the world's best talent acquisition teams. We talk recruiting, sourcing and talent acquisition Each week. We take one over complicated topic and break it down so that your three year old can understand it. Make sense. Are you ready to take your game to the next level? You're at the right spot. You're now entering the mind of a hustler. Here's your host, william Tincap. This is William Tincap and you're listening to the Recruiting Daily podcast.
Speaker 2Today we have Morgan on from Jobbite and our topic is fantastic and timely, of course how generative AI will transform organizations from the top down. Morgan, why don't we do some introductions? Tell us a little bit about yourself and Jobbite.
Speaker 3Yeah, so I'm the chief data scientist at Jobbite. My name is Morgan Llewellyn and I am a trained behavioral economist by training, and I've fallen into data science due to I would love to say great choices, but I think it was just a happy accident, as I came out of grad school, so I've been working in the AI data science space for a long time.
Speaker 2I've been working with the AI data science department for a long time. It's all strategy. Morgan it's all strategy. At this point I said this is the thing about a career that looks great going back. Oh yeah, totally made sense.
Speaker 3Totally made sense. I meant to do that Funny enough, yeah, funny enough.
Speaker 2I remember I came back when I was at Salesforce, I fought like the change title change to data science. I don't think this is a good idea. I just don't think it's a good idea. I don't think the market's moving that way. It's a heavy data no one likes data.
Speaker 3That was just more of the hey. I went to grad school to become an economist. Right, you want to change my title?
Speaker 2It feels like I'm throwing out everything I've done.
Speaker 3I earned that degree, I earned that degree.
Speaker 2Damn it. I want that in my life. I know I'm going to say that, but he's a. He's made a lot of mistakes in his life and a terrible decision.
Speaker 3So yeah, yeah, I can see that, but anyway. So yeah, that's a little bit about me. So I've been a behavioral economist and working in the TA recruiting space for a number of years now.
Speaker 2Yeah, at a certain point, you stop seeing how many years right, you just cut it off. Exactly For a long time.
Speaker 3Yeah, OK, got it.
Speaker 2How long have you been doing this, William? Yeah, a.
Speaker 3Yeah, see you, just you, just you, just. That's when it becomes a badge of honor, yeah, or?
Speaker 2ageism? I don't know, I it's I deleted all of my information before 2000 other than my degrees, all my work information. I deleted everything before 2000. It's it looks like I started working in 2000.
Speaker 3There you go. That's the best work.
Speaker 2Exactly, exactly, and then, when we hit to 2025, I'll go back to 2005. I'll only have, I'll only render 20 years of work experience.
Speaker 3Yeah, you're just worried about guys like me, right? Yeah?
Speaker 1who are?
Speaker 3looking for ageism in AI right.
Speaker 2Exactly yeah.
Speaker 3Don't worry, we I've got you protected. Okay, thank you, you're good in my pocket.
Speaker 2See, that's, that's why this call was so important to me. All right, so let's talk generative AI. So let's give, let's give the audience kind of a prima facie understanding, our primer on generative AI. I'm sure they've heard about it, of course, but let's do that. And then let's dig into the transform organizations for the top down.
Yeah, so let's talk about generative AI a little bit. And how does it differentiate from all the other conversations that folks have been inundated with over the past five, 10, 15 years? And so, when we're talking about generative AI, a lot of that conversation is really around LLMs, right? These what's called LLMs, or large language models. Right, and large language models are taking think of the internet and understanding the context and the content and what's the questions being asked, what's the right answers. And LLMs are these massive models with billions and billions of parameters that are really trying to understand our language, whether that language be English, whether that language be Mandarin, french, what have you? It's understanding the language and understanding the content within that language. So being able to differentiate one of my favorite examples Python, the snakes from Python, the programming language. Different, very different.
Speaker 3But if you think about it, where we were, say, 15 years ago, if we were just doing parsing by words, we have the word Python. And how do we know that this is a snake and this is a programming language, right? Right, then we started developing this idea of context, where we started looking at a couple of words before the word Python and a couple of words after the word Python to understand. Oh, you're talking about typing or programming. Right after this word Python, you must be talking about the software skill as opposed to the snake. And gradually we started getting greater awareness around the context of a word.
Speaker 3But large language models have just blown that out of the water, where now it's not just a couple of words around a couple of words around that word Python, Now it's that whole document and understanding. Okay, we're talking about where the Python snakes live and what they eat. And so now if you ask a question, tell me about this Python snake, it's able to understand from reading thousands and thousands of documents, just like you or me. It's able to understand something about that Python snake and answer questions. And so the generative AI part comes to. How do you apply that? How do you ask a question and then generate a response or code or what has Just quickly, morgan, how do we teach people to ask prompts?
Speaker 2Like years ago we had to teach people how to write search, right? So Boolean, if you didn't know Boolean, and maybe most people don't but there was a bunch of people that had to learn Boolean to then find Things on the internet great. But now it seems like we're gonna have to learn a new language, if you will. It's about asking AI questions or prompts, if you will.
Speaker 3That is an excellent question, william, and I think that really speaks to the heart of what's happening in our space. I Think of this as almost Google 2.0, right, or googling 2.0, where the people who are gonna do really well With large language models are the creative individuals who know how to ask questions. Because, again, you've got this massive, you've basically got the world's dictionary right, the world's history, the world's encyclopedia of information at your fingertips. Now it's how do you ask the question that you want to, how do you ask the right question to get the response that you want or the Information? It's very similar to googling right.
Speaker 3Googling things become more or less an art right. When I, when I Interview you folks historically always been do you know how to Google right? What do you do when you're stuck? And Usually I would hope the answer would be all just Google right. I'm not gonna try and recreate it. That's very much what the prompting is here. It's very similar to googling. It's not like Boolean search where you have to be very specific and things like that. It I would say these large language models are very forgiving in the data that comes in and the data that comes out Is still gonna be pretty good, and so it's gonna be very similar to googling and do you think organizations going?
Speaker 3forward and do you think organization?
Speaker 2Yeah, do you think organizations will start building the competency to teach and train people To then be able to, so that they can get more out of it? Like, where's the I said the onus of learning that? Is it on the Candidate, is on the employees, on the individual, or is it on that company? Again, we know this learning is gonna have to Exist and be created. Who's responsible?
Speaker 3so I can already tell you, william, you are gonna be amazing in the future, because you ask great questions. I appreciate that language models were built for you people.
Speaker 2It's ironically enough. I have three degrees undergrad and art, history of MA and American Indian studies in MBA and most people think that the MBA was the most important degree wasn't. Clearly, the art degree was because I had to learn 15, memorized 15,000 works of art. But the MBA while I diss it, the MBA Taught me how to ask questions, which is crazy good, but they could have just said it's a masters in asking questions. I Would have still signed up for it. I.
Speaker 3Guess two things real quick. Favorite undergrad class was our history, funny enough. So I completely appreciate that response. Now, I'm no expert like you, but I absolutely love that class developed an appreciation, yeah, my, you know ten amount of skill set, yeah, but asking questions, coming back to that, really is it open AI Bard, of all these different technologies? They're really going to reward creativity and I think, personally, that's what's super exciting here is we're going to be rewarding creativity because you, that opens up a world of possibilities, right, that opens up some really exciting things that I can't even think of but you're gonna think of, or someone else is gonna think of, applications with that opportunity to unleash your creativity.
Speaker 3Now, the question that you asked is where's the onus? Is it on the business or is it on the individual to learn these prompt engineering skills? And I think it's a little bit of both. Right, is the easy way out, and let's start with kind of the employee, and then let's talk about the business, because I think along with the business comes those hard questions of this is a disruptive technology and what does this mean for businesses?
Speaker 3So, let's just talk real quick about the employee. Just like an employee uses Google, right, to understand and find things they might not know today, they should be looking for opportunities to prompt engineer to understand and find things that they don't know today. The other reason why I would encourage employees to be investigating prompt engineering techniques is it basically makes you a data scientist out of the box. Right, right, software engineers become data scientists because now you've got these AI models at your fingertips and not the model, the output that can be super useful and then for marketing, et cetera. It's just, it's great to have that.
Speaker 3And software engineering is this idea of a rubber doc. You talk to the rubber doc to hear if your idea is a good idea or not, right, or if your code makes sense. Very similar here, right, it's like that rubber doc that you can pass it. Hey, here's what I'm thinking. Does this make sense? Or hey, why don't you go and create it? And then I'll go and edit, because it's always easier to edit.
Speaker 3And so I think there is an onus yeah, there's an onus on the employee to upskill if they want to advance their career and they want to. I don't want to say stay relevant because we had a plumber out here the other day, like for plumbers. Maybe it makes sense, maybe it does, but if this is your space, if you're generating content, if you're generating code, then yeah, I think it's like any other skill you should be learning. Let's talk about business, though the onus on the business Cause. This is what I think is really interesting, because I don't see that as being necessarily a threat at an individual employee level. Right, it's not going to be open. Ai comes out or-.
Speaker 2It's taking your job tomorrow.
Speaker 3Yeah, it's not taking William's job, right. I don't see it. It could help you right. It could help you generate content, and you should be using it, or it might make your organization more efficient, and so it's churning. Different things happen. Maybe you need to hire less, but I don't see it as taking an individual's job or a business line's job necessarily. What I think we're going to be seeing, though, is it could make entire organizations disappear, because now their business model is no longer valid, and so it's not taking William's job right, it's taking that organization's kind of purpose for being away.
It's interesting to say that. So my son I have two sons, one is 17, one's 13. The youngest I was showing him chat GPT. I'm sure he knows more about that than I do. Anyhow, I was showing him chat GPT the other day and we were, of course, like, acting like eighth graders. Of course he's an eighth grader, but I was acting like an eighth grader, we were just having fun, I was writing my obituary and I was just a fun bit.
Speaker 2And then I showed him on Instagram a bunch of AI models, model, fashion models, and I'm like, and I'll show them a picture. I'm like is this a real woman? Is this AI? And at one point he couldn't tell the difference. And then I said, okay, let's look at some reels. So then I showed him some AI reels, or some reels of AI models eating, walking, talking, the whole bit and I said is this real or is it AI? And literally he couldn't. And I showed my wife the same thing. Couldn't tell the difference. I'm like so do we need the fashion industry? Like, can we just create it? Right? Do we need fashion models anymore? If we can create fashion models and make them look real, like they're walking down the runway, real, why do we need fashion models? And he's looking at me like oh yeah, no, we don't need those.
Speaker 3Here's what excites me about that example and coming back to this idea of rewarding creativity. So let's say, three years ago if I had an idea for a blouse right, or a men's suit right, there is no way I could somehow communicate that and make a visual representation of what's in my head. That's not possible. You need years and years of experience and the right people, etc. But now today there's a possibility that actually, whatever's in my head I can represent visually and be able to share that visual with other individuals and potentially make a reality. And so that's why, like, there's an opportunity to really reward the creativity and not necessarily the hard skills that are typically required to move from creativity to reality. And I think these large language models make that jump a lot smaller.
Speaker 2The interesting about large language. Models are many interesting things, but it's like I was talking to somebody earlier today. I'm like we set out a really high bar by calling it artificial intelligence the artificial part. We got right, the intelligence part. It's going to take a while for things to be really intelligent, like it's artificial, better than human, and then rebranded and then at one point, yes, it is artificial intelligence, but we've set this bar, and so anytime there's a failure, people think okay, we're not there yet. Of course we're not there. It's like talking to an infant when do you graduate college? Yeah, we're not there yet, but no one can dispute the capacity of AI. That's just. If anyone's trying to dispute, that's just that silly. You can't dispute what it's top or it's ceiling, if you will. It's limitless. Whereas every human being has a ceiling on a day-to-day, hourly basis, but over the totality of their life they have ceiling. You can only learn so much and AI doesn't have that capacity or doesn't have that ceiling.
Speaker 3It doesn't, and the other yeah. Ai makes mistakes, as you or I do, and so the question is what's the alternative? Are we going to have Morgan and William, with our limited to your point, right, there's only so much we can learn? Are we going to have William and Morgan make the decisions, or are we going to have this thing with infinite capacity to learn or just read documents? Right? Are we going to have that make a decision? And against the two, william and Morgan versus this infinite thing?
Speaker 2Which one?
Speaker 3do we think is going to be more?
Speaker 2right than wrong. I want to go with that one. But the thing, though, because we talked about top down, it's OK, this change is going to happen. It's going to happen top down. Why do you think it's top? Or what do you think the difference between top down and bottom up, like? Why wouldn't this be candidate driven, or employee driven, or consumer driven, if you will, as opposed to something that executive C or the board sees, or the industry sees and pushes it down?
Speaker 3Yeah, so I think there's a. There's really three kind of reasons why I think it comes top down, and the first can be, again, this idea of there's business risk for some organizations. It's not that Morgan in this particular part of the business is going to be affected, it's our entire business is going to be affected. Right, that's sweet level thinking and strategy that's necessary. So I think that's one and I think that's why you see Private equity VC, even financial markets, reacting to announcements about large language models or P firms and VCs saying, look, everyone in our portfolio is going to be using these because we don't want our business disrupted, thank you also see something from the customer side where, if you look back, you know, specially in the HR tech space, if you look back 10 years ago, it was marketing that was pushing and I to customers right, hey, we've got this thing, we've got this thing, we want to talk to you about this thing. Right, in a couple years ago, I think, we started seeing a shift where more and more customers were coming and saying, hey, do you have this point solution? Right, like, can you help me do this? Can you help me do that? But in the last six months, william, what we've started seeing is customers coming to, coming to us saying tell us about your strategy. It's not just a point solution anymore. Tell us about the horizon and how you're moving towards that horizon. So customers have really, I think, changed the way they think about incorporating AI, and I think that's also driving the C suite to get involved. And then the final thing, when you know the third thing, right. So we already talked about business disruption, customers, and the final thing, I think, is really culture and innovation that we're talking about here and that's why it's the C suite.
Speaker 3So, when you think about a disruptive technology, what it requires is innovation and an innovative culture. Right, some organizations have that typically newer organizations and you're going to need top level, top down leadership pushing innovation among a lot of organizations that have more or less pushed all the innovators out. So if you're a business that's been coasting and using the same technology year after year, your innovators have bled out, right, they've gone and they're looking for, they've gone to find something new. So leadership is going to have to reinvigorate that innovative DNA in that organization and so that's going to come down from the top. Right. They're going to have to instill an innovative spirit among people who maybe have self selected into a not so innovative organization, and so leadership is going to have to figure out how do they get an organization that maybe hasn't been selling a video that is at risk right for deprecation. They're going to have to figure out how to get their people to be innovative, and to adopt and use these new technologies.
Speaker 2So that's why I think it's a top down. You choose two words that are really interesting disruption and transformation, and so I think that's going to be a big step forward for the future of AI. Do you see those as, especially with transformation or transform, do you see that is iterative, like we won't really know what's happening, just we, every day, it just kind of changes, or is it going to be more cataclysm? That's the wrong work. Will it be we wake up one day and things are really different, because I think people are probably not overwhelmed but they're looking at, or is it going to be something that's that happens faster? Like I'm in back in my mind.
I'm thinking about Moore's law. I'm thinking about, ok, is this something? Again, we won't really know the notice, the difference, because we'll just consume it and the change will happen. We just consume it, consume it and it was like, ok, we wake up three months later. Yeah, of course we're all using prompts or is it something that we're going to? Really, this is going to happen fast, really fast. The transformation, disruption especially.
Speaker 3So I think you can see it fast, and here's the reason why let's again take this disruptive business approach. So you've got an organization who is in using some of these powerful models, and you've got another organization that is as a customer. I might switch from the non-innovative organization to the innovative and to me that seems like the flip has been switched Suddenly. I'm going to see larger capabilities, new capabilities that I didn't have before, because I have this new vendor providing me these opportunities that just didn't exist. So I think, if we think about it from a business perspective and the tooling you're using, I think you're going to see some. It's going to be binary. In some cases it's you replace vendors and move towards these more innovative vendors, and maybe for others it might be more gradual, where you're upgrading specific features in technology. But I think at the micro level, yes, it is going to be binary Because at that feature level, that tool level, you are going to see a significant improvement.
Speaker 3Think about something as basic as reporting. An organization wants to report on hires or something like that, and now you've suddenly got this capability where I can tell you the report I want and it's just going to go generate it. No longer do I have to go ask my business analyst to generate a new report or something like that. It's going to solve that problem for me. That, to me, is pretty significant change in the way that we do things. What do you think, william? What's your thought?
Speaker 2You know, I think it's like an animal farm. I think that the definition of words will change and as you go forward we'll learn more. We're peeling an onion of things that we don't really know what the core of the onion, we don't know how big the onion is, and so we're just going to peel back layers and people really notice as much, and it might take a couple of generations. People think that I think they want to believe that it's going to be more like minority report next week, and the truth is minority report was shot over 20 years ago, so it's going to happen slower, but we won't notice the change. It'll be that subtle that the words have changed. We'll just be using a kind of a new idiom, a new vernacular, we'll just be using new words to describe some older things and we just won't even notice the difference. So I don't think most people now, the people that have their finger way down on the pulse of it yeah, probably they'll know more about it, know more about it in advance of other people, because I live in Texas.
Speaker 2When I was growing up we would get music two years later after California and New York got it. So something that was hot in New York, this rap in the 80s or skate music in California, we'd get that two years later, ok. Ok, now internet, ok, we don't get it two years later, but there's still a group of people that will understand this and consume it faster than the mainstream folks, because you always have the adoption curve of you got early entrance and then mainstream and then you've got laggards right. So I think the laggards is going to be generations that just opted out, or people maybe not even generations, but people just opted out of learning something new.
Speaker 2So, I have a more kind of iterative look at it and say, ok, ok, ok, it's gonna happen slower. We won't see the paint drying.
Speaker 3Yeah, I, I think at the business level right. So thinking about from a person, a person level view versus a business level view, I think those laggards are at risk is the way I would describe it, because this is, this really is a different than your UI, right? Yeah, I Think this is the internet.
Speaker 2This is the. This is what the middle 90s there was people in the early 90s that know about the internet and played with the internet. It wasn't, as it didn't look like it does now, clearly, but there were people that that did that. And then there was like a whole host of people owned a web development firm in 90s and, like, talking to people about the internet, then they just look at you like, have you smoked dope today? What are you talking about?
Speaker 2I'm like there's a display, it's called the world wide web, and I think what it feels like that to me, like this, is that again, I think you said Google 2.0, which would really make sense to me. So it's, some people are just gonna get it and go. Oh, hell, yeah, let's do that and go and they'll try stuff, even if it failed to throw it against the wall. I'll see what sticks, and there's gonna be a bunch of people that fight it. Bill Gates infinitely said Internet's a fad. Huh, okay, maybe not, and I think there's people that are gonna do the same thing with a guy. They're gonna. They're gonna be those people are gonna go. I think it's gonna just go past by now. I think it's a week wait for the next thing.
Speaker 3It's like okay, let me throw one more thing at you, and why I think this is a little bit different? Because there's something else happening at the same time that I think reduces the, the cost of adoption for these powerful and models, and that's what's happening in the regulation space. So you've got New York City, their bias laws coming out, what July Fed is when they're going to enforce, but you've got Europe talking about regulations and in the HR and TA tech space, I think Regulation is going to be a boom to the AI industry. Right. Why that's the case is you're gonna take the uncertainty Regarding risk of using AI or automation. You're gonna remove that from the buying equation because now it's gonna be.
Speaker 3Here's the, here's the test. Did you pass or not? And no longer does the organization who's purchasing these AI technologies, no longer Are they responsible to understand. Is this biased? Is it not biased? What algorithm are you using? It can be. What are you doing for me? How does this make finding people, keeping people better? And did you pass the test? Yes, and so I think this other thing is happening Underneath everything that's actually gonna speed the adoption, and so I'm a big fan of regulation.
Speaker 2I'm in this space because it's a force and it also forces us to have more intellectual discussions around ethical AI and audited AI and things like that. So I like and I think it's in parallel Again, there's a group of people that understand that, know that, and then there's a group that have, or oblivious, I have no idea that even that those things are going on more than I could talk to you all day, but I know you got like work to do and stuff like that. So Thank you so much for coming on the podcast. This has been wonderful and, of course, we just by touch the very tip of the iceberg of this thing, but I appreciate you.
Speaker 3Yeah, thank you. Thank you for having me.
Speaker 2Absolutely. Thanks everyone for listening to the podcast. Until next time.
Speaker 1You've been listening to the recruiting live podcast by recruiting daily. Check out the latest industry podcast, webinars, articles and news at recruiting dailycom.