Embracing Change and Bridging the Gap with Devyn McNicholl

Alicia: 00:00

Welcome to Almost Nowhere, the podcast where we explore the cutting edge intersection of data science, AI, and actuarial science in the P &C insurance industry and

Alicia Burke: 00:15

I'm your host, Alicia Burke. I am the director of the CAS Institute and I'm joined by my co-host Max Martinelli, actuarial data scientist at Akur8

Alicia: 00:25

Today we're able to record our episode in Orlando at the Casualty Actuarial Society's Rate Making, Product, and Modeling Seminar. Our guest is Devon McNichol, actuarial and statistical consultant. And Devon, welcome so much. Thanks for joining us.

Devon: 00:38

Yeah, thanks Alicia. I appreciate

Alicia: 00:40

So since we are at RPM, I was hoping we could maybe chat a bit about the keynote yesterday. Because we were talking about that as we were walking over here. We had a futurist talking to us about artificial intelligence. I just wanted to get whatever some of your takeaways.

Devon: 00:55

Yeah, I thought her talk was so interesting because she was really approachable and she wasn't an actuary. So she was really good at speaking. But she brought up some points that I really hadn't thought about. Obviously, think most actuaries, maybe I hope I'm not overstepping, but I think most actuaries are using.

AI in their chat bots. For me, it's writing emails, writing basic code. That's really made my efficiency as a person, as an employee better. But she was really talking about it from more of a holistic view of all the things, like having an agent. So that was interesting because I haven't heard of that yet. I don't know if you remember what she was saying, but she was basically saying, you have these employees that are bots that can reason and decide

what needs to be done as opposed to having to ask. And I think that's really scary for people because you're like, about my job? And she got into that as well about how every time there's an evolution in technology, people lose jobs and then more jobs are created. And I think we hear that in the political world and it doesn't sound as real as it did coming from her.

loved how hopeful she was about it. I loved how she acknowledged the doom and gloom and the scary, know, change is scary and the world is a scary place and it's hard to...

wrap your emotions around having this new big tech that's coming into your life and doing your laundry and doing your job and making calls for you. she was so hopeful about what it could do for our industry, but also just the world. And I walked away feeling so energized by

Alicia: 02:42

was something she said at the end, I think it was in the Q &A, but it reminded me of an earlier study that was floating around LinkedIn a few months ago saying women were less likely to be adopting AI and she brought it up again. So it kind of reminded me. I was wondering is that your experience as well and why do you think that would be?

Devon: 03:00

Yeah, I

mean, I think that's interesting. My head goes straight to, know, a lot of the women, women in tech that I know they take the work on their backs. They feel like they have to prove themselves. they do or don't is a different question. in my world, the women, the few women that I know that I've worked really do feel like...

everything they do is on them and they don't want to take shortcuts and they want to prove themselves, especially in a coding role. And not to say that some men don't feel that way, but I an easier shortcuts make more sense in the workplace to people that have privilege. And so I think.

I don't want to say that's a good or bad thing, but I think it's probably true that women are going to feel a reluctancy to let go of some of the responsibility that they've fought for to an AI agent or whatever you want to call it. to me, I think that's probably the driving reason, but I'd love to see the data on that.

Max: 04:01

Well, so we actually did a little bit of research and we were developing the CAS AI Fast we saw that study and we were shocked and we wanted to look from a marketing perspective, like how did we do? And it seemed, Alicia, from the research that it was very representative of CAS membership. So it seemed like maybe there's some immunity here, but we're still kind of asking this question. It's the second time that research has come up.

Devon: 04:24

Yeah, yeah, makes Like I said, I'm using it. If I'm one data I do think I was late to use saw especially younger people that I was in various roles come on and they're just so ready to use And I think I'm right at that age. I don't want to age myself too much, but I'm in my 30s. So I'm right at that age where

I didn't have that in my official training. And it does kind of feel like cheating. When I go in, when I ask, you know, Claude or whoever, write my emails, there's a feeling that it's not genuine, right? That you're wasting someone's time with fluff. And I think that's another maybe feeling is want to be direct and you want to not waste anyone's time and you want to be genuine. And so we have to find a way to make

AI in the workplace feel like it's not a shortcut, feel like it's a tool that's appropriate to use, and I think you'll start to see a lot more women and older people, which was another thing that she She actually said that older people have been faster to adopt, and I found that really interesting. Because I think anecdotally that's true in my work as well. know, like I see my bosses using it, right? And I'm always like, wow, that's cool.

Alicia: 05:28

cool.

Yeah,

and it was actually opposite of what I would have assumed because they always make it seem like, it's the youngest people who are less likely to be skeptics of it. But it made sense, though, the people who have more experience are able to utilize it better because they can filter out what's the good stuff and the hallucination.

Devon: 06:00

Absolutely,

yeah, yeah, and using that wisdom to decide what is fluff, what is true, what's relevant. a skill set, right? And we're gonna have to get better at that skill I know that I struggle with it. you know, I'll just basically I'll ask my chatbot to make that shorter, right? But maybe there's a better way. Maybe don't make it shorter, make it more, you know, whether it's concise or impactful. always just trying to make use AI to

everything down and I think probably I need to change my perspective on that based on her talk yesterday as well.

Alicia: 06:33

It's an interesting way to think about some of the younger people who are just starting their careers who don't have that experience. Are they just accepting things that maybe they shouldn't?

Devon: 06:44

Right, yeah, especially in code, right? Because if something runs and you get the answer that you expected, I can see that you could certainly say, it's done, right? And coding is all about thinking and what could go wrong.

Max: 06:59

Right. Edge cases upon edge.

Devon: 07:01

That's right. Yeah, so I think a lot of times we write code and 80 % of the times it works. But if you're not the one actually writing it, you're not thinking you don't have that time to think through what could go wrong. And we have to find ways that. But I don't think that's a reason not to use it. I think it's just a reason to teach better skills in using the tools.

Max: 07:23

Going back to the folding laundry, I think every human is excited for a folding laundry robot. It does feel like there's a lot of actuarial laundry. Like I could maybe say, you know, go find the 10 recent filings from these competitors and see how they're pricing this and give me a report. You know, maybe there's value in an analyst doing that the first time and actually see, but I don't envision people actually being excited to spend like a week in the refilings and digging everything up. So it would be nice to just have some agent and say, can you summarize this for me? I need like a quick answer.

Devon: 07:52

Yeah,

yeah, is probably not good to say, but when I was first an actuary, I really liked those types of tasks because I felt like I could do them. It's very hard when you're starting as an actuary to feel that you belong and that you know what you're doing. So those types of tasks where it's like, Devon, spend the week.

copy and pasting 100 competitor filings. sounds awful, but at the same time, I think there is learning in again, I don't think it's something that we should not use AI because of those learning opportunities, especially for young people that are starting their actuarial it's something we should think about. How do we give young actuaries the opportunity to learn the mundane things? People take it for

Sometimes can be hard to email. Or just the small little things that you don't learn in college. If AI takes over all of those, I do think that there's some loss ownership over your day to day.

Max: 08:53

The second order, third order effects of automating this stuff.

Devon: 08:56

Yeah, yeah, But maybe also maybe that's just me being scared and it means you could just skip all of that stuff and go straight to just being a nerd and like nerding out on the results. So I think there's a long way to go us to figure out what's the appropriate ways to use it and how to make it the most effective, efficient tool.

Alicia: 09:18

Yeah, I was a little bit excited but concerned about the futurists talking where we would all be sending our avatars to a meeting and like they're making decisions, not just like reporting out. Not yet. I probably will. did it? Was it easy?

Devon: 09:30

Did you make an avatar?

My avatar is way better than me. She speaks Japanese. wow.

I was shocked. I actually went up to the keynote speaker after because I made the bot in about three minutes. Then I started making videos with it. You can change their clothes and it looks like me. I sent one to my wife and I was like, can you tell this isn't me? mean, it looks like me. you should make one. guess it's what I

Alicia: 10:02

I'm cool with sending my avatar to a meeting with people. I'm just wondering if it's a meeting of all avatars.

Devon: 10:08

Why have it? like can't they just email each other? don't know Yeah, it's well. I think if you're the one person in a meeting with nine avatars. Yeah. Yeah, that's really weird. Yeah, and

Do you feel worthy? I think feelings. I think this is going to be the challenge is how does AI make people feel in their roles? sometimes we forget that part as actuaries, especially when we're trying to create teams and build trust and all those things that are required to have good results. So that's what I'm thinking about when she's talking about it.

The other thing that I think is interesting, she talked about how over each technological revolution, how long it's taken for people to adopt things. Do you remember that? Yes. It started like the steam engine, it was like years, then cars were some smaller amount of years, all the way up to cell phones, which took, I remember it was between five and six years. And then the internet.

was four and then Twitter was two. But chat GBT was some amount of months, right? Where they reached 100 million users.

Alicia: 11:22

than TikTok.

Devon: 11:23

Yeah, and I think that that was the best illustration of how fast this is moving that I've ever seen because it really puts it into perspective of you don't have time to adapt. You you don't have time to get used to cars being on the road. And we have to be really adaptable in our personal lives and in our professional ones, I think.

Max: 11:45

I mean, I recall when video conferencing first became mainstream too. It was a little weird if you were the one actuary video conferencing in and nine other people would be in person. eventually that just became normal. This does feel very different though. That one actuary talking to nine avatars, that feels substantially different, but maybe we will adapt. I'm curious where it all goes.

Devon: 12:09

Yeah, me too. And I think at some point, you probably just stop having as many meetings. I think the step is because that whole section was about you can have 1,000 meetings at once.

I don't need to have a thousand meetings. I can't imagine anything even if I was like the president of United States has too many meetings. And how do you say, even if your bot is going, are you summarizing that information in a way that you can make actionable? And I think that's the part. I don't know. I feel weird about sending bots to meetings, but I'm going to get over it. I'm going to try to take my own advice.

Alicia: 12:47

I do have question about your background. You started in a more traditional actuarial type role and then moved into a data science type of role. Can you give us a little bit about that experience and is it something you recommend that more people do to have skill sets in both areas?

Devon: 13:05

one thing I like about the actuarial career path is that it is very defined. And that was one thing that drew me to it. It's like, if I do this and take these exams and pass this, then I'll have a good salary and I'll like chill. That was my initial plan. I really liked how it was you do these things, you get to this place and you're done.

And then you forget the exams are so very different than the work.

Not that I didn't enjoy the work or I didn't enjoy the exams. I liked both. But I found that once I was done with the exams, the brightness of the career, it was all about business, it all about the loss ratio, it was all about profitability. It was all about, do I want to be a leader or do I want to be an individual contributor? And I know if you're listening and you're working at a carrier, you're like, yeah, those are the paths. And I wanted something different and I wanted to have more autonomy.

I just I really like coding and so when I decided not to go back for my F CAS it was a big decision Especially because a lot of my leaders were saying You're not you're not gonna get promoted if you don't Go for your F CAS and and that to me was well, then I don't want to work here and that was my own personal decision Because I wanted to keep getting better and I wanted to do be a better actuary. I just know that I didn't want to

I wanted to learn what I wanted to learn and I think a lot of mathematicians are in that space where if they're not interested in the topic, it's much harder to get yourself to be happy in that space.

I feel like I didn't know any actuaries that were really doing data science roles. Now everything's opening up, right? And there's so much opportunity. If you do know how to code, if you do know statistics, you're probably in a better spot than if you know how to balance an insurance company's balance sheet.

There's so much overlap. It really just depends on what I think motivates you, what you like learning, and what you're okay doing every day. And everybody has, I mean that's what makes diversity a beautiful thing at a company is not everybody has to be doing filings. Not everybody has to be doing rate making, not everybody has to be reserving, and not everybody has to be building models. But find one that you like and go for it is kind of my advice.

Max: 15:34

And it probably opens up a lot more resiliency in the industry too, because we have, you know, cross like understandings of how these things work, more specializations and everyone can kind of benefit from that. I feel like it's kind of a win-win. There is that element of like risk aversion where people are, you know, they know it's not right for them, but they're not sure if it's worth the risk of making a jump. And I think that's kind of challenging for folks to, you know, get over. you have any advice on making that jump? Because it seems like you made it very well and it was rewarding.

Devon: 16:04

Totally. mean, I the industry is changing. We're facing challenges like we've never faced before what we're facing with catastrophes, weather changing, political the severity, the cost of things, inflation. I mean, there's the industry is changing so fast. And if you want

to stay in your role and have your carrier and your actuarial department look like it looked 10 years ago, the keynote was saying yesterday, you're gonna get left behind. And so it's scary to make the jump and to decide to do something different and go down a different path. But I also think it pays off in ways that are far and above, know, kind of playing the safe route. If you try for something.

if it's, you know, I'm gonna go get a degree, right? You never, or I'm gonna go try to get my dream job. Even if it doesn't end where you think it's gonna end, you're gonna grow and gonna end up being a better actuary. I mean, that's just my opinion. So if you're scared and you wanna make that jump, I would just say close your eyes and do it. If you can.

Alicia: 17:13

Do you have any takeaways from RPM so far? other sessions outside of that keynote that really resonated or networking that you've been able to do?

Devon: 17:23

Yeah, I mean the networking

is always fun at these. You get to kind see that there is this sort of social core to

our profession because there's not that many of us. you do kind even with you Max is I know you. I don't really know you, but I know of you. Right. And so for me, it's these these these sessions are always so cool to just especially talk to young people that are just becoming actuaries. I had a really interesting conversation and I I don't want to name drop her because I'm not sure she wants to be named up. But last night a woman who builds GBMs and her specialization is, you know, getting them

filed with the DOI, which I'm really interested in right now. she just kind of seemed like such a badass. And I was like, wow, you're so cool. And her path was unconventional. it sounds like she's more of a data or interested in more of a data science role. she had a kind of similar story to mine, where she had an interest. She went for it.

Now she's really killing it and she's being sought after as someone explain and represent a

I don't want to say technical, but a technical thing that's going to change the industry, right? Slowly and surely, different kind of models are going to have to be accepted by all the DOIs. And so just having someone who's focused on advocating for change within the DOIs, within the sort of industry, is so cool. mean, so you meet people like that, and I think that's, for me, that's the whole reason to come to these, and I always like the keynote, so.

Max: 19:03

Yeah, well, of course,

if we have you here, we have to talk about behavioral variables. you know, most of people at RPM are pricing folks and segmentation is top of mind. And so there's always that search for new lifts so you can better segment out, you know, just for fairness, right? If someone is a materially different risk and it's not just that class that we've had for 20 years. behavioral segmentation seems like kind of like a core competency for pinpoint. And it seems like you've been there for a while now. So was curious if you could just give our audience at least an introduction before we dive in.

Devon: 19:34

Yeah,

yeah. work at a company called Pinpoint build traditional models, but we use different types of data. think it's, I kind of touched on earlier about how the industry is changing. We have to find ways encourage profitability while also ensuring fairness and.

really looking algorithmically at bias and all these things that we talk about in data science. And Pinpoint is really doing that, right? They're kind of marrying a new type of model because they're using different types of data, behavioral data. you know, think purchasing behavior, you know, interests. And it turns out that those things, if you know a lot about a person from a perspective of their data,

you can add so much lift to your understanding of what their expected loss outcomes are going to be. And then, so that part's interesting, right? But I think we all know if you have more data about someone, you can probably price their insurance better and you can understand their risks better. The more interesting question to me is how do you do that in a fair and appropriate manner? Not just a legal one, but an actual fair and appropriate manner. And so that's kind of my role there is to build processes around both qualitative and quantitative around how do we think about big data and pricing insurance.

outside of the realm of what we're used to. really hard to do. And there's no answer. You can't go to an actuarial paper and say, oh, this is the process for determining my rate. mean, so you're kind of, and I shouldn't say that because the CAS is coming out with a series on race and bias. And they have a ton of

ways to assess that's been really helpful for me because I'm just kind of picking out what a lot of other smart people are

It goes along with the conversation of AIs is how much, how do we balance privacy, bias, and profitability in insurance? Because at the end of the day, insurance companies aren't profitable, we don't have insurance. And insurance is a social need. we have to find ways to balance all these things. And it's going to take a lot going to be heard and there's going to be learning. But we can't not move forward because of our fear

of doing something different. And I think, again, going back to the keynote yesterday, mean, that's what she was talking about, right? Is take steps forward and figure it out. Don't not take steps forward because you're afraid of something you don't even know that is a challenge yet. So that's what we're doing at Pinpoint. I love it there, so if you ever want to talk behavioral data, you just let me know.

Max: 22:21

You touched on my next question. It's a very delicate balance because I think if you read basic rate making, fifth edition, and they're talking about class-based rate making, and there's this consensus that this is the best we can do right now is rating you like the people you are alike, and eventually we could get more individualistic. And people tend to be very receptive to that idea, like rate me like me as opposed to the people who you think I'm like.

But then as we get more individualistic, people start to say, well, don't know if I like this anymore. It's kind of like this duality of like, we don't like the class-based rating, but we don't want the data about our individualism to be considered. And so it's very delicate. And I wonder how you guys walk that tightrope and if you can share that.

Devon: 23:05

Yeah, I mean, it's a really good question because, you know, when I pull my pinpoint square, I'm like, hey.

Max: 23:16

That's how I feel with my roof score, actually.

Devon: 23:21

My point is we're in this world where everybody can know everything about us all the time. I personally believe that your insurance rates should be actionable. don't think that you should be not able to change your...

situation in order to help control your So we have to balance really our, you know, the challenges balancing our industry secrets, quote unquote, not giving our product away while still being transparent enough that the end user is comfortable with what we're doing.

We're finding really creative ways to do that, right? So we're getting our models audited by third parties. this is actually maybe boring, but I think really interesting because insurance is one of the last major industries to not have actual rules around these things. for example, employment law is very strict about how models can work when searching for candidates.

what we're doing is we're looking at what are all of these existing industries that are already solving this problem and how can we bring those algorithms and quantitative solutions to our products. And so we hired several third party experts in that space to help us apply those same standards to our models. So what that means is all of our models meet all the requirements

requirements of employment law, which is way above and that insurance is doing in terms of bias. I mean, we're using credit. We're using a lot of things that have implicit bias. And I don't think we'll ever get away from that. And so we have logical in how we prove

that we are being fair. And as a statistician, I think it's provable by numbers and by, instead of saying this variable is fair and this one isn't, which is a lot of times what the DOIs are doing right now. prove look at the data.

so that's what we're doing. And we produce reports on that. And so we by class how we're rating and what's important, what variables are important to those specific things that are important to people's privacy. And we're still learning. We're changing our processes all the time because we get carrier partners that have different expectations. it's another cool thing about being at a smaller company is you can kind of be flexible and say, that's a good thing I haven't

thought of. Let's attack that problem. hopefully that answers your question.

Max: 25:56

Yeah, well opens up a million more. I don't want to keep you too long. I guess, so one of the concerns someone might have too is that how much of behavior is cultural and know at that point

When we're talking about bias, we're typically talking about like societal implications, but if there's cultural ones, which I would say are different. So for example, my wife is black and my family is white from an Italian ancestry. And these are very two different cultures. The families mesh well together, but very different cultures. And so even like my Italian cousins who had lived in Italy, who are now coming to the US, just even like the generational difference within the culture is so large. And so is behavior

your cultural and would that open up new considerations or questions?

Devon: 26:43

at the end of the day what we're trying to do is match rate to risk. if, I'm trying to think of an example that would be cultural that makes sense, if you're statistically more likely to have a loss, I think the question really has to be should you pay more in insurance? And I think you should try as best you can to remove all the noise about the questions about that.

and prove that that's the case. Now, systemic bias, so for example, if you live in a neighborhood where you're more likely to, or an older home, let's say, you're more likely to have a fire, that's tied to socioeconomic class, right? And there's no pulling those two things apart. So I think the question really is, do we want...

people that have more privilege to pay more to subsidize the systemic that you see in homeowners, for example. not a question for me to answer, right? But I think it's something that we should all be thinking about because already doing that, by the way, with credit and age of home and...

But we're not talking about it. We're not quantifying think to some degree, most actuaries will say, if you're going to have the bigger loss, you should pay the higher premium, regardless of whatever else is happening. And politically, think a lot of people are shifting in the other direction. And so it's up to us be in touch with our morals and be in touch with

with our expectations of ourselves and how we want to be treated and then apply that to our products. And it's not an easy question. And I'm not going to pretend I have the answer, that's all that we can do.

Max: 28:24

I envision positive second order effects from this too, because I feel like if you can parse out the behavioral component to your point about like socioeconomic status and like fire risk. So my understanding, I've never seen the data substantiated, I, I sounds very true that socioeconomic status kind of correlates with like the dispersion of fire hydrants. And so, you know, if you can absorb some of the behavioral component and say, well, territorially there is still some risk. And then we start exploring why that might be. It might say, well, there's actually a balance here between

that actuarial view and that political view, if we solve this underlying problem, you create a better risk that could be priced lower and you solve a problem and you actually mitigate some of those political issues.

Devon: 29:06

Absolutely, mean if we can help identify, and this is one space I'd really love to see the actuarial profession grow, is helping city planners with exactly what you're talking about. Small things like where should fire hydrants be? Actuaries should be involved in that. How should the streets be laid out? I'm not going to pretend like I know how all the engineering works, but we should have a seat at that table because we are the ones consistently assessing.

the losses that happen because of those decisions, right? And so, absolutely. I I think that's a huge untapped actuarial opportunity that I really haven't heard many people be a part of. And we should absolutely pursue that as a whole, as a group.

Max: 29:54

set the blueprint on becoming that non-traditional actuary So we have a template here of how to get actuaries into city planning.

Devon: 30:02

and even catastrophe planning, right? So if we want to talk about what's happening in LA the hurricanes, have to build back better, and I think everybody knows that. we're, a profession, in my opinion, not doing enough to make sure that our insureds are protected when they're indemnified. We're indemnifying without...

as much protection on future loss as I think that we need to be doing. And it's not an easy, it's not an easy thing to fix, but another space where I think we should be.

Max: 30:36

And speaking of the Hurricanes, just before the show you were telling us a little bit about your personal experience. Would you mind sharing that with the audience?

Devon: 30:42

so I grew up and live in Asheville, North Carolina that was hit by the floods caused by Hurricane Helene to be like kind of funny about it, was so, I'm the actuary that it was pricing hurricane insurance for large part of my career. I was so unprepared. Like we didn't have water. Like I didn't have, you know, the go bag or the anything because

In my space, I sit at my desk in my mountains and I look at aggregated statistics and I really had this sense of this could not happen to me and if it did, it wouldn't be a flood, right? it really opened my eyes to we are so good at depersonalizing.

the trauma and loss caused by what we indemnify, whether that's a car accident or a catastrophe or your house burning down or whatever it is that you're facing that you need to come to your insurance company for. Actuaries are so far removed from that trauma that I think it's a problem when it happened to me and I saw my community not get indemnified,

which the insurance companies, know, people didn't buy the insurance. before that event, I would have said, well, they should have been insured. After the event, understanding having those personal ties, and as we move forward, we're all going to have more of those personal ties. None of us are, doesn't matter where you live, your risks are increasing.

from an insurance perspective, an actuarial perspective. It just reminded me to remember the humans that we're doing this for. Of course we're all in it to make our salaries and have our nine to five and do our thing, but at the end of the day, this is so important. And pricing risk appropriately is so important because if we lose this industry, you know, I mean...

I don't know what happens from there, but it's not good. I don't know. I'm on my soapbox a little bit. But all I'm saying is that find that compassion in your heart, because I think it will lead to making better decisions for your product.

Max: 32:51

for sure. I the human element and the qualitative component coming in, I think, will benefit all parties involved. And it's definitely kind of at odds with the traditional, I mean, have a pivot table and just aggregate some data, but really opens up opportunity in my vision. we talk a lot about on this podcast that insurance is just a cornerstone of society. And like you said, we don't want to lose this. I think everyone would lose in that situation. So finding a way to blend this in, to me, it's tricky, challenging. It's new.

lot of positive effects that come from that.

Devon: 33:22

Yeah,

I mean, let's use AI, let's use young people, let's use, you know, I'm personally so sick of the old guard. Sorry, the old guard that's listening. Like, I get it, you need to make money. Like, we get it, we know that, right? Let's also remember the cornerstone of who we are and why we do what we do. bring that into the boardroom. Let's bring that into the conversation more often.

continue to focus on diversity. Let's continue to focus on using AI to be more effective and efficient. And let's put more focus on how do we solve these problems as opposed to react to them. Because if we keep reacting to these natural disasters in the way that we're doing it now, it's a lose-lose for

we've taken it on to be the people that solve this problem. think, part of the solving of the problem. I mean, it's a big, it's a heavy weight to carry, it's one that we all need to remember.

Max: 34:11

Yep.

Alicia: 34:12

It's very powerful message. Thank you for that.

Max: 34:14

as we wrap up, we usually ask our guests if there are any books that you're reading that you want to share, any content that you're watching that you think would, even if it's not purely education.

Devon: 34:23

I was going say the books I'm reading right now are really embarrassing. reading the, I don't even know what they're called, the fairy books. Do you know what I'm talking about? Where they're like vampires and they're riding on dragons. it called that? yeah. So that's embarrassing and I wish I never said it. But I'm also watching Severance right now and I think Severance is like the coolest show ever.

Alicia: 34:52

Yeah.

Max: 34:53

I seen the latest episode. don't know if our audience has seen it yet, so be careful. We're secrets, but great show.

Devon: 34:59

It's such a good show. mean, it's so well done and it's so illogical that you can't... I love to try to guess what's going to happen and I'm never anywhere close to the right answer.

Max: 35:11

Well awesome, any parting thoughts? We'll see you at RPM next year.

Devon: 35:14

Yeah, yeah, I'll definitely be at RPM. know, we're gonna keep doing what we're doing at Pinpoint. I encourage anybody who's interested to reach out. mean, always trying to collaborate. You guys are doing amazing, just bringing people together and you know, I appreciate it. I think the podcast's awesome. So thank you for having me.

Alicia: 35:31

And to all our listeners, we'll be back with a new episode soon. Thanks for joining us today.