‑body psychology gives her a unique lens on why people hurt—physically, emotionally, and relationally.Her book When Your Body Speaks explores how emotional trauma shows up as chronic pain and what it takes to release it.Learn more at satyadating.com.Mind‑Body Psychology — how repressed emotions create chronic pain and why the body keeps scoreEmotional Integrity — the gap between who we say we are and how we actually show upDating Accountability — why modern dating is broken and how Satya is trying to fix itBehavioral Patterns — how childhood experiences shape adult relationshipsHealing Work — what it takes to process old pain so it stops running your lifeBuilding Satya — the mission behind a dating app built on truth, alignment, and real connectionWhen Your Body Speaks — the science and stories behind emotional trauma and chronic sufferingYour body tells the truth even when you don'tIntegrity is attractive because it's rareUnprocessed emotions don't disappear—they rerouteDating apps reward performance, not authenticityHealing requires honesty, not perfectionSerena brings a perspective that cuts through the noise: if you want better relationships, you have to start with the relationship you have with yourself. This episode challenges the way we think about pain, connection, and the standards we bring into dating and life.Website: satyadating.comBook: When Your Body SpeaksInstagram & socials linked through her site

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ https://youtu.be/j0TuosYDQe4?si=7mzUwBe4PrQ-eB2E In this insightful session from the Ultimate Partner Live event in Bellevue, Washington, Vince Menzione sits down with Stephen Boyle, Corporate Vice President for Enterprise Partners at Microsoft, to pull back the curtain on the tectonic shifts redefining the tech ecosystem. Boyle details Microsoft's massive organizational pivot into enterprise and SME/channel divisions , explaining how artificial intelligence acts as the foundational thread unifying systems integrators, software vendors, and digital natives. Moving past market noise surrounding competing foundational models , he highlights Microsoft's strategy to become the ultimate “platform of platforms” by prioritizing user choice, security, and trust. Emphasizing a shift away from infrastructure technicalities and toward practical business outcomes , Boyle delivers an urgent mandate for partners to scale technical talent, eliminate traditional operational silos, and brace for the incoming consumption-driven, agent-based future of enterprise computing. Key Takeaways Microsoft has restructured its global sales divisions into distinct Enterprise and SME/Channel organizations to better target its massive total addressable markets. Artificial intelligence is fundamentally altering the partner ecosystem by dismantling traditional software and systems integrator silos to build interconnected, multi-party solutions. Rather than forcing alignment to a singular model, Microsoft aims to be the definitive platform of platforms by offering extensive choice across over 1,100 language models. The enterprise landscape is rapidly moving past experimental AI pilot phases and entering production setups completely focused on transforming core business outcomes. Tomorrow's service organizations are aggressively evolving into software-minded operations that deploy repeatable, highly specialized internal autonomous agents. Managing tokens and monitoring usage metrics represents the emerging operational baseline for balancing efficiency against the scaling expenses of large language models. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AI frontier, platform of platforms, enterprise partners, global systems integrators, digital natives, language models, token consumption, agent sprawl, citizen developers, shadow IT, business outcomes, technical enablement, marketplace growth, hyper-scalers, processing fluency, sovereign AI, industry ecosystems, data governance. Transcript [00:00:00] Stephen Boyle: This is the biggest, most transformative, iterative change in technology we’ve ever seen, where, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:00:12] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. Uh, I am thrilled to invite our next guest up on stage. I’ve known this gentleman for several years back in my days at Microsoft, and, um, we’ve been friends, actually Microsoft, and then we both went and did different things, came he’s come back to Microsoft in a big way. [00:00:46] Vince Menzione: Uh, Steven Boyle, for those of you don’t know, is recently a named the C. We will talk about it in a second, but I, I need to announce you properly. Is the corporate vice president, which by the way in Microsoft is a big deal for enterprise partners. He and Nicole De and I would say are the two Microsoft leaders in the organization. [00:01:06] Vince Menzione: Nicole is the channel chief. Steven has a, a big remit and we’ll talk about that up on stage. But I’m just so delightful for his support and for making the time in a very busy week at Microsoft ’cause this is CEO summit this week to make some time to come with us and be on stage with me. Please welcome my good friend Steven Boyle. [00:01:29] Vince Menzione: Good to see you, sir. To see. So I’m gonna put you on this side. [00:01:33] Stephen Boyle: Okay. [00:01:35] Vince Menzione: The hot seat. So I’m gonna, I, I didn’t do a justice and I, I wanted you to explain your role. I, I think I know, but I think for the, for the people in the room, uh, talk to us what Enterprise Partners means at Microsoft and what that role remit and remit looks like. [00:01:50] Stephen Boyle: Um, CVPs may or may not be important, but one thing they don’t do is get invites to the CEO summit. So I’m super pleased to be here with you guys. No, no, it’s totally cool. It’s totally cool if that phone rings. No, I’m kidding. Doesn’t. So what does it mean? So I’d like quickly, um. January last year, uh, we split the sales organization into enterprise and small to medium enterprise and channel. [00:02:15] Stephen Boyle: You guys probably familiar with that? Nicole is the, uh, chief partner officer lives in the SMA and C world and drives the channel, um, drives our marketplace business and, and a lot of other things. Um, for that 60 billion, um, you know, total addressable market that we have. Down there in SME and C. Um, at the same time, we established enterprise partner as part of Nick Parker’s overall organization. [00:02:40] Stephen Boyle: Um, but for most of 2025 we ran it as global systems integrators and advisories, ISVs and digital natives. So three separate footprints all focused entirely on, on, on enterprise. Um, in December, January, we talked about establishing an enterprise partner leader that would. You know, aggregate all of this stuff. [00:03:00] Stephen Boyle: Um, I was fortunate to come through, um, some frankly, pretty hairy, uh, experiences, I bet with some of our senior leaders. Um, I, I’ve loved to [00:03:08] Vince Menzione: been in the room for that [00:03:09] Stephen Boyle: questions like, why Steven Boyle and things like that, right? And really have to dig deep to, uh, to justify. Anyway, uh, I’m blessed and honored, uh, to run that entire portfolio of partners, uh, for the entirety of the enterprise partner world, which now from a chief revenue officer perspective, belongs to Deb. [00:03:25] Stephen Boyle: Deb Co. So Deb is the enterprise leader for all of our sales that we do into that space. Awesome. Um, I have three regional leaders, Nina Harding here in the United States, Ehab Ra in in Europe, and Heather Gordon in Asia that mirror and replicate and flow down the things that we decide to do from a strategy perspective for the, uh, for the core. [00:03:45] Vince Menzione: And we love Nina. She’s been, she was at our last event, [00:03:47] Stephen Boyle: super, super lady. And, uh, you know, the US is still 50% of our overall business. [00:03:53] Vince Menzione: Yeah. [00:03:53] Stephen Boyle: Too big to fabric. Every time I talk to Nina, I’m like, Nina, you’re too big to fail. We can’t cover you anywhere else. So you know, you’ve gotta be successful here in the Americas. [00:04:01] Vince Menzione: So I think just for breaking it up, I, ’cause I do want to like, it’ll lead to the next question, right? So you have the global systems integrators, all these systems integrators. Essentially you have all of the software companies we used to call ISVs, we now call SDCs or software development corporations. [00:04:17] Vince Menzione: And then you also have the AI stack, I’ll call it. Right? So under Jason Grafe. Yeah. Many, many might know. Jason’s been a guest on the podcast and was Satya’s chief of staff at one time, eight years. Eight years. Wow. I didn’t realize there was that many. [00:04:31] Stephen Boyle: Carry carried a lot of bags for Satya over the years. [00:04:34] Vince Menzione: Unbelievable. Well, let’s, I mean, so AI is an important component, right? And you saw Jay’s, Jay talking, just talking about AI and all these things. I would love to start here, right? Because, uh, you’re, you’re, I wanna get your perspective as Microsoft, your perspective as Microsoft on the biggest shifts you’re seeing in defining this we’ll call AI Frontier. [00:04:54] Vince Menzione: We’re seeing right now, how should partners translate that into how they position and go to market externally? How, how do we need to think about this time? [00:05:02] Stephen Boyle: Yeah, that is, uh, that is a huge question and I’m not sure we’ve got enough time to go into the, into all of the detail. Um, so let me sort of up level it a little bit for you. [00:05:10] Stephen Boyle: And I think, look, the move that we meet at made a couple of months ago and pulling together those three aspects. Nicole had already done it in SME and C. Right. One partner organization across the world with a very common set of goals. We were working closely together, Sandy Gupta, on ISV, Jason on ai, and myself on on si. [00:05:29] Stephen Boyle: But we were still working closely together across silos. So the opportunity for me, 60 days into this role is AI just allows you to wire the partner ecosystem together differently. Right? And even if you look at how we’re going to market an AI today, um. You know, with, with, with chat GPT, with Claude, with Anthropic, um, I think there’s something like 1100 different, you know, language models on Microsoft today. [00:05:55] Stephen Boyle: So the way I think about AI is we are absolutely gonna be the ultimate platform of platforms. Yeah, choice is incredibly important. Um. It’s, it’s, you know, turn the clock back 12 months, everybody was chat gpt five point x, you know, and then six months ago it was Gemini and now it seems to be clawed. And honestly I don’t know what it’s gonna be next quarter. [00:06:15] Stephen Boyle: So the only thing I can do is offer you choice. [00:06:18] Vince Menzione: Yeah. [00:06:18] Stephen Boyle: And from a partner perspective, I think that minimizes or reduces the risk that you have betting on the Microsoft platform because you can go in a multitude of different directions. I know we’re not in Europe, but if you were in Europe and you were worried about G-G-D-P-R and Jay mentioned sovereignty, you’d probably be like lining up really closely to Misra. [00:06:37] Stephen Boyle: Yeah. And a bunch of other Europe, European partners. So wherever you are in the globe, I wanna be that platform choice. Um, and we will lead with our own first party solutions. I hope they’re not coming for me. Um. I parked safely in the hotel. It can’t be me. Um, but you weren’t vibe coding in the room. Um, but you know, wherever you are in the world, in whichever industry you are in, um, it is our intent to, to offer that platform of platforms and to give the broadest set of partners the opportunity to engage with us. [00:07:07] Vince Menzione: I think that’s really important because I, I have found, especially in the last month or two, people are, it’s almost like a knee jerk. Don’t you feel like people don’t know what to do? There’s been so much noise in the press and the media and, and the markets around open AI and anthropic especially. Where do I go? [00:07:26] Vince Menzione: Seems to be like when I, when I sit, I watch everybody in the room here. I think they’re, they’ve all been thinking that as well. So you can, [00:07:31] Stephen Boyle: there’s a, a little bit of a deer in the headlights moment. Yes. And even I like, I get that. Yeah. Um, you know, I saw, uh, Jay slides. Jay, love the presentation. Love the slides, man. [00:07:40] Stephen Boyle: I’m gonna steal several of them. Um, we’ll talk about that later. We, we [00:07:43] Vince Menzione: have the deck, [00:07:45] Stephen Boyle: but, but in all seriousness, you know, this, this is like. It’s a new paradigm. I will date myself a little bit. Some of you might heard me say this. I sold many computers in the 1980s. Mini computers. Some of you in the room are going, what’s a mini computer? [00:07:59] Stephen Boyle: Um, I sold client server for Sun Microsystems in the nineties. I sold an awful lot of Oracle databases in the Auts, I think they’re called, and I’ve done two stints with Microsoft. This is the biggest, most transformative. Iterative change in technology we’ve ever seen. What, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:08:18] Stephen Boyle: Um, and we are building intelligent systems at scale faster than we’ve ever seen. Scalable, mission critical solutions being implemented today inside of Microsoft and with our most important customers. So, and we can’t do it without partners, right? There is absolutely nothing we can do in this industry. I will, I will put the, you know, the elephant in the room out there. [00:08:40] Stephen Boyle: Our ISD organization has between five and 7,000 people. Our forward deployed engineering organization is about a thousand people. [00:08:47] Vince Menzione: Yeah. [00:08:48] Stephen Boyle: So when you look at the scale of the total addressable market that Jay just talked about. We are gonna service directly like this much [00:08:55] Vince Menzione: used to be 5%. Was it even, is it even that high? [00:08:58] Stephen Boyle: I doubt it’s, I doubt it’s even that. And the billions of dollars that we spend every year helping our customers transform to what we’re now calling frontier firms is gonna be, have to be driven with every single person in this room in some way, shape, or form. Judson is not asking Marla to significantly increase ISD. [00:09:15] Stephen Boyle: Not asking John to significantly increase FDE, although we probably will hire in that area just because of the, the newness and the, you know, bright shiny object that everybody’s like, oh, FDE, I’ve gotta have those. We’ve got a thousand already today that have been around in John’s organization for 10 plus years doing the things that we are doing today. [00:09:32] Stephen Boyle: But we are gonna build out that muscle. But the real way we’re gonna build out that muscle is with all of you in this room. That’s like categorical. That is my like, probably number one goal for the next one to three years is make sure that, that story that Jay just told about Microsoft not being involved in AstraZeneca. [00:09:48] Stephen Boyle: I probably won’t tell Judson that Jay, but I love the story. Um, like if you could all do that for me, like win, um, that is so, you know, from our worldwide learning, through our skilling enablement through our cloud solution architects that I personally own. We are pivoting aggressively towards making sure that the partners understand our platforms better than any other job, number one for me right now, if you don’t understand what I’m selling, like I’m kind of dead in the water obviously. [00:10:15] Stephen Boyle: Well, [00:10:15] Vince Menzione: I was gonna ask you why now? Why Microsoft? Why now? Right? Because there is a lot of noise. You know, Google just announced, you all announced your results on the same day, which was astounding. That was freaky, wasn’t it? It was. It was the first time. And the, the total commitment, customer commitment is over a trillion dollars now, I think 1.2 trillion is what I counted up. [00:10:33] Stephen Boyle: Yeah. [00:10:34] Vince Menzione: But it’s saying a lot about like, what do I do now, like as these partners in the room. Um, how, I think you kind of already, and you’ve talked about this, about differentiating where Microsoft is, I think J Slide does a lot of justice there. It says how, uh, Microsoft Partners came into the room, surrounded the customer. [00:10:52] Vince Menzione: It feels like Microsoft has always leaned in big time on partners. Uh, more so I would say than any other organization out there. What would [00:10:59] Stephen Boyle: you say Joe Roses, my chief of staff, business manager and so many other things was telling me last night that, you know, we used to say 500,000 partners. [00:11:05] Vince Menzione: Yeah, [00:11:06] Stephen Boyle: it’s a, it’s a significantly higher number than that as well. [00:11:09] Stephen Boyle: So there’s an element of, you know, back to the deer in the headlights, which partners are, are more important. One of my other phrases that I say on a regular basis, the winners and losers are yet to be decided in this next wave. Like, I want all of us to on the right side of that argument. Right? But, but it’s gonna be a challenge and, and companies are going through shifts. [00:11:28] Stephen Boyle: You know, Accenture, maybe, possibly doesn’t need 750,000 employees in the not too distant future. Maybe TCS at 600,000 doesn’t need 600,000 human employees. So we’re going through this dramatic shift of, you know, what’s the right balance going forward. What I would say about Microsoft is notwithstanding the fact that we’ve figured this out for 51 years, which is a little bit mind blowing, um, that you know, all the way back in the seventies we’ve gone through so many iterative changes. [00:11:56] Stephen Boyle: People have questioned just like they’ve questions. A lot of other technology companies, are you gonna be around for the long haul? I think we’ve proven time and time again, and I love Jay’s story. I’ve used that myself about how many companies disappear on a, on a decade to decade, you know, business. 10 years ago I had the opportunity to listen to Craig Clayton Christensen, who’s sadly no longer with us. [00:12:15] Stephen Boyle: Yeah. But you know, the books that he wrote and the story that he told to Microsoft 2014, we were nowhere in cloud. [00:12:21] Vince Menzione: Yeah. [00:12:22] Stephen Boyle: AWS was so far ahead of us, it was crazy. And he came in and he’s like. You know what? You guys need to be successful. You need to figure out how to cross this chasm again, and we’ve done it time and time again. [00:12:32] Stephen Boyle: You can go back. You know, Microsoft used to be known as a fast follower in ai. I don’t think we’re a fast follower. I think we’re right up there. We’re right at the front, but that race is still being run and the winners are losers are yet to be decided. [00:12:44] Vince Menzione: I was in that room with Clayton Christensen with you, by the way. [00:12:46] Vince Menzione: I remember, I remember that. That was at a Prism conference. [00:12:49] Stephen Boyle: Yeah. Yeah. [00:12:50] Vince Menzione: You men, you touched on this with the GSIs a little bit. How do you see the roles evolving? You know, we, we, we bucketed all, we’ve always been. Fantastic about bucketing ISVs or SDCs and sis and digital natives. Yeah. How does it, how does that all come together? [00:13:06] Vince Menzione: Does it come together any differently in this new AI platform era, or is it the same? [00:13:11] Stephen Boyle: I look, I, I’ve said this for a long time, like if you go into AstraZeneca, the six plus, you know, frontline partners, there’s probably a whole board of second, third tier that, that we don’t know about doing, you know, things across the AstraZeneca group. [00:13:25] Stephen Boyle: It takes several villages and sometimes a small town, especially in my world, in the enterprise world, strategic five hundreds. Yeah. Um, you know, we, we ran some reports a few years ago and it is shocking how many global systems integrators have a footprint in Shell or Exxon or, you know, bank of America or whatever else. [00:13:44] Stephen Boyle: So I’ve always believed that partner to partner is critical. Yeah. I think it became even more critical in the, in the AI world, and I’ll take my new friends at Anthropic. So I went to the first Anthropic partner Summit. Some of you might have been down there in, in San Diego, um, just a couple of months ago. [00:13:59] Stephen Boyle: Same partners, same people from the same partners. In the room, you know, talking about what they’re gonna do together with Anthropic. Um, and I’m looking out across this audience going, okay, well I know him and I know her and I know those guys, and like, I need to figure out how I’m gonna weave this together. [00:14:14] Stephen Boyle: So it’s not just an Accenture and Anthropic or an NTT data and anthropic, but it’s an NTT data plus anthropic plus Microsoft. Story going forward. And then who’s best at delivering those services capabilities? So it’s it at every juncture that I see in the, in the partner community, and this is the, the reason why I argued vehemently with Nick, that it has to be one organization I’m gonna create maybe given a little bit away. [00:14:40] Stephen Boyle: So if you’re recording, stop now. Um, I’m gonna create an enablement organization that is partner agnostic. I don’t necessarily care. I do care about the digital natives, but I don’t care about how I train them. Right. What I’m more important of is how do I train the digital natives in what the sis are doing, and how do I train the sis and what the ISVs Plus digital Natives are doing. [00:15:01] Vince Menzione: Yeah. [00:15:01] Stephen Boyle: That is my, that’s my game plan. If I fail there, then I think we fail to raise the bar and be differentiated in an AI world, and I’m not set up like that today. [00:15:12] Vince Menzione: I wanna, I wanna ask you, uh, uh, because I was looking at Jay’s slide and the, the managed piece is. And we have a lot of managed service providers in this room today. [00:15:20] Vince Menzione: A lot of them, by the way, come from the old school of managed services. The managed piece seems to be like, if I’m doing something today with ai, we’re gonna talk about security next, uh, up on stage here. It seems like there’s a new set of skills or a different approach to the customer, don’t you? Don’t you agree? [00:15:37] Stephen Boyle: I I [00:15:37] Vince Menzione: think you need to keep your hands on the steering wheel at all [00:15:39] Stephen Boyle: times. I think what it boils down to is you can’t do AI unless you do certain other things. [00:15:44] Vince Menzione: Yeah. [00:15:44] Stephen Boyle: Right. You could be a modern work specialist and you could make a lot of money being a modern work specialist, or you could be a, a dynamic specialist. [00:15:52] Stephen Boyle: We just held our, uh, inner A in a circle conference last last week, which I was disappointed to miss for the first time in a few years. Those, those days are, are, are fast becoming over. [00:16:03] Vince Menzione: Yeah. [00:16:04] Stephen Boyle: Um, why? Because everything that I’ve just said is tied together by ai. Yes. And in order to do good ai, you need good data. [00:16:12] Stephen Boyle: And in order to trust everything that you’re getting, as Judson talks about trust and intelligence, you need to wrap that in a really secure [00:16:19] Vince Menzione: Yes. [00:16:19] Stephen Boyle: You know, en en environment. Now we will do our best to provide levels of security into how we deliver ai. But that’s not the end of the game, right? You have to take it all, all the way to the edge. [00:16:30] Stephen Boyle: So that’s why a siloed partner or a singular commercial solution area partner in Microsoft’s terms, has got to transform its business. ’cause if you’re gonna do ai, you’ve gotta do those other things as well. [00:16:41] Vince Menzione: Agreed. I must see the model changing, and in fact, I see like bigger organizations becoming managed service providers in many respects. [00:16:48] Stephen Boyle: Yeah. Yeah. I mean, look, there’s still, there’s still a role for all the old terminology you mentioned is SV to sdc. Yeah. I’m like, I’m been around long enough. Look, it’s ANB still anv, it’s still an isv. Thank you. Independent software vendor. Um, and it’s, you know, where, where AI is allowing software to be, you know, frankly developed in a number of different places. [00:17:07] Stephen Boyle: We are all citizen developers. Um, you know, I was on a call with our internal leadership yesterday, um, and you guys might have heard this story ’cause I think it came out at Ignite. When we turn the agent 365, around and on ourselves. We found 130,000 agents running across Microsoft that had been developed and deployed internally with, I mean, you could call it shadow it. [00:17:28] Stephen Boyle: I guess that would be one phrase that you would use for it, but the reality is if you, if you haven’t got something to do your job today, you have the tools. To build it really, really fast. Um, and that, you know, that’s, that’s a great opportunity for people to be able to do their work, you know, in a better and in a different way. [00:17:45] Stephen Boyle: But it’s also a huge opportunity to make sure that data governance and security and all the other things that we need to deliver are there out of, out of the gate and out of the platform that we deliver. So security’s absolutely critical. Not saying that managed services won’t grow, um, at, at some level as well, but only if they transform into this multifaceted way. [00:18:04] Stephen Boyle: Yeah. Thinking [00:18:05] Vince Menzione: about, well, that’s what I was, I was gonna lead to here with innovating. It’s happening across, I mean, we’re talking about chips, we’re talking about foundational models, LLMs, we’re talking about applications, we’re talking about agents. How should we think about where to play and how to differentiate as partners in this room? [00:18:22] Stephen Boyle: I think. [00:18:25] Stephen Boyle: So look, I mean, one, one of the ways that Judson talks about it is I think silicon’s gonna change over time. Yes. NVIDIA’s definitely the 800 pound gorilla, maybe the 8,000 pound gorilla. Yeah. Uh, but you know, if you read the press, there’s, there’s things happening in, in different places as first party silicon, which we clearly are, are developing, um, in a quantum direction for sure. [00:18:45] Stephen Boyle: Um, there’s lots of different language models that haven’t even been launched on, on, on the marketplace yet, so. You know, Judson’s trying to uplevel our conversations. You’ll hear us talking about conversations more and more as we go into FY 27, um, that obviate all of those layers. Just like even when I was selling Sun Microsystems, it was about the business outcome and the business solution that we were solving for not necessarily the fastest piece of hardware or the best client service solution on, on the market. [00:19:17] Stephen Boyle: So I think what’s gonna happen over the next 12 to 24 months is we’ll have so many different models to choose from. We’ll have more silicon to choose from, but those won’t be the real buying decisions. The real buying decisions of what? How am I trying to transform my finance organization, my HR organization, and my supply chain? [00:19:36] Stephen Boyle: Because the underlying technology, Judson says commodity I, I guess I can go with that. It will be commoditized and we’ll really start to focus back on what the important things are. We’re moving a lot from pilot to production. You guys have probably seen that. The numbers that Jay just showed about how many. [00:19:52] Stephen Boyle: Projects are failing, is getting less and less because we’re getting smarter and smarter about what it takes to actually drive the business outcome. And I need all of us to be talking that same language. Yeah. Having conversations with head of HR about how we’re gonna transform human capital management in the, in the age of agents, if you like, like the underlying platform. [00:20:14] Stephen Boyle: It’s not, don’t worry about it. You wanna be on a secure platform. Don’t get me wrong. But at the same time, I don’t think we, we spent too much time worrying about that. [00:20:21] Vince Menzione: Yeah. We’re not, what you’re saying is we’re not spending enough time on outcomes. On the business outcomes. Right. And that’s where we need to focus. [00:20:27] Vince Menzione: We’re, we’re focusing on, I, I feel like we’re, it’s a signal to, to noise ratio that we’re living through right now. There’s too much noise. [00:20:33] Stephen Boyle: Yeah. [00:20:34] Vince Menzione: And we’re not focusing on the signal. I think that’s what you’re saying. [00:20:36] Stephen Boyle: I, it’s got to be, I mean, to be honest with you, it’s always been, you know, even when I sold what I would perceive, you know, sun in the nineties was a rockman ship to the stars and, you know, kind of sad what happened to that company. [00:20:47] Stephen Boyle: Um, but we, we were, we were fixated on, we had the best client server. But, but nobody was buying, you know, a piece of Sun hardware as a room heater, which is all it did, you know, like for the longest. But if you had SAP, if you had Cybase, if you had Bond, remember Bond, I mean all of those applications that drove the business outcomes, we’ve gotta get back to that kind of mentality. [00:21:09] Stephen Boyle: Yes. And worrying a little bit less about the underlying architecture. Yeah. It needs to be, it needs to be part of the conversation. ’cause it needs to deliver trust and security and intelligence and everything else. Then you need to rapidly move to what are you trying to achieve and how can we ensure the, the, the success of, of your business outcome. [00:21:27] Stephen Boyle: And look, I mean, Palantir pri you know, sort of came out and said, well, the way we do that is through forward deployed engineering. Um, and they stole the show. And, and, you know, they’re, they’re doing very well as a result of doing that. Uh, but if you go and talk to, um, Tom Siebel’s organization at C3 ai. [00:21:43] Stephen Boyle: They’ve had FDS for quite a while. You know, I told you about John Chuchu 10 years ago. John Chu, Chuck’s job was to go and get all the applications that we needed on the Microsoft phone. Remember that? [00:21:54] Vince Menzione: Yes. Um, [00:21:55] Stephen Boyle: you know, so we’ve pivoted John o over the years to doing what he’s doing now, which is to go sometimes in partnership with, with partners into the customer and say, what is it you’re trying to achieve? [00:22:05] Stephen Boyle: Let me show you how I can build that for you in three weeks or three months. That might have taken you three years. We literally just did a hackathon with one partner last, last, last week with, uh, with our ISE organization, the, the, the forward deployed, uh, group that John runs. Um, and one of the big customers said, I’ve just done in three days what would’ve taken me three months. [00:22:26] Stephen Boyle: Now he hasn’t productized it and rolled it out and blah, blah, blah. But the reality is that is how fast things are changing. And this was not a small company. This was a very, very large oil company, and they were like blown away by how much we can achieve. We’ve gotta do that at scale. [00:22:41] Vince Menzione: Yeah. [00:22:42] Stephen Boyle: You know, we, we have a commitment to scale our FDE community through partnerships to touch all of the S 500 in a very personalized way. [00:22:51] Stephen Boyle: And then, you know, at a slightly, you know, lower ratios down through the, through the majors and into, into Nicole’s SME and C world as well. [00:22:59] Vince Menzione: Jay talks about the decade of the ecosystem. He coined that term back, back on a podcast way back in nine, in, uh, in 2020. Microsoft has been at the, for, we used to call partner to partner back, back in the day. [00:23:10] Vince Menzione: Mm-hmm. Do you remember those days? How do you think about this ecosystem evolving and what steps are you taking to help bring these organizations together? Because I, I, again, we look at the seven seats or 6.3 seats at the table. The customer has the power now that they didn’t have before. ’cause they have the commitment with like with Microsoft and they can buy off of the marketplace and pull together multiple organizations to go, go do that. [00:23:34] Vince Menzione: How do you think about helping to orchestrate that as the leader of the enterprise partner business? [00:23:39] Stephen Boyle: So I’ll start with a really big example, and I’ll try and sort of scale it down a little bit. But my friends at Accenture, with the Accenture, Microsoft Business Group, we spend an awful lot of time, you know, in, in each other’s pockets, in each other’s deals. [00:23:51] Stephen Boyle: We know everything that’s going on in the Accenture, Microsoft Business Group. And a couple of weeks, or maybe a month or so ago, I was told that the Microsoft Business Group is now larger than the SAP Business group. It probably flip flops. [00:24:03] Vince Menzione: Yeah, [00:24:04] Stephen Boyle: it won’t be too long before the Anthropic Business Group is bigger than both of those. [00:24:08] Stephen Boyle: So what I need my Microsoft team to do is to not spend all of their lives in the. A MBG, the Azure, the Accenture, Microsoft Business group, but to go make friends in the Anthropic Accenture Business group and frankly still to make friends in the SAP business group and maybe in the Oracle Business Group and the list goes on. [00:24:27] Stephen Boyle: So at a macro 11, in the very largest accounts where we haven multiple practices, where we haven’t spent time before, I’m gonna. Push my people into uncomfortable zones and I’m gonna push them to go into those other areas and I’m gonna load them up with technical talent and cloud solution architects and ai, you know, forward deployed engineers. [00:24:45] Stephen Boyle: And I’m gonna force different people to talk together that haven’t talked together. So I can do that in TCS. I can do that, Capgemini, I can do that. Um, you know, in Europe with Capgemini and Misra is a classic example. Um, with the, with the Indian sis, Indian based sis, they’re all big enough where I know all the practices exist. [00:25:04] Stephen Boyle: I just need to do a better job of, of talking to them. Now, when you downsize that into, you know, into a, a company that doesn’t have all of that scale, this the same truth still holds. I need to talk to people who aren’t necessarily motivated every single day to do something with Microsoft. I need to talk to people who are motivated to do something with an AI partner or even a traditional SaaS partner. [00:25:27] Stephen Boyle: I noticed yesterday, actually no, this morning I got a notification that we just passed, um, a billion dollars in revenue on the marketplace with ServiceNow. [00:25:35] Vince Menzione: Nice. [00:25:36] Stephen Boyle: Um, and I think AWS announced the same thing, by the way this month as well. Um, so thank you to the ServiceNow people. Yeah. Um, you know, that is that there’s a tremendous demonstration of how far we’ve come in marketplace. [00:25:48] Stephen Boyle: ’cause that’s another one where we trailed AWS quite significantly. But with the right partnerships. And driving the right motions, we can, you know, we can definitely catch up and we will continue to pass, uh, some of, some of the other hyperscalers in, in, in that way. So really the bottom line to your question is partner to partner is still real. [00:26:08] Vince Menzione: Yeah, [00:26:08] Stephen Boyle: how we do it and what we use to tie things together. And I know that compensation drives behavior and we’re not gonna get into a compensation about like how we get compensated and everything else, but the reality is I’ve gotta break down those barriers and those silos and I’ve gotta deliver real meaningful enablement and practice development so that, so that the people who sit in the Anthropic business group and the people who sit in the Microsoft Business Group are spending as much time together as they are with me. [00:26:34] Stephen Boyle: That makes sense. Simply put, that’s what I, I need to achieve at scale rapidly. [00:26:40] Vince Menzione: So to, we’re getting close to time here, but as you look forward, what would define the most successful partnerships in this ecosystem? Is it, is it what you described, the opening up the aperture or for the, for the leaders in the room here today, what should they go do better and differently? [00:26:58] Stephen Boyle: Um, so obviously we’re closing out this fiscal, we’ve got Microsoft start and Microsoft start for partners coming up in July. Um, I mentioned the fact that we’re, we’re driving. Cu customer engagement through the lens of conversations and how do we achieve business outcomes? I would encourage you to, to gravitate, if you like, above the commercial solution areas where you might have understood, this is how I interact with Microsoft today. [00:27:23] Stephen Boyle: Um, and abstract it up to that AI layer. You know, think about trust, think about intelligence, think about business outcomes, and how do I potentially weave together a story? If I’m in the dynamic space, how do I get better in data? If I’m in the data space, how do I get better in. In that modern work environment, but really use AI as the overlay to, to help tie that together. [00:27:44] Stephen Boyle: That’s one thing. The second thing is if we’re not training you in the right direction, it’s stevenBoyle@microsoft.com. Let me know. Awesome. Um, we’ve got programmatic stuff, um, you know, and we’ve got high touch stuff as well. So I think this is, this is another time where Microsoft is gonna over pivot on all of the training and enablement that we need to do to make sure that you’re, you know, you’re grounded in our platform. [00:28:07] Stephen Boyle: Um, I think there’s a huge opportunity with this agenda future to become more of a software partner. You know, even the deepest services organizations are going to need agents, and the more successful ones will be the ones that can turn on those agents in a repeatable way. So. Our agents, the new SaaS. I’m not exactly saying that, but I think that the agen future is one where even the more services oriented companies will, will have teams of agents that they’re deploying. [00:28:35] Stephen Boyle: In fact, I had a very, very large systems integrator, um, in, in the EBC just about a month ago, three weeks ago. Um, and I was sat next to their head of consulting and he showed me what he called his God dashboard. Uh, and right in the middle of his God dashboard there are like 450 accounts. All of whom I recognized, ’cause they were all in the enterprise, right in the middle of his dashboard was, how many tokens am I spending? [00:29:00] Vince Menzione: Yeah. [00:29:01] Stephen Boyle: Like, not like what’s my daily runway? You know, not am I making a profit on that account or anything else like that is like, how many tokens have I consumed? Yeah. Because there is an awful lot of, that is the new juice, if you like. That’s, that’s driving the success. You can have the smartest people on the planet, but you’ve got to still arm them with all the best tools that are available out there. [00:29:22] Stephen Boyle: So it’s fascinating to listen to him, how he had gone through that thing of, you know, agent sprawl, how many are really working, how many are not working? How can we prove that? You can prove it through, you know, managing your tokens. There’s a new version of. Finops for tokens, for want of a better phrase, that’s gonna be critical for us all to understand. [00:29:40] Stephen Boyle: ’cause they’re not cheap, they’re not free, that’s for sure. And, and they might not be cheap if you’re not, if you’re not managing them and using them effectively. Yeah. So that’s the other thing that I would really get on top of. And, you know, we’re gonna make some announcements in the not too distant future about the consumption driven future. [00:29:56] Stephen Boyle: Um, that, that we will, that we will deliver with our first party and third party platforms going forward. So that’s another. Another critical thing [00:30:03] Vince Menzione: sounds like some exciting announcements. Pretty soon. [00:30:06] Stephen Boyle: Yeah, could look close. Quarter four, help me close. Quarter four. Yes. That’s priority number one, two, and three right now. [00:30:12] Stephen Boyle: Uh, but get ready for some, you know, for some new announcements in July. Um, look, the future is incredibly bright with Microsoft. It’s incredibly bright in the industry as a whole, right? I mean, let, let’s be honest, the, the growth targets that we will have for ne next year are astronomical, and we will not make them without the partner community that we have, without training and enabling the partner community that we need for tomorrow. [00:30:34] Stephen Boyle: So like, stay close, you know, stay engaged. Talk to your partner development managers, talk to the talk to field reps, talk to the accounts that that, that you are in, and stay as close as you possibly can to our emerging strategy. And, um, you know, look, I, I think if I had fivefold or tenfold the people I have today, I still wouldn’t be able to touch everybody that I would like to touch in the partner community. [00:30:58] Stephen Boyle: So I’ll apologize in advance. Um, but we’re gonna have some, you know, some really cool ways of learning. Um, and we’re gonna make sure that they’re available to the widest possible audience. [00:31:07] Vince Menzione: Well, we bring the practitioners and the experts in the room to help with that as well. Right? Yeah. Because you can’t always have a partner development manager tied to everybody in the room. [00:31:14] Stephen Boyle: I, I would do hackathons on AI every week with every partner and every part of the world, but I can’t. [00:31:19] Vince Menzione: Yeah, exactly. Well, so good to have you today. Thank you. So good to see you again. I don’t know what your schedule is like. I, we didn’t, we don’t have enough time for questions. [00:31:28] Stephen Boyle: That’s cool. [00:31:28] Vince Menzione: From the audience. [00:31:29] Stephen Boyle: I’m gonna stay around for a little [00:31:30] Vince Menzione: while this [00:31:30] Stephen Boyle: morning and I’m coming back [00:31:31] Vince Menzione: for cocktails. Alright, terrific. So. Stephen Boyle will be here for cocktail hour. Thank you. Four 30 and uh, I wanna thank you, sir. So good to have you. Thank you. Good to see you. Absolutely. [00:31:42] Stephen Boyle: So much. Absolutely. Hey, thanks everybody. [00:31:43] Stephen Boyle: Thanks for what you do today, and hopefully thank you for what you do tomorrow as well. [00:31:46] Vince Menzione: Thank you. An incredible leader. [00:31:49] Stephen Boyle: Don’t forget, ultimate [00:31:51] Vince Menzione: partner Alive is coming soon, June 18th at our executive breakfast in New York. I hope to see you there.Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ I

“Tęsknię za Ballmerem na scenie.” Łukasz po keynote'cie Build 2026, na którym Satya wymuszał z widowni klaskanie - “nie było wow” - a po osobowościach pokroju Guthriego i Russinovicha został korporacyjny autopilot. Bo to pierwszy od lat Build, gdzie zamiast Azure'owych fajerwerków dostajemy Windows, Windows, Windows.

Yoga is an inquiry into the nature of the mind, body, and consciousness. It provides a ritual that fosters belonging, meaning, and purpose, creating a sense of community and support. Through asana, yoga helps focus the mind on the breath, entering a state of stillness and awareness. A balanced practice is grounded in ethics and personal discipline, guided by the 8 Limbs of Yoga, particularly the Yamas (ethical guidelines) and Niyamas (observances). Key principles include non-violence (Ahimsa), truthfulness (Satya), and self-study (Svadhyaya).Asana practice emphasises steadiness (Sthira) and ease (Sukham), with focus on breath, sensation, and internal light (prana). This leads to overcoming duality and attaining a state of yoga. Obstacles such as dullness and self-doubt arise, but cultivating awareness, friendliness, joy, and compassion can help overcome them. Using tools like R.A.I.N. (Recognise, Allow, Investigate, Nurture), we learn to self-regulate and move toward lasting fulfilment and freedom from suffering.To read more and to practice with Zephyr Wildman, click here. To support Zephyr Yoga Podcast, donate here. Hosted on Acast. See acast.com/privacy for more information.

Reid sits down with Microsoft CEO Satya Nadella fresh off Microsoft Build 2026. The conversation goes wide: how AI is reshaping work, business, and society—and why the transformation sweeping through software development today is only a preview of what's coming for all knowledge work. Satya makes the case that human capital and "token capital" are now deeply intertwined, that companies—not just countries—must build their own AI capabilities, and that the organizations best positioned to thrive are those that can leverage their unique expertise inside intelligent systems. Reid and Satya also explore Microsoft's enterprise AI vision, Reid's work with Manas on AI-powered scientific discovery, lessons from past technological revolutions, and why demonstrating real, tangible benefits may be the most important thing the industry can do to earn—and keep—the public's trust. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcasts/satya/

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company's most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @satyanadella | @Microsoft | @latentspacepod | @swyx Chapters: 00:00 – Satya Nadella Introduction 01:48 – Reflections from Microsoft Build 03:12 – Microsoft's AI Training Strategy 05:48 – Complexity of Real-World Deployment of AI 07:33 – Augmenting Human Capital 09:37 – Harnesses for Enterprise 11:49 – Developer Value 15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence? 15:51 – Modern Definition of IP 17:38 – Future of Vendor vs. Enterprise Agents 21:48 – Near-Term Predictions on Model Pricing 24:02 – Durability of SaaS 25:58 – What Satya's Building 28:18 – Future of Engineering Roles 30:54 – How Microsoft Can Be More Ambitious 34:36 – Data Centers and Community Impact 38:01 – AI's Impact on Society 39:52 - AI and Education 42:28 – Conclusion

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Send us Fan MailThe database has always needed a human hand — someone to tune it, protect it, and keep it running. But what if AI could do most of that work autonomously? And if it can, what does that mean for the people who've built careers doing exactly that?In this episode, Al sits down with Satya Krishnaswamy, Director at IBM, to explore IBM's Db2 Genius Hub — a bold step toward autonomous database management powered by AI agents. Satya breaks down what it actually means for a database to run itself, how IBM is thinking about the levels of AI autonomy, and where the human still needs to stay in the loop.They get into the real questions: How many AI agents does it take to manage a modern database? When should you let the system decide — and when should you keep your hand on the wheel? And what happens to the DBA role as automation handles more of the heavy lifting?If you work in data, lead a data team, or just want to understand where enterprise database technology is actually headed — this one is worth your time.Timestamps02:14  Introducing Satya05:23  Db2 Genius Hub06:47  The State of the DB Industry07:34  The Fate of DBAs10:24  15 Agents12:47  AI DB Autonomy Levels15:52  When Should DBs be Autonomous23:42  Taking Advantage of Db2 AI27:32  Human in the Loop, or Not36:32  Db2 Advancements in Summary38:19  The Client Challenge41:04  For Fun Guest LinksLinkedIn: linkedin.com/in/satya-krishnaswamy-b412265bProduct: https://www.ibm.com/products/db2-genius-hubWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Send us Fan MailThe database has always needed a human hand — someone to tune it, protect it, and keep it running. But what if AI could do most of that work autonomously? And if it can, what does that mean for the people who've built careers doing exactly that?In this episode, Al sits down with Satya Krishnaswamy, Director at IBM, to explore IBM's Db2 Genius Hub — a bold step toward autonomous database management powered by AI agents. Satya breaks down what it actually means for a database to run itself, how IBM is thinking about the levels of AI autonomy, and where the human still needs to stay in the loop.They get into the real questions: How many AI agents does it take to manage a modern database? When should you let the system decide — and when should you keep your hand on the wheel? And what happens to the DBA role as automation handles more of the heavy lifting?If you work in data, lead a data team, or just want to understand where enterprise database technology is actually headed — this one is worth your time.Timestamps02:14  Introducing Satya05:23  Db2 Genius Hub06:47  The State of the DB Industry07:34  The Fate of DBAs10:24  15 Agents12:47  AI DB Autonomy Levels15:52  When Should DBs be Autonomous23:42  Taking Advantage of Db2 AI27:32  Human in the Loop, or Not36:32  Db2 Advancements in Summary38:19  The Client Challenge41:04  For Fun Guest LinksLinkedIn: linkedin.com/in/satya-krishnaswamy-b412265bProduct: https://www.ibm.com/products/db2-genius-hubWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

I'm excited to work with Microsoft once again as the presenting sponsors of the AI Engineer World's Fair! We'll streaming live from MS Build today for a special crossover pod with our friends at No Priors and the one and only Satya Nadella. However we did not hold back with this interview - we asked all the burning questions about uptime and Copilot that we know you have in your minds. Lets go!For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn't just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale. We go deep on GitHub's internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub's history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.Full Video PodWe discuss:* Kyle's expanded role across GitHub* How AI got Kyle coding again after years in leadership* Why GitHub rolls out AI through existing workflows instead of forcing new tools* WorkIQ, MCP, Slack, Teams, email, and GitHub as company context* Why massive “mega-skills” are giving way to small, atomic micro-skills* How AI changes summarization, communications, marketing, and analyst work* Why former developers in leadership may have a unique advantage in the AI era* Kyle's “15 agents on Saturday” workflow* How Kyle built an AI-generated executive presentation for CRO/CFO teams* Why AI changes the chief of staff role without removing the human work* GitHub Actions, webhooks, arbitrary code execution, and secure agent compute* The npm acquisition, supply-chain security, 2FA, and token invalidation* Slop forks, vendoring, and whether AI agents change dependency management* What pull requests become when most PRs come from agents* Prompt requests, vouching, AI review, and trust in open source* What counts as a “developer” when AI lowers the barrier to building* GitHub Spark, low-code, and why GitHub refuses to hide the code* 14x commit growth, Actions load, databases, monorepos, and availability* Copilot's evolution from completion to CLI, desktop app, cloud agents, and SDK* Context, memory, rules, and making GitHub “act like Kyle wants it to act”* Ambient AI, OpenClaw, enterprise security, and the new operating system for agents* What swyx should ask Satya Nadella about Microsoft's AI futureKyle Daigle* LinkedIn: https://www.linkedin.com/in/kyledaigle* X: https://x.com/kdaigleTimestamps00:00:00 Introduction00:03:36 Why AI Got Kyle Coding Again00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills00:15:39 The Golden Age for Former Developers in Leadership00:17:31 15 Agents on Saturday and AI-Generated Executive Work00:20:20 How AI Changes the Chief of Staff Role00:21:45 GitHub's History: Actions, npm, Webhooks, and Open Source00:28:45 Slop Forks, Vendoring, and AI Dependency Management00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code00:47:38 GitHub's Hardest Era: 14x Growth, Reliability, and Scale00:59:21 Actions as the Compute Layer for CI/CD and Automation01:02:04 The State and Future of GitHub Copilot01:08:24 Ambient AI, Background Agents, and the Future of the SDLC01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context01:21:41 What Should swyx Ask Satya?TranscriptIntroduction: Kyle Daigle's Expanded Role at GitHub and MicrosoftSwyx [00:00:00]: We're here with Kyle Daigle, COO of GitHub. Welcome.Kyle [00:00:07]: Hey, thanks for having me.Swyx [00:00:08]: You're not just CEO of GitHub. People know you as that. You have a new role.Kyle [00:00:11]: So I have an expanded role now. I've been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I'm also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we're also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they've had with GitHub over the years. So it's a different role in some ways, but it's also just building on the experience that I've had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.Swyx [00:01:09]: We'll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it's appropriate. I think one of the interesting things is I rarely meet a COO who's also a CMO. I think you're a very outward facing and you're very confident publicly. That's rare. Do you actually view yourself as COO? What's What is your thing?From GitHub Developer to COO/CMO: Building the Platform and Operating GitHubKyle [00:01:33]: I think for me, it's been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of theSwyx [00:01:46]: Let's bring that up. You wrote the back ends?Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that's kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub's always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I've, I still code. I love coding but the problem has always been, people. It's a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we're building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what's kept me at GitHub for so long.AI Workflows for Leadership: Commits, Retrospectives, and ContextSwyx [00:03:40]: Apparently, you have— your commits have gone up. What's this? What's going on?Kyle [00:03:45]: Rui's called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what's driving this. So that's, some of it's writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone's tools, But building for me, building for the non-technical leader, though I'm technical and how we're, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it's not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we've posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week's messaging actually was. That's something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It's actually, a recursive loop backwards. I'm always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn't work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I've built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it's running workflows for me. It's just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.Swyx [00:06:47]: Of course. That's where, that's where, stuff is hosted. Man, there's so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You're, you're understanding what happens. When you say we That's three thousand people. How?Rolling Out AI Internally: Skills, CLIs, and Company ContextKyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don't want to have to teach you a tool. I don't want to have to teach you something new. And so for us, we tried out a few tools. Most of them don't work because I got to get you on board? I got to teach you how to use it. What we've actually ended up doing is we've built like a set of skills internally. We have we each have our set of skills, and we've just been distributing even to the non-technical folks, the CLI. And then effectively, we're just giving it access to like read about everything that we're writing. So that's for us, that's usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.Swyx [00:08:03]: Teams and Slack?Kyle [00:08:04]: so we use Teams for video communication, but we don't use it for chat. W-we— GitHub for a long history, right? We're alwaysSwyx [00:08:13]: Also SlackKyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.Swyx [00:08:18]: So even though you have been acquired for I don't know, eight years nowKyle [00:08:22]: we stillSwyx [00:08:23]: You still use Slack?Kyle [00:08:23]: it's a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don't work the same way at all. We still use a bunch of different tools Because it's the purpose-built tools that We need. And thenSwyx [00:08:47]: Well, the same doesn't go for the rest of Microsoft, presumably.Kyle [00:08:50]: like the like various teams like operateSwyx [00:08:53]: They make their own decisionsKyle [00:08:54]: Various ways. I think it just matters what you're trying to what you're trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it's incredibly important for our teams that are working remotely. There's a lot of stuff you miss when you're not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what's happening this morning, today, yesterday. A little automation gets run. We'll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it's about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it's also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it's just like enabling everyone with that power of it's going to take me a week to figure this out. Instead, we're going “Okay I built a skill. Let's put it into a repo. We'll all share that skill together, and then we'll use the CLI or now the app-” “just to run it.”Micro Skills vs. Mega Skills: How GitHub Uses AI at WorkSwyx [00:10:26]: All right. I think, I think we're going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they're Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.Kyle [00:10:50]: like I think we haveSwyx [00:10:52]: And I assume it's a mess a Yeah.Kyle [00:10:54]: there's like I— like I think the reality is there's two pieces. Like first is I think that we're ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we've found and what— I was just with my team, this week, and we were talking about the skill side, and we're really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that's going to do I said, that full report. That doesn't really exist on our side anymore. It's usually how do— like a single skill that's going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweakSwyx [00:11:58]: It's brittleKyle [00:11:58]: Your mega skill and you're screwed? You can't do that. And so now we're really just talking about the Legos we're using and just letting the instruction book be something we're all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.Swyx [00:12:15]: I've, thought a lot about Postel's law. I don't know if that's a term that is, means things to folks. It's the idea that you should be liberal in what you accept and strict in what you output, right? And I think that's like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?Kyle [00:12:56]: I like I think so. I think that theSwyx [00:13:00]: Summarize anything.Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I'm summarizing something for an analyst, that's a very different thing than, probably how I'm going to summarize something for like a customer meeting or an engagement. So that's I think like the difference when we're talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it's just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we're talking about work and enabling the the marketers, communicators there, it's the atomic, this is what good summarization is, and then this is what I care about as for marketing for communications For whatever. And that I think is like the interesting matrix problem when we go from like a developer set of concerns to all kinds of different professions, is that what that word means to me is different than it means to you is different than it means to the analyst or the salesperson, and that's where I think the matrix mess is that we're starting to like still starting to find. It's about these mega skills but they're all just slight permutations, but those permutations are really important. It's the difference between someone reading this and going “Did AI make this?” what Or “This makes total sense, and I would expect this when I'm giving a briefing to Gartner,” or like whatever else.Swyx [00:14:37]: I think the beauty of it maybe is that you don't have to be that careful about what goes in there. It doesn't have to exactly fit as long as it like roughly is contained in there. I used to complain about plugin hell, basically. Like when you have a framework and then you have a hundred things that you need to integrate, everyone does like the GitHub used to be bloated full of these things. And now we don't need them anymore ‘cause now you just use skills.Former Developers in Leadership: AI as a Creation MultiplierKyle [00:15:00]: And like I think the most magical thing is the just that like I can just also crack it open. Like Like yes, I could go like change the how the plugin is coded, or like I could go do that now with AI, but I think there's just something more magical about getting a response back and being “That's not right,” and then you just crack the skill open, you just type English words and it's different. That building block is just, I think very unique. Once I get everyone to kind of understand how to best how to best make those changes to get the most power out of them.Swyx [00:15:36]: Is there a— you have a your peer group that Of people like you. Is there a common framing for Something I'm feeling is, which is true, is that is this a golden age for former developers who are now in leadership? Because you can wield the tools, you would know the right words, you're maybe not too close to the details. Doesn't matter. But like you're more effective than someone who doesn't come from that background.Kyle [00:15:59]: I think that like the secret has always been your ability to identify patterns and solve problems, and I think that for folks that like myself that don't code day to day anymore, that has made me successful as a developer, made me successful as a COO and now CMO. And so now that I have access to get and write code, I'm now applying that sort of like pattern finding and problem solving, and I know enough still about how to then go and say, “Oh, I want to make an app, but I don't want to break into jail or create something that's not going to be able to work or to be deployed scale or whatever.” that ability to apply all that additional business knowledge and still code I think is what makes that so interesting to me. Slightly different than I think some of the other like technical leaders that became business leaders and now are going back to their apps and updating them. Good for them? But I think the more, much more interesting thing is, well, now I have this whole new set of expertise over ten plus years. Why not take that and use that as a developer with these AI tools? So I definitely think that makes me more powerful, but I think that's true for like every dev as well. Most of the dev friends I still have also have some other underlying skill and passion. There's really talented, very kind of linear computer science software devs, absolutely. I just find that the folks that came from a different career, went to school for something else, went off and did this random thing, and then became a software dev, or were a dev, did a random thing, came back. Learning that extra set of information, learning those extra skills, and now having the power of an AI where I can crank up fifteen agents on Saturday while my kids are doing lacrosse, That's like really powerful. And I think it gets me back to that feeling of like creation, and it's very hard to replicate that in most other senses? That first time you build an app and you click it and you show someone that's magical. And so being able to do that not just in code, but across all kinds of different assets that's, that's huge. We were doing we're doing our every year we do our revenue planning. We talk about okay, what is it going to look like for next year? And of course as you imagine, there's, slideshows everywhere talking about what are we going to talk about, what's the narrative, et cetera. And so as you said I'm “Okay, well, I could probably just like build something to build this and then that way I don't have to go build the whole spreadsheet or I have to pass it to my team.” So we went through this process, and I got all the information and used the skills I mentioned. I built like a little app just to make it so I could look at some of the information in a SQLite database, more easily. And I ultimately built this entire presentation without touching any of it and I was “Okay, I'm just going to present this to our CRO, the CFO, their teams,” without mentioning I'd built it with AI. I like built a skill to make it look very much not AI driven. Just not pretty.AI-Generated Presentations, Human Taste, and the Changing Chief of Staff RoleSwyx [00:19:03]: Like a design. Yeah.Kyle [00:19:03]: Not pretty. But just like very clearly not AI. Kind of like don't do anything interesting.Swyx [00:19:08]: That's, yeah, that is valuable.Kyle [00:19:08]: Just go Exactly. We did the whole thing through. It used my notes from Obsidian, it used all the context I mentioned before, the plans, and Never came up once that it was AI generated.Swyx [00:19:20]: It didn't matter.Kyle [00:19:20]: Never once. D It didn't matter. And so now I takeSwyx [00:19:23]: This is a toolKyle [00:19:23]: I can take that tool and go, “Look, I don't want you to go build slideshows.” They're just helping us share information with each other. If this thing can do it With a little bit of crafting from you and then we can look at it together, awesome. There's no value in all that extra work. I think that the ability to, make it look humanly bad and and build a little app to, manipulate the data I think is part of, that upside for devs that are now in leadership roles. Because, the thing that I feel like I said before, this that's all a people, that's all a people problem. I know if you've used a coworker or not to build a slide deck, unless you spent a bunch of time to not do it.Swyx [00:20:07]: I know, but like it was so, I think there's a certain charm to just being blatantly AI. ‘Cause I think that you're well, you're just honest about There may be mistakes here that I cannot vouch for. So how much value is there? But anyway I think, actually the real question I want to ask is, there's a— You were a chief of staff To Thomas. And in the pre-AI world, the that job would've been a chief of staff job of like Can you prep me these slides and all that? And now you do it yourself.Kyle [00:20:35]: I still, I still have a chief of staff. Because, the difference is it's sort of the discussion every time we have some sort of technology evolution is it's not that the jobs the roles don't all go away, they just change? And so yeah, I don't have someone spending all their time building out slides for me and presentations ‘cause I don't need that anymore. But now I need that person that is able to go and find all the different connections between humans in those discussions to help me find out, okay, I should be meeting with this group and this team, and they have an opportunity, and I'm going to be in San Francisco today, I'm going to be in Seattle tomorrow. Those sorts of human connection aspects are still incredibly valuable and has always been a big part of that chief of staff role. But now just like chiefs of staff are not opening up, letters to process, they're doing emails. What It's the same thing. And now they're, they're not building out as many of these presentations because they have the the ability to have a AI take it on for, and share that with me and great. Let's keep moving ‘cause it's allowing us to go faster and make better decisions more quickly.Swyx [00:21:45]: Awesome. Well, so we can dive into more sort of, Productivity insights as you go. I did want to do a little bit of a brief history of colleague and hub. Because, we started here. And then you also involved the NPM acquisition. I did, I do want to touch upon that. And then more recently, I just want to bring up to present day where we're having uptime issues Which transparently we've already Addressed publicly, but we'll, we'll discuss in the pod. Did I miss anything? Like what, any other major highlights? Obviously, it's, it's a lot of years to cover.A Brief History of GitHub: Webhooks, Actions, Acquisitions, and Platform EvolutionKyle [00:22:15]: No the I think one of one highlight was right before the acquisition closed in twenty eighteen, I got to launch the first version of ActionsSwyx [00:22:27]: OhKyle [00:22:27]: At GitHub Universe. So it was OSwyx [00:22:29]: They're that young?Kyle [00:22:30]: It was October of twenty eighteen, I think. Yeah. Yeah.Swyx [00:22:33]: Gee, Jesus.Kyle [00:22:34]: I got to I was the engineering leader on that project and got to launch that. And then, yeah, we did acquisitions of NPM you said, Semmle, Dependabot Pul Panda a whole bunch of things. That was a bigSwyx [00:22:47]: Pul Panda.Kyle [00:22:48]: Abi is doing well.Swyx [00:22:51]: DX. Holy crap.Kyle [00:22:52]: Did well on DX. I and like that was a that was the big shift, after the acquisition. I had to join the sort of business side.Swyx [00:23:00]: So I need to hit you on some of these things ‘cause you were there. Right? And how often do I get to talk to someone who was there? But yeah, Actions. Is that the number one source of security issues on GitHub?Kyle [00:23:11]: Oh, sh I think that the number one source of, security issues is probably like all, the literal code in everyone's like underlying repositories. I would say back further than that is, if you remember I had to show in this graph was this is, I'm, didn't say this before, this is ultimately webhooks.Swyx [00:23:30]: You yeah.Kyle [00:23:31]: Like circa whatever it was.Swyx [00:23:32]: It says Hookshot in there.Kyle [00:23:32]: I forget. Yeah. Yeah, Hookshot's in there. And so like back then, it says GitHub Services. Do you see, it says Hookshot FE for front end, and then it says GitHub Services. GitHub Services back in the old days, right? You we had a repository that was Ruby code, and you could write any Ruby code in there, and then we would execute that On your behalf As a service, and then that way if an if you were trying to integrate with something, it didn't we would run it for you.Swyx [00:23:57]: And of course no containers ‘causeKyle [00:23:58]: No, ‘cause it wasSwyx [00:23:59]: Well, no containersKyle [00:24:00]: Twenty fourteen. And so there was some isolation obviously, but it was mostly the separations on the server level. That's like an example as long as the very old version of Pages, which ran on its own containerization infrastructure, not on Actions.Swyx [00:24:15]: Which like all-time great product.Kyle [00:24:16]: Pages powers the internet at this point to some degree. Those were places where like clearly there were no like issues like to my knowledge. But it was those things where I'm looking at and going “Okay, well we can't be running arbitrary Ruby code,” like on everyone's behalf. Then containerizing all of that up intoUh into actions now where yeah the containerization, is r-really good. The pinning most folks aren't pinning it the like to a particularSwyx [00:24:48]: ImagesKyle [00:24:48]: Sha, et cetera like their workflows, and so that's a big that's a big place Of pain for folks if they're just doing similar to any dependency management, just V1 or newest or latest, I think. But, that journey from that day to “Okay, we're just going to run all this arbitrary code, and, it'll basically be okay,” to now, no, we have, really good containerization. We have a new, underlying, ag-agent, containerization, service. It's like we're using it under the hood. It's through Azure. They recently announced it. The Azure, Dev Compute, but it's, very fast, very fast compute to be able to, spin up your own cloud agents, or whatnot. We're using it under the hood for some parts of the new,Swyx [00:25:36]: Microsoft Dev Box?Kyle [00:25:37]: No. Dev Compute, yeah.Swyx [00:25:41]: Hmm. Not finding it just yet.Kyle [00:25:44]: Oh, it's, it's in there somewhere.Swyx [00:25:46]: All right. Well, we'll cut that out.Kyle [00:25:47]: Sorry. But with, Dev Compute, you can, run, really fast, spin up really, small VMs really quickly, so you're doing a tool callSwyx [00:25:58]: Same conceptKyle [00:25:58]: Just do it containerize exact-exactly. So we're using that so definitely moving that direction to protect us from every every piece of code that we're ultimately running.Swyx [00:26:07]: look, that grows into the full SDLC? Code hosting was just the start and and then it's grown beyond that. Let's talk about NPM may-maybe ‘cause I think that's also, a very major point in the industry. I do think, it was looking for a home. It was, kind of struggling as a business, right? I don't know, I don't know how you would characterize that whole acquisition and how itNPM, Package Security, and Keeping the Internet RunningKyle [00:26:33]: like when we were talking to the team, I think the big thing for the both of us was to find a way to keep NPM, which was basically powering the internet then and way more so now to some degree running. Keep it going keep continuing to scale. It was having scaling problems, if I recall, back at that time. They were doing some rewrites. ItSwyx [00:27:00]: that's cute compared to now.Kyle [00:27:01]: Well, that's the thing is like when I'm talking to folks now, there's there's so many more underlying uses of NPM than there were back when we had them join in with GitHub. But that was ultimately the goal. It was really okay, we used to have pages. We have, the world's code. Let's make sure that we can keep NPM running well for the world. And we put a bunch of time and investment into fixing some of the underlying backend, changes, some of which we talked about some of the manifest work, et cetera. And then now, really trying to bring the the security posture of NPM up to speed. But, it is a unique challenge in that every move that we make to make it more secure will break a lot of people. And security is paramount. And also, we take it very seriously. We're, the any time that we have a problem with GitHub or we make a change that makes us more secure but hurts, there's, a snow day for developers or a really bad fire that they have to go put out. And so we've, have changed the 2FA policies. We've changed the way the tokens work. When we find tokens that have been exposed or potentially, exposed, we invalidate them, andSwyx [00:28:22]: I love that feature in GitHub. Yeah, it's greatKyle [00:28:23]: That creates issues, but, the but that's the thing is we're trying to push the community, forward without necessarily, doing something that is going to break the contract that's been for 15 years or close to it or some amount of years on NPM.Slop Forks, Vendoring, and the Future of Open Source Supply ChainsSwyx [00:28:43]: I think the— So now we're talking about, open source and publishing. And I think there's something here with what people are calling slop forks, which, I think Malta from Vercel is doing. And, part of me thinks, well, the way to get past any vulnerabilities, we just, let's just get rid of the concept of NPM. And we only publish source code. And anytime you want to import it you have your coding agent look at it and then adapt whatever subset you're going to use into your vendor it. But, the AI vendor it. Is that realistic? I don't know. Is it— Will that solve all our security issues? I don't know.Kyle [00:29:24]: I don't think it'll solve I so Mitchell was just talking Mitchell Hashimoto Was just talking about this today, and I think that I-in some ways, it's all all things, old or new again? Yeah, absolutely vendoring everything. Like I do I do remember twenty thirteen, twenty fourteen.Swyx [00:29:42]: This is Yeah. Let's, we must return toKyle [00:29:43]: That's what is We were vendoring everything. We were having actual discussions around, or at least I remember we were “Should we take this full thing?” “Why is this so big? We only need this one file.” And so I do think there's something true there where having either taking only what you need or the dependencies just getting incredibly small over time, I think will help to some degree, but it's not going to solve the fundamental problem, I don't think, because the vulnerabilities in an agent looking at them, there's time and time again, there's a million different ways in which we can convince an agent that this thing is, secure or not and pull it in. Or we can do static code analysis or runtime testing to say whether the code works or not. That is, I think, the step that needs to continue to be, invested in. The question is just on, how much scope. Should it be this enormous project that I'm pulling down, or should it be this piece? Either most companies are running some amount of security checking on the on the packages that they're bringing in or vendoring. That I think won't change. That's like what advanced security does to some degree, Socket does some degree. Like everyone is doing a piece of that. How we each do that like especially when we're talking to enterprise customers, is just like very different. No there's no one wants one single way to do it. And I think that's always been GitHub's, unique position in the world. I talk a lot to maintainers, I talk a lot to folks about this. It's we're— we rarely start like a process and a practice and like push it onto the community. We usually wait for the sort of like RFC process socially or literally, everyone agreeing, and then we'll cement something in. Because otherwise we'reMaintainers, RFCs, Vouching, and the Social Layer of TrustSwyx [00:31:35]: That fits your role in the ecosystem, yeahKyle [00:31:36]: We're GitHub. Yeah, we don't want to shape the whole thing. We want it to be figured out. But like how do you balance that like sort of Role in the industry to keep everything as secure as is possible and make sure that you're you're not going to be compromised as a human, ‘cause that's usually how it all happens. And Not not create a process or lock us into a flow that you're not going to or like Mitchell's not going to or other open source projects aren't going to like. That's always been a tricky balance for us, and I think that's something that we haven't talked about enough is we're not going to be able to fix everything for everyone in a way that everyone is going to like. So tell, help us, tell us what is working. When Mitchell was talking about, the Upvote, the upSwyx [00:32:22]: I was going to bring up his thing. Yeah.Kyle [00:32:23]: I forget what it Yeah. When he's talking to us, I was chatting with him and talking to him about this and I put it on Twitter and we talked to, also over DM, was “We're going to keep working.” but I think the important thing is I do actually want to hear what isn't working for you. And as, be as specific and clear for your project as is possible. And to every piece of credit over the many years that we've known each other through the industry, he's always done that and I appreciate that ‘cause there are places that we need to fix up, and we hear from him, and we'll fix up just like we do all other kinds of maintainers. But that that process between making those types of improvements and being more secure and like creating, I forget what he calls it's not the proof process, not the claims process. Do what I'm talking about? He has that he his projects have a way for you to kind of like,Swyx [00:33:13]: VouchKyle [00:33:13]: Vouch. Thank you. Yeah. He has like the vouch system for saying, “Hey, you should accept my PRs.” That's beenSwyx [00:33:20]: I just built this into GitHub. I don't know.Kyle [00:33:22]: Well, see, but that's the thing is that you say that and like he and his community really likes this and then I'll go talk to other maintainers and other maintainers, globally, and they're “No, this doesn't work for me.” And that is the tension, but also the kind of beauty of GitHub, depending on which way you look at it is we want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this. What You can go use this project, and if it takes off and becomes the kind of mostly standard, then yeah, we probably wouldn't enforce it but we would add it in because that's the flow that we tend to do?Swyx [00:34:02]: I hear a lot of people don't know the history of the pull request. And like like that's how, that's something that GitHub standardized basically.Kyle [00:34:08]: Yeah. It was a very messy process Like beforehand, and now the we have the benefit of it being the process? And now we have to go and Figure out the next best process or what adaptations change, or what does a pull request look like when eighty percent of your PRs are just coming from your agents and not From other devs?Swyx [00:34:31]: Do you like the prompt request idea from Peter?Kyle [00:34:34]: like I think that for each like each idea I think has its merits. I'm not, I'm not avoiding saying anything good or bad, but I feel like I've seen a version of we have that we have entire Thomas' store. Take all the assets of what you've built and put that in. I think that's got great ideas. There's all these various permutations of the PR flow, but I think the reason why there's not a single answer is ultimately we're trying to codify trust. We're trying to say “Okay, if Sean reviews this I'm going to trust it because you're Sean or you're the senior dev or you're the whatever.” And right now, when we are working in a flow where an agent writes code and another agent reviews code and then Kyle goes and looks at it the trust is kind of diffuse. And most of the tools that we're talking about are talking more about verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn't solve, I think, the human problem of I'm looking at a PR and I want to know if I can trust it. And we're still, we still tend to use human signals for that? Mitchell approving it or Kyle approving it or whatever. And so I think that's, I think that's why most of these options haven't really solved it is because, it's a social problem ultimately. It's a it's a human problem to review it and agree. Or you fully trust the tool and you're imbuing that tool with full trust Which I think in some cases that absolutely exists.AI-Generated PRs, Trust, and the Waymo AnalogySwyx [00:36:08]: And so like in the same way that there will be a tipping point in society when we don't allow humans to drive anymore Because machines are measurably better than Than humans. I'm looking for that tipping point, right? Like Mythos is ridiculously expensive. Someday we'll have Mythos on a desktop. I don't know. Will, does that change the equation?Kyle [00:36:30]: I think it's more I took a Waymo here, and I was on my phone and not looking around at all. There are other, self-driving, vehicles that I would not trust while, staring at the road. And I think that trust is something that isSwyx [00:36:48]: Is this a Zoox thing? What is itKyle [00:36:50]: I think that is both. I think that is both. LikeSwyx [00:36:53]: There's Zoox in this robo taxi. That's it. It'sKyle [00:36:56]: Well, depending on what level Of self-driving. But, my point is sort of that I think part of that is I strongly believe that's, a mixture of verifiable proof. Like how many accidents, how much data, and so on, and the human aspect of how I feel when I'm in this car, what it tells me, et cetera. And so that's why I think some of the like Some of these some of our AI tools tend to, imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to go, “Should I trust this or not?” And that's in the soft sense of, startups with high agency, weekend projects, and open source. And then there's enterprises and regulated industries and everything else, and that is an even harder problem to go solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational,Swyx [00:37:55]: Oh my GodKyle [00:37:55]: Multi governments around the world and regulating agencies. And so that's where I feel like until we tip over to your point on the sort of like human EQ side of it. I feel okay this feels okay I've been proven enough. Then the ball will start to roll a lot faster, where we'll end up getting to the “Okay, we can trust this,” and feel good about it in the Most difficult of cases.Reputation, Sponsors, Stars, and Bot Activity on GitHubSwyx [00:38:18]: If human trust is the thing that matters, I feel like GitHub as the developer social network could maybe do more there. Like vouchers are one system But, we have star counts, and then we have Contributor rights, and that's it. And I feel like there should be more in that space. I don't know if there's any other design decisions there.Kyle [00:38:37]: I think that one of the places that we don't really expose right now in this sort of way is, some degree of like hard trust and support, which would like for me is like sponsors is a good example of that.Swyx [00:38:49]: Ah.Kyle [00:38:49]: It like costs you something. To prove that I believe in your project and I trust you To some degree or I want to support you at the very least.Swyx [00:38:56]: Solve payments for open source. Why not?Kyle [00:38:58]: I think that I think that like as we keep moving forward, right, there's more and more projects where I'm, adding more and more dollars into sponsors personally because I want to like support them, but I also like know of I've probably never met them in person, but, I know of enough of their work that I want to support them. I think the thing that I don't love about stars or commit counts or anything else is ultimately, even with all of the various, abuse and de-spamming and deduplication work that we do or anti-abuse work that we do, these are all, not active social signals. They're passive ones that are ultimately gamifiable. And you may trust me, but another open source maintainer may not. And on what heuristic should you be, trusting me? That I think, is kind of where some of our thinking is right now. What signal from me is most important to you? You— If you can define that potentially, honestly in an agentic workflow that's what we see some of these open source projects do, where you have GitHub actions, and then you have like an agentic workflow that's calling AI, and you're setting these rules. Like if Kyle has submitted and gotten accepted PRs across any given project and has a social handle tied to his account in GitHub, and that social account's older than a certain amount. Really complex measures that matter to you ‘cause most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines. You could take that and then go apply that and then just say, “Oh, we're not going to accept this PR.” Building something that is, I think, malleable to everyone's needs, is a little bit better, rather than going “Hmm, this account's too young.” Because what happens? The attackers just go and go and create a multitude of accounts, and they wait Until it ages up. Needs to have a certain amount of stars. That's how star inflation happens. Need to have a certain amount of reposSwyx [00:40:46]: Oh my God. YeahKyle [00:40:47]: With PRs. They all just create repos and submit PRs to each other, and then they come in and do something nefarious. And so, it's hard. It's hard to find the measure. So I think we're, we're looking more at how can we provide you tools so you can kind of choose what's best for you. And of course, we'll give you some standards. But the trust vector, gets down to I don't know, some version of like human digital ID like everyone's been talking about. Like how do I prove that it's meSwyx [00:41:13]: Give me your eyeballsKyle [00:41:14]: On the internet. Give me your eyeballs. Exactly.Swyx [00:41:18]: The I got to keep moving on Topics, but obviously I can go all day on this stuff because, I've been involved in GitHub and open source My entire professional career. Stars. Very superficial. Everyone knows it. But I think time to one hundred thousand stars is the fastest I've ever seen. Like people just reached that in I don't know, months. And then like at the same time I don't trust it right? Like how many of these are real or bot or like whatever. I don't know how to ask this but like what can we do about it? LikeKyle [00:41:49]: JustSwyx [00:41:49]: Is stars broken? Is stars fine?Kyle [00:41:51]: I think that there's kind of two, there's like two pieces. Obviously we're constantly like trying to find ways in which like your users are producing spam, which would, I would include like be like only doing star gamification. When we find them, we pluck ‘em out and we,Swyx [00:42:08]: But it's like a Whac-A-MoleKyle [00:42:10]: It's a hundred percent like a Whac-A-MoleSwyx [00:42:11]: There's no wayKyle [00:42:11]: Now, powered by AI to be helpful. But I think more so what I'm seeing is, a lot of the like fastest time to X tends to be because we're now inviting so many more people into like software development on GitHub That like the zeitgeist is just swarming? And it'sSwyx [00:42:32]: It's not just developers anymoreKyle [00:42:33]: And it's not you and I. Like like however you want to say like what a developer is it's not just folks who have been coding for a very long time. It's folks that have maybe started coding or only joined in since the AI era. And nowSwyx [00:42:44]: what's the latest Octoverse number? I know eighty million was my lastRem- member that a number of developers on GitHubKyle [00:42:50]: Oh, we're over 200 million now.Swyx [00:42:53]: Okay. Well, so you see?Kyle [00:42:55]: Like over 200 million developers now.Swyx [00:42:56]: But it's not developers, right? It's, it's people with a GitHub account.What Counts as a Developer in the AI Era?Kyle [00:43:00]: So, so this is, this is the biggest debate that I would say, everyone loves to have at GitHub at this point. From my perspective, right, I think that there's, there's clearly a difference between, professional enterprise developer and then developers. But I think that I think that the idea that we should be I don't know, splitting hairs or segmenting developers in the early era of software development is, not worth our not worth the time. SoSwyx [00:43:29]: When you get into gatekeepingKyle [00:43:31]: 100%Swyx [00:43:31]: What is a developer?Kyle [00:43:31]: 100%. ‘Cause I wasn't a developer when I started writing code? I was going toSwyx [00:43:36]: Oh, no. I made— I cloned a thing, seven years before I learned to code. And then I and then I wrote about my learning to code journey, and people Just called me a fraud ‘cause I had a GitHub account. And I'm “Well, no, I just use GitHub, but I don't know-” “I didn't know what I was doing.”Kyle [00:43:49]: I I remember that. I remember those sets of posts, and like that's, that's b******t. So I fight very clearly on the line of, if you create code, if you have an idea and you create it into some way of, I'm, I'm going to run it and use the app right now, you may still use AI in that moment, but that's okay. At some point you're going to do the next thing. You're going to create a big— You're going to have to learn about this database. You're going to fix a bug, whatever. We're all on some same journey, and those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever. And those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer, and now I can just click the star button. And so I think that yes, there's clearly some amount of like spamming and game gamification that we're working against, but I really think we're just seeing this whole new cohort of folks that are moving from technology to technology because they're not working on a 20-year-old software application. They're working on a side app that they built on the weekend for their friends or for their new idea or whatever. And that's how you see these enormous charts going up and to the right with With stars.Swyx [00:44:59]: I think something that's remarkable is the persistence or, that GitHub extends to those folks. Usually when I see platforms go into a new audience, they usually have to, have like a second platform with a different name that wraps the main platform. But somehow GitHub has been able to sort of persist and extend, and it's friendly and whatever? So it's, it's nice.Spark, Low-Code, and Always Showing the CodeKyle [00:45:19]: I that's partially why I think as we've tried to move into I don't know, more like low-code-y things. We so we started working on Spark as like a way to, build an app and run it. I think that the reality is that we anytime we try to, kind of put even a veneer on top of it without when we put a veneer on top of something, we still always show you the code. That's kind of like a tenant. We're never going to, hide the code from you ever, because whatSwyx [00:45:52]: Why would you?Kyle [00:45:52]: That's, yeah, that's the whole point? However, I think that what we learned with things like Spark is that really the value of Spark for most devs is, easy runtime. And you may have a runtime or a host that you're going to use for that or you just build something and run it but, the package of making that even more simple isn't really needed for folks that are trying to build software and not just trying to build, an app, which is, slightly different, a slightly different goal. So I want to get you in, I want to get you comfortable. I think the best thing for me as, someone that did not traditionally come into software dev way back, I want anyone to be able to breach that chasm and not be in the I don't know, I feel like we're, we're still in an era of, STEM. I've got a 12-year-old and an eight-year-old, and it's “We got to get ‘em into STEM,”? Over and over. And I like I do, I do the things that good parents do. I was “Oh, you want to do coding?” “Yes, I want to do coding.” Do coding classes. But now they're just not afraid of doing software. And that's, I think, the thing that's honestly kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I'm not going to go into the breaker box ‘cause I'll probably kill myself? But, I can go change that light switch. Everyone should be able to go and say, “This fricking app doesn't do what I want. I want it to work like this.” And that I think, is what's kind of kept us all connected with GitHub through the years and some and during the easiest of times or in the hard times because of that opportunity of, we're the home for all developers, and we want everyone to be able to have that feeling that we've had of, had an idea, I created it and holy s**t here it is.Swyx [00:47:37]: Here it is. All right, I'm going to try to do more spicy questions.GitHub's Hardest Scaling Moment: Growth, Agents, and UptimeKyle [00:47:42]: Great.Swyx [00:47:42]: Is it an easy time now or a hard time?Kyle [00:47:45]: Oh at GitHub? It's a hard time. Like, it's a hard time and also, I was just with my team and I said, “This is also, the best and most exciting time that I think I can remember at GitHub.” BecauseSwyx [00:47:57]: Best of times, worst of times. It's never oneKyle [00:47:59]: ‘cause we've we were talking about Octoverse reports and, usually we do an Octoverse report once a year, and we look at the numbers, and we say, “Oh my goodness.” I was at Universe in October saying, “This was the fastest year of growth that we've ever had,” right? And now we're doing more in a month than we did in a year last year.Swyx [00:48:20]: You're talking about PRs.Kyle [00:48:21]: Commits.Swyx [00:48:21]: Commits, yeah.Kyle [00:48:22]: PRs. Kind of like you name it by roughly every measure that we're looking at, there's some amount of sort of growth that is much bigger, and that is breaking our system in new ways, not old ways. Like webhooks were always notoriously, unreliable over the years?Swyx [00:48:38]: Whose fault is that?Kyle [00:48:39]: not anymore mine, but for a period of time, I'm sure you could pull up a tweet that was “It was me. I'm sorry.” but, now, that got rewritten at a scale level that is still working and is not having problems today. Now what we're finding isn't just the isn't the-The simple stuff that folks are on the sometimes on Twitter or on the internet are “Hey, why is this like this?” Sure. There's absolutely silly problems that we shouldn't exist. But now we're talking about, unique, novel permission problems that happen only at a scale across all different objects or whatever, that now we have to go rewrite this underlying system. And so it's, there are problems that yeah, caught us off guard, which I think I said. Like the growth is astronomical, but also we're making such material progress in that I'm excited once we're once we've kind of like reimagined the underlying foundation layer, or pieces of it at least, what's going to be possible when it's not just all of us and all the new people that are being developers and all of their agents and all the tools like working together. Because that'll still happen in that in that GitHub tool, that GitHub community. But it's a it's a hard day anytime we can't give you what you're looking for. We have the same problem internally. We operate through github. Com. Of course, we have backups when things go down and whatnot for our own operations but we feel it too. If it's not working it's not working for us, and that's kind of like the promise of dogfooding for GitHub. It's always been true. We're using the same tool you're using. We're not using a super secret version. We and so we also need it to be great for us for our customers of course for open source. And now an exponential growth of agents, Doing it too.Swyx [00:50:32]: I wanted to load for audio listeners who maybe haven't seen your tweets, whatever. So one billion commits in twenty-five. Now it's two hundred and seventy-five million per week on pace for fourteen billion this year, if growth remains linear. Is that still the pace? I don't know. It's been aKyle [00:50:48]: it's, it's speedingSwyx [00:50:50]: Roughly.Kyle [00:50:50]: It's still speeding up.Swyx [00:50:51]: It's, it's April, so yeah.Kyle [00:50:51]: Exactly. This was in April.Swyx [00:50:53]: All right. So basically you have fourteen x growth, right? Year on year on year. And I think that's a scaling issue. I think, I'm going to like try to really steel man this thing. People have experienced fourteen x growth. They haven't had your downtime. And that's like— C-can we go dig into that? Why? Like what's the— what broke? What are we doing to fix it? Like just anything for the community to reassure them.Why GitHub Reliability Is Breaking in New WaysKyle [00:51:18]: so there's a Like I was saying, there's a couple different places that we've seen the growth issues. Some of the growth issues, which is why we're t— I was talking about pushing hard on more CPUs is in actions in particular. More tools, more agents, more PRs mean more builds, more builds mean more CPUs. And so we are expanding through not just our data center, but obviously we were talking about moving to Azure and moving to, adding an additional cloud compute because we simply need more CPUs. Not as much GPUs. We definitely need GPUs too, but now CPUs are becoming a factor.Swyx [00:51:53]: It's very CPU heavy.Kyle [00:51:54]: Underneath the hood when it comes to some of the underlying services, we've been breaking up over the years our database infrastructure, so that way we have, more cognitive separation between our the various services. The place that we continue to have pain is in, permissioning. And so right now m-many of our permissioning layers sit into a database that we like internally call MySQL One, and old Hubbers will know what I'm talking about. And so we've been pulling things out of MySQL One for many years, because like and we use we use Vitess and we use other technologies to shard and we do it as one bigSwyx [00:52:31]: Famous thing, PlanetScale was born from this andKyle [00:52:32]: A hundred percent. Sam Old Hubber and friend. And so finding these opportunities to like break this out and then do that globally. The other thing that I think is interesting and both a unique opportunity and tricky is we also run everything I just talked about in a black box container with GitHub Enterprise Server for people that work on-prem. So we take everything I just said, and we also do it on-prem, and we also do all of that and we do it in a data residence setup for customers that need to have their data in a single location. Each of these has the unique characteristic around how we're sort of storing that data in MySQL or in a permissioning setup. That's where some of these outages have oc-occurred, where you're seeing it more like across the board rather than just like the one pieceSwyx [00:53:17]: Filling the databaseKyle [00:53:17]: Isn't quite working. Exactly. And so part of it is that. I think there's been some other places where agents are much more or more projects appear to be moving towards monorepo versus we were going the other direction for many years in the industry. Repos were smaller, but there were more of them, and now we're seeing the opposite. Repos are bigger, and there's, not fewer of them per se ‘cause there's new growth, but, we're just seeing many more big repos. Big repos, big monorepos have always had, a unique performance problem. Because each one, is slightly different if, particularly if the underlying blobs are incredibly big Inside the repos. And so we've done a ton of work that you pro— like most people haven't probably experienced, unless you're in this case of the monorepo. But that Git, infrastructure layer improvement does help the overall, system because, many of the improvements that make monorepos work better make all repo infrastructure work better. And so, I could kind of keep going down the line where it's another thing where we're moving out of, We're changing how we do j I'll just say job queuing for lack of a better, explanation changing the underlying technologies there.Swyx [00:54:32]: I spent two years being a job queuing guy, so.Kyle [00:54:34]: And so it's kind of a little bit of a little bit of piece by piece, and it's mostly because as we were— as it was built, we built everything in a way that assumed, I guess in some ways that the size of the pipe of work was going to remain the same. There's just going to be more people coming through each of those pipes. But instead now in places whereA git push was, generally a certain size for example, is now, no longer true.Swyx [00:55:03]: Oh, yeah.Kyle [00:55:03]: OrSwyx [00:55:05]: I push a thousandKyle [00:55:06]: On the average. 100%Swyx [00:55:06]: A thousand line commits like dailyKyle [00:55:07]: Same thing with PRs. Like PRs same thing. And like we've talked about optimizing that and making changes where, and there were technology choices that did not work there? And it got slow, and it didn't It was not fast. It did not do what the users wanted. And so we've been reeling that all out and going “Okay, that's just not right. Let's stop putting good money after bad and do it the do it the right way or the right way now.” So there's It's a it's a lot of things, not quite when I've experienced scale at GitHub historically, it's almost always two options that we've used. We go vertical scaling, particularly with databases, right? And we go horizontal scaling. Oh, we just have more people using this service. Great. We're going to add more servers, and we rack them in our data center, or we use it in a cloud. And now we're sort of in a like diagonal, where like vertical doesn't really work anymore. Horizontal isn't work either because we're all We all have some CPU or GPU constraints in the world now, and now we have to go in and like crack open services that have been running for 10 or 15 years and go, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. This is like we're We have to do the work. We have to make it better.Swyx [00:56:22]: actually as an infra guy, I'm “This is like one of the most fascinating scaling challenges I've ever seen.”Kyle [00:56:26]: That's that's, that's the thing that's the thing that it's hard for Like when we weren't talking about it publicly, and I was like I came out, and I was “Hey, I just want to explain what's going on.” Part of it comes from a very old GitHub ethos, which is it's our it's our uptime. It's down. W What I know you're a developer, so you're, you're inclined to want to understand more what's going on. But at the same time us going “Hey, this service didn't, perform the way we expected, and now we have to go change it,” we weren't We're not trying to hide anything from you i

What does it really mean to live in your truth?In this episode of Coffee & Crystals, Kadie Chronister explores Satya, the second of the five Yamas in yoga philosophy and one of the most powerful tools for personal growth, self-awareness, and authentic living.While Satya is often translated as "truthfulness," its teachings extend far beyond simply telling the truth to others. Satya invites us to examine the stories we tell ourselves, the relationships we choose, the boundaries we set, and the ways we either honor or abandon our authentic selves.Drawing from personal experiences, yoga teachings, and lessons from her own healing journey, Kadie shares how practicing truthfulness can help us create healthier relationships, make aligned decisions, and cultivate greater peace and confidence in our daily lives.In this episode, you'll learn:• What Satya means in yoga philosophy• Why truthfulness begins with self-awareness• How to recognize when you're living out of alignment• The connection between honesty, boundaries, and self-respect• How to communicate truth with compassion and kindness• Journal prompts to help you uncover your authentic voice• Practical ways to bring Satya into your everyday lifeWhether you're new to yoga philosophy or looking to deepen your spiritual practice, this conversation offers practical wisdom for living with greater authenticity, mindfulness, and intention.Memorable Quotes:"Don't put yourself in situations you don't deserve.""Drop the narrative and focus on the facts.""Every season tests our truth in different ways."Topics Discussed:00:00 Introduction to Coffee & Crystals02:03 The Yamas: Yoga's Ethical Guidelines02:46 What Is Satya? Understanding Truthfulness12:04 Self-Discovery Through Honest Reflection15:42 Practicing Satya in Everyday Life20:25 Relationships, Community & Authentic ConnectionKeywords:Satya, yoga philosophy, yoga sutras, authenticity, truthfulness, mindfulness, personal growth, self-awareness, spiritual growth, wellness podcast, yoga lifestyle, conscious living, self-discovery, emotional wellness, Coffee & Crystals PodcastSummer Solstice: https://www.eventbrite.com/e/1985756813083?aff=oddtdtcreator

The only qualification for sincerely approaching the Lord is that one is akiñcana, meaning that one feels, "I don't have any qualification." Helplessness is our main qualification. In Satya-yuga, because people have such acumen for meditation—they can remain in trance for hundreds, if not thousands of years without budging—they take shelter of that. Although the Holy Name is prominent always in every yuga, the denizens of Satya-yuga then, because of distraction by the mode of goodness, become self-reliant. As is mentioned in the purport here, in Kali-yuga, we have a great advantage, and that is our disadvantage. Kali-yuga, is mentioned in the very beginning of the Bhāgavatam (SB 1.1.10), as you all know: prāyeṇālpāyuṣaḥ sabhya kalāv asmin yuge janāḥ mandaḥ sumanda-matayo manda-bhāgyā hy upadrutāḥ As mentioned by Sūta Goswāmī at the outset of the Bhāgavatam in discussion with the sages, Kali-yuga is a disadvantageous age for meditation or for performing proper sacrifice. After all, the Bhāgavatam mentions that the sages had tried to perform the sacrifice, but they were getting only smoke. I've seen that in some ISKCON fire yajñas! Not only that, they're addicted to scrolling in the age of Kali-yuga, it's the prediction given 5,000 years ago that everyone would have a crooked neck because they can only sit like that, or walk like that, or sleep like this. (22:44) Mandaḥ sumanda-matayo means they have really bad ideas—really bad, stupid stuff. Manda-bhāgyā hy upadrutāḥ—and they're unlucky; there is no good fortune for the people of Kali-yuga. So, the sages are gathered for that sacrifice to find out how to do the highest good for these people who are going to be assailed by all these distractions in the Kali-yuga. And this Jīva Goswāmī also points out in his Bhakti-sandarbha, is one of the qualifications for people in Kali-yuga: it is their disqualification. When somebody is so disadvantaged that they can't fend for themselves, oftentimes the government will give a dispensation. Kṛṣṇa also gives a dispensation for those who are wholly unqualified to take to any other process. So, in Satya-yuga people are self-reliant. As the ages progress—Tretā, Dvāpara—there are adjustments to the mode of worship. In Satya, they're self-reliant, they're all paramahaṁsas, and they're fully equipped to just meditate. Then in Tretā-yuga, we have the fire to meditate upon and put grains into the fire; at least it's a form people can look at. And then in Dvāpara-yuga, with the installation of Deities, you need a big temple as the main object of vision in the whole town, where you walk in and see the Deity. But in Kali-yuga, people are iconoclasts. They don't want the form of the Lord; they try to break the form of the Lord, and they deny the form of the Lord. So, Kṛṣṇa comes in the most accommodating form possible. This is mentioned in Rūpa Goswāmī's verses about the Holy Name, when he says, "vācyaṁ vācakam ity udeti bhavato nāma svarūpa-dvayaṁ." Vācyaṁ vācakam— there is the name and the named. The name of Kṛṣṇa and the word Kṛṣṇa that designates Kṛṣṇa. He said these are the same, right? No, that's not true. He says although we say they're one thing... ------------------------------------------------------------ To connect with His Grace Vaiśeṣika Dāsa, please visit https://www.fanthespark.com/next-steps/ask-vaisesika-dasa/?utm_source=youtube&utm_medium=video&utm_campaign=launch2025 https://vaisesikadasayatra.blogspot.com/ vatam #kttvg #keepthetranscendentalvibrationgoing

What happens when you know something is wrong for you, but you keep ignoring it anyway?In this episode of Self Care, host Geraldine Hardy explores truthfulness, honesty, and why being real with yourself is one of the deepest forms of self-care.Drawing from the yogic principle of Satya, Geraldine reflects on what it means to stop betraying yourself, especially when your body, intuition, and inner voice are already telling you that something is no longer right.Through a deeply personal passage from her book Moments That Matter, Geraldine shares a chapter of her life marked by overworking, studying full-time, holding down multiple jobs, replacing one addiction with another, and pushing herself toward burnout while ignoring the physical signs that her body was struggling. This episode is not about surface-level wellness.It is about truth.Geraldine speaks honestly about the danger of pretending you are invincible, the cost of over-giving, the trauma patterns behind people-pleasing, and the moment we have to admit that we are running ourselves down. She also reflects on why setting boundaries is not selfish, why ignoring your body is never strength, and why real self-care begins when you stop lying to yourself.This is a grounded, honest, and deeply personal reflection for anyone who has pushed beyond their limits, taken on too much, said yes when they needed to say no, or mistaken survival mode for strength.This episode explores: • What Satya means within yoga philosophy • Why truthfulness is central to self-care • How self-betrayal can show up in everyday life • Why burnout often begins before we admit it • The danger of replacing one escape with another • How overworking, over-giving, and people-pleasing disconnect us from ourselves • Why the body eventually responds when we ignore our limits • How yacht crew and high-pressure professionals can mistake exhaustion for resilience • Why boundaries are not entitlement or selfishness • How to ask whether you are being truthful, loving, and caring toward yourselfGeraldine's message is clear:You cannot continue lying to yourself and expect your body, mind, and spirit to carry the weight forever.

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

Full show: https://kNOwBETTERHIPHOP.com Artists Played: Mad Sexual Genius, conshus, Wordchemist, Mr. J, DJ Stranger, Brooklyn Funk Essentials, Stik Figa, Heather Grey, Chinese American Bear, Chuck Strangers, Allison Russell, Von Pea, Odell, corto.alto, MoZaic, MIGHTYHEALTHY, Sankofa, DJ Navin Johnson, Tinariwen, Sulafa Elyas, Señor Kaos, A Producer Named 2, Just Be, JBiz, JuniAli7, Phels, MRKBH, Rico James, Satya, LEX, J57, TLC, OutKast, GOODie MOb, IMAKEMADBEATS

ಕೌಶಿಕ್ ಕೂಡುರಸ್ತೆ ರವರ "ಅರ್ಧ ಸತ್ಯ ಅರ್ಧ ಸುಳ್ಳ" ಕುರಿತು ಕೇಳಿ ಮಮತ ವೆಂಕಟೇಶ್ ಅವರ ಅಭಿಪ್ರಾಯವನ್ನು ದೀಪು ಸುರೇಂದ್ರನಾಥ್ ರವರ ಧ್ವನಿಯಲ್ಲಿ.Listen to Mamatha Venkatesh's opinion on Koushik Koduraste's "Ardha Satya Ardha Sullu" in Deepu Surendranath's voice.Write to us: parichayaloka@gmail.comCo-sponsored by: Prathama Srsti - Buy authentic, hand picked GI TAG products of India and support local art and artists. To know more visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.PrathamaSrsti.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Parichaya Loka presents a new Android app for travellers. Gear up for the weekend! Download "Tour Hoysala" app from the Playstore. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://tinyurl.com/tourhoysala⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Parichaya Loka presents Android app for travellers. Gear up for the weekend! Download "Tour Bengaluru" app from the Playstore. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://tinyurl.com/tourBengaluru⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

I'll be teaching yoga & meditation this September 20-26 at Ballymaloe House in Ireland with Erin Doerwald. It's a profoundly beautiful, nurturing setting for retreat. Join us for rhythms of daily practice, exquisite farm-to-table meals, and cultural exploration. Plus cedar saunas and a cold sea plunge! We welcome you to join us for this extraordinary retreat—more info at shawnparell.com/irelandI was puttering around on my desktop last week—doing anything to avoid beginning this draft—when an email arrived from Satya Doyle Byock, Director of the Salomé Institute of Jungian Studies, psychotherapist, and author of Quarterlife: The Search for Self in Early Adulthood. I'm connected to Satya's work through a yearlong course in Jungian psychology, so it felt synchronous that her voice should reach me in the midst of a procrastination I had entered but not yet named.In her newsletter, Satya reflected on how AI-generated content has begun to drain her motivation to write, or at least to write in the digital landscape:“The existential (or is it creative?) concern is not only that I don't know that I can keep up; it's that I'm not sure I want to.I don't want to feel frenzied for any reason, let alone in order to keep pace with robots.”I felt an immediate resonance. A similar resistance has been gathering at the edges of my awareness in recent months. As ever, I am drawn to the practice of writing; I feel reluctant, though, to keep step with machines, and wary of the subtle infiltration of AI's manufactured voice into the written word. Its outputs are refining by the day, but its velocity, seamlessness, and casual superiority register as categorically inhuman. Even the term content betrays the shift: it names a product, not a process. Writing still implies the grist of a mind at work.Momentarily, I consider abandoning the whole imperfect enterprise of these essays. Why compete with the speed of light? Human attention—my attention—has already been profoundly shaped, even warped, by life in the digital age. Like Satya, I am unwilling to have my nervous system further conscripted into that race.But then I pause. Because keeping pace is not, and never was, the aim of this work.In 1884, William James insisted, in his early challenge to Cartesian dualism, that “a purely disembodied human emotion is a nonentity.” Emotion, for James, arises from embodied sensation—from the interplay of pulse and breath, fascia and nerve synapse. What, then, are we encountering in AI's frictionless outputs, if not language severed from the very conditions that allow for feeling?Beyond its basic communicative function, writing is one of the ways humanity has revealed itself to itself across generations. Its deeper value—like all art—is metabolic. The artist's task is to sustain attention—to lower a bucket into the shadowed recesses of the psyche and draw up something true. Something we can hold up to the light and marvel: this has been here all along.Silene stenophylla—the narrow-leafed campion—offers a botanical echo of this process through millennia. Revived from 32,000-year-old tissue preserved in the frozen burrows of Arctic ground squirrels, its cells were coaxed back into bloom. Its resurrection gestures toward the truth that creation unfolds according to its own tempos, on timescales that exceed human urgencies. No algorithm can hasten such an emergence. It belongs to the potential of living systems. Silene stenophylla stands for all that has yet to be brought to light.My family is moving through a health circumstance that has re-angled the light on everything. I find myself asking, with unusual clarity, what it means to be human. What is this brief and improbable flare of existence, this particular arrangement of spirit and days—and what, in fact, matters within it? I am learning that no intelligence outside nature's intelligence—the one that moves through this body, shaped by my relationships, my encounters, my losses and blessings—can do this work for me. Integration is not transferable. It is slow chemistry, the metabolism of meaning, made possible by contact and time.In this unprecedented modern experiment—this “rough initiation,” to borrow Francis Weller's phrase from In the Absence of the Ordinary—we are tasked not only with preserving our shared existence, but with tending the intricate ecology by which we make sense of being human at all.To that end, I was struck by Ezra Klein's remarks to David Perell about how he prepares for interviews. He described the option of relying on a production team to generate questions—augmented, no doubt, by AI—and his commitment instead to the slower labor of reading and thinking his own way into his subject matter. It is through this integrative process that he becomes acclimated to the terrain of his guest's inner life.Klein's learning process is unmistakably estuarine. Like a river meeting the sea, he begins at the edge of his own knowing and encounters the salinity of another's perspective, allowing it to permeate and reshape his understanding. We may learn to live alongside artificial intelligence, and make good use of it, but this kind of convergence, this gradual and reciprocal deepening of awareness, still belongs to the meeting of living minds.As writers, readers, and human kin, we are now asking questions at the threshold between what can be offloaded and what cannot. We recognize ourselves in one another's fitful impasses and revelations. I find myself wanting, more clearly than ever, a human-paced, human-proportioned life—one in which instinct and intuition remain intact, in which I can not only hear another's words, but discern the place from which they have arisen. And so I must remain faithful to slow, estuarine processes that bend the psyche toward dignity and insight. We feel our way, and grow, and ache, and fall away, and arrive again. This is how we are human.If these offerings speak to you, consider becoming a paid subscriber. The Guest House is a 100% reader-supported publication, and your subscription makes it possible for us to create & connect. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit shawnparell.substack.com/subscribe

$606 million in DeFi exploits in one month. Two of the space's sharpest risk thinkers debate whether lenders are being paid anywhere close to enough. ======================================================== Thank you to our sponsors! Coinbase One 20% off first year of annual plan + $50 Bitcoin bonus. Offer valid until May 31. coinbase.com/unchained Citrea Bitcoin changed how money works. Satya changes how Bitcoin scales. citrea.xyz/unchained ======================================================== One month, $606 million in exploits. And yet DeFi lending yields for blue-chip collateral sit close to SOFR, as if nothing happened.  Tom Dunleavy, head of venture at Varys Capital, did the math and concluded that fair risk-adjusted DeFi yields should sit around 12.5%. Adrian Cachinero Vasiljevic, co-founder of Steakhouse Financial, thinks that number paints with too broad a brush, and that for the right primitives, with the right collateral, the market rate might actually be close to correct.  Host Laura Shin queries them on the TradFi equations that underpin the debate, the DeFi-specific risks that those equations miss, and on whether depositors are sleepwalking into tail risk they cannot fully see. Host: ⁠⁠⁠⁠⁠⁠⁠Laura Shin⁠⁠⁠⁠⁠⁠⁠, Host / Unchained Guests: ⁠⁠⁠⁠Tom Dunleavy, Head of Venture, Varys Capital — @dunleavy89 Adrian Cachinero Vasiljevic, Co-Founder, Steakhouse Financial — @adcv_ Learn more about your ad choices. Visit megaphone.fm/adchoices

The Full Moon in Scorpio is a powerful invitation to release, reflect, and return to your truth.In this episode of Coffee & Crystals, Kadie shares a deeply personal reflection inspired by her recent grounding experience in Hawaii—swimming in crystal blue waters, hiking through the jungle, and reconnecting with both nature and herself. What began as a desire to escape transformed into a realization: we're not meant to run from our lives—we're meant to root deeper into them.Blending yoga philosophy, lunar wisdom, and seasonal insights from both Chinese medicine and Ayurveda, this episode explores how to move through emotional depth, speak your truth, and realign with your path during this transformative time.If you've been feeling emotional, stuck, or called to shift—this episode is your reset.The deeper meaning of the Full Moon in Scorpio and why it brings emotions to the surfaceHow to shift from escapism to embodiment and emotional honestyThe role of Satya (truth) in yoga philosophy and how it supports alignmentHow spring energy (Chinese medicine + Ayurveda) influences your mood, body, and momentumGrounding practices to reconnect with yourself and move stagnant energyHow to use this lunar cycle for reflection, release, and transformationKadie's personal story: from childhood escapism to present-day groundingA reflection on feeling supported by nature, community, and self during her time in HawaiiWhy the Scorpio moon asks for emotional depth, truth, and releaseThe connection between seasonal energy and internal shifts (Liver energy, Kapha balance)How misalignment shows up—and how to gently come back into alignmentSimple, powerful rituals to support your Full Moon resetJournal: What am I avoiding feeling or saying?Release practice: write down what you're ready to let go ofGet outside: walk, ground, or connect with natureWater ritual: shower, bath, or ocean intention settingSpeak something out loud you've been holding in00:00 – Welcome to Coffee & Crystals00:27 – Kadie's journey: from escapism to grounding01:21 – Reflection vs. escape: what the moon reveals01:43 – Full Moon in Scorpio: truth, emotion, transformation02:13 – Letting go of patterns and stepping into growth03:11 – Hawaii reflections: nature, grounding, and feeling supported05:32 – Jenny from Forrest Gump & childhood perspective shifts06:00 – The power of honesty in self and relationships06:59 – Satya: the yoga philosophy of truth07:28 – Practices for clarity: journaling, walking, decluttering08:56 – Grounding into the earth during transformation09:44 – Discipline, growth, and showing up for yourself10:43 – Recognizing misalignment and coming back into balance13:33 – Navigating seasonal energy and emotional shifts14:55 – Summer Solstice event announcement (West Hollywood)15:21 – Closing thoughts: hydration, detox, and energetic supportFull Moon in Scorpio, moon rituals, spiritual podcast, yoga philosophy, Satya truth, grounding practices, Ayurveda spring, Chinese medicine liver energy, emotional healing, self reflection, wellness podcast, Coffee and CrystalsInstagram: https://www.instagram.com/kadiechron/Podcast: https://www.instagram.com/coffee_and_crystals_with_kadie/You're not meant to escape your life—you're meant to come back to it.Use this Full Moon as a moment to release what's heavy, speak your truth, and root deeper into who you are becoming.Electrolyte link: https://amzn.to/4eU5NUQSummer Solstice Event this June in LA: https://www.eventbrite.com/e/1985756813083?aff=oddtdtcreator

People think of Aave and Morpho as competitors. But Morpho only lost $1 million when North Korea drained $300M from a DeFi protocol. The architecture explains why. ======================================================== Thank you to our sponsors! Coinbase One 20% off first year of annual plan + $50 Bitcoin bonus. Offer valid until May 31. coinbase.com/unchained Citrea Bitcoin changed how money works. Satya changes how Bitcoin scales. citrea.xyz/unchained Ether.fi 15% cash back on food and ride apps, 3% on everything else. ether.fi/unchained ======================================================== After North Korea's Lazarus Group drained nearly $300 million from Kelp DAO's bridge, the contagion spread fast, leaving close to $200 million in bad debt on Aave. Morpho, one of the largest lending protocols in DeFi, ended up with about $1 million in exposure.  Paul Frambot, co-founder and CEO of Morpho, explains why the protocol's modular, isolated architecture produced a different outcome, and what it reveals about how DeFi lending is supposed to work.  He also addresses the ongoing debate over whether DeFi lenders are fairly compensated for risk, the institutional reaction to the hack and what it means for the sector's timeline, the moral complexity of Arbitrum's decision to freeze stolen funds, and why formal verification may be DeFi's last line of defense in an age of increasingly powerful AI. Host: ⁠⁠⁠⁠⁠⁠Laura Shin⁠⁠⁠⁠⁠⁠, Host / Unchained Guests: ⁠Paul Frambot, Co-founder and CEO of Morpho Labs Learn more about your ad choices. Visit megaphone.fm/adchoices

Inside the Room.Hayden E. Stafford, President & CRO at Seismic, had a front-row seat to Satya Nadella rebuilding Microsoft from the inside out.The world saw the outcome.Hayden saw the decisions.The ones that got challenged.The ones that got changed.And the ones leaders refused to change.He saw that alignment didn't break all at once.It broke when signals said something isn't working…and nothing changes.That's when teams drift.Priorities split.And small gaps turn into real consequences.You'll discover:- What it was really like in those rooms—and how Satya handled bad decisions.- What Hayden saw that changed how he leads.- What makes asmall misalignment turn into a million-dollar problem.And he's now applying those same lessons across thousands of employees and customers at Seismic.So:When you know something isn't working is your tendency to…Change it—or convince yourself it will?-----Connect with the Host, #1 bestselling author Ben FanningSpeaking and Training inquiresSubscribe to my Youtube channelLinkedInInstagramTwitter

(20 INSTRUCCIONES ESPIRITUALES IMPORTANTES) Hoy hablaremos del pilar, que para mi, es fundamental en nuestra vida cuando queremos crecer espiritualmente: Satya, hablar y vivir desde la verdad. Hablaremos también del peso que implican las mentiras en nuestra vida. ¡Espero te inspire!

In this Age of Kali, hari-kīrtana is very, very important. The importance of chanting the holy name of the Lord is stated in the following verses from Śrīmad-Bhāgavatam (12.3.51-52): kaler doṣa-nidhe rājann asti hy eko mahān guṇaḥ kīrtanād eva kṛṣṇasya  mukta-saṅgaḥ paraṁ vrajet kṛte yad dhyāyato viṣṇuṁ tretāyāṁ yajato makhaiḥ dvāpare paricaryāyāṁ kalau tad dhari-kīrtanāt “The most important factor in this Age of Kali, which is an ocean of faults, is that one can be free from all contamination and become eligible to enter the kingdom of God simply by chanting the Hare Kṛṣṇa mantra. The self-realization that was achieved in the Satya millennium by meditation, in the Tretā millennium by the performance of different sacrifices, and in the Dvāpara millennium by worship of Lord Kṛṣṇa can be achieved in the Age of Kali simply by chanting the holy names, Hare Kṛṣṇa.” (Cc Madhya 6.242) ------------------------------------------------------------ To connect with His Grace Vaiśeṣika Dāsa, please visit https://www.fanthespark.com/next-steps/ask-vaisesika-dasa/ ------------------------------------------------------------ Add to your wisdom literature collection: https://iskconsv.com/book-store/ https://www.bbtacademic.com/books/ https://thefourquestionsbook.com/ ------------------------------------------------------------ Join us live on Facebook: https://www.facebook.com/FanTheSpark/ Podcasts: https://podcasts.apple.com/us/podcast/sound-bhakti/id1132423868 For the latest videos, subscribe https://www.youtube.com/@FanTheSpark For the latest in SoundCloud: https://soundcloud.com/fan-the-spark ------------------------------------------------------------ #mantramusic #spiritualmusic #kirtan #mantra #spiritualawakening #soul #spiritualexperience #spiritualpurposeoflife #spiritualgrowthlessons #secretsofspirituality #vaisesikaprabhu #vaisesikadasa #vaisesikaprabhulectures #spirituality #bhaktiyoga #krishna #spiritualpurposeoflife #krishnaspirituality #spiritualusachannel #whybhaktiisimportant #whyspiritualityisimportant #vaisesika #spiritualconnection #thepowerofspiritualstudy #selfrealization #spirituallectures #spiritualstudy #spiritualquestions #spiritualquestionsanswered #trendingspiritualtopics #fanthespark #spiritualpowerofmeditation #spiritualteachersonyoutube #spiritualhabits #spiritualclarity #bhagavadgita #srimadbhagavatam #spiritualbeings #kttvg #keepthetranscendentalvibrationgoing #spiritualpurpose

Send us Fan MailThis episode is one I've been sitting with for a while — a synthesis of two things that have shaped my whole path: Ayurveda and yoga philosophy.In this episode, I bring those two threads together and look at the yamas and niyamas — yoga's ethical and personal observances — through the lens of kapha season.What we cover:Kapha dosha is made up of earth and water. It's heavy, slow, stable, cool, and moist — and late winter into spring is kapha season. When kapha goes out of balance, we might notice lethargy, mental fog, resistance to change, a tendency to oversleep or overeat, or that feeling of comfortable-but-stuck stagnation. The medicine for kapha is warmth, stimulation, movement, and letting go — and the yamas and niyamas offer a beautiful map for exactly that.I give a brief grounding in the eight limbs of yoga and what the yamas and niyamas actually are — not as a rulebook, but as living, breathing invitations to notice and redirect with curiosity rather than criticism.Then we explore six practices through the lens of kapha season:The Yamas — how we relate to the world:Aparigraha (non-grasping) — where are you clinging, and what wants to be released as spring arrives?Satya (truthfulness) — using honest, clear seeing to notice where we've gotten comfortable in stagnation — without judgmentBrahmacharya (right use of energy) — not about deprivation, but about noticing where energy is leaking and asking: is this giving me life or pulling me deeper into heaviness?The Niyamas — how we relate to ourselves:Tapas (inner fire) — the gentle, consistent showing up; kindling the fire rather than forcing itSaucha (purity/cleanliness) — clearing physical clutter, mental tabs, and anything that's accumulating and pulling on your energySvadhyaya (self-study) — observing your own kapha patterns without judgment and seeking what genuinely inspires youI also talk about my own evolving relationship with tapas — how it looked very different in my pitta-dominant twenties than it does now in midlife, and why the middle path is the one I keep returning to.My invitation for you this week: pick one of these six practices, sit with the questions it offers, and let it be a gentle lens for seeing your life more clearly — without judgment, without pressure, just with curiosity.Free download: Grab the one-page guide to all six practices — with reflection questions for each — in the link below. A simple, beautiful reference to keep nearby as you move through the season.https://canva.link/yamas-niyamas-kaphaMentioned in this episode:Abigail Rose Clark — author and somatic facilitator,  cleaning/decluttering as a somatic practice https://www.abigailroseclarke.com/store/p/k473j0h0mmmarResources:Free Masterclass:  The Alchemy of the Perimenopause PortalAyurvedic Dosha Quick Reference GuideAbhyanga Self Massage GuideWeekend Nervous System ResetNourished For Resilience Workbook Find me at www.nourishednervoussystem.comand @nourishednervoussytem on Instagram

This is the third part in the series, “Doing It Right the First Time.” This week, we explore why telling the truth isn't just morally good; it reduces mental and emotional labor, builds trust, prevents crises, and aligns your inner truth with outer action. You'll hear practical, bite-sized strategies you can start using today to practice Satya with confidence.What you'll learn:How pausing before you respond can keep conversations honest and constructiveHow naming your precise feelings improves communication and reduces misinterpretationHow to use “I” statements to own your truth without attacking othersHow starting small builds lasting authenticityHow writing first can clarify your truth and reduce fear around speaking itHow to work with resistance using curiosity, CBT, IFS, and DBT tools

About Katie FarinasKatie Farinas is a midlife coach, yoga teacher, and spiritual guide who helps women navigate midlife with clarity, peace, purpose, and empowerment. Through yoga philosophy, mindfulness, nervous system regulation, and energy-based practices, Katie supports women in reconnecting with their intuition and stepping fully into their most aligned and authentic selves.✨ Explore Katie's work and offerings:Visit Katie's websiteJoin the newsletter for soulful insights and to receive weekly practicesRead and watch on SubstackBook a reflective Insight Seat to come on the show and receive live coachingSchedule a Clarity Call to see if my coaching is right for you.

Colonial Caregivers: Ayahs and the Gendered History of Race and Caste in British India (Cambridge UP, 2025) offers a compelling cultural and social history of ayahs (nannies/maids), by exploring domestic intimacy and exploitation in colonial South Asia. Working for British imperial families from the mid-1700s to the mid-1900s, South Asian ayahs, as Chakraborty shows, not only provided domestic labor, but also provided important moral labor for the British Empire. The desexualized racialized ayah archetype upheld British imperial whiteness and sexual purity, and later Indian elite 'upper' caste domestic modernity. Chakraborty argues that the pervasive cultural sentimentalization of the ayah morally legitimized British colonialism, while obscuring the vulnerabilities of caregivers in real-life. Using an archive of petitions and letters from ayahs, fairytales they told to British children, court cases, and vernacular sources, Chakraborty foregrounds the precarious lives, voices, and perspectives of these women. By placing care labor at the center of colonial history, the book decolonizes the history of South Asia and the British Empire.Satya Shikha Chakraborty is an Associate Professor of History at The College of New Jersey.Saumya Dadoo is a PhD Candidate at MESAAS, Columbia University Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

Colonial Caregivers: Ayahs and the Gendered History of Race and Caste in British India (Cambridge UP, 2025) offers a compelling cultural and social history of ayahs (nannies/maids), by exploring domestic intimacy and exploitation in colonial South Asia. Working for British imperial families from the mid-1700s to the mid-1900s, South Asian ayahs, as Chakraborty shows, not only provided domestic labor, but also provided important moral labor for the British Empire. The desexualized racialized ayah archetype upheld British imperial whiteness and sexual purity, and later Indian elite 'upper' caste domestic modernity. Chakraborty argues that the pervasive cultural sentimentalization of the ayah morally legitimized British colonialism, while obscuring the vulnerabilities of caregivers in real-life. Using an archive of petitions and letters from ayahs, fairytales they told to British children, court cases, and vernacular sources, Chakraborty foregrounds the precarious lives, voices, and perspectives of these women. By placing care labor at the center of colonial history, the book decolonizes the history of South Asia and the British Empire.Satya Shikha Chakraborty is an Associate Professor of History at The College of New Jersey.Saumya Dadoo is a PhD Candidate at MESAAS, Columbia University Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Colonial Caregivers: Ayahs and the Gendered History of Race and Caste in British India (Cambridge UP, 2025) offers a compelling cultural and social history of ayahs (nannies/maids), by exploring domestic intimacy and exploitation in colonial South Asia. Working for British imperial families from the mid-1700s to the mid-1900s, South Asian ayahs, as Chakraborty shows, not only provided domestic labor, but also provided important moral labor for the British Empire. The desexualized racialized ayah archetype upheld British imperial whiteness and sexual purity, and later Indian elite 'upper' caste domestic modernity. Chakraborty argues that the pervasive cultural sentimentalization of the ayah morally legitimized British colonialism, while obscuring the vulnerabilities of caregivers in real-life. Using an archive of petitions and letters from ayahs, fairytales they told to British children, court cases, and vernacular sources, Chakraborty foregrounds the precarious lives, voices, and perspectives of these women. By placing care labor at the center of colonial history, the book decolonizes the history of South Asia and the British Empire.Satya Shikha Chakraborty is an Associate Professor of History at The College of New Jersey.Saumya Dadoo is a PhD Candidate at MESAAS, Columbia University Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/gender-studies

Colonial Caregivers: Ayahs and the Gendered History of Race and Caste in British India (Cambridge UP, 2025) offers a compelling cultural and social history of ayahs (nannies/maids), by exploring domestic intimacy and exploitation in colonial South Asia. Working for British imperial families from the mid-1700s to the mid-1900s, South Asian ayahs, as Chakraborty shows, not only provided domestic labor, but also provided important moral labor for the British Empire. The desexualized racialized ayah archetype upheld British imperial whiteness and sexual purity, and later Indian elite 'upper' caste domestic modernity. Chakraborty argues that the pervasive cultural sentimentalization of the ayah morally legitimized British colonialism, while obscuring the vulnerabilities of caregivers in real-life. Using an archive of petitions and letters from ayahs, fairytales they told to British children, court cases, and vernacular sources, Chakraborty foregrounds the precarious lives, voices, and perspectives of these women. By placing care labor at the center of colonial history, the book decolonizes the history of South Asia and the British Empire.Satya Shikha Chakraborty is an Associate Professor of History at The College of New Jersey.Saumya Dadoo is a PhD Candidate at MESAAS, Columbia University Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/south-asian-studies

Colonial Caregivers: Ayahs and the Gendered History of Race and Caste in British India (Cambridge UP, 2025) offers a compelling cultural and social history of ayahs (nannies/maids), by exploring domestic intimacy and exploitation in colonial South Asia. Working for British imperial families from the mid-1700s to the mid-1900s, South Asian ayahs, as Chakraborty shows, not only provided domestic labor, but also provided important moral labor for the British Empire. The desexualized racialized ayah archetype upheld British imperial whiteness and sexual purity, and later Indian elite 'upper' caste domestic modernity. Chakraborty argues that the pervasive cultural sentimentalization of the ayah morally legitimized British colonialism, while obscuring the vulnerabilities of caregivers in real-life. Using an archive of petitions and letters from ayahs, fairytales they told to British children, court cases, and vernacular sources, Chakraborty foregrounds the precarious lives, voices, and perspectives of these women. By placing care labor at the center of colonial history, the book decolonizes the history of South Asia and the British Empire.Satya Shikha Chakraborty is an Associate Professor of History at The College of New Jersey.Saumya Dadoo is a PhD Candidate at MESAAS, Columbia University Learn more about your ad choices. Visit megaphone.fm/adchoices

This week we chat with Satya Patel! Satya is the co-founding General Partner of Homebrew, a seed-stage venture capital firm based in San Francisco. Through Homebrew, Satya has backed some of the most impactful startups of the past decade, including Chime, Cruise, Finix, Gusto, Habi, Honor, Plaid, and Shield AI.He's also the co-founder of Screendoor, an investment platform designed to support emerging fund managers by pairing mentorship from experienced GPs with anchor capital from world-class institutional LPs.Before becoming a venture capitalist, Satya served as VP of Product at Twitter, where he built and led the Product Management and User Services teams during a pivotal period of the company's growth. Prior to that, he held venture and product roles across the tech ecosystem, including serving as a Partner at Battery Ventures and as a product management leader at Google.Satya has been recognized on the Forbes Midas List Seed for his track record backing category-defining companies, particularly in fintech and AI. The U.S.-born son of Indian immigrants, he grew up in Las Vegas before building a career at the intersection of technology, product, and venture capital.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Satya Patel: @satyap@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

Join me this week for a deep dive into the art and science of Satya—speaking and acting from your authentic truth. In this episode, we blend practical psychology with spiritual wisdom to show how truth-telling can transform mental health, relationships, and everyday choices. You'll hear how Cognitive Behavioral Therapy helps uncover automatic thoughts that fuel inauthentic behaviors, and how DBT's DEARMAN skills can help you request what you need while preserving connection. We'll explore Interpersonal Neurobiology to understand how naming our feelings can regulate the nervous system, and how Internal Family Systems encourages curiosity about the parts inside us that resist honesty—so Self can lead with clarity and compassion. Plus, we'll ground the conversation in spiritual wisdom about truth-telling from traditions around the world. You will find actionable steps, real-life scenarios, and guided reflections to practice Satya right away—whether you're setting boundaries at work, navigating conflict at home, or simply learning to show up as your truest self. Tune in for practical tools and a thoughtful path toward greater peace and resilience.

One of my favorite repeat guests is back: I’m talking to psychotherapist Satya Doyle Byock about the duality of individuation and community. We get into the difference between individuation and individualism, and why it’s critical for all of us to individuate—to go on our own journeys—so that we can genuinely be a part of the collective, and not just subject to herd mentality. We also chat about our search for meaning, and why Satya encourages people to trust an irrational guide. And we talk about getting in touch with our daemons—which you can think of as your inner genius, a spark that wants to come through you. You can learn more about the retreat that Satya and I are hosting at Omega in May here: https://www.eomega.org/workshops/tapping-what-wants-come-through-you. And for all the show notes, head to my Substack.See omnystudio.com/listener for privacy information.

Unblocking Love: Somatic Healing, Mental Load, and Authentic Connection with Linda SandervilleAre you living a life that requires constant escape? In this episode of the Cupid's Coach Podcast, host Julie Ferman sits down with Albuquerque-based psychotherapist and yoga teacher Linda Sanderville to discuss how to unblock your heart and body for real connection.Linda shares her unique somatic approach, combining psychotherapy with yogic philosophy to help high-achievers manage emotional dysregulation and reset their "mental load". Discover how to stop being "crazy busy," set honest boundaries, and tap into your Capital S Self—the wise inner knowing that already knows how to win at life.Inside this Episode:• The Body-Mind Link: Why emotional dysregulation shows up physically and how to listen to your body's signals.• The Mental Load Reset: Strategies for multi-passionate women to create spaciousness for romance.• Beyond the "Hot Factor": Why prioritizing emotional safety over visual "hotness" leads to lasting satisfaction.• Intergenerational Gifts: Centering family strengths and healing just as much as generational wounds.• The "Legs Up the Wall" Challenge: A simple, 10-minute daily practice to settle your nervous system.• Radical Honesty: Using the principle of Satya (truthfulness) to stop deceiving yourself in your dating life.Whether you are navigating a "dating detox" or looking to deepen your current relationship, Linda's perspective will help you move from victimhood to resiliency.Connect with Linda Sanderville: Visit SatyaCounselingAndYoga.com for consultations, blog insights, and restorative yoga resources.Connect with Julie Ferman: Ready to move from "Me to We"? Register for free and private matchmaking at JulieFerman.com.#CupidsCoach #SomaticHealing #MentalLoad #DatingAdvice #YogaForLove #EmotionalRegulation #LindaSanderville #RelationshipGoals

Join me this week as we discuss the important idea of doing it right the first time. This has a much broader meaning than what I thought it would. There is something deeper when we consider it in the context of truthfulness and not lying to ourselves and others. If we do something right the first time, we avoid all the messiness of going back and fixing our messes and mistakes.

In this week's episode, we talk about what growth and belonging can look like in families and faith communities. Differentiation is the key to deeper and more meaningful belonging as long as your community can handle it.

Satya Mishra was born and raised in India, in one of the smaller tech hubs on the eastern coast. He came to the states 25 years ago on an H1B visa, working in semiconductors. In 2015, he decided he wanted to do something entrepreneurial and set out to do so. Outside of tech, he is married with 3 kids, which takes up most of his time. When he lived in CO, he did lots of skiing and hiking, including snowshoe hiking. Once he went to California, he switched to beaches. Finally, when he moved to St. Louis, he took up improv, enjoying connecting with people and thinking on your feet.Satya and his co-founder, Raj, both when through the immigration process in all of its forms. They realized that no one group owns the process, as it's highly specialized, and usually fell onto the employee to keep track of. One day, they set out to solve this problem, to assist business teams to take ownership of the entire process.This is the creation story of WayLit.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.waylit.com/https://www.linkedin.com/in/satyamishra/Support this podcast at — https://redcircle.com/codestory/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, we dive deep into the concept of Satya, or truthfulness, again. Join me for a few minutes to explore the delicate balance between the need to belong and the need to grow, which is another of Deborah Adele's insights from The Yamas and Niyamas.In this episode, I talk about the difficulty of living authentically, the psychological research on belonging, and how we often sabotage our own truth to fit in. Drawing on wisdom from various traditions—including yoga, Buddhism, and modern therapy—let's discuss practical strategies to connect with our true selves without sacrificing our relationships.

Our podcast guest today is Dr Satya V. Reddy who is an anterior segment surgeon in Louisiana, USA and we talk about one of the most important issues that you will face in your life: how to balance building a practice with having a family (and personal) life that you can enjoy. I have struggled with this and have made many mistakes. Now we realize that the concept of "delayed gratification" may not be the best approach. We talk about the challenges and how to figure out the right recipe and balance for you and your life. I know you will find this very useful.We feature a new podcast every week on Sundays and they are uploaded to all major podcast services (click links here: Apple, Google, Spotify) for enjoying as you drive to work or exercise. The full video of the podcast is here on CataractCoach as well as on our YouTube channel. Starting now we have sponsorship opportunities available for the top podcast in all of ophthalmology. Please contact us to inquire.

I ordered some new equipment, and have not had time to set it up or give it a practice run. This week is a breather for us all while I take the time this week to figure out my new toys. Catch up if you are behind or just listen to some old favorites. I highly suggest any podcasts with my kids - they are always good ones.I will see you here back next week and we will discuss the need to belong vs. the need to grow in the context of the Yogic principle, Satya.

Welcome. In this In Sanity a Piece of Mind episode, we continue the deep dive into the yogic principle of satya—truthfulness—and its profound impact on our relationships. Discover what authentic connection truly looks like as we explore the pros and cons of being real versus being "nice."I've given some practical applications and real-world illustrations and discuss why prioritizing authenticity is essential for meaningful interactions. Learn how the principle of yoga—meaning to yoke or unite—can guide us toward deeper unity with ourselves and others.Join me for a thought-provoking conversation that challenges you to embrace your truth and cultivate richer, more fulfilling relationships!Please share this podcast with friends and family.

In this episode of 32 Thoughts, Kyle Bukauskas and Elliotte Friedman break down a wild Thursday night around the NHL, highlighted by a stunning Hurricanes comeback in one of 15 games on the slate. They dive into Toronto's rough stretch continuing in Seattle, why trade chatter around the Maple Leafs is heating up, and why major moves may wait until after the Olympic freeze (8:24). Elliotte unpacks the latest on Artemi Panarin trade rumours as he runs through each potential suitor (14:50), potential action between Detroit and Winnipeg (39:35), and updates on hot teams like the Islanders and Blue Jackets (40:59). The show wraps with Nashville drawing interest in Michael Bunting and Michael McCarron (49:21), plus a Final Thought from Tampa Bay ahead of the Stadium Series (54:34)Kyle and Elliotte answer crowd questions in the Thoughtline (1:05:57). Today we highlight Moroccan-Montréal pop-R&B artist Satya and her song Realness. Check out her music here.Listen to all the 32 Thoughts music here.Email the podcast at 32thoughts@sportsnet.ca or call the Thought Line at 1-833-311-3232 and leave us a voicemail.This podcast was produced and mixed by Dominic Sramaty and hosted by Elliotte Friedman & Kyle Bukauskas.The views and opinions expressed in this podcast are those of the hosts and guests and do not necessarily reflect the position of Rogers Sports & Media or any affiliates

We often ask "Are they ready for a relationship?" but the more important questions is "Are you ready?" We're revealing 10 questions you need to ask yourself now to see how you relate to others, know when to stay vs walk away, and trust yourself in the process of finding love. We're also doing a final segment with Dr Serena Sterling, the founder of the new dating app Satya, about taking initiative on dating apps to actually make dates happen (and how to make it crystal clear if you're ready or not). Enjoy!Learn more and download Satya today: https://www.satyadating.com/

As we approach the year of the horse – a year full of movement, momentum, courage and moving forward – we wanted to dive into how 2026 will look for each dating archetype. If you're new here, definitely take our quiz (https://howtobedateable.com/) or it's in our book 'How to Be Dateable'! We discuss the superpowers each dating archetype brings, the boost you need for 2026, and what to watch out for. Plus we do another segment with Dr Serena Sterling, the founder of the new dating app Satya, about the 'rate your match' feedback loop she created. Enjoy!Learn more and download Satya today: https://www.satyadating.com/

Happy 2026 all! We're back bringing that new year energy and sharing why 2026 – the year of the horse – will be better for your love life. We kick off this episode with a special segment with Dr Serena Sterling, the founder of the new dating app Satya, about what she's doing to change dating app culture (which includes keeping people in purgatory for bad dating behaviors!). We also discuss how to manifest better dating experiences and relationships for 2026 – beyond just imagining what you want but actually putting it all into action with a four step ritual that anyone can do this year. Enjoy! Get your dating profile reviewed: https://www.findingyourperson.com/offers/dfeankaK/checkoutLearn more and download Satya today: https://www.satyadating.com/Take the Dating Archetypes quiz now: https://howtobedateable.com/HOW TO BE DATEABLE IS OUT! Order now: https://howtobedateable.com/Follow us @dateablepodcast, @juliekrafchick and @nonplatonic. Check out our website for more content. Also listen to our other podcast Exit Interview available on Apple Podcasts, Spotify, or wherever you get your podcasts.WE WROTE A BOOK! HOW TO BE DATEABLE (Simon & Schuster, Jan 2025) is available now: https://howtobedateable.com/Our Sponsors:* Kensington Books: Dawn of Chaos and Fury by Melissa K. Roehrich is on sale now: https://www.kensingtonbooks.comSupport this podcast at — https://redcircle.com/dateable-your-insiders-look-into-modern-dating-and-relationships/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy