Does LinkedIn Hate Women? w/ Martyn Redstone

In this eye-opening episode, the boys dive deep into the shadowy world of social media algorithms with AI in HR governance expert Martyn Redstone. With over 20 years in recruitment and a focus on ethical AI in HR, Martyn unpacks a viral experiment by Jane Evans and Cindy Gallop that exposed shocking disparities. Is this intentional discrimination? Not quite—it's the insidious "proxy bias" at play. Martyn breaks it down: LinkedIn's algorithm penalizes topics common among women. This creates a vicious cycle where established voices dominate, and emerging ones—often women's—get buried. LinkedIn denies using gender as a factor, but as Martyn argues, the real issue is between the lines. From content visibility traps to calls for transparency, this discussion reveals how tech perpetuates inequality in professional networking. If you're in HR, tech, or just navigating LinkedIn, you won't want to miss these insights on building a fairer digital future.

• 0:00Speaker 00

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• 1:31Speaker 03

  Hide your kids, lock the doors. You're listening to HR's most dangerous podcast. Chad Soash and Joel Cheeseman are here to punch the recruiting industry right where it hurts. Complete with breaking news, brash opinion, and loads of snark. Buckle up, boys and girls. It's time for the Chad and Cheeseman Podcast.

• 1:54Speaker 00

  Oh yeah, it's the podcast your mom warns you about, also known as the Chad and Cheese Podcast. What's up everybody? I'm your co-host Joel Cheeseman, joined as always Chad So Wash is riding shotgun as we welcome Martyn Redstone to the show. He's a, he's an expert in AI governance within HR, an entrepreneur and writer for hair, which we'll get to in a second, but he's also the author of a post on LinkedIn entitled, "Is LinkedIn's Algorithm Biased Against Women? A Legal and Technical Analysis." Martin, welcome to HR's Most Dangerous Podcast.

• 2:31Speaker 04

  Thank you very much, chaps. Great to be here.

• 2:33Speaker 00

  All right, let's get to the hair real quick.

• 2:34Speaker 01

  Or lack of.

• 2:36Speaker 00

  You're follically challenged, so I guess hair doesn't mean, you know, the golden locks of some of us. So hair, explain.

• 2:44Speaker 04

  It was definitely ironic, that's for sure. So ultimately, it's a plan words. It's AI in HR, literally spells hair. So, but, but the funny story is when I launched that a couple of years ago, I did have a message on somebody and it started off as a LinkedIn newsletter and I had a message from somebody to say, "Don't know how to tell you this, but you've called your latest newsletter hair and you don't have any?" And I was just like, wow, thanks. I really didn't think that one through properly, did I?

• 3:11Speaker 00

  AI in HR, that's clever. That's clever. Very, very British. Also ironic that we're three white guys talking about if LinkedIn hates women, but let's get to learning a little bit about you before we get into the meat of the article. Tell us about Martin.

• 3:25Speaker 04

  Yeah, the quick story is I've been in recruitment for 20 years. Literally January, we'll see 20 years. Last 10 years has been helping organizations with innovation in TA, technology and processes. Seven years ago, started up my own AI automation agency and basically for the last 12, 18 months, I've been really focused on AI governance, making sure that people are doing things sensibly, responsibly, ethically, ultimately making sure they just don't do stupid shit with AI.

• 3:54Speaker 01

  You're a whistleblower. I love that. We need those.

• 3:56Speaker 00

  You're not busy at all, right? You've got nothing going on if you're watching, watching that space.

• 4:02Speaker 01

  How you got involved because Jane Evans and Cindy Gallop.

• 4:06Speaker 04

  Yes.

• 4:06Speaker 01

  They saw a problem with the LinkedIn algorithm. So they wanted to test it. So, so talk a little bit about that test and then how you actually got involved. And by the way, Cindy Gallop, well, I don't know if you know this or not, friend of the show.

• 4:20Speaker 04

  Yeah, yeah, I saw, yeah, I was talking to her earlier on, it's really, she mentioned that she'd been on the show. So that's great to hear as well. So, yeah.

• 4:29Speaker 00

  I still, I still have the scars from where she smacked me around on the, the episode that we had her on, but that's, that's another story.

• 4:36Speaker 01

  It's not like you didn't deserve it. Come on.

• 4:40Speaker 00

  True. Sorry. Go ahead, Martin.

• 4:41Speaker 04

  So yes, earlier this year, Cindy Gallop, Jane Evans, you know, between them, they've got almost 200,000 followers. They saw their reach just fall off a cliff and it wasn't just low engagement, you know, substantive posts around, about female founders, women, feminism. They felt that they were being specifically buried and it felt to them like a bit of a shadow ban. So what they did was along with two of their male peers, they posted exactly the same content at exactly the same time. So the two women, two men, and the men got between 50 and 140% reach across their follower numbers. And their follower numbers were four digit.

• 5:19Speaker 01

  Quite low. Yes. And that was like a combined. Yeah. It was, I don't even think it was 10,000 combined, was it?

• 5:25Speaker 04

  No, it wasn't 10,000 combined compared to, you know, Jane and Cindy. Jane got 8% reach across her follower numbers. So, so that was the big experiment that kicked it all off really. You know, what the hell is going on here? So then we saw women like Megan Cornish, you know, they changed their profile settings to a male profile. They put moustaches on their photos, you know, they, they started to write in a more male way. I, it's great. There's loads of pictures out there of them.

• 5:54Speaker 01

  It's awesome.

• 5:55Speaker 04

  The result was immediate spikes in visibility. So Dorothy Dalton got involved in this as well. We all know Dorothy through the kind of HR and recruitment world. She said to me, because she knows, you know, I'm, I'm weirdly into all of this, you know, AI biased and, and, you know, governance stuff. Have you looked into this at all? And if the answer is no, I just want to look into it. So I did. I analyzed everything from their kind of all of the research, all of the experiments that have been happening. Analyze the the paper that LinkedIn released about the algorithm. I've got some juicy stories on that later as well. And found a bit of an invisible code, which really for me proved that there is plausibility behind proxy bias in the algorithm.

• 6:40Speaker 04

  And that's kind of how I got involved. And since then, it's all gone a little bit crazy.

• 6:44Speaker 01

  I don't think all the listeners understand what proxy bias is. So so if you can give a little little insight to what proxy bias is.

• 6:51Speaker 04

  Yeah, absolutely. So direct bias would be LinkedIn putting in the code. If user equals female, then suppress their content. That would be just direct discrimination. When we look at proxy bias, it's some of the it's some of the markers that people might understand somebody to be female or to be black or to be whatever.

• 7:10Speaker 01

  Playing softball.

• 7:11Speaker 04

  Yeah, exactly. I don't know what that is. I'm British. But yeah.

• 7:15Speaker 01

  Rounders, rounders.

• 7:18Speaker 04

  Yeah, absolutely. So so if we think about some of the things that are in there, in the algorithm, there's something called verbalization. And what that means is that they're looking at the tone that you write in. So, you know, in the new version of the algorithm, they stopped looking at, you know, your IDs and they started converting your profile into a text story that feeds a large language model. And this introduces something that we call kind of semantic bias. And what that means is that the algorithm loves and we're now hearing this term agentic language. You know, male coded language is like dominated. And so that kind of penalizes more female coded kind of communal language, you know, saying things like collaborated.

• 7:57Speaker 04

  So so ultimately, it's going to start penalizing, you know, females through the way that they use language compared to the way that men use language. And that we mean by proxy bias, it's not direct discrimination, but it's picking up on markers that can create discrimination through that.

• 8:12Speaker 01

  It's like Amazon's algorithm went went haywire. When it looked out into the market and saw, oh, whoa, all these all these developers are male. So let's just get rid of the female. Out of the entire funnel itself because you know it will will up our chances to actually find more developers if we're just looking for males. It's kind of like the the proxy buy side of the house to some extent, and then also being able to kill if you know the types of universities that you might have gone to, which might be off female universities. The type of sports that you play, those types of things.

• 8:44Speaker 04

  Yeah, that that was the interesting proxy markers in the Amazon case was things like which college you went to, which university you went to, you know, what sports you play. If it said female soccer team straight away, you know, it's, um, so yeah, so that's where the proxy buys comes in, not direct discrimination, but it leads to it.

• 9:00Speaker 01

  He said soccer, Joel.

• 9:01Speaker 00

  He did say soccer. He's he's he knows his audience. That's good. Fortunately.

• 9:05Speaker 04

  I'm not a fan of either soccer or football, so, um, yeah.

• 9:08Speaker 00

  Fortunately, Amazon, Amazon always does the right thing and and canceled the algorithm. So just keeping up with their, they're winning, they're winning a record of batting a thousand. There's another, there's another baseball reference for you. Curious about going back to the study, when you say they posted the same thing at the same time, that doesn't mean at two o'clock on Sunday, the 10th. Where they all posted the same time. Was it a Sunday and then the next Sunday? Was it a Sunday and then it it seems like it would be really hard to have apples to apples comparison. So I'm just, I'm just wondering sort of what exactly happened when you say they posted the same thing at the same time. What does that mean?

• 9:44Speaker 04

  So so they all they all had exactly the same post, um, and, um, exactly the same words. So this was a little bit different. This is what kicked off the whole thing really more than anything else. So this wasn't looking really at the language and and this is the interesting bit. So exactly the same post and exactly the same day time, um, of the week and the day. So so this was just to see whether or not there was any kind of direct suppression of women and actually potentially there was.

• 10:08Speaker 00

  So it'd be like Chad and I posted the same thing with a little spin on adjectives and.

• 10:14Speaker 04

  No, exactly the same. So so the original, the original experiment was exactly the same words.

• 10:20Speaker 00

  Yeah.

• 10:21Speaker 04

  At exactly the same time. Um, so there was no spin on anything, but look, you know, there's, there's lots of other things going on in the algorithm and there's lots of other things going on in other parts of how the system works that potentially impacted that as well. You know, we we found in the. Analysis of the algorithm, you know, a prompt instruction that explicitly weights historical activity at seventy percent and profile relevance at thirty percent. So mathematically forces AI to start prioritizing past visibility. So if your posts already haven't been doing well in the past, they're not going to go and do well in the future. And that can impact you. I know people who might have been, I know, giving birth to children and taking a bit of a break from LinkedIn. So there's lots of other kind of thought processes around, again, more kind of proxy bias coming in.

• 11:02Speaker 00

  Cindy Gallop, very popular, because one of my questions are going to be, you know, my followers aren't the same as your followers and quality quantity, but like, there's no, I could say there's probably no doubt I haven't done the research, but Cindy Gallop has quality people following her and engaged with her. So was there anything in regards to number of comments, number of likes or engagements? Did

• 11:22Speaker 01

  Impressions.

• 11:23Speaker 00

  Can you, is there any breakdown of what exactly happened with each of the posts? Did one get no comments and the other got three hundred?

• 11:29Speaker 04

  So all they measured was how many, so you know, on LinkedIn, you see kind of how many impressions, yeah, you're each, you know. So the only data that they captured that that they've made available is how many, how many impressions you got on that post. And they compared that to the number of followers they've got. Because logically, you'd say, well, if somebody's following you, they should see your post. And so you should get a hundred percent visibility across your followers. But it doesn't work like that. And that's how, that's what they've come to realize as well. There is a second paper, and actually, I probably mentioned it now. So LinkedIn's paper that they released around their algorithm, which is the three sixty brew paper, has been available on academic repository, ArtSiv, since the beginning of the year. Over the last couple of weeks, it disappeared. It was taken down.

• 12:15Speaker 01

  Oh, magically.

• 12:16Speaker 04

  Well, just such a shame that I kind of saved it. But there is a second paper called large can I retrieve? No, absolutely. And what that means is they also have things like popularity tokens going on as well. So this is the scout that looks for what to put in front of you. And you know, we call it kind of the get rich, the rich get richer token. It's a popularity token. So like the top one percent goes into the AI prompts to to serve up content in front of you. So this is kind of hard coding a bias where the algorithm is only now retrieving content that is already popular as well. And so it's locking out new voices and again, locking up people who've struggled with popularity or getting reach in the past. So it's actively positive only echo chamber. You know, it's very, very interesting what's going on. Very, very

• 13:01Speaker 01

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• 14:04Speaker 01

  the numbers to roll in. So was there any response from LinkedIn about this?

• 14:09Speaker 04

  Yeah, so recently LinkedIn wrote an engineering blog post, and it specifically said in there, we don't use gender as a variable, so we can't be biased.

• 14:19Speaker 01

  And that's not what you said, because gender, we're talking proxy. We're not talking, you know, if, if, if female, then this, or if male, then that.

• 14:27Speaker 04

  Exactly. And they actually mentioned in the post, what we do look at is, you know, this, this, and this, and this, which totally utterly, you know, proved the point. And the algorithm just reflects what the user wants. So, you know, it was kind of their excuse. So that, for me, really proved the, because proxy bias doesn't need that gender variable, you know, and they did say they, you know, it looks at position, industry, language, et cetera, et cetera. And so we say, well, this is the issue. If you're looking at all of these things, you're going to uncover and promote proxy bias. So, so we're pushing for LinkedIn to really, you know, get a grip on it and deal with it. It's really difficult. You know, you're not going to mathematically remove every bit of bias, but, you know, in this day and age, we expect people to, you know, to own it, to report on it, and to... Do something to try and mitigate it.

• 15:12Speaker 01

  Well, to monitor it and then make the changes necessary.

• 15:16Speaker 00

  Yeah. How much of a black box is LinkedIn's AI? Like, do they publish some of it, but not all of it? Do they keep it all in-house? Talk about their sort of openness.

• 15:25Speaker 04

  They, they had an academic paper that explained how the, the, the Brew 360 algorithm worked and they took that down. So for me, that's a black box now.

• 15:33Speaker 00

  Okay.

• 15:33Speaker 04

  You know, I think that it probably didn't do enough to explain it, but it did enough to ask questions. And so, um, so I would say that now they've gone from semi-transparent to black box, uh, which is a real shame. And there are, as we know, regulations all over the world that, that can start trying to force them to be a little bit more transparent and open it up.

• 15:53Speaker 00

  What are the odds that human beings are the problem? Because going back to Amazon, we were making decisions that were biased and we were doing things that were biased. Like, is there a chance that we're the problem and LinkedIn isn't so much?

• 16:05Speaker 04

  Well, um, yes and no. Um, I mean, with any algorithm, humans are the problem because humans are the ones that are creating the algorithms. Um, so having an engineering blog saying, we don't use, you know, gender discrimination in our algorithms is point in proof. You know, they've coded, they've not hard coded in, you know, if, if gender equals female, then downgrade posts. And that, that's just not an excuse. So yes, humans are the issue. You know, there, there's a few points to that. First of all, the humans that create the algorithms are the issue. But second of all, some of the humans that create a bit of a cesspit when it comes to social media interaction, they're also going to be the problem as well, because the more engagement you give to somebody, the more engagement you give to a post, based on what we know about the way that the, the second algorithm, the scout algorithm acts,

• 16:50Speaker 04

  um, that's going to improve people's reach over time as well. And, and there are some, as we know, across most of social media, but even on LinkedIn, there are some, some bad actors. Absolutely.

• 17:00Speaker 00

  Cesspit, that's a cesspool, right?

• 17:04Speaker 01

  And Joel, LinkedIn is not Skynet, so they are run by humans. So therefore, humans, they are the issue.

• 17:10Speaker 00

  I meant more the users, not the creators of the algorithm.

• 17:13Speaker 01

  The end users.

• 17:14Speaker 00

  Like, I think more of how we, the people, are using LinkedIn, maybe impacting it in ways that aren't healthy.

• 17:20Speaker 04

  It's a vicious cycle because, you know, humans are creating the algorithm, which is telling the system to increase engagement on how people are interacting with, you know. So if you've got bad people interacting with bad content, and that's the other thing, you know, that we know from people like Cindy and Jane is that, and from Dorothy, is that, you know, if they're getting abuse on the platform and they report it to LinkedIn, LinkedIn aren't even doing anything about it a lot of the time. And they're saying it's within our community guidelines. And trust me, it's been eye opening because since I've been involved in all of this, I've been getting abuse as well on LinkedIn, which is just laughable because.

• 17:56Speaker 01

  You've been getting abuse from who?

• 17:59Speaker 04

  From men. I mean, it's been great. Yeah, no, it's been brilliant. I've had people messaging me saying, you know, as a computer scientist, you should know better. You should be teaching these people how to write better LinkedIn posts rather than blaming the algorithm. D and I content is never going to be interesting. I've had somebody.

• 18:18Speaker 01

  Oh, my God.

• 18:19Speaker 04

  A male feminist. I don't even know what that means.

• 18:23Speaker 01

  That you get laid and they don't. That's what that means. Go ahead. Sorry.

• 18:26Speaker 04

  My wife might be hearing this. Stop it.

• 18:30Speaker 00

  Yeah. Martin, as two guys with a podcast that's eight years old, if you don't have haters, you're doing it wrong. So take that for what it's worth.

• 18:37Speaker 04

  I'm delighted that, you know, it means that people are at least engaging with my content, which is great for the algorithm. It's eye opening, though, because you do tend to see kind of what's going on. I think I'm the furthest away from the stereotypical person that you would imagine to be called a male ally or a male feminist or something like that. So these people are just morons, really, aren't they? And they've got nothing better to do in their day than abuse people online. So I can't really take them seriously. But men, you must do better. I think that's probably the best way I can put it. You know, there's no reason for that kind of behavior and it's just going to, you know, damage, you know, the experience of platforms like LinkedIn more so.

• 19:13Speaker 01

  Noticed it's interesting because I had a had a great comparison that I threw out there about NFL teams and their quarterbacks. Right. And how the Green Bay Packers, literally, they have the fewest amount of quarterbacks since 1992, where I think like the Cleveland Browns have like close to 40.

• 19:32Speaker 01

  But the Green Bay Packers has like, they have like three, right? So there's, there's literally just this comparison. And I was attributing that to development of talent. And if we take a look at how Green Bay develops talent differently than the Vikings or, you know, somebody else in their division, right? Or even the Cleveland Browns. It is an entirely different culture, right? We can learn from this and that that post, which is, you can say, there are a lot of females who watch NFL football, don't get me wrong. But is it very much a male sport, right? Males playing it. It's, it's, it's very, very machismo, testosterone, whatever. At the end of the day, that post exploded.

• 20:13Speaker 01

  Now, I can't say it was, whether it was because I tagged the Green Bay Packers or what it was, but it was a very, very, very male, male. You know, Tim Allen, home improvement, oh, oh, oh, post.

• 20:24Speaker 00

  Yeah.

• 20:25Speaker 01

  And it just exploded. So that was, that to me was interesting. And I showed it to Julie, like 10 minutes after I posted it, it just exploded. And she's like, holy shit, you should be posting about, you know, NFL stuff more.

• 20:38Speaker 00

  Yeah.

• 20:38Speaker 04

  Yeah. And, and I mean, the thing is, is that we're still at that point where nobody really understands well enough. And there are lots of gurus out there that think that they've cracked the algorithm, but there are, we still don't understand well enough, you know, exactly how it works. But, but absolutely, you know, it could be because that was very, as you said, Tim Allen, you know, um, I actually did watch that. I've got no idea what you mean about quarterbacks and Packers and things like that, but I home improvement was one of my favorites. Yeah. So, um, so, but, but yes, um, it could absolutely be that, or it could just be the fact that, you know, that first hour rule, you know, you just don't know. But there's certainly something weird going on.

• 21:15Speaker 00

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• 22:30Speaker 00

  Hey Martin, you talk about legal implications in your post and they tend to be scripty and focused, but I want you to talk about the UK Equality Act of 2010, EU acts that might impact this, and probably the lack of any regulation in the US where nothing's going to happen with any of this stuff. Talk about the legal implications.

• 22:49Speaker 04

  Yeah, so as you said, I mean especially over here in Europe and continental, not politically now that I'm in Brexit Britain, but over here in Europe, there's a lot of regulation about this. You know, like you said, you've got the Equality Act in the UK and the Equality Act means that ultimately you need to treat, there's 12 protected characteristics equally and not discriminate against them, one of them is gender. And so there's potentially some issue with that regulation when it comes to the way that LinkedIn is managing their female members on the feed. The other one is the DSA, Digital Services Act in Europe, where there are Rules around what they call VLOPs for very large online platforms and how they also need to manage

• 23:33Speaker 04

  algorithmic bias as well. And then, yeah, we do have the EU AI Act, but that's all over the place right now anyway. So let's kind of ignore that. But you know, we've absolutely got those two that are potentially areas that LinkedIn need to be be careful of when it comes to. And they're already being, you know, investigated under one of those for something completely different in Ireland. So it it needs to be, you know, they need to be careful and they need to. I think the thing is with me is that rather than just kind of brush it away and say, we don't use gender. They they need to respect the lived experience of their users and they need to take it on board and just be responsible and look into it. Even if they look into it and say, do you know what? We've we've found all of this.

• 24:12Speaker 04

  There's nothing to be concerned of. We tweet something slightly, blah, blah, blah. It's brilliant. You know, be transparent, but but don't just ignore and dismiss the lived experience of your users because that's going to start impacting trust and you don't want that.

• 24:24Speaker 01

  Did they investigate the experiment that that Cindy and Jane did? I mean, that's that's the biggest question because it's kind of like if there's a murder, you go to the fucking crime scene, right? You check it out. You you do all the investigations. Did they do any investigation whatsoever?

• 24:38Speaker 04

  Not that I'm aware of. So the processes that have happened so far since have been that we had a member of parliament over here in the UK met with LinkedIn to go over this, you know, and since then, they then created the the engineering blog that we that I talked about earlier. And all of the responses have just been to point people towards that engineering blog. So they haven't given us any more detail around whether or not they've looked into the original experiment or looked into some of the work that I've done or looked into some of the lived experience of other people. All we know is they've written this blog and their boilerplate response is, look at this blog, we don't use gender. So no, we're calling on LinkedIn to do something about it. We, you know, Jane and Cindy have created a

• 25:19Speaker 04

  petition that people can sign and a website as well, fairnessinthefeed.com. And, you know, we're hoping that more and more people can join the call for LinkedIn to actually try and look into this, just look into it, not even asking for a fix because we don't know whether there is something to be fixed. But look into it, investigate it and be transparent in your findings.

• 25:41Speaker 00

  Do you think employers are at risk at all? If LinkedIn does get pinched, can that come down on employers at all? Are they pretty much safe?

• 25:49Speaker 04

  I don't think so because, well, I mean, I would hope that people aren't relying on the feed book. But the interesting bit is limiting economic activity. So when you've got self-employed people who are using their feed to encourage inbound leads and those kinds of things, and they're losing out on opportunity, that's potentially an issue. Because LinkedIn is all about creating economic opportunity for all. But the employer side, probably not, because they do have mechanisms, again, that they have released publicly around how they mitigate for bias and proxy bias in their recruitment products. So they do have methodologies, and actually, that was part of what I wrote, was that we know you do this for your recruitment products, so why aren't you just doing it for your feed as well?

• 26:30Speaker 00

  The unfortunate thing is, this is what happens with monopolies, right? If only LinkedIn had a competitor that people could say, like, I'm going over here.

• 26:38Speaker 04

  Yeah.

• 26:39Speaker 00

  You've mentioned a few ways that people can, quote unquote, "fight the power" on this one. What are some other ways, whether quietly, passively, or really actively, people can get the word out on what's going on at LinkedIn and maybe get them to open up a little bit?

• 26:53Speaker 04

  Yeah, so I think it's just, you know, following the people that we mentioned, people like Cindy, people like Jane, people like Dorothy, and reposting what they've got to say. Like I said, the hashtag #FairnessInTheFeed is the one that they're all using. So follow that, find the posts on that, sign the petition. I don't think we can do it quietly in terms of just reposting and engaging and what have you. But as I was saying earlier to another group of people, this is powering the masses, you know? And I'm not one for kind of mass protest or anything like that. But I think the more people that push back on LinkedIn to say, "We want you to be transparent in this", the more opportunity we have, and that's ultimately through signing the petition.

• 27:34Speaker 00

  Sponsored t-shirts, Martin, that's the answer. We need t-shirts on everybody about this issue.

• 27:40Speaker 04

  Uni-sexy.

• 27:41Speaker 01

  That's it. That's what I'm saying. Hey, Cheese Man, sh- shut up. That's our next, that's our next promotion. That's Martyn Redstone. Martin with a Y, kids. Martin with a Y, not an I. Uh, Martin, if, uh, people want to find out more about this, they, they, they want to get in contact with you. Maybe they want to troll you a little bit. Who knows? Where can they find you?

• 27:59Speaker 04

  Yeah, um, LinkedIn is always the best place to find me. Uh, I'm pretty active on there. Um, I'll accept requests from anybody.

• 28:05Speaker 00

  The third irony of the show. How to follow me. I'm on LinkedIn, everybody.

• 28:09Speaker 04

  If they had a competitor, if they had a competitor.

• 28:12Speaker 01

  That's right.

• 28:12Speaker 04

  But, um, it's the best place to find me. Um, you know, I'll, I'll accept requests from anyone, trolls included. So, yeah.

• 28:20Speaker 01

  You're so thirsty, Martin. You're so thirsty.

• 28:22Speaker 04

  Totally, totally. You know, business is business.

• 28:25Speaker 00

  Yeah, and we haven't, we haven't even, uh, talked about the gun target in the back there as a, as a, anyway, maybe, maybe we'll bring you on again next year to talk about that. Chad, that is another one in the can. We out.

• 28:36Speaker 06

  Thank you for listening to, what's it called? A podcast. The Chad, the cheese. Brilliant. They talk about recruiting. They talk about technology, but most of all, they talk about nothing. Just a lot of shout outs of people you don't even know, and yet you're listening. It's incredible. And not one word about cheese. Not one. Cheddar, blue, nacho, pepper jack, Swiss. So many cheeses and not one word. So weird. Anywho, be sure to subscribe today on iTunes, Spotify, Google Play, or wherever you listen to your podcasts.

• 29:18Speaker 06

  That way you won't miss an episode. And while you're at it, visit www.chadcheese.com. Just don't expect to find any recipes for grilled cheese. It's so weird. We out.