Bonus Episode: Ask A Headcount Expert

May 8

Podcast Overview

Eric Guidice: Ladies and gentlemen, headcount experts, very special episode. I'm gonna try to flip this one in 24 hours and see if I can publish it by Friday, May 8th. But it's the 7th today and Chris and I are gonna do something different called Ask an Expert, a headcount MBA. I don't know what we're gonna call the segments, but we're gonna run through posts from six or seven different creators that have happened this week, talk through our reaction to them, and just give our perspective on how the conversation is shifting as it relates to headcount in the LinkedIn space, the ecosphere. So this episode will also come out next week. We had Reid Gilbert on. But he is a tremendously interesting conversation that will come up about how a company that raised in its most recent round over $50 million is 20 people and thinking about AI. What was your takeaway? What did you think about that conversation?
Chris Mannion: He came into that conversation and I don't think he really held back. He knows our audience is HR and I think the kind of AI replacing headcount is a very sensitive topic, but he gave it to us straight. This is not Reid's point of view. This is the CFO's point of view at most growth stage companies right now. It's not like how do I replace people with AI? It's how does my capacity increase because everyone is now AI enabled, especially on the engineering team. And what does that mean for the future? So I really hope that people watch that with an open mind. And I think it's quite easy to maybe get a little defensive around some of the points that were made, but it's just a fact that this is the finance point of view. And so it's not like an argument as to whether this is happening. It's like, how do we adapt as a HR team to make sure that we're best positioned to lead in this new era that we're seeing? And so there were no surprises, I think, for me, aside from how real the conversation really was.
Eric Guidice: Yeah. Yeah, I think thinking about it as a position versus that individual, it is his individual opinion, but you and I both having customers in the space as well as in the VCPE space, the future is coming. Do you know what I mean? Like that is what is being expected and asked of these positions to then say and encourage. So somebody's giving you $50 million as a 20 person company. They don't want you to hold it. They want you to spend it so that you can grow and grow and grow. And so choosing where you spend it, we've even asked him, what is your next hire? What are you going to do? And he's like, I'm pretty good. I'd rather spend this money on something else. And for me, I think I've been into a lot of macroeconomics YouTubes lately with the whole Iran situation going on. So I'm just very interested in, coming from Uber, the idea that it's very subsidized right now and we have a lot of infrastructure to build to continue this at a scale. So for a 20 person startup that might make sense but for a 20,000 or a 200,000 we might have another workforce planner from a major enterprise on in a couple weeks. But how does the token spend scale and then what do companies do when those prices flip? It's a very interesting conversation. So I was, yeah, thank you for bringing him on. It's great content, great perspective. And I think yeah as we do this episode where we're just hearing what our peers are talking about, it becomes super interesting to kind of hear that finance perspective and then mesh the two.
Chris Mannion: So Reid, 20 person, early stage startup, series B hyper growth, but not necessarily hyper growth in headcount is very different from, as you say, a 20,000 person company that probably doesn't have all the data infrastructure that the early stage startup has and has a lot more change management and a lot more processes internally that they can't just shift overnight. So I think there's different stages that we'll cover. I think we're generally biased towards what's coming that's the most exciting and how's that going to impact? But that's only really impacting smaller teams to the extent that we're actually going to see big changes in the next year or two. I think it's hopefully what we're talking about now is not a what's happening next week, but really, what do we think about over next five, 10, 15 years for larger organizations in order to kind of continue to kind of advance and be competitive against some of the early stage companies that will take 10 years to reach that kind of scale.
Eric Guidice: I made a poll yesterday. I think this relates to what Reid was saying is we're in hyper growth but not necessarily headcount. And so I made this poll: are headcount agents headcount? Before I give you the results of the poll, what do think?
Chris Mannion: I think that's a really interesting question because I think there's two ways of thinking about headcount. One is you think about headcount in terms of the capacity. I think one of the things that I don't think we actually defined it with Reid, but you hear a lot of VCs talking about revenue per employee as being one of the key metrics moving forward. And that's how they're tracking performance of organizations. And that's gonna definitely spill out into larger organizations too, if it hasn't already. So headcount as a kind of revenue per employee, I think it's a really interesting way to think about agents in terms of, for each agent you add, how many headcount does it replace and how much revenue does that agent actually generate? And so if you go from a 100 person organization to an 80 person plus 20 agents, do you assume the headcount is the same and if the revenue is the same, are you actually lowering the overall headcount cost? And so there's that kind of business or finance perspective where it's all about the numbers and the metrics and that's how you can kind of measure and benchmark performance against your peers. So I think that's a really interesting way of looking at it. But I think something that gets missed if you only look at that area is the whole like people element of headcount, which is your headcount is not just a line item on a spreadsheet. It's a person with hopes and dreams and a career plan and a family and everything else that comes with paying for a headcount is almost like an investment into the future. And you could replace someone who's doing a very transactional role with an agent and you probably won't really see much difference in terms of the output of the organization immediately. But what about the future? What does that mean for how the organization grows over time if you have fewer overall headcount in order to do the job in the future? And so there's very much a qualitative side of it which is much harder to measure. And so I think a lot of people are going to shy away from that. But I think there's no easy answer to is an agent a head count? I'd say from a pure business perspective, if I'm analyzing a 10k and looking at a business and trying to compare like for like, I would loop agents or agent spend into headcount. So you have your overall kind of fully ordered cost of all of your headcount plus your agent expend that's essentially replacing headcount and then using that as a comparison. But I do think you need to add that qualitative element in as well and look at what does this mean for the future in terms of the ability for the organization to continue to grow and succession planning and everything else that comes with that. So I'm curious to hear the results of what the audience said and where the audience sits as well. I'm sure different layers and different functions are gonna have different perspectives on this.
Eric Guidice: I'm actually going to write a little bit of an article on my particular perspective about it. But it is about what is a headcount and how do you store the information? How do you add headcount and what's important about the business context of headcount? So if they're not stored in the HRIS and you don't recruit them through the recruiting system, they are additional costs elsewhere. However, they are a part of your headcount spend and they're likely tracked in the finance software. So that's the summary of the article, but I'm going to kind of detail it out in a little bit more of a report, try to make it as finance focused as possible. To a certain extent, everyone agrees they're a part of headcount. They're just not a headcount. Most of the folks are from the TA or HR. There was one or two operators, one or two finance folks in there as well. But the majority were from the HR space, like the people that I follow. But it's an interesting conversation. I think the things to think about are like, how does a company charge for an agent? Is it by tokens or are they actually charging you like a person? And then my other stream of consciousness that we can dive into at another date is if an agent cost whatever they cost today, let's call it $1,000 a year or even $10,000 a year and a person doing that job would be $100,000, there's no way that the supplier of intelligence or productivity is gonna leave a 90k gap. They're gonna charge you 98k and hope that their thing is better, or if it's better they might even charge you more than 100k and then you're deciding. But there's no way it remains 10k forever. That's the way I think, but it's PTSD probably from subsidizing rides at Uber.
Chris Mannion: Haha. There's another way of looking at head count, which is actual butts in seats. And then there's FTE, full-time equivalent. So is an agent, can you measure an agent by FTE is another question. And I think the same criteria applies, but if you have one person plus an agent that then becomes two FTE, then is that agent then one FTE equivalent? And this is probably a rabbit hole we're not going to go down to in the time that we have, but I think it's an interesting perspective when you are measuring financial decisions as to how much do you spend on agents or tokens or tools? And can you actually make that like flight comparison against headcount measured as FTE?
Eric Guidice: Right. And we heard from Reid, the cost of these tokens is not because of that gap. There's no point in measuring the cost of the tokens relative to the productivity because the gap is so crazy. So there'll be a time in the future where someone's as scrutinous to an agent as they are a person. But nonetheless, let's get into some other perspectives. And so I'm going to pull up a window here that has some information. So let's start with Emily Gransky. She's the VP of talent at Formation Bio. She has a really interesting Substack. She's in her gardening era, so am I. So I have a shared interest of two topics, but she wrote something that says, I'm not convinced AI is coming for your job. So let me share my screen here for a second so you can see it. And you'll see this, I'm not convinced AI is coming for your job. And there's a few things that she's essentially saying in this article, right? It's like, we have this, like you said, there's a defined inevitable replacement of human workers, but she's exploring this other idea that it's really enabling this activation of human thought and it allows people to do things that they weren't. It's a productivity play, but the replacement isn't as imminent as the enablement and the productivity. So work will change, but it's not really about total replacement at this point. It's like the same way Elon's talking about there'll be all self-driving, like we're gonna replace the truckers or taxis or whatever. It's like they've been promising that in the next two years for the last however long and macro economics folks are like, well no one's just gonna give up all their existing trucks, they're gonna phase them out over time because it's not free.
Chris Mannion: When I was at business school, so I started 12 years ago, everything was focused on autonomous vehicles and how we were two years away, 12 years ago. And we've always been two years away. It's kind of like fusion, right? We're always a decade away. And that's been the same for 50 years. So the question is, is AI gonna replace all of our jobs? Clearly, no, that's not the case. And I think the way I like to look at it is, you're familiar with the kind of product adoption curve where you have your innovators, your early adopters, your early mid-stage, late mid-stage, and then laggards. And that's how, in the startup world, you define your market. And there's what's called the chasm between your early adopters and your early majority. And that's generally where a lot of startups struggle to actually scale. And it's just based on the adoption of technology from anything. I'm in most cases not an early adopter even. I mostly wait for things to be proven out before I actually use them. I think AI is probably one area where I've just been forced to use it because I'm solo. But when you think about the kind of adoption and what's actually happening, if you look at the news and you look at what people are publishing, myself included, you're probably looking at the top few percent of people who are actually using tools and experimenting and kind of integrating them into their day-to-day, but they haven't replaced themselves with those tools. And so that's kind of a very small amount of the market. And then you look at the actual kind of surveys that places like MIT and Stanford ran and a lot of the big consultancy firms have run them as well. Like how much of the day-to-day is actually being done by AI? And it's almost nothing, right? So most projects fail. Most of the people in the space feel like they're left behind. And it's this kind of FOMO element of, most people are not using it in their daily workflow. They're using it to prompt and kind of like how you would normally use Google. And so if you think about how AI is changing the jobs, it's not everyone's starting to use it for complete workflow automation and the loss of all jobs is imminent. It's more like we're still trying to figure it out and we're going to gradually implement it and figure out what we can do the same as we did with the internet, the same as we did with word processors and every new technology has this same curve. But then as we discover what we can do, we discover more things that we can do outside of the core. So maybe there will be fewer people copying and pasting data from one spreadsheet to another, which I feel like most of my career was spent doing, and have now automated with AI. And more people actually building new things and coming up with ideas and maybe more in a creative space. So no, I don't think it's coming for our jobs. I don't think we're going to see mass unemployment in the next few years. I think what we are going to see is a shift in terms of how different roles are paid and how many job opportunities are available. For us, we saw this huge spike in demand for software engineers. We just couldn't get enough software engineers into the market. And now every software engineer plus an AI tool has become much bigger resource for an organization. So they need fewer overall software engineers, but the role didn't go away. It just changed. And I think the more that the role is paid, the more opportunity there is for replacement. And so there's kind of a bit of a long-winded way of saying, I actually agree with Emily. And I think when you actually get into the weeds, you can see how their day-to-day is changing, but the overall impact to the job of the person who's probably listening to this podcast is probably going to need to adapt, but they're not going to be replaced.
Eric Guidice: Yeah, I think if you're on LinkedIn, you see the Zapier AI fluency, the momentum around things like that, and you're thinking, oh, are all companies doing this? And there are a significant amount in our ecosystem doing it, but at the macro level, it's there. I think with something like the telephone operator, it was like a single industry had to then get repurposed. Same with manufacturing automation, et cetera. But now if trucking and design and copywriting and legal can all do it, it is not a replacement. It's a kind of restructuring, but it's in a lot of different places which has this, you know, negative headlines get clicked. So people are kind of hopping on that bandwagon, but I agree as well. Which then brings me to my next article here from Chris Abbas from the CEO of Talentful. It's a 300, 350 person RPO and he's releasing a new job. Let me get the screen up here. He's releasing a new job that's called the Director of People and Agent Operations. And so what he describes this role as is somebody to own the people side and the People Tech Stack but also the agent operations. And it's one of the first kind of hybrid roles that I've seen created in our space for this function. You can obviously see from the comments and the likes, etc., that it is popular. It's starting to get its traction, but it looks at a couple of shifts, like the idea of the system mindset.
Chris Mannion: Three years ago, I wrote one LinkedIn post and I said something along the lines of recruiting what exists as we know it five years from now, it's actually gonna bifurcate into two different roles. You're going to have a more internal consultant and an advisor who's gonna help hiring managers to make better decisions. And it's all that kind of the people skills that come with a good recruiter. And then there's gonna be this kind of systems and process role that's gonna manage the tools and actually come up with a strategy for how you can automate workflow using the tools that are gonna be developed. Just looking at what was available back in 2023 and how far we've come since then. I think this wasn't me being smart and kind of looking ahead. I just read a bunch of reports on what the organizations were actually working on. And it was this whole kind of Biden AI initiative where all kind of senior execs from various companies went to the White House and talked about this. So we've known this was coming for years. And the way that it impacts recruiting is, as I said in the last question, if I can replace all of my copying and pasting from Excel to Excel and then Excel to an email, I can spend less time doing that and actually more time just managing that workflow, but actually getting more involved with the actual people side of making change happen. And so I think this is a very natural evolution. And I think as RPOs are going to incorporate more agents into their recruiting stack, it kind of goes back to how do you measure capacity? And if as an RPO, your recruiting capacity is your primary cost driver and revenue driver, if you can have half the number of overall recruiters and have the same amount of capacity and actually create the same kind of output, then your overall profit line goes up. And so this is a huge value add to the organization. I think we're going to see more and more. We saw the talent engineer profile get flagged in the last couple of months as well as being one of the new roles. I think it's all down this same bucket of people who understand TA and understand HR and are able to articulate workflows and processes and systems in a way that they can figure out how to adopt AI to replace part of that, while still understanding the domain that they're operating in, can just add so much value to the organization. We were doing this back in 2019 before the AI boom really kicked off. But it's just an evolution of what's been happening for a while. It doesn't mean, to the point we just made with Emily's post, it doesn't mean recruiters are going away. It just means the nature of the role is changing. And I think for the better. Recruiters shouldn't be spending their time in an Excel sheet trying to figure out how the ATS data matches what they're seeing and how to write a Boolean in LinkedIn that actually gets the right candidate and then getting poor feedback from the hiring manager. Like all the admin should be pushed away and they can actually just help the hiring manager, advise and guide the hiring manager or manage these workflows. And I've been bullish on this for years, but it's actually really interesting to see it start to kind of manifest now in 2026.
Eric Guidice: I had two thoughts. Curious to know what you think. One, what's the interview rubric look like? What are the competencies on this role? I understand, I get it. And I think theoretically, we understand what's happening here. But I'm just curious, what is that rubric? What does that interview process look like? And then the second point is, who's evaluating it? Who's the person who's saying, this is what we're looking for? Is it an architecture person? Is it a skills person? Are you looking at individual tool fluency? Are you looking at system architecture of tools together? Are they responsible for a budget? Very curious to see what this job results in. And we're actually looking to book Chris on a podcast in the near future, so we'll get to ask him directly. I thought this was a cool job. I do have some curiosities about it, but let's get into the next one here. It's Kate Stewart. Now Kate, her self-proclaimed title AI native PeopleOps leader, so she might be a great fit for this role. But she does staff people operations, AI and automations lead for Horizon 3 AI, but previously was at Lattice and Loom. So tremendously interesting experience. And if I look at what Horizon is doing, it's a security provider. So it's very interesting to see someone's perspective on this information, but she has a very interesting post that you'll notice another fellow headcount provider. She and I commented on this together. Let me share my screen here. Let's get it up for you. So this is a head of people or a people leader producing a comp tool from Talent Hub via a Glean agent. It's looking at compensation data and job architecture and their pay zone policy and it's creating something that recruiters can just access. And so it's a very interesting tool. I think at headcount we are actively exploring how to deliver this functionality with a primary concern of security. And you'll notice one of the players in the space, Caro, is very, very similarly curious to know what is happening.
Chris Mannion: I think the more you kind of empower the people who are doing the work to build solutions for themselves, the better results you're going to have. But you really need the guardrails in place. And I think that's where this post is really kind of, or the questions on this post, they're really kind of stemming from. When you talk about comp data and HR data, you can't just open up have a complete open architecture and let anyone play with it. You have to have those kind of guardrails. And I think that's where having a strong IT team that understands what the kind of privileged information is and how that needs to be handled, probably with a legal lens as well, is going to be really important. The challenge is how much bureaucracy and paperwork do you add into the process to make it safe enough without actually constraining the ability of the end users to get things done? So I think as you're thinking about the self-building internal tools, I think the comp data is a really interesting one. As long as you can have a permissions list of people who already have access to that data and you know that data is not going to be shared kind of externally, I think, you know, kind of open the floodgates. I think the challenge becomes the education piece of how do you know what data is being shared externally as part of to outside of the organization and what is the risk that that kind of imposes? And I think that's where you really need a kind of fully swept up IT team to partner with you on this. So what my advice there would be, don't do anything with sensitive data without that IT team involvement. But also if you're in IT and you're listening to this podcast, lean in on the HR side and actually try and figure out how can you help them be more successful in their day-to-day roles so that they don't keep coming to you with requests for new tools that you don't have the context for and don't have the capacity to actually build because you're already overstretched with everything else that's going on with the role of AI.
Eric Guidice: Yeah, I think, you know, there are a lot of pay transparency laws that you publish it on the Job Description. It shouldn't be that sensitive. I think there's a lot of sensitivities about like, what does someone specifically make and comparative in an individual job level or range, it can get dicey, particularly with two employees, same title or two employees, different title making the same pay. You know, when I was at Bird, I created this comp philosophy that I held to that was equal pay. It didn't matter what you were doing. You could pick your cash and your equity and at a level, that was it. And it produced its own side effects. I mean, I can go for days on comp. And if you want to download that, go to Unicorn Talent, look at the comp and leveling framework, you can see all the different ways that I've applied comp in the past. But it's a very touchy subject, I would say, as far as the employment culture. And then secondly, the data that is accessed by hiring managers in the full story. So there's the, you know, if you're in Europe, you have social contributions, taxes, benefits. There's all these different types of things. There's bi-level benefits packages. There's a lot of stuff that is more than someone's just budget. And so it's, again, access to the information I'm not against. I would say for most of the companies that I'm talking to in like the vendor provider, we are on the hook. If it goes bad, that's bad. The permissions is an issue. But for most companies internally if you can figure out a way to make it work, I think it's pretty cool. Alright, let's get into the next one. This one's from Logan Marsh. He's the head of talent acquisition at Calendly, and he's got a storied past. I really like his background, but he did an analysis on the Block layoff about the changing in org structure and has kind of three main things. The Build it Today thought experiment is now an obligation. The pyramid org chart will be replaced by a circle, and timing is now an ethical conversation. And my main focus when I was interacting with him on this one was the circle org chart. I was very interested in it's, you know, you talk about a company of 20 people, AI at the center, I get that. But when you're at a thousand people, you have management relationships, whether it's person to agent or person to person. And so I was curious about do we see kind of like a web of circles throughout different? Is that what the org chart looks like or is it a pyramid? Like what are your thoughts? Obviously my comment here, it's gonna be a big circle. But what are your thoughts?
Chris Mannion: I come from the military where you have a clear command chain and it makes it very easy to understand how you as an individual are contributing to the big picture. So orders trickle down and feedback trickles back up and that should happen in the most efficient way possible. Now, when you actually get down to the teams, and I think this is where it can potentially get quite interesting. You think about how teams operate and there's the kind of your rigid structure of team where you have the kind of leader of the team and then they have their direct reports and they have their direct reports. That's how teams are built so that you have that kind of command chain. But when you look at how teams actually operate and you look at special forces, they will generally have a much flatter layer when you are actually deployed. It's generally the subject matter expert takes the lead on whatever tasks they're actually doing. So if you're breaching the building, it'll be the explosive expert. If you're capturing hostages, then it will be the expert on that. If it's a medical recovery, it will be whoever's the lead medic on the team is going to take charge of that responsibility. And so when you think about the future org structure in terms of small teams and the two-pizza teams that Amazon popularized, that's conceptually about the right size. Five to eight people, I think is about the right number for a small team. Having them kind of built around an AI is kind of an interesting concept, but what happens when you go a layer above that? Is it, you then have a whole bunch of people around there and then a whole bunch of people around there? And when you actually look at it from the side, that just becomes an org chart. So we've kind of studied org design over decades and there've been many experiments of having flat org charts and everyone's a CEO or whatever. I think it makes headline news, but I don't think it ever works. They generally go back to some kind of structure where someone needs to take charge of what's happening and someone needs to be responsible when something goes wrong. And if everyone's responsible, then no one's responsible. And so that can, I think really impact the kind of productivity of the organization. So I hate the analogy. I can see the benefits, but I'm very bearish on that as a kind of long-term strategy. And I think if any 10,000 person organization is trying to think, how do we create a circle org structure around AI, I just think they're kind of wasting their time. It's more about how do you actually enable the teams to create that kind of org structure where there is less of a hierarchy where people can actually get things done without having to ask permission. And AI becomes an enabler there. But I think structuring a whole organization around AI, unless you really understand it, I think is potentially a recipe for disaster. We'll see. Two years down the road, I might be wrong.
Eric Guidice: Yeah, we might be coming back to this episode with a big old told you so. I just, I guess what I'm curious about is, one, I am not anywhere near the military nor claim to be an expert in the space, but the conversation about the leadership structure between Iran and America, where there's a decentralized leadership structure, and that could be seen as an advantage by some analysts, where everyone knows who's the leader in America. When it comes to negotiations or whatever, there's a lot of different things, I think. And the idea that one structure is dominant doesn't mean another structure doesn't have benefits is number one. Number two, I was curious, like, is AI the manager or is people the manager? And then who owns the company? And, you know, I'm curious about that. And my third thing was, you know, I've heard of flat org structure before. I've heard of the no management structure. I've heard of those, but I've never heard of circle before today. And it's just an interesting new entry in the conversation. I thought it was just something to read. I'm very curious to see how it all shakes out and Logan always has a great take. And if you want to kind of see the podcast circuit Logan, you want to come on board? Let's talk about it, but let's get on to the next post. This one's from Reggie Williams, Reginald Williams. He calls it the Reggie OS. It's a look at how he structures his personal AI. And you know what Reggie is doing today? He's the head of early stage US talent at Sequoia Capital. Before that he was Bain, before that he was Netflix, Lime, Airbnb, so storied tech experience. And so what he's talking about or what he's showing, I'll just flash the image on the screen so you can see it. What he's showing is like, hey, this is how I structure my agents and there's like, you know, there's talent and communication it has this kind of critic architecture and it looks at different tactical areas and different operational areas. And so what he's talking about is his experience with Claude code and kind of navigating that in his position. And it's having this architecture that may be different. This is not a circle architecture. It's essentially applying the standard management architecture of having this chief of staff that's then managing these different agents and you then become the decision maker.
Chris Mannion: I actually just published a video this morning which walks through the kind of what I've called the three tier structure of how you can actually start what's called start your day in AI instead of starting your day in email. And I picked that up from one of the keynotes from the conference last week, but the whole concept and I realized I hadn't done that. I still open Gmail as my first thing every day. And I'm like, why am I doing that? I've been using AI for years. So in half a day, I built a chief of staff that now kind of parses my email, gives me the kind of top things to respond to, goes through my calendar, looks at issues. Actually I've started to respond to customer support issues from one of my other businesses and at least drafting them, so I'm not completely out of the loop, but I'm kind of giving my veto as the leader. So I'm the leader of the agents and the agents are not acting completely autonomously. So I think it's a really, really great approach because what he's doing essentially is saying, what are the things that I spend most of my time doing that's low value and how can I outsource that to an agent who could do 80% of the work for me? And then I kind of update the rest. I think the one thing I would push on is just watching the Anthropic deep dive a few weeks ago where they were talking about how they've rethought their agentic structure. And the initial thought was that you have all these agents and each agent has a different job and so the agent specializes in that job and you maybe have a coordination agent which is what Hermes the chief of staff then coordinates all these other agents. I think what they found through experimentation with Anthropic in particular was that you actually need one agent and that agent has the ability to write code. And that code writing agent then has a bunch of skills. So you don't have 20 agents, you have 20 skills. And the code writing agent calls on the skill that it needs to solve a particular problem and then writes the code that they need for that and creates that function and just calls in that function at any point in the future. And I think that's where we're going. And what's great about that is the skills are actually natural language. So you can understand exactly what is being defined in the skill and actually update that to kind of match my response. Even since yesterday when I created this three tier system, I've actually gone back and edited a bunch of things. And so now I'm actually getting really quality summaries back from my chief of staff that's calling on the different connectors that have the MCPs for the different tools that I use. That's actually given me so much more information. So I become less of a kind of executor and more of a strategic decision maker, which I think is probably the direction I'm going to. It kind of builds on maybe some of the point from the block layoff announcement of you maybe have more senior folks and less mid-level folks, but that's actually going to create a bigger problem that I don't know if we'll get into. I actually have a post scheduled for Sunday on that one with the whole middle management crisis that we're about to see, which was kind of another finding from last week. Yeah, I love what Logan's doing. I think we'll see more people do it. Maybe we'll have software tools that will do it for us. But I actually think for most people, you spend 15 minutes in code and actually build this yourself.
Eric Guidice: Yeah, I like the idea of managing the agents. It kind of ties it all together. I like finishing on this post because we started off our agents headcount. In this particular case, you kind of manage them. Maybe they're a contractor, maybe they're a digital contractor, whatever, and they're coming into this space. I think the idea that there still needs to be that human in the loop to validate this information doesn't mean they're smarter than us yet. I've had a couple of agents that just they don't make it. They're not there yet. So being the person in the loop is super helpful and being able to control what they do with sensitive information, again super helpful. So I like the idea that there's a conversation happening across all these different posts that we're talking about. It's what is AI to the market? What is it to HR and finance? How are we structuring ourselves? What should we be prepared for? And then how did the individual in each zone, whether you're a company hiring for a director of people in AI or you're an internal TA leader or finance leader trying to apply in your zone. How do you think about the cost versus the productivity? How do you think about your own work? How do you think about scaling? It's all very interesting conversation. I'd love to do more of these types of conversations week over week. So I think I like this. We'll see how it does.
Chris Mannion: AI is now starting to become commoditized. More and more people are using it in their day to day. So AI is no longer a differentiator. It doesn't matter who has the best AI tool. It matters who has invested in the people and how are those people actually building into a capable workforce that's empowered by AI, not replaced by AI. So I think that was one of my key takeaways and kind of tied to that was this flag that as I get into it, I'm more and more concerned about this kind of middle management layer and a crisis there. With a few different forces. One is, as we've seen with the block layoff, a lot of middle managers are now being laid off and the people that remain now have wider spans of control. And so as you increase the span of control, you increase the admin and everything else that goes with becoming a manager, which one burns out the managers faster, but two actually disincentivizes anyone more junior from actually aspiring to that role. And we've already seen that, that Gen Z are less interested in moving to management roles, they want to stay in IC roles. So just kind of mapping that out as workforce planning for the next few years, if you see fewer people going into middle management, more middle managers burning out, that layer gets narrower and narrower. And all of a sudden you have junior and senior with no one in the middle. And one theory is maybe you replace middle management with AI. And as we've just discussed, you're then taking the human out of the loop. So I think that's quite dangerous. Another theory is you actually empower that middle management with more AI to automate the admin tasks, like what we just saw Logan talk about, and actually having their own chief of staff that's an AI agent. So they can spend less time on paperwork and more time on actually coaching their team. But I think really, when you think about how do we actually build and develop the next layer of middle managers, assuming that we still value that as part of the organizational chart and will we actually move to more of a kind of a flat structure where you have maybe a circle of juniors and a circle of seniors and no one in the middle, is that going to work? As I said, I don't believe so, but we'll see how it goes.
Eric Guidice: I'm going to make you made me think of something about your middle manager post. So going to write a response. I don't know if it'll be a LinkedIn post or it'll be multi paragraphs. But I'm curious to see when it comes out. But I'm going write a response to it. Because you sparked something. Do you want to know what it is or do want to be surprised? What can a middle manager do that AI can't? And I think about like the trend that I see of people using AI like a therapist or emotional support, but I'm not saying that that's what middle managers are doing, but there is a component of a management ratio that is about understanding the context of the work and the succession planning and the strengths and weaknesses about the way people interact with a business or a customer or whatever. And there's this, is it good at it? What does a middle manager, if we were gonna compare it, head to head match, you know, you see the two fighters, what can they do and why is that important and what is the value and how many will you need? The thing is like, I know there's different doomsdayers on the entry level is gone. I see that in the legal space or the middle manager crisis or at the center of the management circle is AI. So that is all the jobs getting replaced, but they're all different perspectives on what's going on. And I think that and then the other side is like if AI. Yeah, I think it's a quote from Ford, right? It's like good luck unionizing the robots. It's like good luck getting a robot to buy your car. You know what I mean? It's like, all right, good luck with the AI as a manager, good luck getting the AI to buy your product. So there's just stuff about it that I'm, there's this, I don't know if it has anything to do with headcount. Amazing close to yet another best episode ever. But yeah, that's my curiosity. So I'll write it when I see it.
Chris Mannion: Yeah.
Eric Guidice: Another fantastic Headcount Experts episode, episode 19. If you like this format, let us know. We're going to probably post some of these clips and try to engage the folks that we were on. If you were mentioned and you want to be on here and you want to talk about some stuff, we're interested in it. But other than that, we're going to continue our regular scheduled programming, kind of talking about time relevant topics in headcount. We have a few amazing guests coming out. Ian Jones from Hacker One, Samantha Schulman from Uber. I'm talking to Jeff Moore on the Unicorn Podcast. We can get them over here as well. So there's a lot of great folks coming on board. We're gonna come with some great content, but I really like looking at the current events because I think it's just changing so quick. But you know, another fantastic episode with this new segment, ask an MBA. A lot of people don't have access to an MIT MBA that's in their field to ask whatever question they want. I don't brush off that privilege that I have. So thank you, Chris, for entertaining me and my crazy ideas with this type of stuff. And I really like hearing your perspective. But we're going to do some more of this if people like it. Stay tuned for another episode of the Headcount Experts.
Chris Mannion: Thanks.

Last Week’s Top Headcount & AI Perspectives

This episode of Headcount Experts, Eric Guidice and Chris Mannion dive into the latest insights from industry leaders including Emily Gransky, Chris Abbas, Kate Stewart, Logan Marsh, and Reggie Williams. Each post or article covers themes in headcount, automaton, AI, recruiting, HR or Finance and get’s an honest reaction from the perspective of operators & problem solvers.