Should We Still Build Careers Around Deep Specialization?

0:01David Rice: Most AI coverage tells you what happened. AI Signal tells you why it matters. It's a free, five-minute weekly briefing that helps people leaders make sense of the shifts, trade-offs, and decisions hiding behind the AI headlines. Less keeping up, more knowing what to do next. Subscribe to AI Signal at peoplemanagingpeople.com forward slash AI dash signal.

0:27David Rice: For most of our careers, the advice has been the same. Specialize. Go deep on something valuable. Build expertise nobody else has. Today's guest thinks that advice needs an update. Not because expertise is dying, but because AI means we need fewer people who are deeply in any one domain. The knowledge is more accessible now. The value proposition shifts. On today's show, I'm talking with Tony Castellanos, head of people at Nextdoor, about what actually becomes more valuable as AI takes over the codifiable stuff. His answer? Taste. Knowing what good looks like is becoming more important than knowing how to produce it.

1:05David Rice: And you can't train taste in a classroom. It comes from reps, at-bats, gut punches, accumulating enough wins and misses that you start to recognize the difference instinctively. The org chart conversation is where things get kind of interesting. Tony thinks the generalist is about to be revalued. Today, they're treated as the operational layer. Junior, not quite as useful. That flips. Experts become fewer. The people who can weave expertise across domains, who can figure out what a company can do when you pull from multiple centers of knowledge, those people move to the center. And the concept of a cost center?

1:42David Rice: Tony thinks that label stops defining who gets to take on a problem. The org chart evolves from a map of where information sits to something more fluid, teams forming and dissolving around a problem at hand. So today we're covering why deep specialization increases your AI exposure unless you can connect it to broader business problems, how taste develops and why it's becoming a core leadership competency, the inversion of experts and generalists in AI-era organizations, and why the cost center model may be what looks strangely outdated five years from now. I'm David Rice. This is People Managing People.

2:16David Rice: And if you've been building your career around depth in a single domain, this conversation is worth thinking through. Let's get into it. For seamless performance review processes and enterprise-grade security, try TeamFlect for free, the number one rated performance management platform in Microsoft ecosystem, trusted by enterprises worldwide to align goals, run performance reviews, and develop talent at scale.

2:43Tony Castellanos: Tony, welcome to the show. It's good to have you. Thanks so much. It's a pleasure to be here.

3:15David Rice: reaching a point where being exceptional at one thing is actually a risk unless you can connect that expertise to a much broader business problem?

3:24Tony Castellanos: Important question. And yes, we've all been trained that you go deep on one thing, you're the best at it, and you stand on that platform. And I think that maybe the headline exaggerates as a touch, because expertise isn't dying. Expertise is still really important. We need to know how to do things. I think what changes in the world of AI is we just need fewer of the people who are deeply in a specific domain. And that's because their knowledge, their expertise, and the broader knowledge base is much more accessible to people around it. If I think about the skills that become more valuable, you still need that person who understands what's happening because we need to be able to audit what is AI delivering, how is it calculating this, how is it thinking about that.

4:04Tony Castellanos: But I think there's a ton of value in being able to weave that expertise into how your businesses moving forward. An example of this, right, is if you go back in time, right, there were companies that would have lots of people who were in roles where calculation was the core. I mean, if you were in finance, if you were an analyst, if you were an engineer, right, there was a lot of math being done. And then all of a sudden, Excel arrives. You're like, well, we need a fraction of those people to actually do the math behind what the business is. But that didn't just like eliminate the need for the people, it created a different value proposition for them.

4:38Tony Castellanos: So like the new value in a human was understanding like, what should you be calculating? Like, what's the right model to build? What does it mean? Like, can we make better business decisions from this information? So it's really, how do you use that expertise? And I don't think anybody would make the argument that Excel ended financial expertise. Like we all have those teams in our companies but i think the real value became like you have a few people who are deep experts in this and really what they bring to the company moves up a level in terms of like how do they position that strategically with what they know so i kind of think that's what's happening again it's just faster and it's really across every function that ai touches yeah and so much of it is like

5:21David Rice: there's certain types of work that i think that it accelerates at right like why it's having such a disruptive factor in coding is you write code and it either works or it doesn't right so it doesn't

5:32Tony Castellanos: work you know you know what you got to do it's great for binary outcomes you know right and wrong true false i mean it still gets those things wrong which is why you need the experts to check it but i think anytime you get into rules-based programs or systems it can be really effective there and when you get into knowledge dissemination that is really rooted in specifics rather than lead into judgment.

5:57David Rice: Yeah. Well, you mentioned the piece judgment there. Cause like I was listening to somebody talk last week and they were talking about how do you know when a marketing campaign is ready to go? Ultimately, like somebody with the taste to say, yes, this is all complete, has to stand there and say, I sign off on this. And we talked about this idea of taste beforehand, which is a word that up until now, I don't think a lot of CHROs had in their competency models, like five years ago hungry you know right it's five years ago i don't remember seeing the job description in which taste was involved but i guess if ai makes knowledge and technical capability much more accessible does knowing what good looks like become more valuable than knowing how to produce it and if that's the case then how do organizations sort of identify and develop

6:44Tony Castellanos: something as intangible as taste i think it's incredibly important in this ai enabled world because you can produce so much content, right? So I think we're all being flooded. I mean, if you just go look at your Google Drive right now, I'm sure there's like 10X the docs that you had a year ago because you started all these things with Clot or with Codex or Gemini or whatever. And so, yeah, I do think taste becomes incredibly important for refining that into a way that's going to be digestible for any human that's going to consume it. And like how you develop that, my honest answer is just it's repetition.

7:17Tony Castellanos: It's at bats. Like there's not really a course in training that you're going to get for it. It's the accumulation of the wins and the gut punch bruises when you get something wrong, right? Like if you do that enough, you really start to refine what does good look like and what does subpar look like. And I think that's hard because it requires the opportunities to do that. But this is one where I think AI also just changes the pace at which you get some of those at-bats, right? Because you can keep pressure testing things dozens of times, I mean, in the span of an hour. And that's how we think about AI at Nextdoor, right?

7:54Tony Castellanos: It's very much about brainstorming and experimentation. It's not about generating the answer for you. So you can do it, but I agree with what you said. It puts a lot more pressure on the person to define that because AI is going to hand you something that is sophisticated. It is persuasive and it's going to be just flat wrong, right? So, and like, as a human, you're the only thing standing between that artifact and the audience that it might reach. So I do think it's incredibly important.

8:22David Rice: That's interesting. I like the idea of just at bats, you know, like to use the baseball reference, but I like it. Like reps is what, and you think about like, how did somebody who, let's say they're in a, I use the marketing example before, so we'll stick with that. But it was like, you know, they've seen this in a year, you know, you go back 10 years, they might see that problem come up a dozen times. but now with this they're seeing the same type of thing that they've got to approve 10 times that many times in some cases so the bats can teach us at least more quickly and i think the question i have is will people be able to see the whole picture because of the you described the volume of stuff that's happening now it gets harder and harder for leadership in particular to still see the whole picture when everybody's producing so much more i think that's going to be an interesting thing to watch.

9:11Tony Castellanos: Agree 100%. And if like, I've tried to read most of the docs that have come my way, but honestly, they're just flowing in so fast. But this is where it comes back to like judgment and taste, right? Are you putting something together that is succinct, that is clear, that makes the connections to what you're trying to accomplish? And the nice part about AI enabling that is you can literally just tell it, create 20 versions of this for me. Make sure you prioritize this, this, and this. You look at them, you pick the best one, you refine it. And so like, It's so much faster to get to an end product, but I do agree that the deluge of information that comes our way right now can be overwhelming.

9:47And so there's a lot of pressure on just distilling it into something that's going to be useful for a person.

9:53David Rice: I mean, especially now that we're getting into this era of agents, I'm waiting to see the story where it was like some manager and it did all these things that the person couldn't even process or didn't even know existed because it's just working so fast and generating so much. that story is bound to come out at some point.

10:10Tony Castellanos: So I mean, it just happened with an escaped version. I'm not going to name it because I feel like I might get in trouble for that. And that's one of the things like one of our principles is own what your AI does. Right. And so I think people just can't take it for granted. They can't just ship what AI produces. They also can't just allow AI to ship what it produces right now. And I think getting that right, whether there's a human audit before anything moves outward or whether there's guardrails or an auto trail after the fact that sort of edits it that will catch things that need to be sort of pulled back.

10:44Tony Castellanos: There has to be some sort of mechanism like that.

10:47David Rice: Yeah, you started experimenting with organizing the people team around problems rather than sort of traditional HR specialties. And I think that's like an interesting kind of approach, potentially much bigger change than adding AI to an existing job, right? So if we start organizing around outcomes instead of expertise, what happens to the sort of functional structures that career ladders and ownership models, you know, we spent decades building?

11:13Tony Castellanos: This is a fun one. And like full transparency, we haven't blown up our org chart right now. Like we still have recruiters, we have compensation, we have people, business partners. But you're right. We have started to look at what are the actual problems that that group is intended to tackle together. So we've tried to orient the teams around the things that are true across the people realm. And for us, that comes down to talent, activation, which is a combination of engagement and enablement and culture. And so for every one of those, there's no version of it that's just recruiting specific or PVP specific, right?

11:46Tony Castellanos: There is this entire body of work that goes into making sure that you're bringing in and cultivating the right people and the right talent base, that you're supporting employees in the right way, that you're building an environment where everybody can do their best work. There have been functions that have been focused on specific aspects of that, and that's been because the expertise has lived in those functions. But I think what AI enables is more cross-understanding of how they come together to make those things real. And so what we're trying to do on our team is really to create more of a mindset around what is the ultimate goal here rather than what is the recruiting piece of this pod.

12:26Tony Castellanos: You know, I don't want people thinking in terms of their operating lanes. It's really did the people organization deliver value in this dimension?

12:35David Rice: It feels like it requires people to be a little bit more. I mean, not obviously adaptable, but it's almost like kind of philosophical about their place within things. You know, maybe that's a little too deep of a word, but in a way it's like, where does my skill set impact this overall outcome that we're seeking? That's a different way to look at our work versus how can my skills influence the outcome that we want to find?

13:04Tony Castellanos: it's there and i think it's even a step further that is like what can i learn and apply here like i see the problem and i'm not limited to the fact that i'm a recruiter or i'm a compensation person or whatever it is i'm a member of the people team there is an end-to-end flow of bringing talent into developing it to promoting it and all these things like how do i assist or move that all forward and i think that one of the fun things is that you get a lot more we go back to reps there's a lot more iterative cycles that you can get and like how can you apply your expertise and what can you learn because ai is going to be your partner in that so i really want to extend the way that people think about again their function to really like ultimately what is the end goal of

13:48David Rice: this really there's an obvious danger in pushing everybody towards sort of broader roles and i'm i think anyways i think you can end up with a workforce of generalists in theory right like who know a little bit about everything, but not a lot about everything. I think you kind of answered this earlier. You said expertise isn't necessarily going anywhere. But how do you create people who operate end-to-end across a problem and still preserve their expertise that will allow the business to function should, let's just say, an LLM crashes for a few days, they still can go and do the things that they need to do at the depth that they were able to do it before?

14:25Tony Castellanos: For sure. So if I were to take a step back on this one, right, I think that the thing that I worry about most in a world of like you have more generalists, it's less about lack of understanding because I think people who are intrinsically motivated and curious as they work through problems at their organization, they're going to develop that context. So they're not just going to be flying blind. But I think the real risk is in people just taking the output of AI, shipping that and moving on. And that is not confined to generalists. Specialists can do that too. So it comes back to this idea that it's a problem of breadth and really depth and judgment rather than it is sort of context awareness.

15:04Tony Castellanos: I think that as long as you hire people who genuinely want to understand their environments that they're operating in, I think that's going to become a new criteria for us. The term curiosity is really rising up the chain on how we think about evaluating talent coming in. We want to make sure that people are absorbing what's around them and that's allowing them to make better decisions. There's a lot of opportunity in setting up teams that way. But to go back, sorry, I'm stuck on now the LLM goes down question. I do think that's one where personally, oh man, I would feel far less effective. I actually don't even know if I could do my job anymore.

15:40Tony Castellanos: But in that realm where you have generalists who are enabled by AI and it goes down, I don't think that you lose the knowledge, right? What you lose is the link to the knowledge. What AI is doing for them is it's really expediting. How quickly can I get all the context, all the metrics, all the everything to make this decision or to move this program forward. It doesn't then eliminate that person's mental model for what do I need to make this decision? It just puts them back into the mode of yesteryear when I have to manually go collect, like what actually happened on this product? Like what is the revenue that this generated?

16:17Tony Castellanos: And so it just takes longer to fill in the pieces. But I do think that if that person has been enabled for a long while, they have a really clear picture of what information and data sets they need that will inform their work going forward.

16:29David Rice: curiosity is the big one right like i come back to that as well like you can't that's one thing about humans that i think is where we will be resilient it's like you won't eliminate people's just desire to know things to be able to do different things yes this technology can do a lot of things better than us but people like doing stuff people like learning stuff that's just the way we are right we're hardwired for it and we're not going to undo thousands of years of evolution just by introducing an AI. It's not going to happen.

17:01Tony Castellanos: No, and I think for the right people, that creativity is unleashed through this because you get so much farther, so much faster. But again, I think there's also the risk that it is like you just take things at face value and that's where things start to really degrade.

17:15David Rice: Yeah, that judgment piece. And we've already seen some data come out of executives worried about de-skilling, that they're seeing people just after a period of time, maybe six months, They're really working with the model really closely, validating everything, exercising judgment. And then over time, something happens and you just sort of start to allow it. I'm wondering what's going to be some of the most successful strategies against that happening. Like, how do you keep that? Because it could maybe, I think we're at risk of it becoming a bit boring or just routine. And so how do we keep the people invigorated in it and really obsessing over the judgment piece and the taste piece?

17:56Tony Castellanos: Well, I think what you just said, I mean, that's a human relationship too, right? You have a new hire. You don't automatically trust everything that they do. But over time, they deliver. They get things right. And you more and more trust them, just like you're going to more and more trust an LLM. And that means like as they're sending things to you to proofread, to audit, to check, you're probably doing less and less and less of that over time. It's just a natural human behavior when you trust something. The nice part about AI is it's really good at also all those audit points. It's way better at proofreading a document than I'm ever going to be because my eyes start to glaze over.

18:29Tony Castellanos: We're going to need to create a world where you have the multiple teammates, the ones that you start to trust more and more because you've been in sync, it's gotten things right, you've shipped work product together. But then you have the third skeptic who comes in and is like, wait, you two are wrong. And that can also be an AI-enabled service. And that doesn't mean that either of those are right. But to your point, it's provocative. it puts the human back in the driver's seat to say wait these two services are disagreeing what's the right call what's the right thing for me what's the right thing for my organization so as long as we don't fall into the trap of just blindly shipping and that you are always critiquing what's coming out and you can encode that if you want which i think is helpful i think

19:10David Rice: we're going to be in an okay spot yeah absolutely i agree when we were chatting before this you mentioned a compensation agent and i thought it was interesting because it's not really replacing your compensation function, as you were saying before, but it is distributing some of that function's expertise to recruiters. I'm curious, do you think that that is ultimately the bigger sort of organizational impact of agents, right? They're not replacing expertise, but they allow expert knowledge to sort of travel much faster throughout the company.

19:38Tony Castellanos: Yeah, I think that's one of the huge highlights for AI, for sure. And so what you're describing, our compensation agent is what we've built is it's this service that plugs into our compensation architecture and it looks at our employee base. And what it does is it's accessible to our people, business partners and to our recruiters. And it doesn't look to analyze situations. It doesn't provide recommendations. What it surfaces is factual information so that those people can make informed decisions. And what this does is, you know, so a recruiter can say, hey, I want to make an offer at X. How does that compare to the bands or how should I think about that?

20:13Tony Castellanos: And it will say, here's where it sits within our compensation practices. And here's an approximation of how it compares to people internally. We don't expose any individual employee information and we make sure that everything is anonymized enough that you can't actually get to an individual's compensation. But by equipping the recruiter with not only does it, this is where it sits in the band, but also this is the equities or inequities that it will create internally. It allows them to expand their perspective from closing the candidate to build a harmonious team. And it does that without needing a compensation person.

20:47Tony Castellanos: Because I mean, I grew up in recruiting, right? And I'm so accustomed to it. All right, I'm going to get the offer details from the candidate. I'm going to go talk to compensation maybe a day or two later, whenever that person is available, I'm going to come back. And instead of that multi-day chain where you're debating with somebody because you don't have the relevant context, it's at your fingertips. And so I think it just makes things go faster. And by exposing the actual data, it equips you. We were talking about earlier about generalists. It builds a context layer that you can continually draw from.

21:18Tony Castellanos: So I think it is developmental as well as effective.

21:21David Rice: I'm interested because you've got enough specificity there that this agent is sort of contained and it's the type of a task that it can excel at. What was sort of the conversation when you sat down to think about how you wanted to develop this and essentially goal set for it? Because we talk about agents like they are employees in some ways. And how are you sort of developing the role that you wanted this to take on?

21:46Tony Castellanos: Yeah, agents as employees is a big one for us. So we lovingly have named this compadre, your comp friend. And that is full credit to Orion Cawthorn, who's our head of compensation. And where did this come from is you have recruiters and compensation people who are sitting in these discussions are saying, do we really need to be here? Like, isn't there a different way to do it? And so this started way back when we kicked off a few people team AI days where everybody was focused on solving a problem with AI. And we've been doing this for a while now. And one of the things that came from it is can you build the service that makes this information accessible but does it in a very responsible way where you're protecting privacy but you're also equipping recruiters and PVPs to make the right decisions.

22:30Tony Castellanos: So it came out of the team just not wanting to spend time on this thing because it was a transactional meeting. and that's one of the things that ai is great at is removing the overhead associated with these tactical interactions that we have constantly day to day yeah i can think of about five of them that i'd like to make right now right and i think you know as you start to make them i mean i have a little chief of staff sam wise who does things like meetings prep and agenda organization and will give me previews of the day and there's just a lot of our days are consumed by engaging one-to-one or with groups that just information absorption.

23:08Tony Castellanos: And that's something that AI can do for you. And so when you have that as the precursor to a conversation, it makes the actual conversation much richer, much more informed. So you're not having to take the time of everybody just for information sharing.

23:21David Rice: That's interesting. And you just said the information absorbing thing. And it got me thinking about a conversation I was having last year with somebody who was, we were kicking around ideas for things we could maybe build. The person I was talking to is in Scotland. and he was saying to me, he's like, you know, one universal problem, I think, no matter where you work in the world, so much information comes at you. Some of it will irritate you. Some of it will excite you. Some of it will bore you to death. But none of that has anything to do with what's most important, right? And so if it can just synthesize, at the end of the day, it's going to give you like a five-minute little personal podcast.

23:56David Rice: You can listen to it on your drive home or in your earbuds, and it'll say like most important things from the day, things you need you remember for monday or whatever and it just lists them out you know i was like actually that would be great a lot of us are in information barrage all the time it's like oh my god i can't

24:12Tony Castellanos: even remember everything we're doing i mean have you tried to look at slack yeah it's just like 500 channels totally find it all 100 so my version of this from sandwise is i get sunday evening a brief delivered to me that is about the week ahead and it's a combination of what i've said to is the important bit is give me guidance. These are the three things that are most important to me. So I want you to scour slack. I want you to look at all my meeting notes, et cetera. What are the things that fit in these three buckets that I need to know about for next week? But then I also have a section where Samwise gets to discover things.

24:48Tony Castellanos: And what do you think I should be aware of? What did you come up with that is not in these three things that actually should be on my radar, but don't fit cleanly into this? In that way, look, is there stuff that I'm missing? Probably. Am I relying solely on AI to filter though? No. Like I've given it parameters because there are things that I want to make sure I stay in touch with. And I think the danger is what we just described earlier. We say, show me what's important and you allow AI to decide what's important. That's a world that we can't enter. Yeah.

25:16David Rice: That's ultimately the conclusion that we arrived at around that. We're like, well, yeah, but who's defining important and is it aligned with A, the business, B, your manager, B, your goals, like, or C, you know, like it's just, there's a lot of factors that are going to change the context of what it should produce. And that's ultimately where the big challenge came in for it. Cause neither one of us knew how to quite solve for that, you know?

25:39Tony Castellanos: Yeah. Now I'm thinking I should have Samwise produce a slide deck. Cause I think now Google has an automated presenter thing through Google, Google slides. Right. And so that might be fun to have your service, build a slide deck and then have the, another service presented to you, your own personal, like mini chief of staff team. It'd be amazing.

25:59David Rice: Yeah, actually, that would be incredible. The future employee, right? They need breath. They need judgment. They need the ability to move across problem spaces, right? Doesn't that kind of create a pretty fundamental problem with how we develop people today? Because I think most career paths, they kind of reward getting progressively deeper inside of a function. What would development and progression look like if breadth becomes part of seniority rather than a detour from it?

26:25Tony Castellanos: yeah no this is one where it is counter to the way we've developed people but the nice part is the thing that's creating the problem is also creating the solution ai is really giving people enormous amounts of autonomy over their own growth right they can go investigate and interrogate a topic now you do need the guardrails to know what is real and what is not but you can build a foundation for yourself and take that to an expert for collaboration or for edit or for critique so that you're just accelerating your learning. And in this way, you know, what we found and what we've encouraged all of our employees to do here is don't wait for the learning and development team to build a curriculum.

27:05Tony Castellanos: Don't wait for your manager to find a couple hours to sit down and deep dive on this thing. Go explore the thing that you're wondering about or that you're curious about or the problem that you see. And the great part about doing this with AI in your company is that you're not doing this in an academic sterile setting, right? You're doing this in your company's context. So, I mean, even if you're, you know, things go haywire, you're still learning about what your company does, but really the benefit is you're coming with an informed point of view on what your company actually does. And so you create this amazing feedback loop between your manager, whoever, where if you're trying to build breadth, you go to the party that's adjacent to your function or who's involved in this, and you can actually get feedback on something real and tangible versus a made-up exercise.

27:52Tony Castellanos: And in that way, we don't have people sitting around waiting for assignments or stretch opportunities. You can actually just stretch yourself every day. It's a form of experiential learning, but it's just a lot faster.

28:05David Rice: One thing I think that's so dependent on right now is exposure. So I don't know what I don't know until I'm exposed to that fact. How do we have to think differently about workflows right now? So people are getting exposed to going, oh, they're getting that curiosity, right? Because they want to learn about what this other thing that they've got to tackle, but they don't quite know it yet. And so they can develop a curriculum where they can work with other people to kind of learn what it is they need to know. Is there like a major workflow shift coming? Because that's kind of what I'm hearing.

28:39Tony Castellanos: I don't know if it's a workflow shift. I think it's you, you, you hit it there in the question though, that it isn't like we've come back to curiosity so many times. And I think that it's actually going to be less about workflow and it's going to be more about mindset and behaviors. Because when I hear you ask that question, right, it's like, well, the nice part about AI is you can actually start with the question. I don't know what I don't know. You can literally start with that question. But I think that we're going to see, maybe not more pressure, but I think you're going to see great managers start to push on the five whys.

29:07Tony Castellanos: And that's going to get people into this mode of really investigating why does this thing exist the way that it does? What is it trying to accomplish? And if I were to revert to more first principles thinking, would I do this this way with the tooling that's available to me today? And that's going to be one of the biggest things I think that we come up against is inertia and entrenched process. Because it's really hard to get back to like, what are the doors that we closed because it wasn't possible to pursue that path back then? But AI has unlocked so much potential that recruiting is a good example, right?

29:44Tony Castellanos: Where it was a given. I used to work at Google a long while back. It was a given for most companies that, you know, when you're at that scale, you can't actually review every single resume. Now, we actually did make a concerted effort to do that, which was crazy because we had people whose jobs it was just to review resumes. But for most companies, you just assume like, look, I'm getting tens of thousands of these things a day. I'm not going to get to all of them. with AI, you can actually, it can get some review. It can't get a human review. It can go flow to a system that's going to look at the content there.

30:14Tony Castellanos: And that's just not a thing you would have ever thought was possible way back when. So how many other doors have we mentally shut is going to be where the real opportunity is.

30:24David Rice: Yeah. I just have to challenge myself on that all the time. I'm like, you're just used to doing it this way.

30:30Tony Castellanos: Which is terrible because like old dog. I don't want new tricks. We're being forced into this world.

30:35David Rice: Yeah, you have to remind yourself like, no, you need some new tricks.

30:39Tony Castellanos: Yeah, that's true. Fair.

30:42David Rice: You gave this example of something as ordinary as hiring a contractor, right? You know, it touches people, finance, legal systems and approvals, right? You know, today we've designed the organization around all those sort of functional handoffs. But if agents eventually start to carry context across that whole process, do we still have to organize humans around the same boundaries i'm curious if not what's one part of today's work chart you think ai could make look strangely outdated five years from now

31:12Tony Castellanos: yeah that's a fun one i do think the threat of this conversation right i think we're going to see this inversion that we've been talking about where functions are going to have a limited number of experts and they're going to be really surrounded by generalists who know how to apply that knowledge across domains. And, you know, I think in today's world, we still have generalists, right? But they're more or less treated as this operational layer that is more junior, that is not quite as useful. And so I think that flips and those people become the folks who are really focused on company level capabilities.

31:44Tony Castellanos: Like what can we do if we pull from all these different centers of expertise? I think that if you were to go five years out, the thing that might seem strange to people then is this concept of like cost center actually and not not because like i think cost centers are going away necessarily you're measuring your investments uh you you have to right there are certain accounting and compliance requirements there but i think that we stop letting that measurement dictate how things run if i think about a cost center right now like yes it's doing that accounting function but in many ways it's also defining where specific problems live because that's where historically the expertise has been.

32:24Tony Castellanos: I think that those two things start to come apart in a way because we now have this ability to tap into that expertise from different parts of the company because it's either flowing through AI or systematized or it's encoded, what have you. And the org chart becomes, it evolves from this map of where information sits to something that is much more amorphous, where teams are forming and dissolving depending on what the problem space is that they're tackling. So, you know, I think we'll have fewer experts. We'll have more people who think about how to wield that expertise. And this is not, I want to say fewer experts.

33:04Tony Castellanos: It's not a story about the companies or the teams shrinking necessarily. It's saying that the proportion of the team that is an expert will be smaller. The remaining team will be deploying that expertise and actually solving problems and advancing work. So in a lot of ways, I think everybody's problem space gets much bigger than their cost center. It's just that that label stops defining who gets to take on a specific problem.

33:31David Rice: Well, I'm sure HR leaders who have heard themselves referred to as a cost center will be relieved to hear that.

33:37Tony Castellanos: Oh my God. Believe me. Like when I think about the term GNA, like I do take a little offense.

33:45David Rice: Oh, that's great. That's a great answer.

33:48Tony Castellanos: The GNA one is like, just to tides off, is a good example of, I think that you'll stop thinking about what is our GNA investment and what is our legal investment? What is our finance? All these things, you know, talk about like, what does it take to run the company? And I think that's a much healthier place for humans to be.

34:01David Rice: I agree. And I like the idea of like, everybody has access to the expertise and now you're going to go put it into play. I think that is a much more efficient use of people's time, but I don't mean efficient in the sort of like, you know, we're trying to drive efficiency way. It's just more so. How do we help people achieve their goals that support the business in a better way?

34:24Tony Castellanos: It's a lot more fun. You know, again, like the accountants who got the benefit of Excel, right? Now they get to not just be deep in the calcs, but actually get to go advance different work products. So I think it's a lot of fun for all of us.

34:37David Rice: Tony, this has been a great conversation. I appreciate you coming on.

34:39Tony Castellanos: Thanks so much for having me. It's been a ton of fun. and like i can't wait for this to play in a year in my head and to see all the things i got wrong

34:46David Rice: oh i i can't wait to see in a year how much more of this is gray so

34:54David Rice: yeah it's always interesting listening back to them later because you're like yeah that was a big problem at the time and now we're just sort of don't really think about it oh my gosh things

35:04Tony Castellanos: are changing so fast you know what a world it's a ton of fun to be in though one thing you can't

35:08David Rice: call it is boring.

35:10Tony Castellanos: Very true. All right.

35:13David Rice: Well, listeners, until next time, head on over to peoplemanagingpeople.com forward slash AI signal. Get signed up for our newest newsletter product. It's an executive breakdown of all the things you need to learn this week, or of one thing you need to learn this week. Not all of them. Actually, that's the whole point is to find the signal and harness it, right? Rather than listening to all the headlines and chaos. And until next time, don't stop developing your expertise. it's not necessarily going anywhere. Yes, indeed.

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