How AI Velocity is Rewriting the Rules for Engineering Leaders | ChatPRD's Claire Vo

Claire Vo 0:00 I look at the speed at which people are able to build things, and I think velocity will be a massive, massive differentiator. I think teams have got to get on this train because they're gonna be competing on the ground feature for feature, capability for capability. And if you don't embrace this, I just cannot imagine you you don't get left behind.

Conor Bronsdon 0:28 Welcome back to Chain of Thought, everyone. I am your host, Conor Bronson. Our guest today is Claire Vo. Claire is the chief product and technology officer at LaunchDarkly, the founder of Chat PRD, and also the host of the How I AI podcast. Claire, welcome to the show. It's great to have you here. Thanks for having me. I appreciate it. I love that you've got this diverse perspective and approach across AI. Not only are you diving deeper with other folks and exploring

Conor Bronsdon 0:55 and creating content, not only have you founded your own AI enabled application, but you're also building AI products as a leader at a scaling company like LaunchDarkly. And I have to imagine this gives you a varied perspective across the space, a unique vantage point even. And that's exactly what I wanna explore with you today. From the incredible product development velocity that AI enables to

Conor Bronsdon 1:22 how it changes the equation for risk and why leaders need to be building their own vibe coded apps just to keep up. And by the way, I'm doing a terrible job of this, so I I'm feeling held accountable at this conversation already. So let's start, though, with this demand that we're seeing around agentic AI, something we've talked a lot about on this show, but I think it's really important to continue to dive into. And, in particular, I think your perspective is one I wanna I wanna understand deeper.

Conor Bronsdon 1:49 With Chat PRD, you're seeing a significant demand for more agentic AI experiences over copilot like models. From your vantage point, what are the key drivers behind this growing hunger for agents within AI that perhaps didn't exist six, twelve months ago?

Claire Vo 2:07 I think there's a couple things that probably can contribute to this rise of demand for more agentic experiences. I think the foundational one is people are just much more comfortable with the concepts of generative AI and AI products. And so they're able to wrap their heads around the things that were keeping them from adopting any AI products, let alone agentic ones. You know, where is my data going?

Claire Vo 2:31 How are these responses being generated? Who can I trust? Is my data secure? Does it have enough context? Is it gonna hallucinate? If you don't have that foundational understanding of how these products work, you're certainly not gonna embrace a form factor of the product that's a little bit more independent, a little bit more asynchronous, and a little bit more connected to your data and your products. And so

Claire Vo 2:55 I think one baseline comfort and understanding with AI definitely helps here. And then I think the second thing that we're seeing is working with AI tools is still work. You still have to, like, sit in front of some sort of tool and figure out what am I gonna prompt this thing? What can it do for me? What can it do well? It's still really coming from a push from the human

Claire Vo 3:20 in order to get these outputs from AI. And I think, you know, what people are really wanting when they come to a GenTick experience is is I wanna discover what you can do for me. I want you to be able to do a broad set of tasks for me. And once I set you off on a path, I want you to take it to its logical conclusion. And so I think there's this form factor or UX of the AgenTic experience that is actually a little bit easier to adopt than other kind of like AI products that I've seen. And so I think it's a little bit of the user experience as well makes a difference.

Conor Bronsdon 3:56 While the user experience may be superior and I think will continue to become superior, There are considerations on the back end as far as creation that you therefore have to take on. You've, I guess, shifted left the the challenge. Instead of having it be in the co pilot experience, it's earlier on in the development process, in the guardrailing, in how you evaluate and improve those systems.

Conor Bronsdon 4:19 And yet we're still in the early days of AI adoption. Yeah. This may not be the form factor that sticks long term. We don't know yet. Can you talk about the characteristics that you're seeing leading edge teams that are comfortable offloading more and more tasks to low supervision AI like agents? Like, what are those characteristics those teams have?

Claire Vo 4:40 Yeah. I think one is risk tolerance, and I don't think this has to do exclusively with AI. I think there are company cultures that are just much more risk tolerant and have much more of a embedded experimentation mindset. So if you're coming to this age of AI with a culture where you know what the appropriate level of risk to take, you're willing to let people experiment and fail and optimize,

Claire Vo 5:07 and you want to work towards outcomes and you're willing to tolerate learning and sort of like that growth path, through that process, you're gonna be set up really well. Because foundationally, the number one thing I see with leading leading edge adoption AI teams is they're just open. They're just more open. When someone says, hey. Can you try this AI tool? They say, already have. It's so cool, or I can't wait to do it. As opposed to other companies where they say, it's never gonna it's late. Never gonna work in our code base. Never, never, never. And you spend so much time upfront objective hand objection handling

Claire Vo 5:42 and not enough time actually figuring out what works. So I think that risk tolerance is really important. And then I think there really is operational maturity, at least at large scale. You know, if we're talking about startups, startups have the advantage of their small, their code bases are relatively less complex, their customers are probably fewer and lower risk, they can do a lot more. But if we're talking about any company

Claire Vo 6:05 of scale that's adopting AI in a meaningful way, they're operationally mature in a way that their engineering practices already protect for quality, already protect for velocity, are already to set up to make engineers productive. When you have that foundation, it's very easy to add in agentic engineers or AI IDEs or any of these sort of automations that then accelerate

Claire Vo 6:33 because the same practices, the same technologies, the same operations apply to when you're adding these tools in your stack as when you're adding, you know, engineers to your stack, you're adding new tools to the stack. It all applies. And so I do see this combination of risk tolerance, operational sort of maturity, and then honestly, tops down or at least centralized

Claire Vo 6:57 focus and accountability on adoption. So somebody has to say, it's my job to make sure we become the leading AI, you know, powered engineering organization. And we've seen this lately at the CEO level. You know, we've seen all these, like, CEO edicts. So we're now this AI company. You know, that needs to happen somewhere in the engineering organization. And LaunchDarkly,

Claire Vo 7:22 it started a little bit with me. Bless them. They're stuck with me. So AI AI native whether they like it or not. But, honestly, the the shift that made the biggest difference in engineering adoption was we made one of our most senior tenured engineering leaders and engineers the, like, sort of, like, AI czar at at in the engineering organization. And that centralized accountability

Claire Vo 7:46 and week to week execution just makes the practical adoption of these tools a lot a lot easier. So

Conor Bronsdon 7:54 how did you decide who the right person was for that AIs are, and what did you do to enable them to be successful? You might be surprised by the answer, which is I picked the AI skeptic.

Claire Vo 8:07 I like that. I picked, you know, not I wouldn't say, like, the AI skeptic, but certainly somebody who wasn't as naturally pre inclined as maybe I am to be bullish on the AI opportunity. I picked somebody who had one, a really good robust sense of our architecture and code base. Somebody that I knew kinda knew everything about our core monolith, knew how our engineering organization

Claire Vo 8:35 worked, and understands understood, you know, where there are dragons, as we say. And so somebody with good foundational understanding or a code base is really important. Secondarily, somebody senior enough with enough internal tenure to both have credibility in the organization when they say, hey, this really works or this doesn't. We trust that person, but would also be able to sort of foretell

Claire Vo 9:01 and avoid some of the big, like, landmines in adopting AI. And then the, you know, the third the third category is I really wanted to give this person has done so much for the company, and they're a wonderful leader and they're a wonderful engineer. I wanted to give them a win, honestly. So part of it is part of it was motivated by them having the right attributes to be the technical leader for this. And part of this was a career development opportunity

Claire Vo 9:26 I wanted to give them saying, you've been here for a while. You need to figure out what your next level, next wave of impact's gonna be. Congratulations. I am plucking you the the the the plum prize of you get to put AI all over your resume, and you get to be the leader there. And so those were kind of the three reasons why we picked this person. Zach, thank you very much for doing it.

Claire Vo 9:49 And it's been exceptional because he's not, you know, overly enthusiastic. He's not gonna say everything works, but he's also opened up his mind to what does work, what doesn't. They identified some technical places where we can invest to make the adoption easier, and we know who to go to for questions. Zach, how do I get access to x? Zach, how do I figure out how to get this product to work with y? We have a centralized person that makes it easier for the team to kinda understand where to go when they're trying to adopt these new technologies. Do you think leaders broadly need to rethink

Conor Bronsdon 10:26 their approach to innovation and risk with AI given the opportunity that's ahead here? Yes. A 100%.

Claire Vo 10:34 I just you know, the thing that keeps me up at night is some hot upstart company with a clean code base who's just ripping through features with AI. Like, it it really it really terrifies me. I look at the speed at which, you know, people are able to build things, and I think velocity will be a massive, massive differentiator. And I think people are gonna wait too long. I'm massively

Claire Vo 11:06 pair like, super paranoid about this. And so I think teams have got to get on this train because they're gonna be competing on the ground feature for feature, capability for capability. They're gonna be competing for talent. And if you don't embrace this, I just cannot imagine you you don't get left behind, especially when these times the teams can access massive rounds of of funding. You take the combination of, like, AI native,

Claire Vo 11:37 super high velocity funded. That makes me paranoid. And so we have an incredibly healthy company, great brand, amazing engineers. Like, imagine if we had the guts to say we're just gonna operate completely differently. We're gonna embrace this. We're gonna go as fast as possible. We're gonna build some really cool stuff.

Conor Bronsdon 11:58 I think the teams that can embrace that and say that's possible and that is for us are really gonna remain relevant in this next stage. And I think folks that don't maybe will not. I will ask about one point of what you said here. Because while I broadly agree with you and think any team that's not at least attempting to adopt AI is likely to be left behind, you did mention a team that has a clean code base and it has a high velocity of AI development. Then I'll just ask about that, given that that's one of the,

Claire Vo 12:29 key areas that I think many skeptics will will push on. Yeah. Yeah. I mean, look. It is very different. You know, you mentioned at the beginning I have this diverse, you know, perspective. I get two things. I get my darling petite little chat PRD repo. I've, you know, written every line of code with with cursor, with an I, like, I know that whole thing by the back of my hand. It's not that big,

Claire Vo 12:52 and it's built on a modern stack, and it's built on a stack that AI knows how to write for. It's just a dang delight to work in. It's so so great. And then I have LaunchDarkly, which is an amazing, scaled, proven, production grade enterprise product that has been built over the course of ten years that, you know, maybe wasn't built optimized for the languages and frameworks that AI seems to do the best with. That is complex,

Claire Vo 13:21 that, you know, has some tech debt. Those are very different very different situations. Now what I think people underestimate though in the situation where you have a legacy code base is a couple things. One, the ability for AI to accelerate cleaning up your gnarliest parts of tech debt. You know, like your front end framework used to be I promise you, I've done this two or three times. Eighteen to twenty four months of ripping out whatever JavaScript framework decided to deprecate that year and, like, move to the new hotness.

Claire Vo 13:53 I've done that almost every job of my career. This makes it a lot easier. I know somebody who, at at their startup just decided we're ripping everything out. We're replacing it with with Tailwind and with Shard CN, and we're just gonna have these, like, beautiful simple components, and I don't care. Let's just rip and replace everything, and now they can move very fast. So one, I think you can replatform some parts of your product a little bit faster

Claire Vo 14:18 and clean up some tech debt. Two is you can do purpose built things to make your repo better to work with for AI. And the bonus of doing those things is you make your repo better to work with for everyone. If an agent is having trouble running your code base locally, an intern is going to have trouble running your code base locally. A senior engineer is going to have trouble running your code base locally. Like, if it takes three days to get your local environment set up, that sucks for AI, and that sucks for humans.

Claire Vo 14:55 And if your code base is not well documented, that sucks for AI. That sucks for humans. And so I I do think one of the most effective tactics I've heard my peers do that we also try to embrace and launch directly is do a spike on how can we make this code base better for AI to work with. And that actually pays out pays out dividends in terms of efficiency. And maybe the last thing that I would say is a lot of people a lot of people say, it'll never work. It'll never work in our disgusting,

Claire Vo 15:31 disgusting old repo. Just it's impossible. And one, say that sounds like a you problem. But two, like, have have you tried? Like, have you given it a real go? Because I think people maybe try one PR with Cursor or they try one task with Devin, and it doesn't do well. And they don't learn why it doesn't do well. And they don't take the accountability of, like, maybe my prompt was bad,

Claire Vo 15:59 and then they give up. As opposed to what I think we did at LaunchDarkly, which is I said, just go experiment and report back what works, what doesn't. And for every one total dud, we got two helpful wins. And on the net, that's that's positive. And so I do think there are things you can do in legacy or more complex code bases to make it work both technically and operationally. You just have to give it a go.

Conor Bronsdon 16:23 I completely agree. I think there's a broad expectation misalignment based off of some of the marketing of AI around this is just gonna magically solve your problems. And it actually does solve a few of them, but there's still setup work that needs to done. There's still iteration that you need to do to make sure that your infrastructure is in the right place, that you have the context provided to

Conor Bronsdon 16:45 Devon or Cursor that you may need for it. And if you spend the time to do that, the dividends that you will receive are fantastic. And I think what you're saying out of all this is essentially that leaders are still underestimating the opportunity with AI and underestimating the risk involved with sticking with a human only engineering plan.

Claire Vo 17:08 Yeah. I mean, what I think is people are so worried about what if AI ships a bug that they decide not shipping anything is better. And that is just such a backwards way from a business perspective to look at development and innovation. Bugs with humans. Like Yeah. We we ship bugs with humans, and we ship them quite slowly. Yes. And so I just think people really underestimate

Claire Vo 17:33 the opportunity cost of moving slower than than they could. And I also think leaders really underestimate how irrelevant they will become if they do not know how to do this in large organizations. You know, as as I said, I gave, the kinda engineer that is leading our AI initiatives at LaunchDark. I was like, I gave you the career gift. This is like he at LaunchDarkly and beyond. Congratulations.

Claire Vo 18:03 In 2025, you led the transformation of an engineering organization from one that operated in a legacy way to one that's operating in AI enabled way. You have all the learnings, you know what works, you know what doesn't, you have the success stories. If you as a CTO, VP of engineering, engineering manager, staff, principal engineer are not developing those stories for yourself, I guarantee you,

Claire Vo 18:28 in two years when you go into interviews, you are not gonna be at the top of the list. If you say, we just we didn't really worry about that. That that wasn't gonna ever work for us. Or I did a couple things, but I don't really know those tools super well. You are just not going to have the hard skills to do the job. And I really do think it's a hard skills issue right now. It is

Claire Vo 18:49 a new type of engineering skill you need to develop. Can you say more about that? How

Conor Bronsdon 18:54 like, Give me some more depth on into it. Like, what does that hard skill look like? Yeah. I think I think there's a couple of things. So one,

Claire Vo 19:02 you know, using all the tools available to you. So I think coding is gonna go to AI enabled IDs. Just is. It's just better. It's a better way to live. Know? Happening. It's already happening. And so if you do not know how to manage context, effectively prompt, set up rules, access MCPs, all those things, if you have not set up your toolkit for how do I use this new set of engineering

Claire Vo 19:30 tools well into an advanced degree, you're not gonna have the hard skills to be a software the software team in a couple years because you will just not know how to use the toolkit. And so I think that's one very specific example. As a engineering leader, if you do not know how to integrate and operationalize the use of coding agents or automations, either in your

Claire Vo 19:57 DevOps or in your engineering operations, if you don't have a sense of how those things have or have not increased overall velocity in your team, if you have not spearheaded initiatives to, as we talked about before, make your code base better for the entire organization to work with using AI, you're gonna be sitting next to and interviewing against people who have done those initiatives, who have figured out those operations.

Claire Vo 20:23 And so, again, I think this is just as we look at how we evaluate the progression of engineers from, you know, SWE one all the way through principal engineer. If we think about what it means to be an engineering manager or director of engineering VP, CTO, I think we need to add AI fluency into that list. And I think people need to come up with a very specific list of

Conor Bronsdon 20:46 skills that they evaluate for both in terms of promoting people and hiring new folks. I'm already seeing in some of the hiring discussions I'm in where people that would be fantastic candidates, you know, two years ago are not as well rated because they haven't dove into AI feet first. Yeah. And I expect that to happen even more so over the next couple of years, and not just in technical roles, in marketing roles, in sales roles. Yep. If you are not

Conor Bronsdon 21:16 embracing this technological revolution, you are at a risk to be left behind. And therefore, I think what you're doing at LaunchDarkly of building this culture, cultivating this culture where the risk reward of velocity is seen as a net positive and where AI is embraced and experimented with is so important. So, you know, you mentioned appointing an AIs are helping them to transform the organization.

Conor Bronsdon 21:46 What other steps have you taken to really create a culture where everyone within the R and D teams

Claire Vo 21:53 is enabled to take this on? Yeah. We do a couple things. I think the first thing is very tactical, which is you have to get finance and security out of the way. And by out of the way, I don't mean you don't go through finance security. I mean, you have a very simple framework for evaluating tools and getting budget for them. And so Demonstrate ROI. Yeah. I established very early on, we're gonna spend some money on AI. It's gonna be totally net positive. I know in my in my soul,

Claire Vo 22:19 and we just gotta figure it out. And then info sec, I need, like, a fast turn evaluation, and I need you to be cool. Like, be cool, be risk aware, do not put our customers at risk, do not break any of our contracts or compliance codes. But, like, otherwise, I got you gotta be cool. Like, we gotta be able to try stuff. And so I think having those two teams deeply aligned to this being something we're gonna do is great. Luckily, we had

Claire Vo 22:44 no friction friction there. And in fact, the finance teams are always excited because they see the the potential efficiencies gained with these these kinds of tools. That that's the first part. Then I really believe in this building public culture when you're trying to adopt AI. So we created this Slack channel. It's called project building with AI. It's got, like, 200 people in it. And you just every time you do something with AI, when it works, when it doesn't,

Claire Vo 23:10 related to work, not related to work, dump it in the channel. So people can see, hey. I did this PR, with Cursor, and it totally blew blew me away. It built all my tests for me. I was super happy. This is great. To, you know, public chats with with Devin or another agent where, like, really, really ate it on this one. And everybody's, like, yelling at the agent to go to sleep, and it's very, very funny. So we just put it all in public. And the benefit of putting it all in public is, one, you normalize it. You say this is not something we hide or we're ashamed of or that is wrong or is not allowed.

Claire Vo 23:46 It's all open in public. Two, you get this nice, like, learning across the it's the best way to socialize socialize learning is across the organization. So I love that that public channel. It's my favorite channel. It's super fun. And then another thing that we do kind of related to building a public is we have kind of this like AI Friday power hour. It's basically like a Twitch stream of internal people using AI tools. So we all get on ten in the morning Friday.

Claire Vo 24:16 Everybody's in a good mood because it's ten in the morning on Friday. And, you know, two or three people try something live with AI or show an AI workflow that that worked for them. And so that is also something where you can just kind of, like, look live and watch them in our own code base, try to figure things out, explore new tools, evaluate the quality, get some, like, champions

Conor Bronsdon 24:40 out there. And so I found that to be another really effective tactic. I like them a lot. And I have to say I've I've really enjoyed the various vibe coding livestreams that I've had the opportunity to watch. There was one that Microsoft built a few weeks ago with, Brendan Burns and the team at GitHub that I I really enjoyed where they're just like, oh, we're just gonna build this reminder app for my family that I wanna do. Let's just spend two hours and kinda knock it out and and, you know, quick five code session. And I think seeing others, you know, essentially

Conor Bronsdon 25:11 pair programming with also an AI with pair programming also with an AI enabled, you know, is so valuable to give the context of how how others are doing it and to to to build in public and share these learnings. But I know it can be hard. There's often hesitancy. People are are nervous about not being as good at something initially or not wanting to to show things off in public. Are you finding more hesitancy with maybe senior level engineers who,

Conor Bronsdon 25:45 are like, oh, I've been doing this for years, and I know what I'm doing over or are junior level engineers nervous about not excelling? What where are things sitting? You know, it's hard it's hard to quote, but my heart wants to say, like, you know, you get old and curmudgeonly.

Claire Vo 26:01 You get stuck in your ways, and you get a little True for me sometimes. Paranoid. And, you know, the more senior you are in your career, ultimately, like, the more it becomes your problem if something goes wrong. Of course, my directors of engineering are, you know, a little bit less risk tolerant for this because you know who gets paged in the middle of night, who gets yelled at when we have a sub zero? Our directors of engineering. Like, of course, because accountability

Claire Vo 26:25 rolls rolls up. And so I think they're appropriately, not skeptical, but just, you know, risk adjusted for for some of this stuff. That being said, you know, I do think across the board, there does still exist AI hesitancy for a couple reasons. One, as I said, you're asking people to learn a new hard skill, and people just do not have time to learn anything new. Like, okay. I could spend two hours

Claire Vo 26:52 spinning up this agent and installing a new IDE, and I like VIM and blah blah blah blah blah. And or I could just, like, knock out this PR. Which would I rather spend my time on? And so it's like all l and d initiatives. You have to have the time to carve it out. Two, AI is not one shot, a 100%, totally accurate all all the time. And so, of course, you are gonna get

Claire Vo 27:19 these instances where you work with AI and you get you bet get bad outcomes. And that to me is expected. It's cost of the game. I think it nets positive, but that can be a real detriment to to adoption. And then I think the the last piece that I found really interesting is stylistic. Right? There are are both individual coding styles as well as organization wide

Claire Vo 27:43 best practices and styles that teams have just gotten used to. This is how we write tests. This is how we document things. This is how we do our front end. And when an AI says, I could do it I could do the same thing. I'm just gonna do it differently than you like. People get frustrated. So I think there's a lot of reasons for folks to be skeptical. And then I do think

Claire Vo 28:05 leaders have this really challenging line to walk, which is, look, we have to get more efficient. We have to get more efficient because the market is getting more competitive. And sucks to be us, but that just means we have to do more with less. And I think that has been the case for several years. Nobody is saying, like, I have way more headcount than I used to. And everybody's saying, like, just hire to solve your problems. No one's saying that.

Claire Vo 28:27 And so I do think there's this reality that there is this efficiency, you know, program to some of this. And when you say that out loud, people say, you're replacing my job with AI, and I don't like that. And so I do think it's very complex. You have to have a very healthy culture in order to, like, put your arms around this, make people feel like it's part of developing

Claire Vo 28:48 both their personal career as well as the value of the company, which benefits them from a financial perspective. And so I think there are ways to get over to over the hesitancy hesitancy. But you have to be really precise about what the hesitancy is, address it heads on, and then kinda say what we call, like, say the stinky fish in the room, which is people are afraid they're gonna be replaced with AI. People are afraid that they're just gonna be asked to grind out more work and more PRs and more features and more and more and more and more with less, less, less, less, less. If you can say those things and you can address them, you can hit them heads on, and then hopefully you can get over some hesitancy and get back to building. I love it.

Conor Bronsdon 29:22 And I've also heard you describe, junior engineers with AI as, perhaps a loaded gun.

Claire Vo 29:31 Can you expand on that a bit? Yeah. You look. I love them. Give me all day a like a kinda junior, early career engineer who's all in on AI, who knows every prompting trick in the book, who has tried every coding open source coding agent before you've even heard of it, who is, as I very gently say, like, too dumb to know better in terms of, like, what they can bite off and what they can't. Give it to me all day. You need those in your team. You need

Claire Vo 30:00 big early career energy because, you know, sometimes you get some magic out of that and it keeps the rest of the organization on its toes. And for folks that are early in their career that have those attributes, the advice that I would give to you is you have been given an incredible opportunity, and it is also wise to know what you don't know. It's just like super wise to know what you don't know. And so if you can go in and say,

Claire Vo 30:31 you know, was bored last night, so I built an entire MCP for our app. I wanna put it on our public repo. And you know, but I'm not sure I handled auth right. Or is this gonna be maintainable for the labs team? Or and, like, just knowing what you don't know so you don't come to these, you know, code reviews or come with these proposals without a good sense of where you need to look around the corner, where you need advice, where you can learn,

Claire Vo 31:00 you're just not gonna do well. I mean, I've I've heard plenty of my peers who have hired that sort of like cracked AI engineer who in an interview is like, can build this and that and answers the questions well, and you're fine if they use cursor because it's great. And then you realize they're just shipping a bunch of code they do not understand and don't care to understand.

Claire Vo 31:18 It's just a bad a bad a bad situation. And so, love early career, love a good AI powered YOLO, and, like, know what you don't know and know know how to grow your grow your own skills. You have this great, engineering tutor in AI, but you also have great mentors on how to work for the team, how to work in a big code base, how to solve scale problems, how to solve technical challenges, and I think you should take advantage of I really appreciate you bringing this broad perspective across

Conor Bronsdon 31:49 how to enable an AI first team and how you've approached the transformation at LaunchDarkly. But perhaps most interesting for me is how you've been solo building an AI startup on the side, chat PRD. And you're moving at a velocity that would seem impossible to someone who was trying to do this while also having their main full job a couple of years ago. And you've said that everything you think of, you build in a week, you don't really have a product road map because, hey, I'm you're just shipping.

Claire Vo 32:28 What's that experience been like? I think it is such an important experience. Again, it's probably the source of what makes me, as I said earlier, so paranoid. Like, what I stay up at night and think about is, like, what if there's a Claire out there that is just ripping in our product space? That makes me paranoid because as somebody who has built this myself and has a career that spans over two decades, like, I've done a venture founded startup myself. I've worked at many startups. I worked at large organizations. Like, it is different.

Claire Vo 33:01 I raised capital ten years ago to build a product, and I swear on my life, I could probably build that product before lunchtime today again if I need to. Like, it's just totally different right now. And if you, as a leader, do not take a minute to really feel how different it is. Not can I get cursor adopted by, like, finance and my engineering organization?

Claire Vo 33:28 Can I get my PMs to write PRDs in in ChadGPT? Like, not that. Like, put your hands on a keyboard and feel how different it is. Put your hands on a keyboard and try to rebuild your own product. Like, until you feel that moment of, like, holy moly, it is so different right now, you really are just not gonna be prepared what for what's what's coming next. So I think that has been the most valuable thing about ChatParity. I tell everybody,

Claire Vo 33:55 I love ChatParity. It's doing exceptionally well, better than I could ever expect. And if it goes to zero, it will have been worth it because I've I've learned this lesson. So this is like my number one piece of advice to people is learning how to build something like this and what it really takes and what it really doesn't take is super valuable even if you remain in larger organizations and bring those learnings to your kind of career in a in a larger org. Let's extrapolate

Conor Bronsdon 34:20 this experience you've had and align it to the concern that you say it brings up for you of, hey. What if there is a Claire out there who's rebuilding our product right now? What are the biggest operational disruptions that you believe small AI native teams will cause for larger incumbent organizations?

Claire Vo 34:40 I think, price disruption can be one of them. Right? You can offer some large percentage of feature capability for some small percentage of cost. That that can be one. I think perceived innovation velocity is another one. If you are just perceived at innovating at a lower pace than your competitors, whether or not your competitors are really operating at any scale in the market doesn't matter.

Claire Vo 35:06 Optics, do have an impact, then you're gonna be perceived as, you know, a laggard company. I think that's something to really consider. And then talent attraction is another one, which is for as many AI skeptics as you have in an engineering organization, you have just as many people who want to build, you know, modern, best in class engineering skills. And if your organization does not provide those for them, then they're gonna go look elsewhere.

Conor Bronsdon 35:33 Yeah. I think you're absolutely right that if you're not enabling folks to have the opportunity to learn, they will either be doing on the side and maybe not bringing those learnings to work, maybe they will, or they're gonna look for a new organization that's gonna enable them. Because the best engineers out there right now are fully aware of what is happening, and they are seeing

Conor Bronsdon 35:55 this opportunity and can't afford to let it pass them by because most of them aren't retiring next year. Most of them have several years in their career, and they want to continue to be great. And many of them simply are curious and excited, if not both. So what would your advice be as we wrap up this conversation to the different categories of folks within their career? Let's say maybe,

Conor Bronsdon 36:18 you know, more junior engineers who are getting started, leaders who are are farther along, maybe they're director plus level, and then the folks who are that senior AIC to, like, maybe engineering manager team lead levels. How how would you advise those different groups to approach

Claire Vo 36:36 this AI movement? Yeah. So for early in career, I would say, you know, embrace your your natural enthusiasm for the new and share your learnings. I think the best things that maybe early in career folks bring into an organization is an experimentation mindset, a sort of fearlessness in trying things that maybe will require some work on the back end, but at least can get to to prototype version. Then really staying in touch with, like, the new hotness. What what's new out there? Share it. We we wanna know.

Claire Vo 37:10 I think for leaders, this is the the the folks that I really wanna speak to, which is close your eyes and imagine what an engineering organization is really gonna look like in five years. What is it really gonna look like if you just cast all this forward? What's the shape of it? What are engineering managers going to do? Are we gonna have PMs? What tools are you gonna have? How is software gonna be built? How are you gonna attract talent? Cast forward to that, you know, five year future and then start preparing

Claire Vo 37:38 to get your organization there now. I think that's so important. It is yes. You have to worry about the day to day today, but you really need to figure out how this is all gonna shake out in a couple years and and get it figured out. And then those kinda like senior ICs, love them. My favorite group. So so I think you all are going to be the highest impact in this new era. Like, I actually have said this for a while now. I think this is the era of the super IC.

Claire Vo 38:05 Like, you are gonna be able to get so much stuff done. You're gonna be able to have so much impact. You're gonna be able to command a very high salary because you have a combination of experience and, like, breadth of impact powered by tools. If you just lean in, like, this is your time. And guess what? Bonus. To make more money and get promoted, you don't have to manage people. What a treat. Like, what a treat.

Claire Vo 38:32 You don't even have to have one on ones. You don't have to do, like, performance reviews. You don't have to deal with people's complaints. You can just, like, build stuff. And so I do think this is, like, the era of the super IC, especially senior ICs. I think leaders out there, you gotta figure out a path to pay them more money and give them better bigger titles without forcing them to take on teams.

Claire Vo 38:56 And so I would say, like, embrace that era and figure out what you want the shape of your career to look like, during that time. You mentioned something

Conor Bronsdon 39:03 which I wanna drill down on, which is, are we gonna have PMs in a few years? And we're already seeing this transition into AI PMs. And obviously, that's a bit of a nebulous term so far, but it certainly involves creating MVPs a lot faster. It certainly involves moving a lot faster and and changing the approach. What how do you see the role of a PM evolving

Claire Vo 39:25 within engineering organizations or disappearing? I mean, I famously murdered the career of PMs at Lenny's confer Lenny's conference last year, with my PM is dead talk that rattled a bunch of people. Look. I think the role is gonna change. I just fundamentally think the role is gonna change. I think there are going to be sort of two archetypes of product managers. I think they're gonna start to come from very different practices.

Claire Vo 39:50 I think you're gonna have, like, the prototype manager that is much more of this, like, combo UX engineer PM who'd like to finds, like, product experiences and can get you to a high fidelity sense of what that product experience looks like and how it needs to technically operate very quickly. I think that's one attribute. And then I think for those that maybe are not that attribute or in in addition to attribute, you're gonna have these, like, very commercially

Claire Vo 40:15 minded GM style PMs who think a lot more about what market am I selling into, how am I making money, what is the positioning, All those sorts of things. And so I just think this, like, middle ground of, like, I'm the keeper of what users want. And, you know, I'm a people person, damn it. Sort of, like, I just talk to the engineers because the engineers can't talk to the designers, and the designers don't really wanna talk to the executives, and the executives don't really talk to the humans. Like, I just think that piece is just not a real,

Claire Vo 40:45 like, a real robust enough job with enough impact when you take into consideration these tools. And so I do think the product manager role is gonna shift. I am building a product manager agent that I think can take a lot of those tasks off the plate off people's plates with Chat PRD, and then let them focus on things that I think humans are really good at. Talking to other humans, figuring out what they want,

Claire Vo 41:12 selling, creative inspiration, unique user experiences, like special insights. I just think the more we can clear our minds of, the kinda like tactical day to day operation stuff and the more we can focus on, like, depth of creativity, better our products are gonna get. So I think it'll change. I will definitely be wrong for many years, and then suddenly I'll be right. So I look forward to that. I love confidence.

Conor Bronsdon 41:38 And I highly recommend folks go check out chatprd.ai and explore more of Claire's work. Claire, where else should our listeners go to learn more about you and to follow what you're up to? Yeah. I'm on x at Clairvaux,

Claire Vo 41:52 also LinkedIn. That's my name. I'm on TikTok. We're reviving the TikTok. You heard it here first. I have chief product officer on TikTok, so look forward to that content. And then tune in to the how I AI

Conor Bronsdon 42:04 podcast where I talk to other people about how they use AI. Fantastic. Well, Claire, it has been a distinct pleasure having you on the show here. Thank you for an entertaining and wide ranging conversation. I'm excited to think through some of your advice and implement it myself. So it's been a ton of fun, we really appreciate you having you on. Thanks so much. And for everyone listening,

Conor Bronsdon 42:24 make sure you check out our YouTube to see so much more, me. Make sure you check out our YouTube to see so much more behind the scenes content. You can find it at RunGalileo on YouTube. There's demos, webinars, many more incredible podcasts with guests like Claire, and we'd love to have you there. Thanks so much for listening, and we'll see you next week.