Ep 502: Sustainable Growth with AI: Balancing Innovation with Ethical Governance

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Leveraging AI can kind of be like a tight wire rollback, right? Like you want to be innovative, you want to, you know, take advantage of the latest and greatest that AI and large language models have to offer. Yet at what cost? Is anyone out there reading terms and service of, you know, all these random AI tools that you and your team want to take advantage of? Do you know what happens with your data once you sends it, once you send it to one of these big AI tech companies? Do you care about governance or do you really just care about keeping up and getting ahead and using the latest AI update from one of the big players? I think these are important conversations that are worth talking about and that's exactly what we're going to be doing today on Everyday AI. What's going on, y'?

All?

My name is Jordan Wilson and I'm the host. And this thing, it's for you. Everyday AI is your daily live stream, podcast and free daily newsletter helping us all not just keep up with what's happening in AI, but how we can get ahead to grow our companies and our careers. So it starts here with this live stream and podcast. But if you, that's, that's where you learn. But if you want to leverage it, you need to do that on our website. So if you haven't already, please go to your everyday AI.com Sign up for the free daily newsletter. Yeah, you can go listen and watch and, and read hundreds of website, hundreds of back episodes where I've interviewed some of the world's leaders on topics all over across the board. But also we're going to be recapping valuable insights and takeaways from today's conversation. So make sure you go check that out. Technically a pre recorded one. So if you're dropping in for AI news, that's going to be in the newsletter as well. All right, enough chitchat y'.

All.

I'm excited for today's conversation, so please help me. Welcome to the everyday AI show. We have Rajeev Kapoor, the President and CEO of 1105 Media. Rajeev, thank you so much for joining the Everyday AI Show.

Jordan, it's my pleasure. It's an honor to be here and I'm glad I'm here and I hope people get Moku Value.

All right, I can't, I can't wait to talk about this. But before we kind of Talk about this balancing act of innovation and governance. Rajeev, can you first tell us a little bit what is 1105 media? And tell us a little bit of your background as well in the AI space.

Yeah, so 1105 media, we're a B2B marketing media technology company. So I guess the best way to describe it is we're like a politico but only for technology, B2B. So we do everything from face to face events to lead gen to newsletters to webinars to those kinds of things. And we cover big data. So I have a company within 11, within the 1105 umbrella. There's a company called TDWI that's one of my companies. It's one of the largest big data analytics and AI training companies in the country. It's phenomenal. So people go to tdwi.com and check it out. Then we have another business that does cyber and physical security media and marketing of another business that does enterprise technology. So our customers there are like Amazon Web Services or Google Cloud or Azure or people like that. They come to us and say hey, we want to get more developers of xyz. Can you help us get our product out to those people? So essentially that's where we're a middleman that basically helps connect buyers with sellers is kind of what we do there. And we cover and one of our big partners is in terms of doing some of those things we do as Microsoft, we do a lot of things in the Microsoft stack. And we have the largest non Microsoft event and Microsoft headquarters coming up in August. So we'll have about 7, 800 people there at Microsoft headquarters for our Venus Live event all around. What's happening with Microsoft? That's great. So that's the 1105 media site. Now to answer the question, my AI world, I've actually been involved with AI for a long time. So about 11, a little over 11 years ago actually sold a small AI startup in the machine learning space and we were building AI algorithms used for audio technology. Originally when I kind of became CEO of that company, it was kind of VC backed it. We were building chips and processors. But we quickly found that the TV guys, the phone makers, they weren't going to redesign their boards for another chip. They wanted less chips. Right. So we actually took the algorithm out of the chip build out, you know, and basically build AI algorithms where we tested sound audio quality 3, we called it 3D sound. Now you hear it as spatial audio. And so as a matter of fact like you know, if you, if you, if you saw over the holidays here in the States, there was that Apple commercial where the, where the daughter gets a guitar and dad sad because he can't hear a plane. And then they give him the new AirPods and he puts them in and he can hear. That was kind of the technology that we had built that we used AI for that. So that company got sold and then I went took classes on AI at MIT and I got a dual AI certification at MIT. And then here at 1105, we've been kind of covering the machine learning side of AI for the last 8, 9, 10 years. The generative AI stuff is obviously very brand new over the last two years. And so we've been all over that. And then I remember the morning ChatGPT came out, I jumped out of bed and I remember looking at my phone going, oh my God, this is going to be the greatest thing since electricity, right? To change the world. And initially I was met with some skepticism from people, but I think I've been proven out to be right. But, you know, but literally like within 24, 48 hours, I decided, you know, I'm going to write a book about this. So I wrote a book called AI Made simple and it was the number one bestselling AI book on Amazon for about seven months. It get published in May, June of 2023, I think. Yeah. And so, yeah, and then I had a second edition and now I'm working on a third edition and now also working on another book kind of around prompting and AI for the executive. So anyway, so that's kind of my experience with it. And you know, I'm a techie. You know, I was an executive at Dell Computer for a long time. So always kind of been in the tech world my whole career. Prior to that, I worked for an old computer company, you may have remember it called Gateway. So yeah, so that's my technology and AI background.

Love it. So let's, let's maybe skip to the end here and then we can unwrap this a little bit. Rajeev. But as we look at this balancing act, right, like companies want to take advantage of every new model update. Every, you know, every single shiny AI tool in the corner ever wants to jump into it. So how do you balance keeping up and using all of these AI models, but with the ethics side, with the governance, how do businesses do that?

You know, that's a really good question. And I think that's an area where people right now are just learning and understanding and realizing they actually have to put a little bit of effort, energy into. Part of it is I sit on a board of a kind of ethics and governance AI company called Luminova. Basically it's like the watchman where who watches the watchman? So it's basically an AI platform that watches AI for the most part. But if you think about, you know, how to think about ethics, as you know, in the same vein as making sure you don't hamper innovation, you got to take a little bit of effort and start creating some sort, whether it's a cross functional AI ethics board, for the lack of a better term, like how do you grow the teams to optimize for speed and scale. But then how do you use the ethics team to protect the long term license to operate and to provide value to your customer base? Right. So what's, what would that look like? It could include legal people, scientists, ethicists, technologists, end users of your product. Kind of like a core solution. How do you then go about mandating the review of all the different AI models that you might be using? And then one thing is, I think one opportunity might be tying some compensation to the executives based on ethical outcomes and concerns, not just purely revenue and EBITDA based type solutions. So there's, I think that that's a good way to start another one is understanding realizing that AI as we know and you know, it has got, and people listening know it, it's got some biases and the AI system LLM you're using is going to inherit and amplify those biases. So unless we're using it, unless we're fighting it, like literally every step of the way we're doing regular third party audits or looking at the training sets, the models, we're building some sort of explainability into the models. Is there a way to partner with diverse communities to look at what's happening? So that's kind of like, I think where I would start in terms of looking at how to balance that.

So a lot of times people in companies we've worked with, they always look around and they're like, who should be in the room?

Right.

Like you talked about kind of this like AI ethics board or a team of people who needs to be around that table. Because sometimes, you know, people are looking at it or CISOs and like, you know, you know, sometimes it's just, oh, C Suite or HR marketing, right. Like who needs to be around that table when we talk about the, the ethics team or the people that need to be involved in making those ethical decisions on AI use?

Look, I, I Think like you remember what happened with over Christmas with Sam and the OpenAI group where you, you got gov and you came back, you know, all that stuff, right? If you remember that that nonprofit board, part of their charter was to kind of be that kind of ethical board makeup right now. They didn't win out in this whole power struggle thing. But I think ultimately the answer to the question is that if you want to do this right, you need to have, you have to look at stakeholders across more than just your company. You know, I'm not saying you have to give these people power, but you should, you should give these people the ability to voice their opinions, their concerns, whoever they might be. Maybe you have one or two frontline users that rotate every six months onto this board. Maybe you might look at, is there a technologist for maybe one of your customers that might make some sense? Like, like, like, you know, for example, if you're OpenAI, maybe one of your biggest customers is, is make it up like some big healthcare organization, right? Then let's get, let's get the, let's get know the head of HR for that group to be part of it because HIPAA laws and all these things and there's so much opportunity on the medical side of AI, as you know, that might make some sense, legal scholars and whatever. So I, I think, I think it's going to be a combination of two, three or four core people from within the company and then probably three or four people from outside the company that can come together, work with the CEO, work with the team and the board, the regular board, to really understand and, you know, then, and go from there. And so that's how I would look at this if it were me. But I can imagine that not everybody is going to be like me and all of that, but it does concern me and I think the more you hear about deepfakes and these kinds of things, I think more. And long term, I think you and I were talking before the show started. I think the long term winner is the people who can really figure out how to do both very well.

So I do want to get into deepfakes in a little bit here, but I think it's worth diving a little deeper onto the data side. Right, because, because I think, you know, speaking of Microsoft, you mentioned Microsoft earlier, you know, their CEO Satya Nadella a few months ago said, you know, LLMs are a commodity, right. And I think we've slowly come to realize over the last year or two, you know, that using large language models, generative AI isn't going to be, you know, your company's moat, right? Like in, in competing with whoever else you're competing with. It's actually gets to your data. So you know, how can companies really both separate themselves with their data? But also, I mean, I think that's probably one of the most overlooked pieces in terms of, you know, guardrails and even ethics and how you use that data. So let's talk about both sides of that.

So I've spoken to probably 3,000 CEOs in the last 20 to 24 months and one of the questions I ask them before I do my talk, I say how many of you have a good command, not a great command, a good command of your first party data? I can count on two hands how many hands went up, right? Because I think what happens is CEOs look at data as an expense rather than an opportunity for growth. I think they see CapEx, I think they see cash going out the door. I don't think they see how they can, how they can turn this into something really valuable. So to me and to you and to probably a lot of your listeners, data is the new oil. But what's missing is the refineries that sit on top of the data to turn it into something, right? You can't do anything with just raw, with raw oil you need the refineries, refine it into something. The same thing goes with data. You got to understand your data, you have to have the right practices around your data. You have to look at data privacy and then you have to understand how do you now mine this data, how do you refine this data to use it to your advantage. And quite frankly, if you can figure that out, I'll tell you just by doing that one step, which is arguably a little bit more machine learning, ish, probably the generative AI short term you might actually just build that mode that you didn't think you could build because no one else is doing it. And so if you don't have a data scientist on staff, if you're not spending any, a little bit of capex money on figuring out your data issues, you're going to fall, you're going to fall by at some point. So, so, so take some time and effort to understand your data. So that's where I would start first. Now in terms of the privacy side and there are the things that the problem is that if you just do this on your own and you half asset, it's going to be garbage in, garbage out, right? You know, and it's going to you know, you're going to have to really understand how you can make. To me the challenge is how do you make your privacy a real differentiator? Now, some would argue Apple's probably like the, the golden child when it comes to privacy. I like what they're doing with them. Granted their solutions keeps getting rolled out, whatever. I'd rather them roll it out, push it back than launch something, Apple Intelligence with this shitty product. But to me, I think if they're the gold Standard and the LLM's going to run locally on the phone and all that there's more security then that minimizes data collection, it gives the user a bit more control and opt out capabilities. There's potentially the ability to really have really good transparent usage logs, I guess for the lack of a better term understanding. So that's sort of the real opportunity I think is how can you turn this into a feature? How do you turn your privacy and your ethics into a feature of your offering as opposed to an expense that might cost you some money? How do you turn it into a feature that helps you drive revenue?

So let's, let's dive a little bit more into just governance, right? I, I think it's this, this word that, you know, sometimes it's, it's, it's, it's, it's like, oh, you're, you're safe AI. Bingo card, right? Like I need to say data, you know, privacy and I need to say guardrails and I need to say ethics. And as long as I say those things, you know, people are going to nod their head and it's like, all right, we're doing AI the right way. What does it actually mean when we talk about not just governance but you know, when, like tying in governance in an ethical way. Let's break that down a little. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this video very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use gen AI. So whether you're looking for chat, GPT training for thousands or just need help building your front end AI strategy, you, you can partner with Us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen.

Look, I mean, I think it's. Are you reviewing your major AI initiatives? Are you understanding your AI product roadmaps, partnerships, you know, are they all, how are they being linked? You know, are you leading from a, are you leading kind of from this, I like to call this enlightened leader perspective, right, to where these types of thing matters, you know. You know, I mentioned earlier are you have a diverse group of folks in the room helping you understand and realize what, what type of AI you are deploying, you know, and do you have real good explainability of your model? For example, you know, do you have a monitoring and feedback loop for, you know, for, for your model? I think all those things are, are really there. You know, I think one thing is, I think two things. Number one is, do you have like, are you really, when you have your AI model, are you really doing your worst case testing? Right? I think there needs to be some of that. I think you need to have just like, I think like Microsoft, Google will, will pay hackers to hack their software. You know, you got to basically do the same kind of thing. You know, I think, you know, those are some of the things I think that, that are, that, that are absolutely, you know, you know, necessary to start thinking about governance and how do you build it in and then it's, you know, understanding, realizing is probably never done. And then how do you keep iterating and learning from and going back and giving that feedback loop and mechanism? And I think those are all the things and I, you know, I hate to say it, but I'm not, I don't know if companies, especially the LLMs out there, are going to really put a lot of effort and energy into this unless it's something that's being done on a global basis. Because I think the last thing they want to do is do something that's going to tie their R behind their back in terms of innovation, because if they do it, but then no one else is doing it. So for example, if OpenAI says, oh yeah, we're going to do it, but then Grox says we're not going to do it, then you're gonna have issues. So anyways, but you know, I think another thing here is to Me, it's also making sure that the consumer really understands what you're doing with the data. So I'll give you an example. So I'm a big basketball fan and I'm born and raised in la, so I'm a Laker fan. And the Clippers open a brand new, beautiful, beautiful, amazing stadium into a dome. It's gorgeous, like probably arguably the best stadium in the world for basketball. And everything's facial recognition. And I've talked to so many people that don't want to go because they don't want to pay with their face, you know, because they don't know what's going to happen with the data, who's getting that data, right? So a small example. Even though if you got TSA pre check, you walk up, you're taking your picture anyways, you know everything about where you're going, right. Or global Entry, right. So you know, the company and the government, everybody has everything about you anyways, probably. But there's just something there where I don't believe that they've done a good enough job of explaining why this is better for the consumer. So you have to be able to do that.

So, you know, I think it's worth exploring a little bit more because even the concept of AI innovation, you know, I think has changed. Like, yes, we have listeners from all over the world, but the majority of our audience is here in the US and you know, know, with the, the, the presidential transition, you know, things have changed drastically. Right? The whole like AI Safety Institute was essentially dismantled. You know, it seemed like we kind of had this like yellowish light, you know, for the last couple of years and now it's like, oh, there's no stoplights. We're just going with, with, with innovation. We'll see if we break anything, see what happens. Right? How can companies both, you know, keep up with the, the pace of AI, right. Which is crazy to do, you know, and this is coming from someone that does it every day. But also, even when you look at the, the, the regulatory aspect, you look at the, you know, the federal, the government's involvement and, and how it's changing, right. And all those people sitting around the boardroom, you know, if you're bored, if your leadership is only meeting, you know, once a month, once a quarter, whatever it, like, whatever it is, how can you keep up with the regulatory side, let alone everything else that, that, that that's happening on the, you know, the LLM, the tool side.

Yeah. I mean, look, the cop out answer is you probably can't. But it doesn't mean you don't try. And I really think the companies that are long term going to thrive and be there are going to the ones who can figure out how to do both and what are some of the things that they can do. Look, and by the way, I went to the White House about 10, 11 months ago before the election and I met with people from the Biden administration, was on the White House grounds and met with people from the Office of Technology and Science. All the Alphabet agencies were 100% into this. Under the Biden administration there was thousand, a couple thousand people literally dedicated AI and understanding this challenge at issue. I don't know what's happening now, but I think we can guess what's happening now. But I think to do this at the end of the day, companies are going to have to regulate themselves if they really care. And my point is, is that I don't think, I don't think the big guys will because if they do, they could very well end up hampering their ability to be innovative and grow and it could cost. So again, I'm only going to use an example. If OpenAI says yes, we will do this, we're going to publish AI impact reports, we're going to look at smart regulations and we're going to, I don't know, we're going to create our own bill of rights for people or shared, shared industry standards for AIUs. We're going to do this ourselves, we're going to self do it, we're going to self regulate, we're going to self govern, we're going to do it ourselves. But unless Meta and Google and you know, X slash Grok and others, half the people in hugging face or whoever they are, unless they also step up and say do it, it's going to be difficult and to do. And so, so look, I mean you and I were talking earlier like, you know, the future, it, it's, it's hard. And the future, if the United States wasn't built on people who said it's too hard, they never did it. Right? It would have been hard. If that's the case, the United States wouldn't be here today if it was too hard. Right? And so, you know, so how do you do this? So technologists, CEOs, founders, entrepreneurs, somebody out there, you know, they're going to figure out how to do this and you know, they're going to figure out how to embrace this and figure out how to do both. And again, I come back down to, you know, the people are Going to figure out how to build it anyways. They're going to build it better. They're going to bring ethically and smarter. And if you've got challenges, concerns about AI's dark side like we talked about earlier, like deepfakes and these kinds of things, then do something about it and lead and lead with vision and a purpose that stands up and says, we're putting our foot down. Here it is. And oh, by the way, I guarantee, I have a feeling that the first company that really comes and does that is going to get a lot of positive buzz and feedback and they actually might see an uptick in their, in their adoption of their, of their opportunity and solution, in my opinion. But I give you.

So I, I'd say we can't not talk, you know, when talking about innovation and ethics around AI, you can't not talk about deepfakes. Right. Think there's, there's obviously a very, you know, a very defined line in the sand between, you know, your digital twins, you know, people using it for corporate use and then just unauthorized deep fakes. Right. Which are extremely easy to use now. Right. Anyone with, you know, 10 minutes and a couple dollars can make something convincing that can, you know, really fool a lot of people. What's, what's your take on both, you know, the innovative side of kind of this, this digital twins and you know, enterprise companies have been using them for a while now. But also the downside for deepfakes and deception and misinformation and disinformation. Like, where do you lie on that? Kind of like innovation versus. Ah, this is risky.

Look, I mean, you know, the Internet has, was risky at the end. There's good things about the Internet and there's bad things about the Internet. There's good things about social media and there's bad things about social media. The good news about AI is that everybody has access to it. The bad news about AI is that everybody has access to it. Right. This is kind of the way it is. So anytime there's something good, there's going to be something bad. The ying and the yang of life will always be there. I just think that companies who are really leading this effort need to do a much better job. And I believe they have the technology. You would probably know a little bit better you just came back from gtc unless something was there. I think that companies have the ability to watermark something that is a deep fake. You know, I really worry about society when someone can take your voice or my voice for 11, 10 or 11 seconds. Put it up in 11 laps. Replicate our voice. And I was saying, do. Do something that we never did. Right? It could damage the reputation. Us, Me, you, people listening, you know, somebody taking our. Somebody taking your daughter's face and putting your, you know, on someone's body.

That.

That's unfortunate, right? Or something happened or. You hear the stories now, right? You. I don't know if you heard the story about that CFO in Hong Kong. So if you know where the. Where the employee of a finance institution in Hong Kong got a deep fake invite and went to the zoom call. And it was basically a deep fake CFO and a deep fake controller who convinced him to wire $25 million because it looked and sounded just like the CFO, and that's his boss. And he was like, okay, all right, boss, I'll do it. Then you hear the story of how there's a school principal in the Midwest. You know, I don't know if you heard this story, but there's a school principal in the Midwest who reprimanded one of his teachers. The teacher got angry, created a deep fake of him saying the N word that it wasn't him. Right. And. But. But fortunately, this person had some sort of connection to the FBI, and the FBI got involved, and they discovered it was a deep fake, and they were able to trace it back to the. To the person that he had reprimanded. So, you know, not everybody has access to that. And then you heard the story about character AI and what happened with that poor kid, you know, that I don't want to get into because I'll make me sad. But, you know, so that's really risk and challenge. And quite frankly, the onus of that has to come to. Has to go to YouTube, has to go to Meta Instagram. It's got to go to whether it's SNAP or whomever they might be or, you know, X. You know, to really police these things. I mean, it's better for society and for humanity, and, you know, and again, everybody's susceptible. And because it's so personal to me, deepfakes could potentially be. And I'm being. I might sound a little hyperbolic with this statement, but to me, I think deepfakes could be as bad on a. As. As nuclear weapons. You know, so there has to be. I think there has to be some sort of regulation around deepfakes. I mean, it's almost like creating the AI. AI the AI, you know. You know, the AI you know, AI Agency for information tracking or whatever. Right? So there has to be something at some point, but we'll see. We'll see what happens. I'm hoping that we do have a fairly influential AI person associated pretty close with the President. So hopefully he'll be able to really tackle this. I hope, and we'll see where it goes. But it is a concern and everybody should watch out for it.

All right, so Rasheed, we've covered a lot in today's conversation from how companies can make data their differentiator, how to set up ethical AI alignment, and then even a little bit on deepfakes. But as we wrap up here, what is your one most important takeaway or piece of advice for business leaders trying to walk this tightrope between AI innovation and the ethical side?

I kind of mentioned it earlier and I don't mean to like come back to what I said about five, six minutes ago, but just because it's hard doesn't mean it shouldn't be done. And now's the time where CEOs and leaders in this space really need to lead with a set, with a set vision, ethics and quite frankly, courage to really stand up against the norm of what's happening now and really lead from the front, because that's how they're going to win. And I really believe that the company or companies that figure out how to manage this and figure it out and really put this forward, this idea of privacy and this idea of really of governance and really understanding and protecting the consumer, the end user, they're the ones who are going to eventually, I think, win in the future.

I think it's great advice and an extremely important conversation to have. You know, especially with all the developments and regulation and all these uncertainties we have floating around, I think today's was an important conversation to have. So Rajiv, thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it.

My pleasure. Thanks buddy. To see you.

And hey, as a reminder y', all, we covered a lot. If you miss anything, don't worry, it is going to be in our newsletter. So if you haven't already, please go to your everydayai.com Sign up for that free daily newsletter. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'.

All.

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