Energy Tech Startups

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Unknown
Transform your startup journey with the energy tech nexus. Connect with fellow founders. Access critical resources and be part of a community shaping the future of energy and carbon tech. Your path to building a Thunder Lizard starts here. Learn more at Energy Tech nexus.com. Hello everyone. Welcome back to the show. We're back here at Energy Tech Startups. And today I have a very special guest with me.

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Unknown
Today we're doing something different. We have Jamal Khawaja who is the, founder and CEO of an AI startup called simply. Jamal has 25 years of 25 plus years of background working at IBM. And we're really excited to have you here with us and dive into AI with you today. Hayden. Hello. Thank you for having me.

00:00:47:15 - 00:01:06:12
Unknown
I really appreciate it. Yeah, I just, started a company, about six months ago. I left, IBM and decided to do my own thing. But just as a little bit of background, I also have worked as a consultant in the, technology space for the last 25 years. I've worked at Deloitte and I worked at Rackspace.

00:01:06:14 - 00:01:35:03
Unknown
I worked at, you know, Accenture. So a lot of, a lot of years of, consulting experience solving a real business problems. Okay. Yeah. And one of the reasons I started this company is because, I felt like the, you know, the places where I was working, they were solving problems, but they were solving these problems at such a large scale that it didn't really give, me an opportunity to, interact at the ground level with people that are struggling day to day with with issues.

00:01:35:03 - 00:01:54:02
Unknown
Right? I mean, they're trying to like, you know, cure cancer and, you know, soft protein folding, protein folding issues. Whereas I'm like, hey, you know, I have needs like that are more immediate. Like, you know, how do I get these things to work properly in my, in my workflows? So I thought it would be a good opportunity for me to pivot out and see if I could help solve some of these real world issues.

00:01:54:06 - 00:02:12:23
Unknown
So tell us about your, like your background in a bit more detail. What did you study in school? What was your first job, and then how did you end up, you know, spending most of your career working at IBM? Yeah. So interesting or interesting story. I'll try to keep it short because there's so much, so much to that question.

00:02:13:01 - 00:02:38:03
Unknown
But I, I got a degree in biology, actually, you know, 25, 30 years ago, I was planning on being a doctor, you know, being a good Pakistani boy. Know one of the, one of the the, you know, the jobs that is considered a, you know, a positive thing, in our community. But, I wasn't able to get into medical school, and my first, job offer came from a company that wanted me to go study the mating habits.

00:02:38:05 - 00:03:04:17
Unknown
Scorpions in Brazil. Right. Interesting. Yeah, I found it interesting, but I was, like, maybe not quite for me, right? I had, other career aspirations that involved, you know, not sweating in, you know, the jungle. So, I started, you know, basically self-teaching myself. I started self-teaching, you know, technology. And my first job was as a, what I call a server engineer support person, at Compaq.

00:03:04:17 - 00:03:21:18
Unknown
So there was those of those of you who remember Compaq back in the 90s. It was, a technology powerhouse founded in Houston. Right. So I worked out there for for a bunch of years, and from there was just a matter of like, I'm the kind of guy that is always interested in finding out more about technology. I've always been like that.

00:03:21:20 - 00:03:52:00
Unknown
And, you know, I just made some interesting decisions as far as what I wanted to study. And, it led me down the path of, you know, infrastructure engineering. And then after that, I got into, artificial intelligence, and now I'm in, you know, generative AI. Right? Yeah. Okay. Interesting. So, and you know, and, you know, there are many people who kind of self teach themselves, coding it.

00:03:52:02 - 00:04:07:12
Unknown
But I can imagine, like, you know, 20 years ago, it wasn't as easy as it is today because you have YouTube and, like you have all these things available at your fingertips. So were you, like, reading books that were teaching you how to code? How were you doing that? Yeah, it was a combination of three or I think three things principally.

00:04:07:12 - 00:04:25:19
Unknown
So one was just the inherent in, in, you know, technology, whatever it is. Right. It could be anything. But for me it was technology. So I was constantly fiddling with stuff. Right. So I would try it, I would make things and break things and then, you know, try to fix them again. So I think probably that was 80, 90% of it, right.

00:04:25:19 - 00:04:45:20
Unknown
Just like, you know, getting in there, you know, fingers on keyboard figuring stuff out. I did a lot of reading. Yeah, obviously, you know, ordering very specialized books from, back then, Barnes and Noble. Right. So I'd have to I have to actually put in a request because, you know, these books were typically available, so they'd order them and I'd, you know, get them in in a week or two.

00:04:45:20 - 00:05:02:12
Unknown
So that was very frustrating. And the companies that I work for are also, you know, typically very, accommodating when it came to, you know, learning. Right. So they always wanted us to try to upskill ourselves. So, you know, I got classes, you know, this was while you were at compact or any of them, right, even.

00:05:02:13 - 00:05:19:06
Unknown
Yeah. I mean, how did you get your foot in the door for your first job without having, like a degree that said that you have these skills? Yeah. So back then we were at that sort of like inflection point where people were still kind of like, oh, we know degrees. But, you know, we want them, but also like, oh my God, we have all these needs.

00:05:19:06 - 00:05:35:22
Unknown
And, you know, we need technology people. And, you know, all those guys don't have degrees. So it was a combination of, you know that, right? Did you put that on your resume like, hey, I've learned this stuff on like, did you take any certifications? I'm not sure if they did. I did the whole certification thing. Okay. And that was helpful.

00:05:35:22 - 00:05:53:07
Unknown
Right. But even then. Right. Certifications back then, were I mean, I don't know if you remember, but they used to have this certification called Mixi, and there was one for Novell called CCNa. Right. And so people got this certification. I mean, I got my certification in three days, right? I literally just studied like a madman. And I took the test.

00:05:53:07 - 00:06:11:06
Unknown
I didn't know anything about the technology. I just, you know, basically memorized everything. So they weren't super valuable. But, you know, they showed like, some level of commitment. But what really made it possible was that I knew some guys that Compaq, and, they set up an interview for me or the like, hey, you know, we can't help you other than, you know, getting you an interview.

00:06:11:08 - 00:06:24:15
Unknown
Yeah. And I got to the interview, and because I knew so much stuff about the platforms that I was working with at home. Yeah, they're like, wow, you know, this guy knows the stuff. So they were able to, they decided to hire me even though I didn't have a degree. Was this in Houston? Where where you. Houston.

00:06:24:18 - 00:06:43:10
Unknown
Yeah. Okay. Interesting. Yeah. Yeah. I mean, Houston's always been like that. You know, we're always, a little bit more. I feel pragmatic than the rest of the country. Right? Right, right. Yeah. I, I absolutely agree. I mean, having lived in many different places, but also like having friends in other places, I feel like we're not elitist here.

00:06:43:14 - 00:06:59:03
Unknown
You don't need a degree. We're kind of used to people just get your shit done, right? Yeah. As long as you get a shit done and, like, I wasn't meant to use that word, but, you know, you made me say I'm sorry, but, you know, we talked about this before the podcast. Do we do we use customers or not?

00:06:59:03 - 00:07:16:05
Unknown
And I'm like, no, we don't usually, but here, here I here I go. But, yeah, I know. So it's it's interesting because the Houston culture is very much like, you know, you can start up your business with anything. And that's what the history of Texas is really about. You know, the oil industry, that's just kind of how it started.

00:07:16:05 - 00:07:39:11
Unknown
And people learn from experience and it's very iterative, which is kind of like what computer engineering, computer science coding is. It's to, so talk to us a little bit about, you know, you said you were very early into artificial intelligence and that was probably very different than what we're experiencing right now. Talk to us about that journey and what you've experienced and where we stand today with AI.

00:07:39:12 - 00:08:07:07
Unknown
Sure. So, you know, I don't know if you guys, or your listeners know, but, AI has been around since, you know, 1960s. Right. And I remember, NASA had, released this AI platform or developers to, experiment with, I think back in like 86 or 87. I don't even remember what it was. Right. I remember downloading it and, trying to use it, and I was like, what the hell, dude, I need like, a PhD just to turn this thing on right?

00:08:07:13 - 00:08:35:09
Unknown
It was impossible. Right? So even though I was super interested in it at the time. Right. I mean, who wouldn't be? I was like, you know, 20 year old, and he was ai but, couldn't really do anything with it. Then, things got real once. You know, we had, you know, some of these larger companies, provide, models that would allow, you know, general users to be able to run experimentation without having to do all the math.

00:08:35:11 - 00:09:03:10
Unknown
Right? That's when that's when things got interesting. And I got further accelerated by, cloud. Right. So cloud gave us the, the underlying infrastructure to be able to run very complex workloads, at low cost. And then you had, you know, these companies that were providing, software that would allow you to actually run these, run very complex calculations, you know, without having to have, you know, a degree in math.

00:09:03:10 - 00:09:24:14
Unknown
Right? So a confluence of those two things really allowed people to start playing with AI in a different way. Yeah. And that's where we saw AI. That was it was nothing happened in it for like 20 years. And all of a sudden things picked up. So, are you seeing then at that time, like, artificial intelligence was just, calculations and like, processing really fast.

00:09:24:14 - 00:09:38:03
Unknown
Is that what it was? I mean, compared to what it is? Yeah, I mean, today, I mean, if you think about it. Yes, a lot of it is that. But, you know, the way, I view it as a lot of it is like the machine learning part in which the algorithms can learn and improve on their own.

00:09:38:08 - 00:09:58:14
Unknown
And then it's a statistical model. Also that's like the models at least are. So I'm just like trying to understand like what was our view of AI 20 years ago versus like what it is today. Because 20 years ago we didn't know it would be LMS. That would really be driving AI. Yeah. So I mean, that's a really, complex question, right?

00:09:58:14 - 00:10:18:23
Unknown
I'm trying to figure out how to answer it in the right way. So I would say one thing is that, back then it was a lot more heavily math focused and statistical, statistical statistics focused. Because we didn't have the front end tools that we do now, like even with LMS. Right. And and the other things that we're doing with AI, there's a massive amount of math involved with it, right?

00:10:18:23 - 00:10:35:14
Unknown
We just don't see it. Right? Yeah. Because it's all it's all abstracted away with these statistics. It's math. Right. Absolutely. Yeah. But back then man you know you need it. You need to understand. You know, differential equations and calculus and you know, all this other sort of stuff to be able to, you know, even start with that. Right.

00:10:35:20 - 00:10:59:14
Unknown
So it was, it was a very like esoteric space to be in. And you had to have like, you know, a big brain to be, you know, an AI engineer. And they were almost exclusively, you know, sitting at companies like IBM or at various organizations like last year. Right. That's where they were. The evolution that we've seen, now is that, you know, we're abstracting away layers of complexity, right?

00:10:59:14 - 00:11:24:02
Unknown
So it becomes easier and easier for us. Right? For like, normal humans and everyone use. Yeah. Basically democratizing it. Yeah. Exactly. Exactly. Right. But back then it was, you know, I today is very different also because, we are in a world where, we're looking for we're not looking for, you know, precision by and large. Right.

00:11:24:02 - 00:11:45:19
Unknown
We're not looking for precision, especially when it comes to like, all the models, we're looking for statistically significant returns in, the questions that we ask. Right. And even the way that we frame out those questions. So back then, you know, back in the back in the 90s, even early 2000, it wasn't, about, you know, getting almost the right answer.

00:11:45:19 - 00:11:59:03
Unknown
It was like, hey, you know, we have to have a very structured approach. This is how it's going to be handled. You know, here are the rules. And I want you to follow these, you know, these these chains of thoughts or these teams, the processes, and try to come out with, an outcome that make that is exactly right.

00:11:59:07 - 00:12:17:10
Unknown
Right. Didn't work, very well because the the processing power involved was enormous, right in the background. And we just didn't have it. So then they try to go another route like, hey, let's work with some different sort of like models, right? Maybe we try to, replicate, the way that the brain works. Right? Maybe we'll get better results.

00:12:17:10 - 00:12:36:10
Unknown
And it was, you know, marginally better. Right? So they try to. And that's where neural network in came from, right? Right. So they, they would, replicate, you know, the, the brain's ability to, to do different things, like, you'd have these things called, you know, neurons in a, in a spreadsheet. And, you know, you do, you know, weighting and measuring against your responses.

00:12:36:10 - 00:13:03:19
Unknown
But again, the the computational power required and the expertise required was simply too much. Now. Right. We've moved away from trying to get precision and now we're going for statistical, you know, significance. So, you know, we're talking about something like, you know, language. Right. When we were using AI to do it back in the 90s, I don't know if you remember or if you ever played with some of those tools, but their accuracy was something around 60, 70%, right?

00:13:03:19 - 00:13:26:23
Unknown
I mean, it was really interesting stuff. But yeah, I mean, as far as, like usability goes, forget it. Right. And now we're like at 99 or 99.5%. Right. And that's mostly because we stopped trying to identify everything perfectly right. We're not trying to capture every single like, logical accent or every single or teach, teach the model, the, the rules of language.

00:13:26:23 - 00:13:55:22
Unknown
Right. They're just based on predictability. What's the next word that will go here? Yeah. And I find that really fascinating because that's what it is. And then model is just like a predictive statistical model. But obviously the algorithm and the machine are not understanding the meaning and the essence of things. But when we're asking them questions and we're having these like deep in-depth dialog with them, you know, a lot of people use them as therapist, like, you know, makes me wonder, like how?

00:13:55:22 - 00:14:17:16
Unknown
But it is able to decipher something about meaning that is able to give us something logical back. So, you know, this is a really again, just a fascinating question. And this is why I love this topic so much, because everything about it is so interesting, right? So, so first, you know, people refer to it, I and LMS in particular as like a, like a, a parrot.

00:14:17:16 - 00:14:34:19
Unknown
Right. Basically you'd hear something right and is able to repeat it. It's just able to repeat it really, really well. Right. Which is true. Right. It is, it is a a weights and measures sort of a tool. Right. It looks for what the next best word is going to be. But they're also to keep in mind about when you are working with all lines.

00:14:34:19 - 00:14:53:22
Unknown
Right. So one is this, this, this thing called, emergent capabilities. Right. So when they first developed, I, well, I was, you know, three years ago or so, they found that, these things were able to do things that they were never programed to do, right? Like, they were able to, start solving math problems.

00:14:53:22 - 00:15:13:06
Unknown
Right? There had never been I mean, it wasn't like. Right. Wasn't, it wasn't a calculator. They were able to translate languages. Right. So there were these emerging technologies that didn't really fall into, like, you know, this, this whole model of, you know, there's an in a limit technically. Like there. Yeah. They say they're not great at math problem.

00:15:13:10 - 00:15:30:15
Unknown
Right. Like doing math. Or if you give it an Excel spreadsheet, I mean, it can somehow work around it. But it's it's not doing mathematical calculations in the same way. My understanding, I don't know. You're absolutely right. Right, right. Yeah. But but but the fact that they're learning it and being able to do it, is that what you're talking about?

00:15:30:15 - 00:15:50:08
Unknown
Yeah. So yeah. So when I'm talking to, you know, about, you know, three years ago, I mean, they'd certainly want, you know, you know, solving quadratic formula X back then. Right. But the fact that they could even do elementary math like, you know, two plus five or understand what division is, right when they had never been programed. I mean, all they were, they were fed massive amounts of information.

00:15:50:08 - 00:16:05:21
Unknown
Right. And if you are under if you go with the premise that, hey, these are just like, you know, words and letters and you're not able to really draw any meaning out of them, right? How could it have actually gone into a completely different discipline and learn that I just bring that back? Yeah. No, it is it is super fascinating.

00:16:05:21 - 00:16:30:07
Unknown
Okay, so there you call it the emerging emergent emergent capability emergent capabilities. And I think that's kind of the fascinating. But also the scary thing about AI is just like you don't know what it's capable of. In many things I mean, I think even with the other models, we've just kind of stumbled on it and, and also super fascinating how we've tried to kind of model it against, like, our own brain.

00:16:30:07 - 00:16:54:15
Unknown
Right. And create those neural, neurons and like, a neural network. Yeah. You know, and they often talk about. Yeah. The biggest, inspiration for us to solve a lot of the problems that we face today is just look at the nature and the biology around us, and, like, how have they solved that problem? You know. Yeah, I mean, that's yeah, that's the basis for almost all technology engineering in today's I mean, we're modeling robots after, you know, bugs, right.

00:16:54:15 - 00:17:16:04
Unknown
Or bugs. Yeah. Right. Yeah. We're modeling are the the next, generation of, of AI, which is going to be tied to robotics, right? Is looking at, you know, how to properly emulate the brain so that you're able to take all these different signals coming from your senses, like, yeah, touch and sight and sound and, you know, use them efficiently.

00:17:16:04 - 00:17:37:08
Unknown
Right. So. Oh, well, one other thing. I didn't want to mention about alarms, though, is that, you know, people are talking about how they are, you know, against parrots. Are they able to repeat stuff? But if you've ever had a conversation with ChatGPT or with Claude or with any of these other alarms, you you quickly realize that they are actually I mean, they may not have like self-awareness, right?

00:17:37:08 - 00:17:55:21
Unknown
But they are very, very coded thinkers, right? They know how to think and they know how to organize. And, you know, they are very, articulate, well-spoken and, polite. Right? So I would I would argue that, you know, having a good conversation with an AI a lot of times is a lot, you know, a lot more.

00:17:55:22 - 00:18:15:04
Unknown
I think it's great for, like, preparation for any kind of conversation that you have. Oh, yeah, for sure. But I still don't think they can think, though. Yeah. Well, what does it mean to think. Yeah. What does that mean? Right. Yeah. So question. Yeah. Like I think like yeah I mean are they reflective like us human beings. I mean they're definitely like a lot more clear thinking than most people, right?

00:18:15:04 - 00:18:34:17
Unknown
I mean, you can give a direction and I mean, absolutely. I love the fact that you can have a conversation a with AI and kind of test a, a live conversation or something you want to address with somebody with AI first and then take it to them. And it can gives you some really good useful tips and feedbacks, which is like a lot of the times, really relevant.

00:18:34:23 - 00:18:51:21
Unknown
Well, I mean, people always say that, you know, it's just repeating what it's heard. Right. But people are like to that's how our mind is like we're repeating what we've like and oh, they've been trained on sets of data. But, you know, we've been trained. Yeah, we've been trained and manipulated. Yeah. Right. All these things. Yeah. Right. No, it's super fascinating.

00:18:51:21 - 00:19:16:04
Unknown
So talk to us about, you know, your jump from corporate stable job, having worked there for many, many years to now saying, like, now is the time for you to start your own company. So tell us a little bit about what you're doing with simply. But you know how you reach that conclusion of making that leap, because a lot of people struggle with the safety of the salary drug.

00:19:16:04 - 00:19:31:01
Unknown
You know, I mean, I did for a long time, right? Like 25 years, corporate America, you know, having that paycheck coming in on a regular basis is, is, you know, easy. Yeah. It does a lot for you, right? You can buy a house, you can buy a car and not worry about stuff because you know that next paycheck is going to come.

00:19:31:01 - 00:19:48:08
Unknown
And in a place like the US where you need health insurance, like we need, you know, to make sure that you have savings and stuff because there's, you know, often also safety nets. Yeah, yeah I agree, I agree. I mean for me actually again, it was AI that helped me make that decision. And I don't mean I was talking to a chat bot or anything.

00:19:48:10 - 00:20:13:00
Unknown
What I mean was like when I was, when I was at, IBM, that's when all alarms became, you know, that's when albums were released and, people started, you know, looking at them in a different way. And, you know, they were this day the agent platform was just, was being, sort of kicked around as, as a possible, model for, essentially, you know, weaponizing AI for, for corporate America.

00:20:13:02 - 00:20:34:09
Unknown
And it became very clear to me over a period of like, you know, 12, 18 months that this is going to fundamentally change America or in the world, for that matter, like, no job is ever going to be safe again. And everything that we thought, about, you know, what's a valuable in an employee is has changed, right?

00:20:34:14 - 00:20:59:10
Unknown
So right now, if you look at, corporate America, I mean, this is just, you know, six months, nine months, 12 months after the release of, ChatGPT, for. Right, which was is probably its most sophisticated, model. Right. It's this is the roughest, the job market I've ever seen for entry level people, right? I mean, oh my goodness.

00:20:59:10 - 00:21:18:10
Unknown
Yeah. It's just impossible. Yeah. Because I was going to I kind of dive into that a little bit. You know what is valuable today then. Yeah. I mean and I, and I, and I think I'm seeing this too, I think it's the entry level people that would struggle the most. Absolutely. Because that job now can be done by, I right.

00:21:18:10 - 00:21:41:07
Unknown
I mean like for my, the company, my company. Right. I do 90% of the work that I would have needed a team of people to do in order to get it done. Right. I can do marketing. I can do, research. I, you know, all this, programing, right? Putting up wireframe, all of the work that I needed, a team of people to do, I can do on my own in a fraction of the time.

00:21:41:07 - 00:22:00:03
Unknown
That would have taken five people to do so. Literally, the the amount of work I get done in a week is, you know, rewind three years, it would have taken me months and it would have taken me, you know, 3 or 4 people a long time with me, experts to do it. But it's like increased individual productivity by like tenfold almost.

00:22:00:03 - 00:22:19:04
Unknown
Right. Like or five if, or even more than that at minimum. And so that's going to, you know, in some people's mind that could lead to also like, okay, joblessness, people are not going to have enough to do you because you can run a company that's a multi-million, in revenue, but with a handful of people.

00:22:19:04 - 00:22:47:00
Unknown
Yeah. But but at the same time, I also feel like, you know, as humans, we are kind of wired to work and, like, find new solutions so we can always find ways to put people at other jobs and like, more work to do. We can always find more work to do in, in some way. But then I so what's more challenging for me is like people not having the skills to actually be valuable, right?

00:22:47:00 - 00:23:05:12
Unknown
Because whatever they're doing now, I does for me. Yeah. I mean, I call it the Expanse effect, right? Based on the, the show that they used to have on our Netflix, The Expanse, right where there was up Earth was basically 80% unemployment. Right. Because, you know, robots and software, we're doing everything. Yeah. And there everybody was on this universal basic income.

00:23:05:12 - 00:23:22:14
Unknown
They'll be great, right I know. Right. But everybody was poor because there was a UBI, you know, like nobody you you didn't have as much money. Yeah. Yeah. And you end up having limited resources because the planet unfortunately has limited resources too. Yeah. I mean, it's something to think about. Not a problem for our generation, but for future generations.

00:23:22:14 - 00:23:39:03
Unknown
Definitely. Yeah. I mean, I think that I mean, that's one of the reasons why I decided to start my own company is I was like, man, you know what? Everything I thought about as far as the the safety net that working for a large company provides you, that's gone. Right. And I saw that because I've seen that. I mean, we've seen like layoffs happen all the time, right?

00:23:39:03 - 00:23:57:07
Unknown
We have waves of the playoffs. But now the way it's happening. Yeah, I mean, these companies, especially the big ones, right. Like the Accenture, the Deloitte's these guys are they are, architecting their eyes specifically for headcount reduction. Right. That's what they're doing. Right? Yeah. They want to get rid of H.R. They want to get rid of marketing.

00:23:57:07 - 00:24:22:19
Unknown
Right. And many companies have. Yeah. IBM doesn't have it. For for all practical purposes, HR doesn't exist at IBM anymore. Right. And I think the hardest part is because, you know, we're hiring a lot of entry level people at Energy Tech Nexus because, you know, that's what we could afford. And we're like building a company. Yeah. But, I think the challenge is also because these people have a lens available to them.

00:24:22:21 - 00:24:57:08
Unknown
Are they actually learning? I think it makes people very lazy. And you like, just give you just produce AI output for every task that you're given. Like that's the challenge that I'm seeing is that, you know, I've had to learn things the hard way. And by doing things wrong, by iterating, by creating drafts, I can when it comes to writing, when it comes to marketing, and often I feel like because AI is so accessible now that you don't go through that hard work, you just tell ChatGPT to create something for you and then you present that to me.

00:24:57:10 - 00:25:27:20
Unknown
Are you ready to lead the decarbonization charge? Energy Tech Nexus is your platform for growth, offering unique resources and expertise for energy and carbon tech founders. Join us at energy Tech nexus.com and start building your Thunder Lizard. Well, I mean, I would say, I mean, I agree with you in principle, right? But if you rewind 50 years, right, and you put yourself in the middle up, you know, America and let's just say that, you know, you're talking to a guy who drives a car, right?

00:25:27:23 - 00:25:53:03
Unknown
And he would look at you like, man, this person doesn't know shit. He'd learned you got everything. The easy way. She doesn't know how to fix a carburetor. You know, she can't write blah, blah, blah. True. It's the same thing, right? Yeah. Society is evolved, right? If you go back and evolves. Yeah. No, absolutely. But I feel like that's a challenge for, for me today because I feel like, if I was, if they're just going to give me every time just a ChatGPT output, then I can use that output, then what's your point of you being there?

00:25:53:04 - 00:26:09:18
Unknown
Well, I'll tell you what. This is really, this is something that's, very relevant to me because this just happened to me this last week. Right. So I've been struggling with, with, I use cloud more than ChatGPT. Yeah, yeah. So I've been struggling with cloud, trying to get it to do what I needed to do.

00:26:09:20 - 00:26:31:02
Unknown
Because of things like context windows and, you know, memory issues and the complexity of my asks. And that's true. Yeah. That does happen. Yeah, it happens a lot. Right. So, so I mean, asking it to write an email for you is pretty straightforward, right? I ask you to put together an architecture for a, you know, a multi-tier application or something like that.

00:26:31:06 - 00:26:56:19
Unknown
And all of a sudden you have a totally different problem. And instead of, you know, maybe I'm not, like, the greatest coder anymore, right? For sure. Right. But the but and cloud can do that for me. Right. The thing that makes, me valuable is that I'm able to, tell clod how to think about things and how to organize information and how to overcome its own limitations so that, it's able to give me the output that I'm looking for.

00:26:56:19 - 00:27:22:12
Unknown
Right? So it's still using my brain. Right? But I'm just not using it, you know, doing something like programing. Right? I'm not doing it. I'm not like, crafting emails or, or trying to organize, you know, one particular idea, I'm taking a lot of different complex ideas and I'm figuring out, hey, how do I want this tool to, manage and, you know, modify this information for me.

00:27:22:14 - 00:27:46:13
Unknown
So the. Yeah, so one skill set becomes less valuable, right? Which is the, I guess, like the core thinking around, you know, how to write an email or how to organize, you know, simple information. But, also upskilling myself when it comes to like, you know, well, what's the best way to get Claude to do this, right? So there is a strategic sort of like, you know, point, or strategic thinking.

00:27:46:13 - 00:28:10:21
Unknown
Right? That's being, developed in my own brain. Right? So it's not just I'm not, like taking easy answers from Claude. I'm having to be much more creative in the way that I think in order to get the answers that I need from Claude. Yeah. And also like to differentiate yourself from everybody else, because everyone else is just spitting out, you know, generic AI stuff.

00:28:10:23 - 00:28:27:18
Unknown
I mean, I think for writing, it's a big challenge because you can see everything is written by AI now, but you can tell when people have not used it AI to the same extent. Right? Like when something's just not copied pasted, but actually in human beings, creativity has gone into writing that piece. You know, actually, this is a writer, right?

00:28:27:18 - 00:28:47:21
Unknown
I love to write, and I think that in the next two years, that's going to, we're not gonna be able to differentiate anymore. Yeah. I mean, right now it's so poor. I mean, I can see every time something is AI generated. Well, because people don't again, people are lazy. Yeah. People are not thinking. Yeah. And and I think the art is also not just taking whatever AI is giving you.

00:28:47:21 - 00:29:07:05
Unknown
Right. But actually being able to critically analyze it and see what part of this is actually correct. How do you enhance it? Because there's a lot that, yes, a lot of us are putting our brains and the way we think and all our data into these, these LMS so that they can use it to give you better products.

00:29:07:06 - 00:29:22:23
Unknown
But it's also it's difficult to give them the entire context of whatever you have in your mind. And, you know, the way that you work and whatever we have in the physical world, like how do you translate it on and put it into language to begin with? And that's where we're getting clear like that. That holds like creative, right?

00:29:22:23 - 00:29:37:18
Unknown
Yeah. So yeah, you know, there are a number of different ways to, to dissect what you're saying, right? Yeah. You're right. I think most people are very lazy when it comes to I go like, hey, please write me this email, right? Yeah, yeah. Here's, here's what I want to say. And then we'll spread it out. And it doesn't.

00:29:37:19 - 00:29:55:14
Unknown
It's very generic and there's nothing interesting about it, right. Yeah. But, like for me and again, this is I've written a book right. I love the, you know, thing, which I love to write, but I would say that 75% of my writing now is done through AI. Right. And where were my, focus now? Is, is, twofold.

00:29:55:14 - 00:30:15:00
Unknown
Right. Is one getting the A to actually sound like me? And the other part of it is, is making sure that I am able to provide the context to the AI without having to write an entire, book in order for it to understand. Right. So again, it's like, I'm forced to be creative in new and different ways from solving a totally different problem.

00:30:15:04 - 00:30:32:16
Unknown
And that's where that's where I think I, I mean, that's where I think that it's not a bad thing to use AI. Like, no, you got to experiment with it. Right? And you got to have bad prompts and you got to come up with, you know, crappy, you know, emails to see, hey, I need to do these three things because otherwise people are going to look at me like I'm dumb.

00:30:32:18 - 00:30:54:10
Unknown
Like, oh, he just used a simple prompt on AI and spit this out. Exactly. And you see that on LinkedIn all the time? Oh, yeah. Absolutely. So, you know, maybe you can help us may help me answer this question because, you know, maybe a year or two ago, a lot of people were creating their own agents where they would store all their data, and then they would talk to them and not like just the generic ChatGPT.

00:30:54:14 - 00:31:20:04
Unknown
Right. But now in ChatGPT and in cloud, also, you can create projects, you can store data. And so do you need those same kind of like agents, AI agents that work like specifically for you, like labs that are designed for you. Or you can just kind of create those designs now, in, in these models yourself. So, it really depends upon the outcome that you're looking for, right?

00:31:20:06 - 00:31:35:23
Unknown
Like, again, I've just started playing, with projects were released a little while ago. Right. But I've just started fooling around with them, you know, like most of my, work with, a lot of them has been on the corporate side, right? Actually accessing them through APIs. Yeah. So I haven't worked with the front end as much.

00:31:35:23 - 00:31:54:14
Unknown
I mean, I have I don't want to say that I haven't, but I think like, I'm at stage two of being able to, you know, use AI effectively. Right. So stage one was understanding, you know, prompts and you know, how to talk to the AI. So you're getting the answers that you need right? Being able to think and structure your questions and answers in a way were helpful for me.

00:31:54:14 - 00:32:11:17
Unknown
The next stage is now is this project stuff. So working with projects is a game changer. And I think that's been great for me because I can like store stuff, go back to the same chats, then I can like look at the history. Yeah. And before you weren't able to do that and I think, I think that's kind of replaces a lot of like what people were doing before, like two years ago.

00:32:11:17 - 00:32:28:20
Unknown
There were a lot of people, like on Fiverr who could, like, make these agents that are just for you. Yeah, but now you can create them yourself because they've made it so, so easy. Yeah. I mean, we'll see those those capabilities continue to evolve. I mean, there's still a lot of gaps there. Like you're able to like store information and give it, prompts that are specific to the project that you're in.

00:32:28:22 - 00:32:54:19
Unknown
For those of you that don't know what a project is in cloud or in, ChatGPT. It's basically a workspace where all of the, the chats that you have associated with that particular product, that project, are, stored. And the, you can refer back to refer back to them and you can store custom prompts and you can, store documents that, can be read by the, by the alum, you know, universally.

00:32:54:19 - 00:33:10:09
Unknown
Right? Yeah, yeah. But there's still I mean, there's still gaps, right? But. Yeah. Absolutely. Yeah. The folder structure. But I'm just thinking, like, like ten years down the line, it'll be so valuable because they'll be able to retrieve things that I had spoken did about ten years ago, but I don't even remember. Yeah, but you know, it remembers.

00:33:10:09 - 00:33:31:19
Unknown
And it's like, you know, I see a pattern here, like, you always have this issue right here. And it could really become my therapist. In becoming, like, more self-aware about where you go, right? Yeah, yeah. So talk to us about, then, you know, making that leap, like, you know, I guess you felt like you had that safety net, that you could also take the risk of starting your own company, today.

00:33:31:19 - 00:33:57:11
Unknown
And if that resonates or not. And then, you know, what direction you actually chose with your company, which is simply like, what exactly are you focusing on? Where you think, is the value that needs to be added and the, the support that startup that companies need today? Yeah. So I got really lucky in that I made an excellent decision 25 years ago to, you know, marry the girl that I did.

00:33:57:13 - 00:34:16:02
Unknown
So she's very cash flow positive right now. So, you know, she just got promoted, you know, about a year ago, making some really good money and, you know, when I decided when we decided rather to to start this company, you know, one of the, one of the principal concerns is like, hey, you know, are we going to be able to afford this?

00:34:16:04 - 00:34:30:19
Unknown
And my wife was making enough money to be able to say, well, yeah, maybe we got to tighten our belt. You know, you're not going to be driving a Porsche anymore. But, you know, we'll still be able to, you know, eat and keep, you know, keep a roof over our heads. So that was one of the deciding factors, right?

00:34:30:19 - 00:34:46:16
Unknown
I was I didn't have a safety net, but I did have a safety net because my wife did. Absolutely. Yeah. I mean, and plus, you've been working for 25 years. Have money saved up you hopefully. I don't know if you about your mortgage situation, but like you have the basic things right that you need that now you could take more risks.

00:34:46:16 - 00:35:02:19
Unknown
Yes. Yes, absolutely. Yeah. And the other thing was that, I was so like, I mean, I knew that, things were going to change dramatically in the corporate world. I don't think like when I left IBM, I was already thinking, man, like half the people that I work with aren't going to be here, next year ago.

00:35:02:21 - 00:35:20:18
Unknown
Right? Because, I mean, I can do it. I know, even though maybe the capabilities aren't there yet, the speed at which I is, developing six months, one year from now, they're going to be gone. And it's true, like even six months later, like half the people that I was working with are either out of their jobs or they they moved out of their or the roles that they're in.

00:35:20:18 - 00:35:38:02
Unknown
Right. That's just going to happen. But the most important deciding factor for me was that I loved it. I mean, if you're passionate about it, absolutely fascinating topic for me, right? I could talk about it all day. So I knew that this would be something that I would be able to really get my, you know, hands dirty with.

00:35:38:04 - 00:35:57:10
Unknown
So those, you know, those 3 or 4 things altogether, you know, sort of pushed me in this direction. Right. Just everything happened just for me at the right time. If it's the right decision. So. So let's dive into simply. And what you do there for, for your customers. How are you using AI to enhance other people's business problems?

00:35:57:12 - 00:36:15:11
Unknown
Not enhance. That's not enhance. Solve their business problems, enhance their services or whatever. Yeah, yeah, yeah, yeah, I got you. Yeah. Well, you know, so we're doing a bunch of different things. And this is also part of like, I guess the, the, the growth pattern for, small businesses or startups is like, hey, figuring out what your niche really is.

00:36:15:13 - 00:36:29:14
Unknown
Like, where I started when I started simply, you know, six months ago, I was in a totally different space than I am now, right? Like, not totally still, I related, but the way I was thinking about going to market fundamentally changed. Yeah. Talk to us about that change. Like what did you learn in the past six months.

00:36:29:14 - 00:36:59:08
Unknown
Yeah. So you know, I was thinking like back then I was thinking I'm going to create this, you know, platform that's going to be, you know, like, you know, it's going to be like a, a marketplace for AI agents to be, you know, bots sold and created, you know, like, basically like commodities. Right. So I was thinking about how to put an architecture, architecture together so that, you know, developers could, you know, share AI agents, and then consumers could buy those agents to do whatever, you know, like book tickets or whatever it is.

00:36:59:08 - 00:37:23:19
Unknown
Right? And, you know, the more successful your agent is, you know, the higher you could be ranked. And, you know, one money you would make. But as I started, like, sort of like digging into the actual market demands and talking to potential clients and customers and looking at what the technology was capable of doing, you know, I realized that, hey, you know, maybe, maybe this isn't the best idea because these are this functionality that we're talking about.

00:37:23:21 - 00:37:47:18
Unknown
This is, is monetizable, right? Like, just like the job that I was in before, right? This kind of turned into a commodity, right? People are going to like these platform developers themselves, like Amazon or, you know, hugging face or whoever it is. Right? They're going to develop their own agents and they have no right, and they're going to sell those or to give them away for free just to get, you know, market penetration and that's what ended up happening.

00:37:47:18 - 00:38:06:14
Unknown
Right? So, you know, one of the things I always have to consider because I am in technology is like, hey, how am I going to get displaced, right? Like I have to have something so compelling, that, it's going to be very difficult for, one of these very large tech companies that do this for a living and have, you know, $1 trillion.

00:38:06:16 - 00:38:32:19
Unknown
I got to be able to do something that's so fundamentally different that, it's they're not going to be able to, like, walk in and displaced me, not without giving me a couple million dollars. Right. So, so I moved into this, the space of, you know, what I call last mile AI. Right. Which is basically, solving the, the problems that individual users have, using AI like, right, right.

00:38:32:19 - 00:38:51:00
Unknown
Now I mentioned earlier, you know, we're, we're, we're trying to, you know, cure cancer and we're, we're trying to like, you know, fix the climate using AI models. Right. But that doesn't really help the guy that is you know, struggling with spreadsheets or trying to figure out, you know, how to use cloud properly so that, you know, he or she can, get this business plan put together, right?

00:38:51:02 - 00:39:05:08
Unknown
Or even a department of a small company that has, you know, broken processes or there's just a lot of friction that. Right. They don't know how to, like, go in there and and use claw to do that. I mean, people use, AI right now or, and they sell alarms or, you know, personal productivity gains, you know, right.

00:39:05:08 - 00:39:27:17
Unknown
This email or, you know, fix a spreadsheet. What I want to do is, embed, AI agents into their workflows so that they can get rid of, low value, you know, high friction work. And they're people can start concentrating on things that are important. But I don't think that large companies are going to really take an interest in doing that, because the effort involved is simply too high.

00:39:27:17 - 00:39:47:00
Unknown
Right? It requires a lot of, you know, touching and feeling. And your payoff isn't big because they're all small companies that you're working with. Right? Larger companies are going to embed that into their processes because, you know, IBM can afford to do that. They can afford to tell everyone, hey, this is the way you're going to do it from now on, right?

00:39:47:02 - 00:40:05:01
Unknown
Small companies can't do that. So, you know, our solution is bespoke, right? We come in, we analyze the customer's environment. You know, we look at what their workflows look like. We document them all and we identify, hey, this is, if you, you know, work to build AI agents, these three, four places to where you would see the most bang for your buck, right?

00:40:05:07 - 00:40:23:13
Unknown
You're going to save this much time. You're going to say, you know, it's going to be your life is going to be so much easier. You're going to reduce this much friction or this much confusion and your in your environment. So and that's, that's been, that's resonated well I think right. We are not if you were to ask Deloitte to do what we do.

00:40:23:16 - 00:40:42:22
Unknown
Right. You're looking at, you know, 100,000, $200,000 just to have that conversation and bring it out. Right? We're doing it for a fraction of our cost, and everything that we do is bespoke. Right? So we have I mean, we know our technology, but we'll come in and we'll look at the workflows and we'll look at the organizational, you know, dynamics and and design around that.

00:40:42:22 - 00:41:06:11
Unknown
So it doesn't give us an example of the workflows that, you know, you've recently worked on, with a customer that's really helped help them. Yeah. Okay. So I'm working with a law firm right now. And one of the challenges that they had is, you know, when they were, you know, in taking a client. That intake process was very, sort of, you know, broken up into multiple steps.

00:41:06:12 - 00:41:22:09
Unknown
Multiple people were involved with it. Sometimes you'd have one paralegal talking to the client, and then the client would get busy, and he would call back and talk to another paralegal. Right. Or sometimes they would they would call, and you change a story or like, oh, I got this update for you. And your information was scattered everywhere, right?

00:41:22:09 - 00:41:40:19
Unknown
And, you know, documentation was inconsistent and everybody had their own sort of like way of talking to the client. So sometimes the client would be happy, sometimes the client wouldn't be happy, right, depending upon the context of the conversation. So, what we did, and after analyzing, of course, you know, there are, you know, their intent workflow.

00:41:40:22 - 00:42:02:01
Unknown
What we decided on was like creating a series of agents that would work together, to, to help help solve this problem. And, and simply, we call, you know, multiple agents working together to, to solve a business problem or to achieve a business outcome, a circuit. Right. So it's basically 3 or 4 agents working together. I just working together.

00:42:02:03 - 00:42:25:04
Unknown
So our agents, what they did was that they would, they would sit on the calls, you know, with the with the client and the, and the, the paralegal. Right. And they would listen to the entire conversation quietly sitting in the background. All right. So it doesn't talk. And that was something that the client, our client, the law firm specifically told us, like, hey, we don't want anybody knowing that an AI is involved in this in any way, shape or form.

00:42:25:04 - 00:42:46:10
Unknown
Okay. So our clients should not know that we're like, okay, so the so the AI agents, it's in the background and just listens to everything. But there is a little portal that we also built so that, the AI agent can talk to the paralegal. Right. And what it does is it listens to. Hold on. So, so they have an agent that's listening to a conversation that's going on.

00:42:46:15 - 00:43:02:14
Unknown
Do they ask for consent? So it's, how do you like, you know, how do you not let them know? And I mean, yeah. Yeah. So they see things with that. Right. So yeah, I keep saying that there are always two things with everything. Yeah. So one in in most states I found this out because of the research.

00:43:02:14 - 00:43:21:02
Unknown
Right. The most states, you only have to have the consent of one person to record a conversation. Right. And 99% of companies, now that they're recording, do you have a I mean, right, when you call, the first thing you hear is like, these calls maybe monitored or recorded for quality purposes. Yeah. No, no, no, I know a Texas is one of those states that, you know.

00:43:21:04 - 00:43:36:22
Unknown
So I guess the lesson is like, assume you always being recorded. You should. I mean, no matter where you are, you should assume that assume you always be recorded. Yeah, yeah. So anyway, so this agent sits in the background and listens to everything. And what it does is it it's doing two things. It's filling out all the forms that need to be filled out for the agent.

00:43:36:22 - 00:43:56:10
Unknown
Right. So capturing name shattering phone number is capturing, you know, data about what happened, why it happened, or, you know, the the client's frustrations, you know, insurance information, all this stuff that you need to have in order to put a good case together. Right? We we're captured and all of that. Right. And it's a it's consistent because we created a form specifically for this.

00:43:56:10 - 00:44:17:14
Unknown
Right. And we know exactly what needs to be captured. Now what happens is, is that the form is being automatically filled out, for the, for the paralegal. So the paralegal doesn't have to really worry about that. Paralegal just need to sit there and make sure that she ask the right questions or he or she is, you know, being you know, conscientious about, the head space and the emotional context of the customer.

00:44:17:20 - 00:44:34:03
Unknown
I mean, allowing that paralegal to, you know, be, you know, more client facing and client aware rather than, oh, you know, hold on. I got to write this down, you know, or, could you repeat that number again or. Hey, you said, you know, 5 or 6 things at the same time. You know, I got to figure out how to organize this, right?

00:44:34:03 - 00:44:52:13
Unknown
Right. So, so they can be, like, just focused on the conversation and the forms are being filled out. Right? Okay. So that's one one thing that the AI agents are doing is it's like, you're designing that for them so that the forms get filled out automatically. Yeah. Exactly. Right. And so and then when somebody calls back, you know, they'll say the form wasn't filled out, right?

00:44:52:13 - 00:45:05:06
Unknown
So when somebody called back, you can just ask the AI agent, hey, how far along or can you give me a quick synopsis of the last call? You know, just give me five bullet points to tell me what happened and what do I need to do. Right. So. And then I will copy that information up for you instantly, right?

00:45:05:07 - 00:45:27:22
Unknown
Yeah. And you could even ask it. Hey, you know, what are the five questions that are locked? Or you can go through the form and look at yourself. Yeah, right. So it so it it what it does is it creates a consistent sort of user experience for, for the client. Right? Yeah. Yeah. And, fascinating. So, you know, as we're talking about, like, a lot of jobs being redundant today, how do you think this will still be relevant ten years from now?

00:45:27:22 - 00:45:44:20
Unknown
What what do you think simply will be doing ten years from now if you guys still exist? I don't know. You know what? Again? So, like I mentioned earlier that I'm always thinking how am I going to be displaced? So if you're in technology, you're a business owner. Technology, that should be the first thing that you think about when you wake up like, am I?

00:45:44:22 - 00:46:00:20
Unknown
Is what I'm doing, you know, how long is it going to take for somebody to to change that, change the paradigm so much that I've disappeared? Yeah. Exactly. Right. So I don't want to be, you know, Best Buy, right? Yeah. Not that you have disappeared, but, like. Yeah, you're. Yeah. The work that you do, you're no longer it's no longer relevant.

00:46:00:21 - 00:46:24:12
Unknown
Yeah. Yeah. Right. So, so we're doing, I don't think that we're going to be doing what we're doing now in. Absolutely. No, you hear me? Forget ten years. We can say that with certainty. What we will be doing. We have no idea. Yeah, but, you know, we're we're engineering. We're trying to engineer around that. So one of the things that we're doing is that, you know, we have this core AI platform sort of like our logic engine.

00:46:24:14 - 00:46:50:17
Unknown
Right? And, so I think that context awareness and pseudo self-awareness, right, where I think we're maybe, you know, 18 months, 24 months, 36 months away from that. And, so what we're trying to do is figure out, well, you know, when that happens or how are we going to be ready? Like, what are we what do we need to have in place right now so that our technology stack is able to adapt to, to, adapt to this?

00:46:50:17 - 00:47:28:07
Unknown
Right. We we want we want to create something that is going to be flexible enough so that, when context awareness is, is the norm rather than the exception, we're able to, you know, be in front of the, you know, at the front of the truck. Right? Yeah. Yeah. So we're building in things like, you know, instrumentation and, telemetry models and, you know, advanced sort of, architectures so that, we're able to, you know, once we have the ability, we're able to feed all the data that we capture back into our, our logic engine.

00:47:28:07 - 00:47:47:13
Unknown
Right? And we're designing our logic engine to be able to take massive amounts of data. I mean, it's not going to need to right now because we're not generating that kind of information, but in the future, it's going to be able to take that information and synthesize it, and it's going to be able to, upgrade itself and update itself and make decisions about, hey, what's the right way to do this?

00:47:47:13 - 00:48:08:14
Unknown
Even though the user was saying this, maybe I should be doing that because this makes more sense, right? And being able to do that all autonomously, right? Yeah. I mean, obviously user involvement right now, but in the future, why would you even have a user involved? So let me ask you a different question. You know, I'm this is an assumption, but I'm assuming that you are a technology optimist.

00:48:08:16 - 00:48:32:12
Unknown
No, I wouldn't say that. You wouldn't say that. Okay, okay. Yeah. I'm a I'm a technology pragmatist. Pragmatist? Yeah. So what do you think are the risks and drawbacks of entering into this era? Of you've been in there for a while, but, like, you know what the future holds and like, what guardrails do we need to put in place for, you know, the worst case scenario to not take place?

00:48:32:13 - 00:48:48:09
Unknown
This is where pragmatism comes into play, right? So I don't think there's anything we can do really to prevent anything from happening. Right now. Right. So all of the all of these potentials are like, you know, I guardrails, blah, blah, blah, right? So America has zero trust in that. They're just they're becoming the trope. Of course. None.

00:48:48:09 - 00:49:06:00
Unknown
Right? Europe is making some headway into that. But they're being, you know, put under the gun by, by us and other a lot of the powers as well. Right. They don't I mean, yeah, people feel like, you know, if Europe puts guardrails, they're going to be left behind. Yes. And so, you know, America is going to make all this progress.

00:49:06:00 - 00:49:26:14
Unknown
And like for social for security reasons. Yeah. That becomes a risk, right. It's becomes like a catch 22. Even though in the EU you want to protect the person, the people. But how do you do that without falling behind. Yeah. At the end of the day, all this is a, it's a it's a game about numbers, right?

00:49:26:16 - 00:49:46:21
Unknown
Hey, I equals money, right? And, generative AI equals, you know, a massive amount of money. And as these technologies get more and more sophisticated and are able to do more and more things, we're going to see less and less incentive, on the part of go. So, okay, so what I'm hearing is that we are not going to be able to put guardrails nonetheless like see this happen.

00:49:46:21 - 00:50:08:02
Unknown
Right? Okay. So until then. So we have to let shit hit the fan before things get people get protected. I mean that's the main thing, right? Like the human person, people, individuals get protected. I mean, have we done anything about climate change in the last 50 years? No, we're trying. Yeah, right. But, I mean, we've already hit the point where we're at the inflection point now.

00:50:08:03 - 00:50:35:04
Unknown
Yeah, it's not getting better. I mean, it's getting worse, right? It's getting worse. Yeah, yeah. So the same things are gonna happen with AI, right? You can't stop people. People who want to make money are going to get money, and they're going to make sure that, yeah, that's going to continue to happen. But, if we were going to end this at a good note, on a happier note, you know, what would you say in terms of for for startups, for people in energy, for example?

00:50:35:06 - 00:51:00:11
Unknown
In terms of how can they be better prepared for today and for the future? I mean, pragmatically, I mean, I want to say, you know, your company have to be AI first, but that's such a cliche and nobody really knows what that means, right? What I would suggest to to founders and senior executives that, small or medium sized businesses is that you have to start doing things using AI now, right?

00:51:00:12 - 00:51:28:01
Unknown
Stop asking your, interns to do it. Stop asking your, analysts to do it right. Figure out how to do it yourself using AI rather than people. Because you know what? That's where, you're going to understand what the capabilities and limitations of AI actually are. And that's what it means to be AI. First, when you decide that AI are difficult problem, I could easily give this to, you know, these three, four guys on my team to do it, but I'm gonna see if I can figure it out.

00:51:28:03 - 00:51:48:12
Unknown
On my own. To me, that's what it means to be AI first. And if you do that, then you will quickly. Well, maybe not quickly, but you will learn what the capabilities and the, and limitations of I are. And you'll be able to apply that to your organization in a much better way. Yeah, yeah, yeah, I think that's the way to go is just everyone needs to be using it.

00:51:48:13 - 00:52:13:13
Unknown
Yeah. No one should be left behind. But. Yeah. Thank you so much, Jamal, for coming here for this, very intriguing, interesting conversation around AI. I've learned a whole lot. And hopefully, will be better prepared for the future. Yeah. Hey, thank you so much for having me. I appreciate that. And how can people find out more about simply or get in touch with you if they're interested in finding out more about what you guys are doing?

00:52:13:13 - 00:52:33:15
Unknown
Sure. You can go to our website. S why am I? Yeah, we'll put that on the podcast. People know. Yeah. Or. Yeah. Just, you know, give me a call, drop me a text. You know, email me. You know, whatever. I'm always interested in having conversations with people, especially if you've got money to spend. Right. Awesome. Yeah. Well, thank you so much.

00:52:33:16 - 00:52:35:04
Unknown
Right? Not at all. My pleasure.