What Recruiters Know About Your Next Actuarial Job with Jacob Galecki

Welcome to Omos NoWare, a podcast from the CS Institute, exploring AI, data science, and actuarial thinking in the P&C insurance industry. I'm Alicia Burke from IKAS. And I'm Max Martinelli. Today's episode is sponsored by Kalecki Search Associates. We're joined today by Jacob Kalecki, founder and managing principal of Kalecki Search. We're going to be talking about trends in actuarial recruitment, including some things you probably have not heard anywhere else. Talk about how the profession can prepare the next generation of actuaries for entry-level roles in the AI era, and by climate and other emerging risks are increasingly relevant to actuarial work. Jacob, welcome to Omos NoWare. I'm super excited you agreed to do this because when I first was talking with people about having sponsored episodes, when people could kind of break the fourth wall, say what they need to say. You were the first person that came to mind. You were the first person that conversation with, and I'm just really glad it's happening now. So, great to have you here. And great to be here. Thanks for having me, even though it's this is sponsored. But I think that cuts both ways because I hate to sound like, oh, this cuts both ways. But to me, it's actually good because people can say what they need to say, and it doesn't have to be coded. And we've always been really good on this podcast about making shit. The content is high energy, not boring stuff. So, I think you're the right guy for that. Well, I hope I can deliver. Jacob, did you want to just briefly tell the audience a little bit about yourself and Kalecki Search? Certainly. So, I've been in the insurance industry now for 25. I think I have to start, I think this wouldn't you stop counting and you just say 25 plus after this year. I started my career intentionally choosing to be an insurance, which you know, so many people accidentally find themselves into insurance. But I chose deliberately to be insurance. However, eventually, I accidentally fell into recruiting. So, I went to school to be an actuary. I studied actuarial science at Temple University and had a long-term part-time internship all during college at Milliman was my first job. And as I say, to most people, I successfully eliminated one career option for myself. So, I rolled right out of undergrad into graduate school and studied of all things, history and folklore. Somehow insurance pulled me back in and at one point I became a producer. And then after doing some soul searching, I decided to take a second stab at the actuarial track, but this time focusing on P and C because I was in retirement initially and started sitting for exams and I met a recruiter and the recruiter found me a job and it was in recruiting. And then 17 years later, I'm still recruiting and then throughout my recruiting career, I spent the first five years at an actuarially focused exclusive actuarial search firm. I spent about four years at Liberty Mutual. I was the in-house data science and actuarial recruiter and some other stuff as well. I think I did product analytics and some strategy roles and handful of executive stuff there as well. Then eight years ago, I started this. And so we focus on actuarial underwriting product analytics and executive recruiting across property and casualty insurance. Post the stories from back in the day. I think this is just really good for the Gen Z folks to see how much stuff has changed every career. So, like, you're really equipped to kind of project the trajectory of where we're going. I've been talking about how I worked with Lotus notes in what Lotus 1, 2, 3 in my actuarial job because I did. Yeah. Do you want to talk about how we used DOS programs? And this was like 2000. You think about how much has changed since 2000. And to be fair, a lot of those, like, kind of outmoded softwares were kind of vestigial things that were hanging around. It's not broke. We don't have to fix it. So let's just keep using these tools in this environment. Do you want to talk about Oregon Trail? Are you playing that during the work day? No, no. But, like, going way back to the dawn of time. I mean, God, I started college in 1997 and didn't even have an email address. Think about how far we've come in my first actuarial job. Yeah. We used some old tools and things that do not exist today. Things that would look very, very wrong in today's environment. No coding whatsoever. Nothing even resembling data science occurred. Here's a funny story. So my first job as an intern at Milliman was to annualize people's salary because it affected their pension calculation. So there was a strike. And so the people were out of work for two months. And as part of the collective bargaining, they said, okay, we want that to not count against our pension calculation. But we don't trust computers. So what you have to do is we're going to give you a giant pile of dot matrix paper. And you're going to use an adding machine, annualize the salary on the adding machine, take the ribbon and staple it to the dot matrix paper. That was my first actuarial job. Yeah. It feels like, like, when I'm visualizing this, it's like in black and white. Like, it's just so foreign to what we say now. Maybe we trust computers a little too much these days. At the time, I was out of the Philadelphia office. So this is the crazy thing is at the time, the CEO of Milliman, like, was based in Philly. And his office was right across from my cube. And I was over there, like, stapling things to dot matrix papers. We have come along and it didn't get much better than that. And people wonder why, like, it's like, why did you leave? I'm like, I wonder, again, this was not PNC. Wow. That was the first mistake. So you've definitely seen a lot of change. Do you feel like though in the past couple of years, maybe since like 2022, 23 has that change really accelerated a lot? I think it's been like water behind a dam for a long time, you know, and I was kind of thinking about what we were going to talk about today. I was thinking about to my time at Liberty Mutual. So I started at Liberty Mutual 12 years ago. And we know one of the things I tell people all the time is that like, well, okay, AI, that's nothing new. We've been doing this stuff for 25 plus years. What's different is that all of a sudden, we have a lot more compute. We have a lot more data. We could actually do something with this now. So I think back to like 20, God, let's just say 10 years ago. So I used to attend Liberty Mutual had an internal data science conference once a year. So everybody who had anything to do with data science at Liberty Mutual would go to an offsite. And it would be like a conference, like a CAS conference, there'd be like sessions and learnings and all kinds of stuff. And it was really valuable because everybody worked at the same company. So everybody could share, you know, what would be considered trade secrets at a general-purpose conference, but could discuss openly techniques and things like that. But they were talking about, you know, deploying early machine learning models at the time. And one person stood up and says, well, this is never going to work. If we want to do an image recognition, there's not enough doors in the world to train the model on what a door looks like, not enough doors in the world. Somehow we've managed to figure that one out. And so there were a lot of limitations as to what you could do with machine learning just 10 years ago based on training data, based on compute, but there was a strong desire to deploy it. But it was, it's not there. So now all of a sudden, you know, there's just been how many trillions of dollars of investment into data centers cloud computing. That wasn't a thing. All of this stuff is really pushing the envelope. And so there's so much more possible now. So, you know, all it took was money. Is it changing the actual skills or experience that is relevant to employers today? I've been giving this advice out for for years now. There'll be an entry-level actual aspirant. They haven't had an internship or haven't had their first job. They passed two exams and they come to me and say, "Well, I can't find a job. Should I take another exam?" Einstein, I think it was. Maybe this was attributed incorrectly, but the definition of insanity is doing the same thing over and over again and expecting a different result. I've always said, it's like stop taking exams. It's like two is the price, you know, for entry. What people seem to forget is that people are hired to do something. Your time might be better spent learning how to do that thing. And what that is, I've seen it evolve quite significantly in my time. It used to be just like, "Okay, are you good at Excel?" If you can believe it, that was something that was like a a really cool addition to someone's skillset coming out of college. And then like, then it was like VBA. Like, can you code in VBA? Can you run automations within the Microsoft environment? And then slowly we started seeing R, then we started seeing Python, then we started seeing more sophisticated machine learning. And then, you know, in some cases, a actuarial job description does not differ or look in any material way from a data science job description today, with the exception of just scribble out PhD and, you know, write an F-cast. And then of course, you know, having the context around insurance is super important. Just like any data science scientist off the street isn't necessarily going to understand what the nuances are. This is the power of the sponsored episode, where the CIS can't censor what you just said about the exam. So like, hopefully we keep that in. But I'm not saying don't take exams ever. I know. I know. I'm saying like, pause for a second. You have to be a well-rounded individual for someone to want to hire you, right? Yeah. There's a strong correlation to how quickly, well, this is not an absolute statement. So let me, let me be very clear before I say what I'm about to say. But there is from a recruiters perspective, and at least my perspective, and maybe this is just my opinion. There's a strong correlation between an extremely fast exam taker and someone who doesn't have strong marketable skills. Because you know what they did. They spent all of their time studying. Part of the magic of the actuarial career is that you learn while you earn. And so it is, or earn while you learn, you know, the other way around. But it is meant to take a minute. It's not meant to be this, okay, I'm going to walk in. I'm going to sit for 10 exams tomorrow, and then just, you know, then I'm going to cash in. And I was like, well, guess what? You can't because you don't know how to do the job. There's so much like, insurance is messy, right? It's complicated. And there's so much judgment that goes into it, goes into actuarial work. It's like, then they're done that. Seen that. Oh, I've seen that go wrong. I've seen that go right. We should try this. Oh, that didn't work. Let's try this. Right? So I'm not saying don't take exams. I'm just saying, if you're struggling to secure your first actual opposition, you need to look inwardly and say, "What can I bring to the table besides passing exams?" And you could take a third exam, but you probably should learn how to code an R. But then is that irrelevant today in 2026 when Claude can do that for you? So that was maybe where I was going with the next thought is that, because I think what you mentioned was about the blurring of the lines between the data scientists and the other. So the technical skills are desired, but the actual is differentials, maybe that institutional knowledge, or I'm sorry, that industry knowledge, where the data science might work. Well, I was just saying the judgment, right? The judgment. And, you know, if I may make one sweeping comment about AI generally is that almost everything will be replaced, but judgment, because guess what, AI is really bad at that. We've seen some examples. Every time I open LinkedIn, it's about a crazy example where AI made a foolish recommendation. So it seems to be really good at certain things that humans sometimes are good at, sometimes aren't, but there's definitely this judgment component and the thing that we should have. Give it a spreadsheet of like 500 things and say, "Tell me if this is like blue or yellow. It's going to tell you." And it probably did a few of them wrong, but, directionally, it'll be right. You know, it's like, you know, it's good at that kind of differentiation, sifting through things, creating a little bit of signal, not all the signal. But again, and this is where I have a lot of opinions, you know, AI has been trained and developed to produce the average. And so you're getting kind of like the lowest common denominator answer for everything, right? A poem. You're going to get a pretty mediocre poem, right? A novel. You're going to get a mediocre novel. Is it readable? Sure. Are there like thousands of them on Amazon right now? Yes. I put you on this because this is where I kind of go with that conclusion. I think if there's a pattern of data scientists kind of blending with actuaries, right? Or the lines are blurred at least. Do you see that? Because like, I think actual work doesn't always have that, you know, there's definitely the judgment component that where the numbers are just crazy. You have to kind of whistle through the noise and figure out what it is supposed to be. But do you see that kind of extending into like strategy, analytics, product roles, like actual listings or conversations that you're hearing from employers? Like we kind of want a product person, but they start listing what they want as a differentiator. And it's kind of like, well, it's this technical actuary. Are you seeing the lines blur there also? Yeah. I mean, I made the prediction and it remains a prediction that like data science, actually we're on underwriting, we're all going to merge into a single discipline at some point. They might call it insurance decision science or something. And because at the end of the day, you're, you're looking at a risk, you're putting a, you're evaluating it through some kind of quantitative means, right? And you're, you're trying to figure out what the price is. And then you're making, you're placing a bet. And the market's naturally doing this already. Like you're seeing that playoff. No, no, I would say there's still a pretty strong differentiation. Well, I mean, when I train people on insurance, I, you know, encouraged them to think about the data. And I was like, well, how much data is there in so much about like how much data there is? Like really informs us to like what the company needs. So this is like from a recruiters perspective, right? And so if you think about like, you know, personal passenger auto, there's a lot of data, right? And accident happens. However many seconds, like every three seconds or something, that's a data set progressive wrote the book on this. They, they know how much it'll cost. If, if this car hits this car on this intersection, it is a knowable thing. There's so much data that they basically have become omniscient. Do you need an underwriter when you already know when you're omniscient? No, you don't. It's a matter of categorization, right? Put it into the right box. And then did you know, you know how it's going to play out? In personal lines, there's become no need for an underwriter. Now, is there underwriting? Yes. But is are there underwriters? Not really. Do some companies still have desk underwriters for personal lines? Yes, they do. How long will they last? Probably not very long. But then you get into like specialty lines and just like okay, terrorism and political violence, like how often does that stuff happen? Yeah, somewhat frequently. But not to the extent that we can compile enough data to become omniscient. That's when you need these underwriters that have a lot of judgment that understand the things that play. Okay, well, you know, this person, this country, like thinks this and this country thinks this. And so if this person goes here, they're exposed to this type of risk, right? And so, and then if this happens, this is what we're on the hook for. And they need to know all that and have all that judgment. They become like a CIA analyst. And so there's a lot more judgment needed where there is less data, where there is more data needed, there are technical skills needed. But this is how I think about, this is how I think about talent. It's like how much data is there. And so is sliding scale. So lots of data, they need more technical people, less data, they need, they just need people really understand the risks and really understand insurance. And then these, these types of people are very different from these types of people. And so something Alicia and I have been talking about recently, like is the, where is the, the bigger gap specifically for actuaries in supply and demand? Is it on the technical skillside? Because we always hear about that. Or really, okay, because you know, I don't know many actuaries that specialize in like the judgment for terrorism and like, you have to consider supply from the actual, from the, from the talent perspective as well. And so in each, you know, 10,000 plus of the members of the casualty, actual society is a thinking feeling human being. And they have within them the desire to take their career in however many directions. Things that we hear perennially as recruiters is, I want a new job. I don't want consulting and I don't want reserving. You have to consider to like the desires of the actuaries as well. There's just different types of work that is just not, not, not that interesting. Of course, like you think about like personal lines reserving, super short tailed. It's like, you know, that's like kind of rubber stamping work. But then you get into, I'll pick on the terrorism stuff again, but like it's, you know, super long tail. And that's where it gets, gets to be a lot more interesting. You can introduce a lot of data science into the reserving methodology. What could be, or should be known to the, to those listening is that not every reserving job is created equally. There are some that actually require some really sophisticated thinking and understanding and judgment versus something, you know, are truly more routine types of roles. You know, some of the, some of the highest paid people at Liberty Mutual were reserving actuaries and mainly sitting atop what Ted Kelly called the ticking time bomb of insurance, which was a spestous and workers compensation. They were there constantly lording over those two long tailed risks and just really worrying about them. They had a whole department for it. So can you ask the question again, because I said I was circled back to the original intent of the question. Yeah, the supp, the supp, I mean, you kind of answered it, but the supply and demand, if we look at the scale of like the specialization for that actually is either the technical skills or like the deep industry knowledge for that specific subset where it's really thin data. Like, where's the bigger gap? Yeah, well, I don't know if it's useful or helpful to think about it in terms of gaps and so much as just like, I do think that people in general are products of their own careers, right? And so maybe that said I needed to say something profound. So how about I might at least just say something obvious. When you graduate college and you're looking for your first job, there's a handful of people who can be choosy, but most people can't be choosy. So they might end up in personal lines. They might end up in commercial lines. They might end up in ERM. They might, you know, it's wherever they, wherever they might land. And then they're going to start developing a strong skill set around that thing. And then that's kind of like, that's their marketable skill set. It's very difficult for someone to transition from like one silo to another. I'll just go back to like my like guiding principle, which is like people are hiring people to do work. And they need to know how to do that work. And if they can't do that work, then their their skill set is all of a sudden less valuable in terms of a salary perspective. So certainly like one can move from one thing to another. There would be potentially a pick out there. I wanted to touch on more like less traditional employers of actuaries. If you're thinking of like insure tax or other other orgs that are not insurance companies, are you seeing any trends there that are different or that you know, people should be considering. I hear I hear non-traditional thrown around a lot, but at the end of the day, a lot of these insure tax are really involved in traditional insurance. They're approaching it. It might be from like a let's let's just make it more efficient. Let's quote bind issue faster. Let's underwrite faster more efficiently. Let's price better. Let's pay claims better. And I'd be happy to kind of because I've had a front row seat to the insure tech journey. So it might might be helpful to kind of get the the history lesson on insure tech and where it started and where it is today. But I'll answer the question first. What I'm talking about here are the insure techs quote unquote that are really producing insurance, producing and distributing insurance in today's environment. There are three types. The makers, the sellers and the enablers. The makers are the MGA's or carriers. The sellers are the brokers, the agents and the enablers are everyone else who have like, you know, a good wildfire model or, you know, a better way to pay claims or more efficiently to underwrite, etc. In the in the latter bucket enabler, you know, we see some actuaries make their way over there and they are typically involved in what's called sales engineering. That is they are a subject matter expert that will interface with like kind on on the side of a buyer. If they're selling to an actuary, they might want to have an actuary to talk actuary to actuary. It's like, okay, I've been in your seat. I understand your pain. Here's how it is going to improve it. We see them over there, you know, less so much in the selling side of things. I don't think that an actuary's job looks distinctly different on the maker side. So within an MGA or carrier, they're pricing and sometimes doing some reserve analysis before there's riots in the street. Yes, MGA's do do some reserving because there's this magical thing called a sliding scale with their capacity providers and they have to they have to show the the wall strands. Yeah, well, we want to talk about the what candidates don't hear. So this is the the content you're not going to get anywhere else. Well, let me let me say this much. I think maybe everyone knows this, but they probably just still need to hear it. Is that insurance is incredibly small. I have been recruiting for 17 years and I'm a baby. I belong to the National Insurance Recruiters Association. There's a gentleman who belongs to that who's been recruiting for weight for it. 60 years. He's an outlier. But you do this long enough. You know who everybody is and reputations start to build backstories start to form and people talk people know people have back room conversations. Am I a big fit? Am I a fan of this? No, not at all. But it does it happen 100% does it happen? Yes, it's discussions are being had about people who've worked with other people all the time. I see this more than anywhere else in insurance than an actuarial. So so and so applied. Let me check with like these other 85 people that I've that you know worked with them or her. And find out what the deal is. I think that's something that people may not realize is going on. But it happens with great regularity. It's important to be mindful of the fact that you know every day you're building a reputation. Either good or bad. What are they saying about me? But moving on. But it's interesting you say that because I've definitely had people two examples come to mind. One was someone hit me up. Said, hey, you're connected with this guy. I'm LinkedIn view. Do you know him? And I was like, no, I really would just connect to them LinkedIn. And I ended up following up later on like, oh, did you hire that person? They were like, no, no, I messaged someone else. They said, you know, his quality of work was good. But he'd be offline like half the day we couldn't get in touch with them. And so it was just like, you know, some some piece of information you wouldn't otherwise know. Yeah, that's all day, every day max. It's it's I got a conversation on Friday. You know, talking with someone and I didn't even ask. They just sort of volunteering. And this just goes into like the recruiter bank, right? It's just like so and so good actuary, but takes forever on their projects. So and so was in a couple of meetings and said one thing in one meeting and another thing in another meeting. I add that to the, you know, proverbial file. And then especially when, you know, these are senior positions that we're recruiting for for instance. And like I go through the landscape and I was like, okay, so and so, blah, blah, blah, blah, next person, blah, blah, blah, blah, blah. And like I just I give them the whole like everything I've ever heard about them, you know, and it's like, yes, this person's good. But you should know this. These discussions could happen like what you were discussing Max and then also from like a professional perspective because like, you know, the reason why a company would hire me to recruit a senior level position is because I know I have the dirt. Like I know who these people are. I know who's good and who's not. Do I know everything? Absolutely not. But do I know enough to be dangerous? 100% kind of in that vein of things. Maybe the candidates don't know. Are there specific things you're hearing hiring managers say behind the scenes that candidates would benefit from more specifically? You know, typically a lot of the things that we hear interview skills, communication skills, providing concrete examples. I think a lot of people kind of just go through the motions day in and day out. Don't really like aren't thinking about the value that they're adding to their organization. And then they when it comes to an interview setting, they want to know it's like, what value have you? So basically it's like the question is what value have you added to your organization? And there's like, I don't know, you know, I did this one calculation once, you know, like, oh, okay, well, you're hired. Not really. But the it's, you know, I just think being mindful of like what you're doing and like in the broader picture, I find this specific to actuaries that a lot of times they get very, very like just closed up to what their little slice of the pie is and are not looking at the big picture. And Alicia, when we spoke a couple of weeks ago, we were talking about going to how do like actuaries get into the C suite. I was like, well, there's not really like a prescription for it. But one thing is for sure is that they really need to have as a holistic understanding and picture of insurance as possible and not how to like build a lost triangle for one county in Nebraska. I love this idea of thinking outside of the little actual slice. And you know, in this new role, I have I'm talking to CFOs more and or current pattern is a little bit of frustration with actuaries. I don't think they're actually involved in the hiring per se, but you certainly the budget. And I don't know how much this anecdote extrapolates to the rest of the market, but I had one guy tell me like the actuaries are the only ones asking about work-life balance and the hiring stuff. And like I have people working late some nights, but I got to know if I hire for this actuarial profession, this person doesn't do that. I might lose them. And it's like why even take a chance on that candidate? Whereas, you know, on websites, they always have like top 10 best jobs for work-life balance actuaries. So a little bit of odds with where the industry has had the pressure on the CFOs right now. So do you see that trend continuing as that? Okay, tell us about that. Like we're still like, you know, I'm calling it the COVID hangover. It's COVID happened. We were in a recession for like five minutes, right? And then all of a sudden, like the economy was pumped with trillions of dollars in stimulus and then hiring went wild. Ask any recruiter in the world, like what their best year was in the last 10 years, they'll say 2022. But what goes up must come down. And so once all that stimulus dried up, the hiring rebounded or whatever the opposite of rebounding is, debounded, trademark, Jacob Galecki. We saw a huge reshuffling. And so because all this is coming to an end, is that, you know, you know, I'll pick on Jim actuary. So Jim actuary is from and I'll pick on Nebraska, Lincoln, Nebraska went to the University of Nebraska, you know, but because, you know, actual jobs are a few and far between. He ended up having to move to Virginia to get his first job. So then COVID happens. And Jim actuary saw his saw his chance as a remote work. I'm moving back to Nebraska and moved back to Nebraska, bought a farm, growing corn, having a good old time. In the meantime, the all the companies are returning to office and doing hybrid and all of the stuff. And it's there's putting there's a lot of downward pressure. We're not even talking about AI yet, but that's putting pressure to the day will come in very soon. That Jim actuary is going to be out of a job because he made a lifestyle move to Nebraska. And there are not a lot of there. Maybe he'll be lucky and find a job local, but maybe he won't. Maybe he'll have to move to California. Maybe I'll have to move to New York. But the expectation 2026 and beyond is that people are going to be where the work is. They don't have to be in the office five days a week, but they're going to have to be proximate to the office. And the conversations is starting. Max, you're party to it that they're like, well, they actually are one caring about work life balance. Well, at some point, they're going to say, like, you know what? It's just not worth it. We want the people in the office. And we're going to have what we want. And we have the leverage. I'm in the business of when there isn't someone in the spot. We do the work. And so business is good. I think like you said, it's kind of already happened. And it's not how I want to get my business. But you know, the other thing too is like, we just announced the return office for the younger folks. And it's not like a malicious that it was just like, we were not setting them up for success if they weren't close to someone where they could get help. So it's like, it's for them. And those candidates who are like, yeah, we want that. There's candidates who don't. So it's a very tricky situation. But like you said, it's kind of a lifestyle choice. It is. And I think that there will be remote jobs available for people. They will become increasingly competitive. You know, and there'll be a they'll be a ceiling on the on the progression for that. And people are some people are okay with that. You know what? And like I said, every actuary out there is a is a feeling emotional human being. There's different definitions of success, right? One person's definition of success is, you know, being the CEO of a Fortune 500 company. Another person's definition of success is like, I have the time bandwidth to spend time with my children. You know, it's there's something out there for everybody. But like if if your definition of success is be CEO of a Fortune 500 company, you better move to the office and be seeing every day. Are there any other things maybe that somebody put on a resume that they think is helpful, but is actually raising more questions for employers? I don't know raising questions, but certainly I've seen my fair share of resumes in my life and most of them are bad. You know, not that their experience is bad. I'm not saying this is a bad candidate. This is a bad professional. It's like, it goes back to what I was saying earlier about what value are you creating for your organization, having the wherewithal to be able to express that in written form earlier. I was talking about that in interview form, which is also important, but you need to think about it in terms of cataloging your accomplishments versus your, you know, your job description because it's someone's just like, you know, I did this. I did that. I did that. I'm like, okay, well, what was the result? How much value? How much The money did to save the company. How much money did it make the company? What were the implications? Why were you brought in? What did you specifically do? So there's, I think every bullet on our resume should be a story, start to fetish. Should I have a beginning, middle, and end? - About gaps, like I worked with the guys. Like if I say two gaps, I just skip it. I don't even look at it. - I, that guy you work with is a Bond Villain interviewer, is the definition I give that. And so there's a couple things Bond Villains do. One, they like to set elaborate traps. They have arbitrary and weird criteria. They, in many cases, are bald. And in some cases, have cats. So did they have a gap? Because like, did this person have its gap because they were taking care of their like sick mother and then like they had the misfortune of having yet another sick person in their family and had to take another time off? It's like, I think that's really inhumane to not consider someone under those circumstances. - How prevalent are the Bond Villains though? 'Cause that, even if it's inhumane way. - A lot. Ask any hiring manager. They're gonna have these weird things. It's like, well, if I see one tapo, then I'm not gonna, I'm not gonna interview them because they don't know how to tap. And then like, you know, there's, God, just, so you ask anyone, trust me. They're out there everywhere. It's like, they have some kind of like, secret formula, it's a successful hiring and it's like the stupidest stuff. I'm sorry to say that. But like, that's just like, that's not a good, you know, yes, a resume should be type of free, however, mistakes happen and how do you recover from those mistakes is what's important, you know? The key to successful hiring while we're on the topic is understand what it is that you need, the job that needs to be accomplished, look for demonstrated success in their past in something that is relevant to the job, talk to some candidates, measure them against the criteria of the position and make a decision. - Yeah, I think this is because there's a, whether something is right or wrong, but also a reality component. And so the next logical thing that I thought of after gaps was, you know, when I first started my career, people were staying in the position five to 30 years. And now it feels like the modal person, especially if they're on the younger side, they're very accustomed to just stay in a role for like a year or two and then jump. Is that becoming more frequent? Is that something that-- - Yeah, it is becoming more frequent. However, I don't think that the appetite, you know, that's, you know, yes. That's the thing that hiring managers are most personicity about is frequent job moves. And so we have, like, you know, when we interview candidates and we bring them forward, there's like, I call them like, it's like the absolute musts. Like if we get anything out of these people, like in terms of information, before we present them to a client, that we need to know these things. And one of those things is why did they make each move? Why? And I'm actually, I'm personally, as a recruiter, less concerned about it. But universally, the hiring managers are extremely concerned about it. And it makes sense. It's like, what were the circumstances? Why did you just choose to do this? And some answers are really bad. Well, you know, somebody called me and I said, yes, and now I work there. That doesn't seem very intentional. - Like, I'm a consulting side. I don't think I'd be as worried or like he's said, I could be shown, well, you know, maybe there's someone who rapidly progressed through their career, right? On the carrier side, that I'd say, like it takes two months to three months just to get them access to all the databases. And if this person has had five jobs in the past five years, I don't know if I'm only going to get nine months out of all the institutional knowledge that I invested the first three months, like it feels like a fair complaint. Just shake it. Something on the street. - I mean, I think it's a fair complaint. I think that, you know, do we take jobs that we do not like and they're not good fits for us? Yes, we do. Let's say you're at your first job for five years, then you take your second job and you're like, you start and you're like, holy crap. This is not for me. - Move on. - You know, don't stay, leave. Think about it as like, so that third job, you better, you better stick around. So you need to be really careful about like, you know, that choice. I think anyone can be afforded a little grace in terms of that. You know, I just said I'm less concerned about people's like logic why they move jobs. But like now that I'm talking to this as like, I am concerned about why people move jobs because it's a pattern, it's a pattern of behavior. So a lot of this stuff comes up when we're doing executive recruiting where it's just like, okay, what's the pattern of behavior? Why did they move from one job to the next? And then you know, some of them are like, more money, more money, more money, more money. And like those are easy, easy codex is to crack because I'm like, just give them more money. And he'll take your job. But then as soon as someone else comes along with more, yep, more money, they're gone. And we know that. Like, we understood the decision logic. And then some people, you know, it's like short tenure, short tenure, short tenure. And you're like, well, what happened? Well, I got laid off, I got, I got fired. I got laid off again, you know. If you're laid off once, big what? If you're laid off like last five jobs, maybe you're not crucial to the organization. Maybe you didn't create enough value. And these are the things going through the hiring manager's minds. It's just like, there is somewhat of a stigma attached to being laid off. And especially if you've been laid off several times, there's like, they get, they put you under the microscope. They won't understand why. They want to know like, why, why you and not the guy next to you? And so it's like, well, it was a layoff. It was summer just indiscriminate indiscriminate layoffs. Those are easy to talk about. Some, I always have a follow up question, which is, you know, why did you leave your last job? I was laid off. I said, how many were involved in layoff? One, it's like, you sure it was a layoff? You know, that kind of stuff, you know, you have to think about like everything on your resume, everything about your career going back to like, what I said earlier, like people talk, people know. It's like, it's all discoverable. It's all knowable. Even if you don't tell them on the resume, if you don't tell them in the interview, like, it's knowable, it's find outable. Like, they're going to find out. So you got to be careful with how you progress your career and where you go. So yes, so like the short 10 years are becoming an increasing problem and there's not much tolerance for it among the hiring managers of the world, and not just the bond villains. - I have another bond villain sort of question. So for the job seeker who has, you know, 20, 25 years of experience, who's worried about maybe facing age bias, do they need to adjust anything and how they're presenting themselves on the resume or in their interview to get the, you know, the best advantage? - It's hard to create or suggest blanket recommendations for something like this 'cause everything is on a case-by-case basis, right? Some, I have one client who really values deep, deep, deep insurance knowledge that you can only get by years and years and years of experience. So they almost have, they have a bias for more tenured individuals versus less tenured individuals. There are organizations out there that, you know, have a bias for less tenure. My understanding is that unless you've done something that's really wrong in actuary that has a lot of judgment and experience should not hurt for a position anywhere. What should they be doing from their resume? I think presenting an honest and accurate picture of your experience is important. And then also going back to what I said earlier is that people hire you to do something. So when seeking a new position as a more tenured individual, you need to be thinking about where can I add the most value. What do I know the most about? And that's where you're gonna be most marketable. So maybe let's move to something a little more taboo. And it's the compensation part. And you and I have talked about this a million times 'cause I'm always texting you like Jacob, I need a septillion dollars. I think most humans want us to- - You need to get there and I'll take my like, my honorarium. - I mean, I can't be the only human though that's hitting you up about compensation. It feels like we've been talking about things that I think suggest that there are positive forces or actual salaries, but also challenges if you're in certain subsets. Like where are you seeing the market going right now? - Well, salary growth is not linear. Let me say that much. Ask 100 out of every 100 actuaries is gonna say it has to be linear. And what I mean by that is is that again, building valuable skill sets is what's gonna create the most lifetime earnings, right? And so maybe taking a step back in compensation to get a valuable skill set is not the worst thing in the world if you can afford it. I have a lot to say about compensation. - So we have to hear it. - We find that in property and casualty insurance, the most responsive to compensation to their detriment are actuaries. And so in fact, I joke internally that I was like, well, you know they're only criteria as compensation, right? So it's like literally as long as the title is actuary, they could be like making cat food. And as long as it's more money, they would take that job. How valuable is that cat food making skill set in the actuarial circles? Not very. I've seen a lot of bad behavior. And especially back in the 2022, there was a lot of movement. We estimated that 25% of the actuaries turned over in that year alone, which is huge. And average attrition at any organizations about 10%. So we can assume all the actuaries out there correct me if my math is wrong. But we can assume that from a population of 10,000 actuaries about 1,000 of them are going to move every year or so, right? We saw a quarter of them, 25%. And as you would imagine, that's created a lot of kind of downstream supply issues, right? Because despite what we've just talked about, short job ten years, Most people stick around for about five years or so and we're still within that, we're ending that five year period shortly, but a lot of people are job hugging because they have a plum gig. They, you know, they moved somewhere for lifestyle reasons. They have a remote position. They're not leaving because they've written their meal ticket and they like what they've been given. If everybody can get one nugget of wisdom out of this and I've been trying to understand it about actuaries for so long is that like of anyone of everyone in the insurance space who could better understand lifetime value. It would be an actuary, right? It's just like they could bottle it out. How is it that they are moving primarily for more money and not considering how that will affect future earnings? You're building a portfolio of skills that are marketable for a price. Some skills are more valuable than other skills. There's a reason why meta and open AI are paying people nine million dollars a year, right? Because there's only like 12 people who know how to do that thing, right? It's like learn that thing. Actuaries could really benefit from just stopping, pausing, and thinking is like, how does this advance my career and how does this advance my lifetime earnings? Let me say this, money's not everything, right? Like I said, there's multiple definitions of success and whatever your success is, whatever your definition of success is, you need to frame your decisions within that, right? Does this get me to where I want to be or does it not? I had not thought about that with actuaries, the risk management being able to look at it more holistically. They don't consider it. I literally had one hiring manager call me and say, okay, we have another actuary opening. They left. They literally told me that they love their job and are just leaving because the other companies paying them more money. Do you're going to leave a job that you love and you're going to go somewhere else from marginal gains, potentially less happiness for like $5,000? Is it worth it? No, Max, what do you do it? I mean, well, no, no, no, I'll steal me on that argument too. I've had a lot of people where they leave for another $5,000, $10,000 and you get the text after the third month when they're like, this isn't working out, like I made a mistake. So I've definitely heard that. I get that text maybe two or three times a year, actually, if I'm being honest, but think about how many times it's actually happening more so than you're aware. Right. It's crazy. I have done this for a long time, and I say internally, it's like the only criteria. It's money. Half the kid doesn't even talk to us until we tell them how much the job pays. I'm like, do you not want to hear about the job? Maybe it's interesting. Maybe it's your dream job. Well, would you take a $5,000 pay cut to like walk out of your job into your dream job today? Probably. Most people would. I mean, three hours a week. I wonder if it's a low sample size where they haven't had a bad job yet to know what the opportunity cost of that marginal increases, and maybe they don't vet. It sounds like you're saying they're so fixated on compensation. They don't actually vet that opportunity being right for them. No. No. No. No. And the more junior, it's total commodity market. It's like, you know, I'm selling corn for $5, who is it a bushel? And like, it's like, I'm going to go over here $6 a bushel and it's total commodity. And I would like to talk about this. I think that, you know, my, my ilk is very much to blame for a lot of this. And it's the recruiter community in general. And I'll lump myself into it. I'll be guilty as well. But there's been, I think there's a lot of recruiters who focus on actuarial and they have to make a living too. Actuarial is a hard to get recruiters are well compensated for their efforts. Should they be successful? And so there's a lot online. So there's a lot of churn going on. There's a lot of like, there is a lot of transactions occurring that probably should not have occurred. You say, oh, this isn't working out. This isn't working out. And it's, and it's hard. You know, when faced with more money, it's hard to turn down. I get that. I understand that. I'm faced with that in my work. It's like, well, okay, well, this is a, you know, this is a good paying role to work on as like, but it's going to be really difficult to fill. And we could probably do like three smaller jobs, like faster and easier than this one big one. Right. And so then it's hard to say notice some of this big stuff. And sometimes we have to for the greater good. Right. And the greater good of my organization, my career, my time and energy and emotional energy. People don't talk about emotional energy and work so much of it to be given. Right. Okay. So that's, I mean, this is why we want to have an episode with you because I think people need to hear this. But maybe if I was just anticipating what the audience's objections would be to this. And maybe they should hit you up to have a deeper conversation because I find when you and I talk sometimes run the phone for like an hour talking through this stuff. So maybe that you should just start billing for therapy sessions. But so one might argue, well, you know, maybe the other professions aren't doing this as much because that opportunity is not as present. I mean, that's one thought. The other is that compensation is such an easy filter to even sit like, you know, if it's below, you know, maybe it's if it's like 20% below, I'm not even going to consider it. So just a really easy filter. Well, yeah, I'm talking about marginal. I'm talking about marginal differences here. And like, look, I'm not I'm a free market capitalist. You should be paid for the work that you do. And I'm not an advocate for people making less money. So let me make that clear. I'm just saying, hey, maybe take a step back and consider other aspects of the job other than just the pay, right? And so I'm also a recruiter. And so from my point of view, everything's negotiable. There's been very few things that I have not been able to negotiate through. At the end of a good negotiation, everyone should be unhappy. And so usually that's the that's what happens. Things things are negotiable. So that's that's the message that I'd like to put forward is that don't just consider the compensation, consider the full picture and use the recruiters as as as a resource for you because they have the relationship with these companies and they know the tolerances like I tell people all the time. It's they're like, well, I make this and I was like, okay, well, they could probably handle that. But then there's some that are just like not having it. It's just like when we say the budget is X, the budget is X and not a penny more. Like literally the candidate could try to negotiate an extra penny and salary and they would not afford it to them. And I know that. And so it's like where are their tolerances? Where is their wiggle? How important is it to you? Look at the full full picture of benefits as well. You know, I talk to a candidate this morning. And I've, you know, they work at a company where I know the retirement package is like crazy good, probably best in the industry. And that's a, hey, look, I just want to flag this. It's like the retirement plan that you have is best in class and you're not going to see that anywhere else. And that's like, so just know that you have the best retirement out there. And so you're going to go from like 12% to three, pretty much everywhere else. I thought I was doing the right thing. Ultimately, they still made too much money anyway. So in see, here's a great example practicing what I preach. The client will max out at, you know, one dollar figure and they were an order of magnitude above that. And I was like, there's no way they're going to go above that. And so, and trust me, I have tried. And so that on top of the really good retirement question was stemming off of like when those people maybe take just the higher pay. And then a few months later they're texting saying, this is not what I thought it was going to be. What kind of like questions do they need to be asking in the job interviewing process like what red flags should they be looking for to be able to tell like this is a good or not so good fit. Think about what I said earlier is that people are like poking around in the background asking about you do the same for the hiring manager do the same for the team do like do your due diligence ask to speak with a peer. How do you like working here? And they're like, if they're like, I like working here. And I'm like, do you really like working here? Yes, yes, I do. You know, it's like look at the body language, understand, read between the lines. I think most people are going to tell you that they like working where they where they work. But like if you can like, you'll be observant, really listen to what they're saying, ask a lot of probing questions. Why do you why did you take this job? This is a great question. Why do you stay here? What keeps you here? Right? That's a good question. Their answer might just be like a silly answer. Like, well, you know, it's where I work. For better or for worse, you got to like find out who you're working with. And actually, we don't want that's a good, a good point that I made. But the, it's like so much of like why you like work, like think about all of us, like the jobs that we've liked the most. It's like it's the people, right? It's like who you're working with. It's like, so the more people you can meet, is this a group of people that you want to spend your time with 40 hours a week? And of course, no matter how good your teammates are, if the manager, your ultimate manager is not a very nice person, that's not going to be pleasant either. So you need to figure out who they are. You also need to understand how companies make decisions because, you know, what I always tell people is that what a gift the interview process is is that you have a front row seat at the very beginning to the company's decision making model. And how quickly do they make decisions? How do they make the decision? Do they, are they a consensus building organization? Which are, you know, some people love it, but consensus equals slow. And it's like, are they more of Like as Bezos says, like commit or disagree and commit, right? It's like does someone make a decision but everyone follows up behind that person. And so you can learn so much with just how frequently they communicate with you, how quickly they make a decision, how transparent they are about the decision. Like when I go to do business with companies, if they come back with my contract and they have red line half of it, I just say no, thank you, I'm not gonna work with you because I know that they're gonna be a pain in the, you know, but I was like, this is how you wanna start the relationship is just trashing everything in my contract, which is industry standard and like nothing crazy in there. It's not like I'm, you know, like a rock star, like you know, writer and like asking for all the, you know, brown M&M's or whatever, you know, it's just like standard terms. So it's like you have such, you know, the interview process is such a gift, you know, I'm always surprised too when like, it's like well, it took them like three months to make a decision and, you know, I was so grateful and then they take the job and it's like, you know, they don't react positively to very reasonable requests like, you know, through the offer negotiation. It's like, oh, you know, I just need an extra, however many dollars because XYZ and they're like, nope, you get what you get and you like it. And so you're always gonna get what you get and you're gonna have to like it if you work there. That's just who they are. Pick up on cues and the interviews. Think about the very observant with how, how they make the decision and how quickly they make the decision because you have the front row seat to their decision making process. - So I wanna talk about does AI change recruiting itself? And this actually just like sprung into my mind because you're talking about the gift of the interview process. And I think I just saw like a kind of a parody video where AI was doing the interviewing process. I don't know if that's just a parody or that's like, actually starting to happen now but it's just curious if that or any other changes come in. - So yeah, so AI is interviewing you. Do you wanna work at a company that doesn't value you as a human? (laughing) Right? It's like, the interview tells you everything you need to know about a company and their decision making process. - Other changes you're seeing to the process like with you. - So in recruiting, so recruiting like everywhere else is trying real hard to implement AI. And I don't know who's using AI to any success in any walk of life right now. I'm sure there's somebody. I'm sure they have a YouTube channel and I'm sure they are very proud of themselves. I'm not gonna implicate any particular software but I've seen it all because I hold out hope that one day maybe recruiting can be a little bit easier and as of 2026, it's still hard. Which is good because hard work is what's most valued or difficult work is what's most valued in the market, right? So most recent, things that I've observed or demoed in recruiting were these like candidate finders. So we put in a job and it goes find the candidates. The output made me wonder what on earth was the AI thinking? And so because it wasn't thinking hard out of 200 returns, 999 were incorrect. Like in not even in insurance, one person, you got one person right. Is that more efficient? - No. - And so that's from the finding perspective. So like recruiting is like his job, find, engage, you know, process, right? So the finding is not working. So. - Finding the heart is part. - Is that the part? - It can be depends on the job, right? So we tie it back to the actual world. There's a lot of recruiters in the actual world, right? And it's because actuaries are not hard to find. There are lists, there are directories. There are credentials. There's easy ways to find actuaries, right? It's when it gets into these like squishy executive jobs and stuff where there's lots of like weird little things that need to be in the skill set that are not going to be evident. Like we're working on a job right now that like we have to find someone who's had a material impact on, on propping up a reciprocal exchange. And it's like how are you going to find that? That's not going to be listed on someone's LinkedIn profile. So you have to go find every reciprocal in existence and then like find all the people. Like who? So from the finding perspective, AI is not there for recruiting. It, you know, and I will warrant that it might work well for like if you need like an entry level software engineer or something like that, something that was a lot of them and they're like well-defined in terms of like what, what it is that the job is, you know, like a dentist. You're your dentist or you're not a dentist. And so it's, you know, if you have a DDS, you're probably a dentist. Some companies are implementing AI, job application review. I've yet to be party to. So when I was at Liberty Mutual, we looked at everything. Humans, humans looked at it. The closest we came to anything was introducing a Harvey Ball. And I don't know if Harvey, is Harvey Ball like a thing? I got to Google this. It's a, it is a graphical representation. And it's just simply a green, yellow, red. And so they did that with a few. I found that the Harvey Balls weren't all that helpful. So it would, it would yellow some greens and green some yellows, red some greens. So I didn't trust it. So as soon as you find like one red that should have been a green, you no longer trust it. I haven't seen any like software that evaluates it. I haven't looked at that. I feel very strongly about humans looking at humans and looking at resumes. The problem is it's like AI versus AI, right? It's like, and so I think a lot of these applicants are like pumping it out. And so it's like, okay, well, we'll see you and we'll raise you, you know, AI application review. And then it's like, who's AI can like beat each other out? We put knockout questions like, I love this. Like, it's like, do you have like 12 years of property and casualty experience, yes or no? And then we'll ask like an insurance question. Like, what does PAS mean? And some people like, of course it means, you know, something that's not policy administration system. And they were like, were you truthful in your answers? And then they'll say yes, even though they lied. And we call them double liars. The ones I like the most are the ones that lie. And then they answered, no, they were not truthful. Those are my favorite. And I was like, well, at least they're honest about being dishonest. And they're only single liars. Yeah. So, but it's happening. It's companies out there trying to make a living. They're trying to add some value. But what they're doing is just inserting a layer of complication into something that was probably relatively simple before. And so I get it. So companies out there is like, oh, maybe we should create like a AI application review machine. It's like, we can make some money. Because like, there's like, you can make a lot of multiples on a SaaS company, right? And so there's a lot of economic consideration behind putting effort into stuff like this. And it, but it's not adding a lot of value. Certainly, that's not the benchmark that most companies are measuring. They're success by least these SaaS companies. They just use it, the users. So it's like they can get people to use it. Then they're worth something, right? And so they're pushing. So what I'm observing with all of these tools entering into the marketplace is like the increasing need for human beings and human to human interaction. And so it's going to be more important for bringing it back to the actual community here to participate within the CAS, volunteer network, be present, be active, know the other humans. What a great profession, right? It's like, there's an organization behind you. And everybody who does what you do belongs to this organization. It's like, nobody has it as good as an actuary in terms of like being able, like you have like a real change. It's like, I'm not saying that you should do this, but you could know all of them. You could know every actuary. You could certainly know the ones that are most likely to be hiring in the next 10 years or whatever, and they're typically involved in the CAS or at least they're very present at a meeting. Grab a coffee, do something. So because it's like, if your algorithm is battling the internal algorithm, your resume will never see the actual hiring manager. But if they have a job and you know it's like our friend Jim actuary who's the hiring manager, you could just call Jim and say, hey, I just applied to your job, I think I could do this, this, and this. Would love the opportunity to be considered. That's it. You know what, guess what? Jim's gonna call Claire Recruiter and say, Claire Recruiter pulled this person's resume, and let me see it, and then you'll probably have an interview. So it's like, and that was all done through human means with a little bit of LinkedIn maybe in the middle. - So the classic networking is still the best path. - I think it's even more important. - Yeah. - I think, 'cause Max wanted my point of view on AI, I think most of it, most of it's trash. And especially when it comes to like recruiter tech, it's like, it's sorry, all recruiter tech, people please don't listen to this podcast, but not very good stuff. - A slight pivot away from AI. But I remember for one of the CAS conferences that you were sponsoring, you got a booth, and you're basically just using it as a billboard because most of the actual folks tend to go to one tech vendor, to the next tech vendor, and then they kind of skip around the recruiter. Like they just don't wanna have those conversations. And one of my hopes with this episode was that this would maybe drop people's guard down, so they can actually start having those conversations. - Yeah, yeah, let me say this, I get it. You're there with like six of your coworkers, as like, do you wanna be chatting it up with a recruiter? Probably not. But, you know, there are discrete ways to go about this. You can message them, say, hey, I'm gonna be at the, whatever meeting, can we, can we grab a coffee? Can we meet in some undisclosed location? So that my coworkers don't see me chatting it up. or it's just like, I'll say hi, we're not going to have a material conversation at the conference. Let's talk offline because that's most certainly in the most confidential way to go about it. I want to be mindful of confidentiality. I want to be mindful that people don't want to be seen necessarily talking to a recruiter. People have opinions about different recruiters, right? But guess what? Like, I know all the actual recruiters. They're all pretty good. The ones that show up consistently to the CIA's meetings are all really good. You know, I'm not going to name anyone specifically, but let's just say if you see them regularly at the conference, chances are they know what they're doing. There are ones that you may have never seen at a conference. Maybe they don't know what they're doing. Maybe they haven't dedicated to supporting the actual real function. Maybe they're dabbling. Maybe they are good. Maybe they're not. I don't know. They're just unknown. And so I think in general, I don't want this to necessarily be a commercial for me, but so much as like, you know, being an advocate for the recruiting profession and especially the ones that have dedicated years to deeply understanding the actual real function within the insurance industry. And are you seeing any differences in what the employers are looking for of entry-level actuaries? We're thinking specifically as new tools may be replacing some of the learn by doing work that they would have done. It's been a while since I've been on the entry-level side of things. So I'm going to have to speak in vague terms. I do think that is going to become increasingly more difficult to secure entry-level positions, which has to be really wondering what's going to happen when there's no junior talent to progress within organizations. And so which will create an issue unto itself. What I have read and what I understand, and if you were an entry-level actuary asking me for this advice today, this is what I would tell you, is that one, the exams and the material in the exams are really important to knowing how to code is, like I said, 20 years ago, it was like no Excel. And then today, it's like, you've got to know Python, you've got to know R. And then an argument can be made that the clause can do this. And I was like, yeah, I can, but it does it do it well. Code better than clause, and you're fine. It's not stable code. I actually think it's a great resource to learn how to do it. It's still not there to be able to produce deployable, integrate enterprise grade software. It's not replacing anyone. Yeah, from what I understand, too, is just cultivating. And maybe this is, this is not a helpful comment, but I'll make it, is cultivating some level of flexibility and learn how to roll with it, right? Because I think that's that's the name of the game in 2026 and beyond is like, okay, well, none of us know what tomorrow will hold. But if I am adaptable, then that is the most important thing that I can do. So I was on a like was flipping through Charlie Munger's book, you know, poor Charlie's Almanac this morning and was talking about how his hero was Benjamin Franklin and Benjamin Franklin, by all accounts, knew everything and how how did he do that? Because he was adaptable. He read widely. He knew a little bit about everything and was deep in on some things as well, building that general knowledge base, understanding the human condition. I might even advocate a little bit for the liberal arts. I'm a big fan of liberal arts. I think actually some of the best actuaries I've met, the smartest actuaries I've ever met are the ones that have liberal arts, educations versus actual science, educations, no knock on actual science. I have a degree in it. It's just like having that kind of divergent thinking, I think builds adaptability. However, you build that that's your own journey. There's no prescription for it. It's like no a little bit about everything, no context. The more human you can become, the more valuable you will be because the robots will increasingly do the robotic stuff. One of those areas to know a little bit about I wanted to touch on was climate. Because when we talked a couple of weeks ago, you mentioned kind of specialized in that area, and is there anything the general actuary, not a catastrophe specialist should be thinking about in terms of climate and property insurance? I think the two big issues of the day, right, are AI and climate, and they're actually tied together. And AI is exacerbating climate change. With climate change, accelerating, it's like tomorrow, September 1st, and it's going to be 100 degrees where I live in Memphis, Tennessee, where it should be like 70. It's a real problem. Things are progressing. It's a peril that I think really needs to be more deeply understood in how that will evolve over time. And so educating yourself on what's happening, especially like wildfire, like one of the most valuable startups, if not the most valuable startup in recent times, a flood insurance company. They are essentially printing money, and I'm really impressed with what they're doing. They have enough money, so I'm not going to give them free advertising. So you know who you are. I can't speak to the specific mathematics and the modeling, and all of that behind the scenes. What I would encourage every actuar is to be cognizant of it, because it's going to be really a big driving force in insurance. And then after all, like what, what's one of the biggest drivers of market, market hardening and softening is claims activity as it pertains to cat activity. And I watch this stuff very carefully, too, because like as the market softens, as it hardens, so goes hiring. So are the employers looking for that skill center? Here's what I'm asking. I remember at a former employer, I hired the principal climate scientist. She was brilliant, you know, worked at a bunch of universities. She was a think tank sun climate. She came in, and her first thing was to look at what the actuarial teams and the catastrophe modeling teams were doing. She was like, this is not science. And so I wonder, is it something, is it a gap that actually needs to fill, or is it just outside of the actuarial core competency, what are employers looking for? Well, I mean, God, you know, we're insurance, right? Are we climate scientists? No, no, you're not. Do we ensure climate risks? Yes, we do. So in so far, as the insurability of said climate risk, we should understand that. And if you don't know the answer, you need to have someone close to you that knows the answer, right? And so in the example you're given, talk to climate scientists, talk to the people who do know, understand, at the end of the day, we just have to really deeply, as deeply as we can understand the risks that face insurance. We are not necessarily going to know it all, but there are people who are academics, scientists that do really deeply understand this stuff, but they don't understand insurance. And so it's like, how do you ask the right questions in order to like translate that into some kind of like actionable model and actionable strategy for your company? We only got through like two percent of the questions. We have two percent. I don't know. So three percent. I don't know. You had the bright idea to have a recruiter on and recruiters do talk. So that's yeah, it's it's good content though. I mean, I think like I'm getting more out of this than I I would have thought to. So it's very good content. But like to one of Alicia's questions a few points ago, when she was asked about the like what to put on the resume for the junior folks, what maybe if you go to the positions that you're actually helping to hire for the more expert or technical folks, what's the version of that question? Like what's the new R Python that they should be like the skills they should be building? Well, we're starting to see we're starting to see Claude and it's not without an iRoll from me, by the way. It's starting to creep in, especially with like the newer companies because they're trying to gain perceived efficiency. And I use that word perceived very deliberately. These frontier models I think are providing marginal value today. What I would like to see is a deeper investment into homegrown models that actually do something good versus losing these frontier models to do something that's mediocre. The stuff that like Claude and OpenAI have been built upon has existed for 50 years. And there was a lot of amazing work that was being done at Liberty Mutual when I was there around some of this stuff that was really good. And it was all homegrown and very, very impressive and value ed. So it wasn't just busy work. It was real value add industry changing stuff. This was 10 years ago. So this was crazy. It was like it was indistinguishable from magic. So I'd like to see more of that and unless a dependence on some of these frontier models. But you know, to your question, people are asking for it. And so I would invite anyone interviewing at a company that is asking for it. So what do you use it for? What value have you seen from it? Ask a lot of probing questions from what I could tell the best use case for frontier models today or building these like little like self help applications that can help you like grind through stuff really quickly. Like really good for that kind of stuff. But anything that's like actually like deployable at an enterprise scale, not there. And I don't think it's intended to be that. The math is getting more Matthew. If I can like say it simply, we go back to like the like lots of data, no data scenario where there is lots and lots of data. You can do some pretty crazy stuff. Crazy good. Things with with mountains of data. You can get to a level of specificity that is really ridiculous. Then you have like price purity, which I guess is like the actuarial holy grail, right? It's like the true price pinpointed per risk. People who can do that kind of stuff, they're the ones that are in demand. And then the day-to-day actuarial work can be performed by any actuary. And we talked about this briefly. So let me throw it in here before we run out of time is that you know there there is a bit of a kind of bifurcation occurring in the actuarial profession where some are just like tried and true day-to-day actuarial work and others are really skilling up and separating from the herd in ways that are really profound. And this could be done in a lot of different ways. So you can take a qualitative route. or quantitative route. On the qualitative side, if you like deeply understand insurance and how the pieces fit together and what the implications are, that's a strong qualitative point of view. And that's valuable. And then there's a strong quantitative point of view where you increase your technical skills and become increasingly more technical. Your math is more mathy. I have a mathiest math. That's right. That's right. I think it's so Jacob says just to have these conversations network talk to the recruiters. The good ones, not the bad ones. Not everybody is for everybody. And I am included in that. It's too humble for a sponsored episode, Jacob, but we'll take it. Thank you. Thank you so much for being here and thank you to Galecki Search Associates for sponsoring today's episode. So everyone will see you next time on almost nowhere. The views and opinions expressed by the hosts and guests on this podcast are their own and do not necessarily reflect the official position of the casualty actuarial society. It's subsidiaries or the employers of the hosts and guests. This podcast may discuss topics related to insurance and regulatory issues, but the information shared should not be considered as legal or regulatory advice.