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Samer Hijazi · Calm Under Fire

• Col. John Boggs: Good to be with you today. I'm Colonel John Boggs, and this is Calm Under Fire. This is conversations about leadership when it actually matters. So get into it. Today's guest is Dr. Samir El-Hajazi. ⁓ an AI researcher, a systems engineer, a technology entrepreneur, and decades of experience building high reliability systems in environments where failure is not an option. He's a co-founder of CEO of AZ Care, healthcare technology company developing HIPAA compliant AI infrastructure. to friction and access and coordination. Samir holds a PhD in electrical engineering and has held senior engineering leadership roles at Cisco, Cadence and Tensilica. So across startups, large enterprises, healthcare, his work has focused on translating advanced algorithms into systems that perform reliably in the world. Now, reliably in the world. This is not just on paper, So thank you very much for being here. I greatly appreciate it. spent your career working in environments where systems ⁓ have to work. Not just sound impressive, because ⁓ definitely sound impressive. So ⁓ when the mindset really take hold for you to do this sort of thing that you're doing right now? Samer: well, thank you for the introduction. I really appreciate it. And again, thank you for your service to our country. ⁓ you, sir. It's honor to be on this ⁓ show. I like to learn from you as much as I would like to introduce myself. My, as you said, my background started during my PhD. I ⁓ mentored of my younger candidates at that time. And I learned that there is Col. John Boggs: Mm-hmm. It was an honor to serve. Thank you. Samer: two types of people who just get things well and people who just don't. And that has substantial impact on what transpires out in the industry. One of the good things about, one of the best things about our American education, graduate school, it really implants that in you. We have a very robust graduate school system in the US, across the board, and we have a peer peer-reviewed publication system that helps in disciplining the candidates into how to get into what matter and what doesn't matter. Now there are a lot of critics of how things run, but in the overall, we have probably one of the best graduate education systems in the world that have helped thousands of people gain the discipline needed for that. But that's not enough. After that, I have the privilege of joining some of the brightest people in the world. I, I'm grateful that I have right after then, after finishing my PhD, work with AI for wireless con, ⁓ ⁓ a from bill labs. That once that was spun off ⁓ into gear and these just phenomenal. the discipline, what do you think of when you think of bill labs, it's all real. These are the real deal. They educated me in the Col. John Boggs: Mm-hmm. Samer: tenure I spent with them on like it was from 2005 to 2011. I learned, I would say in this time, many times X of what I have learned during my PhD time and, uh, at Kansas state and Colorado state. And these people don't like it from around the world. Uh, I don't want to name names and forget some, but they are from Ireland, Australia, New Jersey. Col. John Boggs: Hmm. Mm-hmm. Samer: name it and there are somebody from there. ⁓ there are just the best of them and the brightest in the world. And that's kind of what kind of opened my eye of how do we do things here? How the industry can be good. You to focus on who can do well ⁓ and with them. It doesn't matter ⁓ who's what his background is. There are good people from everywhere. are bad people from everywhere. And you need to focus on what people do. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: and help them do it and build them up for it. I remember one of my mentors, name is Albert Molina, he kind of made the comment, I was late on one of my deliveries in 2008. And he recognized the reason I was late because I was trying to help another colleague. And he knew that I'm gonna be late. And he stayed up till 4 a.m. and he was in Spain waiting for me to be late. Col. John Boggs: Mm-hmm. Wow. Wow. Samer: Because he wanted to teach me a lesson. And remember at that time, he said, Samer, you cannot be a superhero. When you make a promise, you keep a promise. you focus on what you are good at. The things that you are not good at, yes, it's good to learn it. But you need to focus on what you are good at and ⁓ the best at it. again, have the leverage and the privilege of being around great mentors who have shaped me and taught me and pointed me when I was wrong. and I am grateful to all of them. Col. John Boggs: Wow, that's interesting. He said when you make a promise, you keep the promise. That's one of the things I tell a lot of my clients is you have to be impeccable with your word. If you run your mouth and you made a promise, you wrote a check and that check will get cashed. It will absolutely get cashed. that's very sound advice. Tell me, what is it? that I see leaders, as a matter of fact, I just had a client on the phone not too long ago, and they wanna deploy out technology, they just wanna get the technology out before they really understand the system that they're entering into. Do you run into that? Samer: Yeah, kind of a little bit. So I don't think about maybe let's jump into it. AI, think that's where really the problem or opportunity lies both at the same time. have been working on AI before it becomes popular, ⁓ it's cool. ⁓ my, as I said, I did my PhD and using AI for wireless com. And since then I used focused on different things from ⁓ computer vision to audio and most recently into Col. John Boggs: Mm-hmm. Mm-hmm. Samer: Healthcare related problems or interconnectivity actually in healthcare Which is kind of a going back to where I started where you're applying AI for community for correspondence and communication now the problem with AI it is impressive when it works and a lot of people see that and they say I want some and that's a problem why we have the first AI winter and then and then Col. John Boggs: Mm-hmm. Samer: because people had a hype of the promise and then they think they didn't deliver what they expected. And that's why we had an AI wonder, but in reality, AI never went away. AI has been in deployment all along. AI became popular in the past, let's say five years, my last startup, ⁓ Labs, we built an AI model to remove noise from phone calls. Col. John Boggs: Mm-hmm. Samer: And we'll get acquired by Cisco in 2000. That's again, and before that we, we, was in Tensilica, were building, uh, IP chips or soft IP chips for AI applications that are being in deploy, in deployment in many applications. didn't call AI back then. We used to call it machine learning. Some people sometimes call it statistical modeling. All of these are disciplines of, or fields of AI as we know it today. Col. John Boggs: Mm-hmm. Samer: Now, what people call AI today, when people speak about AI today, they are thinking of one branch, is GenAI, which is just an offspring of all the things before it. Now, and all of them share something in common. AI is a statistical model, meaning it gives different answer every time you acquire it. It converges into a mean behavior. Col. John Boggs: Mm-hmm. Samer: what's called expectation in nerds' world. But every time you prop it, it gives you a different answer, as it should be. That's how our design. And that's Gen.ai or the AI models we developed for noise removal. The outcome is somewhat different every time you prop it. The question is, is this output reasonable or not? That's when Col. John Boggs: Mm-hmm. Samer: And the more reasonable, the more probable that out of the output would be reasonable, the better the AI training has been. it is guaranteed sometimes these statistical models will a wrong answer. This is not a question, will it happen? It is guaranteed to happen. And by the way, we are model as well. ⁓ We are guaranteed if you ask me the same question a hundred times, you will get the same answer because it depends on the circumstances. There's a lot of Col. John Boggs: Mm-hmm. Samer: And it's not necessarily that I'm lying. You could give the same different answer for the same question because you interpreted it differently. circumstances was different for the, for what, what that question was. And the we solve this problem for humans is we redundancies and we put safeguards. Same is true for AI. ⁓ But there is ⁓ one problem for AI to more than it is when you deploy a human to do some task ⁓ is that. Col. John Boggs: All right. OK. Mm-hmm. Mm-hmm. Samer: I have a limited capacity. I'm limited in space and time. So my damage of my statistical behavior is bound to where I can be and the time I can be in. AI models are scalable. So if I have a model and that's deployed without safeguards and it has a problem because it guaranteed to have problem, the scale of the damage an AI model can perform, even if it is better than a human, is substantially worse than what a human can do. Col. John Boggs: Mm-hmm. Samer: The damage I can do is limited with the space and time I'm available through. AI model works 24-7 and can act across geography. AI model can answer 1,000 calls at the same time. You want it to answer a million calls, it will answer a million calls. And the problem with the AI model will manifest itself on the million calls simultaneously. I can answer one call at a time. So whatever problem I have is limited to that one call. it's the point I'm making. Col. John Boggs: Mm-hmm. Samer: clear or should I dig deeper into it? Col. John Boggs: it's clear. when AI is wrong, it's way wrong, is basically what you're saying here. Because it can give you million mistakes at once, but it's the same mistake. Samer: Exactly. Well, there's different mistakes, different variants of mistakes, but it's a million of them. because AI can't scale, I cannot scale. there is, and that's where, but the way we solve that is the same way we solve it for me. We need to put safeguards and we put redundancies to catch these problems as they happen in real time and rectify them. This is what I call when you deploy an AI model, you need to have, and not all problems are created equal. Col. John Boggs: Okay. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Mm-hmm. Samer: Some problems are important and you need to deal with them. And this is I call them catastrophic failures. And there are some problems that are just natural. It's within the variation of what's plausible. you let them be because you are not going to catch and solve ⁓ the problems that the AI model is going to produce. And where we come. It's an engineer. It's a laborious process. It's like when you try to put a system with humans. It's a laborious process to manage people. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: AI is the same way. We need to think of deploying AI the same way we think of managing people. Yes, it's not as smart yet and it's getting there, but even when it becomes as smart and even smarter, we still need to put it in a system that will catch the problems and resolve them and reward good behavior. Not because AI needs reward, but because the people who deployed it need to know when things go well and reinforce it. Col. John Boggs: What should leaders understand most about AI before they use it? Samer: They need to understand that AI can go wrong and will go wrong and it's guaranteed that it may go wrong sometimes and they need to keep that in mind as they are deploying it and the way to solve that is humans need to think be creatively think about where and when will it go wrong what are the catastrophic failures and put in place the systems that will catch these failures and resolve them Col. John Boggs: Mm-hmm. Samer: Or mitigate them. Resolve is a big word. I think it's more mitigation rather than resolution. And this is, it's, it's a human problem systems since the stone age. It's the same problem now and it could, it will be the same, but the only difference here we are. You hire two people, you hire two people with AI, you hire two people and the AI and the AI will scale beyond what you think. So we need to have very robust systems. very robust management and governing system around the AI at all times to be sure the problems that the AI will inherently experience, the statistical behavior of it getting wrong sometimes not derail whatever you are trying to accomplish. give point in case, I think there was a publication. Col. John Boggs: Mm-hmm. Samer: published by MIT. there is many papers came after it. But this one, the one that got the most headlines. I think it was middle of last year. It was called the Gen. AI Divide. And in that publication, they highlighted that 95 % of all AI deployments lead no value. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: ⁓ is ⁓ major problem because if continues the way it is, this will potentially usher a third AI winter where people will lose faith. ⁓ The news is the amount of progress has been done on AI is substantial and people are willing to tolerate some of these failures more than what they were willing to do in the past. But it always just a major Col. John Boggs: Mm-hmm. Mm-hmm. Mm-hmm. Samer: case of AI failure that has not been safeguarded for the people trust in the whole system to erode. And that would be unfortunate because there is a lot of good can be attained by deploying responsible and well-managed AI models. And as well, a lot of damage can be done by deploying AI haphazardly and randomly. it's really falls on us, people in charge to recognize Col. John Boggs: Mm-hmm. Mm-hmm. Samer: That deploying AI is not magic. Deploying AI ⁓ is a process that requires thinking of systems behaviors and people need to put these systems. It's not going to put itself and these people need to be compensated, managed, respected, treated well, and the opportunity to think through it, take the time to deploy it. I'm in the AI world. I'm in the startup world. This is my third startup. I understand how... Col. John Boggs: Mm-hmm. Samer: It's important to rush to deployment, but it's equally important to deploy something that works. I understand the concept that need to deploy something as soon as the MVP concept that you need to have something as soon as better than what's out there, deploy it. But also at the same, I believe in no harm. You cannot deploy something that's just without an out there and may harm some people ⁓ that cannot have that out there. If you are going to do it, please don't. Col. John Boggs: and well you lead me right to healthcare. Healthcare is one of the most regulated human-centered environments there is. and AI is being deployed in, well, you've done it. You've done it. So. How should a leader be thinking, particularly in a field like healthcare? What I see, what I hear, well, I see it, I hear it, I work with people who have done it, let people go because, well, AI will take care of it. We have AI doing this. And I stop and I cringe a little bit because I get the sense that, is it better not, is it not better to make those individuals AI smart, AI savvy to understand the technology so they can deploy the technology rather than think the technology is replacing them? Samer: So I understand you and I will first of all, me say what does azcare do? Because it's important to recognize what do we do at azcare. At azcare, we are in the connectivity business. We are trying to solve the broken front door problem of the healthcare systems in particular and the on hold crisis of America in general. That's the kind of what we do. How we do it, we are building called calling workflows. Col. John Boggs: Mm-hmm. Mm-hmm. Mm-hmm. Samer: workflows that require the phone call in it to act on behalf of the consumers, of the users, of the patients or the employees, not on behalf of the system. You find a lot of people trying to deploy agents for call centers, either to receive phone calls from call centers or to make phone calls on behalf of call centers. That is not what AZCare do. AZCare are making phone calls on behalf of you and me. To a system to do a service for us. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: The first use case we are deploying is making doctor appointments. So you want a doctor appointment, you go to AZ care, you select the doctor you wanted, and you request AZ to make the appointment for you. AZ will call and make the appointment. That's the first workflow. And we have many other workflows that will come after that, both in health care and beyond, because we are ⁓ care workflows first, but at the same time. We are targeting the general connectivity problem, the on-hold crisis we all experience where we stay on hold every day for some time. think the average head of household spend around a week on hold in America. This is a problem. But anyway, coming back to health care, think in the MIT study that ⁓ referred to earlier, give a specific point, which was kind of an eye-opener. There are some systems, there is a lot of this kind of transcription services that are out there. I'm doing great work trying to save the doctor time from transcribing things. again, when things deployed without safeguards, things can go wrong and things will go wrong. And one of the things went wrong is that was a mix up between the doctor name and the patient name. And now you have a health record that has the patient name wrong. Col. John Boggs: Correct. Yeah. Samer: OK, that's a problem. You cannot have that out there in the field. That is not OK. Can that be mitigated? Absolutely, you could have. It's examples from that paper. And there are other many other examples, but I don't want to go negative about anybody. That's a public example from the MIT paper. Col. John Boggs: That's a problem. Samer: Yeah. So the, the problem with like these is not, it should not be allowed to happen. It should be safeguarded. And the way you safeguard them the same, same old way on software engineering, you, there are fields that are extremely important. You don't allow it to be arbitrary. There is a limited set of options you allow in here. So for a doctor name, there is a limited number of doctor names and I, and you know, which meeting is this? So there is only one option be allowed in there, maybe two options, but you cannot have. Col. John Boggs: Mm-hmm. Samer: arbitrary, the AI act in an arbitrary way, the same way you wouldn't allow an entrant to come to the meeting and just write their notes and arbitrarily put them in the system. It's the same, get good systems, I'm sorry, good policies lead to good systems and good systems lead to good outcome. And what we see in lot of these cases is people just have bad policies that leading to out to bad systems and bad systems leading to bad outcome. But the good news again, this problem is solvable and And people taking notice. And I hope the year 2026 is the year of correction where we would reverse some of the bad outcomes that has been attained in 2025. And 2026 will be a better year of deploying more responsible and more reliable AI systems. Col. John Boggs: Mm-hmm. Tell me, where do you see leaders? Where do you see leaders overestimating technology, underestimating people? Samer: This is always been the case. is not having started with AI. It's always been the case. Leaders want to scale. They want to cut costs. And I understand that this is a reasonable thing that we all want to, since the advent of the technology era, recognize that economy is not a zero sum game. That technology increases the pie. And the more scale you make on your operation, the bigger the pie you can attain. Col. John Boggs: Hahaha Hmm. Mm-hmm. Samer: The problem with AI in particular, it's not only AI. We can go back to the When automobile industry came to exist, there was a horse industry. Not just horses, there was a horse industry. There was a guy who was making the shoes for the horses, the guy who was fixing the wheels for the buggies. There was a guy who was scooping the poop the streets. Actually, one of funny things in New York, the gray stones, they go up. On stairs there is the first floor is always go up on stairs and the reason for that is to be elevated above the level of the proof on the streets so we forget we forget how we have built we used to have a city built around horses and that disappeared within a decade with automotive industry came to exist did that mean we have ⁓ means what ⁓ result we have more people working today Col. John Boggs: Yeah? Samer: on the automotive industry, either driving cars, trucks, delivering milk, whatever it is, then we had people working in the horse industry. And that's where it's important to recognize AI will damage some businesses or some industries will be impacted. Damage is a big word here. Will be impacted by the advent of AI. But there will be more people working because of AI, ultimately. Col. John Boggs: Mm-hmm. Samer: than the number of people that AI will take their jobs. And that's where it is important to recognize when deploying AI is to deploy it in a way with augment people in a way that makes people benefit from their time more. They benefit from their skill more. And these are going to be the most valuable deployment. I'm going give you the example we are trying to do at azcare. We are trying to take most dreaded experiences you have. I don't think anybody is waiting to be, OK, I cannot wait to call and stay on hold. I think that is a thing that any Maybe somebody wants that, but the majority was done. So that's, think, where things where AI can do very well and will help us. In Babel Labs, we built AI that remove noise from phone calls. And the output of this, actually for personally, I am a father of four kids. ⁓ thank you, sir. And I ⁓ to have them around me when I'm working. I live concept called work-life integration. So you'll find my kids. Col. John Boggs: Yeah. ⁓ wonderful. Samer: around me all the time. That's enabled by AI, because I can be in the living room with all my kids around me fighting from age 4 to 12. So they don't keep quiet. But you will not hear any of the noise because of the technology we developed. So it brought me closer to my kids and family and enabled me to do things I could have not done before. These are the kind of things. Col. John Boggs: Mm-hmm. Samer: that we will see AI doing more of. Col. John Boggs: So what kind of discipline will that take for a leader to have to learn ⁓ the time, to learn the proper way of using the technology, ⁓ AI? What kind of discipline will that take? Samer: that ⁓ can go wrong. And that's the same discipline that it needs to be a good leader. People, ⁓ you're trying to manage people, the first thing you need to know is that people ⁓ do mistakes. If you expect your team to do perfectly all the time, you are not in the right business. You might be a good boss ⁓ people will tolerate you because you are paying their paycheck, but a good leader will recognize that people will make mistakes and it's your job to find when these mistakes happen and Col. John Boggs: Mm-hmm. Mm-hmm. Samer: help them to go over it. Same things with AI. When you deploy AI, you need to recognize things may go wrong, and you need to let your people find that out. And when things go wrong, you should recognize, accept that it did go wrong, and find a way to solve it. And it's a gradual process, incrementally. Again, it's a good policy leads to a good system. Good system leads to a good outcome. If you, as a leader, put a good system around the deployment, or the policy around the deployment and that that policy lead to a good system, you will end up with a good deployment of AI and you might deploy at the end something that you didn't think you are going to deploy and that's fine. And you know, in the startup world, you make a plan for your startup, but the only reason you make the plan is just because that's how you're going to act today. It's what you learn from the feedback from the market is what's going to deploy, dictate on you what you're going to do, what you're going to deliver. That's kind of one factor. Col. John Boggs: Uh-huh. Samer: Another factor that I personally like to do is eat your dog food. Be in touch with what you do. it is OK to build startups as a financial model. ⁓ not going to pretend successful financially is really good. But at the same time, if you care about what you do and what your team do, it makes a whole lot of difference. As I when ⁓ we built Labs, Col. John Boggs: Mm-hmm. Samer: my co-founder and mentor, Rowan. We doing this. We were living this. This is not something that somebody told us about the problem. We were in the trenches building these geographically distributed teams, dealing with the brightest people around the world, hiring them wherever they are. So we were in touch with the problem. So when ⁓ we building solution, we knew how the solution evolved. And as we are deploying it, we are in touch directly with what's coming out of it. In AZ care, this has come for me very personal. My late mother who passed away two months ago. Col. John Boggs: ⁓ sorry. Samer: Thank you. She has been struggling with health issues for the past decade, more so in the past five years. ⁓ that gave me a first seat experience or firsthand experience of drawbacks of our fragmented healthcare system and how makes life for the patient who suffer from chronic problems and their caregivers. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: can substantially alter their quality of life. And think this is a place, ⁓ great for AI to come and help. Help is needed here. We are an aging country. We are. It's not going to reverse. I would love ⁓ for it to reverse. I would love for our leaders to have policies that will encourage young people to have ⁓ kids and reverse this phenomenon. ⁓ Col. John Boggs: Yes, we are. Mm-hmm. Mm-hmm. Samer: That's beyond my control and ⁓ my sphere of influence. And in the now and in the foreseeable future, we are an aging country and we will be dealing with more and more chronic care and we have less and less people to care for our elderly. ⁓ up in an environment where you need to respect your elderly. You elderly deserve respectful and graceful exit when it comes, when time comes. And that's the job of their Col. John Boggs: Mm-hmm. Samer: the younger generation whom they spend their life raising. And I think there is a great opportunity and mandate to deploy AI responsibly, not to offload the care to AI, it's just to take some of the mundane inefficiencies that is in the system so that those who are working on the system, they can do what they are doing best. It will open the door for others who will when they see the health care as built now. We have we have an exodus of doctors and nurses from the system. We are not only don't have enough, we have the people working and they are being burned out and exiting. Col. John Boggs: Mm-hmm. That's yes, very much. Samer: So it's a ⁓ major problem. We have more demand. We don't have enough people coming in and more people coming out. need to fix that. We need to fix the system both for the patients and the doctors and the nurses and for the people who are the families of these seniors are ⁓ our Doesn't need to be seniors for those who have chronic issues, who need sustained amount care at all times. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: this problem has to be solved. AI offers a great opportunity to solve some of the problem gradually and effectively. Col. John Boggs: you think about, ⁓ ⁓ of the things you've said, love you said, eat your own dog food, ⁓ responsibly. And you said good systems. You said that actually quite a bit. Good systems, good systems. Are you, what does a good system look like? What should a good system be able to do, particularly in healthcare? Samer: They said, good policy lead to good system, and a good system lead to good outcome. This is not my phrase. I wish I would love for such a phrase to be mine, but this is a phrase I learned from some of my mentors time. ⁓ so as a leader, your job is not to do the work. As a leader, your job is to put the policies in place so that those who are doing the work will converge to the solution. Col. John Boggs: Mm-hmm. Mm-hmm. Mm-hmm. Samer: And the keyword here is converge, because the solution is not known. Maybe in some cases the solution is known, and it is known what it is. But more often than not, good things that are worth doing, the answer is not clear. you ⁓ depending on your team to converge to that right outcome. So you need to put in place a system that will help them, motivate them, reward them. Col. John Boggs: Mm-hmm. Samer: as they are converging to the right outcome. And if you observe that this is not happening, then sure, you need to check, is this the right team member? Because there are toxic people, they exist. But actually, if your team is toxic, you need to look in the mirror. is wrong if your team is toxic. But ⁓ aside, assume you're a good guy and you build a good team, but there is something consistently going wrong. Col. John Boggs: That's correct. Mm-hmm. Samer: An outcome is not coming the way it should be. Well, quite often we go, and let's fix the system. Well, actually, no, you should fix the policy. You should go back. What's the policy you have instituted that resulted in the system that exists that led to this bad outcome? And then you fix the policy first. Then you go and take a look on the system that generated from the policies you have put in place. did not solve a problem and then you can fix the system because the policy has been modified. Because you can go and fix the system as much as you want. If the policy is not in place to bring the system where it should be, the system will evolve back to where it was. Col. John Boggs: Mm-hmm. Wow, what a thought. you start with policy, you fix the policy first, before you start playing with the system, fix the policy. That's profound and I hope our listeners heard that. It starts with the policy. Now we're talking technology, we're talking about leadership in technology, but ⁓ not gonna find anybody better than Samir to talk about it. And first, what did he tell you? He tell you fix your policy first. Holy smokes. Okay. So when you think about your next chapter, next thing for you, what principles matter most to you now? What matters most to you for your future? Where you're going? Samer: Okay, I need to go back in order to answer this question. Col. John Boggs: Okay. Samer: My dad is a doctor my mom is a nurse ⁓ I have the privilege of having from a highly educated family. ⁓ was set on track that you are going to go and get your PhD and that's what you're to do. Luckily I had the intellect for it, but I didn't, ⁓ wasn't like this is, there was alternative choices. This is what our family do. That's how it was. After getting my PhD, ⁓ Col. John Boggs: ⁓ Mm-hmm. Mm-hmm. Samer: And I, again, I has the privilege of, or had the privilege of working with the best and the brightest in the world. I could have not gotten a better job than to join that, the Bell Lab team that I had the privilege of joining and learning from them a lot. But despite of that, was not happy. I was, I wouldn't want to call as depressed, but I wasn't satisfied. I had a good job, good pay, bought a house very rapidly. in a good neighborhood, in a good area. I had, have, have things were going well, but something was missing. It's well, what is, what's the point of all of this? And I met a very lady from Lehigh University. She was at Lehigh University where they were, they were starting a new MBA program, focusing on entrepreneurship. And we had a discussion. Col. John Boggs: Mm-hmm. Mm-hmm. Samer: And she said, why don't you come? We have an open house. I went to the open house of the MBA program. You will not find that on my LinkedIn or resume because I am a nerd at heart. Although I have some other credentials, I focus on my, what I'm good at. Yes. I learned other things along the way. got some other skills, but going back to what Albert told me, focus on what you are good at and become the best at that. Yes. You might be other things that you need to learn and you gain them. Col. John Boggs: Huh? Samer: But they are not the focus of your life. You focus and you keep focusing on what you are good at. The other things, even if you didn't solve them, it might be OK if you just became better and better at what you are good at. So coming back to the point ⁓ I went and there in the house, there was a professor, Professor David Santura, if I remember. And in that he explained the difference between corporation, small business. Col. John Boggs: Mm-hmm. Samer: and start up. And he explained the point that the main thing that distinguishes small business and corporation from a startup, that the objective for a startup is to build something that can scale on its own. The objective of a corporation is sustainable growth. The objective of a small business is to sustain the livelihood of the business owner. The quality of life of the business owner. So I had that distinction that I learned. And from that, I learned that the biggest amount of good, the fastest way to do good and conquer new territories is through the startup world. And that's it clicked for me. If I want to make the world a place, I have the skills, I have the opportunity. I want to be part of that. So, and I believe in a phrase that I think one of my heroes, Muhammad Ali, growing up, I remember he was in a presentation where he refused to go and fight in Vietnam. And people ask him, well, why are you doing this? You could have done like all the other rich people, get yourself scanned. You must have so many bruises on your bones, you'll be dismissed. You didn't have to say, I'm not going. And he said that's something profound. have a platform. And it's not about me. It's about the people who hear me. And because I have a platform, I am obligated to say what I believe to be the truth. I be wrong, but I believe that going to Vietnam is the wrong thing for me and for America and for my people and for the people I'm going to go and kill. And I'm going to say it that way. I'm not going to just avoid it by showing that I have problems and just not go. It's not only about not going. It's about saying the truth, utilizing my platform. And I believe all of us have platform one way or the other. And you have a platform, I believe you are obligated to use it to make the world a better place. And you need find what's your platform and what are you good at and use that for what you can do. So connecting from back then fast forward to today and what I want to do in the future. the American system of entrepreneurship is of the best systems that we at America have put together. It enabled some of that, not some of actually all technical revolution that we know of came out of this system that we have put in here, put in place here. We should be proud of that. We have a good heritage in this country. Yes, we have done something. We have done wrong things as well. But we did also some things good, good things. And we should be proud of what we did right. And we should have for policies that will enable us to fix things when they go wrong. Col. John Boggs: Well. That is, profound. That's very good. So, wow. I'm tell me. ⁓ for leaders listening who care deeply about building AI systems learning more about leadership and AI. And thank you very much. mean, holy smokes, you blew my head away here. The American system is the best for entrepreneurship. American system for entrepreneurship. Wow, that's wonderful. How people reach you if... If in fact, you know, if they want to learn more about you how to reach you to get involved with AZ Care to have it work for them, how do people reach you? Samer: I am my LinkedIn. ⁓ that's the best way to reach out to me. Just search my name. I'll put easy care or without it. think you will, I have ⁓ the I, that my LinkedIn is very one of the, ⁓ if search Samar Hijazi, you will find me at the top one. I have the privilege of people around me who like what I do and support me and that have helped me to be in where I am. And I am. Col. John Boggs: Okay. Samer: humbled and respond to every message I get on LinkedIn. Col. John Boggs: Wonderful. Well, ladies and gentlemen, that's it for today. And we had a phenomenal guest here who talked about leadership and AI. I mean, holy smokes. There were some fantastic nuggets that we should all be taking away. love eat the dog food. ⁓ love the... thing that got my attention was 95%. And I've heard this before, 95 % of AI projects have no value. So that's for those of you in leadership positions, before you start, I'm gonna get AI and we're gonna do this with it, make sure your policies are in place that makes sense. I believe that's what Tamir is saying, make sure those policies make sense. Otherwise, you're going to go down the wrong road and you're going to go real fast and it'll be real wrong and it'll be real ugly. and gentlemen, that's it for today. I will talk to you later. See you later. Out here. Samer: Thank you. Really appreciate it.