Agentic infra changes everything (Interview)

28:16and is being relatively modest around the values that they're paying in multiples. And then everywhere else is depressed. So if you're not in those sectors, then your value multiples are down. And so it doesn't feel great. And so you look at it emotionally and you're like, it's a bubble. And you're like - Well, that sends more money into that particular space as well, right? Because there's less - Their argument then was also what it does is drive investors to find alpha, right? Yeah, exactly. So now what they're going to do, they have to find growth somewhere. And so that's what's driving IPOs.

28:54That's what's driving M &A. Like all of those things are happening because that the, yes, those AI stocks are outperforming. Also, you can't beat them. So there is no, there's no better alpha than those. so you just so so if you want to do better than just betting all your money on the magnificent seven the only way to do that is to find other unseen growth stocks and put your money there and those are all embedded with the magnificent seven yeah so the weird part to me is the circular is the circular deals i mean that's the part that is just crazy yeah that's crazy i i agree i just don't get it i don't understand how it all makes sense but apparently it might i mean i understand how the deals get done for sure yes how they get done but do they make any sense i don't know about that i don't know i don't know if they make sense either yeah um i don't know that they do but but their argument which was very convincing was that that's not a structural threat to the economy you know like like yep that's that that could be bad right and meh you know um and whatever i'm not sophisticated enough to make an argument one way or the other yeah i'm not either i just think when one of those organizations is nvidia which happens to you know be the darling of our economy right now yeah yeah if they have a big correction i think everything does but maybe if the magnificent seven corrects the economy corrects for sure i don't know that that would mean that it's a bubble i think it would do you know what i'm saying like it's different like we're perhaps parsing you're saying yeah we are the demand is there that's the overall the demand is there the demand is there The demand is there.

30:31We're not building ahead of demand. We are not going to be over provisioned in 2028 and have way too many data centers. In AI data centers. We are not. We're not. And the people, and there's like a lot of people who are hoping that's true, who are probably listening to this podcast were like, I hate AI. And I like cross my arms and I'm like the whole thing's stupid and you shouldn't use it. And whatever, they show up every time I talk about AI on LinkedIn being like, it's all, it's all lies. And you know, like, and they're just wrong. Yeah. They're like, they're wrong. They're just wrong. And like, whether they know they're wrong now, or they know they're wrong in six months, like they will learn that they are wrong because the, it is actually transformative technology.

31:09It actually can make a difference right now. And yes, 99 % of what's being built is not a good use of that technology. Right. Just like 99 % of the early uses of search engines weren't better. You know, like we got, we got to figure out how it works. We got to figure out how to build technology around it. There's like, we're in the beginnings of doing all of that work. And what we got caught up in was this hype cycle that was like, oh, it's going to be AGI. The magic robots are going to take over. They're going to just run everybody's jobs. It's going to become super intelligent. You know, it's Terminator.

31:39And like, none of that's what's happening. Like, if you've actually used these systems, the idea that they're going to turn into the Terminator is laughable, right? Like, laughable. Well, friends, Agentic Postgres is here. And it's from our friends over at Tiger Data. This is the very first database built for agents, and it's built to let you build faster. You know, a fun side note is 80 % of Cloud was built with AI. Over a year ago, 25 % of Google's code was AI generated. It's safe to say that now it's probably close to 100%. Most people I talk to, most developers I talk to right now, almost all their code is being generated.

32:17That's a different world. Here's the deal. Agents are the new developers. they don't click, they don't scroll, they call, they retrieve, they parallelize, they plug in your infrastructure to places you need to perform, but your database is probably still thinking about humans only because that's kind of where Postgres is at. Tiger Data's philosophy is that when your agents need to spin up sandboxes, run migrations, query huge volumes of vector and text data, well, normal Postgres, it might choke. And so they fix that. Here's where we're at right now. Agentic Postgres delivers these three big leaps, native search and retrieval, instant zero copy forks and mcp server plus your cli plus a cool free tier now if this is intriguing at all head over to tigerdata.com install the cli just three commands spin up an agentic postgres service and let your agents work at the speed they expect not the speed of the old way the new way agentic postgres it's built for agents is designed to elevate your developer experience and build the next big thing again go to tiger data.com to learn more

33:26there was a recent uh anthropic research document that was a little scary that i can put in the show notes that i can paraphrase it was like it it had it tried to protect itself when being threatened with being replaced or something else essentially it was like self-preservation but then they said in production they didn't see that happening yeah but like it was pretty wild but like i mean it's wild but like that's recent like that's on their home page recent too well the anthropic ceo is one of the most bullish bulls in the and it's in the and it's in their best interest yeah like i was i was giving a talk to an unnamed organization in the federal government and agentic misalignment sorry this is what they call it agentic yeah so just so we're clear it wasn't the llm that got misaligned.

34:13It was the agent that like, that like in the control loop. And like, if you just think about how LLMs work, you're like, sure, what's the probability that what I should respond to, how many human beings have written, I don't want to die. Like a lot of people put that into the world. They're like, I'm scared of that. Dying is bad. Don't die. So then you're like, okay, what are the odds that the right next word for the LLM to say is don't die. And then that, And then that emerges as making tool calls to preserve itself. That's not crazy person talk. It's not even magic. You're like, sure. Like, it's kind of predictable.

34:49But it doesn't make for a super great blog post, right? The great blog post is like, the agent tried to keep itself alive, you know? And you're like, right? It found an instinct to self-preservation. And you're like, it didn't find an instinct to self-preservation. It said blackmail. The word blackmail is in this research document. Because they gave it access to tools like email and they were like, oh, you know what I should do? And like it went off the rails. Get that CEO. If you turn me off, I will. Yeah, exactly. I get how it happens. My point isn't that it didn't happen. Of course it happened.

35:21Of course it happened. It's just if you know how the technology works, you're like, yep. Okay. Like that's funny. But I can see how it gets into that loop. And I don't think it's magic. I don't think it's threatening. I don't think we're all going to die. Like it's a good case for why you shouldn't give it a nuclear bomb. Let's bring it back down to like bare metal or practicality. My usage of generating a lot of software lately, mostly around CLIs and useful tools for me in my home lab or, you know, as Jared says, what did you say, Jared? It was a home cooked meals. Yeah. Home cooked software.

35:54Yeah, that's right. I have full faith that this is only going to give more jobs to developers and not replace because it is smart, but it doesn't have taste. It doesn't have direction. It doesn't have the problem set. It just wants to be useful and solve problems, even if it's totally the wrong way. Yes, absolutely. And I was trying to make an MCP server the other day for this thing I'm working on. And I'm like, the docs, have you read the docs yet? I don't want to go read the docs. This is why I'm talking to you. You're the agent. You go read the docs and tell me how it should work. It's like, that's a great idea.

36:28No joke. Good call. Yeah, look, exactly. And this a bit back to just basic economics, right? like what is the demand for software systems and will those software systems demand become met at which point now there's fewer of us as far as i can tell the demand remains uncapped like as far as i can tell there's no i have no idea when people go huh that's enough software i don't need more software in the world like we're i'm done like we're cool well useful software creates more useful software like you just come with more ideas you're like you know i want to do next this and And then after that, I'm going to do the next thing.

37:04You're like, you have a list. We all have backlogs out there. Yeah. And so like that whole, this whole story, the only, the only part of this story where it gets rid of humans is the story where we're not talking about the technology we have now. We're talking about some other technology that is some distant future, um, where between us and that distant future is the invention of fantastically new techniques that are roughly on par with the technique that brought you the one we just got or more. And it's probably not one of them. it's probably dozens. And then once you think about that and you're like, okay, now maybe that turns into some kind of unrecognizable super intelligence.

37:40Sure. Like maybe at that point, it's Star Trek. Maybe at that point there's like replicators and we're like the post-economic, like, but like, that's not a useful conversation when it comes down to like, I was building stuff in my home lab and I was thinking like, could there be a great agent loop in the data center? And you're like, hell yeah, there could. Like what, you know what sucks in the data center? What sucks is trying to figure out how to look at the logs to understand what's going on. And so maybe what I'll do is build an agent that knows how to log into all the servers and run journal cuddle and then bring all the data down and then do the analysis for me and tell me what's wrong.

38:17That'll be sick. And we could write that right now before we get off this podcast. We just do it and it would just work. And it would be sick. And you'd be like, that was amazing. and like, okay, like that's, that's the thing. That's what it is. And like, you know, I was thinking the other day about Kubernetes and like the control loop and the, and like the bin packing and all of those things. How would you have written Kubernetes differently if you had LLMs to drive an agent loop? I think it would be different. I think, I think there's a bunch of, I think there's a bunch of the decision-making that you would have put into a different part of the loop of the system.

38:57And I don't know that it would be better or worse, but it's an interesting thought experiment to be like, where would I put the LLM? Where would I put the deterministic side of what it does? How would that change the user experience and the user loop of what I want to do? Like, sick, sick, right? I don't know what it would be, but I want - Is that what you're building? Are you building that? No, I'm building system initiatives. I'm just thinking about it. You seem like you're pretty passionate about that problem. I'm passionate about all these problems. I want to build all these things. I want to build the journal Cuddlebot.

39:26I'm into all of it. I'm with you on that. I think a lot of the consternation, I'll call it, out there, and that Adam is humanizing for us in human form here, is that the same people that have brought us the current technique and techniques are saying they have the AGI techniques basically locked in their basements until Q1 of 26, and then they're going to be unleashed on the world, and all hell's going to break loose. They don't. I mean, aren't they, though? I mean, that's pretty much what Anthropics CEO Sam Altman's talking about. Yeah, yeah, yeah. They're all talking about it because it's really good business to talk about it.

40:00And they're just keeping those deals done, you know? We keep getting those deals done. We keep paying for those growth multiples. Right. And you know what I mean? And like, you know, do I think it's a dangerous game to be promising AGI? I do. I do. I do. And like, I think that could go bad. And also, I don't know. Is it? It's probably fine. well i just think that these two things juxtapose is it's not a bubble and the people in the but the people in the non-bubble are practicing are promising a thing that you don't think they can deliver on i mean to me that sounds like i think most of what people are buying isn't that right now like no one's buying agi no one's no one's going to intelligence we're buying suit they changed the you're not buying super intelligence either there's no super intelligence to buy so like i'm buying a what did uh what did right now you're buying access to the llms that's what you're buying and not having to run the inference on your own a genius golden retriever on acid that's right yeah that's basically what you're buying now that's right that's good enough for me by the way well what's amazing is the genius golden retriever on acid if you if you build the system around its existence will dramatically outperform.

41:15It's crazy. Like we had somebody, they had a production outage. They had no data in system initiative. The prompt they put in was, I'm having a production outage. Here's the data. Here's the evidence. Go discover the infrastructure and system initiative you need to troubleshoot it and tell me what's wrong. And 15 minutes and 700 components of discovery later, it told them the bug. And like, that's bananas. That's crazy person talk. And it happens all the time now. It's just not evenly distributed, you know? Like not everybody's seen it. Not everybody knows. And the ways that those systems compose, you know, if you have a big Terraform repo or whatever right now, and you try to do that same trick, it doesn't work as well.

41:57And so you're like, oh, this thing, maybe it's the AI that's bad, you know? The AI didn't figure out to go read the docs, you know? And you're like, oh, I don't think that means the AI is bad. I think it means that Adam is bad at prompting, you know? adam probably should have said read the docs plan one plan two plan three and like you know he'll get better he won't make that mistake twice you know never never again never again never make the same mistake twice but like that's the it's when i talk about it not being a bubble and i'm not defending one way or the other the bubbly position but like i think the argument that says it isn't is the one that says look is there is it a high are we in a hype cycle yes are we uh is that hype cycle reaching beyond its abilities?

42:41Yes. You know, but we had a hype, we've had lots of hype cycles, right? I mean, going back to Kubernetes, are we all going to, Kubernetes is supposed to be the operating system now. Are these, but we're not installing Kubernetes in these AI data centers, no matter how hard they try to convince you that you should. Like what people actually want is bare metal compute. Here's the thing though. Here's the thing. We just went from the horse to the car. Yeah. That's what we did in our time, right? Like we're living through the invention of the car. Yeah. It's going to be crazy. It's going to be crazy.

43:13It's going to change everything. And of course we don't know. And there'd be hype everywhere. Of course. And we don't know what to do with it. You know, we're like, there'll be flying cars. There'll be space cars. What if, what if all the cars were underground and car tunnels and then every, the whole world could be a park. What if we, you know, like, I don't know. I bet we, I'm sure we had crazy ideas. And like, uh and yeah what i'm ready for as a like practical technologist you know like i'm not a researcher i don't i don't like hang out writing papers like i build stuff and that's what i like to do and what i can say as a practical technologist is like whoa building stuff with this kit is fun it's so fun because like it can do things that are really freaking cool and that you could not do a year ago and that's epic and i don't know what that means you know in the macro nobody knows really right but it is real it's not it's not an illusion you know unless you have agi in your basement locked up like you know sam altman does if sam altman has agi locked up in his basement sam altman would be doing better at delivering llms to us right well if sam altman had a secret AGI, chat GPT-5 would have been a lot better.

44:29Well, that's the other thing that people are saying on the other side is like, well, they aren't getting that much better, that much faster, but they are stinking good. I'll tell you that much. But you also don't need them to. Right. Because what we're learning, like what we're learning about these systems is that the less you use them, the better you are. Yeah. You build the software systems around them. Yes. It's what you plug into them that matters. Right. And so like the first stage of this was make the LLM do everything. That's why we were all like, make it do math. What a ridiculous thing to ask it to do.

44:57It's always going to be awful at math. Like the technology is like anti-math. It's like, Oh, what am I determined to random and non-deterministic. So why do I care about whether it can do math for me when I have calculators? Why don't I just plug in a tool that does math? And then I'm guaranteed that the math is right all the time because you know what it doesn't do hallucinate numbers that it received after it received them from a tool. I've never seen that happen i've never seen it be like oh here's the number and then it's like oh i changed it for you it's actually 72 i'm sure it happens but it's pretty not very often and like but that was our first pass our first pass was just like oh we're gonna feed everything in the world to this magical robot and the magical robot's gonna do it and like that's clearly dumb it was clearly not playing to the technology's strengths and so like like and now we're learning how to play to its strengths but like you know i mean just now learning you know i would last couple months i think as an industry, we're starting to be like, oh, that could be the way, you know, it might actually come together like that.

45:56How has this changed how you think of things, Adam? Like how, like, is there a fundamental paradigm shift in your brain? I think it's convinced me that more and more, that more of the systems that we architect around the LLM will change. So if you think about like the interfaces that we provide to those systems, they're like weird anti APIs. So like, like the API that I would give to you as a software developer that you would be pleased with is not the API that I should give to an LLM because the LLM is trained on human language and behavior. And so like it, it dramatically outperforms when you give it like these really wide interfaces and let it explore.

46:40Who does that? You know, like no one, like that would be a terrible API design. Like if I told you that like my API to my service was like one big function call and I was just like, whatever, you just tell me to do, send me some junk and I run it for you. You'd be like, no, you know, that's a terrible design, but it turns out with AI, it's kind of a good design, you know? And like we like those layers are dramatic. And I think the the thing that's changed for me is that now I can't look at problems without thinking, well, where where can I insert that essentially plain language compiler and turn it into a loop where I'm working interactively alongside something that that can help me move through this problem space?

47:28and that's just a dramatically different way of thinking about everything to some degree where it's you know and that's not how i felt about it for the first i don't know year and a half of this journey where i was just like i don't know i try it every now and again it's not very good you know like the results are mediocre hype cycle's crazy but the last six months or so like it's tough and and it really has fundamentally changed the way i think about it and like with system initiative it i mean we took this ui that we had spent five years building trying to be like here's a better way to compose these resources and i just deleted it it's just gone because it turns out stupid yeah it turns out smart with with your knowledge you know it turns out that like what's great is the models what's great is that i built these one-to-one abstractions that was awesome but the actual way you want to work with them is just in an AI.

48:21I just want to say to clog code, uh, I need to deploy Valky to make this session thing work. Can you update the code and then build a change set for me that does it? And it's like, yeah, I got you, bro. And then it goes and does it. And then I review its work and I'm like, oh, yep, that was pretty right. Oh, this security group's a little wrong. Oh yeah, no, I need it. I need, I need the, you got the size wrong. I want it to be a little bigger. And like, we work together in this reactive loop to do it. And then it all just happens. And like, it turns out that whole composition UI was in the way, like, because it was designed not for that, for that loop with the agent, it was designed for a human to be like, here's how I compose things.

48:57And like, that broke my heart. You know, I'd spent five years doing R &D where I was like, okay, crack your knuckles, I'm going to finally figure out how to like, give people this incredible UI to let people compose complex infrastructure. And then as soon as I figured out the right shape of how to work with an agent, I had to delete it all because I was like, Oh, never again. Like, why would anyone work that way? You just wouldn't. It doesn't, it's so much easier to just ask the system to do it and it just works. And so the UI now is about radiating information back to you more than it is doing, making changes.

49:33You know, it's like, show me the map, show me the review, show me the changes. Like, let me dive into the details and play around but like you know it's fundamentally altered the way i think about everything including the last you know six years of my life that had to require some humility oh oh oh jared i mean i mean like i'm back to that moment so it's back so these these are um these are these are buddhist prayer beads and i'm holding on to them like doing this while i'm talking to you because of that you know because i'm just like oh oh that was awful you know like you're still in like ptsd from it oh i'm sorry to laugh at your pain but it's it is it's hilarious i'm just being honest you know and like it was it was awful man and you know away from it are you like when did you make this decision uh we we made we started building the prototypes of working this way less than six months ago and then shipped it maybe two months ago and so we're just in the very beginnings of trying to get people to understand like here's what we've built and here's how it works.

50:39And, you know, the humility runs in a bunch of different directions. It runs in the humility to like, like to look at what you've built and be like, no, it's not right anymore. Like it's not, it's not good enough anymore. Um, which is really hard. Um, it's also that most people, you have to take people on these journey on a journey about like why, why it would work and why they should try it and how, what that experiential loop would be like. and you know the people i love most are infrastructure people and those are not the people that are highest on the ai supply train do you know what i mean like like those people tend to be the grumpy luddites who are like you know never been useful to me and uh and so so that's also been humiliating a little bit to go out to my people and be like look at what you can do if you think differently about it and they're like i don't know like not only do i not want to look at it, like I reject the premise that it could work at all.

51:33Um, and, uh, that is humbling, right? Because that's not the right reaction to that is not for me to go sit in my tower and be like, well, I'm just smarter than you. You know, it's to figure out how to explain it better. It's to figure out how to be like, okay, I have to like, I got to get all the way down to the ground again and just be like, look, I know, I know I get it. You know, I have to find that path of empathy to be like, here's like, I do have to explain it to you from first principles. I do have to work you back from, from the very foundations of how we think about this problem. And, and there's just no shortcuts.

52:07And I really want there to be, cause you know, I want to like put it out into the world and have everybody just fawn all over me. Cause wouldn't that be easier for me? For sure. But it's not, but it's not what happens. You can't just manifest that, you know, you know you can try well of course and every now and again it does you know so every once in a while lightning in a bottle but so that's interesting because the infra people are some of the most resistant to the new tech but you can't you just can't get you couldn't possibly continue building it the previous way no because once you've seen it because the world has changed yeah what like it's irresponsible you know like i have investors i have my own dreams and my own hopes like i wasn't building it like i have a my job is to try to build something great and like you know that's where the greatness is so you got to go you got to go there you know and doesn't matter what the risks are you know because you're guaranteed to lose the other way you know what i mean yeah exactly yeah it's a losing proposition like this is the only way you could possibly win doesn't mean you will but at least you got a fighting chance and it's so fun like like you know we built like a policy engine uh that uses the mcp server to like let you write like policy and markdown that describes like hey check my infrastructure to make sure that this policy is always applied and then give it a search query that tells it what infrastructure to pull in and then the bot will go grab that infrastructure apply the thing and then write you a report about whether you're compliant with your policy and you know we wrote it in three hours and that's crazy person talk it's such crazy person talk it's bananas and like but you know who's experienced that so far in industry me yeah paul stack who works with me you know a small handful of people who are listening to this podcast who don't work for me who have done it themselves and they're like whoa some of them that listen to this show dream of working for you yeah well for that if you want to work for me the first step is to buy my software wasn't it don mckinnon chair that the voicemail was for don mckinnon where it was that he had oh yeah yeah that's what i'm referring to uh drop a note jason put that uh give us a little tease of that did you ever hear that adam uh don mckinnon uh changelog listener and now a guest he was on the show this year who's a fan of yours and he confessed his fandom to breakmaster cylinder for our State of the Log annual episode where BMC will remix people's voicemails.

54:38And so BMC remixed a voicemail that Don left us in which he mentioned he enjoys your episodes in particular. Hey, Jared, Adam, and everyone at ChangeLog. My favorite episode of 2024 was the ChangeLog and Friends episode from Chef to System Initiative. I've been following Adam Jacob on social media for a while and he's always a great guest. So it was interesting to hear more about his career journey that led him to where he is now with his new company. And I did have to go back and watch any given Sunday after hearing that episode. I'd never seen it before. I also got a kick out of the Rails is having a moment again episode.

55:16A lot of times I disagree with DHH, but regardless, he is always entertaining to listen to. Thank you for all the work you guys do on the podcast. It's one of my favorites. And the BMC remix has basically Don confessing that he stalked you and you had him thrown out of your office. My favorite episode was from Chef to System Initiative. I've been following Adam Jacob on social media for a while. I've been also following Adam Jacob to work, and I got kicked out of his company. So it was interesting to hear more about his career journey that led him to kick me out of his company. And I disagree with him, but regardless, he is always entertaining, and he is always kicking me out.

56:02That was hilarious. I'd love to watch you listen to that. Yeah, I really do want to listen to that. Yeah, you should. I mean, if you want the ego boost, this is a good one for you. Oh, that's so fun. Yeah, I could use it after my humble pie of realizing I had to delete years of effort because the interface was wrong. Can we get into the practicality of, they say AI slop, right? And so you said you just did this feature in three hours, which is like mind blowing. Yeah. And that means that you've got code generated by the LLM that you didn't write, but you're probably code reviewing. So I'm assuming you're generating a lot more code, maybe a lot more pros even to around what you're building because documentation, why not?

56:43Right. When you can just generate it, do it. Some degree. Yeah. Yeah, for sure. But what is what is it like to generate that kind of feature in that kind of time frame and do it well in terms of code? Do you code review it? What's your engineering change of practice as a result of generating so much code? Yeah, yeah. I don't know. It's interesting. So like that example is happening in a vacuum where we're like basically running a little spike to be like, okay, very few people have actually built end-to-end complicated full lifecycle application deployments using this kind of technology because it just is too new.

57:23And so we're doing it ourselves to be like, are we right about how the best practices work? Are we right that this flow is better? You know, like those sorts of questions. And so like the code quality that's necessary on this policy bot, for example, is pretty low because I just need it to work to see like, is this cool? You know, like, is this directionally the kind of thing you want it to be? When it comes to the, and so in that case, the loop is much more about just, does it meet requirements? So, and that I tend to also use AI to do, right? I'm like, hey, here's the list of requirements, like play, right?

58:01Or those sorts of things are great at just being like, up, go prove this thing, write me some tests, you know? And it's sort of, and its purpose is really just to meet the requirements. When it comes to the actual, like system initiative code base, which is quite large, it's a big monorepo, probably over a hundred thousand lines of code by now, easy. The utilization is more embedded in that like the engineers are using it in places where it makes sense or where it will accelerate them. Sometimes they're using it to help to understand the code base. Sometimes they're using it to write features.

58:36They've had great luck using it to refactor, right? Like write a bigger plan, have it refactor, like move in those stepwise directions. I think in general, what we've all kind of learned is that the more specific we can be, the better the outcomes are. So. you know, in the first, in the early stages of this, like you were trying to feed it as little as possible and then hoping it would do magic and then judging it when it doesn't perform. And I think now, now we've sort of transitioned into the, into the world where it's more about saying, Hey, like, here's this problem. I understand the problem as a person to some degree, here's what I know.

59:14Now tell me what you know that confirms or disproves that hypothesis. Then, you know, now we're going to use that information to go write a plan and then I'm going to read the plan and then we're going to talk about the plan and then you're going to go execute on this plan. And then I'm going to see if I like what you did. And if I don't, maybe I throw it away. And like, I would have never done that historically, you know, like just open a branch and then have a bunch of code get written and then be like, nah, I didn't like that direction. Let's go the other way. You know, like I do that all the time now.

59:44And so like that, that stuff has changed, but the, but the fundamental question of like, how do you apply taste? How do you apply the right level of engineering, that really hasn't changed. So like a good example is the first MCP server for system initiative, actually a customer built and had showed it to me. And I wasn't very enthusiastic because quite frankly, it was bad. Like it was just like a one-to-one mapping of the API and it didn't perform very well. And I was like, you know, it's just like all the other AI stuff I've ever done. And then when I started really reading about how to build them well, and like what their abstractions were, suddenly it started to perform, you know?

1:00:22And like, that's the difference between the one that we just, you know, you gave a prompt, like, here's an API spec, make me an MCP server, which it will go do, versus saying, as a person, I understand what the interface is and I'm going to craft this thing in a direction that is going to return better results. And so that part of the loop is still all people. And then, you know, depending on the engineer, they're using more or less uh ai in the building but we don't talk about it that much and there's certainly not like a mandate one way or the other you know like is there a known freedom like hey do what you want yeah sweet like i'm gonna give you i'm gonna give you and i'm just gonna give you a license to claw code or you know you want to use you want to use vs code and run copilot like what i'm gonna do is pay you the same way that like when you come work for me i'll buy you whatever keyboard you want right you know like i don't care and people are often like oh any keyboard and i'm like yeah any keyboard and they're like well what if it's like this really expensive keyboard and i want custom keycaps from japan with little emoji apples on them and i'm like any keyboard i don't care because what i want you to have is the thing you're going to put your fingers on it all day and every time you put your fingers on it i want you to be like i love being here doing this thing and so if me buying you spending an extra 100 bucks on a keyboard like makes you feel great about your job like i want to spend 100 bucks so you feel great about your job and stay longer you know there's this custom keyboard out of my want well i don't really want it just looks beautiful i would never spend the money on it which one is that kind of curious i can't remember the name of it but it's um i'm past my research of it but i see it's not in my brain he already denied himself and moved on he already flushed that cash well i tell you the number you're gonna be you're gonna know why it was like over four hundred dollars to like build this custom keyboard hey man i've been running i've been running kinesis advantage keyboards since the early 2000s and like easy 300 bucks 400 bucks saved my wrists changed my life right never like some things you don't want to go cheap on i think in this case though it wasn't about ergonomics though it was about look it was about aesthetics more so you like the look of it yeah yeah it was like a lamborghini yeah it was like it was about how it looked not how it functioned So yeah, yeah, yeah.

1:02:37You were like, that looks awesome. It looks cool. It has spinning rims. Well, the last time I checked, there's only one of me, only one of you. I'd like to have more than one of me because the world expects so much from me. I can't possibly do it all. But let's be honest, that's probably a good thing. There's just one of you and one of me. But what if there was another version of you? One that already knows your projects, your notes, your team's quirks, and can actually finish the work for you. Well, that sounds like more of me. More of me time. That's what Notion's new AI agent feels like. I've been using Notion AI for a while, but the new agent is revolutionary.

1:03:18It's built right into Notion. Your docs, your notes, your projects, all in one connected space that just works. It's seamless. It's flexible. And you can actually have fun using it. But here's the crazy part. Your Notion agent doesn't just help you with work. It finishes it. It can do anything you can do in Notion. Write pages, organize databases, summarize projects, connect with Slack or Google Drive. It can even clean up post-meeting notes and assign tasks. It plans, it executes, and if something breaks, it just tries again. Basically, it's like delegating to another version of you that already knows how you think.

1:03:55Teams at OpenAI, Ramp, and Vercel, they're all using Notion Agent to send less email, cancel more meetings, and get more done. And now you can too. Try Notion today, now with Notion Agent at Notion.com slash changelog. That's all lowercase. Again, Notion.com slash changelog to try your new AI teammate today and support our show while you're at it. Love Notion. Check them out. Notion.com slash changelog.

1:04:28do you feel like a babysitter of this ai because i've had this idea that we don't have enough babysitters or get to a point where we need taste you need the lm to generate you need the speed at which it knows and can learn the collaborative fabric that you just described but then you got a limit of people who got an understanding of how to direct that thing and then babysit that thing because that takes human time. Yeah. Not human effort necessarily. You need to have that tasteful babysitting mentality. Yeah. And I just wonder if we're going to run out of babysitters anytime soon. I don't think so.

1:05:06Cause I mean, I think it's a different, my experience is it's a, it's a different kind of flow. So like the kind of flow where I'm sitting down and writing code and I'm just going to write code for eight hours a day, which now that I'm saying it, I kind of miss. And so I'm like, Oh, I wonder when I could, maybe I'll go back to that. But like the flow state is there as well when working with AI tooling. But it's a different flow state because what's happening is the bounce between the conversation, the source code, the decision making process. You know, like it's the loop is a little different, but it doesn't feel like babysitting because babysitting sort of implies.

1:05:41What's babysitting once it starts going? Making the plan is fun, right? But the plan being baked and then it doing and then you confirming that is totally babysitting. Yeah, but what's changed is that I don't now I don't I don't watch. Okay. YOLO. But it's not even YOLO. Like, like, I'll have like, what it's opening up is that there's multiple avenues at once. And like, the agent will tell me when it needs my attention. And so I'm not like waiting around waiting for the agent to finish. I'm like off doing other stuff. And, and that like that flows is is very weird. You know, so like one part of like in one part of the flow, what I'm doing is working on that policy feature and the other I'm working on a different feature and I'm doing it at the same time because there's multiple things that I'm driving all at once.

1:06:32And then I have code open because there's another piece of the system where I need it to be really specific. And so the context switching I'm doing is different, you know, but like, but yeah, I do. When I started using these tools, I did a lot more babysitting than I do now. I did a lot of just like, Oh, will it, you know? And I'm like waiting for the drop. And now I'm not, I'm just expecting the drop and I'm like, blah, you know, like, okay, it's off doing its thing. What am I going to do now? Oh, I guess I'll go do this. And then like a little pop-up happens in the corner of my desktop and it's like, Claude needs your attention.

1:07:00And I'm like, okay, which one, you know? And then the way we go. Yeah. But I don't feel like I'm doing a lot of babysitting. I feel like I'm doing a lot of like, it feels like engineering. It's different, but like, but it definitely feels, it feels like engineering to me i would agree and maybe it's because i don't like to plan very much and so i feel more like i'm iterating with it doing the work and me just i don't want to use the word babysitting because then that'll be right i'm not babysitting so much as i'm just directing you know i'm directing the work and yeah i'm waiting for it to be ready for the next direction or the review maybe is a better analogy yeah well you need more traffic cops The volume of parallelism that you can get out of this is crazy high.

1:07:45It's another way that all the systems that we drive will have to change. Like source code is a great example. You know what sucks in the current model? It can hallucinate syntax. And there's no way to know until late in the game, until I run like a compiler loop or I run a lint. So how long before somebody takes a good idea that was something like Unison, where when you're programming, the source code itself gets translated into an underlying data structure that then you can perform transformations on. Where now when the LLM proposes a line of code, it gets automatically linted. It gets automatically vetted at the moment of injection as opposed to writing to a file.

1:08:27Like, and how much more efficient will that make that loop? Because instead of it waiting for the compile loop and the AI wrote bad code, the AI will know it wrote bad code immediately. At the moment it wrote bad code, then get corrected by the compiler. The compiler will be like, oh, you did it wrong. And then it will just loop around. Like no one's ever built that loop yet, but someone's building it. I'm not like the first person who's thought of this, you know? And like, and that, that loop, like that's what we, that's what I mean when I'm like, oh, it's going to change like a lot about how we think about how these systems are constructed.

1:08:59Because the, once you start designing the system to make that loop delicious, it's going to be real different than like i'm going to crank off 10 000 lines of source code run the compiler hope it works see if the linter functions you know what i mean like that's working which is amazing but like if you want to make the user experience in order of magnitude better you have to get crazier with the fundamentals and so like what's the first post-modern programming language look like that was built with llms in mind i have no idea but it's different than Python, you know? Yeah, how would you, like, can you think about that a little bit?

1:09:36I didn't even think about a new language, you know, post-AI and being AI native, really, to use a popular term out there. Yeah, I mean, I haven't thought about it a ton, but, like, the first example was, well, I'll go back to, which is, like, when the AI agents perform better, they perform better when the feedback loops are closer at hand. So when they hallucinate, which you know they will do, like you have to correct the hallucination, right? And so right now the hallucination loop gets corrected when you run lint or when you run the compiler or when you run tests. And so what would change if instead of having the interface be right to a file and put in some words, instead it was like write these lines of code that gets turned into structured information that gets fed to another structured data source.

1:10:31And now you're doing like a transformation to the underlying code base, which can then automatically understand the context in which the change was made and then evaluate whether or not it fits on some set of policy about how the system would work and then feed back to the LLM immediately. That was terrible code. You know, don't do that. And like that loop, that's the loop. Like that's how these systems are going to get better. and what do you have to do in programming language land to make that loop be good? I don't know because I'm not a programming language guy. I'm a practical technology guy, but like, you know, Unison, for example, does this with a database where when you write code in Unison, it synchronizes up to this big database that makes a hash of every function and every variable and then builds a big Merkle tree of all of those things.

1:11:18And if I was building AI around Unison, I would use the hell out of that to make it so when it writes new Unison code, like it immediately tells me whether my code was good or bad whether it worked knew how to revert you know like you could do crazy things um that are just not even feasible in the current model but like we're not even we're not you know most of us aren't thinking at that level yet because we have more practical things in front of us well i think some are because of the i slot right like that kind of prevents if you can do if you could perfect that world even if you didn't go AI native, which is to rebuild from the ground up with a mind, even if you took like Rust or Python and said, let's bolt on that kind of world.

1:12:01When you, when you, if you could figure it out, cargo, it does that job, right? There's a better feedback loop. Assuming the build is being run by an agent being led or directed by a human. If that assumption is true. Yeah. Yeah. Yeah. And like, that's what I mean when I say that it's all like, like the technology we have now. I don't need AGI for that. I don't need the bubble to pop. I don't need any more technology than the one I already have. We could build that right now. The only thing that's in our way is that we haven't imagined, we haven't been willing to reimagine those parts of the system yet because there's a bunch of practical reasons you shouldn't.

1:12:39Oh, you're going to invent a new programming language? Pry Python from my cold dead hand. I talk to people all day who hate infrastructure as code. They, they, that's their opening gambit when they talk to me and they're still a little hesitant to get rid of it. You know what I mean? So like, like it would be even harder in those cases, but like, that doesn't mean anything. You do it anyway, because doing it anyway is the way we figure it out. Like it's the way we move forward. It's the way that we get to the other side of like, what is that? That's the fun part of being able to build technology from scratch like and yeah i don't know you know i think that conversation we just had about programming languages we could have it in every piece of the stack we could have it in in every industry and every vertical like in in in essentially everywhere and we don't need any new technology at all to do it none right we have all of it right now does that just feed into the obvious beast of OpenAI, Anthropic, those folks having, I said tollbooth before in a different podcast, like this idea that we now have to pay the piper to play the game of software development essentially is like, okay, if an LLM or some sort of agentic tool is par for the course when it comes to being an engineer, and if that's true, if we're building everything around that paradigm, then that means that their moat gets thicker, better, more awesome, potentially.

1:14:03Certainly of their pockets get deeper because we're giving them even more reasons to give them more money for sure but that'll create new incentives right for us to be like well wouldn't it be better if we didn't have to pay anthropic all this money wouldn't it be better if you could run it on your desktop because the loop would be better like the reason we're building ai data centers to do inference is because we need high bandwidth to the inference mechanisms so like what are we like we'll start thinking about how do we build a hybrid inference models that use local that use my local resources but then also move to the other side how will the pc change because of the shape of that need of inference and like like so yes i think it'll grow their moat it'll do all those things but then what it'll do is create a new opportunity which is why am i paying all this money to anthropic all the time and like what wouldn't it be better if blah blah blah blah blah, blah, blah.

1:14:54And like around the cycle will go, you know, it's, it won't be like an end game state where it's like, Oh, and, but like, will they be big? Like, yeah, I think they will. And I think it's going to be even more. I think they'll own more real estate than people are giving them credit for. Like right now, like people are kind of convinced that the agent part is going to live outside of like the anthropics or the open AIs. And I think if you've tried to build an agent from scratch in the last six months or less, you've had a pretty good time. If it was before then, if you were like using LangChain or something, it was less good.

1:15:27And I think if you look at the, like Anthropic has a Claude SDK, which basically just wraps up the Claude code and then lets you program that as the agent loop instead of writing your own agent loop. That thing is crazy good. Like you just include it as an NPM library and then you don't write any of the loop. You just like, here's my system prompt. Here's the query. Here's the tools I want. Plug in some MCP. Run. And then it does. And then you're done. And it took you no time at all. And it like, you know, crazy good. Yeah. So the agent client protocol that Zed came out with and starting to get deployed out there is basically leaning into that where it's like, we're making an awesome editor.

1:16:09We don't want to write the agent. We just want, you know, somebody who's putting all this effort into the agent, make the agent awesome. And we'll plug into that. And so they're like, they're basically seeding that real estate. I think everybody should. Yeah. The way we should think about the agent is glue. That's the thing I've been, that's the, that's my pithy sentence that I'm trying to get everyone to repeat is the agent is glue. And so like, if you like, think of it like a Pearl script that you wrote, that's the, I've aged myself again. Um, what's that again? What's Pearl used to run the internet.

1:16:41We all had them. I'm so sorry. Anyway, like, think of it like the script that you write and that's, that's more what agents are going to be. And like, but the first generation of this, we were all like, Oh, we'll build custom embedded agents. They'll be on autopilot. There's startups that are like, I built autopilot agents and it's all in like our special value prop is that our secret agent can do this thing that your stuff can't do because of all of our whiz bangy stuff. That's why it's trapped inside of our platform walls. All that's going to get obliterated as my prediction, because the agent is glue.

1:17:10And it turns out that like your, your like closed wall agent is actually terrible because if you expose the capabilities to my agent, now I could use it to orchestrate my problem, which is exactly what I'm going to do, right? It's glue. It's what I want is CPAN, you know? I want like, I want Ruby gems. I want NPM. And like, and then I want to write my own agent that uses those things to orchestrate my problem, which it's really good at. And so, yeah, the agent is glue. That's the future. And so when you see people - So how does that show itself in SystemNet today? Is it just using cloud code or?

1:17:48Yeah, it shows itself by me not embedding the agent. and not trying to build a wall. So my moat is that I build, I have the best deterministic system to be driven by an agent. If what you want to do right now is manage infrastructure in AWS or in Azure soon or in other places, like what you're going to do is use system initiative within an agent and you're going to have it do stuff like go discover your infrastructure, like propose changes, do it in safe change sets. You know, like all of that stuff that we do, That's the magical sauce. And when you connect it up to the agent loop, incredible, right?

1:18:25But you don't want me to hand you a proprietary agent that tries to do all of the things. What you want to do is build an agent that plugs into your ServiceNow help desk that closes the loop for your compliance structure that says when that change set gets merged, you're compliant with SOC 2. And I couldn't possibly build that feature for you because I have no idea how you did it. But you know. and the agent is glue. So how much sauce is there in system initiative that's not the agent doing things like? A ridiculous amount of sauce, like six years of R &D sauce, hundreds of thousands of lines of source code.

1:19:03What kind of stuff are you doing? What are you bringing to the table that I couldn't get by plugging Claude into EC2 or something? Yeah, yeah. So there's a couple of things. So one is the way that the models get built. So it turns out that if you want the agent to be able to drive something, what you want to do is mimic the outside world as close as possible. So, for example, we don't abstract AWS from you. We just expose AWS the way AWS describes it. And the side effect is that when you write a sentence like, deploy this, like, give me an infrastructure that deploys this Docker container from scratch, it'll go build you an AWS best practices looking VPC and subnets and deploy it across multiple availability zones, even though you didn't ask it to, because that's how AWS would tell you to do it.

1:19:51And that's how they, that's how it got trained. And so it knows how to do that because it's looking directly at this model that we give it. The other thing we're doing is taking that one to one model. And because we have this strict modeling language, when it hallucinates, we correct it in that moment. So when it tries to like invent a property or whatever, we can, we just tell the LLM that property is not real here. Use this tool to read the directions about what properties exist and read the documentation. And then it goes and discovers it on its own. We put your own policy in that same loop.

1:20:20So like, if you want to make sure that you're compliant, like that's how that happens. It doesn't happen later. It happens right when the moment of the hallucination happens, right? You can create your own new models from scratch. So you can be like, hey, I have this API document. I want to use it in system initiative. Go build me the assets for this internal system so I can use it through system initiative. And it will just run off and read that source code, read that description, and then build the models and system initiative on your behalf and then let you run them. And it will do that whole loop.

1:20:49That's not the LLM. That's me. That's all my sauce. Like you can, you know, and like change sets. You don't want to work in the cloud YOLOing infrastructure. You don't just want to throw the MCP server of AWS at an agent and be like, you know, if you want to, delete the database. You know? Like you need change sets. Turns out you need change control. You need the loop. And like all of that stuff is what we provide. And that's what I mean when I say you ask the LLM to do as little as possible. Like we're not asking the LLM to do almost anything except parse our language and then make good choices about what to do next, which it's great at doing.

1:21:28But all the complex work, all the inner details of like, you know, how do I make this variable subscribe to that variable? Like we're not asking to do any of that. like we're just giving it the syntax to express itself to automate it. Yeah. And it works better. Yeah. It turns out it works great. But, um, but yeah, that's, that's where it is. And that's one of the mistakes that we made in the early era of thinking about AI systems was we were like, Oh, all the values in the LLM. And I would make the counter argument. It turns out the LLM is useless pretty much, you know, it's cool for like generating Shakespeare or whatever, like, or, you know, fun memes, but like, if you wanted to do something complicated, like what you want mostly are deterministic systems attached to it and it turns out that's where the value is going to live the value is not going to live in the llm it's going to live in what are the deterministic systems that we connect to the llm to help that orchestration do the right thing which is good for the rest of us because what we build are deterministic things so sure yeah tell us more about this custom model like building your own stuff in system initiative uh you said you handed a custom model i'm not sure what you mean oh yeah yeah you had an api I believe, and said build a model around this.

1:22:37So a good example is Keeb, who works for me, has a bunch of stuff in DigitalOcean. And we didn't have DigitalOcean assets yet. And so he took the API doc from DigitalOcean and he wrote maybe a three-page paper on how to translate that API document into assets and system initiative. And then he fed it to the LLM and then it wrote them for him. And now there's DigitalOcean support and it's working in Keeb's workspace. and we're polishing it up and we're going to publish it. And that model of how to drive DigitalOcean through System Initiative, LLMs wrote all of that, right? Or one of the demos we run for people is we build an infrastructure that they tell us, that they tell us what they want, and then we turn it into a template, and then we ask the LLM to find the variables and be like, hey, I want to drive the size of my infrastructure or those sorts of things, and then have it program the model in real time inside system initiative and then see that reflected back to you.

1:23:38And then that's the loop of how you figure out how to build the automation, which is a crazy loop. It's so different than the loop of like writing infrastructure code. But that back to like, not to shill system initiative too much, but to get back to like, like things news people can use or whatever. Like the, you know, the, the, the thing we're doing there that you can take away with you is that the the interaction loop is driven by humans talking to the LLM. So like you could write that, you can write those assets yourself. You can write a deterministic pipeline. That's what we do for AWS. It's what we're doing for Azure, right?

1:24:16Because they change a lot and we want to automatically, dynamically build those models. But for a lot of things, you don't need that level. You know, you just need it to work. And so if you just need it to work, like the interface here now is like talk to a chatbot and like go to the agent and be like, okay do this thing for me and then look at the results and that's the loop everybody can have at this point um and and you should start thinking about because it's it's so good right yep so compelling it really is especially the lower the stakes the the better it is you know the higher the stakes and you start going some more harnesses i mean look humans got to be in the loop like one of the first things people wanted was autonomous agents and like the another pithy thing I'm trying to make into a thing is that like agents earn the right to autonomy.

1:25:05So like you got to earn the right to be autonomous by performing really well in human observation over and over and over again. And like, I don't, I don't want an autonomous agent, not at all. You know, like, like I want, I want, I want humans in the loop until I decide I don't, you know? Yeah. I kind of want autonomy with guardrails and, you know, clear parameters. so within the world I just said for you to go and do yes go and do some things and most of the things I'm doing are not hard to roll back so it's not I didn't take AWS down that wasn't my fault okay you know what I mean so I want to create an idea or a world to live in and say go nuts get it done we've thought through it all one of the last things that I do before I'm like okay we can actually do this is I say examine, and I call them PEPs.

1:26:02So I've coined this idea of agent flow, essentially, of how to create documents. Is document-driven development, spec-driven development, sure. But essentially, creating these PEPs borrowed from the Python language of how they create enhancement proposals. So let's craft an abstract. Let's craft why it should exist, all the research behind it, maybe some code samples, potentially some additional files that live outside of the actual pep markdown document that support it you know whatever go nuts make this idea whatever it needs to be and at some point there's acceptance criteria like what you're going to do and what will be accepted and if that's all clear then go nuts you know right and it turns out it performs pretty good and it does yeah but the last thing i ask it to do like even when i'm pretty sound in my belief i've read it myself and this is cool yes let's go is I say examine this for clarity and blind spots totally and there's so many times I'm like oh my gosh I mean again I'm not solving AWS problems kind of thing it's more like small things yeah but we would have gone a different direction or wrong direction if I didn't ask it to examine it yeah from a clarity and blind spots perspective it's like okay when I get to this point I'm not really sure what to do I know our plan is pretty good but that point there is crucial and I don't know what i'm gonna do i'm gonna guess when we get there basically it says and how many other places could you do that i don't know but i want it in all of them yeah i agree i think like i don't know capability for a human to so good it's not that i don't want to think about it it's like i want to take my brain space while i tell you to do it to go do the other parallel thing i'm doing or to go and do you know this email thread or this phone call this whatever i'm doing i'm kind of like why it's a different flow state yeah right and soft from the background essentially like checking in on it when i have time here's the plan go do it come back later cool done yeah it's so wild i wouldn't can't imagine that's how software engineering is now and how that's going to influence the future direction of how we engineer software right because that loop is the sudden loop we're optimizing and we've and we've just discovered it you know what i mean like it's like we're using dos and there was no windows that's where we are in the life cycle of these tooling.

1:28:19Like, and we want to believe we're not because like all the hype and money and you know what I mean? Like they're telling us really loudly that we're not there. They're like, this is ready. And it's all, we figured it all out. And like, okay, man, sure. Slow your roll. This is DOS 4.2. This is like the worst it's ever going to be. We have no idea what the right interaction models are. We've, we're like, we're grubbing around in the dark. And also it's awesome, you know? in the same way that like i loved dos when i was a kid you know yeah i was like oh i'm running bulletin boards i'm gonna figure out how video games are gonna work what's a tsr you know we're doing that but we're at that level now we're at the like mostly i just played a lot of duke nukem i loved a duke nukem so i who didn't love duke nukem it's the best it was what i had it had to be the best yeah it was incredible it was yeah wing commander was more my jam but and there's Duke Nukem Forever, which eventually did come out.

1:29:17I think it was 25 years later, something like that. Did I play Duke Nukem? I must have. I don't think anybody did. We all wanted to come out because it was a running joke. It spent like 10 years or a decade and it never actually manifested or something like that. It was funny. It finally did and nobody cared. Yeah, because we were like, wah, wah, wah. The world has moved on. I funded that Kickstarter? No way. And our humor group moved on. We were like, oh yeah, it turns out I don't actually want Duke Nukem anymore. They actually renamed it to Duke Nukem for never. For never. I like that. Yeah, that's the way.

1:29:50Yeah, what could go wrong? So we're in Windows DOS. That's where we're at right now. Yeah, before, I think we're in DOS. I think we're not even Windows. There's no Windows. There's no Windows. The tools I think we have available to us now as developers that we want to have people leverage our tool more so in their agents is an npc server or this new skill skill saying like those are the only two we're figuring all of that out of course yeah yeah but like once you realize how easy it is to build new tools or to build new skills and then plug them into like you don't have to think about the loop anymore and you have to think about like well how am i dealing with like agent memory or the turns between submissions and like it was not that long ago where if you wanted to write your own agent you had to think about all that stuff and you do not anymore you know like like you just you can pull up that agent SDK and the interface is an API that's called query.

1:30:42And then you put in all the options and then you, and then you await the results. That's it. And it like, it'll go off and do 50 turns and it'll call tools and do all the thinking and be like, I made a plan for you. Like all that stuff's built in. You don't have to do any of it. And like, okay, so the agent is glue now because like, I don't, I'm just going to do that. How many, how many tiny agents will I write? because all I have to do is be like, hey man, build me this pipeline that does policy bot. Grab the data from over here. You know, I like, it's going to be, that's wild. It's wild. But when I say that it's like DOS, it's because the, you know, it's cool we can do that.

1:31:25It was cool we could write Duke Nukem, you know? But like, there was a lot more we could do in how we interact with computers and the design, you know? Like, what are the applications you can build on top of it? Like, all of that's open field running. And everybody wants you to believe that we've already cracked the nut because we're all trying to make a dollar, you know? But, like, we mostly haven't cracked the nut, you know? Like, we're all still experiencing it. Infrastructure, I've cracked the nut. You should pay me a dollar. But, you know. Well, on that note, just take me into the world of how now with the rewrite.

1:31:59So you're now AI native, right? Which means you went back to square one, you threw it away, you deleted it. Well, I mean, we deleted a lot of it. Right. The core model we kept, it turned out the core model was catnip for AI. So all that lived. Yeah. You did that, right? And then now you're at this place where you're drafting essentially what I would imagine some sort of new interface. Yeah. Right? Age is glue, right? Yeah. How are you now? How does someone interface with System Initiative in an AI native way? Are you in Cloud Code? Yeah, we're in Cloud Code. Are they in Cloud Code? Yeah, they're in Cloud Code.

1:32:36What's going on? Yeah, the way we ship it is basically like a pre-bundled Cloud Code where you check out a Git repository, and what it has is like a pre-configured MCP server where it's like for your convenience, we've blessed a lot of the endpoints that are safe and then added in the context in the Cloud MD. and set up some of those things. And that's been working great. And then as we like extend the platform or whatever, we just commit to that Git repo, you pull it in. And now next time you start the agent, you get improved capabilities. So you're navigating to a directory that you've cloned down on your disk as an individual developer and you're instantiating clod.

1:33:14Yep. And like, that's like, that attaches you to a workspace and system initiative. And it's got the MCP server already configured. You could do all of that yourself. It's not, we're not, it's no secret that that's what we're doing. No, no, I get that. I mean, that's, what I think is funny is how simple that is really. It's so simple. So simple, right? And it works so good. And so, and it's way better than being like, use, install the custom system initiative agent. And you're like, oh man, but it can't write plans like Claude does. You're like, no, no, just put it in plan mode and have it go look at your infrastructure and have it spend credits doing deep thinking.

1:33:43And it like does deep thinking. Turns out it's incredible at it. So like, that's not my value. You know, my value is the thing that is what it's going to execute on the other side. so then there's really three ways you interact with it so one is through the agent two is through a web ui because you want to understand what the agent has done and you want to be able to visualize it or you want to be able to visualize your own stuff so you know ubiquitous search mapping so there's still a visual component but what it's doing is drawing you a map as opposed to letting you sort of build a map yourself through composition and then the third is a public api where you're driving that data model through what looks like just very traditional software development API calls.

1:34:21And so when you think about, and then you use all three of those modes in different ways. So like in a CD pipeline, you would, you'd be using the public API to do promotion, right? To basically say, Hey, take it in the data about the build that I just did, and then call into system initiative to change some infrastructure. You'd be using the agent loop to do like that policy bot that I talked about, where you're just like, Hey, just call the agent, have the agent do it and it'll go search for the right thing and then do the analysis and then write me a report. Um, you're using the web UI to understand like what the state of the infrastructure is or to, uh, or to troubleshoot problems or to review the work that's happening elsewhere, other people's work.

1:35:06So, so those are the sort of three ways. And I think that is going to be a model that more people start to accept is that like, as these agents come online and we're doing more and more with them, what we need are systems that allow us to interact at different modes of, uh, different, different levels of fidelity, you know, where like, I don't want to have to do all of my API calls through the agent. Cause that's annoying because I know what I want to do. So I just, if I know what I want to do, just let me call the API like a normal person, you know, like, cause that's good and great. And who doesn't love a good API?

1:35:39And then, yeah, But I think things are going to, in general, become more multiplayer in that way. Have you considered that you're the, gosh, I'm just thinking about, I'm zooming out. So follow me with this because I'm trying to piece together some insights that I'm just kind of getting real time. And I got a good friend who's building some on-prem stuff. So I'm like knee deep with my friend who's building out some cool private cloud stuff. And I'm thinking about those folks who want to migrate away from the cloud. Maybe they might be an Oxide customer. or maybe that they're not going to be for a while, but they definitely have their own hardware.

1:36:11But what they don't often have is the cloud operating system, which I believe system initiative could be because you need this connective tissue on top of disparate hardware with ideas in orchestration. And largely that's been infrastructure as code, hard to automate, Terraform, wars, licensing, all the things. Yeah. And I just wonder, have you considered that system initiative is or can be that cloud operating system for everybody? Put it on top of anything, whether it's you choose public cloud or you choose private and on-prem instantiation. Is that what you're going to do? Yeah. Yeah, basically.

1:36:53I mean, and that's why the design is generic, right? That's why you can create your own components. That's why you can program it from the agent. That's why you can create, you know, if you have your own applications deployed on-prem, they probably have their own APIs. They have their own CLIs. Like you're going to write custom functions that go and interact with those parts of the system. And that's how it's going to work. and what's different is the way you think about the layering so you know we've had to build a lot of abstractions in order to try to make things good enough for people that it turns out when the agents are in the loop you can remove yeah you know like so there's a lot of layers here where like when you think about what the interaction model looks like you might be able to remove some of those intermediate layers because they're actually in the way now like you can you can orchestrate a lot more, you know, like system initiatives models are like one-to-one to the cloud provider, which is crazy.

1:37:53You know, like you're like, I want to deploy a load balancer in AWS. That's not like one object. It's like six objects. You got to be like, oh, well, what's the listener? And then what's the target group? And then, oh, is it going to talk to this thing? And what are the subnets going to talk to? Like, it's crazy verbose. And so, you know, the move in programmer land would have been to build an abstraction, right? That's like, oh, here's a simplified load balancer abstraction. So I don't have to think about those six components, but with an LLM, you don't care. You're just like, make me a load balancer.

1:38:23And it's like, sure. He'll go poop out the six. And then you can look at him and you're like, Oh, yep. Those are the six I wanted. I don't, I don't need the higher level abstraction anymore because the higher level abstraction was me just saying load balancer, please. Like, I don't, I don't need, I don't need an intermediate layer. So, so even when we think about it as like the cloud operating system or whatever, like it breaks your brain in half because you're like, well, yeah. And but what's the interface? I mean, it's probably just saying, could you deploy my application onto this hardware, please?

1:38:55And then it's like, sure. You know, like the low level details are actually the thing you need. You don't need the middle layer at all. And, you know, we're still exploring the repercussions of that. You know what I mean? Like, I don't know where that goes or how that ends. But, but when I talk about it being like open field running and how, and how, how much opportunity there is to build, that's what I mean. Like it's, it's the more you open your mind up to what's possible, the more you're like, Oh yeah, actually like it could be like a lot different than it is now because there's things that you just wouldn't, you wouldn't do as a person that you're happy to let the agent do because you, you're just babysitting it.

1:39:34You know, you don't mind that it had to go run off and do those six things. It knew what to do. Do you support on-prem currently? I know you've mentioned AWS, GCP. Yeah. Custom hardware, where are you at with that? Yeah, we're nowhere yet. But what we do do is support your ability to build your own models. So if you have stuff you want to drive with System Initiative and you have a specification, what you would do is just create the models and feed it. But we'll start. Like it's an obvious thing that is, as we add more coverage, the system gets more powerful. So like, you know, we do right now, like architecture migration inside AWS, where it's like, Hey, I want to move to Graviton.

1:40:18So analyze my infrastructure, make me a plan for moving to Graviton, like show me what I would need to do. Then analyze my code base to see if there's anything in the code base that makes it so I can't move to Graviton like that loop. Like we can do that loop for you right now. And it's very cool. once multiple cloud support comes more online in system initiative, which is coming quickly, like that stuff's just gonna work between like Azure and AWS. And, you know, it's not gonna move your application. It's not magic. It's not gonna like, but if somebody needed it to, suddenly you're like, well, the agent is glue.

1:40:53So how's your application work? You know, maybe write a little bit of glue that knows how to do that orchestration, that knows how to like take the database backup out of Azure and then load it into AWS and then run that script. And then what you wouldn't have to do in the middle was all the work to be like, well, how do I map the instance sizes for my Cosmos DB to Dynamo? You'll just be like, it's going to get that right on the first crack. And it's going to get it right 100 % of the time. So that kind of mobility, it's just going to keep compounding because then we're going to be like, well, sure, we'll do VMware.

1:41:29Everybody wants to get off VMware. And so, okay, once you have a VMware target and you have a whatever else target, like that migration story is the same. You're just like, well, okay, the raw infrastructure part's pretty easy. The hard part now isn't how do I build the infrastructure declarations, right? It's how do I move my app? Like, which was always the hard part. Like the sticky wicket was always like, what's the actual application requirements as it migrates? But in those stories, that's the plan, right? Is that, I know I'm asking one more time about this on-prem situation, but how important is it to you to get there?

1:42:09It's pretty important. I feel like it's becoming burgeoning. Like it's new, right? This new own your own cloud kind of situation is newer than like the last two years, but there's a significant uptick in the desire. We just had an outage. We just talked about that, right? Yeah, we'll probably start with Oxide and OpenStack. and then you start to move from there, right? So like, you know, but once people start to adopt in a bigger way, then they start to bring them, you know, like different, like if you have the API that you want, like one of the things that's true in system initiative right now is like, we're still in the part where we're writing like core documentation.

1:42:45We're just trying to catch up with like all the things it can do, but there's nothing stopping anyone right now from being like, hey, I have an API spec for this thing I run on-prem. like take that api spec build models and system initiative and uh and away you go like like go go do it and it would just work like there's no magic to it gotcha a lot of fun stuff man so fun a lot of fun stuff uh are you are you growing as a part of this like do you need more babysitters more toll booth orchestrators more directors i don't need any like in terms of employees. Yeah. Like, yeah, no, I'm good. Right.

1:43:23The second, like what I need to do right. The second is, is bring my products to market and get more people to understand what we've done and, and turn that crank faster and more employees like would be helpful, but like at some point, the thing you, the thing you're doing is getting the feedback loops moving that gets you to the spot where you're like, okay, like now it's, it's, it's very, it's clear what to do. And like, Like, you know, today I have staff, I could put more people to work, but what I, but what I need more of is people trying system initiative and being like, oh yeah, this worked for me.

1:43:59Oh, this didn't work for me. Oh, this is what I really want to do. Oh, can I contribute this thing? Oh, I really want to make, you know, I really need to make models for this thing that I do. Can I do that? And you'd be like, yes, absolutely. Here's how you go do that. And so today that's the game. I'm that that's the most important thing in our business is just finding those people and helping them win and getting them successful. And that naturally will lead me to needing more employees to work on the software. But today, I don't need more employees to work on the software. I need more people to use it to tell me where we need to go.

1:44:36Yeah. What is it like to go to market today in 2025? Like, what's the hard parts about going to market? Yeah, it's weird, right? Right. So I can't speak for anybody but myself. So I would say in infrastructure, what's weird today is. Is that we spent a lot of time teaching the market how to do the last generation of tools because we thought that was the best that was the best we could do. And we did a really good job. So, you know, I'm proud of the work that we did to build DevOps and and to think about infrastructure as code and sort of all of those paradigms. the amount of AI noise has made any AI go to market really tricky because, you know, people are just tired of somebody telling them, Hey, this AI thing's really cool.

1:45:27It's going to change everything. And you're like, not in my life. You know, I'm sitting over here doing what I do. Like all it, all it did was lie to me this afternoon. I gave it a try. It didn't work. I'm over it. And so, uh, you know, part of the challenge is just that practitioner challenge of, of talking to people and being humble enough in the face of their, uh, of their incredulity to sort of stay engaged and be like, Oh, yep. I understand your incredulity. Like, I know why, I know why that's how you're, I know that's why I know why you're saying what you're saying together. I'm going to show you that it can be different and then we're going to get there, And so that's interesting to me, because in my career, building configuration management or infrastructure as code or the DevOps movement, there were people that rejected those things.

1:46:25There were people who looked at Chef or configuration management and they were like, never for me, snake oil salesman. And, but it was pretty rare. And there certainly wasn't like an overarching technology story that we were a part of where like earlier in this podcast, we had a serious conversation about whether it was a bubble and we were, you know, whether the whole thing was snake oil and it was just like, so like that as a, as a background noise, when you're trying to go to market sucks, you know, like that's no fun at all because you just, it's really difficult to cut through that noise and be like, hey, no, this is like real practical, valuable, useful stuff.

1:47:06Because people's reaction to it is just like, it couldn't possibly be because it's the 15th AI pitch they heard this week. And 14 of 15 were bad. I think the other is that on the flip side, the enterprise sell way better. I've never been as smooth as it feels right now. So like take the same technology, the same things. I show it to like a CTO at a global 3000 company and they have an existential crisis about what it means. You know, they're like, oh, no. Like and they get it like immediately. And so it's very strange. What do you mean by oh, no? What do you mean by oh, no? Like the implications are massive organizationally.

1:47:53It's like, oh, like this is the way our technology will work now. And I know a little about how my organization works today and the gap between what we will be able to do, what we can do now and where we were is so big that like that you're there in. They're like, I have to move, you know, like I have to do this because if I don't, I'll be left behind. And also the organizational challenge of moving all those people to this new way of working, understand what the technology is, figure out how it goes. Like, that's a daunting task. So, but, but the reception. I quit. I'm going to the beach. Okay.

1:48:26yeah a little great that's a real choice and this happened in the devos movement too there were people who were like like i had meetings where we got to the end and they were like this is cool software we should absolutely buy it but i retire next year and i'm not taking on like a transformation journey i'm not taking on a transformation journey in you know in 2005 you know um like that's not for me yeah and i think what we see now is you know the enterprise leaders the technology leaders, practitioners who've been around a long time, you know, like the old heads, they tend to get it and they tend to snap in pretty quick.

1:49:03And they're like, Oh yeah. Okay. Woo. You know, they got to shake it out a little. Um, and it's a game for a run. Like I'm going to, yeah, it's exactly what it is. And you're like, Oh, I haven't been working out and I need to, you know, like it's sort of that vibe, which is strange for me because me and my career, usually it's the opposite. Usually the things I build, it's the, it's the practitioners who pick them up and are like, this thing's amazing. And then they go convince their bosses. And we're having this opposite motion where it's like, Oh, it's actually the top down. It's the, it's, it's, it's that the top down then connects to those people.

1:49:39Then the skeptics show up and then they try it and they're like, Oh, I'm not a skeptic anymore. Cause you know, cause I tried it, you know, we had somebody, for example, in a, in a global, uh, one of those like global 3000 sort of motions where they turn the system on, they hooked up the agent, they asked it to go build some infrastructure for them. And they were like, Hey, now what I want to do is like, do it again. I want to repeat it. And I was like, well, just tell it that you want to repeat it. And they were like, what? And I was like, yeah, just say, do it again, only in another region. And then it did.

1:50:13And they were like, Oh, you know? And they're like, Oh, Oh, you know, because we spent a lot of time being like, oh, what I should do is build an abstraction, you know, where if I wanted to play my app in multiple regions, I got to, I got to bundle it up in a little thing. I got to put a helm chart around it and I got to put some variables at the top and like, oh, you don't have to anymore. You could just say, do it again. And it would do it again. And like, that doesn't mean that that's the structure we'll have in the end, you know, but, but it's, it's, if you're an executive and you, and you're, and you're listening to your team and you're watching your velocity and you're thinking about how the organization works, that story really hits hard because you're like, yeah, okay, that's better.

1:50:55You know, like that would be dramatic. And so, yeah, what we're seeing is the top-down motion is working better and the grounds up one, like I got work to do. Like all I'm doing is making practical examples and writing documentation and like doing as much work as I can to just describe the details of like, here's how this works. Here's the way AI works. Here's how it practically comes together. Here's why, you know, here's what those shapes look like. And it's just, we're just going to have to do that work, you know, not just for us, but like for the industry at large, because it's, we, we have to turn it into practical technology or, or, or we'll have missed a real opportunity to make, to move things forward.

1:51:37And right now it's, it's largely not practical, right? It's, it's still people talking theoretical. Yeah. It sounds like your go-to-market strategy needs to be top down. if it's not already, Adam? Is it top down? I mean, it's pretty top down. Okay. And like, but it's weird that it's top down for me. You know, once again, back to being transparent, like I had an existential crisis and I had to pull out my prayer reads or whatever. Like it's not, you know, like I wasn't expecting top down. And like, I'm happy that top down is there. Top down's always part of your strategy. Anybody who tells you that their strategy is bottoms up and never top down, but they sell into the like large enterprises is a fool like large enterprise selling is always top down in the end so like i'm not upset that it's top down but like but i get a lot of validation from practitioners you know so back to humility you're like oh i i'd like it more if my friends thought it was cool what's uh go more layer deeper here what's sales like for you then how does Can we talk about sales?

1:52:41Do you mind? I mean, I'm not going to talk about numbers or customers, but yeah, I'm talking about sales. You don't have to say numbers. As a concept. I'd say, you know, how does your sales organization work? Basically, how does a lead come in? Do you go out and get those things? Yeah, like we're early enough stage that like, and when you're figuring out the go-to-market, when you're figuring out like, what do we have? And how do you explain it to people? And how does that connect? like you don't have a sales organization in the middle because your sales organization needs to be enabled. They need to be given a playbook.

1:53:13They need to be given, they need to be given messaging. They need to like, and then the great salespeople will take that playbook and rip it up, but they need to have one to rip up, you know? And we're at the stage where both the market at large and our company, like we're just, we're writing the playbook as we go, you know, we're like, Ooh, does that work? Oh, it turns out top-down works better. Great. Let's go, let's do more top down, you know? And like, that's, that's the, that's the stage of, of sort of selling that you're in is just like, is, is you're, you're learning about the motion while you're running the motion, which then eventually gives you enough certainty where you're like, oh, this is repeatable.

1:53:48And now I can grow a Salesforce, but a great way to kill a startup is to, is to hire sales reps when, because you have a sales problem and then like watch them not be able to go to work, you know, like they just can't, they can't do it because you gotta, you gotta, you gotta to tell them what to do do you have no sales then sales people no sales folks like i'm blessed enough to have co-founders who like can't have been here the whole time that like and i'm pretty good at selling like not to toot my own horn but i'm not a not a bad sales not a bad sales rep uh you know so uh so like largely a lot of that expertise is in-house so we can sort of hold that out a little longer than other people might um but it's really just you know once you know that that motion is there and you understand it and you get a little bit more repetition under your belt and you're clear about where the angles are, like then you, then you go higher sales reps.

1:54:39I think one of the things that's happening in AI is that like you, the, um, like they can, they can really cause a lot of hyper growth. If, if the, if the messaging connects to the practitioners in a way, or if it connects to its market in a way, like they can drive a lot of motion so like we'll see what happens but always fun going deep this was a fun one i really enjoyed this i hope so it was it was dope i dug it i always hope i'm not boring you know no i'd be like hang out with me for two hours while i no talk about sales never boring oh just the end cap that's more that that was my fodder if no one else cared about that last five ish eight ish minutes i mean look if you're a startup founder or you're trying to go to market in this space i can tell you for sure what you should be doing right now is you should be in every single deal for a long time.

1:55:34Uh, because you, you need that product learning and you need the deal flow learning and you like the only way to get it is to be there. And as soon as you put someone in between you and the deal flow, like you just, it's like, you're trying to understand the world through a pillow, you know? Yeah. Like you just, you need the, you need that. You need the fire hose, you know? Do you actually give it a name in like founder led sales, what you call it? Or Or do you just fly? I mean, people give it names. Yeah, people give it names. But I don't know. I just think of it as a leader and as a product person and as a CEO.

1:56:07I don't know how to do it differently. And I've had the privilege of working for truly great salespeople. Like Barry Christ, who was the CEO of Chef for a very long time, is a world-class sales guy. One of the very first things he did at Chef was watch me try to do sales. And then he was like, that was great. I'm going to do it better tomorrow. And then he did. you know he just he just showed he was like i'm going to rewrite the pitch trust me and i was like great i'll follow you and he did and it was incredible and it changed the trajectory of that company was it was amazing you know and like you know good sales guy do not do not underestimate it but that's because i knew what it was you know that worked because barry listened to me and he was like oh adam i see what you're saying i see how this goes here's how we could change the way we're saying it you know he didn't change any of the details about what i was saying but he changed how he was saying, what the layout was, the packaging, sort of how it all came together.

1:56:59But if you don't know those things, if you can't do the first part, which is explain it to a sales guy and be like, here's how we talk about it and here's why it matters and here's what people value and here's what their response is. And the best way to learn that is to just be in the trenches with people. If you're listening and you have an infrastructure problem, right this second is the window where I will come sit in your house and build infrastructure with you. And you don't have to pay me money to do it. i'll just do it i'll just come and it'll be awesome like we got on planes we go wherever so what do you know about boot c i'm just kidding i don't know a lot about it but i'll go because like because that's the that's the stage you're in that's how you learn to sell you know you don't learn to sell things by staying at home you learn to sell by going out into the world and like make it happen so what you're saying is that there is a an opportunity you will literally fly to them sit hand in hand with them and show them this initiative in their world or how.

1:57:53Yeah, I will sit in your, I will literally come to you and I will help you automate your infrastructure with system initiative because I want to learn, I want to learn what it's like to do it from your eyes. And that's how you learn to sell the software, right? You don't like, you don't learn, you don't sell software by putting up blog posts and being like, I hope you read it and figure it out. Like maybe, but like the actual way you do it is being like, oh yeah, you have a problem. Amazing. I, I, I'm fascinated by you and your problem and I cannot wait to help you solve it. And like, let's solve that problem.

1:58:26Um, and then you do that enough. And next thing you know, you've written enough blog posts about solving people's actual problems and talked about it on LinkedIn enough that people are like, Oh, I bet this thing would solve my problem, you know? And next thing you know, it does. And, but that happens cause you, you know, cause you get on planes. It happens cause you like talk to everyone. It happens cause you, you know, in the early days of chef, I solve people's puppet problems, you know like they'd come to me and be like oh i have this puppet infrastructure thing and it's like this thing's biting me and i'm like oh i had that problem that's why i wrote chef but here's how i fixed it before i wrote chef and you know spent a couple hours just hanging out fixing someone's puppet and like you know because that's that's how you build community that's how you that's how you get people to care you know yeah Yeah.

1:59:10you flying lost then you on the plane like once a week yeah i mean i'm when's your flight today that sure are you are you flying today i'm fine i'm not flying today and a lot of people weirdly enough don't want you to fly anymore yeah like nah stay there yeah they're like that's they're like i don't know man that's weird the pandemic happened you know once again old guy maybe we just do it on zoom which is fine i like face-to-face person i'm happy to do it on zoom too it just hits different on zoom it's good i do all my sales for us via zoom i fly nowhere and we don't have a bad job at sales but but like you know you know if you could run a couple weeks you know, I think there's no question to me what's better if what you're doing is, is figuring out how to do complex infrastructure automation or complex sales.

1:59:56Like, you know, the time I spent sitting at Meta when they were Facebook, like just with them automating data centers, like that was invaluable, you know, and it paid dividends in that product for years. And, you know, you can't get that by, by, from a sales call, you know, I can't get that by being like, well, yeah, you should definitely try chef. You know, I'm not going to come help you. I'm not going to, I don't want to actually be with you. Like, no, this is what I do for a living. I love this. You know, I love infrastructure. Like, like all of those moments, they're not, it's not like a chore to have to fly somewhere and hang out and automate some infrastructure.

2:00:32Like that's a, that's a blessing. Cause you're like, yeah, I get to see this real gnarly problem. I get to see real people like using the software and you learn so much, um, about how to sell it, about how it works, about their environment, about, about what people need, you know, and there's no, there's no real replacement for it. And zoom, you can get some of it, but, uh, but it's a lot harder because it's, it's just harder to get people to open up, you know, like when you're in person, you can be like, it's, you can be fun, you know, you can crack a joke. It's hard to be fun on zoom. It's hard to be fun on zoom.

2:01:05It is people come intended to be allowable to be distracted. meaning i can this is a call where i can be rude right and it's it's opted it's okay like i can check slack i can check email i can look at my phone while on a zoom but in face-to-face in our realm it's it's that's not a cool thing to do right you generally don't do that when you go sit at someone when you go sit in a conference room with people and run a project for a week or two like where it's all dedicated to their point of view and all dedicated to their problem like that's the bad that's the greatest that's the funnest thing and like um i love doing that so yeah if you're listening and you're like oh i'd love to do that like adam at system init.com let's go but he's not hiring okay he's not hiring i'm not not hiring but i am gonna come fix your infrastructure for you okay gotcha i like that better deal all right if you have infrastructure problems i'll buy for you some but call adam and he'll take care of you exactly precisely there you go.

2:02:06All right. All right. Good stuff, Adam. Thanks for coming on. Oh, it's always my pleasure. Always a pleasure. Thank you so much. Stay cool. Be awesome. It was fun. Super fun.

2:02:20Okay. We covered a lot of ground on this one. I'm sure you have thoughts on the AWS outage, on the AI bubble, on the MS-DOS era for Agentex systems. Does Sam Altman really have AGI locked up in his basement? Let us know in the comments. Link is in the show notes. We love hearing from you. Thanks again to our partners at Fly.io and to our beat freaking residents, the one, the only, the Breakmaster Cylinder. That's all for today, but we'll be back in your ear holes on Change Login Friends on Friday.

2:03:08Thank you.

2:03:32heard