Ep 511: The Mad Dash To Cash in On AI. Is AI overhyped AND underhyped?

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.

It's probably a question that many of you have pondered to yourselves. Is this AI thing overhyped or is it maybe underhyped? And we might not know the answer to that question for many years, but it doesn't change the harsh reality right now, regardless of what your views on generative AI are, everyone's doing the same thing. Everyone's in this mad dash to cash in on AI. So I'm excited for today's conversation as we tackle this with guests that's been there, right? One who's actually written the book on this very topic. So I'm excited for today's conversation and I hope you are too. So what's going on, y'?

All?

My name is Jordan Wilson. I'm the host of Everyday AI. This is your daily livestream podcast and free daily newsletter helping us all not just learn what's happening in AI, but how we can actually leverage it to grow our companies and our careers. So it starts here with the podcast live stream. That's where you learn, but you leverage everything that we learn by going to our website at your everyday AI dot com. So there on the website you can sign up for our free daily newsletter. We're going to be pulling out the best insights from today's conversation and breaking it all down for you there, as well as keeping you up to date with everything else that you need to know in the world of AI to be the smartest person in AI at your company. Also on the website you can go listen to more than 500 episodes with some of the brightest minds in AI. All right, I'm excited for today's conversation on this mad dash to cash in on AI and talking about if it's overhype or underhyped. But before we do, let's start off as we do on most days by going over the AI news. A lot actually going on today. So first, Adobe announced that it is adding image generation AI models from OpenAI and Google into its Firefly app, broadening the range of AI tools available to users and making the app accessible also on mobile devices. So Firefly users will be able to create images using OpenAI's GPT image Gen. A lot of news on that one today, as well as Google's Imagine 3, Google's VO2 video generator, Flux 1.1 Pro and more. And they will be adding models soon from Luma and Runway. So Adobe is aiming to serve different user needs by allowing customers to choose between its proprietary Firefly models for production level work and also third party models, whether it's for idea ideation, experimentation or whatever else. So pretty big news there from Adobe. All right, next, but pretty big as well. Geez, a lot of for a random Thursday here, the middle of end of April, a lot of AI news. So Microsoft has officially unveiled its co pilot wave 2 spring release for 2025 with some new updates and just some old ones as well, putting it under the spring Wave two wrap. So a couple new things. The chat interface has been redesigned to make it easier for users to find and use tools, access previously created content, revisit past conversations, and engage with favored AI agents directly from the chat. Previously, Microsoft has also rolled out AI agents called Researcher and Analyst, powered by OpenAI's latest reasoning models, which will soon be accessible through their Frontier program. Also, a new agent store will allow users to browse, select and pin preferred AI agents for quick access. And then probably the biggest one out of this is, well, they're going to be rolling out ChatGPT's new GPT4O image generator and also integrating that as well throughout their copilot platform. The Copilot notebook feature will soon transform notes and data into actionable insights, including dynamic charts and those ever popular audio summaries narrated by two hosts. So yeah, a lot going on there on the Copilot side. And last but not least related actually to those last two stories is OpenAI has officially unleashed their image gen model to the API. All right, so you might be wondering what does that mean? Well, all those viral images that are being created in OpenAI's GPT4O image generator, well they've all been inside of ChatGPT, but now OpenAI is opening that up to developers everywhere. So what that means is you're probably going to be seeing a lot higher quality of AI generated images where you, you know, previously, especially for these companies that maybe still use OpenAI's dolly technology, the images weren't that great. But now we are going to see a much broader rollout across the board to probably hundreds and major companies already such as Canva, GoDaddy and Airtable are already exploring or even already have implemented the new model, which on the API side is called GPT image 1. Terrible name anyways, but this is going to definitely streamline creative workflows and offer image generation capabilities directly in so many more enterprise tools. All right, for more on those stories, make sure to go to your everydayai dot com. All right, let's get into the heart of today's conversation. This is, this is one I'm stoked for bringing in Chicago writer and reporter, right, who who used to be at the Chicago Scene. So for our live stream audience, please help me welcome to the show Gary Rivlin, the author of AI Valley. Gary, thank you so much for joining the Everyday AI show.

Great to be here. Thanks, Jordan.

All right, I'm excited for this one live stream audience. Thanks for tuning in, Kyle and Jean and gosh, we got, we got a couple dozen already in the house. So if you do have any questions on today's topic, make sure to get them in. Might have time for them at the end, but let's start at the top. Gary, give us a little bit about your background in Silicon Valley specifically and how you've been kind of reporting and following AI since the early days.

Right. So I had been a political reporter at the start of my career, but it was the mid-1990s. I was in the Bay Area, San Francisco Bay Area. And you know, I had actually gone to school as an engineering major. I had program but just got interested in politics. But suddenly the dot com Internet was rising and so I kind of changed topics in the mid-1990s and wrote about the rise of the Internet, the dot com boom and bust and all and you know, kind of since. And, and then kind of went on to, I was working for Wired. I went on to cover Silicon Valley for the New York times through the mid-2000s, you know, kind of the Google going public, kind of the revival of, of Silicon Valley after kind of the.the2000.com fall and you know, just I'd kind of moved on. But then I just got this random email at the end of 2022 from Reid Hoffman. Reid Hoffman, founder of co founder of LinkedIn, investor in Facebook, Airbnb, you know, kind of just a central figure in Silicon Valley. I had gotten to know him in the early 2000s, right after LinkedIn went public, excuse me, launched I wrote a feature for Wired. So I've known him since the early 2000s. And I was on his me. You know, he's like dear friend. I'm like one of 2,500 friends. You know, at a different day I might have hit delete. But I read this thing and like he's like, hey, I'm having the, my, my first, I've, I've co founded my first startup since LinkedIn and it's around AI and he had this one line just remind me of me being a frustrated programmer earlier in my life. Like instead of us learning the machine's language, the machine is going to speak our language and like the light bulb, like, wow. And I realized like this I had great time is right before the world kind of went crazy with ChatGPT again. The end of 2022, it's like, huh, maybe I should move back to Silicon Valley. You know that I mean, move back to Silicon Valley as a topic. And so since the start of 2023, I've been following, you know, his Reid Hoffman's company and other founders, other investors looking into Google, Microsoft, how are they planning on cashing in, taking advantage of this moment.

So I love that story, right? Like getting an email from, from Reid Hoffman and being like, huh, like, like, like really thinking about it and first being like, is this real? And then figuring out, oh yes, it's definitely real. Right? So walk us through. How did that initial email from Reid eventually, right down the line, turn into the book AI Valley? 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 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 ChatGPT 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.

Right? So you know, I mean, just because one person is telling you like, hey, this moment is here, you know, you don't necessarily get on an airplane and you know, start reporting. So I just, you know, make calls and like, you know, just figuring out that generative AI, really this was a breakthrough moment again. It, it helped that my timing happened to coincide with ChatGPT. So the wider world was waking up to the potential of generative AI, but you know, just kind of recontacting sources of mind from over the years and understanding like, wow, all this has happened like the transformer model out of Google, you know, GPTs 1, 2, 3, 3.5, you know, before this that like, wait a second, you know, there have been these key moments and this is the inflection point. So you know, early on I just realized like, wait a second, this is one of those before and after moments. Like before ChatGPT, you know, a small group of people were involved in AI, but after ChatGPT, this is gen of AI. This isn't a product like Google Translate behind the glass. This isn't a recommendation engine. This is something you can talk with, you can interact with. And so you know, I just kind of instinctually understood that this is an inflection point and I should, I should jump on, jump on this moment, you.

Know, and we'll make sure to leave the link to the book in the newsletter today because I think it's, it's definitely on my list of books that I need to read very soon. But you know, walk us through maybe what are some of your main findings right from being able to, to talk with, with some of the leaders that are building all of the AI systems that we're using now as well as just, you know, being privy to everything, right? As a former Pulitzer Prize winning journalist, you know, privy to probably things that we didn't get to see on the surface. But walk us through maybe some of your main findings of the book in, in AI Valley.

Well, I mean one of the first things is how did we get here? And you know, looking into the history was fascinating to me. I tell it through a couple of characters but you know, since the 1950s there was this over optimism that AI is just around the next corner. So for like decades, since the 50s, it's been just around the next corner. You know, there was this massive wrong turn that, you know, kind of rules based this idea that you have to like teach a teacher program line by line by line how to react. You know, that was the main approach. People ridiculing neural networks. Neural networks date back to the 1950s. But you know, people didn't see any promise in that. And in fairness to those folks, there really wasn't much promise because computers weren't powerful enough until that point. We didn't have the digital data for training. That wouldn't really happen until the second half of the 90s and the rise of the Internet. So that was one thing. Like just kind of how did we get here and sort of, you know, Just, just sort of the. To understand this moment. It helps understand what had happened before it. And I also started realizing like I'm in deja vu. This is the mid-1990s all over again. We could talk about a couple of differences. The change is moving faster than it did in the 1990s. But with that said, there's this idea. I learned this early on my second interview ever in Silicon Valley. This really smart guy, Paul Sappho, a forecaster, kind of help you understand the future, said, you know, we tend to overestimate the short term impact of a technology, but underestimate the long term impact. And that's the Internet. You've asked me about the Internet in 1997. Like I understood, lots of reporters understood like, yeah, startups, you're not really going to get. This isn't going to happen fast enough for you to get, you know, fabulously wealthy overnight. And you know, most dot coms did in fact out of business with no return. But I also didn't quite understand in the mid-1990s how pervasive this would be. In fact, I remember talking to a venture capitalist back then, John Doerr, very well known venture capitalist, and he had just invested in this company called Amazon.com and he says to me like, oh, they're going to do more than books. And so to my little mind it was like, oh, they're going to do CDs too. You know, they're going to, you know, it's just, they're going to do. And so, you know, I don't think we understood it was going to be the everything store. And so much of the world, business, education and your personal life would be transformed by the Internet. And I think the same, you know, two years on the front lines of AI, I think the same thing there. I use models, I use large language models every day, all day. They're really useful tool. I could tell you, we could talk about, you know, the strengths and limits, but you know, it's a powerful, powerful tool. Whoever it's limits like people talk about 2025 is the year of the personal agent. Like, yeah, but it doesn't really have much of a memory. And so the whole advantage of a personal agent is they know like, you know, Jordan likes aisle seats and Gary likes window seats. You know, they get to know you over time. They really are having a hard time remembering you from session to session. But imagine 10 or 15 years from now. And I think we'll say the same thing about AI that we've said about the Internet. It's changed everything. It's, it's turned upside down. Organizations, education, you know, personalized businesses across the board.

So you know, I'm curious, and that was some great background there and drawing these parallels to the, you know, the dot com, you know, boomer bust era of the late 90s and ultimately what that led to. But you know, I'm wondering, you know, what other insights did you glean and you know, like that, that comparison specifically. Right. I'm always thinking about it because I think, you know, companies specifically, they had, you know, three, five, sometimes 10 years to you know, figure out their kind of Internet strategy. Right. But on the AI side, I tend to think it's not really the same yet. I think people are, are really drawing that comparison in my opinion, maybe too tightly. Right. With, with everything on AI and kind of the dot com era and they're like we've been here before. So you know, I'm curious from, from, from your findings, your reportings, are you seeing that as well companies trying to follow kind of their, their dot com business plan a little too, too tightly?

Yeah. Are you talking about kind of potential customers? You're talking about the creators of these, maybe even both?

Yeah, that's, that's, that's interesting to tackle it from both.

Yeah, I mean, you know, for the, for the creators, you know, let's understand that when you know, an open AI or an anthropic takes, you know, billions, tens of billions of dollars and venture, you know, venture dollars, they really need to satisfy their investors. And so, you know, I, I really do think they're following that dot com playbook of over promising. And I think that's really going to hurt them. I mean I wouldn't bet against OpenAI, but you know, just sort of, there's an issue that the accelerationist, the zoomers, those who say like full speed ahead, let's put no guidelines on AI. The general public is, the majority of general public is fearful of AI. Less than 1 third of folks in, in, in polling are excited about AI. So I, I do worry that hype is going to cause a similar fall. I don't think we're going to have a dot com like bust with, with AI. We're not seeing all these AI startups go public. It, it'll be different. But I really do worry about kind of a boomerang. So if I was talking to a business audience, I would say yes, you should be experimenting with this. But again, understand this is idea of autonomous AI, this idea that, oh, we'll just have AI Employees, digital employees, and they'll do the work. I think in the short and medium term. That's the wrong construct to me. In fact, I love Microsoft's name for its, you know, for its chatbot copilot. That's perfect. It's like it's gonna help you. You know, we were talking before the show like you can't type into a prompt, you know, make me a Martin Scorsese movie, you know, enter like it's, that's not the way it works. You are the creative. It's human centered, you know, right now it's a fabulously powerful tool. It's a limited tool, it makes mistakes. It's not quite where it's gonna be in five or 10 years, but it's a really powerful tool that can help you do whatever you wanna do it. For the book, you' this like the first thing I did when I thought of the, huh, should I write a book about AI? I went to ChatGPT, like write me a 5,000 word proposal that would win me a contract on a book on AI. And you know, I got two things from that experience, you know, one, it is far better read than I am as a far superior memory than me. But on the other hand, the second thing is like, it can't really, it's very mediocre writing, it's very flat. And so like, however, when I say, hey, here's a paragraph and I'm not loving it. I like the ideas and it helped me improve it. That's when it's really strong. Get I'm the creative, I'm giving the ideas, I'm giving it the approach and it could help me see that through, it could help me with the execution. And so like, you know, I really think that's important for businesses to understand that this is an autonomous AI. We're not getting rid of the human, you know, at least not quite yet. I can't predict 10, 50 years from, from now, but it, it, it, I want everyone, students in school to business leaders and everyone in between, like use this. Understand what it's good at and what it's not good at.

So Gary, you, you, you, you brought up an interesting fact as, as, as part of a study, you know, saying that less than one third of people are excited about AI, yet the six largest companies in the US by market cap, are sticking every dollar they can into investing in these systems, technically hundreds of billions of dollars in, in capex and in AI spending. Right? So how, how can you kind of like explain that dichotomy Right. Because people aren't necessarily thrilled about AI yet. The biggest companies in the world are sticking hundreds of billions of dollars and kind of saying like, hey, it's coming no matter what. So you know, I'm curious, you know, how can we make sense of that kind of dichotomy?

You know, I think it's very rational for a Microsoft or Meta to be putting in these tens of billions of dollars will probably be soon each company hundreds of billions dollars because if they miss this, it's going to be a multitrillion dollar miss. And so, you know, you could almost see it as a hedge against the future, but I don't think it's a hedge against the future. I come back to we tend to overestimate in the short run and underestimate the long run. Like these companies, I mean, you know, Microsoft sitting on $100 billion, they can afford to do this kind of investment because they see, and I think they see correctly, that 5, 10, 15 years from now they're going to get a nice return on this, this investment. Right now we're capacity on these data centers. They're predicting that by 2030 we'll have a doubling of data centers. So I don't see that diminishing anytime soon. There's an expression among the venture capitals like go to where the puck is going, not where it is right now. And I think that's what these companies are doing. They see we're making an investment not for the next quarter, but for a five or ten year time horizon.

And that, and that seems very difficult. Right, right. To, to even like my, you know, because I think about the, you know, skate to where the puck is going, which, which makes sense and has historically made sense for, you know, at least dealing with tech innovations over the past couple of decades. But at least for me, when I look at everything as, as someone that covers AI every single day, it's almost like, you know, we're in a new game.

Right.

Like there's no longer a puck. Right. And there's no longer skating these, you know, machines are starting to, to do these things for us. Right? So you know, not, not asking you to be a business consultant here, but, but how can the majority, you know, of Americans and our listeners kind of, you know, kind of take on that, that, that mantra of skating where the puck is going when AI development is so freaking fast, right?

So I, I, I was talking about kind of the venture capitalists, the founders, you know, the, the tech leaders skating where the puck is going. I think for People right now, you know, again, start using it. I'll use. I wrote a feature article about this incredible company, Runway. They do text to text, excuse me, text to image, to video, text to video. And you know, they're one of the leaders in video generation. I mean, they're up against OpenAI, Google, et cetera. So who knows who's going to win. This is three artists in New York City taking on deep learning PhDs. And so far, if they're not ahead, they're right there in the competition. And, and so in doing that article, I said, like, it's really important that who's actually using your product. Hollywood is using their product. They, they like, instead of having like, they want to do a 60 second scene that is on a mountainside with a thousand soldiers. In the mid, you know, in the Middle ages, that's prohibitively expensive. It'd be $10 million more and they probably wouldn't do it. But now with AI, they can do it. And so, you know, what are the practical things? I talked to a global production production company that uses a Runway all the time. And like, you know, how is this helping? They said, you know, in the past they'd have 50 or 60 people for a film shoot. You know, kind of people organizing the talent. The talent, the production people, the post production people, all of that and 50, 60 people. Now using Runway, they have 15 to 20 people, you know, the timeline. Instead of being, you know, typically 16 weeks from thinking of an idea to, you know, launching to releasing it, it's now six weeks. So 16 weeks to six weeks. And they say like the average project, we're saving 50 to 80%. See, that's a very concrete way. They have figured out like, okay, we're still figuring this out, as you correctly say. Like, who knows in five years what a video model, what will a cutting edge video model look like? But they're figuring out how to use this thing that exists now that's a limited but powerful tool or a tool that's very powerful in certain ways and not quite what it's going to be. And so they're figured out like how to save 50 to 80%. That's a big, big savings. And so, you know, across the board you've, you know, you've got your marketing team, you know, and is it marketing team of 10? Like, I think using AI, you can have a marketing team of five that's, you know, using the AI to iterate and you know, kind of, oh, here's my idea. Do a quick illustration. Oh, no, change it, change it, change it. You know, you'll still have an illustrator or two, you'll still have creatives, but I don't think you'll need as many. I think, I think right now AI is a great cost savings tool. Rather than like, oh, we will replace our marketing team, you know, with digital labor.

Yeah, I think the Runway example is great. And yeah, obviously, you know, Gen 4, extremely impressive. And it is cool to hear kind of the story. Right? It's artists and not, you know, people with, you know, 20 years of deep learning experience. And, you know, yeah, they did their partnership with Lionsgate.

Right.

Like a huge studio there. So extremely impressive, you know, so I'm wondering, you know, Gary, kind of like going back to this concept of, you know, AI maybe being overhyped in the short run and maybe under hyped in the long run, even for you, as someone that was covering this, you know, from the front lines, so to speak, and being able to talk, you know, personally with some of the leaders in the AI industry. What were maybe some things that even took you back and you're like, oh, wait, you know, wow, right? Like, I have those moments all the time, but I'm very much on the outside looking in. So from an insider's point of view, you know what, over the past year or two or a couple of months or a couple of weeks. Right. Is really even shocking you.

Well, you know, this idea and this definitely, you know, conquered up memories from the dot com years. This idea that venture capitalists are so scared of missing out on the moment, the fear fomo. Like, we could have gotten early into X, but we missed this multi billion dollar opportunity. So they're throwing money at these half baked ideas. I mean, Ilya Suskever, the co founder of OpenAI, who famously initiated the coup back in 2023, he left OpenAI for obvious reasons. So he's founded Safe Superintelligence, I think it's called. They don't have a product yet, but they've raised billions in venture capital and they have a paper worth of, I think, $32 billion. And so I kept on seeing this happen over and over again. There's, you know, a promising team like, oh, two top Google machine learning specialists teaming up with someone at, you know, Meta, who left another machine learner. And they, they create a company, they write a memo and they raise, you know, $10 million on $100 million, you know, valuation. Like, you know, I think two things like, that's insane. And the other thing is like, I sort of get it These are really promising people. We have no idea. We want to get in on a hot deal. We might lose our $3 million we put in towards the 10 million. But if we don't, you know, if, if we, if it pays off, you know, that 3 million, you know, could be, you know, billions. I, I example. So I was covering Silicon Valley for the New York Times in the mid-2000s, and there's this new company, thefacebook.com and Excel. Jim Breyer, famous venture capitalist, put in, I think he put in $12 million at 100 million valuation up and down Sandhill Road, you know, all through Silicon Valley. They're all mocking, you know, Prior, what a fool, you know, for this college only app, he's putting in all this money, $100 million like that 12 million became like, if you, if he kept it through going public, it was 4 billion, something like that. And if he held it, it'd be worth, you know, tens and tens of billions of dollars. And, you know, that's the venture capital game. Most bets are wrong. I mean, they either show nothing or they show barely any return. But it's a game where that one big hit can make up for everything, and that's venture capital in Silicon Valley. We're just going to slap down bets and just hope that one or two of them are huge. So you asked me why to conjure it up, this kind of, this insanity. That's understandable.

So, you know, one thing that you mentioned earlier, right when we are talking about, you know, less than a third of people, you know, being excited about AI, yet all the big companies are, you know, investing in it, you kind of talked about this, this boomerang effect. Could you, could you talk a little bit more about what that means?

Well, you know, so there's business and there's the personal side business. I put in a different category. Like, okay, if your work is saying like the Shopify CEO, I want every employee to be using it, you're going to be using it. But, you know, kind of this whole idea of personal agents, this idea that the public's going to be the, you know, the consumer, the general consumer audience is going to be using these things. Like we're asking, you know, these personal agents are going to have all this power, they're going to have access to your private information and, you know, kind of in an existential sense, like humans are soon not going to be the smartest entity on the planet. It's kind of frightening for people. I mean, I blame the media some for that. I blame Hollywood, you know, for, you know, the Terminator, like movies like, oh, the robots are going to take over. That's not my, my concern. But you know, so I, I feel like they're going to get ahead of this and I think it's inevitable that something bad's gonna happen because AI, you know, just to make one up, like a trillion dollars is siphoned off from the global economic system before a human even understands what's happening. A deadly pathogen, you know, AI for good, it could create, you know, vaccines and cures is also one that could create a deadly pathogen. So I think, I think it's inevitable that something bad's gonna happen and I really fear that's gonna turn the general public off to AI. So I wish those working on frontier models would be a little bit more aware of where the public is, assure them that trust and safety is central. As I'm sure your listeners know, trust and safety used to be a major concern of all these companies. But once the starters pistol went off, once ChatGPT went out there, the competition cashing in became the priority and trusted safety became a secondary concern. I think that's a mistake.

That is fascinating. Right. And even these, you know, frontier AI labs, their, their safety teams have, you know, kind of shrunken over over the years or maybe, you know, it was front and center previously and now, you know, it's, it's kind of an afterthought. Right? Like the safety of models. Right. Like what are you seeing, you know, what are you maybe seeing as, as, as similar trends. Right. So that's, that's one, you know, hey, safety used to be all about safety. Now the starter pistol went off. It doesn't really matter. Right. What are some other trends that, that you've picked up through this experience of, of reporting, you know, in Silicon Valley that, that maybe the rest of us haven't picked up on yet.

See what really scares me. So, so I approached this book looking at startups. I felt like I wanted to find who's going to be the next Google, who's going to be the next Facebook meta. And what I discovered after, like all my reporting is like the next Google is likely going to be Google and the next meta is going to be meta. These things are so expensive. When I started it was like they were talking about this is start of 20, 23. Tens of millions of dollars to train, fine tune and operate these large language models. By the time I was done reporting, you know, it was hundreds of millions of dollars. Now it's billions if not Tens of billions of dollars. And like a large venture capital outfit in Silicon Valley, raises $1 billion. But if anthropic, as Dario Amode, you know, said they're going to need, he figures, $100 billion, you know, for their next iteration, their 2027 iteration, how are they going to raise that money? So I'm scared it's going to get so expensive. It's so expensive that either they're going to have to sell so much of their company, they don't really own the company. You know, anthropic, to use that example, Google's put in billions of dollars, Amazon's put in billions of dollars, or probably more likely they'll end up getting bought by a Google or an Amazon. So, you know, my big fear is that big tech, which created a big mess with technology in the 2010s, that they're going to be in charge of AI. There's the trust issue. You know, AI had really bad timing, right came out. You know, I mean, AI has been around for decades, but you know, generative AI coming out at the end of 2022, you know, trust in Big Ten, Big Tech was a pretty much an all time low. You know, Facebook, Amazon, Google, people really are entrusting those companies. And yet I do think AI is going to be in the hands of Google, Microsoft, maybe an open AI. And you know, I'll point out, by the way, OpenAI, founded in 2015, was a counter to Google. Rather than, you know, start as a nonprofit, famously they're trying to rid themselves of their nonprofit parent. But it's, it started as kind of this idealistic thing rather than profit driving AI. This is too essential, this is too powerful. We want it in the hands of an entity that isn't profit driven. But OpenAI is as profit driven as Google or any other company out there. So Even if an OpenAI breaks into that Google, Apple, Microsoft, Amazon category of trillion dollar giants, I think they're the same as those companies. I mean, Sam Altman, it's amazing what he pulled off. He's an amazing CEO.

But.

We'Ve seen all the folks who have left OpenAI, I mean the anthropic team before ChatGPT came out, but many people since then keep on saying the same message, that safety is taking a backseat to shiny toys. Again, I get it. Like let's, let's, these are great tools. They could do amazing things for medicine, for science, for education. I see fabulous upsides into these things. But there's also like any technology, there's goods and there's bads. And I really wish we'd be more deliberate of ensuring that AI is a net positive and not a net negative.

Yeah, I think that's the one thing ultimately on everyone's mind. So, Gary, we've covered a lot in today's conversation, but I'm sure something that remains on people's mind is, okay, is AI overhyped or is it underhyped? So maybe for those business leaders, you know, they've, they've been using generative AI. They're, they're, they're implementing in their company, but they're still wondering that, is it overhyped, is it underhyped? What's your quick takeaway on that as we wrap up today's show?

I would say individual companies are overhyping their specific products and what they're able to do, but I think AI is underhyped. I really think AI is going to transform this world. Amazing things are going to happen because of AI. Yeah, under. I'm going to say under hyped.

Great, great question. Great answer there, and a great way to wrap up today's show. So, Gary, thank you so much for taking time out of your day to come on the Everyday AI Show. We really appreciate it.

My pleasure. This was great. Thanks.

All right, there's a lot there, y', all, and there's going to be a lot more. If you missed something that Gary talked about or if you want to dig in more, that's where it's going to happen in our newsletter. So if you haven't already, please go to your Everyday AI dot com. Sign up for the free daily newsletter. If this was helpful, tell someone about it. If you're listening on the podcast. Thank you. Make sure to subscribe. Leave us a rating if you could. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'.

All. 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.