#609 AI Can Assess Leaders. It Shouldn’t Replace Judgment | Logan Yonavjak

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Mehmet: Hello, and welcome back to an episode of The CTO Show with Mehmet. Today, I'm very pleased, joining me from the US, Logan Yonavjak. She is the co-founder and CEO of Founder Readiness Engine. Um, the name give a hint to the audience about what we're gonna discuss today, and this is one of the topics I always love to, uh, you know, discuss with my guests when it comes to relation between founders and investors and, you know, all things related to founders actually.

Yeah, this is where we're gonna talk about the founder readiness a lot today. Without further ado, Logan, again, thank you very much for being here with me today. I really appreciate, you know, uh, joining me here on the show. So as I do always, I pass it to my guests. Tell us a bit more about you, your background, your journey, and then we can discuss, uh, further after that.

Logan: Sure. Well, it's great to be here. And so let's see. [00:01:00] I am a impact investor turned entrepreneur. This is actually my second go around. I had a previous company in a boutique investment banking group that helped impact companies raise capital. But my journey's been, uh, one of tr- you know, working to deploy capital into things that matter for society and for the environment.

I care very much about how we think about capitalism from a widening the aperture perspective. Like, how can we make sure that we're embedding risks, um, and opportunities associated with some of the e- externalities that we tend to not consider in traditional finance? Um, things like clean air, clean water, communities, things like that.

So I started my career actually working in an environmental think tank and think, um, thinking a lot about how to solve environmental, um, considerations through, um, a lot of research and white paper writing. And then I got the invest- [00:02:00] investing bug. I ended up helping the CFO of that organization transition the investment portfolio of the organization into a sustainable investing portfolio, and that just got me really excited about being in the investing world.

Um, so that's basically where I spent my career: private equity, university endowment, um, farmland investing platforms. So I've done a lot in also constructing, um, financial instruments that have some sort of impact lens. I have an MBA and, um, more recently I got al- in addition to becoming more interested in, um, early stage technology and entrepreneurship, I got really interested in the role of leadership in actually, um, promoting innovation outcomes.

So thinking about how teams are constructed, uh, leadership failures, leadership, um, you know, visionary leaders who are really accomplishing great things because they're great leaders, but [00:03:00] sometimes, um, leaders who maybe don't know themselves as well, don't have those same capabilities, trying to take the helm and not really succeeding, um, in, in what they're trying to accomplish.

So it's kind of a leap, but it's been a fascinating journey. I teamed up with a, a data scientist and psychologist to develop an assessment, a platform to assess, uh, leadership capabilities for senior leaders. Um, we did start with the founders and the early stage investing community. We have since expanded what we do into, uh, bigger corporates and into the investing world, like gone upstream a bit.

So still very much excited about, um, how to support founders and early stage investors, but have also since pivoted, so... Great. And thank you again, Logan, for being with me here today. Now, you know, it's cur- little bit

Mehmet: curiosity, maybe a little bit traditional question, but [00:04:00] you, you know, in your introduction you mentioned something interesting. It's, it's about, you know, uncovering, which usually we, we don't know, right? So even sometime you said the founders or anyone who might be able to do the leadership and, and, you know, drive a company to success, they don't know about it.

In your opinion, or maybe from your, um, you know, experience, what usually is considered something subjective, uh, about, you know, the qualities of leaders that usually are the founders that people don't focus a lot on that? So in other term- Founders are measured and, you know, usually to be leaders or leaders are measured on some qualities, uh, in traditional ways.

So what led you to, to think that there's another way to, to- Yeah ... to measure this? 

Logan: Well, um, I have been through a number of leadership [00:05:00] assessments myself, and so I'm familiar to some degree with some of the traditional ones like MBTI, uh, which is the, the Myers-Briggs, um, DiSC, StrengthsFinder, and Predictive Index.

I've taken numerous, and they tend to be focused on-- They're self-reported to begin with, and they tend, tend to be focused on personality preferences or skills. Uh, those are valuable, but they don't cons- consider the developmental trajectory of an individual or team. Meaning that we all grow over time, and there's a way you can track levels of someone's growth that is actually quite precise.

And so there's this whole field of developmental psychology that because AI just kinda came on the scene, it didn't really-- it wasn't able to come into the mainstream for a couple reasons. One is, um, a lot of the approaches to analyzing developmental stages are based on transcript data and [00:06:00] humans sitting down and scoring, kind of painstakingly scoring these transcripts.

Well, now we can use AI for that. The second is that people sometimes are a little allergic to being ranked, um, based on, you know, their peers. And so the-- I think we've s- spent a lot of time focusing on the presentation of the information and showing that it's not that if you're developmentally lower in some category, you're a bad person or you can't grow.

It just means you might not be ready for a certain kind of role that has a lot of complexity or that's very high pressure or that's highly relationally oriented. So we're really looking at factors that haven't really been in the fold at scale before and accessible from a price perspective. So a lot of the, um, traditional approaches that I mentioned, this hand scoring, that's obviously costly.

Uh, we've been able to drop that, um, so that it's a lot more accessible to people. But, you know, to back up a little bit, I think, [00:07:00] um, there's just this sense that I've been part of a lot of interview processes on both sides. Um- Even if you, you're really good at reading people, I mean, how many times have one of us, uh, anyone who's listening, made a wrong call in- Hmm

a conversation? I mean, you're sitting there, you're talking to a really charismatic person. You just really like them. You think they'd be a great fit. You look at their resume like, wow, they worked at X, Y, and Z investment bank. I'm thinking about the finance world. Um, you know, they went to this school, and I know a couple people in common with them.

You get a decent reference, and then you go, "Okay. Well, it seems like you're ready to go." And then they come on, and they're non-responsive, or, um, they don't actually, um, know how to manage a lot of complexity. Like, they don't know how many... how to juggle multiple balls, and you throw a, you would throw a task at them, and they're like, "Well, that's not really what I signed up for."

So they're not really very entrepreneurial-minded, [00:08:00] you know? Um, so there's just a lot of ways in which hiring goes wrong. There's stats on this. Uh, 40 or f- 40 to 50% of corporate hires tend to fail within the first 18 months, um, meaning they're not meeting their deliverables, and also most people are unhappy at work.

Um, there's a lot of disengagement, a lot of turnover. People are staying at jobs less time. So we're not really... If you look at the research kinda writ large, we're not really being very successful, um, in aggregate at hiring, promoting, et cetera. So, um, it just seems like there's a better way to do it. 

Mehmet: I believe also the opposite happens, Logan, right?

So because you mentioned just an example where someone who comes great resume, you know, awesome experience, like, uh, flagship name companies maybe. Mm-hmm. And yeah, as you said, like it turns out they're not ready. But what about the other, you know, the other [00:09:00] side of the story when we miss on people? 

Logan: Yeah.

Well, I think that's something I like to talk about a lot. So have you, uh, have you thought, um, have you used like gap analysis or negative space analysis in any of your work? Like, do you think when, when you're kind of using AI, do you ask it to do, run negative space analyses or gap 

Mehmet: analysis? I learned to do that.

I, I, I learned to do that in the hard way, I 

Logan: would say. Well, my, my co-founder kind of introduced me to this c- way of talking to AI and it's changed my life because, um, really what that means in plain and simple terms is you're looking at what isn't being said or analyzed. So you're asking either AI or a person to say, "You know, in that conversation, what wasn't discussed?"

Or, "What did you notice that that person glossed over or didn't talk about?" So we're able to look at those patterns in someone's transcript so we can kind of alert someone to [00:10:00] saying, "You know, um, in all of the transcripts that we ran or the assessment questions, they just didn't talk about people at all.

They didn't talk about managing teams. They didn't talk about trust or relationship building." Um, that's good to know. And so that's one, one factor. And the other is that I think we come in with positive and negative biases whenever we're evaluating someone for a hire or, um, trying to build our teams.

Some of those are positive biases, but others, you know, you might judge that someone isn't capable of a certain role based on information you have or kind of projections that you are, are putting onto them, and actually they're, they're gonna be very talented. And so we're helping just augment decision-making with, hey, what we see from this person is they, they can hold a lot of balls.

They can, um, manage complexity. They're very good under pressure. Um, they're very good relationally. Mm-hmm. And so those are just things that can help make better decisions. [00:11:00] 

Mehmet: Right. You know, like, it's very interesting. Uh, multiple points you mentioned that I was, like, staring, like, because, um, it's coincidence.

I'm, I'm, I'm reading this book, um, you know, about AI from the sense of decision-making and, uh, you know, the bias part of it, and, uh, I will explain to you why I, I brought this now. So the book is called Nexus. It's by Noah, uh, Harari. He's famous author. Like, he wrote, like, The Sapiens and, and multi other books also as well.

Hmm. Um, so in this book, still I didn't finish it by the way, but I'm, I'm, I'm trying to finish it as soon as possible. Um, he, he-- it's kind of philosophical, political, and future, you know, o- of the tech. Now, one thing which, although I come from a technical background, I never thought about it, is that when we deal, you know, with any technology, he's focusing on AI because it's, like, top [00:12:00] of mind.

Logan: Right. 

Mehmet: We trust blindly the answer that comes. I'll give you example. Search engines like Google, Yahoo, whatever, you know, right? So we, we trust. So we, we establish the trust. 

Logan: Mm-hmm. 

Mehmet: Now, the scary story here, Logan, and I want to ask you about this in the sense, uh, or the directions of what you're currently doing, uh, with the Rhythm Engine.

The bias of the AI itself, because it has to be trained on some data, um- Let's say, in the opinion of many people, Steve Jobs is the greatest example of a great entrepreneur, great leader, but we know the negative sides of Steve Jobs, right? So if we bring someone who might have the same character, of course, there's no two people the same, but the AI by bias might tell you, "You know what?

This, this [00:13:00] person is aggressive, not so friendly sometime. Um, we don't think he will be able or she will not be able to do it because, you know, they, they look similar to this." But we know that people like Steve Jobs, they had this negative, you know, markers, let me call them, but they were very successful.

So here, when it comes to, to, to understand the expl- explainability, I think they're calling it nowaday, of what AI is telling us, how much it's important specifically in, in what you're building and what you're doing with Agilis Engine? 

Logan: Well, there's a philosophical and then a kind of practical way that I would, I would respond to this.

I mean, the first is that we never wanna give away our agency to technology. Um, you know, I believe very much in humans having agency and discernment, and so I never promote this as the only decision-making factor. I think that we need to be able to justify, um, [00:14:00] when we're hiring or when we're promoting to our colleagues, like why we're making the decisions we're making.

And I think tools like this can augment our decision-making and kind of force us to be more transparent. So I've been in many in, um, interviews where, um, I felt that I'm, I'm a decent interviewer and I felt that I sometimes got roles just because I was more, I was able to mirror and I was, I was likable.

And yes, I have a lot of great credentials, but- Sometimes I've been asked to do roles that maybe I was not the best candidate for, and I think, I don't know how many of those people were required to justify their decision in hiring me, and I think that's kind of unfair. Um, and so that's just like a general comment that I, I just believe people, it would behoove, um, society and, and business, um, settings to just have more transparency around why decisions are being made in [00:15:00] general.

Um, and so but never removing the agency of the, the human being and just using this as another tool, uh, in our tool belt. The other is that, um, in the, in the developmental, uh, leadership and vertical development space, there's been a lot of, um, historical data sets built on human transcript analysis. So I would say, in fact, the scoring methodologies, even though our constructs are slightly different and we're measuring, um, a broader number of things in a lot of the companies that are currently out there, they've actually been doing the hand scoring.

Um, so I would say the foundational data sets, and we're, we're working with some of these companies to demonstrate equivalency, which means that we can look at our constructs and look at what they're measuring and look at the correlation between the data that we have, that what, what we're measuring. Um, and then as we build more data, we'll obviously have more, um, to show.

But to me, the [00:16:00] foundational data sets have been built on human evaluation and rubrics. And so there's a lot of, um... I think that's important to highlight So yes, how we build AI and the, the inputs that go into it are critical. I think in this case we've developed scoring manuals that have been evaluated by humans, um- Mm-hmm

and, uh, reviewed by, uh, humans extensively. So we're doing the best d- that we can. Um, and AI is getting better all the time as well. 

Mehmet: You know, I'm, I'm happy, uh, you mentioned this because actually I didn't want to tell you that, that, uh, part in my question. So the author, which I agree, uh, argue, which is exactly what you mentioned too, the solution for this is to have humans always review, and he called this, like, self-correcting mechanisms, and he gave examples from other domains also as well.

So, you know, it's kind of the feedback loop that we [00:17:00] always know. And, you know, teaching ourselves and teaching everyone around us that, you know, you need to have the ability to question an answer and understand exactly, you know, why this answer was provided or why the score was, was provided. Or like, uh, uh, you, you, you work in investment banking and you, you, you know like this.

So sometimes also now due diligence is being done using AI, for example, and we need to understand why the, the model or, you know, the, the, the system decided, like, I get this score or like I didn't pass. So all, all this is, it's very important. Now, you mentioned some, um- Well, I 

Logan: wanna- ... character- Can I just bring up one more piece?

Mehmet: Yeah, please. 

Logan: So- Yeah, please ... I find this fascinating. I don't know, I don't remember all the details, but the, the gist of, um, the body of work is around judges, and I think this was a US-based study, but it talks about how judges will make poor dec- Yeah ... will [00:18:00] make more stringent decisions in the afternoon and, and later afternoon than they will in the morning.

Yes. Um, I think that says a lot about the ability of humans to evaluate situations. AI's never gonna get tired. True. So there's also a scenario where, you know, the judge has a second brain or, um, they've looked at all these case files and input to AI, "Listen, this is a pattern that we notice with judges."

And AI's able to flag, "Okay, it's after lunch." Like, should we take a break? Should... You know, like, there's ways in which you can build in, um, support from AI, uh, in situations like that so we're not kind of, um, falling prey to human, um, uh, factors that are causing bias. And so yeah, I just wanted to flag that 'cause it's not related to obviously what we're doing, but it was a very stark finding and very [00:19:00] concerning for the lives of people that are kind of putting their trust into these judges in terms of, um- determining their cases.

Mehmet: It's very interesting study indeed. I advise everyone to read, because, you know, it's eye- it's eye-opener for a lot of things that, you know, we, we take for granted, and we need to understand the rationale behind it. But to your point, AI doesn't have this problem, at least the biological problem, getting tired- Yeah

uh, don't, uh, you know, not wor- don't want to work after maybe having a, a heavy lunch, let's say, right? 

Logan: Exactly. 

Mehmet: Something like this, yeah. You, you kept mentioning, Logan, about some traits or characteristics that shows that, you know, uh, there would be a success with this person in a leadership role, whether it's a founder or, like, he's leading a business.

So what are, like, these common traits usually, if, if we want to, to speak about it? 

Logan: Sure. So just to be clear, we built our [00:20:00] tool, and when I say tool, we have a technology which analyzes transcript data and finds meaningful, um, markers and then provides an output. And so that's, like, our platform, but we have, um, what we're measuring is currently, we're very kind of flexible in what we can measure, but we measure six core constructs, is what we call them, and this was designed for, uh, startup founders originally.

So we looked at about 300 research studies that showed directional correlation, positive correlation with the constructs we measure, and then, um, startup successes, so things like follow-on funding, exits. Um, this isn't a perfect, um- Body of work in the sense that it needs to... Each construct needs to be validated with additional data, and so that's something we're working on.

But it does... The basis of it is looking at the research and what it said. Um, so [00:21:00] I would say the most important factor that we've come across, and this has been validated anecdotally by the VCs I've talked to, which is, it's about 150 of them at this point, um, coachability and identity flexibility. So is a person, um, on the one hand, on the lower end of the development spectrum, are they kind of, um, focused on self and defensive about change associated with themselves?

Do they have a fixed mindset? Or on the other side of the spectrum, are, do they have a growth mindset, and do they see feedback, um, and themselves as flexible within the context they're working and, uh, the complexity of the situation they're in? And so for someone to grow and build a company, you really wanna see a high level of coachability and identity flexibility.

While the other things are, are very important, um, it's difficult to evolve [00:22:00] if you don't have a, an, a, a flexible identity. So I would say that's... If, if you leave with nothing else, that's the most important factor that we measure 

Mehmet: 100% agree. I think it's, it's, it's one of the, you know, it might kill the, the company if they are like founders.

Uh, it, it might destroy the department if they are leader of that department if they don't have, you know, this growth mindset and, you know, the, um, if you a- allow to add also, I would say empathy also as well. Um, because yeah, of course, we, we s- as leaders we need to be, you know, uh, like the, the general picture is like, yeah, you, you're like firm, you...

But I, I need, I mean, you don't need to lose the empathy because when you lose the empathy that will lead people to think like you have some ego, like it will push people far from you. This is something I witnessed, so this is why I, I wanted to add it. Now- 

Logan: Yeah, of course ... I'm sure-- Yeah, please. Yes, relation- relational, [00:23:00] um, intelligence, what we call it, is, is highly important.

Although there's the Steve, Steve Jobs factor. Yeah. I think that sometimes we accommodate, um, weaknesses in certain founders because we see a very talented like cognitive ability or ability to hold a lot of strategic complexity. Um, and so there have definitely been concessions given to particular founders who have been very successful.

Um, so it's not that, you know, y- we always have the most, most empathetic leaders, but it's good for those people to surround themselves potentially with people who have a higher degree of relational intelligence just so they don't have blind spots. Right. And so I think that's how I would frame it. You know, if people ask me, "Well, Steve Jobs or Elon Musk, you know, they're, they're kind of known for being harsh or, um, not as empathetic, but they've been wildly successful."

I would say they're probably, um, you know, more the exception than the rule and we, [00:24:00] you know, obviously very brilliant individuals. But look at all the people who failed. We don't know them. All the people that failed because they weren't relationally intelligent or, um, were missing those factors, so.

Mehmet: Exactly. This is what I want to, to, to, you know, follow up on, and you just mentioned the exactly two example that I had in my mind. And I think, you know, the exception about them is- simply we call them genius, right? So- Yeah ... because I think they are- It's a good just short-hand 

Logan: word. 

Mehmet: Yeah, so they are so talented.

Like, if, you know, if I imagine, you know, I have a spectrum in front of me and, you know, you can, you know, see the caliber moving, so of course, like, something will give you a, um, you know, kind of a, um, common, uh, g- you know, grounded grade, right? So, so about, like, how much you sc- or score, let's say. So because they are so genius in, in some areas, so even that one will

it's not neglectable , but I mean, it doesn't affect the overall score and still make them, like, [00:25:00] super successful because they over ex- you know, uh, e- exceeded in, in, in some areas that cover on that. 

Logan: And I would say they probably have on their team, it probably maybe hasn't been made explicit, but they probably have, uh, more relationally intelligent, you know, colleagues that were able to- Yes

kind of help them, um, work through some of the relational issues that they faced. And so I would argue, I, I don't know who those individuals are, but they're probably in the background or foreground kind of, um, taking care of some of that. 

Mehmet: That makes complete sense to me because I, I read the, uh, biography of Steve Jobs, the Elon Musk one.

I don't know why they, they... I, I don't like when people, you know, do biographies before they pass away but anyway- No, I 

Logan: know. It's- 

Mehmet: I didn't, I didn't, I didn't re- It's hard ... I didn't read that yet, but, you know, of course from the videos and all this. To your point, um, I think one of the factor that they were good at, despite all the controversial, you [00:26:00] know, image they have and, you know, character they have, I think they are good at attracting the right talents.

Logan: Sure. 

Mehmet: And somehow creating the synergy within the team so everyone get focused on, you know, one single goal, which is like- Yeah ... making this product successful or, like, achieve this milestone, which is, again, in leadership, it's, it's very, very important. Now, um, have you seen, Logan, you know, any patterns that you correlate, like for example, if they have this and they have that, so that means they're gonna have stronger execution?

Anyf- anything you, you, you discover that, uh, surprise you also as well? 

Logan: Well, I would love to come back and, and talk about, when we finish our research study, I would love to share more- Sure ... because what we are doing is we're taking publicly available data from Y Combinator and Techstars founders and looking [00:27:00] at, um, you know, obviously they got funding from, from those groups, but then any follow-on successes.

Um, and we're looking at patterns in the leadership that show up from tran- publicly available transcript data. We'd love to work with those organizations directly and, and see if founders are willing to opt in to some of the internal conversations as well. Um, but we wanna start showing some of these, uh, bigger trends in terms of, like, correlations between, uh, constructs or, you know, higher levels of certain constructs and, and success, success factors.

So we just need bigger data sets to make those claims, but that's definitely something we're, we're working on. 

Mehmet: Right. You mentioned few minutes ago about, you know, coachability, right? So you, you, if they, we c- we can make them coachable. Um- Now, from conversations, meeting or transcripts maybe, uh, what tend to [00:28:00] give us these signals?

Like, uh, and not only coachability, because there are, there are, like, two other things which I believe as leaders or founders, they have to have it. First is resilience. Yeah. Um, it's not, it's not easy to be a leader or be a founder. And adaptability. And I'm mentioning adaptability because to, to back to your intro about, you know, also, like, the innovation and, you know, if you don't have this mindset of adaptability and accepting that things can change, and especially now we're living in the age of AI, where if we discuss something today, probably, like, in two weeks 

Logan: it will 

Mehmet: be obsolete.

I know, it's really maddening in a way. 

Logan: It will be obsolete. So- And so many tools, it's- 

Mehmet: Exactly. So it's not only the tools, Logan, I think it's also the way we think, the way we take decisions. And as leaders, main things we do all day [00:29:00] is we need to take decisions, right? So this is where you spend majority of the time as a leader, taking decisions.

So what signals tend to show that, yes, we have the adaptability, the resilience, and the coachability? 

Logan: Well, what, what we're fundamentally doing is we're looking at sentences within transcripts, um, and it's called quantitative linguistics. It's we're looking at how someone likely thinks, behaves, and acts under pressure and complexity.

Um, one of the things we're looking for is on the strategic complexity, which is one of our constructs, is how many references they make to, um, frameworks or, um, basically how many balls they can juggle at once, but that comes through by them referencing a bunch of different perspectives. And so someone with a lower level of strategic complexity will kind of focus on one or two things at once.

Um, and someone who's at a higher [00:30:00] level of complexity will be starting to look very meta at a system and referencing all sorts of dimensions of a system, their stakeholders, um, environmental issues, um, philosophical issues. You know, they're really drawing in interdisciplinary thinking, and so that's something you can map and trace in the way someone tells a story or speaks to, um, different perspectives they're holding.

So that's just one, one piece. On the resilience front, you know, we ask a lot-- We ask questions in our assessments about, like, how someone, um, how someone handles stress. And so what you wanna see is somebody who's built systems around themselves to handle stress. So we're all gonna experience, um, some sort of stress.

We're, you know, we all are b- beholden to our, um, nervous system being kind of charged by fight or flight. It's hard to, you know, get over that entirely because these are [00:31:00] instinctual responses. But what you can do is create habits and patterns of dealing with that. And so when someone tells us, you know, or references, "Oh, I have a breathing exercise I use," or, "I have a bunch of, um, um, people that I call upon who are diverse thinkers to help me solve a problem when I'm stressed," you know, they just-- they're, they're making mention of that they've built a system, a robust system of resilience around themselves, so there's less risk that they're just gonna collapse when a stress, an exogen- exogenous stress factor comes in.

Mehmet: Right. How do you think someone who takes our assessment can benefit, um, you know, becoming better rather than feeling that they are just evaluated by a, a machine? 

Logan: Well, and I, that's what we're trying really hard with. W- we, we have plans to gamify this. We have plans to make it much more engaging for people.

I think, [00:32:00] um, a lot of people are afraid of development or, you know, being evaluated because they're, they have a low coachability score, honestly. I, the people that I know that are excited about this tend to score higher on coachability because they see more input as, uh, helping them grow. And so, um, I think it just depends on who you're talking to.

But we give three kind of core areas to focus on based on the assessment, um, on things you can materially work on that'll get you to the next level. It's not a sl- it's not a, a fast process. A lot of these factors take time to improve, but you can see the s- some of the skills associated with them improve quickly.

So, um, you know, one example is, and it, these can be pretty, um, non-threatening in my opinion. Mm-hmm. You might get feedback that your resilience score isn't very high. Well, I wouldn't, you know, there might be encouragement to find some things that relax you or, [00:33:00] um, practice, uh, and, and build in systems that can help you reduce stress.

That to me doesn't feel very threatening to someone's, um, way of being. It's just kind of offering, uh, alternatives and hopefully improving one's life. Like, I don't think anyone really enjoys being in a stressed state all the time, and there are ways to improve that, you know? So- 

Mehmet: Yeah ... 

Logan: I, I feel like what we're doing is building a system and a roadmap and a way to climb the mountain for people, uh, and it just brings all this together at once instead of you having to go figure it out on your own or getting kind of, um, disparate mentorship.

It's just kind of like coalescing a roadmap for improvement in one place. 

Mehmet: Do you see, Logan, based on what you just told me now, that this can become kind of, you know, because it, it, you will have a lot of data, right? And, uh, uh, of course the system will, will, or the engine will c- we, we, will keep [00:34:00] enhancing with time.

Do you have the ambition or the plan or the roadmap to convert this into kind of, and I'm sorry if it's not the right similarity here, but the thing that came to my mind is, you know when I want to go take a loan from the bank so they go and check my credit score, right? So do you see it something where leaders, when they want to promote other leaders, they have kind of a guarantee, okay, hmm, they have this score.

Probably if we do promote them, they will have, you know, good outcomes, business will benefit. Same thing for, um, for, for, for investors also as well. 

Logan: Well, I mean, one tangible thing I've thought of before is, you know, our FICO scores in the US are, um, there's a lot of issues with them and, and a lot of low income and disenfranchised communities struggle to build credit scores and thus get access to capital to do things like start [00:35:00] businesses.

I, I just wonder if there's ways to embed something like this to show that a person would be a good, um, prospective lendee or would be great and, and responsible at taking on capital and could actually perform the function of the business that they're, they're talking about taking a loan for. Um, I mean, I think there's a future where that could augment existing lending evaluations that kind of rely on antiquated metrics in some ways.

Um, so I do think there's potential for a lot of different analyses, and I see it as a developmental infrastructure layer. Like, I think we've just been missing this kind of, uh, tool, and again, the AI capabilities just make it scalable. 

Mehmet: Right. Um- Do you have any concerns about, you know, someone would, um, you know, argue about, you know, the ethical considerations, uh, about, you know, organizations [00:36:00] utilizing, you know, some-- It's not like, uh, I would say too much personal because they're gonna be talking about the business, but maybe like the privacy part of it and, um- Yeah

you know. So, so w- what, what are, like, the countermeasures, you know, we can do to make sure that it's con- it's, it's done the right way, it's not misused? Uh, again, back to the bias point we just discussed before. 

Logan: Yeah. Well, I think a lot of it's disclosure and just, um, you know, like in some of the work we've done, we just have opt-out options for people who, uh...

You know, there's some religious, um, considerations that I believe, uh, in some Muslim contexts, like, uh, people don't wanna be on camera, for instance, especially women. You know, there's just some re- there's some considerations that, you know, disabilities that we need to consider. We certainly are, um, aware of them, and there's-- You just have to give people the, the opportunity to [00:37:00] opt out if f- and give a good reason for it.

And then we have to be clear on how we're using people's data, so we have, you know, obviously terms of use and privacy policies we disclose. I mean, that's kind-- We have, um, values of our organization, you know, that we post publicly that we adhere to as a, as a company. That's super important in my opinion.

Um, we're not-- Our intention is not to, uh, put people at a disadvantage. It's to help make better leadership decisions and more effective ones. I mentioned the stats from, you know, earlier in the conversation. We're not seeing great results in terms of people's satisfaction at work or, um, the success of promotions or hires.

It's costly to companies. It's, um... And it can be very uncomfortable for the wrong people being hired. I would also say we're all being judged without a lot of criteria right now. So like, you know, you go into an interview, you have really no idea what the other person's [00:38:00] thinking. Um, so, uh, there's, there's those dimensions as well.

Um, yeah, those are some of the ways that I've contextualized it. I think there are bad actors and people who will certainly try to, um, use this, but that's something we always have to deal with in a new-- with new technology. And it's not that assessments aren't-- They're not new on the scene. I mean, a lot of companies use them for hiring and promotional reasons.

So I think we're just using, um, kind of a different way of, of scoring and leveraging AI for the-- to make that possible. But there's-- Assessments are not a new thing. 

Mehmet: Definitely they are not a new thing, and I think, you know, I believe everyone listening to us or watching us, that they have taken an assessment once in their life at least.

Uh, maybe when you enter the college, because there are assessment, they ask [00:39:00] you like these questions when, when you apply for jobs, as you mentioned, they, they do this and, you know, like when you do even trainings and, you know, they survey you and all those kinds of, of stuff. Now, if we want to think about the future a little bit, Logan, um, how much do you see this, uh, people analytics fitting in, in, in the due diligence?

Like how, you know, critical it's gonna be, um, in the future? 

Logan: Well, I, I think that it's, um, it's logical that it, it will go in the direction of people analytics. I think, um, especially I've seen a lot of interest from the fund ma- like, the private equity community and, um, pension funds and kind of like larger institutional investors.

The area I've seen kind of less interest, which has been a bit surprising, is the VC community. Um, just a lot of kind of, um, perspectives like we know how to pick [00:40:00] winners or we have a process that works for us, which is mostly gut checks and warm references and, um, cred- you know, where you went to school.

But, you know, that's just the, the mentality I've run into for the most part. So, um, yeah, we'll see how it shakes out, but I, I feel like w- there's def- and a- and amongst corporations, you know, this is, again, assessments aren't a new thing. I think, um, we just have to find the right context in which we can embed this, this work.

Mehmet: I'm a little bit surprised that the VC community is, is telling you this because, you know, I read every single book. I, I went into trainings about the topic and, you know, like always they say, you know, like you, you, you need-- they judge themselves usually, any VC, any investor, um, in, in the VC community, they judge themselves on the failures and not being able to pick up the right winners, uh, whether it's idea or whether it's, it's, it's the founder.

Uh, a- and this has really surprised me because there is a lot of [00:41:00] literature out there about this topic, and I discussed it honestly here with both sides, by the way, uh, with VCs and with founders. And, you know, we agree on common ground that this is one of the big problem is, you know, especially first time founders, I think they face this problem a lot.

Um, and, you know, again, they, they're called first, first time founders bec- for a reason, and we see, um, yeah, they di- they didn't do it before. Again, to your point of coachability, and not only in the, in the startup space, I think we need this more into the workplace. Uh, and this is to your statistic that you talked about, about people not being happy, uh, in, in, in their work.

I think this is the main reason because, you know, I gotta tell you something. I, I want to, to, to hear your opinion about it in, in the same thing. So we, we see people complaining about the AI. It's robotic. Um, you know, like we, we, like we're just [00:42:00] following, uh, you know, uh, prompts and all this. But if you really think about it, actually AI is a mirror of what we are doing, and I think, you know, we are so robotic in the workplace, um- 

Logan: Mm-hmm

Mehmet: from leadership perspective. Okay. One-to-one meetings, um, I don't know, weekly calls, like- A- and if you bring any leader from one company to another company, they just inherit with them the same mentality, the same thing. If you think about it, it's just a robotic way. It's, it's, uh, you know, it's the same way we, we, we accuse- Mm-hmm

the machines of being robotic, so actually we do it ourselves. Not sure, you know, if, if that makes sense- Interesting perspective ... into this readiness engine that, that you're working on, Logan. 

Logan: Well, so, you know, this is more of a pie in the sky, uh, opportunity. But so as everyone's building their second brain, not everyone, but you know, a lot of people are, um, building their second brain and- I'm trying to.

You what? [00:43:00] I'm sorry. I'm 

Mehmet: trying to. 

Logan: You're trying to. 

Mehmet: I'm- 

Logan: Well, w- imagine a scenario where you've, um We've developed your second brain into your highest version of yourself. So we can actually train it to, um, give you advice from a higher developmental perspective than you currently are. And so it's kind of like your aspirational self talking to you and giving you some context on certain situations.

I think that could help, um, reduce, like, roboticism at work, and it's something that I've just been, you know, more fantasizing about it this time. But, um, I think there's potential for role playing and for, um, teams getting together and bringing their best selves to the table and, like, trying to problem solve from different angles and perspectives.

So I'm really hopeful about the use of AI in that context, and that o- obviously is predicated on people's willingness to, to use it. But [00:44:00] that's just some of the kind of, uh, visionary stuff I think we're working on. 

Mehmet: It's indeed visionary, and I'm, I'm, you know, excited to see, uh, how these things change because I'm, I'm maybe repeating myself.

Um, we tend to hide these things. Of course, like, there are some trend maybe last year or the year before about the, what they used to call it? The silent, silent, uh, silent resignation or something like this. Oh, 

Logan: yeah. Quitting- Quit 

Mehmet: silent, yeah ... quitting silently. 

Logan: Silent quitting. 

Mehmet: Yeah, yeah. Yeah, yeah. Quiet quitting, yeah.

So, so, yeah, it, it was a trend, like, and then it faded, but I think it didn't disappear. It was always there, right? So, um, and the goal is to utilize technologies, and AI is, of course, now on the forefront of everything to elevate our capabilities to develop our, uh, you know, skills. Actually uncover [00:45:00] skills that we maybe don't know about it or maybe we thought- Exactly

that we're not, we're not good at and, you know, a- a- and you know, uh, of course, like, I'm not building something on the scale you mentioned, but, you know, the reason I told you I'm trying to because I'm trying to utilize AI to s- to s- to, you know, spot something that even myself I didn't discover all these years, like in my 45 years in, in, on, on the planet Earth.

Maybe 

Logan: there is something- We can't see ourselves entirely, you know? 

Mehmet: No, uh, but what pushed me to do this more is the story about how- You know, it all started with the generative AI. Of course, it didn't come like this. So go back, understand, you know, the... I don't want to go technical here, but I mean, you know, how deep learning works and how the machine is able to do things us as a human be- not because we're not, uh, smart, but because we're not capable mathematically to do it.

And, you know, go read about, maybe this is the second or third episode I'm repeating the same [00:46:00] story, but I think it's important. Go read how the whole thing started, how they did the AlphaGo, where they played the Chinese game Go, and the, they were surprised that the machine goes and, you know, do a move that for centuries, maybe thousands of years, no human thought about this possibility, and actually this is how the machine were e- able to beat the human.

So moral of the story is, you know, technology can uncover a lot, and why- Yeah ... not, you know, our own capabilities and abilities? 

Logan: Yeah, it's like running M- a Monte Carlo analysis on yourself and, like, how, you know- Like it ... could we get into that point where we, just the, the number of scenarios and probabilities and patterns that AI can, can look at at once is far beyond the capabilities of, I would say, every human um, at this point.

Yeah. And so, yeah, that's kind of what gets me excited is, like, but you have to make sure you're comfortable with the underlying data and how it's, you know, [00:47:00] how it's actually formulated. So, um, but yeah, fun things to explore for sure. 

Mehmet: Absolutely. As we are coming close to an end, Logan, maybe final, um, words, maybe something you wanted to say and I didn't ask the right question for you to express it, uh, and where people can get in touch and find out more.

Logan: Sure. I, I mean, I just think we're living in the most, um, exciting era for personal development, for self-actualization, for access to information. Um, and so instead, you know, I encourage everyone to embrace that and to see themselves as an experiment ultimately. And, um, you know, we're always learning and growing even if we have a fixed mindset.

Um, and so it's just, I hope that more people kind of tune into the possibilities of being their best selves. Um, and we can be found at readinessengine.io, [00:48:00] www.readinessengine.io. Folks can sh- send me an email, logan@founderrl.com, and I'd love to hear from you. 

Mehmet: Great. Thank you very much, uh, Logan. For the audience, the links that Logan just mentioned, they will be available in the show notes if you're listening on your favorite podcasting app.

If you're watching this on YouTube, it will be available in the description. Just one final thing about what you mentioned. Yes, you know, uh, the future-- I, like, tend to think the future is bright, tend to think that there's nothing stable. If you go... And, and, you know, we are very lucky living in, in, in this time where we can go back and read about things that happened.

Logan: Yeah. 

Mehmet: I, I couldn't blame maybe someone living in the 1950s or '60s if they have the fixed mindset because, you know, the resources weren't, like, that abundant similar to, to the days we are living, uh, in now. Absolutely. So-- And I think the best thing that happened to humanity is the internet and, you know, the archive of the internet because, you know, like, now literally you can go and [00:49:00] search and see and even watch, you know?

Now we're recording ourself, so some-someone might come years, you know, tens of years later or maybe even after centuries and go look at it and, and see, you know, how people were thinking. Right. So leverage this to know that nothing is fixed. Everything keep moving, uh, and you can learn a lot from, from, from, you know, other people's stories and, you know, from, from- 

Logan: Yeah

Mehmet: reading. Keep learning and never stop, you know, trying to, to enhance yourself. So Logan, again, thank you very much for, you know, the time today, for this great conversation, for the insights you gave us and of course, all the best with what you're building at Threat Intelligence Engine. And as I said, the links will be available in the show notes and the description.

And this is how I end my episodes. This is for the audience. If you just discovered this podcast by luck, thank you for passing by. If you enjoyed, give me a favor, share it with as much people as you can. [00:50:00] We're trying to share knowledge. We're trying to share, you know, some thoughts with you to, to think about it later.

And of course- Don't forget to subscribe. We are available on all the podcasting platforms, Apple Podcasts, Spotify, and all the others. And if you are watching this on YouTube, also we're, we're available there, and subscribe and share it. And if you are one of the people who keeps coming again and again, the loyal fans, you know, I call you fans because you became kind of not only fans, even family.

Thank you very much for all the messages. Thank you for the feedback. Thank you for even the silent ones who send me from time to time, "Hey," like, "we listen, but we didn't have the time to tell you, 'Yes, exactly. I agreed with your guest.'" Thank you for doing so. I can understand your situation. To put your point, Logan, many people are not happy in the work, and this is how I get to know about it.

So thank you for, for you also as well, because all what you have done, and this cannot happen by itself. I don't do anything. I don't push [00:51:00] the podcast to go onto the Apple Podcast charts. So we keep trending since 2025 all the way now we are in mid-2026. So we are always trending somewhere. It's not always fixed.

It's, uh, you know, we keep changing between countries, and I'm happy that finally I saw North America. So Canada was an entry. I'm still waiting for my friends in the US to, to give us some pushup. So thank you very much for the trust, and as I say always, stay tuned for a new episode very soon. Thank you.

Bye-bye.