Tech Debt, Communities, and the Rise of the New Kingmakers with Rachel Stephens

Rachel Stephens is a Senior Analyst at RedMonk, where she covers emerging growth technologies and markets while helping clients understand and contextualize technology adoption trends. In this conversation, we discuss tech debt, layoffs, software development trends, and more.

Where to find Rachel
LinkedIn: https://www.linkedin.com/in/rachelstephens/
Twitter: https://twitter.com/rstephensme
RedMonk: https://redmonk.com/rstephens/author/rstephens/

Follow, Like, and Subscribe!
Podcast: https://www.thecloudgambit.com/
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Show Links
Lessons Learned: https://atlassianblog.wpengine.com/wp-content/uploads/2024/01/lessonslearned.pdf

AI, Code Generation: https://stackoverflow.blog/2024/06/10/generative-ai-is-not-going-to-build-your-engineering-team-for-you/

Zach Akil: https://www.zackakil.com/

Transcript

Intro: 0:00

Rachel Stephens is a senior analyst at RedMonk, where she covers emerging growth technologies and markets, while helping clients understand and contextualize technology adoption trends. In this conversation, we discuss tech, debt layoffs, software development trends and more.

William: 0:29

Rachel, welcome to my luminous, whimsical, mesmerizing podcast about cloud things and other stuff. How are things going today for you?

Rachel: 0:44

Things are great, I'm so delighted to be here.

William: 0:45

Thanks for having me. So do you want to briefly introduce yourself to the audience? You know who is Rachel Stevens.

Rachel: 0:50

And what do?

William: 0:51

you do for Red Monk.

Rachel: 0:52

Yeah, so I work at Red Monk. My name's Rachel Stevens. So those are the two key ones you got right off the bat. Where people probably get lost is who the hell is Red Monk?

Rachel: 1:04

If you've ever heard of Gartner or Forrester some of those bigger firms that help do market research in the technology space the fancy way to say it is we're a boutique version of those.

Rachel: 1:14

The other way to say it is we're a teeny, tiny, eight-person version of those. But we're a small firm, small but mighty, and our job, as we see it, is to help the industry contextualize and understand technology adoption trends. But we specifically try to do that from the lens of the developer and the practitioner. So a lot of the times we know software is a team sport. You have to have a whole bunch of different teams and everyone at the kind of from people working on the software all the way up to your leadership team have to be in alignment. Kind of from people working on the software all the way up to your leadership team have to be in alignment. So we get all that. But the point of view that we care about the most is that developer point of view of what is it like to use the tools, and what does it mean in terms of how technology gets adopted within the enterprise?

William: 1:57

Yeah, I think that's so great and I was actually just giving you some kudos. You know, before we started recording. You know I recording I'm all about clear and concise mission statements and just simple elevator pitches. Elevator pitch shouldn't take me five minutes to read. If it does, it might not be an elevator pitch. In the Red Monk About section on the website you make that distinction pretty awesomely, where you make that distinction between enterprise sales folks dressed to the nines, which are the decision makers of old, and then I think you call the new decision makers the king makers, which are your coders and t-shirts and flippy floppies. You know right, in the software that makes the company money, you know. So you all engage really at the developer level, which is awesome, instead of the mapping the traditional big vendors to the senior execs type stuff. Did I summarize that okay?

Rachel: 3:00

No, I think it's perfect and I think it's good context.

Rachel: 3:03

So, even though we're an eight person company, it's worth saying that, like we're not a startup company.

Rachel: 3:06

We've been doing this for a couple of decades now, and so our founders actually kind of started this where they would go to these conferences and the CTO would be up on stage saying this is the stack we're using and this is what we're doing, and then the founders would take developers out for beers afterwards and ask them what they're actually doing.

Rachel: 3:24

It's like I don't know, like we've got Mongo all over the place. Of course we're using open source things like that, and so they were, if you're thinking about, like going back to 2002 when the firm was founded, that was pre-cloud, early open source in the enterprise in terms of kind of commercial viability for a lot of these companies, social viability for a lot of these companies, and so the initial signs and things that the company originally saw is that, like the leadership team didn't realize all of the things that developers were using to make their lives easier and get their jobs done, and that's kind of the founding thesis of the company is what is it that developers want to use? Because it's probably in your enterprise stack. They do want to use it.

William: 4:06

Right on, okay, yeah. So I guess, before we move on to the fun stuff, if you will, you're in the Denver area, aren't you?

Rachel: 4:15

I am.

William: 4:16

Awesome. So everybody I talk to from anywhere near or around Denver is into rock climbing. Are you a rock climber?

Rachel: 4:26

Um, I haven't been rock climbing in about a decade, so I'm going to put an end to your trend. I'm so sorry. I do like hiking though, but like I'm a foot on the ground, hiker, not not a foot on the wall hiker.

William: 4:38

That would be me as well. Yeah, absolutely Okay. Yeah, I was in Denver last um, yeah, last September for a conference and I met so many rock climbers it was just like nonstop. I was like, wow, everybody's a rock climber around here.

Rachel: 4:54

There's a lot of people with outdoor hobbies in the state.

William: 4:58

Pretty awesome. I mean, I love the outdoors, don't get me wrong. But yeah, I like, yeah, kind of like you like my feet on the ground or in ice skates. I play hockey, so that's kind of my thing, my jam.

Rachel: 5:10

One of my worst persistent nightmares is fears of getting my teeth knocked out. So we'll have to unpack that later, but I could never play hockey. It happens.

William: 5:23

It happens a lot in that sport, especially if you don't wear a cage. So another thing I wanted to add. So I went through, I've read quite a few of your blogs and just thank you first of all for making reading really easy.

William: 5:35

So you have some great, great writing. It's really well put together and it's just not the dry like oh, I need to go take a walk and think about life after I read this thing, sort of thing. You write really well and you wrote a really good blog with a really awesome name, I might add, about tech debt. I think it was. Maybe the real tech debt was the friends we made along the way. I think was the title Such a good title. I saw that and I stood up to me. It's like the first thing I clicked on. I was like, okay, that's awesome. And I think this one was written right in the midst of, you know, the COVID roller coaster that we all so fortunately had to go through.

Rachel: 6:23

If I'm remembering right, that might be kind of that first wave of tech layoffs that started to hit us. I think that's what might have specifically been that first wave of Twitter layoffs. But yeah, this one is just kind of talking about the socio-technical systems that we've built. Built and, um, what, if what, what kind of weight, um do we carry as humans in the system, rather than just the, the code and processes and everything that?

William: 6:52

connects it all together. Right, exactly, yeah, but so you sort of said something. I'm probably gonna pivot now a little bit before we come back to tech debt. But the layoff stuff I remember it's so funny because you know I've been working in this whole tech thing for like close around 20 years now I guess, and layoffs in the days of old they still happen a lot. They happen quite a bit and they were. They were scary. I think social media now just things spread like wildfire and things are also very scary.

William: 7:22

But like I remember the first massive layoff that I saw working for a big company I wasn't impacted, thank goodness, but I remember they called us all in this big room and they just made an announcement. Nobody knew it was coming and it's like okay, like okay. Now we have these boxes, you have people that are going to escort you out, and that was it. It's brutal, that's how it went, it was absolutely brutal. And then you're sweating for the folks around you. It makes it.

William: 7:52

You know, back then when you were working in the office, it made it a lot more real when you see the person next to you that you've been working with for who knows how long and you know you got to wave goodbye long and you know you got to wave goodbye. Yeah, um, it's treacherous, um. But one thing I was going to ask is do you see, like I know that you know, in conversations I've had over the years, like there was a lot of, I think, over hiring and the time of free easy money and you know you know, okay, yeah, you want to do this gigantic. You know vc round and you know, here you go. So I think a lot of companies overhired leading up to COVID and then, I think, during COVID, things really got difficult and I think part of what I could be wrong, but I feel like part of what we're still seeing are phase layoffs that are based on some of that overhiring. But I don't know, I don't have any research or data to support that.

Rachel: 8:44

I don't have any of my own data other than just having read lots of people's hypotheses about this, but I think you are absolutely right that the tech industry in the era of low interest rates in the 2010s had just enormous I guess it's just investors in search of growth, which means that you don't really have to worry about that profitability side of things, which means that you can make a whole lot of resource decisions that wouldn't be rational in other environments.

Rachel: 9:18

And then you add on top of this COVID, where the entire world basically moved their entire life online for the better part of a year, which created some really skewed views of where growth is coming from and where growth is going. So I think people further made some interesting choices there. And so then you come out of the world, where people have returned to more or less a more normal pattern of life, and then you also have a different interest rate environment and all of these things combined. And then you add AI on top of that, which has created its whole own layer of like what does resourcing look like in the future? And all of those together means that people are very much changing how they are thinking about hiring.

William: 10:03

Yeah, yeah, absolutely, and it's. It is funny to seeing the swing back like the polarization of hybrid work versus oh, you have to come in the office, versus oh, you can stay at home. And like I feel like in this, for some reason or another, like the past few years, there's this whole movement of either it like has to be completely one way or it has to be completely the other way. Like I feel like we've sort of lost that idea of having a balance in some of these, some of these areas. And I feel like you know, I worked in health care. I was in health care like right around the time that covid started and seeing, like I don't know, it was just kind of funny from my perspective seeing tech folks complain, you know, when they had to come to the office.

William: 10:52

But you have these frontline workers, you have these folks in hospitals. Hey, they don't have an option. They got to to be there. They're, you know, they're. They have to, you know, for these things to keep going. And yeah, sometimes I feel like I mean even myself, like in my mind, thinking, oh, you know, I got to go to the office, what are they thinking? And then just kind of like sort of resetting my expectations of this stuff based on some of those things like I need to quit. I'm I'm becoming spoiled. Come on, yeah, I'm 100 in the same boat.

Rachel: 11:22

It's like I I've worked from home since 2016 and like I don't know if I could go back to the daily like hour commute, like I really couldn't go back to where I'd slacks again. Like I used to work in finance. Like, yes, like I don't think I'm meant for fitted waistbands anymore. All of those things are like 100% positions of privilege and I recognize that, but I think I'm totally not going to remember the name because this came out earlier this year. But Atlassian put together a really lovely report on like what it means to be a successful remote and hybrid team and it's really like full of so many just practical ways that they have found that their team has worked well together and the things so like when you're saying like you either have to be all one or all the other, they're not entirely wrong about that, because if you're doing it all one way, then you can fully rely on like everybody getting information the same way.

Rachel: 12:19

So we're either all sharing information online or we're all sharing information in person. And so if you don't have, like, a collaboration strategy that is reflective of a distributed workforce, or even a partially distributed workforce, and you have some people in person and you have some people online, you're just going to have things that get lost in the shuffle, or things where you have asymmetrical information across your teams. And so if you haven't thought out your communication strategy and you, all of a sudden, are trying to navigate this hybrid world, it really is a big cultural adjustment that has to happen in order for people to be successful.

William: 12:53

Yeah, I remember it's so funny Like one of the teams I was managing at the time, I had folks that and we were at I mean there was a certain timeframe where we couldn't allow this through COVID but I had folks on my team that wanted to come in and work from the office even when there was nobody in there, and that kind of threw me for a loop. But then you know, kind of when I was talking to a few of them, I kind of understood, you know where they were coming from, why they, you know, they had this whole separation of how they their life, their structure of things.

William: 13:28

They work at work and they do home things at home. And I think one of these folks had a bunch of kids, you know, around the house, so that can kind of contribute to that as well. For sure, for sure, for sure a place to be. I, I totally understand that.

Rachel: 13:40

So, yeah, there's a human element to it too, like maybe I don't know if it's personal preference or what you would- yeah, I think there's just so many things that can say it can be your family situation, it can be your living situation, like, do you actually physically have the space to work, like do some people really do just operate better in separate spaces? So this is where, like, home is here, work is here and I never the twain shall meet. So I think all of these things are valid opinions and you, just as an employee, need to find an employer who shares those opinions. It's kind of like a dating game you have to match how do you work and how does your employer work.

William: 14:22

Yeah, yeah, good points.

Rachel: 14:24

But now we're in a world where employers are just kind of unilaterally demanding the return to work, and so then you don't get to date anymore, you're just arranged marriage.

William: 14:35

Yeah, well, there's trade-offs with that too.

William: 14:41

You will lose some very valuable people if you go all in that way. Yeah, so there's trade-offs and there's consequences to those types of unilateral decisions. Of course, absolutely so. Tech debt so back to that blog, that really well-written blog, tech debt so back to that blog, that really well-written blog. Has your perspective kind of changed at all as far as tech debt is concerned? Because I think in that blog you were at the beginning, you were kind of pondering, okay, I think you're reevaluating what tech debt meant to you at the time, or how it had changed based on those layoffs. But yeah, any changes as we hit the midway point of 2024?.

Rachel: 15:20

So, like I mentioned, I have a finance background. So when I originally started contemplating tech debt, I was kind of in a place of, like this doesn't feel like debt at all, at least in the way that I understood debt from a financial perspective. A lot of times when you're talking about debt and finance, you're talking about a mortgage or a bond or something like that. There's other forms of debt like those are going to be the most common ones that you encounter and in those scenarios, like you have a known amount of principal, you have a fixed interest payment, you have a fixed interest rate, you have a fixed repayment schedule Like it's not at all what we talk about when we talk about tech debt where, like, we have made these decisions hastily and then, all of a sudden, there's going to be an unknown repayment window when things blow up, like it's a different theory of risk and so, like I think a lot of times, when we try to talk about tech debt in the technical world and try to communicate with our partners and stakeholders about what we're doing, then try to communicate with our partners and stakeholders about what we're doing Like we're trying to convey that we are making a trade-off between doing work today and doing work down the line, and it's going to be an unpredictable amount of work when and where that happens.

Rachel: 16:26

And so a lot of the times, I think what we're trying to convey is risk Like risk is what matters, and so I think sometimes, like when I originally started thinking about debt, I was like this is kind of an incomplete metaphor if you're trying to talk to your business colleagues, and then when you added the socio-technical layer on top of that, what are people actually adding to this system and what does it mean to pull people out of the system? And then it just gets even more convoluted. And so I guess where I have landed is that tech debt is really complicated to talk about.

William: 17:02

I don't think any of us have a good handle on it. It is, I'll tell you what. So, from like a sort of like a technical leader perspective, like my sort of definition of tech debt was okay, like we're just okay, like I, you know, again, I worked in healthcare. We got mainframes for days, cobols, all the you know, in the driver's seat, you know, with the software, um, there's certain things that are easier to modernize. Data is really hard to move, but we want to innovate anyway. We want to use cloud. So, okay, we're, we're building like, new things in cloud, like web front ends, but a lot of our data and systems are still in the data center and we, we have a lot of ideas of just, uh, sunshine and rainbows and lollipops from leadership.

William: 17:45

You know, they go to some conferences, they talk to some other cios and ctos and stuff and they come back with some, um, pretty, pretty big ideas and a lot of times you have to go through some of those experiments and prove out that they're not going to work before you can go back and say no. Because, let's face it, if you're a technical decision maker, you can't really just say no to some VP of engineering or something. You can make your case and try to push back a little bit, but there's a point where you have to stop and say, oh, we're going to do this thing. And then, as you're, you're trying to pivot and you're doing these things, you have teams that are pivoting to do a thing. You're, you know every zoom call you're on. You're paying all these folks to be on that zoom call when they could be doing other things.

William: 18:32

And you know, there's just each year that enterprise goes on. They have like a budget and they try and do so much just within that time window and then it's like reset.

Rachel: 18:43

Yeah, that's the really fun, like last 20% of our sprint to an airport ad that the executive saw on the way home.

Rachel: 18:49

It's always a great time, but I do think that what you're saying, I think, resonates in a lot of ways, because it's of have you ever heard of like the theory of pace layering? I think Stuart. I think it's Stuart Brand. Now I'm going to have to double check this. We might have to correct this in the comments.

Rachel: 19:08

But basically it's like different layers of the world and this also applies to your technology stack move at different layers or different rates of velocity. So, like, if you're thinking about like your data layer can be really really intractable, hard to move, your JavaScript framework layer at the top probably could change every two weeks if you wanted it to. I hope it doesn't. So you have this sense of like different things move at different rates. And so, like if you start to think about technical debt and like kind of like an archeological dig of going down, down down, it's those ones at the bottom that you have to kind of start to figure out like how can I shift what's down here without like making my Jenga tower that's built on top of it all fall down? And so it's really like this exercise of digging down, figuring out like where is everything coupled Then, where can I decouple it and how can I start modernizing in pieces, and that's kind of like the one version of the technical debt is like how we start to change things.

Rachel: 20:06

And so if you're an enterprise leader and you're like I want to, I want to make sure that we are changing, we are evolving, because we do need to be doing this constantly, like it's not a one and done thing, like we should be evolving our stack all the time, but we shouldn't be evolving all layers of the stack all the time. And so if your enterprise like strategy is like your executive comes in and like tries to rip something out of the bottom, then that's no fun. But if you have an enterprise leader who's willing to engage in these conversations with you and you can start to come together in some kind of coherent strategy about how we're moving forward, then that's more useful.

William: 20:41

Yeah, communication is a huge part of this for every level. That's why I think it's so much harder for an organization that's so heavily matrixed across like you have like so many layers of management, because you know a lot of times what happens if you have like the compute side, the network side, the security side, and decisions have to go up to a leader and you have to make a decision, and then you have it's almost like decision by committee at the top or the VPs, and it slows things down so much. And you know one thing I saw a lot is like when we were adopting at one company. We're really going, you know, all in on microservices and what we came out with at the other end was some really distributed monoliths under the guise of microservices. And it's very easy to do when you have to take into consideration all these different layers that you kind of talked about, because you just can't simply it's not a cloud native thing, you're not starting from scratch, it's not greenfield, it's still very brownfield that you have to take into consideration.

Rachel: 21:44

Yeah, and I think there's like the two ways where it's like I have two. My app is monolith, it's a big mud ball. Everything is two coupled. And then you also have people who swung way too far the other direction, where it's like I have just this massive web of dependencies. I have like duplicated microservices. I have way too much going on. I have like traces that go way deeper than they need to go in order to get things done, and so like I think it's one of those things where it's like you can talk about them from either extreme and finding like the correct balanced architecture is just a challenge.

William: 22:20

Yeah, and it's got to be complicated for, especially for new developers coming in to just finding really like I remember when I was coming into the industry, you're really trying to find out who you are still in terms of how you fit. If you're even going to like what you're doing and a lot of new developers it's you know back to the point of, oh, what, what you know. I want to be careful about what I put my time into. What programming language should I focus on? And you know it always used to be a thing of okay, what are you best at writing, what's really valuable? But some of these are coming and going now and there are some that, if you learn them and you're efficient, you can fit into different niche areas or even be more hyperscaler focused and such. Do you have any thoughts on that?

Rachel: 23:08

I do have thoughts on that. I have many thoughts on that, actually. So RedMonk has historically tried to, so we've done this since, I want to say, 2012. So we have a long history of just trying to track what we can see about program language usage in the wild, and we fully fully acknowledge this is not a representative sample of how programming languages are used everywhere, like you mentioned. Like COBOL is still out there. That doesn't really show up in what we're doing, because what we're looking at is GitHub and Stack Overflow and COBOL exists not in those places, or at least not so much in those places.

Rachel: 23:41

But our general goal is like what are people talking about? What are we seeing in public commits, and just kind of tracking them over time, and so it's imperfect, but it's just like, rather than just trying to deal with what we're hearing qualitatively, is there any kind of quantitative metric that we can try to apply to what we're seeing? Not perfect, but if you look at it over time, it provides some interesting trends and what we see a lot that is interesting is that there's remarkable stability in those top 10 languages. So Java JavaScript, python, php is still up there. So Java JavaScript, python, like PHP, is still up there C++, there's like all these languages that you've kind of seen and have heard about, like those continue to have a creative value in the industry.

Rachel: 24:26

And then you also see that there's up and coming languages. So it's like you see Go, you see TypeScript is one of the ones that has come up, so it's like all of these ones were at rest, so you can see trajectory of up and coming languages. But you also see that there's really a lot of stability. And so I am of the opinion a lot of times, and especially in the age of generative code assistance, where it can help you transfer or like transport your ideas between languages in a way that's easier than it's ever been before, is that find a language, focus on learning programming concepts and then adapt as you need, but like don't get super hung up on learning just the right language, because I think that that's not going to be. It's a little bit like trying to time the stock market, like pick something that's reasonably well known that solves the problem you're interested in, and then trust that you can learn other languages as you go.

William: 25:19

Yeah, I mean, I remember the first programming language I ever used was, I guess, bash scripting could technically kind of be like programming but, pearl was big when I started out, like on the sysadmin side, and then, honestly, transitioning from pearl to python was like it was like beautiful, it was so nice, it was amazing, you know. So, like I've been, you know more or less python, with some go thrown in there these days a little bit of typescript. But yeah, I mean, you're absolutely right if you learn the fundamentals. Um, I know you have functioning or functional object oriented, like there's different things, different. You know architectures and stuff and different trade-offs and things that you have to make. But yeah, you're absolutely spot on. You know, learn something that's valuable, you're good at it, you focus on it. But the fundamentals and all the software design patterns that underpin those languages, you know that you're.

Rachel: 26:18

Yeah, those are, those are the things that are really important and are transferable and the other thing that I've learned from trying to pick up programming languages and I program so poorly that we should probably not even mention it in false.

William: 26:31

You don't use ChatGPT. Your coding could look really good.

Rachel: 26:36

I used to do DBA work and I'm not going to lie, I really just don't write my own SQL queries anymore. And I'm not going to lie, I really just don't write my own SQL queries anymore. I throw a general schema and what I'm wanting into ChatGPT and then I sometimes have to tweak it, but I really love not writing SQL anymore.

Rachel: 26:53

It's delightful, Some of those really confined tasks does a great job and to find tasks where you can easily verify the outcomes, like did this query run and did it give me what I'm actually expecting? Like verification still an important part of that, yes, but what I learned in trying to learn programming languages is like having a specific problem that you're trying to solve is the most important part of all of the learning things. Like sometimes it's like don't just pick up a language and try to learn it for the sake of learning it, but like what are you trying to do with it?

William: 27:28

and I think that's a really important part of learning process that is such a that's yeah, that is so big, uh, that can't be. Yeah, I mean, it's kind of like.

William: 27:40

I mean I picked up when I started working at a desk full-time I was like I've to like pick up a hobby where I can do something with my hands, or I'm going to go crazy. So I started woodworking and let me tell you, if you start out and you're like, oh, I'm just gonna think, okay, like how do you do this? Or how did you do? No, you need a plan. You need something that you're going to build, that's going to solve some function, so you can connect the dots between the tools you're using, how you're using them. You know reading a plan and and you know the outcome that you want. You know just jumping in and you know leroy jenkins, you know it doesn't really work that well.

Rachel: 28:15

Um, okay, so we're gonna have to add woodwork working to our hockey conversation after we stop recording, because I have more questions there right on yeah, that'll be fun.

William: 28:26

That's it's been one of the most amazing. I started like maybe 10 10 years ago and I got really into it because it's just so peaceful, like it's just yeah, anyhow. Um, so what, what do you think like as far as developers, you know another. So I get a lot of folks reach out as far as like hey, should I do this, should I do that? You know folks that are new in the industry and like really what they want is they want to, not that I'm someone that has like gone out and done a lot of things, but they want somebody that's been around for like long enough that can basically maybe give them some tips for how to maximize their time and help them, you know, be more successful quicker. They kind of want to dodge the potholes, if you will. But it's got to be harder coming into tech as a developer today than like ever before, because, I mean, let's face it first of all, everybody wants to be a developer. It's like that's the number one trade. You know people want to write code.

William: 29:20

They want to write software and there's so many different platforms or so many it's just got to be confusing. Do you have any tips for how a developer might think about how to get how to start going from crawling to walking?

Rachel: 29:37

Yeah, it's a tricky one, I get. My heart goes out to people who are trying to break into the industry right now because it is macro economically speaking it's a tricky one. My heart goes out to people who are trying to break into the industry right now because it is macroeconomically speaking, it's a challenge. And then we just have all of these complexity things that you've mentioned and then a whole lot of weird AI vendors are trying I think, deliberately not to talk to people about. Like this is going to take your job. Like this is like human in the loop, this is going to augment your work, it's going to make it so much easier. But like I feel like a lot of the companies who are buying or listening to these AI vendors are hearing a different story where it's like we can not like yes, we're just going to augment our workforce. We can not like yes, we're just gonna augment our workforce. It's like no, it's like you have a lot of people who are not hiring juniors in a way that they assume that, like Gen AI is going to like take over the drudgery jobs, but like I think that's really short sighted and I think we're going to find that that is not successful in a lot of places.

Rachel: 30:42

So I think what you really need is a place where you can learn.

Rachel: 30:47

So Charity Majors just had a post that came out yesterday about software being an apprenticeship industry and the importance of learning from one another.

Rachel: 30:57

And so if you're a junior or someone who's trying to break into this industry and you don't find that opportunity for yourself, like maybe you can find it via, like if you've done a boot camp or something like that, maybe you can find it if you're lucky and you can get hired and you can learn on the job. Like you can also try to find this in open source. Like if you can find friendly open source communities and you can start to build a public facing credibility, you can start to learn from what's already happening elsewhere and I'm not going to say that all open source communities are super friendly to newcomers, but there's plenty that are. So if you can find ways where you can learn, grow and then also just have a track record of working in public, like, those are all great things. And so I think if I were in a place of trying to encourage people to get to that place where you can start to make a difference, I would consider thinking about what can you do in open source.

William: 31:52

That's really good and you're so, so right about the communities. I just had a guest on that's a CNCF ambassador and she was talking to me about just what it meant to be in the observability group you know, for the CNCF, kind of going into some of the details and some of the value. And the beautiful thing about that is, like I've been in this, I'm not an ambassador, I'm not in any way part of you know officially part of the CNCF community, but anybody can join the Slack.

William: 32:22

Yeah, part of the cncf community, but anybody can join the slack. Yeah, you can talk to anybody. And they have to be the most friendly community that I've ever. Just, I'm not not to say that I'm even a part of it, but I'm there if you have. You know, they're just so friendly they are friendly, it's so easy to talk to these folks and you don't know until you try I love.

Rachel: 32:39

So I was just reading because it was the Kubernetes 10th birthday last week but the way that the community shaped that technology is such an intrinsic part of why that technology was successful and so, like, I think the core of the CNCF ecosystem is kind of founded on that ethos and I think it carries into a lot of the different working groups. There's not it's not to say there's not bad actors or not problems with some of them, but it's, by and large, a pretty welcoming community. Python you mentioned being a Python person. I think Python is also one of those communities that is very welcoming. So, like, pycon is a great event. So, like all of those things I would recommend checking out either language-based communities like open source, platform-based communities, like check it out, see the vibes and see where a lot of them will have things tagged as like good first commits for newcomers, things like that, so you find your way in and you can also look on these Slack communities and see if you can find mentors.

William: 33:37

Yeah, that's a good thing right there. So I never, when I first started in tech, I never officially asked for a mentor. I never that I had one.

William: 33:45

So I worked with someone that sort of took me under their wing and he was. He was kind of a a rough person. He was rough around the edges. He was military, ex-military, very rigid. Um he, he did not sugarcoat anything, you just got directly what he was thinking all the time, uh, but he took me under his wing, he taught me so much and just spent so much extra time with me and a time when I needed it and you know, when I the influence was really important in shaping the direction I went in my career.

William: 34:18

He was a good, good person and you know, you know that was in the time where social media and like it was not easy to get a mentor. You basically had to go work somewhere or go, you know, email someone. It was just very hard. But now there's so many folks out there, you know you can reach out, you can. You know there's so many community events that you don't have to pay for Local meetups. There's just so many opportunities now that you can go and just talk to people and you know, sort of begin that journey and open source. You're right, it's such a good place to, you know, to start out. How do you, how do you think AI is gonna play out over the next few years. Like for me, like it's kind of like.

William: 35:02

I don't want to say it's a complete nothing burger at this moment, but it's. It's hard. I think it's it's started with. Okay, ai can solve every problem that we have out there. We've got to just look at everything through the lens of AI ML, so I think it's going to start getting more more real here soon getting more, more real here soon.

Rachel: 35:28

So I have lots of thoughts on this one too. Um, I think one of the things that's a little challenging about ai is that ai is following web 3 and so, like I think web 3 kind of went through like a huge rise and fall in hype, and so I feel like AI kind of feels like to have it come right on the back of Web3. And so it feels super hyped and it feels the same as what we just went through. I do think that the core of the AI possibility is more substantial than what we saw in cryptocurrencies. I do think that there is a lot of undue hype right now.

Rachel: 36:05

We are very much in a hype, hypey phase of this technology, but I think that the underlying promise of reducing tedium in at least so like I think the LLM thing was like we want like a general purpose platform that can do a whole lot of natural language processing that's kind of what kicked off this wave and that will get us. That's getting us to where we are now. I think the next wave is going to be like hyper specialized models that enterprises can use in really specific ways, and I think that's what's coming next and that's where the interest is, and so I think, kind of where we are now in terms of what technology is available and what enterprises are actually excited about, there's a bit of a gap, but I do think that it's a gap. That is something that we, as an industry, will probably overcome. So I do think that we're going to see AI models augmenting us as teams, as humans, but I think that it's going to look a little bit different than it is today.

William: 37:03

You're totally right about coming off the coattails of web. You know, just right after Web3. I remember seeing some of the funding announcements for some of the Web3 companies, like whoa. They're investing how much in this Web3 startup. That's crazy, yes, and then it's. You don't even hear about it anymore.

Rachel: 37:21

Yeah.

William: 37:21

You're right. You right, ai definitely solves real problems and of course, there's a lot of bullish VCs out there that are dumping a lot of capital into some of these startups and that's probably going to continue. But at the end of the day, they're going to get weeded out pretty quick because they have to build something that's very valuable or profitable, or they're not going to make it past the next round.

Rachel: 37:44

So I think for me, one of the things that's interesting about AI is, if I'm in enterprise and I have this AI FOMO, I need to figure out what my AI strategy is, because my board is saying I have to have an AI strategy. Am I going to go give all of my enterprise data to a company that has a $20 million seed startup and no terms of service? Or am I going to like, oh look, salesforce has AI, now All of my cloud providers have AI? I think the incumbents are actually in a place where they are most likely to clean up on this AI windfall just because people are already trusting them with their data, and I think that trust with data in this phase is going to be tremendously important, and so I think it'll be interesting to see. But there will be some of these VC investments that pan out and come through, but I think you are right that there's going to be a lot of weeding out.

William: 38:46

Yeah, I mean, at the end of the day, it has to be profitable for the VCs. It has to be founders and the really rock star engineers that are at these startups doing cool things. There has to be a reward at the end of the tunnel, or it's just not going to be. It doesn't work for the consumers that are buying a thing and it doesn't work for the folks that are building it. It just it sort of breaks the system in a sense. Yeah, yeah it'll. It'll be interesting to see what happens. It is so funny, the web three. Um, it is a pretty interesting comparison, for sure.

Rachel: 39:25

It's just the timing of it, you know yeah, and I mean it really is like the hype around ai is like people are legitimate to be skeptical about the hype, but I I do think that we'll probably come out the other side starting to see some interesting and viable use cases, because we're already starting to see those yeah, I mean even transposing.

William: 39:45

You mentioned this earlier a little bit, but I back to my Perl days, my good old Perl days. I actually took a Perl script that I had that did some things with some on-prem infrastructure F5s, load balancers and I put it into the good old chat GPT and said, hey, can you transpose this to Golang? And it did it and it actually worked, which was really crazy.

Rachel: 40:10

Like we would hire agencies to help transition code bases to, you know, newer languages or different languages for different use cases, and that's pretty compelling yeah cases and that's pretty compelling, yeah, so one of my favorite ways about thinking about whether or not AI is useful for a specific use case it comes from Zach Akil, and he talks about the IVO model, which is immediately verifiable output, and so you either need to have, kind of, as the user, enough information and understanding of what you are doing to understand whether or not the model has output something useful, or the model needs to be trained in a really specific data set so that it can't hallucinate, or you need to have, like a model that can source something out so that you can check there's.

Rachel: 40:55

There's a lot of different ways, or you can. The easiest way is that the model will output bullshit and then blame the user because the user was not the person who was supposed to be using this model. But as you're thinking about Gen AI, it's thinking about that idea of can I verify the correctness of this? And if it's something where I'm familiar with this script, I know what it's supposed to do and then I can see if it does this in the new language, then that's great. But I think if we're in a place where we're saying like, dear junior engineer, go learn to code using Gen AI and you don't have that foundational background knowledge like that's where we're going to get in trouble.

William: 41:31

Yeah, totally agree. Well, this has been an absolute blast talking to you and I'm going to go. I'm going to link in the show notes your blog, for sure, and I'm going to link in the show notes your blog, for sure, and I'm gonna.

Rachel: 41:44

I I noted the atlassian oh, I gave you atlassian, I gave you charities. I sorry I I name dropped a little bit more than I intended. I just I. I feel like there's so many people who say smart things about them that I cannot take credit for all these smart things.

William: 41:56

Yeah, well, the atlassian thing, I hadn't heard about that. I'm interested to read that myself, so I'm definitely going to link these things um in the show notes perfect, I will provide links for you and looking forward to talking with you more, because this was a delight um, so absolutely. And where can uh folks find you?

Rachel: 42:15

oh, in this weird fractured landscape, maybe just come to the red monk blog and go from there, so that I don't have to tell you like 19 social media platforms to come find me.

William: 42:24

So you're not on Twitter like day in and day out, just tweeting constantly.

Rachel: 42:28

I am there, unfortunately, but I don't feel good about being there.

William: 42:34

I hate it.

Rachel: 42:34

It's the worst. So come find me on redmonkcom. Me and my colleagues post there frequently and we also have links to how to find us on social media elsewhere.

William: 42:45

Awesome. Thank you very much, and we will have to do this again at some point.

Rachel: 42:48

This was a delight. Thank you so much.