379 Unlocking AI: How Leaders Can Tap Into Generative AI’s Power with Rajeev Kapur

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Mahan Tavakoli: . [00:00:00] Rajeev Kapur. Welcome to partnering leadership. I am thrilled to have you in this conversation with me. 

Rajeev Kapur: My pleasure, man. Thank you for having me. I 

Mahan Tavakoli: look forward to getting some of your thoughts and insights on generative AI.

Specifically, you've written a book, done a second edition of it, AI Made Simple. But before we get to generative AI and its impact on organizations, Rajiv, would love to know a little bit more about you. Whereabouts did you grow up and how has your upbringing impacted the kind of person you've become?

Rajeev Kapur: Thank you. First of all, it's a pleasure to be here , . I was born and raised here in Southern California.

. I've been a technology geek and nerd pretty much my whole life, my whole career. My first job was a hundred percent commissioned sales working for an old computer company called gateway. You might remember with the cow spotted boxes. So that was out in South Dakota.

And so I moved out there from South California. That was crazy. And talk about cold and dark and snowy and horrible weather. That was probably the ultimate you can get. But,. It was a good experience I was there for two years and I built up my resume and [00:01:00] I tell young kids all the time, you have to go to the job and I went to the job and that quite frankly, if I hadn't done that, I don't know if I'd be standing here today because from there I got recruited to Dell and not only did I get recruited to Dell, I got recruited to be one of Michael Dell's first executive assistants back in like 1994.

So you can see how sometimes you have to take a risk and bet on yourself. And that's what I did by going to gateway first, then eventually they came my way to Dell and I was at Dell for about 12 years. First seven odd years, I was in Austin, Texas, and then ended up going to Asia for Dell for a big chunk of time, helping to get Dell China going.

 Dell South Asian markets were in that for a while, helped to launch Dell India from a domestic consumption perspective. So just a lot of Dell experience. And that's where I really grew up and learned my skills. And then from there I was recruited to be president of an e commerce company back here in Southern California ran that business for three, four years.

And then I got recruited to be the CEO of an audio technology company that, We were actually building technology using AI back then. It was all machine learning AI, not generative AI, obviously. But [00:02:00] that's the thing that I don't think people understand is there's really two kinds of AI. There's a machine learning AI and the generative AI side.

And so we were building audio algorithms , on the machine learning AI side. And we ended up selling that business. Eventually the technology we were working on, eventually became a lot of what Apple spatial audio is today. So that whole idea of 3d surround sound in your head through AirPods and these kinds of things is what we essentially built.

And I did that and then I sold that business and then for the last 10 plus years I've been CEO of a B2B marketing and media technology company and we cover on the B2B side, we're very much in media and events play and we cover from a media perspective B2B big data, B2B AI, B2B cloud, B2B cybersecurity, and then, how we make money is primarily through events, education, training. Do a lot of B2B lead gen, newsletters, webinars, those kinds of things. And when chat GPT came out, I remember laying in my bed on November 30th, two years ago, and I remember falling out of my bed because after I sold that AI company. I took some courses at MIT to get [00:03:00] certified in AI from MIT. And so I was somewhat in the AI space and understand it, used it, understand how it works.

But the generative AI stuff was all brand new to me. Like I didn't know. And when it came out, it floored me. It caught me by surprise. Caught a lot of OpenAI released at GPT and how it went viral. So immediately, literally that next day, I started writing the first edition of the book. Because in my mind, it was.

Mahan Tavakoli: Every single person on the planet is going to have to get trained in AI, period. There's no choice. You just have to learn it. 

, now you've been. In technology for almost your entire career, different aspects of it, different organizations. I'd also been fascinated with aspects of AI pre GPT. When I saw GPT, I was also floored saying our world and knowledge work is going to change drastically.

Mahan Tavakoli: A little more than two years have gone by and it's incredible how many Organizations I see [00:04:00] that are dabbling, but haven't really thought through the potential implications of generative AI. And you specifically mentioned that I agree. It just one aspect of AI technologies, which can be transformative.

Rajeev Kapur: Why do you think that is? 

There's a couple things. Number one, there's no silver bullet with the tools yet. Yet, number one. Number two is, it's still early, right? So if you think of this as a baby, as the tools as a baby, the baby is still in the crib. It's not even crawling yet, so there's that piece of it.

Rajeev Kapur: And it's not super instinctive in terms of where you can potentially see efficiency gains. There's a lot of fear to be used when it comes to AI in an organization still Hey, if we start using AI, going to lose my team? And whatever the case might be. So there's some of that fear going on, but I think, ultimately this is just it's not a matter of if it's more a matter of when, and it's really more of a matter of what are those tools that are, they're going to come out in the next 24 months.

Because remember, [00:05:00] usually the investment lags the opportunity. So right now there's about there's anywhere over the next couple years, 200 to 300 billion, billion dollars being invested in the AI space. So if you think about all the new companies that are gonna be coming out over the next 12 to 24 months that were in incubation over the last 24 months, all these Y Combinator companies, all these new companies that are coming out, these companies are coming out, are gonna be the new Uber's, new Airbnbs.

If you think about your iPhone, like it was, we had iPhone one. It wasn't really until iPhone six or seven where you start started seeing really good. daily, comprehensive, life changing use cases because it was powerful enough to handle all the data processing that need to be done. The same thing is going to be here, right?

I'd argue that there's also already life changing and business changing opportunities. Matter of how do you use it? How do you deploy it? Now do you start taking advantage of it? But it's not just on the gen A. I said it's also on the machine learning side. And if anything, I think what needs to happen is I think CEOs Number one, you need to develop a strategy and implement that top down strategy and really mandate it across the organization.

The only thing I can tell you for sure, [00:06:00] the only thing I can guarantee is that in the next five years, people and companies that don't use AI are going to get replaced by people and companies that do use AI, right? Now, again, I said five years. If you think about how processing develops and if you think about it our laptops Our phones by 2030 are going to be 10 trillion times smarter than they are today.

Right now, JGPT has a high school level of education. By end of next year, it's going to have a PhD level of education. That's 2025, 2026, right? So it's just going to get smarter. So these use cases and they're going to start coming out. You're going to start seeing more and more people use it.

It's just a matter of time. And it's usually, there's usually a lag of a period by the time the investment makes, by the time the companies come out, it's going to be like the Internet, right? If you think about the Internet, there was like 10 search companies before Google really took off, right? You remember things like GeoCities or whatever the case might be, right?

There was so you'll see some short term applications, tools, and they'll give away to bigger opportunities and it'll transform different business perspectives, CRMs, marketing, HR, I think [00:07:00] law firms are in for a big rude awakening in the future, right? So I, things of that nature.

So I think some of those things will be there. You're starting to see some use cases, for example. In healthcare, you're starting to see, where AI is starting to be deployed for triage. You're starting to see where AI is being used to scan breast cancer images or cancer images, because doctors and radiologists get tired, and it can catch things better than them.

You're going to see, and this is not just about AI, you're going to see a blend, a marriage of AI and robotics and IoT. Over time. And so I think that's where it's headed, but it's going to take some time and we're probably still 12 to 24 months away from it really having big, huge impact, but you got to start now.

You have to start now. You can't just let you can't do nothing. And, even if you're using it to create LinkedIn jobs or HR, whatever, You know, things where I have to analyze, you can take it right now, use the advanced data analysis feature, like one of the things I do is I'll use it to analyze asset purchase agreements.

So I'll take an asset purchase agreement from a company we might want to buy or a division we might want to sell, [00:08:00] put them in there and have it compare the red line versions or whatever, what's changed, what's different, little things like that, people, your finance teams can start putting in their healthcare trend analysis, through Excel and those kinds of things and whatever it might be.

So you can start using it now, but you have to just be willing to be open minded about the tools and what's available. 

Mahan Tavakoli: And that's what I would love to get some of your thoughts on there, Rajiv. First of all, as you mentioned, I am right outside of DC. Most of my clients are DC based and DC region.

Inside the Beltway, we have more than 70, 000. Attorneys and a lot of law firms. And I think many of them have no clue what is about to hit the sector, partly also as a result of the potential capabilities that it provides everyone, including what you just mentioned, as well as the way they typically bill clients, the hourly rates, all of those things are going to be challenged.

Now, as. [00:09:00] Executives or CEOs are listening to this, Rajiv, and you talk to them, they say, okay, I buy it two to three years from now, the world is going to be transformed. You're telling me to be strategic. So what should they do? In trying to be strategic because this technology is moving at a super fast pace.

They have their ongoing operations, all the details, all the deals, all the strategies they have to worry about. How would you guide them to think about AI, generative AI and the robotics, internet of things, all of those connecting and Pursuing a robust strategy with respect to that as well. 

Rajeev Kapur:  That's a good question.

And I think the number one thing they have to do is get their handle around their first party data. The number one thing they have to do every day, then I think every CEO is listening to this podcast. You don't have to listen to me, but [00:10:00] what I'm telling you is if you want to be successful in the future, you have to get your hand all around your own data.

And then let me give you an example. So I'm a big baseball fan, right? And I have my little L. A. Dodger baseball fan, whatever baseball here. And so the reason why I bring this up is there's a company called Traject, T R A J E K T. Tragic AI. And these guys have built a pitching machine. I use baseball as an example because most, everybody knows baseball here in the U.

S. primarily. And, and everybody knows a batting cage. A lot of people have been in a batting cage. People have kids who played baseball. They played baseball as kids. So they understand that. They understand that pitching machine. So they've built an AI and robotics pitching machine. This pitching machine has taken the data in order to be successful.

First, it needed the data. It took the data of every single pitcher that's ever pitched, every pitch they've ever thrown, every scenario you could possibly think of. They have the data [00:11:00] and they program this pitching machine. So there's a Dodger player named Mookie Betts, right? He was an MVP. He did really well and he was struggling.

So he basically went into a machine like this. And he said, okay, I'm going to, I'm going to pitch against this pitcher, Garrett Cole for the Yankees or whatever it might be. Here's the scenario. Here's the count. And I've been really struggling hitting his curve ball. I just want you to throw me curve balls by this pitcher, Garrett Cole, and the machine will simulate exactly pitching and the ball actually comes out of the screen, it's not virtual, so an actual ball comes out of the screen and it's thrown exactly the way Garrett Cole, the pitcher from the Yankees would throw the ball.

Exactly. But in order for that, and that's all AI, because it took all the data to create that algorithm to be able to do that's the low hanging fruit. The low hanging fruit is going to be in the machine learning data sets if you own your own data. So that's the first and foremost step. Own your data.

That's key. Data is going to be the new oil. Data is the new gold. What's missing are refineries sitting on top of the data to out to give outputs. So think of your CRM. Your CRM [00:12:00] is data. Use AI. Salesforce has a system called Einstein. Use that to help do predictive analytics on your CRM, right? Those are some examples of things that you can start doing right now.

Think of your end user. Think of your end user, who you might be, whether you're B2B, B2C, B2G, whatever you might be. You can definitely drive Solutions by looking at machine learning. If you have your data, let your data tell you what to do. Use it for doing predictions. Okay, so that's step number one on the generative AI side.

Like I said, there's no silver bullet yet, but there are tools. Canva. With chat. PT is a cheat code for marketing, for example. Copilot is an is, is getting better and better all the time is great. It's, you have AI chat PT solutions integrated right into Excel. Word PowerPoint. There's a PowerPoint program called Gamma where you go and it'll help you create your slides for you.

Your team probably spends and stupid amount of time creating stupid PowerPoint slides for meetings, right? [00:13:00] Use these tools and it can cut that by 80%. That's time to get back. So then, so it's own your data, look at the tools, but do the one, but if you do that, then what's, it's going to happen, you're going to do one thing money can't do, which is you're going to start augmenting intelligence and by time, so a task that maybe would have taken six hours now might take 16 minutes.

The 16 minute task might take 60 seconds. I'll give you, let me give you two examples. So I recently worked with. A company in Europe, they're one of the oldest beauty brands in Europe, and they, in fact, one of the oldest beauty brands in the world, and they were, they wanted to create a new product.

I can't tell you what the product is because of NDAs, but they were trying for three years, Mahan, three years to come up with the right formula mix for this particular product. And they just couldn't get it to work. And they kept trying and they knew that if they could figure this out, it would add a billion dollars to their bottom line.

That's how excited they were. So they kept trying. So I was there. I was in their board meeting. The board members were there. [00:14:00] CEO was there. She was there. Our marketer was there. And then our chief scientist was there and there's seven, eight of us in the room. And the board meeting was it is in the South of France.

And so we had this meeting and this question came up and we pulled up chat GPT and this was in October of last year. So this was not in 2024, Okay. And now Chachapiti has gotten so much smarter. So we put that Chachapiti, we wrote a prompt and learning how to write a prompt, being that storyteller in the prompt is going to be a skill that CEOs and people have to develop.

So data storytelling, prompt engineering in that little window is going to be so key. You're going to be only as good as your ability to write your story in that prompt. Just bottom line. So the more information you can give him the prompt, the more you can use that prompt and learn how to learn what a prompt is, that's going to be the outcome is going to be great.

So we wrote a prompt that took about 16 to 15, 16 minutes to write the prompt. And in 60 seconds, it spit out, basically the prompt was, Hey, here's the problem. We're trying to do this product. We haven't been able to work, blah, blah, [00:15:00] blah, that's what we've tried, not tried etc. It's about a 500 word prompt, hit enter, 60 seconds to spit it out, it's oh, you should try this, and this.

The chief scientist at the end of the table literally fell out of his chair, goes, oh my god, we never thought of it, oh my god, we never thought of that. So I talked to them about, Four or five months ago, and they're in the process of testing that mix and the CEO's telling me that they absolutely think it could work, and it took 15 minutes to figure it out.

So we, so the first thing we, the first, my first point is that if you're a CEO, it's an amazing brainstorming partner. It's an amazing brainstorming tool, and you have to get over the hang of that. You're talking to an AI rope, an AI algorithm on the other end, pretend you're talking to a human. So one of the things I do is I actually give MyChat Achi, GPTA name.

Because it's now multimodal. It's on my phone. It talks to me and I can have a full two way conversation with it. I do that all the time. I'll give you another example. I had a, there's a friend of mine, he's got a business in Mississippi and they do sheets. Did they make custom sheets for like hospitals and nursing homes [00:16:00] and whatever.

So one of the things they got was a request saying, Hey, we want you to make sheets that don't stick to bandages and they didn't have anything and they didn't know what to do. So we asked chat GPT what to do, and it spit out a formula. And it gave a great result. And so now they're in the process of doing that.

And it's going to help transform the company because a lot of the RFPs were coming in with this and they're one of the only firms that's going to have the solution for a period of time. So again simple, easy things. There's another, I'll give you my third one here. I have a really, I have a friend of mine who's down in South Carolina.

And he's going through some, he was having some mental health issues. It's cause the CEO, a pretty big size company. And I said, use chat, GPT as your therapist. I don't want to see a therapist. So then use chat, GPT, but what do you mean? So I made him download the app. He is he opened it up.

It's now multimodal. And he now a couple of times a week will drive to work and just sit there and have a full two way conversation with ChachiBT because he gave ChachiBT a [00:17:00] persona of a therapist. I do it sometimes. I will give my ChachiBT, right now, for example, I'm in the process of thinking about launching a new marketplace.

And I actually say, okay, the persona I gave it was I want you to be, I want you to act like Steve Jobs, a combination of Steve Jobs meets Elon Musk meets the founder of Airbnb and help me create A per help me with my marketplace idea, whatever blah blah blah and ask me a question So I know a full two way conversation So it's like giving it that persona if I want to solve the national debt you're in dc I can go to chachi beat and say hey, I want you to give me a bite.

I want you to pretend That you are both Donald Trump and Barack Obama, and I want you to give me a bipartisan solution on how to solve the national debt and then you know, you can go back and forth and what about this? What about that? And you by point, you can have a full now conversation with it.

And the reason why this works is because it's been trained on every single parameter of the Internet. Now, yeah. So that's the good news. So it's all about just experimenting, using, playing, trying, and once you get comfortable with it, you're fine. And if you're too worried about trying it from a business perspective, use it from a personal perspective, use [00:18:00] it to say, Hey, I want to learn how to, maybe you want to run a 5k.

Give me the plans to run a 5k or tell them to tell the judge DC. It's cold outside, whatever it rains, here's the, whatever. This is where I am, I want to order 5k, what type of shoe I should have, this is my weight, what, blah, blah, blah, you tell them what. Or family, you want to use it to plan a vacation, help it to plan a vacation.

You want to go to safari in Africa, ask Chachapit to give you a five day itinerary, and it's for safari in Africa. So if you don't use it for business, you start using it for personal and family. So just like you would Google or anything else. So those are just some examples of ways to start transforming lives.

Mahan Tavakoli: Absolutely love those examples, Rajiv. Specifically, because what you did in those examples is taking a big business problem or challenge, whether with respect to new products or new opportunities, and using GPT in those instances to come up with additional thoughts, creative ideas, ways of approaching it.[00:19:00] 

It's not a hypothetical interaction with GPT, which ends up being not as useful. It's actually looking at a business issue or problem and having in this instance, chat GPT or some prefer Claude as a cognitive sparring partner that can support you in that process. 

Rajeev Kapur: Yeah, I think it's great. Look, I think You mentioned Claude.

To me, I think long term it's going to be, if you, if this was a horse race, I can tell you Chachapiti is going to be the lead horse for the first place. I think Gemini is going to be second and Claude is going to be a distant third in terms of the general GPTs, right? And then after that, you're going to have, Metas, GPT, you're going to have Brock.

Which are primarily trained on Twitter and on all the Facebook, Instagram, WhatsApp messages, right? So you'll have that, and then I think long term also, but then beyond that, I think every company is going to have their own LLM. Just like every company has search on their websites. I think long term every company, every organization is going to [00:20:00] have their own LLMs.

I don't think it has to go that way. I think you'll see private LLMs popping up. I think you're in the beltway, right? So pick, whatever, any company in the beltway that are experts, in, in in the government space or whatever it might be, they'll just have their own LLM to be able to talk about the defense industry or whatever they want.

So I just think that's the way it's going to head, but yeah so short term, Just start using it for your, for yourself and, to have that banter back and forth. And just get over the fact that you're talking to an AI, just pretend you're talking to, another executive or a friend who happens to be outsourced.

That's it. And you're fine. 

Mahan Tavakoli: So in doing that you also talk about the fact that generative AI is In essence, democratizing access to this advanced technology. How do you think that is going to impact organizations, whether the opportunity for startups, the shift in industry dynamics, [00:21:00] how does this democratization of access to this very powerful technology in your view over the coming years, impact businesses and organizations?

Rajeev Kapur: Yeah, that's a great question. I just, I don't think you'll need as many  people , let's just use , legal firms as an example. So if you're a law firm and you've got a hundred pair of legals, guess what? You might need only 30 or 40, long term.

If you have 12 people in your marketing team, you might need only five or six. If you're a startup, if you want to launch a new company, a new division, typically you might need five or seven or eight or nine or ten people in that division or a startup to get going and whatever. I'll tell you right now, there are people, kids somewhere, there's some 20 something year old sitting in his garage or in his mom and dad's garage somewhere writing an AI program , that will be a billion dollar business with two employees.

It's coming. It'll happen. That's where we are. [00:22:00] So it's, to me, I think short, I think you're going to see huge efficiency gains. I think you're going to see revenue gains by doing a lot of predictive analytics, being able to predict the future. If you use AI the right way, you'll be able to hopefully, You know, you could possibly predict the future using past data.

That's essentially the baseball model that we talked about a little bit, and and then it's about understanding and, and realizing that at the end of the day, your efficiency gains are going to come from maybe not meeting as many people and just automating more and more tasks.

And, it's like what it was when the first PCs hit the desk. Right back in the late eighties, early nineties, right? The first computers were X eighties, two 86 computers with a 40 megabyte external hard drive. With no mouse with a 13 inch monochrome monitor and a big thick case, and it was an external dial up modem maybe, or whatever. And that thing was 7, 000, and people were scared then. Oh my God, I'm gonna lose my job with the computer. That led [00:23:00] to the creation of whole new industries, then you had the creation of, the mobile revolution, the iPod revolution that created a whole new industries, Ubers, Airbnbs, probably because of the iPod. Mobile gaming, for example, got transformed, right? So whole new industries were birthed from it. Whole industries got transformed with it. And then you have the social media revolution. And, now we're just in the, now we're just entering a new age of, a new industrial age, which is going to be AI, robotics, IOT, and all those things mixed together.

We're going to enter a world. We're in the next five years, five to 10 years, we could cure cancer. We're going to enter a world where people who are blind can see people are deaf, can talk, people who can't walk, can walk. That's the world we're entering. And that that's the type of world we're going to be into because the processing power is going to be there to handle it.

The processing power wasn't there before. In the next, by 2030, the pro our laptops and phones, like I had mentioned earlier, are going to be faster than can process more data faster than the human brain. Google this [00:24:00] week sorry, it was maybe it was last week, it was last week that they launched their quantum computer.

Maybe you saw that, right? That, that was crazy. So if anybody's listening go Google quantum computer. And they gave it a problem that under normal processing would take seven septillion years to solve. And this Google quantum computer solved it in five minutes. So imagine taking that and solving the problem of cancer or solving the problem of whatever, right? And having that help you figure that out. So that's where we're headed. That's where we're going. And we actually may be, we actually may end up in a world where the work week is only three and a half days.

We actually may end up in a world where there's gonna be so much abundance, that everything's gonna be available. , 

Mahan Tavakoli: it is incredibly exciting for those who are aware of the potential and the opportunities and where things are headed. A couple of challenges that I see in organizations, Rajiv, and would love to get your thoughts and [00:25:00] perspectives on is with some CEOs or executives who are using it themselves.

They're having a tough time bringing their teams. along. In part, people are scared. In part, there is hidden use of these tools. There are concerns about Ethics plagiarism, you name it, I've heard all kinds of things within organizations for why people aren't embracing the technology.

What are you seeing and how would you suggest bringing people along with this transformation rather than having them resisting it? 

Rajeev Kapur: I think, , it goes back to that earlier point I was making before, which is this has to be a top down strategy driven into the organization where the CEO, first the CEO has to be bought in.

If the CEO is not bought [00:26:00] in, no one's going to be bought in. Your choices are, you don't have to do anything, but you're going to be out of business in five years, or you start doing it now and you start affecting your competition. Those are your two choices. Pick one. Which one do you want? I would hope that people listening to this will say, you know what, we're going to take the path that says we're going to start now so we can go affect our competition versus our competition affecting us.

So that's number one.

Number two is it's about the marketing of what the tools can do for you. If people hear AI, they get stuck on the word artificial and they hear they are fake, they hear, oh, it's not real, whatever the case may be. It's not about artificial intelligence, it's about augmenting intelligence. It's about a tool that helps.

Think about it. Excel is a tool. PowerPoint is a tool. Word is a tool. These are all tools that we've had that have helped make us more efficient in our day to day lives. Especially at work, these are just going to be tools to help augment what you're doing. If you can take a task, like marketers are hired not to make PowerPoint presentations.[00:27:00] 

Marketers are hired to drive revenue into the organization for salespeople or whatever the case might be increased brand awareness whatever they, that's what they're hired to do. HR people are not inherently hired. To answer questions about what's in the HR workbook. They're hired to develop organizations.

They're hired to make sure that they have development plans and growth and opportunities and help build a great culture for organizations. By using AI, by using tools like HHPT or the Canvas or whatever else, might be out there that you want to use, it's designed to make all those people more efficient in their day to day work so they can do those things that they're really paid to do.

My point is that you may not need as many of them. So if you have, for example, have an open headcount in marketing, my challenge to you is don't fill that open headcount. Instead, make your team more efficient and give that work to the team there. And oh, by the way, then give them a small little increase to cover the extra costs.

To cover that quote unquote workload of taking that more responsibility because now that workload is going to AI, you're not hiring another person. So if you're going to hire another 100, 000 a year person, [00:28:00] if you went to your staff and said, okay, I'm not going to hire that 100, 000 a year person. Instead, I'm going to let you use AI tools to get more efficient in your day to day job.

Oh, by the way, I'm going to give you, I'm going to, across the five of you, I'm going to give each of you A 6, 000 raise, which is 30, 000 of that 100, 000, everybody wins, right? And so it's, but it's also about making sure that people understand the case study, sending them to conferences, making them aware, my company, I pay for the tool.

I pay for chat GPT for my company. I think to me, it's one of the best investments I make is it forces them to learn. So that's where we are. 

Mahan Tavakoli: . So for the audience to follow your work, Rajiv, find out more from you, where would you send them to? 

Rajeev Kapur: So the book AI made simple on Amazon is a great place to get the book.

Follow me on LinkedIn, by the way, there's a lot of Rajiv Kapoor's. It's like John Smith in India, right? Rajiv Kapoor is a very common name. So just make sure you find the right one I've got a nice little banner with my book on it and I'm the CEO of a company called [00:29:00] 1105 media. So check out. 

Mahan Tavakoli: We will put the book and a link to your LinkedIn in the show notes as well.

Really appreciate the conversation and you throwing down the challenge for all of us, including with the great examples of the use of the technology. Thank you so much for the conversation, Rajiv Kapoor. 

Rajeev Kapur: Thank you, my friend.