Stop being skeptical about AI for development with Charity Majors

Why are there firmly two camps within software engineering when it comes to AI? Those hating its effects and those who are AI-pilled. Charity majors emphasizes both camps and things they are talking alongside one another. Charity is a co-founder and a CEO of Honeycomp, previously worked at Facebook and Parse and is one of my favorite voices in engineering. Today, we discuss what it would take for us engineers to ship code we have never read and why this is more of a one question, not an if question. Why are reliability is getting worse across the industry and why it will take some time to recover? Career advice in this age of AI. Why middle managers should consider going back to being IC and why junior engineers will be okay. If you want to hear from someone who was skeptical about AI in 2025 but has changed her mind based on the evidence, this episode is for you. In today's episode, Charity will say, "Spoil alert" that the question is not if we will stop reading code written by AI but when. And we should take lessons from ops and QA and how they prove that software that other's code works in prod. And she's got a very good point. As any ops engineer or essay will tell you, that's how software has always been written by unreliable agents from their point of view. That is software engines like me, your colleagues or you. And let's face this, you probably haven't read all the code in your code base either. This is where I need to mention our presenting sponsor, Antisysis. Antisysis verifies software written by unreliable agents. It runs your whole system in a hostile simulation and roots out the bugs for you. It does this by using an approach called deterministic simulation testing or DST. Antisysis turbochargers testing by running your whole system under aggressive fault injection. Imagine Antisysis hundreds or thousands of versions of the Mario game running, each instance aggressively trying to break the game with increasingly weird input combinations. If it finds a breakage, this is where the determinism comes in. Instead of you having to try to reproduce a tricky bug you saw in production, Antisysis can provide you with a perfect deterministic replay of anything it finds every time. With Antisysis, you can specify properties at the whole system level and Antisysis will actively try to disprove them. So you can be confident that if your system holds up in Antisysis, it will hold up in production. Head over to Antisysis.com/Priigmatic to learn more. Charity, it's so nice to do this in person. You're in my city, this is amazing. So today I wanted to kick off with AI, but before we kick off with AI, I just want to make it kind of clear for people who don't know you that you're not an AI hater or an AI lover. You actually built a lot of cool stuff pre-AI, starting out, which is how Lyndon Labs was at your first job. My first job at Lyndon Labs right across the street. Right across the street. We were just talking about that. So you were building second life. Yeah, we were building second life. And then from there on one of the big kits was parts of the developer tool, which was beloved by developers back in for all, best back in from mobile services I used to use it. And then what happened? Facebook bought you. Facebook bought it. That was my first great lesson in most acquisitions fail. Most were terrible. This one failed. They shut it down. But ultimately I'm very grateful to have had the experience because if it wasn't for that, I've always been a startup kid. And so nobody in my name, and it wasn't until I was leaving Facebook that investors were like, oh, would you like some money? And that's how we started honeycomb. And then you saw stuff at Facebook, right? It inspired you that. Yeah. Yeah. Facebook. And then we got to use the internal Facebook tools. And Facebook had this tool called scuba and it was we were experiencing hockey stick growth. It was just like we had over a million mobile apps hosted on parts by the time I left. And every single week a new one would break. It would hit the top 10 and iTunes or something out of nowhere. It would be like, and these apps need only a stack, you know, and it went from, it would take hours or weeks. We have to get lucky. We finally find, because it's not a, it might be one app that's spamming the logs, but that might not be the reason they might all be backed up behind the reason, you know. We started getting our data sets in scuba and finding them. It just went from being a really hard engineering problem with a lot of luck to just being like a poor problem. Click, click, click. There it is. And it was just mind blown. Like you just, that was a huge problem for our entire existence and then it was solved. It was scuba. And then when you started honeycombe, so what was this a bit of inspiration that you want to build something that feels like scuba dead? Yeah, I just the idea of I was planning to go be an engineering manager or an engineer slacker striper something. And I was just like, I would be so much less powerful as an engineer without this. And so, you know, the grand plan in the beginning, I'm just like, well, I'll start up this fail. So, you know, we'll fail, but I'll go sit in a quarter and write go code for a year or two. And then, then I'll open source it and I can take it with me wherever I go. And then that that's how honeycombe started. And we'll get back to like observability or honeycombe, but before we do now with AI, you know, it's changing everything. But I kind of had a bit of a blast from the past, which is one of the first places we connected was in 2020, so almost five years ago or so. Someone submitted a question to both my blog and your blog. And it was the question was, can you measure individual developer productivity? Now I wrote an answer and you wrote an answer. And I wanted to ask you, that was five years ago, no, AI, no, nothing. Today, someone choose to have questions saying, hey, charity, can you measure one of an engineer's individual productivity? You know, they're using AI tools and all the all the stuff. Would you tell them? I would tell God, I don't remember what I said. I remember that one, but we both agreed by the way that it was, it was that you can measure some dimensions and they're not going to give you the full thing. And they will, for example, not tell you how a team is doing if someone is is actually really a key part of the team. And that as long as you measure individual things, we both agreed that you need to be in the details to know and as good manager or a team lead, you will know. You will know, but you have to have Gator to back it up. It's like color and a painting on the wall and is it good heart slot? Yes, it's good heart slot. So like never go, well, it's this thing that matters, right? You need to actually understand, but you need it to not just be your opinion that was tossed off because you have an opinion about some part, you know, we're all biases, we are selective. You need to look at the picture. I also believe that there's been this whole push towards individual output, but teams are still what matter to me now. And honestly, if there's one thing that I am encouraged and excited about with the AI, I think it's forcing us all to ask ourselves early and often, what does good look like? What does good mean? What does productivity mean? What would better look like? What would great look like? You know, and these questions are hard. I think it's telling that we all jumped so fast to speed. Oh, fast. We can go fast, sensing faster, you know, boom. And I've come to feel like that is a very immature description of what better is. Yeah, just today I saw the and tropic team posted a podcast with Spotify's head of engine or VP of engineering, I'm not sure which one in which they talk that while Spotify with Cloud Code, they're shipping 4,500 changes per day per week. I'm not sure. But they talked about speed. And I was kind of thinking like my experience has been different because I struggled to publish any like some of my episodes in Noguang, Spotify because it was down. And yeah, they're talking about speed, we're not talking about quality, we're not talking about more functionality, better functionality or just things that people want. And in a comment, some people are asking like, okay, so what exactly does that mean that they're shipping more frequently? Yeah, do customers really want the buttons on their app to move around all the time? I don't think they do. Yeah, it's an interesting one. It's the easiest thing to measure. Let's jump back to last year in 2025. You wrote a blog post right at the end of the year looking back saying that 2025 for AI was what 2010 was for the cloud. Can we talk about it before we go into like what this year, but like last year, like how was your perspective? Of course, you were working at a zero-wally company, AI will give you lots of like business as well. But you said it one mainstream, right? Last year. Yeah. And I gave Akino at SRECon, we gave the closing talk. And it's Fred and I standing in front of the term vibe coding had just been invented. Oh, yes. And we were like, you guys should try vibe coding. Pause. Growns. Audible groans. Just like people laughing like, ha ha ha. Our big pitch was that people should learn AI because you can complain better if you learn it, which is legit. I mean, I really mean it. But at the time, I think I still saw it as a really big feature or like a bigger than a programming language, like the cloud, but not like generational, you know, not changing everything. And I think that was accurate. For me, it was November of 2025 when they released Opus 4.5, but actually wrote about this recently, a couple blog posts back about how in retrospect, you could see it coming sooner. You could see, and it wasn't actually the models. It was the harnesses. It was all the tooling. And it was people starting to say that and around July, they were like, this is coming faster than you think and this is what it's going to look like. And there's those people who were saying it, the word wants to be playing with either a cloud code or maybe pie or open codes. So the harness is your ride. - You were getting better at the tooling that, you know, it went from just being kind of a shell script that would try again to like, they built a lot of stuff around it. And then, you know, the opposite kind of, it was a weird time at the beginning. It's been a weird time every time for a long time, but the early months of this year, it felt like everyone around me was just trying it again and changing their mind. Everyone. - Yeah, I think we were just talking about right before we started recording that, both you and me respect people who do change their mind. - Yes. And I don't think we were wrong to be skeptical that the first time it's a pretty extraordinary claim that AI is going to write code about as well as the meeting software engineer can in, you know, for limited amounts of that. - Well, especially 'cause if we look back at the history of software engineering, this claim has happened again and again. - Yeah. - You know, near-all-nets should have been doing something magical. - There's a sticker in your pack that says, we already have a programming language that lets you code all as a punchline. So I don't think we were wrong to be skeptical. - And also don't forget no code and low code. - Oh yeah. - I mean, we know it turned out to be a joke, but the promise was the same. And we were skeptical and we were right. And now we're skeptical again and we were wrong. - What I was saying in that piece though was, I think we were right to be skeptical that time. But now I see the same thing playing out with, would you be willing to ship it some code that you didn't read? There's no point in arguing about if it will happen or when it will happen. Talk about what it would take. - Mm-hmm. - What would it take for you to be comfortable shipping code without you reading it and understanding it? 'Cause that is, that's engineering. - And it goes back to like, you know, my gut reflex would have been saying, oh no, I would not do that because I'd been used to that. However, you're right. You know, if I could have a way to, for example, I could see the change. I could, I could tell that this was tested in like a harness or something. It's the same way where, for example, pre-AI, if there was a, I had a team member who said, I vouch for this and I've hammered it and I trust that person. So like, you're right. There's these things which are, of course, would, never, but I thought that, AI or something can do anything like that. But if it could, you're, and that's what you're doing, right? - For example, if you and AI would, would both do it in tandem for a few months and you would be like, you would get to, how much are they catching? How much am I catching? Is it about the same? Is it more or less? And you're training it and it's getting better. Whether it takes five days or five years or whatever, I think it's pretty clear that directionally that's where we're going. And the other thing that I would say is, this is good for us. If you spent much time with the Phoenix architecture stuff that Chad Feller has been writing out. - You have been quoting, yeah. - I've been quoting liberally. I should probably let you get to it in your own order, but I just feel like anyone who's ever done painful rewrites should be on board with us. - Yeah, and but here's a quote from Chad Feller, "immutable infrastructure, stateless services, containers, blue-green deployments, infrastructure as a code." These ideas all share common premise. Never fix a running thing, replace it. AI pushes this premise beyond infrastructure and into application code itself. When rewriting is cheap, editing in place becomes risky, mutation accumulates entropy, replacements, reset set. - Yes, code is cash. - This is a very interesting idea because you've compared, chaff compared, and you also of course share this, that when we look at how infrastructure changed before, like specifically a server you need to be configured, and I think we call it like pet. - Pets versus server. - Having pets versus, and at some point, we stopped configuring individually. We stopped fixing individual machines. We just throw it away and have a new thing. And with code, we've always been used to the history of the profession, 60 plus years or maybe a bit longer, is that we edit code. And are you thinking this might? - Because of the economics of it. I mean, if you think about it, you could generate 10,000 variants of a function faster than you could write at once. And so when you start thinking about it that way, it's like, well, okay, we're gonna need a lot of e-vals, we're gonna need a lot of tests. But the generation is so cheap, that it really, I think it forces us in that direction. And I think that the, the expensiveness of writing code and maintaining code and the expense of software has always been bound up in its maintenance. And the lines of code, the reason that we trust something is because, we've been using it, because we, then we know we, we, like there's this deep thing about productions like, well, it's trusted, we know. And I've been, I know that as well as anyone. And I will also say that anyone who's ever done a hard database migration should have some real humility about our ability to extrapolate those contracts, store them, like I am not one of the people who's like, this is, we're gonna generate all code. I don't know how much code. I believe that we can go some distance in that direction and it will be good for us. I don't know how far we can go. I believe we can go farther than we are now. I just hand the last project I did at Paris. So we had spent like six months writing the original of Ruby on Rails API. - Yep. - About two years rewriting it and going. - Wow. - Yeah, it was, it was, it was. - And what was it two years? Because new sub being added that you could support. - Just some extent. And also GoLang was a pretty immature language at the time we had to write the MongoDB drivers and like all the other bunch of things. And also just like when you're writing in Ruby and MongoDB and JavaScript and everything is, you know, there's no type safety and it's just painful. Just, you know, in the stranger figs that they do, you build the architecture outside the architecture. You literally find the contracts with your users by breaking them one after the other. Like that just does not seem like the ideal artifact. We should be able to store them somewhere. We should be able to have architecture diagrams that we can review and discuss that generate that code to spec. - This is very interesting because some of these ideas, they've been around decades ago, specifically, you know, if we had created a bootch as a third person sitting here, the idea of like, hey, we can have archives and architecture diagrams that translate to code. UML started there. I think Grady would disagree that like he never wanted it to go there, but a rational software back in the '90s, they say, hey, you'll define UML, it generates code, it will be beautiful. Now it wasn't beautiful because I guess some complexity and turns out a generating code was still expensive and reviewing it. But I wonder if some of these ideas now might be just feasible? - That's my hope. I mean, I'm just barely old enough that my first job, it's like 17 university, I was assisted then. I remember when, you know, I was really aware of what was going on with just the kid, but yeah, I remember how stressful it was and how people were agonizing about how we'll never be able to get that information back and everyone adapted just fine. I think I wrote the systems, you know, they built the systems that replaced them, but not as in replace them and worked them out of my job, they built the systems and they spent their time writing code instead of like running updates by hand on every server in the closet. - And I guess this is an interesting one because clearly like the syshabeman role and professional has been, it doesn't exist today. It's kind of a legislative, it's been eliminated. However, the people who were syshabements, they did understand the operating systems, they understood hardware. - Yes. - They were in a really good position to adopt and a lot of them just became either software insurance, product managers, I know someone became a text sales person. - Yeah, yeah. - So it's almost like, like-- - And I will hold that our generation of engineers still the best debuggers. I'm glad that people don't all have to learn about CPU and memory and all this stuff, but like there's value in knowing that stuff. It comes in handy. I think there's some analogies there to the generation of code stuff. - Also, you know, you took a bunch of inspiration in your recent writing about both syshabements, but also QA, and you wrote something interesting. You said, lines of code are not the ideal artifact review, and I'll quote a little bit from you. The tools to do this don't exist yet, but many of the ideas do exist. Most come from operations in QA, two domains of software engineering has historically been rather snobbish about. So really, there is our relationship to QA and Ops, where I feel we always put ourselves as software engineers here and Ops and QA somewhere, and maybe time to see some humble pie. - Ops equals toil. Right? Yeah, I think it's time. I mean, Ops and QA have always been more concerned with what is software engineering has always been much more concerned with what, how should it be? - So Ops and QA have always been more concerned about validating about correctness, about does it work as expected? - Does it work? - Does it work? - Does it work? - Yeah, I mean, it's always weird to me, just how much software engineers really seem to believe that the world exists in the repo. It doesn't. It's production, you know? The code has part of the information. Some of it, it's very necessary. We need that, but like, I know some of the software engineers who, well, okay, some places don't even let software engineers look at production. just like how? I know. a lot of people were very upset about AI, but the things get me very excited to know the excited about AI are that it is pushing the discipline in directions. We have desperately needed to go for a very long time. Production is not what happens after development. It is a stage of development. - And you've been saying this consistently for pre-AI, I'm just gonna say it for those of you who don't, because I remember we also bonded a little bit over. There was this thing called training on Twitter. I remember when it was still Twitter, and it was tech Twitter, everyone was there who mattered. And there was a trend going, it's Friday, don't deploy. Some something that there was maybe a hashtag even, like I'm not sure if don't deploy Friday or something like that. And the point was it was well-meaning. It said like, look, when you deploy off and there's an outage and on the weekend, we don't wanna do. So there was saying every Friday, it went viral saying don't deploy on Fridays. And you came in and you said, you know what? You should be able to deploy anytime without fear because you should be able to just know, however that might be CICD. And then on top of this, you were like, no, you should actually just not even have a user acceptance testing environment at UAT. You should just deploy its production, like an testing production, right? - As soon as you merge, it should go be going out. You should have to stop the train to make your code not go into production as soon as you've merged, absolutely. - And one more interesting thing is you had a long train thought about like the AI and what it could be. As one thing you said, our brains are not built for validation. Almost everyone I talked to, including Andrews Hayesberg. He said that look, like it's very clear that code generation is cheap. We are generating more code. And the bottleneck for human engineers is for code review. And everyone's trying to figure out how do we make code review easier? How do we build nicer tools? Uber has built amazing tools to like, try to like surface important code reviews. But everyone's pushing like, all right, let's do more code review. As an engineer, I'll be honest, like I never liked doing a code review when there's very little to do. And it's with someone I care about, I'll entertain it. - It's more of a coaching opportunity then, right? - But as soon as there's an AI, it's kind of like, I don't know, I don't really care. Like I'm just being honest here. Like do you care when? - I don't, I've never, so the problem, one of the problems is that I think code review means so many things to so many people in so many places. And so a lot, there's a lot of projection going on. A lot of people are, if you say that you don't want code review, you're saying you don't wanna talk to your coworkers, you don't wanna mentor juniors, you don't wanna, you know, which is not true. We've just bundled so many things into this, like, you know, - It's like, - Usually overloaded. - Usually overloaded. And some of those things are really good. Some of those things could be done better in other ways. You know, some of those things are very cultural, very specific. My friend, David Pol, who I worked with at PARS, and he's now working at GitHub on poll requests. - Amazing. - I love the PARS, Mafia. - Yeah, exactly. He's like, to me, the code review is when we decide, do we want this in our product or not? Oh my, well, that is a great discussion. That is what humans are good at. We should talk about, is this mental model coherent? Should we add this? Should we not? Like, love that. Architectures, you know, but like, the code is not, this is really a great artifact for all of those. So should we be talking to people? Yes. Is the code review the right form factor? Maybe, but I think that the emotional reaction that's when people are getting to the, like, the validation, in my book is at the very bottom of the list. - I'd like to, like, touch, like, stay here a little bit more. Can you break out the parts? Because it feels me code reviews, you're overloaded. But the parts of code review or the things that you have seen are good things, and maybe we don't need to do as code review. And the things that are just like, just have never been that good. - Yeah. - And maybe we just need to throw out a way. - Yeah, I mean, I think, do we want this in our product? Is that is great. I mean, ideally you'd talk about that before, you write the code for it, but, you know, whenever. And, you know, is this, is this API design? You know, those are great conversations. Reading for syntax and bugs and that sort of thing. It's not evil, but it feels like it could be, it's a teaching opportunity. If that's the best teaching opportunity you have, and I guess some folks at some point maybe you need them, but it doesn't feel high. Does it feel like a great use of anyone's. - It feels the only time, whereas useful, is if someone joins a team, I'm initially, it can be a little bit of feedback, especially when there's like, nothing is written down. There's no guys, there's no linting rules that would give you that. - We'll see that again. - Yeah, it's it. We can fill in the cracks if we haven't built the guard rails. We can fill in the cracks, well, kind of ways with, with our own time, but there are so many things, I think, that we never think to extract out of the process of building and validating software. We rely on us. So I am a huge fan of Intercom, you know, Finn, they're engineering org, and I have been forever, like I noticed, Ferris CTO, Decay to go, had this saying, "Shipping is your company's heartbeat." And I love that. They ship a Ruby monolith, like 10, 15 minutes, hundreds of times a day. That is not trivial, it's not trivial thing to do, right? So they're kind of a high water mark, from my mind right now, for teams that were founded, Pre-AI, have a lot of engineering discipline, who have become AI native. And they wrote a great post about how they do PRs that are AI validated. And the bar for them is very high. It's like they have all the wisdom of their most senior engineers looking at every single diff, and that is fantastic, which means that you don't have to worry about remembering and looking and nitpicking, and all the things that we're not good at anyway, and they can talk about, is this the direction we wanna go? Is this the right path? You've also written that non-deterministic systems require more engineering discipline, not less. So what is the thing about these non-deterministic systems? We're specifically AI, right? We're talking via let's just name it. That is, we see that AI does amplify both discipline and lack of discipline. Why do we need more? And when you say discipline, what specifics are we talking about? - Well, I mean tests, and evals for one thing, right? Like if we're treating the code like a trusted artifact and we're trying to predict everything with our human brains and everything, then we're writing the tests that we can predict that it might break, and anytime the system breaks, we like trying to test for that, but that's not an especially high bar. And so I think this sort of the behavioral tests, or the, I don't remember the where it starts to see, but the QA folks have these tweeted tests. We're a capture. - There's also smoke tests. - Yeah, there's so many. There can be like performance tests, there can be low tests, there can be, yeah, there can be like just kind of fast testing as well. - Something that's like, okay, if I'm not gonna read this code, how do I know it's going to perform within boundaries of the last code that I generated? That is conformance testing. - conformance testing. - Just as important for lots of workloads as, you know, absolute performances, it's just not changing too much. And so we, I think we're gonna need, the trust test to go somewhere, right? If you're debiting from this trust account in the creation of the code, it has to get built up somewhere else. And I see like one of the things that I'm really excited about in the coming months is just, I actually really like thinking about it, less as AI, and more as deterministic and non-deterministic systems that have to play nicely together, because determinism is not going anywhere. It's incredibly valuable. And we have to learn to make AI kind of boring, you know? It's a non-deterministic tool, which means that it is all over the place, but it's so valuable, but it's all over the place. We have to learn how to give it carve pathways and places where we kind of crawl it, where we use it in the way that it's a superpower and not in the way that like erodes our foundations. - This is interesting, as Martin Fowler a year ago when he was on the podcast, the thing that he talked about is how the biggest change with AI is a non-determinism. And when we think back in the history of software, it's always been deterministic, same for neural nets, but that was most of a software and he's been really touched too much of it, 'cause it just wasn't that useful for us. But we've been used to that when we program, it just happened the same way, you know, test were easy, because you just run them once, you don't run them twice, 'cause why would you? And I wonder if this is, we need to just realize how big of a deal this change is, and that the any business that employs us, like, you know, they want software that works the same way. We just had a recent post on hacker news, there's this ATS application tracking system, scoring system that hacker rank outsource, which scores your resume. And so software, you're just like, and you can run it locally, it's open source, you can use a local model, I think they recommend Gemma, Google's small model, and when you run it, like a hundred times, it will score the same resume anywhere from like 66.3 to 99 points, and typically most companies have 85 set as the bar, and you're like, hang on, so we've turned what is, what they were advertising as a tool to help your recruitment, we just prove that it's just a coin flip. Like that's bad. - Yeah, and we have to be able to say that it's bad. AI is not the right tool for every use case, you know? And I think, I think every company is going through this in microcosm, and something I was saying to folks just earlier, today we are. We've been doing this series of conversations on our AI norms. values. And it was like a year ago, I don't trust us. Like a year ago, if we were like, yes, we should use AI. I know we didn't know, we didn't know enough. We've gone on such a journey over the past year. And we knew so much more now, though like if one of my coworkers was like, AI is the wrong tool for this job. I'm like, I trust you. You know, you've got to get worse before you can get better. So tell me about where you are right now with your, how inside of honeycomb higher thinking about AI, how you're thinking about how to think about AI. And then what values you came up with the works right now for you. Yeah, it's Church was just acknowledging that the bar has gone up for all of us. That's what happens when we get powerful new tools. Has the bar gone up or has the, you know, the floor gone up? That is a great question. Maybe yes, maybe yeah, I don't know. We're definitely in a sort of wandering in the wilderness phase. But you can't not wander or you will be left behind. You know, we acknowledge that the bar is going up for all of this and that the only viable way to define that bar is better outcomes. And asking ourselves like is this good? Is this better? What does it look like? Another, another thing we point out is just there is no human in the loop. You want the loop? The loop is yours. The loop is mine. It would not exist if it was not for me. So I am the owner, right? There's no, oh, Claude said this. So no, no, no, it's your work. You want it? Charity just talked about owning the loop. Owning the loop also means controlling with every agent inside of that loop is allowed to do which brings us to our season sponsor WorkAWest. Today agents are increasingly able to act on their own and the old off model was never designed for that. Who is this agent? What's it allowed to touch on whose behalf? You really don't want to get answers to these questions wrong. WorkAWest is built exactly to solve this problem. WorkAWest is fine-grained authorization FGA designed for how agents actually operate plus SSO and skim and not just user auth with agents bolted on after. The fastest growing AI companies and tropic open AI cursor pervelexity already trust WorkAWest. Check it out at WorkAWest.com. I also want to talk about build kind the CI or criticism platform trusted by cursor, open AI and tropic and video uber canva and more. Charity talked about owning the loop. But here's a challenge. Thanks to AI your agents are writing a lot more code. To trust this code every change at an agent makes still has to be built tested and proven safe before it ships. So obviously you need CI more than ever. But when agents are pushing 5-10 or 50 times of commit volume to your pipelines faster CI runners won't be enough to keep up with it. Shaving 30 seconds off a single build is meaningless when the queue is 100 plus jumps deep. What you really want is a CI system that gets faster as the volume grows and CI that offers instant parallelization to give you unlimited concurrency and to intelligent route changes at run time. This is what build kite does and while global software leaders at every level continue to rely on it. The same architecture that observed the scale of Shopify and uber a decade ago now runs about 1.4 billion job minutes a week across cursor, meta, reddit and snowflake. While the rest of the CI world are crackling under the weight or re-architection their platform, build kite continues to reliably grow. Agents run on your infrastructure or on build kites. Any cloud, any chip, your secrets, your scale. Every artifact and log is captured. So when something fails, either you or your agents have immediate insight for why. As you're injuring the context you give to your agents, think about how you verify what they hand back. If your system is buckling under the increased volume, head to buildkite.com/pregmatic. 30 day all access trial, no credit card and an actual human engineer on standby. His name is Ola and he's very helpful. And with this, let's get back to charity and communication norms with AI. I think there was this frenzy of, oh my god, I can do this. Oh my god, it's so cool. And I know you have also become very weary of this software. I just don't even read it anymore. As soon as I can tell. As soon as you know you're recognized. This might have been AI. It's trash. Here's a baseline. You cannot send as anyone something you have read. And in fact, if it would take them longer to read it than it took you to make it, it's probably slop. That's really disrespectful actually. And I think, like, just like asking someone to, like, you're asking, anytime I give you something, I'm asking for your time and attention. And if I'm giving you something that I don't even know what's in it and I'm putting it on you, it costs you instead of me. That is not good. I also think that even before that is, I've noticed as I start working on these norms and values, I'm noticing myself as I start to ask someone a question without trying to look up the answer. Oh, I shouldn't do that. Or if I'm giving someone something that I kind of generated and I'm like, oh, you know, it's a part of it is just self-awareness. It's interesting because everything you talked about, it reminds me of when a new joiner would join a team, a junior engineer, a new grad. Either they had emotional intelligence or they picked up on really quickly that, for example, you go and ask a senior of their time once you put in a little bit of work and you start to respect their time as well. Obviously, it doesn't start like that. We don't want them, but there's this balance. And I almost feel it's the same thing where we're like, look, like respect your colleagues, respect fellow humans. If you are communicating with them, make sure that you're not wasting their attention. Because now I guess attention is where we're kind of running low. We have all of these, all of these, like a bunch of people have a bunch of ages doing, but boy, that's kind of the currency. And as long as you respect that, it doesn't matter. I think we're not talking about don't use it after this or that. Like you use it as much as you want or would make yourself more efficient. Just don't degrade because it really degrades those personal skills, right? You can use AI as a shortcut to help you not have to think too much and you can use AI to help you think more deeply and more rigorously. And both of those use cases have their place. But when it comes to your core job function, we primarily want the second one, right? Especially if you're involving someone else and you're asking them to review or, you know, and this is not absolute. There are people who English is a second language and they use it as people who like neurodivergent and these. And that is, again, that is still being respectful, you know? So it's not like, like you said, it's not no AI. But it's like, the reasonable asks of each other and, you know, we don't need to reinvent a new bar for quality or respect because we have great bars already for quality and respect. We just need to apply for a while there. I think that there was a bit of, oh my god, this is so cool. Do you see what that's cool thing can do? And I think we're all just like so overranging. Well, the reason I really respected you came from this, this is, you know, this is the Sysdev background. You also, you're very more an SRE. These are folks who have been pretty skeptical of AI. And you mentioned how you're seeing two camps, two very clear camps. There's like kind of the AI-pilled folks who like get it. And then the people who seems like they just hate AI. And you're you said that you're not seeing these two camps have any sort of way to go between any feedback. Can we talk about what you're seeing? And like maybe, you know, like where you see some of these camps forming. See, the problem is that neither side is making it up. Like they are seeing really scary trends. They're see, they're grappling with real hard problems that are getting worse. You know, and on the enthusiasm side is like, they're acutely conscious that it's, it's a bit of a race. And that we need to push ourselves out of our comfort zone. And they see other companies moving faster, catching up, leapfrogging. They're really worried about, you know, we're falling behind. And the first thing, I don't want to make it sound like fault equivalence because while there are elements of this that are true, I think every company is more one or more the other, but like they're not wrong. They're not wrong. These, we've never seen technological change this fast. We're on the inside of an exponential curve, which is very rare. I'd never usually last that long, but it's still happening, you know. Things are happening that shock us. And we would be wise to be prepared for them. So like, that's real. That's real. And, and these folks are usually at most companies. Usually they are the small minority and they are constantly filling out. Man, one of the things is ironic though is that both of these sides feel like they are the tiny minority and they're out mad. And they're being suppressed. And they are standing up for what is truth and valor in the face of the big AI folks or the big skeptics, but the other side. So in this often, start to come down to the group that is on call and the group that is not. Oh, yep. Because the people who the buck stops with them, they are seeing melting mental models. They're seeing slot. They're seeing all their hard work just dissolve and they're seeing and they don't see any end in sight. So just just to be clear, we're seeing that the people who are on call for a lot of these systems are seeing more incidents. They're seeing carelessness being caused by it. They're actually seeing that since that group starts to use more AI, our systems are getting way worse. Way worse. Yeah. And it's that's a very real not making it out. No, no, no, actually, I was just talking to someone inside of me. There's been this big drama where people have been reassigned. Oh, God. I know I'm sorry. You're posed. So not just my poses since then I haven't written about this sense that I'm not sure what this podcast came out. I might have not talked about it is inside of me. They track Sevese zeros, which is the one of them. I remember. Well, you remember Sevese zeros. There has been a flurry of Sevese zeros. So many of them. And you cannot hide like this is you know, meta like this is black or white. And the past about two months, it's been crazy. And and just so it happens, it's happening inside of Instagram, it's happening inside of WhatsApp, where the, the trust and safety, the basically the, the reliability folks have been acts removed. So it's impossible to deny the connection as well. That there, of course, it's not a direct one, but again, and each, each one has as a post-mortem, but meta has not had this badge for closer to the decade. Yeah. The fast and break thing. And you put two posters together. And when I told this story at a conference, people came up to me and they said, I'm so glad you talked about this, because my company, the different company often VC funded or publicly traded. Like, say, look, it's happening. People are like whispering to me like, like, we are not met up, but the same thing is happening. Same thing is happening. And you know what they all told me? They told me I thought it's just us. Or I thought it's us and then my buddy who works at the center company. And suddenly it was like, oh, it's all of us. No, it's all of us. Yeah. No, what's the real thing? And the inner-con folks, you know, what I love about them is they publish the real, gnarly stuff, right? They don't color it out. And they showed that for 18 months, reliability and code quality went down and it had just started to possibly be going back up. But it's still not there where it was. And they're very honest about it. And they're honest about it. Finally. So, this is the thing. Like stop like spitting in my, and telling me that, you know, like it's just, this is my thing. It's like, we need to hear the winds. We need to hear what's we need to hear about what's possible. We need to hear what's exciting. But you got to couple it with the costs. You got to couple it with it. Is it worth it? You got to couple it with, what are we doing? What is happening? And I feel like part of the reason that both of these sides are getting so frustrated is because they're not, they're not connecting all. And so the people who are seeing really incredible, there are some really incredible things happening in software right now, like with rewrites and with, you know, that automating away, like real toil and like, not a single person that I've talked to would give it up. Yeah. It's amazing. Like they don't, they don't get so excited. Nobody wants to take it away. But half of the people are seeing the winds. And they're not connecting it to the cost, which makes them think that their co-workers are just fuck nuts who are just like, oh, they're just someone to lose their jobs. They're just afraid of getting automated out of existence. They're just blah, blah, blah, blah. Like no dude, you be on call and then see how you feel, you know, and, and there's a mirror effect happening where the folks who are on call who are responsible for this stuff, they don't actually believe that these winds are real. They think they're all cooked because they're not hearing the quiet parts that out loud that yeah, we're seeing this wind, but this is what it costs. We're still cleaning this up. We're still, and so that, that's my, that's my bag to everyone who loves GearGuy's podcast and listens to this is tell the whole story. Talk about the costs. We're all doing it. We're all in it together. Yeah, because you're right. Like this technology is not going anywhere. It will make really big positive change at a bunch of places. It's here, but it's not magic. It's not magic. And I think this is what you said in, in, in the make AI boring and another great article of yours, which you said is AI is just technology. Just technology. And you were arguing that let's just realize it's technology. It's a tool and let's learn to use it well. Now one other thing you said, which is very interesting, is software will be the killer app with AI, which is very unique. Let's talk a little bit about that. Software is made of logic and language. AI is made of logic and language. And because of that, we can bake in card rails. We can bake in checks. We can bake in validation that we, I don't know how we do that in other parts of our lives or other applications. And so it totally makes sense to me that software is what AI is best at. I mean, you see like in the courts, they're starting to get lawsuits for for the court is suing lawyers who are submitting briefs that have hallucinated crap. And how do you check for that? You know, with the same, we have structured data. We have, you know, a hole. And I just don't know how you account for that in the same way. It might also mean that whatever will work outside of the software industry for AI, it will be a subset of what will work in the second. Basically, if we can do something with AI, if we can automate a process or something, you might be able to do it in other industries, but maybe not. But if we cannot do it, good luck. You will not be able to do it because we have the domain where you can validate stuff. We have, we have incredible training data on code that compiles. Yes. Yes. I can in a place, but you might have like training data like with magazines. You might have like low quality magazines or whatnot. I see what I mean. I mean, back to your point about humans like their determinism. They like things to happen the same way. And it's very interesting because as I think of it, you know, one of my businesses is writing. I write a new setter that is, is I like to think it's good and it's worth reading. It is. And I would have said, if you asked me, what is AI really good at? Now, obviously, it's good at coding, but before that, it was good at writing. It was like, my mind was blown that it can actually control the language. When all the newer models come out, I do this test where I said, all right, like, you know, write an article in the style of the pragmatic engineer. And every single time I can tell it's AI generally because it's repetitive. It has a sink. So my point is AI is actually not as good as writing pros as it's a lot better in writing code way better. When I asked to write code, like, I often I'm like, yeah, this is something I could have written. Whereas when I asked to write words, I'm like, I would have never written this and it has training data on me. So who knows? This is my proof that software is the best fit. I think it is. Software is a simplified version of language for a purpose. Yeah, I, you know, at first, everybody was like trying to come up with ways to be more efficient and write with AI and everything. And I sunk a lot of cycles into that. And I have decided not to sink anymore because writing is thinking on paper. And there's no shortcut for doing that thinking. Anything that I write is not content. You know, it's not content where it's just like, well, generate me a couple thousand, which I'm not shaming anyone who generates content. But that's not what I'm trying to do. I'm trying to think through hard and interesting problems and share them with people. And I don't think AI is the appropriate tool to use for that. I use it for our structure. I'll be like, hey, read this and give me feedback and stuff. But you know, I think we should not forget that as we improve our skills or capability or experience or our thoughts, we do become more valuable. And I have this idea and this might be a flawed idea, but I think it will be correct that five years from now, how will people be hired? Now, of course, we know the tools will be better and all that. But in the end, I think it would be like this. Someone's sitting here and I'm going to be interviewing it. I'm going to be trying to get into your company, honey, probably honeycombe, right? And we will be having a conversation and you will judge me based on how I respond. And the more I have spent thinking and bettering myself, the more valuable I will be to you because you will have all these candidates and some of them will have outsourced all the things to AI and they will have a blank because that thing is off. Guess who you will want to work with, right? I am so excited about leaning into the parts of being human together. I don't like the feeling of chatting all day back and forth between agents and people on Slack. Like it feels way too similar. It's just gross. Honeycombe is a fully distributed company, which was never, we always wanted to have a hybrid model, but the office has not come back. And I feel all kinds of ways about this because I love not leaving the house. But at the same time, I crave this more full. I'm so glad you're here. It's so nice to see you. We're just talking how it is different. We've done a podcast remote and it was a decent one, but this is more enjoyable. Yes. And so part of what I hope we do is just remember that we're in charge of the machines. They serve us. And this is still what matters. I want to pull back to back something different. So to talk a bit more about ops and DevOps and the given of your spicy sakes. So now that we have AI, we can actually just bad melt some of the other thing or just be real. Let's talk about DevOps. Just can we go back a little bit in time? You were there. Why was it created? In the end, there was this massive DevOps moving into 2010s. Do you think it succeeded? Do you think it failed? So before DevOps, we needed a DevOps because there was devs and ops. And there was the proverbial wall that code got thrown over, right? And ops were the people who were in charge of the IT. They deployed, they managed to serve us. They set the Linux version. And crafted Linux. Yeah. You know, plugable storage models and everything. That was always a bad idea because it's split brain. Half of you are writing the code and you have to half our understanding it. I would argue that you can't really understand the code you write unless you're operating it. So, you know, the DevOps movement did a lot of good. Trying to knit back together that sort of original original sin. And you know, around the time that I was a citizen, there was this big push, "All right, ops, people, learn to code." And great, I'm glad that happened. Everyone who works with computers should be writing code. I feel like the wave after that is a little less successful, which is like, okay, software engineers, time to learn to understand your code in production. But I also think that in my mind, 20 years of DevOps was really about one thing, trying to create one feedback loop that connected people writing code to that code in production. And it failed. I mean, it failed to this day. Like, they're done by, they're two different domains, you know, there are some people who, I mean, yes. - And I'll show you this diagram that you drew. We now added agents, we'll put it on the software as you can see it. That this is your, I think it's a really nice draw-up of how there's no feedback loop. Like the office people, or oftentimes we call a platform teams, they manage the infrared error and engineers deploy there. - I'm so stupid. I think that's actually good and finding healthy. I think that there are separation of concerns where you can't expect anyone to do everything. And the nice separation of concern is, do I own am I responsible for the stability of the things that you put code on, or am I responsible for the code that I put on the thing? Right? That is a nice scene because you want the infrastructure to be stable, like to protect itself, to be resilient and all these things. And you want your code, like to be oriented towards, is every single user had a good experience. You can have one of those things be true and that other not be true, like they are decoupleable. - And actually this is like even the most modern companies, I often refer to a anthropic as this company which operates in a very different way to most companies. They're very successful despite doing a lot of different things. However, internally, they have platform teams. They had a cloud platform teams and then they have applied AI, who, which is more of the kind of the feature teams, the integration, and the two, I talked to both of them, they just have a very different outlook. They have a very different view on even basic stuff, like will software engineers be obsolete? The people on the platform team were like, "No, we're working really hard." And on the apply, they were like, "Well, maybe we'll have to." - That just not surprised me when Tony Biodet. - But so this company, anthropic, that starts with a blind page, they arrived at the same place. - Yeah, yeah. No, I think it's the right separation of concern and I'm not trying to erase it, but I think that to be a good engineer, you need fast feedback loops. And this is part and parcel with the whole, "Oh, the source of truth is the code." If that's where you live, if you live in the land of how it should theoretically work, no, and I think that with agents, they're breaking that, right? They're breaking that and they're forcing another thing on the observability trip is a lot of people, if you say like what is observability, they'll be like, "Ah, well, there's three pillars, there's metrics logs and traces." We talked about this last time. - Metrics and logs, I would say, are system exhaust. They're the exhaust pipes there. And they're never going away because every team runs a ton of third party software. They didn't write it, they don't own it. They just have to run it and it's outputting shitch. - Yeah, and you want to-- - And you just got to put it somewhere. - You observe it, you see what-- - Yeah, yeah, yeah, yeah, you do stuff with it. - Yeah, and you know, you should put it somewhere cheap. There's a ton of it. It's not super high value, but you definitely need it, right? And you can't do anything about it. You just take it and put it somewhere. Then there's your code. There's your crown jewels. The code that makes you a company. And for that code, your telemetry should be a product decision. It should be, you store it once with all the connective tissue because the value of rich data goes up, not linearly, not even exponentially, combinatorially. If you have a wide event or a trace with 29 bits of data and you add a 30th, that 30th is more valuable than all the others. Well, it is just so powerful. And with non-deterministic software, you know right up front, you can't predict what it's going to do. You have to, like, that is a product decision to capture that trace. So it's less specifically about modern observability and like companies that are, you know, like either building AI, really a code or just complicated code that they're generating. In the old world again, like I'm just being, you know, like, observably one-on-one back in a day, the way I would have written the code is you write the code and you think like, hmm, something funny might be going on here. Let me do a log or an info or a warn. And then I would also try to maybe if we're printing some production. I realize like, okay, well, I guess it's crashing and we don't have any logs there. So I guess it's some other part. Let me do a tool that will like log everything and now have a bunch of stuff. Now this is the old, the simplest way of thinking. In kind of a modern business where I'm like, I know this is high-value stuff. What are ways that I can go about that section maybe a bit like more practical than, 'cause I just will use super basic one. Auto-instrumentation, it has gotten so good in recent years. If you're using open telemetry and everyone should be using open telemetry, yeah. All of the common patterns, like all of the models are trained on them. So it is literally faster and easier to build with instrumentation than not to. And with instrumentation, do you just once I have the code in a compile step or an extra step, it just adds it to the right lines? This is what's important. Like, this is part of just developer intent, right? This is how you declare your intent and that's how you check up on your intent in production. It's honestly gotten so much simpler. I don't fault developers or anyone else for not kind of closing that loop with DevOps because the fact is it was prohibitively hard and time consuming and difficult because your old school software engineer and you sit down, write some code, you're like, "Ah, here I should instrument it and look at it in production." So you're like, "Okay, we've got a bit of data and I want to do something with it." All right, is it a metric log trace, an exception, an error profiling? You know, just like, "Okay, if it's a metric, is it a counter, is it a gauge, is it. " You know, just like, "Oh, down, it takes. " And then, well, what type of data is it? Is it gonna have high cardinality? Is it gonna be a. - Hard nowadays. - You know, just like, and you can blow it, it's just like, if it's a log level, do I append it to it? Like, it's just, you could double, triple, quadruple the amount of time that you spent writing the code, trying to instrument and then, still wouldn't be dying, like you deploy it and then it's like, "Okay, I know the name of the thing that I added, but how do I find it? How do I display it? How do I create a dashboard?" It's just like, that was prohibitively, that was really hard. But now, we can bring all of this to you right in your development environment. It is easier and faster to instrument with telemetry without it. And you don't have to leave your development environment to go and get it, you know? You could have the agent, like we've built some really cool shit, "Ah, you come, where it'll just, it'll be like, "Oh, hey, that thing that you wrote, you know, maybe you want to look at this." And you can control how, both it is, you can, you know, but it's right there, and that's how it should be. It should be part of your development loop. - Do we talk about what spans are? Because I'll quote Eric Riddock, who was in rural LinkedIn, the basic idea of a thoroughly, for applications is don't use logs or metrics, just put it all in spans. What are spans? - Spans are bits of a trace. I mean, a trace is just structured log with some fancy fields, right? And so the span is the subset of the trace that makes up the entire duration. And, I don't know if you've followed me this, but like the default building block has been the transaction for as long as the web has been around. - Yeah. - That doesn't work anymore. - With specifically with the AI. - Yeah, we just, we just ship something called timeline. That is like, it sits on top of spans. So, you know, if you, you know, if you run something like Intercom, you've got a chat thing and a customer's like, I'm coming. - Conversation going on. - Yeah, customers like, I'm complaining. You're like, okay, so you've spent up an agent, supervisor agent that spends up more agents and each of them calls APIs, each of them calls like storage back ends and stuff. And then the customer asks another, I could span hours, right? And you need to be able to do that and visualize the whole thing. It's super cool. - And so this is a new primitive that you came up for, these use cases where there's a conversation or like an element is involved and you have like this. - It's like a meta trace. - Okay, yeah. So I guess this-- - A trace of traces. - So we need these new building blocks, actually, just be able to work with. - Yeah. - Interesting. So I guess this is something to keep in mind. Like any engineer who's like building on top of LL, who is an AI engineer now? As we know. - It's either that or you've just got all these tabs open with traces, you're just copy pasting IDs from one to the next. - Yeah. Or if you're a large enough company, you might have built a whole new-- - Or you might have built your own and that's true, but we know that's doable, but it's painful. - It's doable, it's painful. I'm really looking forward to seeing over the next few months or whatever, just the marriage of tests and telemetry perspectives. - With agents and AI agents being around, a lot of them are now very useful to connect to observably stores. You can go and do stuff. However, one question that comes up is, well, agents have a finite context window. and observability, you can really easily overload that. What are approaches you've seen of agents either using honeycombs or some other data sources to make them productive? Have you seen some patterns? There's a lot of trash data out there. And a lot of traditional telemetry data, metrics, log traces, with all, it tends to fill up your context window with crap when the most important part of the data is again the relationships between the data. So if you can, in fact, one of the AI SRE startups posted this great piece a couple of months ago about how they see the agents that they deploy in the wild bypass the observability data most of the time and they go upstream to find richer intact telemetry data. So that's what I would say. Either you gave your agents, but it's the relationships that matter, right? Because that's what actually helps the AI make decisions. And what is observability? I cannot not mention your book observability engineering and you have a second edition. Can you tell me why you felt the need to write it and what's new in it? Oh, man. The whole thing is new. So, rarely any time a book is considered successful and if the topic is still relevant, they'll ask if you want to write a second edition. So that's not really. But I was really excited to write it. The first book I don't want to say I wasn't proud of it. You're like your children and your books are not supposed to like say anything bad about them, you know, because it's fine. But it was written 2019 to 2021. The definition of observability meant one thing when we started and another by the time we ended and there was at no point where I was like, oh, this book is great. Look, ship it. It was just like, oh, God, I can't do this anymore. Just like to at least take it and I hope that's enough. Now it feels like the definition of observability is more settled. It's everything else in the world that's like changing and crazy and also I think it's a good book. I hope it can help a bunch of folks. It's got six parts. So the first part is an I wrote parts one and six. First part is just kind of like grappling with what does it mean to run deterministic systems, you know, and then, you know, my co-authors, Liz and Austin and George, the part two and three is how do you instrument your code and how do you understand it? And there are parallel tracks for doing this with or without AI and a couple of great guest columns from from Jeremy and then parts four and five are we have a whole lineup of guest authors and use cases and deep dives. Hanson, who did one on front end and interesting mobile. We've got some great ones on CICD. Clickhouse did one on column or storage. Some really, really stellar things. There's a chapter from Keshe at Fin on how they use it iteratively to like do observability. Oh, so this is a brand new book. It's not a lot of second editions are like, oh, we added like you know, two chapters. This is an entire rewrite and it's twice as long. The first one was 250 pages. This one is 600 pages. Okay. So I'm interested now. I'm going to get this book. And the part six, it's my baby, and it was originally supposed to be three chapters for observability engineering teams. And it turned into it's a third of the book. It's 200 pages, but it's it's topics for observability governance for leaders. And it starts with an open letter to CTOs telling them why all their big aigles are blocked behind our ability to make sense of their system. And then we talk about you know, software delivery for no buzzwords. And you just systems theory. Right. Just if you like Danella Danella, uh, not of the stuff then you would like it. And then and then stuff. And then there's a chapter on how to quantify the impact of observability for your finance. How to treat observability as an investment versus a cost center. And when you should use observability as a cost center and when you should treat it like an investment. Because it inherits the type of software that you're observing, you know. And there's a great guest chapter from Rick Clark on staff plus principal distinguished engineers. We're trying to drive massive change without authority. How do you do that? And how is observability vital to that? And then there's a chapter on build versus buy versus open source. And it sounds to me that anyone who is inside or wants to be inside a platform engineering team, may you be an engineer or a leader. You're in charge of you probably want to read this book. And at the end, there's a chapter that is possibly one of my favorites that which is it's called the art and science of vendor partnerships. And it's just talking about how we can't build all the software that we need. And great vendor partnerships are ones where you have influence over their roadmap and they trust you to do these things. And like talking about how most transformations fail. The ones that succeed succeed because someone on the inside has trust and credibility. People believe when you say something, it is true. You know, it cuts through bureaucracy like a hot knife through better. When it comes to partnering with, you know, the sales work of another company, you do not have trust and credibility. You work to build trust through reciprocity. You learn just how much you can trust them over time, right? But the best vendor relationships are the ones where you generally you feel like their successes or your successes, your successes are their successes. You're happy to see each other because each of you are delighted because you know you're getting something from it. It feels like you are two different teams working at the same big company. That is rare. Doesn't usually happen. And that's fine. Most vendor relationships are ones where you shake hands, you exchange money and services and that's fine. But I think in an era of AI, these are durable skills. These are durable skills for very senior engineers who care about impact. Senior engineers and also engineering leaders and anyone who wants to become an engineering leader because I guess like I mean, both of us have been in an engineering leadership, like you've been in much higher positions than I have, but I think it's fair to say that the way for you to get to that CTO role, that head of engineering, that director of engineering is to do the work for six or eight or six months a year to year and a half and to do so you need to know these things. I feel of zero engineering, I'd be underselling this book. I'll be honest, the title, but I'm also going to get it and I'm probably thinking of ways to share a bit more, but thank you for writing and thank you all to your co-authors. But speaking of leadership, I'd love to talk about a little bit of engineering leadership because there's a lot of things that are changing. But I love one of your very recent takes on leadership and I'm going to quote you. The most effective leaders are kind caring humans and skilled business operators. The second most effective leaders are terrible humans and skilled business operators and after that comes anyone else. They're apparently good kind humans who are sloppy operators and bad at business because being good at business is very hard and you said this in relation to what happened at Twitter/X referring to as Elon as a terrible human, but a skilled business operator. I don't know that I would call on the skilled business operator, but my point was that Twitter had 16 years to figure it out and everyone could see that they were not figuring it out and whatever else he was figuring out the business specifically. Being without the business, building products, reaching folks and you could argue that X has gotten better or worse, but you can't argue that he is running it with 20% as many people. Yep, and it's working. And it's working. And some of that, you know, 30 engineers on the core product and another 30 and the like 60 engineers, there were 1700 before. You know, and you could argue and I think it would be true that it's some of the work that those engineers did that a lot. But like this is the point, if we don't do it ourselves, meaning hold ourselves to a high standard, build with efficiency, constantly be like trying to get better, we don't do it ourselves. Someone will come and do it to us. And this is what what you also said you close saying, if we want to remain in leadership, if we want to set the culture and the tone and take the ethical sense that we believe in, we first have to win at the business. I think this is like, especially now that there's so many changes happening in technology changes, there's real world wins, business will go up and down, I guess it'll remind her that like you want to keep your eyes on the prize, which is especially if you're a leader. The 2010s, there was so much money sloshing around in Silicon Valley and time started to get tough and all of these companies canceled their DEI programs, blah, blah, blah. Yeah, they never believed in that. They were just trying to buy people off. You know, and that is very telling to me. And I have taken a lot of lessons away from that, which is just that it's not enough to be a good person. I believe that people who are kind and care about people can and usually do do better than social pass in the same roles. But only if they're good at business learn the business, say close to it. You got to. With AI, now that coding has become cheap. Now that engineers are running agents. How do you see the role of good skilled engineering managers and engineering directors change? What has changed? Well, the first thing that's changed is I think everyone has to get to be hands on specifically to generate some code to ship to production to some extent. You should know what it feels like to submit a death to get a PR through. You should know what it feels like. It's just easier now than it's ever been to pick it back up to fill in the blanks. It's always been the case that leaders were better if they had a hand in it. Now it just does no excuse. Not too. Teams are getting smaller. In general, I think this should be a good thing. If we can figure out how to own more surface area, it should be a good thing. I worry that the way it's happening is being done by CEOs who are like, "Oh, well, this other company is doing it or it's magic or we're going to do layoffs." I really dislike the anti-management term. So no argument that power tends to drift towards managers over time and needs to get pushed back into years. No argument. There's a tendency to have too many managers. The bureaucracy generates a sort of, "It's easier to say yes than it is to say no." These things happen so they need to be pushed back from time to time. I believe that middle management is deeply essential. I look forward to seeing how that works out for them, not having any bit. The role of a manager in middle management, in my view, is sense-making and context-giving. I don't believe in a world where engineers are just given tasks. Here's your jury. Go do the things. "Hey, I can do that." I want people who understand what we're trying to do, understand how we're trying to do it, or who are there to help us figure out how we're going to do it. You can't engage emotionally creatively, collaboratively without understanding. The understanding is incredibly difficult to build, and it's fragile and it will last very long. For those of us listening to our middle managers, it's been a tough few years. Because what they're seeing is there's a push to have fewer of them. A lot of their colleagues, if they're on lucky places, they were made redundant, and on many of them have struggled to get similar positions. We're talking director positions. We're talking head of engineering, senior-engine manager. That role is disappearing faster than ever. I think directors might still be there. For folks who are in this position, and they do like middle management, they do believe they're good at it. What do you think tactics could be to give them a bit more career options? Tactically, I would say go back to BNIC for a while, even if you know it's not what you want to do. If you're at all capable, if you're not capable, then I would try to work. You've got to get AI in your resume. You just have to. And this is a huge career risk. If you're working somewhere where you're not getting these skills, that is a massive risk. I would do whatever I could. This is very interesting that you're saying get AI in your career. Because I remember about a year, a year and a half ago, I started to pay attention to like, okay, this is happening. I remember a year ago, we were article about how to become an AI engineer. I talk with engineers who just like at their workplace, started to do AI and now they're AI engineers. Next thing I'm hearing right now is people who have like two to three years of AI engineering experience are so in demand. I'm doing a research on a job market and they're like, this is the best job market ever. However, the people who are like, okay, I have none, but I want to get it. They, and let's say they're out of the job, they're struggling because no one's giving them the benefit of a doubt. It is really hard and I'm not saying it's right, but it's how it is. And I guess the reason we're bringing this alarm bell is we know this change has not been asked fast. So do it now because later now the next time you go out for a job interview, anyone, you're going to be asked and you're going to be filtered out if you don't have it. And and the dealt between those who are just getting started, most of them doing it. It was here for a little while. It was very easy to get started. No, it's here. And it's, but it's opening the longer it goes, the more the harder it will be to catch it. You just got to get it. So let's talk about directors. Yeah, directors are usually the ones who they have been in management for like 10 years, usually. And there's a real feeling of fear often of like God tech has changed a lot in 10 years. And and this is where I would say your body, like the way we experience anxiety and the way we experience excitement is physiologically almost the same. Like I used to play piano, right? And before a performance, I'd be like, I'm excited. I am so excited to do this, you know, because I'm like trembling and but like the difference is agency. If you sit back and wait for the water to come to you, you're just going to be freaking out. But if you run towards the waves, if you're like just like run towards a try, you know, if you have a job now and you're a director and you're afraid of it, it's always seen as kind of noble when managers want to go back to being ICs, I think it's very well respected. Own it. Run towards the waves. Own it. Be part of the wave, the frontier of people who are like, I'm so excited to be a IC again. It's never been easier to go back and try. I'm going to do it and I'm going to talk about my experience and tell everyone else about just you got to own it. Don't wait. And then let's talk about junior engineers. Obviously it's a harder time to guess her as a junior, but how do you think about the value that they bring? The hardest thing about quantifying the value of junior engineers is that we don't know how to quantify the value of any engineer. It's all vibes. You know, it's so interesting because I feel like we're over here doing all this hand ringing about will juniors be okay? Will they ever learn the basics? But like my friend Boris who has a new observability startup and he talks to these high school college kids all the time, he's like, they are cooking. They are. They don't know what the software development life cycle is, but they are just like, they are doing so much cool shit. I believe that the kids are going to be okay. We just have to hire them. We just have to give them a shot. They're going to come up with a lot of the conclusions and the ways and the hows that are going to be things that we wouldn't have thought of because, but we just have to hire them. We just have to be willing to give them a shot. I did this week in SF, I've talked with a bunch of founders, young startups. And they've been telling me the stories of this open source contributor who was outstanding. So they want to hire him or her. Turns out it was a 17 year old kid. They still hired and now they tell me like, oh my gosh, the things they do. So I think when you're saying the kids kids are going to be fine, just give them a chat and give them a shot. Even if it's an internship. Yes. Totally. I feel more accomplished because internship is a little risk, little duration. And even if that person doesn't work out with an internship under a deal belt, yeah, so much better. Totally. Totally. One question that came up when I asked that you're going to be in the show when I should ask. They said, AI fatigue like someone with someone else, can you please ask charity as an engineer if I'm starting to get just really, really drained of this? Have you had this? Do you see people having it? And what is a good way to just deal with it? We know it's here. We know it's here to stay, but it's still. I mean, my follow-up question would be like, which variety of AI fatigue? Okay, tell us some other varieties. You know, because for some people when they say AI fatigue, they're talking about receiving slop. Some people are talking about all the hype and the, who have you heard the phrase or the term doom trolling? No. But, Cal Newport, I think his name, he's a computer, he's an AI researcher, professor on the East Coast. And it's his term for what the CEO of Anthropic and Open AI keep doing about, oh my God, this might be the end of blah, blah, blah. And he's like, it's just doom trolling and they shouldn't, they need to stop it because they're stressing everyone the fuck out. And stop because it's just not responsible. You know, so like, yeah, I think there's a lot of fatigue around that. I think that a lot of people, their family members are afraid, you know, it's just, it's always before the history of technology. It's been something cool or fun or this will make the iPhone, it'll make your life better. And now it's just like, beer. It's pretty crappy. So there's that, there's, there's the fatigue of like, I found myself being off social media because I'm just so tired of all of the AI slop post. It's just like I'm not interested. There are a lot of different varieties here and yes, we are all feeling it. So I guess I would repeat my call for us to remember that we are in control. We are in charge. I think the universal nature of the frustration means that this is a great time to propose experiments where we take back control. Maybe you and your team agree we don't actually want anymore AI generated PR descriptions. We don't, we don't, none of us use AI on Wednesdays. Maybe we take a week, you know, just like take control back, try something, propose something. I guess because changes so big, experimenting has never been easier and I guess most businesses, most directors, most leaders will welcome team saying, you know, we're going to try out because their answer will probably be, I mean, you're in this position. Your answer, I guess, will be sure. Better yet, don't even tell me, come and tell me what worked afterwards. Yeah. And what didn't? And then other teams can learn from that, right? I think sometimes people are waiting for top down permission, but like we don't know what permission to give it until it works so much better when it's bottoms up when people are just trying to take control of your time and your calendar. And I guess maybe we just forgot that. that there have been major changes in the industry. I remember the iPhone change. And I remember the people when the iPhone came out, iPhone and Android's smartphones. The people who were the most kick ass iOS engineers, you know who they were. They were typically like 18 or 19 year old kids who went into this and they tried it out. Guess what, two years later, they were the domain experts. The staff engineer was a 22 year old and then the entry level engineer was a 40 year old and again, not always. But my point is, when there's such a big change, you can actually become an expert by-- Very little time. By you taking-- Just taking charge? Taking charge. And also, no one's really going to tell you no because no one knows what's working. Exactly. There's some liberty there. So as closing just to go back to a little bit of being human and slowing down, what are one or two books that gave you something? Ooh. I really got a lot out of catastrophe ethics. I haven't seen it mentioned many places. And I think it might be, I think, will philosophy nerds would be like, ugh, that's kind of a pop book. And I think that people who are not real philosophy books are like, that's kind of a lot of philosophy. But you know, he's a bioethicist, I think. Travis Reader, our IDE, or catastrophe ethics. And he talks about how the puzzle of modern life was that it feels like everything were implicated. Every choice we made-- are you going to use milk? Well, the cows were tortured. Are you going to use almond milk? Well, water is a problem. Well, you swim like a little horror mode. And it's just like there is no-- whatever you do, you are hurting someone. And it feels like the problems are so large that none of our decisions really matter. And that tension, like what-- and then he kind of walks through traditional ethical frameworks like utilitarianism and stuff. And it just shows how there is no recipe. Anyone can follow. That doesn't lead you to some really stupid-- and he's like, this is just no God's no masters. We are-- which doesn't mean that everything's relative. Doesn't mean what it means is that the way to live an ethical life of integrity is you need to educate yourself about the world. You need to know things, right? And then listen inside. Where are you drawn? What suffering really speaks to you? What caused you-- because no one can tell you what matters. You have to decide what matters. And so that introspection and-- it's so at odds with the sort of performative rage, which I'm just so exhausted. All right, so that's one. Number two, this is a book that I've recommended a couple times when I'm just going to keep recommending it, because it's so good. It's by Adam Becker, and it's called "More Everything Forever." And he is a journalist based in San Francisco. He has a philosophy undergrad and a PhD in astrophysics. And he just demolishes all of the AI religion, but singularity and the effect of altruism and accelerationism and the whole-- what if we could have infinite growth forever, and he's like the heat death of the universe. You guys literally the only thing we know about, and about exponential growth is that it must end. It must end, and it's scurver and a crash. It must end. And he's got this dry sense of humor. And there are a couple times where he's just describing some of the very real thing. It's just like, why do Oxford ethicists want this? He's talking about taking over star systems. It's just ridiculous. And he also-- he gets in a whack. He just talks about these people who were working so hard on life extension. And he's like-- these are a bunch of sad little boys who miss their daddy. And I was just like, oh my god. It is the oldest fear of humanity. It's a fear of death. And you just see it. You can't not see it. So yeah, those are my two. They're both so good. You're a charity. Thank you so much. This is finally what happened. Finally, it's a good time. I always really, really enjoy talking with charity. I hope you also liked it. I appreciate it how charity talks about the trust account. If we are debiting trust from the creation of code because AI wrote it and no human read it, then that trust needs to be refilled somewhere else. Testing evils and guardrails are always to add more trust that we lost by using AI. I also appreciate it how she talked with empathy about both AI camps. The enthusiasts or AI-pelled folks are seeing the practical wins, while those operating production systems see the slop. Nearsight is wrong, but they should talk to each other more. So if you see wins with AI, share with the broader team, but also talk about it when it creates more work, reduces reliability, or when it degrades quality. And for those of us feeling anxious about all of this change, especially the directors and managers, I'll leave you with charity's advice. Anxiety and excitement are psychologically almost the same, but a difference between them is agency. So instead of waiting for change to come to you, take charge however you can and make changes yourself. Do check out the show in the below for related to the private Icongerial deep dives on how AI is changing software engineering and for another discussion with charity on observability. And I can very much recommend her book Observably Engineering Second Edition. If you enjoyed this podcast, please do subscribe on your favorite podcast platform and on YouTube. Special thank you if you also leave a rating on the show. Thanks and see you in the next one.