Episode Transcript
[00:00:00] Speaker A: Juan, thank you for joining our show today. I'm really excited to talk to you.
I want to do something a little strange. We're going to start with a little bit of myth debunking.
I'm going to say some things that might be true or false and you're going to tell me right or wrong and give an explanation. We're going to jump right in. Being an entrepreneur is easy.
[00:00:19] Speaker B: False, false.
[00:00:20] Speaker A: Why?
[00:00:21] Speaker B: It's the hardest thing I've ever done in my life and I've really done very hard things already.
[00:00:25] Speaker A: So I argue that being entrepreneurs is somewhat like no offense to people who actually have the disease, but it's something like manic depressive. You sometimes have the best day of your life and sometimes it's the worst day of your life. What was your hardest part of? If you look at your history as an entrepreneur, CEO, innovator, what was your hardest day that you can share with us?
[00:00:46] Speaker B: I wish it was the best of my life and my worst day. It was months of my worst day of my life and even years of the worst of my life. So just having the patience and the belief in myself and my team. But specifically, the market has changed so much with COVID When we started with our product, with the industry we work in, with HR tech, with AI coming online. So we've had to pivot four times. And every time is a completely new product, a completely new pitch, a completely new website, completely new marketing materials, training salespeople, retraining all of our existing clients that we've moved to something else and carrying everyone along.
Oh my God, that's the hardest thing I've ever done in my life. And it is not.
[00:01:29] Speaker A: When we listen to all the famous people, they're like, oh, how did I succeed? By trying again and again and again. I never gave up. Is there a time that as an entrepreneur, you need to give up?
[00:01:39] Speaker B: Yeah. So three months ago I was interviewing CEO's to replace myself. So I was giving up. I tried everything. And you go through and think sometimes it's the product or it's my team or in this case, I got to the point where I'm like, it's not the product, it's not the team, it's me. And so, yeah, I do think an entrepreneur should give up at a certain point and should not be driven by when they run out of money. You need to make that decision sometimes based on the fact that if you're not scaling and it's not working, just get out of the way. Either close it or find somebody else to do it better.
[00:02:10] Speaker A: And I'll share that every single successful person has failed at least more than half of the times that they've tried. And if it's exactly half, then they're incredibly successful. And I think that's surprising to people because I don't think it's apparent how much failure is a part of the journey.
Let me ask you one last question on our kind of true or false line. If you had to compare working in corporate America to working in a startup, how much do you learn in each? What's the journey look like from a self improvement perspective?
[00:02:48] Speaker B: At a corporate environment, you get to see what scale looks like, you get to see what not to do, you get to learn about industries, you get to learn about how people communicate, how to lead, you get to learn how people get disengaged, you get to learn the problems of scaling. All that is very, very helpful for when you do a startup. As you get to each phase of growth now, the ability to be agile and to reinvent yourself and change directions and move at the speed of light, you never get that in a corporate environment. Actually, corporate environments make you lazy, make you slow, make you like inactive. So there's a whole nother set of skills that you could just not get at a corporate environment that you can only get at a startup. And probably why many CEO's and founders fail, because even them doing it as a startup, although they can learn doing it, it's not for everyone. It's not easy to throw away everything you've worked on the last two years and start from scratch every couple of years. Like that is just, it's exhausting. But I think corporate is Chinese and startup is Hindi.
They're both languages, you got to learn them.
[00:03:51] Speaker A: Love that. Would you agree that in a startup environment, whether you're an individual contributor or an executive, you're learning ten x faster and more than in a corporate environment?
[00:04:05] Speaker B: Yes. The rate at which you are thrown problems to solve is a ten x rate. Therefore you are, if you do figure out how to solve them, then you are learning at that ten x rate faster. Some people get thrown all the same problems but don't learn that fast and so they don't actually succeed. But if you're able to chug from a hose, it is a ten x rate of learning.
[00:04:25] Speaker A: Appreciate that. Okay, so with all that said, this is a hard journey. It's not built out for everyone, but you're moving at the speed of light. At what stage did you figure out that you wanted to be an entrepreneur or this is something that fit you.
[00:04:45] Speaker B: I might be a little different than most entrepreneurs. I never wanted to be an entrepreneur. I probably am not the prototypical entrepreneur. And even if I sold this company for a billion dollars tomorrow, I would not go back to being an entrepreneur. So it's not like I dreamed of it. And I had the aha moment like I'm an entrepreneur. I became very spiritual at the age of 40. And I started doing, 15 years ago, ayahuasca and psilocybin on a almost monthly basis with shamans, some of the leading shamans in the world. And I started doing it for consciousness and access to higher consciousness and creativity. I had heard that Tony Robbins does it. I had heard that all the people at Facebook and Google and several friends of mine who were the most successful people I knew later shared with me that they had been doing it for ten years. And so I had been doing this. And in my ceremonies of deep meditation, which is basically akin to what ayahuasca is doing, I was given the message that I could do a lot and change the world, and that if I really wanted true freedom, working at large companies was not the way to acquire freedom. Freedom both in my own time and having my own schedule and doing what I want, but also freedom in wealth and wealth creation. And so I was given actually a roadmap and an idea that was the initial product of human intelligence. And so that's kind of how I got into it. It was really in an ayahuasca ceremony and deep, deep meditation.
[00:06:05] Speaker A: I think that a lot of people would agree with few entrepreneurs, but I don't think it's the, let's say, cool thing to say. It's not what people want to hear. That this is a hard journey that CEO's and founders, they ask themselves at least once a week, am I doing the right thing? Should I stop? Should I fail?
And the answer of, you know, should I keep going or should I stop? Is not a clear one. So I really appreciate your honesty and humbleness in sharing with us today. Let's jump into what you're doing today. Where did the company start? What did they do?
[00:06:41] Speaker B: My career was a zigzag career from the corporate world of 15 years. Companies like Procter and Gamble, Reebok, Puma. I then did also software at a company called Siebel Systems, the forefather to CRM and Salesforce. They grew from 8 million to 2.5 billion in two years, and from 100 employees to 12,000 employees in one year. I had all these corporate experiences. Then I became a headhunter, executive search consultant, if you will, placing executives at large companies. I was really good at that placement and finding Fitzhen. So my superpower, as a lot of people like to say in the US, is that I could see someone, interview them, and identify where they'd be a good fit with the hiring manager, the team, and the culture of the organization. However, that is not scalable and never has been. And so the genesis of human intelligence, after one of these spiritual ceremonies that I did in deep meditation, was to take everything that I had learned and all these different experiences of leadership, of training, of hiring, of coaching, and find an assessment tool that could actually identify people's behaviors, motivators and work styles, leadership styles and communication styles in the shortest amount of time. And so we found the tool. It actually found me. It was kind of weird. So I got lucky in finding the tool that in ten minutes measures what nobody else in the world can measure in an hour. So, bar none, the best psychometric instrument out in the world. And then I built a software company so that everyone in the world who does recruiting could use it for hiring, reducing time by about 55 0% and reducing turnover by about 40% in any company we work with. And so that is what I built as version 1.0 of human intelligence. It was called talent fit for recruiting. That was the genesis. Has nothing to do with what we do today, eight years later. But that is how we started.
[00:08:22] Speaker A: So jumping forward, eight years forward, you went through, I think you mentioned four pivots. Tell us about the recent pivots.
[00:08:30] Speaker B: Yeah. So I'll give you the little story I should tell. First pivot was Covid hit. So we lost all revenue. I would have loved to have been a restaurant. They did not lose demand for food. They lost how to get food to customers. So we became a portal where everybody in a company could pull up the psychometric insights about Ari, about Juan, about any employee, as well as any team, and kind of really neat graphs and pictures, and you could really understand without having to read very much. Problem with that was usage and adoption. You get 90% usage in a portal. People log in, they think it's exciting. Six months later, it's 30%. A year later, you have 20% usage. So that's not a good SaaS business model. Then the next thing, during that time of 2021 and 22, I saw Grammarly, the small little company came out of nowhere, become worth $10 billion. And what did they do? They did nothing new. They did not come up with the vocabulary, the dictionary, how to write. They actually did everything that you could do in Microsoft Word. The key insight here, and probably really important for any entrepreneur out there here is all they did was make that content that word had in clunky, click, click, click, click in your workflow, in the document. So there's no kind of, it's seamless and it's fun. And so I saw and told my team, let's pivot. You got to make a product that has the psychometric insights that are in workflows. And then also at the same time, layer on top of that. What was coming online was AI. And so we put psychometric insights in workflows with AI so that this next version, you could write an email, it gets rewritten for your audience, go to a meeting, it tells you the dynamics of the meeting before you get into the meeting or while you're virtually in a meeting. And that was great. Problem with that was it was an amazing product. People loved it, but it wasn't commercializable. Nobody solves the problem of collaboration at companies. Nobody cares. There's no budget, and it's hard to trial a plugin that you need it support. So that then pivoted to current and last version, which is finally the version that's taking off. That should make us a unicorn very shortly. And that is HT coach, human intelligence coach. Imagine the coach that companies pay $30,000 for an in person coach to help leaders develop a leader about how to lead their team. Team dynamics psychometric assessments. We now through an AI bot like the chat GPT bar, but with predetermined questions. You can just pick from a list. You can get everybody up and running in one day, no training necessary. And you can ask any question about any person at the company, any team, about how to work with them, how to lead them, about any division. Why is the division not performing? And so it literally is AI with psychometrics to answer any question you have at any second, all for the price of a buck to $10 per person per month. Truly transformational.
[00:11:03] Speaker A: So that seems impossible. So let's jump into it.
Tell us a little bit about what is the problem, who is using the software and what's the pain that they have? Regardless of the software, what are they trying to do on a data basis? Where are they failing or struggling?
[00:11:23] Speaker B: Great question. So I'm going to split it between small and then large companies. Small companies today do not have the budget or the time to small companies today do not have the budget or the time to hire a talent development, talent management or learning development team or group that costs millions of dollars just to have the team. And then you have to bring in Iowa psychologists and consultants to do workshops with leaders across the organization. So they don't even have the chance or choice to do these kind of programs. That is one problem. Small mid sized companies can't do it. Large companies do it and have those teams. The problem is they spend millions of dollars to only do this for the high potentials, let's say 10% of your 20,000 employees. And when they do the workshops, it's all conceptual, it's outside of work. And the information, whether it's Hogan or Lomminger or predictive index, nobody remembers it, nobody uses it with their team, and two weeks later, the value to the organization is zero. So that is the problem. How do you get an ROI out of these tools that have been used for 80 years, but that everyone knows are completely ineffective when leaders and teams get back to work? And it's only for the privileged. This tool gives you $50,000 of value for every single employee for a couple bucks a month because now everybody has their own coach. Every small company can now have a starter kit to do all these same things without having to hire anybody and just letting the AI do it for you. And actually, the AI results are better than any in person coach you could ever hire.
[00:12:48] Speaker A: So let me break this down, right? Because really what this is is democratization of a mentor. That's really what it is, right?
I have, you know, I've been fortunate enough to go through those programs. You know, with me, it was corn ferry, I believe, and you kind of go through it and I. It does have an impact at the moment.
[00:13:12] Speaker B: Yes, it's great.
[00:13:13] Speaker A: Every single person who goes through these programs, the feedback, they say, is that it was hard for them to do that adjustment from the insights that they had into. How do I do this in the work environment?
[00:13:24] Speaker B: Because personalized for their situation, for their Mary, for John, for Peter, who worked for them.
[00:13:30] Speaker A: Exactly. So I want to break this down even more and talk about a specific. And no names, no company names or people names, but a specific example of a specific person, maybe from your own experience, where you encounter a specific type of problem and the system can kind of help you, guide you through it so the audience can get a real tangible understanding of the tools and techniques that can be used in the process.
[00:13:56] Speaker B: I'm going to give you four layers, because it's four layers of how you aggregate the data of the psychometrics of all the employees. Okay, first is understanding others, right? So if you're going to work at Coca Cola and you're working with somebody in London and you're in Atlanta and you're now put on a new team together, instead of waiting three months to have friction, why don't just pull up this tool, this AI bot, and say, and let's say Ari's in London, Juan's in Atlanta. How do I collaborate with Ari? Question mark. You hit return and just like a chat GPT bar. Okay. And actually you can just pick the question. You don't have to write it. It'll say, always extremely decisive, loves detail, loves data and, but you want, you're really deliberate and you're conceptual. Both of you have to come to the middle to see where each other's at so you can work best together. That's kind of understanding others.
[00:14:42] Speaker A: Or let's say, let's break that down for a second. So one of the, one of the, I think the most impactful social psychology biases that I learned about because I felt that that was the one I was failing at, is called egocentric bias. Now what egocentric bias is, and I'm sure you know all about this, is that you assume that other people are like you. So if you're a heavily data driven decision maker, you're assuming that the person across the board is exactly like you. So what you're going to do is you're going to throw data at them. But that's not always the case, is it?
You could be facing somebody who really cares about the why?
Why are we doing this? How is this going to benefit the organization? You could be facing somebody on the side who wants to know how is this going to affect their team? And in fact, they might know that it's a great opportunity, but they have a fear that this might affect their team in a negative way. They're going to be out there guarding their team first and foremost and you have no idea they even care about this.
A lot of people discount these, let's call them for a second. It's not exactly that, but a lot of people really hate the personality tests because they have to a large extent been disproven by science. But there's a different aspect of it, which is really how do you adjust your communication and your work approach to the person across the table that has been proven to be incredibly effective. So if we kind of separate those two things, this is an incredibly important and scientifically proven aspect of social sciences. I just wanted to break that down for the audience. So having an understanding on how to ask yourself questions and find gaps in your own logic on how to communicate yourself is actually incredibly helpful.
[00:16:28] Speaker B: And we bring it to life for all employees at a company, in any language in the world, in one day.
[00:16:36] Speaker A: That is incredible. Right. So that was the first aspect of it?
[00:16:41] Speaker B: Yes.
[00:16:42] Speaker A: What's the second aspect?
[00:16:43] Speaker B: Yeah, and I'll stay on the understanding of others. Another thing is, if you're a leader and you're giving a sensitive performance review, and we've all been there before, and you don't want to hurt the person's feelings, you can literally just say, how do I write this performance review for Ari?
And then drop in your performance review that you wrote and it'll rewrite it in a way that's best and sensitive for Ari, the way you like to communicate, the way that's going to engage you and be sensitive to your strengths and weaknesses. Different from Victor, different from Mark, different from Carrie. So that's another individual layer. Experience. Now you work up to leadership questions and team. Well, if you roll up the data of psychometrics of a team, you can see really powerful insights. But today, most people don't ever see that. And if you get a consultant to do it, it takes three weeks to give you a hundred page report and you never look through it again. But now, let's say you're a new leader and you just got promoted. You're 26, you've never gotten leadership training, and you're running a new team and they're all in their fifties, and you're the Gen Zer and they're the baby boomers. Now, you could literally pull up in a question, say, what are the best ways to engage my team, question mark? Oh, this team, they all love working creative and innovative ways. They're all motivated by XYZ, and they communicate in this way. Or who in my team is going to butt heads with you the most? So I already know going in as a new leader, who's going to kind of rub against me? Oh, it's Mark, because he's only 10% similar to me. Who are the two people for a project, for instance, for creation and changing a process? Who are my most innovative people on this team, question mark. Boop. It'll just tell you. Right? So for leadership and for understanding teams, that's the second level of kind of where the data can be very useful.
[00:18:18] Speaker A: Wonderful. I want to play the devil's advocate for here for a moment. If the system is telling you, oh, this is the best approach this is the best person for this. But people evolve, people grow, people change. People also want to try new challenges. Are we not biasing our executives or team members based on information that the system might have wrong, or maybe was right a month ago, but that person is actively working to become better and maybe is, maybe they've had a breakthrough? How do we balance, let's say, this biased information and the human nature of people to improve and get better and not be put in a corner?
[00:18:58] Speaker B: Great question. So several points to that question. One is, this is a tool that we use that is scientifically validated for 35 years. And at 92% across tens of thousands of people, we've had over 3 million people take it for every question. It's 92% or more accurate for every 10,000 people who take it. Only one turns it off after they take it. Those are exceptional numbers. We also make it voluntary at every company. So 94% of people actually do take it, and 6% end up withholding from taking it. You can also write, hey, I'm actually, sure, naturally, I'm deliberate, but I've learned how to be very decisive. So you can actually write and say, this is eight out of ten, and you can actually qualify. What's different? That gets shown in your results. But yes, people change. And people, the most agile people and the best people in sports, on a basketball team or a soccer team, are not the ones who are only right handed or right footed, but the ones who also learn how to be left handed and left footed. The same thing happens with your soft skills. Those who are decisive are the best team members and leaders at a company and employees, if they also learn how to be deliberate based on the context. Nowhere in life being extreme helps you. And that's definitely true at work. So to be motivated by structure is great, but also enjoying and being in a creative environment, being able to work in that environment is also good. So our tool is built and identifies where people have their strengths naturally, but also where they can grow and have this duality across 28 different variables. And so, yes, people do change. The right handed basketball player on a team that everyone knows if it goes to his left, is going to throw the ball out of bounds. When that person does start learning, though, everyone notices that they've developed that other left hand, or in soccer and football in Europe, that left foot. And so our tool does allow for that, allows people to talk about that, allow people to record that, and you can actually retake the tool every year and it captures the differences and you can actually compare yourself with yourself over time.
[00:20:51] Speaker A: That is such a hugely important thing, because when I talk to the HR people that are firmly in the camp of, oh, you just have one personality, you're not going to change that. Just. And, you know, social psychology clearly proves that the behavior of a person is actually the of two things. Sure, their personality, but more so if a larger impact is their environment.
[00:21:15] Speaker B: Yes.
[00:21:16] Speaker A: And I think that's the thing that most HR people discount. It's the environment and the ability to change the environment proactively. So you just saying, hey, this is a thing that is actually being updated live all the time, and you're retaking the evaluation, you know, once a year. That's just such a hugely fundamental, different change of a mindset. I absolutely love that because I am a huge skeptic when it comes to putting people in a box and defining the personality. But having, and as you said, it's not a once a, once a, you know, in a, you know, you do this offline and somebody tells you you're an ex, and there it finishes. This is a working tool that you're involved in on a day to day basis, that completely changes the paradigm, in my opinion.
[00:21:56] Speaker B: There's two other levels of the data of use cases. We started with the individual. We started with an individual leader and a team as a second. But there's also where you aggregate the data across a whole organization. So let's say you have a manufacturing plant in Ohio and a manufacturing plant in Kentucky. The one in Ohio has 20% defects. The one in Kentucky has 2% defects. Now, with a tool like this, what used to cost $10 million of research, and usually subjective research by a McKinsey or Bain or a BCG, now, you can just open this up, and if you have the right credentials in terms of hierarchy at the organization, and you have access to both organizations, you can say, what is the difference? That explains why the Ohio manufacturing plan and all those employees create 10% defects versus Kentucky question mark, whoop return. And it actually will go through the 40,000 pages of psychometrics in 15 seconds and tell you, for example, oh, all the people in the Kentucky manufacturing plant who have the 10% defects, they are all self starters, too decisive for quality control, and don't have a predictive nature. Whereas the ones in Ohio do have these other qualities. Right. Imagine then you can roll it up to a fourth level, which is company ownership, not just divisional, but a private equity firm that's doing a roll up in a space. And they have one company, 10,000 employees, another one, 5000. They're going to acquire. Company A, they own. Company B, they're going to acquire. And they want to know where will the integration problems occur to by functions and divisions? And so all this data gets uploaded, it's already there. You literally just ask the question, where's this going to happen? And boom, you just found out that maybe the three plants in Latin America are similar, but the ones in Europe are not. You're going to be more sensitive to that and that the sales teams in each organization are completely different and you shouldn't combine those, but everybody else is enough similar, you should. 80% of m and a does not work today because nobody can answer that question. This will be the most transformational tool in the world for private equity and m and a, and mergers and acquisitions.
[00:23:57] Speaker A: I mean, the possibilities here, if we look at five to ten years in the future, they are just mind boggling.
[00:24:07] Speaker B: Those are coming out in three months.
[00:24:09] Speaker A: Oh, well, hold my beer for a second. Imagine that not only do you have all this psychometric information, but what if the manufacturing processes were also in the system? Imagine that those actual quality results coming from the machines, coming from the quality assurance stations were also in the system. I can imagine a future where anybody in the team can write up a query that gets saved and gets run on a daily basis saying, hey, if you see a drop in this query result, go and look at what is different across plants in regards to the processes and the actions taken.
Then at the end of the day, you'll have 100 or ten things every day, which the system just comes at you and says, hey, there might be one of these three problems, go and research it. So I don't see AI fundamentally doing our job for us. I see it actually pointing us to really difficult human problems that we need to then go engage human to human and think through and brainstorm and solve together. So for me, this is hugely exciting because the future is actually one where you need to work harder because of the AI, and you can't kind of just brush it off, ah, you know, give some bullshit, because the AI is giving you data and saying, here's what I saw. So I think it's super exciting because it's pushing us towards data driven decisions. Would you?
[00:25:31] Speaker B: Yes.
[00:25:31] Speaker A: Would you agree with that?
[00:25:32] Speaker B: Yes, I do. And we have a tagline. We tell people all the time in our, in our pitches, and you kind of alluded to it and paraphrase it, we are actually, with our tool, we are AI that does not replace humans. We enhance humans and we enhance their decision making. We're actually AI for the first time. That makes work more human.
[00:25:55] Speaker A: That's delightful. That is absolutely delightful.
There will be, if you're a copywriter, then sure, I'm sure there will be people who will be displaced by AI.
All they do is fix up verbiages of text, because chat GPT can do that very well. And even somebody like myself who's dyslexic, I use chat GPT. I put in my text and I say, rewrite this for grammar. And it does an amazing job. It's still my tone, it's still my data, it's still all my ideas. But I don't need anybody else to help me. So I'm sure there's a bunch of people who will be affected in a negative way by AI. But when we start looking at the bigger picture, it's quite incredible to see how much new demand and requirements and jobs that we have no idea what they will be is being created by the shifting technology. And in fact, we saw that. We saw that with the printing press, we saw that with the trains, we saw that with the Internet. We have no idea what's coming. I wanted to shift gears a second and talk about any specific customer that you can who had a very transformative or positive experience with their software, and you can share that feedback that you received.
[00:27:08] Speaker B: Yeah, I'll talk about a company called bank of the west. They're the Banque Paribas division in the United States. They had acquired this bank. I think they have 10,000 employees. Their model of talent development and leadership coaching was $30,000 per leader. They had about 600 leaders, and they couldn't have all these people going through this expensive training every month. So it was a three year program. They would take 600 people over three years and give them these kind of quarterly sessions for 3 hours to sit with a high end assessment tool and sit there with all the different assessments for the team underneath that leader. And there's 600 of these leaders, and each personalized coaching around how to lead these teams.
They started using our tools and in three months completed three years of training and development of these leaders. And not only was it done faster, but because it's software that was then integrated into daily usage, they then were all using it at an 80% usage rate every single day. Whereas the other tools that they were spending over three years was just to check the box and say they went through training, but there was really no ongoing usage. And so the ROI and the benefit of a tool like this, I mean, it's just, it's just, it's extraordinary. Like, it's incalculable. It's so hard.
[00:28:29] Speaker A: That's right, Juan. This is such an incredible, I think, frontier for AI to help people be better. It's amazing. I want to ask a different question. If you had to go back and you've been through your ups and downs, if you had to go back to 20 year old Juan, what would you advise him?
[00:28:49] Speaker B: 20 year old Juan okay. I would advise two things. One, search for and discover spirituality as soon as you can. That was the most transformational thing in my life. That led to everything from my family to this company, and more importantly, that led to the capacity and consciousness to be able to hold space for both a wife, children, and the complexities of any business, whether it be large or startup, so that, you know, go pursue and learn about spirituality earlier. Second, I would advise my 20 year old self, don't get caught into the rat, caught up in the rat race of being like everyone else and doing what everyone else does more quickly. Try to figure out what your, you know, what they call Ikigai, or your purpose is. You know, where they're gonna pay you for what you do. You love what you do, and it's adding a service to society.
That intersection will most quickly get you to the vibration of living in freedom, both freedom of schedule, freedom of wealth, and not adhering to anybody else in the world and being able to chart your own journey as a soul. Those would be the two things I would tell 20 year old Juan bit.
[00:29:57] Speaker A: Juan, this has been an absolute delight. Thank you so much for joining the show today.
[00:30:01] Speaker B: Thank you.
