Everyday AI Podcast – An AI and ChatGPT Podcast - EP 604: Beyond the Hype: Turning AI Ethics into Competitive Advantage

Episode Date: September 5, 2025

AI growth with no rules? That’s not bold. It’s reckless.Everyone’s racing to scale AI. More data, faster tools, flashier launches.But here’s what no one’s saying out loud:Growth without governance doesn’t make you innovative. It makes you vulnerable.Ignore ethics, and you’re building an empire on quicksand.In this episode, we’re breaking down how to scale AI the right way—without wrecking trust, compliance, or your future.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Questions for Rajeev or Jordan? Go ask.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Balancing AI Innovation with Ethical GovernanceIntroduction of Rajeev Kapur and Eleven o Five MediaRajeev Kapur's Background in AICompanies Balancing AI Innovation and EthicsFormation of AI Ethics BoardData Management as Competitive AdvantagePrivacy and Ethics as Product FeaturesGovernance and Ethical Standards in AI UseImpact of Regulatory Changes on AI UseDeepfakes and Their ImplicationsEncouragement for Companies to Lead Ethically in AITimestamps:00:00 Navigating AI: Innovation vs. Risks04:00 "AI Startup's Spatial Audio Journey"06:49 AI Ethics Oversight & Governance10:04 Strategic AI Advisory Team Formation15:34 AI Strategy and Governance Essentials16:55 Global Standardization Needed for AI Policies22:47 AI Ethics: Innovation vs. Deepfakes25:48 "Regulate Deepfakes Like Nukes"27:17 Leadership Vision for Future SuccessKeywords:AI innovation, Ethical governance, Large language models, Data privacy, AI ethics board, AI governance, TDWI, Microsoft stack, Generative AI, AI algorithms, Spatial audio, Deep fakes, Data differentiation, Machine learning, Cyber security, Enterprise technology, Rajeev Kapur, 11:05 Media, AI safety, OpenAI, Data utilization, Ethical AI alignment, Regulatory aspect, AI models, Innovation vs. ethics, AI data privacy, Explainability, Data scientists, Third-party audits, Transparent AI usage, AI-driven growth, Monitoring feedback loops, Worst case testing, Smart regulations, Digital twins, Disinformation, AI bias mitigation, Data as new oil, Refining dataSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. Leveraging AI can kind of be like a tight wire ropeback, 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 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's Jordan Wilson, and I'm the host, and this thing is 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 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 read hundreds of websites, 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 chit chat, y'all. I'm excited for today's conversation. So please help me welcome to the Everyday AI show. We have Rajiv Kapoor, the president and CEO of 1105 Media.

Rajiv, thank you so much for joining the Everyday AI show. Jordan, it's my pleasure. It's an honor to be here. I'm glad I'm here and I hope people get real good value. All right. I can't wait to talk about this. But before we kind of talk about this balancing act of innovation and governance, Rzeef, 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 and 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, those kinds of things. And we cover big data.

So I have a company 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 if you 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 the way. 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 is Microsoft. We do a lot of things in the Microsoft stack. and where 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 VES Live event all around that'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 a little over 11 years ago,

actually sold a small AI startup in the machine. 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, processors, but we quickly found that the TV guys, the phone makers, then we're going to redesign their boards for another chip. They wanted less chips, right? So we actually took the algorithm out of the chip, built out, you know, and basically built AI algorithms where we tested sound audio quality. We called it to be, we called it to be, sound, now you hear it at spatial audio.

And so as a matter of fact, like, you know, 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 that dad's sax, he can't hear her play, and then they give him the new AirPods and he puts them in and he can hear her. That was kind of the technology that we had built,

that we used AI for that. So I company got sold. And then I went, took classes on AI at MIT, and I got a dual AI, dual AI certification at MIT and then here at 1105, even kind of covering the machine learning side of AI for the last eight, nine, 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 chat GPT came out. I jumped out of bed. 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'd say, you know, I'm going to write a book about this. I wrote a book called AI Made Simple. And it was the number one best AI book on Amazon for about seven months. It would 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 to the tech world my whole career. Part of that, I work for an old computer company. You may have remembered 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, Rijib. But, you know, as we look at this balancing act, right? Like, you know, companies want to take advantage, you know, of every new model update, every, you know, every single, shiny, you know, any 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 to. Part of it is I sit on a board of a kind of ethics and governance AI company called Lumenova. Basically, it's like the watchman who watches the, watchman. So it's basically an AI platform that watches AI for the most part. But if you think about

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 would that look like? It could include legal people, you know, scientists, ethicists, technologists, end users of your product, you know, kind of like a core solution.

You know, how do you then, 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 and realizing that AI as we know and you know it is got, and people listening know it, it's got some biases. And the AI system, the 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. And there's a way to part. partner with diverse communities to look at what's happening. You know, it's, it's, so that's kind of like I think where, where I would start in terms of looking at how to balance that. So a, you know, like a lot of times people, uh, in, companies we've worked with, they always look around and they're like, who should be in the room, right? Like you, you talk about kind of this like AI ethics board or the, you know, a team of people who needs to be around that table? Because sometimes, you know, people are looking at,

at IT or CSOS and like, 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 ethics team or the people that need to be involved in making those ethical decisions on AI use? Look, I think, like, you remember what happened with over Christmas with Sam and the opening AI group where you got the other, gov and he came back and, 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 went out in this whole power struggle thing but I think ultimately the answer 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 give these people the ability to voice their opinions their concerns whatever they might be maybe you have one of the to 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. You're like like it, like, you know, for example, if you're opening eye, 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 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. of AI, as you know, that might make some sense.

Legal scholars and whatever. So I think it's going to be a combination of, you know, three or four 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 you can go from there. And so that, 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 follow that. But it just concerned me. And I think the more you hear about deep fakes 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 winners that people could really figure out how to do both in very well. So I do want to get into deep fakes in a little bit here, but I think it's worth diving a little deeper onto the data side, right? Because I think speaking of Microsoft, you mentioned Microsoft earlier,

you know, their CEO, Sadia 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, you know, 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 competing with whoever else you're competing with, it 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 in 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 do you? many hands went up, right? Because I think what happens is, 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 turn this into something really valuable. So to me, and to me, 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 to refine it into something. The same thing goes to 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? 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 gendered of 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 a little bit of CAPEX money on figuring out your data issues, you're going to fall behind at some point. 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,

you look, and another thing, 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 the to me the challenge is is how do you make your privacy a real differentiator now some would argue apples 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 than roll it out push you back that launch something apple intelligence of this shitty product but to me to me

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, so 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 into a feature that helps you drive everything? So let's let's dive a little bit more into just governance, right?

I think it's this word that, you know, sometimes it's it's it's like, oh, your, your 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 knob 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.

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library of pre-built workflows for common creative tasks like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. Look, I mean, I think it's, you're reviewing your major AI initiative. 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 things 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 for your model? I think all those things 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 add just like, I think Microsoft Google 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 are absolutely, you know, necessary to start thinking about governance and, you know, 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, 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 buying their back in terms of innovation because if they do it but then no one else is doing it so for example like if open AIS says oh yeah we're going to do it but then Grock says we're not going to do it then you're going to 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 dough. 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 precheck, you walk up, they're taking your picture anyways and everything about where you're going, right? Or global entry, right? So, you know, the company and the government 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 with 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 though, the concept of AI innovation, you know, I think it's changed. Like, yes, we have listeners from all over the world, but the majority of our audiences is here in the U.S. And, you know, with 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 innovation. We'll see if we break anything, see what happens,

right? How can companies both, you know, keep up with 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 regulatory aspect, you look at the, you know, the federal, the government's involvement and how it's changing, right? And all those people sitting around the board room, you know, if you're board 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 uh let alone everything else that that that that's happening on the you know the lLM the tool side yeah i mean look you know 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 you can figure out of be 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 and 11 months ago before the election. And I met with people from the Biden administration. I was on the White House grounds. I met with people from the Office of Technology and Science. You know, all the alphabet agencies were 100% into this under the Biden administration. There was a couple thousand people like literally dedicated AI and understanding this challenge and issue.

I don't know what's happening now, but I think we can guess it'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 that I don't think the big guys will. Because if they do, they could very well end up ampering their ability to be innovative and growth. And it could cause. So again, I'm only going to use an example. If open AI 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 industry standards for AIUs. We're going to do this ourselves. 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 GROC and others, half the people in Hugging Face of whoever they are, unless they also step up and say, do it, it's going to be difficult to do. And so, look, I mean, you and I were talking earlier, like, you know, the future, it's, it's hard. And the future, if the United States wasn't built on people who said it's too hard, then they never did it, right? It would have been hard. If that's the case of 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 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 deep face and these kinds of things. then do something about it and lead with vision and a purpose that stands up and says,

we're putting our foot down. Here it is. And by the way, 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 adoption of their opportunity and solution. There might be. But I could be. So 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? Because I 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 deepfakes, 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 your take on both, you know, the innovative side of kind of 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. And then there's good things about the internet. There's bad things about the internet. There's good things about social media. 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 that this is kind of way it is so anytime there's something good there's going to be something that you know 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 i think that companies have all 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 her voice. And I was saying, 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's unfortunate, right? Or something happens. Or you hear

the stories now, right? You don't know if you heard the story about that CFO in Hong Kong. So, you know, where the employee of the 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 sound like just like the CFO.

And that's his boss. And he's like, okay, all right, boss, I'll do it. You know, then you hear the story of how, you know, 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 unfortunately this person had some sort of connection to the FBI and the FBI got involved and discovered it was a defake and they were able to trace it back to the person that he had reprimanded so you know not everybody has access to that then you heard the story about character

AI and whatever with that poor kid you know which I don't want to get into because it'll make me sad but you know so that's really a risking 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 snap or whomever they might be 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, deep fakes could potentially be, and I'm being, I might sound a little hyperbolic with this statement.

But to me, I think deep fakes could be as bad on the, as, as, as nuclear weapons, you know, so there has to be, I think there has to be some sort of regulation around deep fakes. I mean, it's almost like creating the AI, AI, AI, you know, know, you know, the 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. You know, I'm hoping that, you know, 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, Rashid, we 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 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 is 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 flown around. I think today's was an important conversation to have. So, Rezeef, 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. 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 everyday AI.com. Sign it 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. Meet Firefly AI assistant. Now live in Adobe Firefly, the Allman One Creative AI studio. Just describe what you want to create in your

own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversation. interface. You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly. Adobe.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.