Ep 115: Humanizing the Hiring Experience and Earning Candidate Trust with Jerry Jao

[00:00:09] Hey everyone, welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, talent transformation, responsible innovation, and the future of work. Today I'm joined by Jerry Jao, he's the CEO of Employee, a leading talent technology platform that brings together many powerful hiring solutions, including Lever, JazzHR, Pillar, and more.

[00:00:31] In our conversation, Jerry shares how lessons from his entrepreneurial background in AI-driven consumer personalization are now shaping his vision for a more human, more effective hiring experience on both sides of the market. We get into how Employee is using AI to streamline the candidate journey, reduce the burden on TA teams, and address some of the unintended consequences that emerge when AI gets deployed on both sides of the recruiting process.

[00:00:56] If you believe that finding the right job or the right person for it should feel less like a numbers game and more like a meaningful connection, this is a conversation worth your time. Thank you as always for listening and for being part of the Elevate Your AIQ community. Let's go talk to Jerry. Hey everyone, welcome back to Elevate Your AIQ. I'm your host Bob Pulver. Today I have the pleasure of speaking with Jerry Jao. How are you today, Jerry? Hey Bob, good, so good. Thank you for having me. Absolutely. Yeah, I'm really looking forward to this conversation.

[00:01:25] Congrats on your new role at Employee. I want to hear all about it and we've got a lot to dig into, but I thought we could just start with you giving my listeners a little bit about your background coming to the US and school and what you studied and all these great ventures that you started before coming to Employee. So I'll let you just give a little bit about your background. Sure. I guess there's always a little bit of entrepreneur at heart. I moved to the States when I was 13.

[00:01:51] So it's been a very privileged right to grow up in the land of opportunities and start businesses. I used to sell Game Boy games on eBay to selling Costco candies at schools. And along the way I've discovered sort of the magic of e-commerce. So I started a company retention science back in 2012.

[00:02:14] We're leveraging AI and machine learning capabilities to help brands to create a personalized experience for the customer. So think of brands like Dollar Shave Club, Shinola, based out of Detroit to like fix scrubs. We help these incredible businesses communicate to their customers in a way much more personalized. So Bob and Jerry have different preferences for colors or style.

[00:02:40] We're able to understand sort of based on your behavioral norms, how often would you be purchasing or prefer to be communicated? And so it's much, much dated prior to like GPT making AI sort of a thing that is today. So super excited to be taking some of those lessons and apply to where we're going in terms of hiring and employee. Yeah. Amazing, amazing journey. You remind me of my brother who always had that entrepreneurial spirit.

[00:03:08] He was running businesses, I think, in high school and definitely in college before he started many of his ventures as well. So yeah, really, you know, admirable to have that kind of initiative and curiosity and sort of learning as you go, as you figure it out.

[00:03:24] One of the things that I thought, well, I guess I was curious about is, you know, you're shifting from, you know, the consumer space and all the analytics that you have, that you had at attention science, which in itself brings me back to my IBM social analytics days. But, you know, sort of translating some of that consumer intelligence and consumer experience to the candidate experience.

[00:03:51] And I saw this through line. I saw some of your opening message to the employee team, I guess, a couple months ago now around like human connection and strengthening human connection. And so I thought that might be not to put words in your mouth, of course, but that might be one of the sort of through lines that sort of bridges those two universes.

[00:04:13] Yeah, and I think, and I love the name of your podcast. Like I think about AIQ and the reality is I think hiring is so, there's so much people component. And in terms of really assessing, hey, would Bob be a great culture for the companies, a part of a cultural fit? And also what I would like to say, it's really cultural ad. And so I think that's something that AI is not going to be able to truly assess, at least not quite yet. Who knows how just how capable AI will become.

[00:04:41] And certainly it can sort of eliminate a lot of manual things. That's a part of the hiring process, which I think we all believe is sometimes could be quite manual and just in terms of different, you know, whether it's scheduling an interview or screening a resume, which I know we'll talk a little bit more later. But even prior to getting to employ, I was a customer of Lever for about six years with the companies that I've started. And I've always thought like, it's just, there's so much more we can do with hiring.

[00:05:11] And I certainly believe, I really believe in the mission of the business. And what I'm most excited about is in a time like this is certainly create a more effective process for people to be able to provide for their loved ones by getting to their dream jobs. And on the flip side, it's helping employers finding the perfect candidates and slash job seekers. So that's certainly something I'm really excited about with or without AI.

[00:05:37] Yeah, for sure. I mean, it's just like if you were building any, you know, sort of two-sided marketplace, I guess you got to look at, you know, the needs and expectations of both sides and try to improve efficiency and effectiveness at the same time. You know, I like the way you sort of frame that because I feel like, you know, we as people have so much potential if only, you know, we were given, you know, different opportunities.

[00:06:01] That's everything from training and, you know, reskilling, upscaling to actual, you know, roles to see how people do and acclimate in new environments and, you know, handle adversity and conflict as well as collaborate and innovate. I know you spent a lot of time thinking about, you know, how do we innovate? And you've also spent a lot of time on the responsible AI side where I should say responsibility even maybe before AI was such a household term.

[00:06:30] But really, how do you think about, you know, moving forward in a constructive direction so maybe move fast and don't break so many things is one way to think about it? Totally. But yeah, I mean, it seems like some of your early experiences lend themselves well to what you're trying to do and you've got a lot at your disposal in terms of the component, you know, parts and how do you make them all more effective, you know, simultaneously.

[00:06:58] And I think, Bob, one more thing to add is I think of like at the end of the day, we're all people. So drawing a sort of a connection from my previous companies, selling to consumer and through consumer lens, like I think we're hiring managers or job seekers, we're all people at the end of the day. So how do we and if you look at whether it's job matching or skill screening, a lot of that it is really about ultimately it's like how do we personalize that experience?

[00:07:26] So most importantly, I think job seekers, a lot of times they find it's just really frustrating to find jobs and they feel like they're not as resume, maybe like get over overlooked. So how do we really make sure it's a thoughtful process for both sides? I think there's just a ton of opportunity moving forward. Yeah, for sure. When I think about the, you know, the ways in which people are using AI on both sides of this, right, the candidates use of AI, the recruiting teams, their use of AI.

[00:07:55] I mean, it's tough because you're trying to build these relationships. You're trying to build, you know, win-win, you know, teaming and find the right people for the right roles. And yet it sometimes seems like the use of AI is almost exacerbating some of the adversarial, you know, aspects of the process. And so, you know, I imagine that's top of mind for you every day, right?

[00:08:21] Just thinking about how do I make both the candidate and recruiter experiences, you know, positive ones. And yet here we are trying to, you know, play this cat and mouse game a bit. Yeah, definitely. Yeah. And I think on the job seeker side, again, you have this sort of two-sided marketplace. Job seekers are frustrated because they are afraid that they're not creating a resume that has all the keywords or whatever.

[00:08:48] So whatever filtering systems that companies use to sort of review resumes, they don't want to be missed out. And if you're looking at the job site was just with one of our customers two weeks ago, for every role they have opening, they're now getting two, three thousand applications. And that's a lot for us, even for a hiring manager and a recruiter to screen through. So they're asking for help as well. So how do you kind of balance that?

[00:09:14] And especially in a time like this where inevitably I think there's going to be a shrink. It depends on the role and depends on the industry. But right now I think macro trend is telling us that there are going to be a little bit of reduction in terms of jobs available. And then so how does everyone get the opportunity to be properly and thoughtfully reviewed for a job opening is something that's really important on top of mind for sure. Yeah. Yeah.

[00:09:43] So when you think about, I want to sort of flip a little bit to like, you know, your philosophy and how you're sort of reorienting the team, right? So you, you, you came in, you kind of assess things, you, you brought on PJ, right? As your CTO and started really thinking about how you redesign, you know, some of the teams and, you know, to add some agility to, to what they were doing.

[00:10:11] And I imagine also to make sure that all the people in different, you know, whether you're in, you know, lever or you were, you came from pillar or you're in the RPO or whatever, how everyone needs to sort of think about how we can, you know, sort of do better together. I thought we could just unpack a little bit about your, your philosophy and some of the moves you've already made. For sure. So I think, first of all, I think it's the employee business has gotten, there's, there's scale to it.

[00:10:37] And whenever there's scale and different product lines, it comes with complexities and, and internal challenges. And so it's sort of the new guy coming in, I'm sort of really trying to figure out how do we, how do we make the business a little simpler? And then also how do we flatten the organization a little bit? So it empowers our teammates to be able to make decisions a little bit more real time versus having a lot of discussions for alignment and therefore driving decisions and therefore driving accountability.

[00:11:07] I think over the course of the last couple of years, because of the scale that we've gotten and because of the, the several product lines that you just mentioned, I think some of that have gone lost a little bit. So sort of put my founder's hat on trying to think about like, can we create smaller scrum teams? And so think about these engineering pods. And so PJ and I are both coming from startup background.

[00:11:32] PJ has run very, very large engineering teams, scaled to 400, 700 international global, globally distributed engineering team. But he spent the last year and a half also founding his own startup in an, in a AI native first sort of way. So we're thinking about how do we build our product engineering delivery team to make sure that we're delivering more features in a, in a, in a time that there's AI enablement along with it.

[00:11:59] So, so we're not falling behind and, and certainly finding the organization. We're, we're, we're, we're large, but also not that we're, we're no Salesforce and Oracle. So, so we shouldn't have layer and layer of team. So I, I, I think hierarchy is important sometimes, but certainly at our scale, I think that it, I want my job is empower every teammates to do their job and make smart decisions. And I, I would trust them.

[00:12:24] So, so, so, so we want to make sure that organizationally we're built to, to accelerate those development. Oh, that sounds like a good plan. One of the things that I noticed in some of the research that you guys have done is on the candidate experience numbers.

[00:12:44] I mean, you've, you've clearly made progress across your customer base, but candidate experience, you know, still needs some work, but you've made, definitely made some progress on lowering attrition and, and some other key metrics. I also couldn't help noticing that some of pillars numbers looked quite impressive in addressing some of those same metrics.

[00:13:07] So I was just curious how you think about what, and for people who don't know what, what pillar is, this is around interview intelligence, right? So basically, you know, using AI to listen to, you know, human to human, you know, conversations and get a lot more context and add sort of a third dimension to what you would find on, on a resume, certainly. And also note taking. Yeah. And I know you had Mark on the show before. That's right. That's right.

[00:13:33] And so, so yeah, so I'm just curious how, you know, pillar can, can do even more work on behalf of employee as a whole to, you know, improve some of the metrics or you've already seen some market improvement and continue down some of those paths or, you know, are there other metrics that you guys are thinking about that maybe aren't being.

[00:13:55] Hi, I'm George LaRock and I'm looking forward to exploring the critical trends shaping the future of work and technology with you over on the WorkTech podcast. Now this podcast is a little different. I bring together industry leaders, innovators, and investors, and we go deep into market intelligence that matters to HR pros and tech providers alike. So give the WorkTech podcast a listen here on the WorkDefined Podcast Network and please subscribe if you like it. See you there.

[00:14:26] Attracting enough attention or aren't being tracked at all, frankly. Yeah. Yeah. So from pillar and internally we call AI companion, interview companion. We also have screening companion, talent fit, which is our assessment of based on Bob's resume, how likely would you be a very strong match for a job that Jerry's looking to fulfill as a hiring manager? We have a ton of metrics that we're tracking.

[00:14:51] But across the board right now, last year we closed the year, process over 96 million applications, Bob. So I think when we think about the candidate experience, a lot of it's like, can we help reduce the work that a job seeker needs to go through to apply for a job? And then on the hiring manager slash company's perspective is like, hey, what can we do to make sure that we...

[00:15:17] I think to me, to us is about better accuracy and efficiency just because everyone's so busy. And quite candidly, a lot of our TA team, they don't have the biggest team. They don't have the biggest resources. So our job is to make their lives simpler and would like to say we want to help our TA team get home a little sooner just to get back to their kids or take a 30 minutes mental break.

[00:15:41] Like if an AI capability can help them just get 30 minutes back in their day so they can have more control over their days. Those are the metrics I would look at. In terms of the candidate experience, I think overall, we think about it like in terms of reducing number of clicks or steps that you have to go through to complete an application process.

[00:16:02] Some of that's controlled by the companies because a lot of companies that we work with from small businesses to EVI to Fortune 100 companies, they all have different level of compliance and requirements that they need to ask for the job seekers. So, of course, there's things that that's out of our own control.

[00:16:20] But to the to the extent that we can help enable or systematically reduce the number of steps or clicks for the job seekers, we're trying to make sure that entire experience is just a little more streamlined, a little more automated to this to the extent that we can. So taking some of the information that you previously filled out, if it's within the same company and from a privacy regulation perspective, we can provide those data so you don't have to manually enter it again.

[00:16:47] We want to figure out how do we basically enable that. So so so certainly we're looking at ways we can improve for the candidate experience. Yeah, no, that makes sense. Did you see any patterns across the ATS platforms? Because I know like Jazz and Lever, you know, these these are serving different types of companies. I think if you look at some of our biggest customers, I think some of the patterns that we're seeing.

[00:17:14] So let's say someone like a Marriott International or Mercedes or KPMG. We're we are seeing the same candidate applying for more jobs within a company that they're posting, whether or not they're qualified. I think it's a little bit of casting a wider net behavior that we're seeing. So so I think that's one definitely pretty definitive trend. So therefore increase the number of application count per job.

[00:17:40] Another common threat that we see is that because of A.I., I think most of the applicants and job seekers have also honed in their ability to write a perfectly written resume.

[00:17:54] So whether or not not resume continues to be a strong indicator to get that first foot in the door, I think there's a lot of intellectual debates out there right now just because as much there's A.I. for screening and interview, there's also A.I. in helping you craft the perfect resume. And what we're also finding and hearing from the hiring managers like, hey, a lot of people sound incredibly amazing. Jerry sounds like the perfect candidate for the job.

[00:18:21] But once I got on call with Jerry, he knows half of the stuff or half the things that he claimed, like some of them just simply maybe, you know, it's it's just a little bit less experience than what the resume had maybe potentially imply. So I think there is a little bit of both.

[00:18:36] Again, we're seeing that both sides of the market sort of trying to figure out how to use A.I. to like advance their application or advance the screening process, but certainly making A.I. have also making that matching process probably a little more complicated than used to be as well. So you've basically taken and modified a version of what Pillar had originally built and you apply it to the top of the top of the funnel, right?

[00:19:05] So as soon as someone applies, they get to interact with a screening companion if they so choose. Yes. Yeah. Yeah. It's it's an opt in. You could choose to opt in. So are you seeing improvements in or dropout rates in terms of after people apply, they hit the easy button on those mass supply things and and they just think that that's going to get them, you know, an in-person human interview?

[00:19:34] Or are they embracing, you know, the screening companion to add more color to the resume? I think I wouldn't necessarily increasing dropout rate, but I would say definitely there is now more resume screened and more candidates interview to get to a final offer. That's for sure. On the on the on the hiring manager side.

[00:19:55] Yeah, I know there's increasing candidate acceptance of, you know, talking to an A.I., you know, screener early in the process because because what's the what's the alternative? Right. An alternative is you don't hear from that company again. So if it's a choice between, you know, expressing what you want to express versus, you know, in natural language, of course, versus not hearing back from the company at all.

[00:20:21] Obviously, you you want to move forward, but you're also hopefully, you know, weeding out people that weren't real candidates to begin with as well. Right. Yes. Candidate fraud or over embellishment. For sure. And I would say over also with the that tends to happen probably more with frontline workers or, you know, I don't personally love the term, but like blue collar jobs are tend to be more A.I. screening at the beginning.

[00:20:45] And then if you're looking for a highly skilled role, typically, yes, there's some sort of filtering, but then a hiring manager also are willing to kind of take that first leap and then just kind of get on a call with the candidate. So, you know, if historically, if historically, this is a very difficult job to hire or sometimes as the TA team will call it, like if this is a new unicorn, by the time they check a lot of boxes, they're, they're trying to, you know, trying to get on a call as much as early as they can as well.

[00:21:12] Well, I mean, I also think about like the example that you gave with this overwhelming, you know, quantity of applications that come in. I mean, how is, how is one recruiter or even a team of recruiters supposed to get through two or 3000 resumes? So you need a, another sort of intelligent, you know, filter to help you sort of down, down select and move forward. Yeah. Absolutely.

[00:21:40] I wanted to circle back on the, the responsible A.I. piece, cause I know you guys, you know, take this seriously. And so I was just curious, I know you've got, you're working with IBM, the IBM Watson team, which is where I got exposed to A.I. spending a lot of time with, with IBM research and, and those teams.

[00:21:59] And so I was just curious how, how they're helping you, not necessarily just from a, you know, sort of governance, you know, application perspective, but how they help you think about, you know, responsibly, I, you know, more, more broadly. Is this becoming another sort of layer underneath your infrastructure, just like, you know, data privacy and cybersecurity? Or another sort of plumbing layer?

[00:22:27] So how do you think about your, your partnership with IBM in that regard? And, and just, you know, responsibly more broadly. Yeah. And then I'm, I share with the IBM team in, in next month in May. And I think so, a lot of great things happening there. So first of all, like, I think, like you said, the plumbing, but I, we, it's the backend infrastructure and making sure that we're, cause some resume data, they are, they are highly, highly confidential.

[00:22:51] And we certainly want to protect the, the, the rights of the, the job seekers and making sure that we don't cross your resume ever and things like that. So, and then I think another huge area that IBM has helped us sort of think through in terms of thought leadership is also just how do we think about AI hallucinations? So there's like reducing bias when, when we're looking at, you know, using AI to quickly swing through thousands and hundreds of thousands of resumes.

[00:23:18] As I mentioned earlier, like this last year, we process over almost a hundred million job applications. So this year we're on trend to, to process a lot more. So how do we, how do we balance this delicate sort of unique view of scale, but also with absolute assurance that no one is left out for whatever AI bias that might exist within the model?

[00:23:47] Which again, some of that modeling hallucination, right? We know AI wants to please and therefore wants to give you an answer. And then, so how do we make sure we build our internal model? How do we tweak AI screening and interview companion slash pillar in such a way that we're reducing some of those natural biases that are just because of the way the,

[00:24:12] the data or the sample data that was available to the model and therefore created certain hallucinations and reduce that as much as we can. Those are the things that we're thinking about and certainly partnering with IBM to think through. Yeah, no, I think that's great. The, you know, there's always, regardless of, you know, regulation or lack thereof, you know, you've got a trusted, you know, spot in this, in this talent ecosystem.

[00:24:39] And, you know, you're leaning in to make sure that, that, that you, you know, expand on that perception and then you continue to build and enhance that, that trust as you build relationships with, with others. And so, so I think that's really important. And, and then, you know, as part of just AI, you know, fluency readiness, however you want to sort of describe that, you know, within the organization, I think those are the things that people, you want people to, to think about.

[00:25:10] Is this the right thing to do? Is this the right application of AI? Can I trust its output? Can I validate its output? And in my own, applying my own sort of human judgment, you know, we're talking about someone's, you know, life and livelihood. And as we're evaluating the data coming through the recruiting funnel. And so I've just got to do that with the utmost care.

[00:25:33] Not, I'm not saying they don't do that, you know, naturally, but everyone has a human tendency to say this, you know, this AI output actually sounds, sounds right. It sounds pretty good. It sounds, the logic seems sound, but you still want to apply your own, your own knowledge, your own intelligence. And, you know, these human attributes layered on top of that so that ultimately it is a human making these decisions.

[00:25:59] So as you look forward, it's so hard to predict where AI is going to go, but, you know, certainly we, we know that, you know, the future of work is going to be this, this human and AI, you know, partnership. And so as you think about that in, in the talent acquisition space, you know, what are some things that you think about as you try to navigate, you know, a lot of this, you know, uncertainty?

[00:26:27] How do you sort of envision, you know, some of the, the things that you build and the impact that that has on the experience, you know, across talent acquisition? Yeah, I think, I mean, right now the, the hot, the hot topic everyone is talking about is probably fraudulent.

[00:26:42] So there's definitely the notion of deep fakes and people showing up in interviews that are not the person that ended up accepting the offer and, or the person has like three other identities slash avatars that there's, there's definitely versions of those stories. And, and, and so I think it's really kind of figuring out what that looks like in the new world that we're living.

[00:27:06] And like you said, Bob, a lot of us don't know exactly where that's going to end up, but we certainly know is that we need to have like the necessary guardrails to protect both the hiring teams, as well as figuring out like as much, as many jobs, as important as it's for us to make sure it is easy for job seekers to apply for jobs.

[00:27:29] How do we also kind of, how do we also kind of, how do we also kind of vet out early enough in a process that, hey, this job seeker potentially is, you know, should be flagged because, you know, either it's not a real person or whatever, because it is absolutely happening.

[00:27:42] And, and, and, and, and so that provides necessary risks is that once, if you accidentally hire someone like that and let the person into the company and assist, like have access to the system, it could be, the impact could be potentially really detrimental to the business. So, so I think it's definitely that. The use case podcast is where technology vendors get to talk about themselves.

[00:28:06] And it's a wonderful place for vendors, investors, uh, and practitioners to listen to the story of the solution, the features, the benefits, the attributes, et cetera. And, uh, we get to know the CEO or founder, uh, during the, during the call. And we also get to know the tech. So subscribe to the use case podcast. Where I think another big area and topic that we're thinking about is also just sort of how do we actually,

[00:28:36] cut down and back to some of the things that we talked about earlier was how do we reduce the manual work that we're doing or that art, the hiring team or the job seekers are doing. So scheduling comes to mind. So as you can imagine, scheduling between multiple parties, just never, I think there's just no perfect science. It's probably more of an art. And then, so how do we do that in an AI first world where things could be potentially more automated? Or, you know,

[00:29:05] we schedule an interview and then candidates forget that they have an interview because they're on, on the run applying for, you know, 50 or a hundred different jobs. And there's sometimes something gets overlooked. Like that happens day in and day out. So how do we improve that part of the candidate and the hiring manager's experience is also something we've been thinking about as well. yeah, that is an interesting area on, on the scheduling piece. I feel like people underestimate this,

[00:29:34] how complex that is, especially for, you know, group interviews and you've got a lot of things to, to coordinate. And, and also depending on the type of interview it might be, you might need, you know, certain people, but you also need to appreciate that they have work to do as well. The interviewers I've worked to do besides helping the hiring teams. And so, so yeah, there's, there's a lot of complexity to that. So now I think that's, that's an important area. When we think about AI literacy,

[00:30:05] you know, certainly I imagine you guys have some AI literacy, you know, training that, that happens, especially in this space where you've got sensitive data that you need to contend with. But I was thinking about the increase in AI skills that are looked for within, you know, job descriptions and that that's increasingly the expectation when you go into these roles,

[00:30:33] probably a little bit less for the frontline employees, perhaps, and some of the hourly workers that you were alluding to. But I don't think anyone's job these days is completely devoid of, you know, the use of technology in some way, even if it's, you know, back office kinds of things. And so are you seeing that trend as you talk to your customer base? Are you seeing that they are,

[00:31:03] you know, evolving what they're looking for in candidates? And, and how do you see employees role in addressing some of that when it comes to perhaps additional interview questions or a different type of assessment that you might give to candidates? Right. So, well, so first of all, we're definitely seeing that. And, but I think it's the early innings in which back in the days,

[00:31:30] employers looking for candidates who are proficient in Microsoft suite or Salesforce or Marketo for marketing. I think that's the early days of check the boxes. I think now a lot of companies are looking for, are you AI proficient? But that language typically is pretty vague because does that mean you just simply know how to super prompt or from an engineering perspective, are you a prompt based engineer? And so, or you simply know how to, you've,

[00:32:00] you've been in cloud code or you've used a Windsor, Windsor, that kind of like cursor, those tool and capabilities. So certainly we're seeing that in the actual job description. And then in terms of your second part of your question, as a, as a hiring suite, we're not necessarily the one that's vetting out the actual skillset that the job seekers are claiming, but certainly we have web templates that companies can use for their job posts.

[00:32:27] But just like what hiring managers are doing these days, they're using AI tools to come up with job descriptions as well. And we have those capabilities as well. So I would say, yeah, everyone's sort of requesting some form of AI capabilities and everyone's screening it differently. And then, and, but we're, we are seeing that in the, in the, in the requisitions and a job post. Yeah, you're right. I mean,

[00:32:54] there's still a lot of gray area when it comes to identifying some of these things. I mean, I could rattle, rattle off a dozen tools that I've used. I can't say that I'm, I could use them like in a production setting. If you hired me to, you know, you know, do your social media campaign with, you know, VO and other types of like video tools or whatever. But, so there's a big difference between tinkering and actually building something, you know, concrete,

[00:33:24] and enterprise grade in some ways. The, yeah, I think, I think that'll mature over, over time. And, but I do think at some point you've got a sort of a, if AI is a significant portion of that job, at some point you've, you've got to, you know, make that, make that determination. That's going to take probably more than a, just a conversation and taking them at their work. Yeah, totally. I know you've got, you've got plans to launch your own,

[00:33:54] podcast, right? What are you, what are you thinking about? And what's, yeah. Tell me about that. Thanks for asking. Well, it's simply, it's an idea I had, because we work with so many incredible people that help people find jobs and simply want to tell their story. So it's very exclusive to, in a, in a TA world, we're calling people actually a nod to one of my favorite holiday movies, love actually. I don't know if it's something you're in your family have seen. It's a little, it's kind of cheesy, but happy. And then,

[00:34:24] so it's simply to tell the stories of hiring process interviews. I, I want to learn more about how people interview. What are the, the questions that people always ask? Like, for example, the other day I learned that one of the questions, someone I really respect is what are your non-negotiables? I was like, Whoa, that's a, that's a very direct and like strong question that I was like, that's a, that's a good way to end a resident interview. So, so yeah, we're super excited about that and, and sort of a,

[00:34:54] a, a place, a playground for, for hiring managers to tell their stories, maybe even horror stories. Some of the worst interviews they've had, if they're willing to share it, because we all know there are a lot of stuff happening in the interview conversation. So, so I think it'll be, it'll be fun. People are going through so much right now. I just, I feel like those honest, you know, conversations to your point, you know, good, good, bad, and the ugly, you know, let's, let's hear it. Right. Cause I think whether,

[00:35:23] maybe it's turns out to be, you know, humor, humor, humorous, maybe it turns out to just be something that's, you know, relatable. But I think, you know, one of the aspects of, of human centricity that I don't personally, admittedly talk about enough is like psychological safety and, and what everyone's going through as, as a job seeker. There's a lot of fear about job displacement. They know how difficult it is to find a job. It's, it's,

[00:35:52] can certainly be discouraging when you hear numbers like you cited where 2000 people are, are applying. It just makes people probably think if they don't apply in the first like 10 minutes, their resume is never going to get seen. So no, I think it's great. And obviously I'm an advocate for podcasting as, as a medium. I kind of do more video. So I imagined you guys will probably lean into, to video.

[00:36:18] I was encouraged by open AI buying a podcast the other day. They, they, of course they, that podcast is, is generating quite a bit of revenue on their own and as a sizable audience, but you know, it gives me, it gives me some hope that people are paying attention. And, and there's a lot of growth in, in the area as well. I was just reading something this morning that said, you know, podcasts, especially video podcasts are almost becoming like,

[00:36:46] or the platforms are almost becoming like the new record labels where you're trying to attract. Oh, I haven't heard of that analogy. You know, podcast hosts. And yeah, I mean, it makes some, some sense. And it's interesting how AI is, is involved in that process as well. It's pretty, pretty slick with some of the video editing capabilities that you can do now. And so, yeah, I'm trying to learn some of this stuff as fast as I can. I can take some of those pre and post production activities off, off my plate.

[00:37:15] But I do like that people have a deep appreciation for, you know, these types of human to human conversations as opposed to just, you know, throwing a body of text into notebook LM and just creating instant, you know, podcasts. Right. So. I think something you said about psychological safety, I think when people could see or hear people talking, right. So it's not just a bunch of like cliff notes or a list driven type of insights.

[00:37:45] And then back to like hiring interviews. I think that there's definitely a lot of, I think less about fear. I think there is certainly a level of that, but then I think what among job seekers right now, I think it's more around just the uncertainty that we've talked about earlier. And then how people are reacting to the uncertainty, what time gets worst. You're, you're starting to hear like the massive, massive companies sort of going through a,

[00:38:14] going through a time of transformation and whether or not, you know, they're the right reasons or not, there is certainly positioning us like, Hey, AI is enabling us to do X, Y, and Z. And I know just, you know, we're today's in April and I don't like Oracle just announced another big round of parting ways with an employee base. That's like, I couldn't have like bath them, but then they're pinning it on establishing, you know, an infrastructure.

[00:38:43] And then they have to recuperate costs somewhere, which is kind of, I can imagine that's a very hard news for, you know, job seekers to sort of process. And then, so I think it just comes down to we're in a very uncertain and unprecedented time. It's almost like as big of a change as when the internet first arrived back in like, you know, late 1990s or early 2000, I think like the dial banner, I was having a conversation with some friends. It was like, remember like, like you and I remember AOL,

[00:39:12] but like all the younger generation probably don't remember what dial band is, or you used to have to get on a modem to, to be able to get on, right? ICQ, like to be able to chat. So certainly it's, it just, it's really just unprecedented in every aspect of life and work. And certainly in looking, finding a job, building a technology, everything that we've talked about today. So it's a fascinating time to live through. Yeah. I don't think, I don't think my daughter would know what to do if I handed her one,

[00:39:42] one of those AOL CDs. I used to give you with a hundred free hours of dial up at 16 K or whatever it was. I've got a typewriter on my shelf and my sons are like, what are those daddy? It's funny. I, you know, dating myself. When I first got to IBM in the mid nineties, before they acquired Lotus. So we didn't even have. Oh my God. Now you're really dating yourself Lotus. Yeah. Lotus one, two,

[00:40:12] three and word, word pro, I think was their word processor. Anyway, if I needed to send a letter to one of my customers, I had to go over to the, I don't know what the appropriate term for this area was, but this, the secretarial bay where there were typewriters and I had to go over there and actually type out a letter. So that's how, that's how old I am. But yeah, Jerry, this has been a great conversation.

[00:40:39] I really appreciate your time and working through some of our technical glitches in the process, but yeah, no, this has been great. So, so thank you very much. Any, anything else coming up that you wanted to mention? Any new research you guys are working on? Anything else you want to share? Yeah. We do publish our job seekers rapport and job also recruiters nation rapport twice a year. So comes with a lot of job data, industry data that we're seeing. So please look out for those. And yeah, like you said,

[00:41:08] thank you so much for having me and talking about things that need to be talked about, whether it's, you know, just the responsibility of AI, how do we use it ethically in a, in a, in a job, in a, in a professional environment, psychological, psychological safety, all the things that it's important, but we don't probably don't have enough time to talk about. So thank you so much for having me on the show today. Absolutely. Yeah. Pleasure. And yeah, keep up the great work and best of luck to you and the team.

[00:41:38] And yeah, thank you again. Thank you, Bob. All right. Thanks everyone for listening. We'll see you next time.