Stop being skeptical about AI for development with Charity Majors

21:04And then on top of this, you were like, no, like you should actually just not even have a user acceptance testing environment at UAT. You should just deploy production, like and test in production, right?

21:13Charity Majors:As soon as you merge, it should be going out. You should have to stop the train to make your code and not go into production as soon as you've merged. Absolutely. And one more interesting thing is, is you had a long train of thought about like AI and what it could be, is one thing you said is our brains are not built for validation. Almost everyone I talked to, including Andres Heisberg, 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?

21:48How do we build nicer tools? Uber has built amazing tools to try to surface important code reviews. But everyone is pushing, like, all right, let's do more code review. As an engineer, I'll be honest. 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.

22:06Charity Majors: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. I'm just being honest here. Do you care? I don't. I've never. So one of the problems is that I think code review means so many things to so many people in so many places. And so 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 want to talk to your coworkers, you don't want to mentor juniors, which is not true. We've just bundled so many things into this, like, you know, it's like hugely overloaded.

22:41Charity Majors: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 Pohl, who I worked with at Parse, and he's now working at GitHub on Pohl Request. Amazing. Love the Parse Mafia. Yeah, exactly. He's like, to me, the code review is when we decide, do we want this in our product or not? I'm like, well, that is a, that's a great, 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?

23:21Charity Majors:You know, but, like, the code is not necessarily a great artifact for all of those. So should we be talking to people? Uh, yes. Is the code review the right form factor? Maybe. But I think that the emotional reaction that so many people are getting to it, like, the validation in my book is at the very bottom of the list. I'd like to, like, stay here a bit more. can you break out the parts because it feels to me code review is 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 and yeah maybe we just need to throw it away 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 whatever and you know is this this API design, you know, those are great conversations.

24:13Charity Majors: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. It's like a great use of anyone's. It feels the only time where it's useful is if someone joins a team and initially it can be a little bit of feedback, back. Yeah. Especially when there's like nothing is written down. There's no guidance. There's no linting rules that would give you that. Well, see, that's again, yes, we can fill in the cracks if we haven't built the guardrails.

24:48Charity Majors:We can fill in the cracks all kinds of ways 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, Fin, their engineering org and have been forever. Like I noticed their CTO a decade ago 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 a trivial thing to do, right? So they're kind of the high watermark from my mind right now for teams that were founded pre-AI, have a lot of engineering discipline who have become AI native.

25:39Charity Majors: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 want to go? So is this the right path? You've also written that non-deterministic systems require more entering discipline, not less. So what is the thing about these non-deterministic systems?

26:14We're specifically AI, right? We're talking about AI. 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?

26:28Charity Majors:Well, I mean, tests and evals for one thing, right? Like, if we're treating the code like a trusted artifact and we're, you know, trying to predict everything with our human brains and everything, then we're writing the test that we can predict that it might break, you know. And then anytime the system breaks, we, like, try and write a test for that. But that's not an especially high bar. And so I think the sort of the behavioral tests or the, I don't remember where it starts with C, but the QA folks have these suite of tests where it captures. There's also smoke tests. Yeah, there's so many. There can be like performance tests.

27:07There can be low tests. There can be, yeah, there can be like just kind of fuss testing as well.

27:13Charity Majors:Something that's like, okay, if I'm not going to 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 performance. Is it just not changing too much? And so I think we're going to need, the trust has to go somewhere, right? If you're debiting from this trust account and the creation of the code, it has to get built up somewhere else. And I feel 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.

27:56Charity Majors: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 carved pathways and places where we kind of corral 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 safe same for neural nets but that was most of us software engineers didn't really touch too much of it because it just wasn't that useful for us but we've been used to that when we programmed it you know it just happened the same way unit tests were easy because you just run them once you don't run them twice because 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 they want software that works the same way we 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 engineer just like and and you can run it locally open source you can use a local model.

29:20I 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 points to 99 points. And typically most companies have 85 set as the bar. And you're like, hang on. So we've turned what they were advertising as a tool to help your recruitment. We just proved that it's just a coin flip. That's bad.

29:43Charity Majors: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 every company is going through this in microcosm. And something I was saying to folks just earlier today, we've been doing this series of conversations on our AI norms and 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, no, we didn't know enough. We've gone on such a journey over the past year, and we know so much more now, that like if one of my co-workers is like, AI is the wrong tool for this job. I'm like, I trust you.

30:19Charity Majors:You know, you got to get worse before you can get better. So tell me about where you are right now with your, how inside of Honeycomb, how you're thinking about AI, how you're thinking about how to think about AI and then what values you came up with that works right now for you. Yeah, it starts with 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, the you know the floor gone up that is a great question maybe yes maybe yeah i don't know we're we're definitely in a sort of wandering in the wilderness phase um 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?

31:10Charity Majors:What does this good look like? Another thing we point out is just there is no human in the loop. You own 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 own it. Charity just talks about owning the loop. Owning the loop also means controlling what every agent inside of that loop is allowed to do, which brings us to our season sponsor, WorkOS. Today, agents are increasingly able to act on their own and the old auth model was never designed for that. Who is this agent?

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33:28As you're entering the context you give to your agents, think about how you'll verify what they hand back. If your system is buckling under the increased volume, head to buildkite.com slash pragmatic. 30-day all-access trial, no credit card, and an actual human engineer on standby. His name's Ola, and he's very helpful. And with this, let's get back to charity and communication norms with AI.

33:47Charity Majors: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 sloth. I just don't even read it anymore. As soon as I can tell. As soon as I recognize this might have been AI. It's like trash. Here's a baseline. You cannot send anyone something you haven't 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.

34:25Charity Majors:And if I'm giving you something that I don't even know what's in it, and I, and I'm putting it on you, it costs you instead of me, that is not good. I also think that even before that, it's like, 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. Ooh, I shouldn't do that. Or if I'm giving someone something that I kind of generated and I'm like, ooh, 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 You put in a little bit of work and you start to respect their time as well.

35:16And 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. 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. Like we have all of these, all of these, like a bunch of people have a bunch of agents doing. But the point is 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 AI for this or that.

35:45Use it as much as you want or make yourself more efficient. Just don't degrade because it really degrades those personal skills, right?

35:52Charity Majors: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 most of those use cases have their place. But when it comes to your core job function, we primarily want the second one, right? Right. And especially if you're involving someone else and you're asking them to review or, you know, and this is not absolutist. Like there are people who English is a second language and they use it. People who like neurodivergent and that is, again, that is still being respectful. You know, it's so it's not like like you said, it's not no way I.

36:27Charity Majors:But it's like make 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 this cool thing can do? And I think we're all just like so over it. The reason I really respect that you came from the cis, you know, the cis dev background. You also you're very involved in SRE. these are all folks who have been pretty skeptical of AI. And you mentioned how you're seeing two camps, two very clear camps.

37:06There's like kind of the AI pills folks who get it, and then the people who seem to like they just hate AI. And 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 maybe where you see some of these camps forming?

37:26Charity Majors:See, the problem is that neither side is making it up. Like they are seeing really scary trends. They're grappling with real hard problems that are getting worse. You know, and on the enthusiast side, it's like they're acutely conscious that 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 false equivalents because while there are elements of this that are true, I think every company is more one or more the other.

38:10Charity Majors:But like, they're not wrong. They're not wrong. We've never seen technological change this fast. We're on the inside of an exponential curve, which is very rare and it never usually lasts that long. But it's still happening, you know? Things that are happening that shock us and we would be wise to prepare for them. So like, that's real. That's real. And these folks are usually at most companies, usually they are the small minority and they are constantly filling out. One of the things that's 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 and this often starts 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.

39:07Charity Majors:They're seeing slop. They're seeing all their hard work just dissolve. And they don't see any end in sight. So just to be clear, we're seeing that the people who are on call for a lot of these systems, they're seeing more incidents. They're seeing carelessness being caused by it. They're actually seeing that since that group started to use more AI, our systems are getting way worse. Way worse. Yeah. And that's very real. I'm not making it up. No, no, no. Actually, I was just talking to someone inside of Meta. There's been this big drama where people have been reassigned. Oh, God, I know. I saw your post.

39:38So not just my post since, and I haven't written about this since, and I'm not sure when this podcast came out, I might have not talked about it, is inside of Meta, they track SEVZeros, which is the highest severity. Oh, I know. I remember. You remember SEVZeros. There has been a flurry of SEVZeros, 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 just so it happens, it's happening inside of Instagram. It's happening inside of WhatsApp where the trust and safety, basically the reliability folks have been axed, removed.

40:19So it's impossible to deny the connection as well. Of course, it's not a direct one, but again, and each one has it as a postmortem. but Meta has not had this badge for closer to a decade. Yeah.

40:33Charity Majors:Move fast and break things. And you put two plus two 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, different company, often VC funded or publicly traded, like, the same thing is happening. People are like whispering to me, like, we are not Meta, 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 this other company and suddenly it's like, oh, it's all of us. No, it's all of us.

41:03Charity Majors:Yeah. No, it's a real thing. And the intercom folks, you know, what I love about them is they publish the real gnarly stuff, right? They don't color it out. 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. Still not there where it was. And they're very honest about it. And they're honest about it. Finally. So fun. 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 wins.

41:46Charity Majors: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 both of these sides are getting so frustrated is because they're not those they're not connecting at 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 automating away like real toil and like not a single person that I've talked to would give it up.

42:23Charity Majors:Yeah. It's amazing. Like they don't, they get so excited. Nobody wants to take it away, but half of the people are seeing the wins and they're not connecting it to the cost, which makes them think that, that their coworkers are just fuck nuts who are just like, Oh, they just don't want to lose their jobs. They're just afraid of getting automated out of existence. They're just blah, 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 kind of 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 part said 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 beg to everyone who loves gear guys podcast and listens to this is tell the whole story Talk about the costs.

43:22Charity Majors:We're all in it together. Yeah, because you're right. This technology is not going anywhere. It will make a 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 Make AI Boring Again, another great article of yours. What 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.

43:59Charity Majors:Software is made of logic and language. AI is made of logic and language. And because of that, we can bake in guardrails. 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, the court is suing lawyers who are submitting briefs that have hallucinated crap in them. How do you check for that? You know, with the same, we have structured data. We have, you know, a whole, and I just don't know how you account for that in the same way.

44:44It 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 incredible training data on code that compiles, right?

45:11Charity Majors:Yes, yes. Like in a bunch of places, you might have like training data like with magazines. You might have like low quality magazines or whatnot. Do you 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 read a newsletter that 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.

45:45When all the newer models come out, I do this test where I say like, 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 generated because it's repetitive. It has this thing. So my point is, AI is actually not as good as writing prose as it's a lot better in writing code.

46:04Charity Majors:Way better at writing. When I ask to write code, I often I'm like, yeah, this is something I could have written. Whereas when I asked it to write words, I'm like, I would have never written this. And it has training data on me. So who knows? This might prove that software is the best fit. I think it is. Software is a simplified version of language for a purpose. Because, yeah, 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 think anymore because writing is thinking on paper.

46:38Charity Majors:And there's no shortcut for doing that thinking. Anything that I write, it's not content. You know, it's not content where it's just like, well, generate me a couple thousand words. 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 structure. I'll be like, hey, read this and give me feedback and stuff. But not to write. So I think we should not forget that as we improve our skills, our capability, our experience, our thoughts, we do become more valuable.

47:18And 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'll be like this. Someone's sitting here, and I'm going to be interviewing with you. I'm going to be trying to get into your company, probably Honeycomb, 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 or other things to AI and they will have a blank because that thing is off.

47:56Guess who you will want to work with, right?

47:58Charity Majors: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. Honeycomb 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, like, I'm so glad you're here. It's so nice to see you. We were just talking how it is different. We've done a podcast remote and it was a decent one, but this is more enjoyable.

48:40Charity Majors: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. Now, I want to pull back to something different. I'll just talk a bit more about ops and DevOps and get one of your spicy steaks. So now that we have AI, we can actually just badmouth 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? And in the end, there was this massive DevOps movement in the 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?

49:25And ops were the people who were in charge of the IT. They deployed, they managed the servers, they set the Linux version. And crafted Linux, you know, pluggable storage models and everything.

49:37Charity Majors:That was always a bad idea because it's split brain. Half of you are writing the code and the other half are 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 sin. And, you know, around the time that I was a sysadmin, 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 was a little less successful, which is like, okay, software engineers, time to learn to understand your code in production.

50:16Charity Majors: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 two different domains. You know, there are some people who, I mean... And I'll show you this diagram that you drew. We're now at an agency. We'll put it on the, so viewers can see it. 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 it platform teams, they manage the infralayer.

50:59Engineers deploy there.

51:00Charity Majors:And so to be clear, I think that's actually good in finding healthy. I think that there are separations 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 seam because you want the infrastructure to be stable be 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 the other not be true.

51:38Charity Majors:Like they are decoupleable. And actually, this is like even the most modern companies. I often refer to Antropic 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 have the cloud platform teams. And then they have applied AI, which is more 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.

52:12And on the apply, they're like, well, maybe it will happen.

52:15Charity Majors:That does not surprise me one tiny biota. But so this company, Antropic, that started 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?

52:57Charity Majors: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 pipe. 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 shit. Yeah. And you just got to put it somewhere. You observe it. You see what. Yeah, yeah, yeah. And then 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?

53:28Charity Majors: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. Like, 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.

54:16Charity Majors:You have to. Like, that is a product decision to capture that trace. So let's talk specifically about modern observability and like companies that are, you know, like either building AI related 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.

54:50So I guess it's some other part. Let me put a tool that will like log everything and I'll have a bunch of stuff. Now, this is 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's actually maybe a bit like more practical? Because I just will use super basic one.

55:10Charity Majors:Auto instrumentation has gotten so good in recent years. If you're using open telemetry and everyone should be using open telemetry. 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 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 right it's part of just developer intent 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 you know 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, you know, you're an 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.

56:07Charity Majors:So you're like, okay, I've got a bit of data and I want to do something with it all right is it a metric a log a trace an exception an error a profiling you know just yeah okay if it's a metric is it a is it a counter is it a gauge is it you know just like all down it takes so and then well what type of data is is it going to have high cardinality is it going to be a cardinality you need to worry about that yeah it's just like if it's a log line to which log level do I do I append it to 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 done 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 than without it.

57:11Charity Majors: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 at Honeycomb 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 robust 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. Can we talk about what spans are? is I'll quote Eric Riddock, who recently wrote LinkedIn, the basic idea of observability for applications is don't use logs or metrics, just put it all in spans.

57:44What are spans?

57:45Charity Majors:Spans are bits of a trace. I mean, a trace is just a 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 any of 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 AI. Yeah. We just we just ship something called timeline that is like it sits on top of spans. So, you know, if if you you know, if you if you run something like intercom, you have got a chat thing and a customer is like I'm conversation going on.

58:28Charity Majors:Yeah. Customers like I'm complaining. and you're like, okay, so you spin up an agent, supervisor agent that spins up more agents and each of them calls APIs, each of them calls like storage backends and stuff and then the customer has another. It could span hours, right? And you need to be able to zoom out 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. Oh, okay. Yeah. So I guess it's a trace of traces. So we need these new building blocks actually just be able to work with.

59:07Charity Majors:Yeah. Interesting. So I guess this is something to keep in mind, like any, any, any engineer who's like building on top of LMS, who is an AI engineer now, as, 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've built your own in-house tool, but we know that it'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 year or whatever, just the marriage of tests and evals from a telemetry's perspective.

59:44With agents and AI agents being around, a lot of them are now very useful to connect to observability stores. You can go on 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 like make them productive? Have you seen some patterns?

1:00:13Charity Majors:There's a lot of trash data out there.

1:00:18Charity Majors:And a lot of traditional telemetry data, metrics, logs, traces, whatever it's 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 and in fact one of the ai sre startups posted this great piece a couple months ago about how they 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. So that's what I would say. Either you give your agents, but it's the relationships that matter, right? Because that's what actually helps the AI make decisions.

1:01:05And when it comes to 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?

1:01:14Charity Majors:Oh, man. The whole thing is new. So O 'Reilly, anytime a book is considered successful, and if the topic is still relevant, they'll ask if you want to write a second edition. So it'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. Like your children and your books, you're not supposed to say anything bad about them, you know, because it's fine. But it was written 2019 to 2021. when 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.

1:01:54Charity Majors:Let's ship it. It was just like, oh, God, I can't do this anymore. Just like, please 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 all. So 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 and i wrote parts one and six first part is just kind of like grappling with what does it mean to run deterministic and non-deterministic systems you know and then you know my co-authors uh liz and austin and george the part two and three is how how do you instrument your code and how do you understand it and there are parallel tracks for doing this with or without ai um and a couple great guest columns from uh from jeremy and then parts four and five are we have a whole lineup of guest authors and use cases and deep dives hansen ho did one on front end and um interesting and mobile uh we've got some great ones on CICD.

1:02:59Charity Majors:Clickhouse did one on columnar storage. Some really, really stellar things. There's a chapter from Kesha at Finn on how they use it iteratively to like do observability. So this is a brand new book. A lot of second editions are like, oh, we added like 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 topics for observability governance for leaders.

1:03:38Charity Majors:And it starts with an open letter to CTOs telling them why all their big AI goals are blocked behind their ability to make sense of their system. You know, and then we talk about, you know, software delivery for, no buzzwords, just systems theory, right? If you like Donella Meadows stuff, then you will like it. 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.

1:04:18Charity Majors:And there's a great guest chapter from Rick Clark on Staff Plus Principal Distinguished Engineers. who are 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. I mean, it sounds to me that anyone who is inside or wants to be inside a platform engineering team, maybe you'd be an engineer or a leader, 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.

1:05:15Charity Majors: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 butter. When it comes to partnering with, you know, the sales org of another company, you do not have trust and credibility. You work to build trust through reciprocity. You learned just how much you can trust them over time, right? But the best vendor relationships are the ones where you genuinely, you feel like their successes are 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.

1:05:54Charity Majors: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 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.

1:06:42I feel Observer of the Engineering I'd be underselling this book, I'll be honest, the title, but I'm also going to get it and I'll probably think of ways to share a bit more. But thank you for writing and thank you to all 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 are changing. But I loved 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.

1:07:17And after that comes everyone else. There are plenty of 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 slash X, referring to as Elon as a terrible human, but a skilled business operator.

1:07:35Charity Majors:Yeah, well, I don't know that I would call him 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 is. Figuring out the business, specifically. Figuring out the business, yeah. Building products, you know, 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, but like 60 engineers, there were 1 ,700 before.

1:08:18Charity Majors:You know, and you could argue, and I think it would be true, that it's some of the work that those engineers did. 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 closed 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 and i think this is like especially now that there's so many changes happening and and technology changes there's a whirlwind business will go up and down i guess the reminder that like you want to keep your eyes on the prize which is, especially if you're a leader.

1:08:59Charity Majors: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 sociopaths in the same roles, but only if they're good at business. Learn the business, stay close to it. You got to.

1:09:36With 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?

1:09:51Charity Majors:Well, the first thing that's changed is I think everyone has to, gets to be hands-on. Specifically to generate some code, to ship the production to some extent. You should know what it feels like to submit a diff, to get a PR through. You know, 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, you know. And it's always been the case that leaders were better if they had a hand in it. And now it's just, there's no excuse not to. 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.

1:10:33I worry that the way it's happening is it's 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 or like it.

1:10:45Charity Majors:And I really dislike the anti-management tone. So like 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. You know, the bureaucracy kind of like generates a sort of, you know, it's easier to say yes than it is to say no. And so these things happen, so they need to be pushed back from time to time. But I believe that management and middle management is deeply essential. And I look forward to seeing how that works out for them, not having any bit. But like the role of a manager, middle management, in my view, is sense-making and context-giving.

1:11:29Charity Majors:Because like, I don't believe in a world where engineers are just given tasks. Here's your JIRA. Go do the things. AI 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. And you can't engage emotionally, creatively, collaboratively without understanding. And that understanding is incredibly difficult to build and it's fragile and it never lasts very long. For those of us listening who are middle managers, it's been a tough few years because what they're seeing is there's a push to have fewer of them.

1:12:07A lot of their colleagues, if they're in unlucky places, they were made redundant. And many of them have struggled to get similar positions. We're talking director positions. We're talking head of engineering, senior engineering 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?

1:12:37Charity Majors:And 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 of it, then I would try to work. You've got to get AI on 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. And this is very interesting that you're saying get AI in your career. because I remember about a year, year and a half ago, I started to pay attention to like, okay, this is happening. And I remember a year ago, I wrote an article about how to become an AI engineer.

1:13:12And I talk with engineers who just like at their workplace start 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 research on a job market and they're like, this is the best job market ever. However, you know, the people who were like, okay, I have none, but I want to get it. They, and let's say they're out of a job, they're struggling because no one's giving them the benefit of a doubt.

1:13:38Charity Majors: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 ringing this alarm bell is we know this change has not been asked fast. So do it now because later... Do it 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 the delta between those who are just getting started, those who've been doing it, it was here for a little while. It was very easy to get started. Now it's here. And it's, but it's opening, the longer it goes, the more, the harder it will be to catch up.

1:14:09Charity Majors:You just got to get, you just got to get some. 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'm so excited to do this you know because I'm like trembling and sweat 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.

1:14:56Charity Majors:But if you run towards the waves, if you're like, just like run towards, try it. 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. Just tell yourself. It doesn't have to be true. I'm so excited to be an 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 it.

1:15:32Charity Majors:Just you got to own it. Don't wait. And then let's talk about junior engineers. Obviously, it's a harder time to get started 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. So it's all vibes. You know, it's so interesting because I feel like we're over here doing all this hand-wringing 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.

1:16:05Charity Majors:He's like, they are cooking. They are, they don't know what the software development life cycle is, but they are just like off to their, 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. But we just have to hire them. We just have to be willing to give them a shot. This week, NSF, 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.

1:16:39So they wanted to hire him or her. Turns out it was a 17-year-old kid. They still hire it. And now they're telling 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 shot, even if it's an internship. Yes, totally. I feel more company should, because internship is low risk, low duration. Yeah. And even if that person doesn't work out, with an internship under their belt, it's so much better for everyone.

1:17:05Charity Majors:Totally, totally. One question that came up when I asked that you're going to be in the show what I should ask, they said AI fatigue. Like someone asked, 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 what's here. We know what's here to say, but still. I mean, my follow-up question would be like, which variety of AI fatigue? Okay, tell us the amount of varieties. You know, because for some people when they say AI fatigue, they're talking about receiving slop.

1:17:40Charity Majors:Some people are talking about all the hype and the, have you heard the phrase or the term doom trolling? Cal Newport is, 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 OpenAI keep doing about, oh my God, this might be the end of blah, blah, blah. And she's like, it's just doom trolling. And they shouldn't, they need to stop it because they're stressing everyone the fuck out. Yeah. 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.

1:18:22Charity Majors:You know, it's just, it's always before the history of technology. It's been something cool or fun or this will be the iPhone. It'll make your life better. And now it's just like fear. It's pretty crappy. So there's that. 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 posts. 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.

1:19:06Charity Majors:Maybe you and your team agree we don't actually want any more AI-generated PR descriptions. 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 change is so big, experimenting has never been easier. And I guess most businesses, most directors, most leaders would welcome teams 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.

1:19:42Yeah, and what didn't. And what you learned.

1:19:43Charity Majors: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 until it works so much better when it bottoms up, when people are just trying to take control of your time and your calendar. I guess maybe we just forgot 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, the 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.

1:20:19Guess 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 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.

1:20:42Charity Majors: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... And I really got a lot out of catastrophe ethics. I haven't seen it mentioned in many places, and I think it might be, I think real philosophy nerds would be like, that's kind of a pop book, you know? And I think the 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 Reeder, R-I-E-D-E, or catastrophe ethics and he talks about how the puzzle of modern life is that it feels like everything we're implicated every choice we made are you going to use milk well you know the cows were tortured are you gonna use almond milk well water is a problem well you swim like a little hormones 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 And then he kind of walks through traditional ethical frameworks like utilitarianism and stuff and just shows how there is no recipe anyone can follow that doesn't lead you to some really stupid...

1:22:01And he's like, this is just no gods, no masters.

1:22:06Charity Majors: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 know you need you need to be you need to know things right and then listen inside you know where where are you drawn what suffering really speaks to you or what what caused you you know because 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, you know, 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, but I'm just going to keep recommending it because it's so good.

1:22:51Charity Majors: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, the singularity and the effect of altruism and accelerationism and the whole like, what if we could have infinite growth forever-ism? And he's like, the heat death of the universe, you guys. Literally the only thing we know about exponential growth is that it must end. It must end in an S-curve or in a crash. It must end. and he's got this dry sense of humor and there are a couple times where he's just like describing some of the very real things it's just like why do Oxford ethicists want this he's talking about like taking over star systems and it's just ridiculous and he also he gets in a whack he's just like talks about these people who are working so hard on life extension and he he's like these are a bunch of sad little boys who miss their daddy.

1:24:00Charity Majors:And I was just like, oh my God. It is the oldest fear of humanity is the fear of death. And you just see it. You cannot unsee it. So yeah, those are my two. They're both so good. Charity, thank you so much. This is finally made it happen. Finally. It's a good time. I always really, really enjoy talking with Charity. I hope you also liked it. I appreciated 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 all ways to add more trust that we lost by using AI.

1:24:34I also appreciated how she talked with empathy about both AI camps. The enthusiasts or AI-pailed folks are seeing the practical wins while those operating production systems see the slop. Neither side 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 directors and managers, I'll leave you with Charity's advice. Anxiety and excitement are psychologically almost the same, but the difference between them is agency.

1:25:06So instead of waiting for change to come to you, take charge however you can and make changes yourself. Do check out the show notes below for related to Pragmatic Engineering 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, Observability Engineering, 2nd Edition. If you enjoyed this podcast, please do subscribe on your favorite podcast platform and on YouTube. A special thank you if you also leave a rating on the show. Thanks, and see you in the next one.