← The Artificial Intelligence Show
The Artificial Intelligence Show
#168: The AI Economy, How People Use ChatGPT, AI-Native Companies, Meta Ray-Ban Display AI Glasses & How Americans View AI
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- [00:00]
A
Years ago, I would say AI was eventually going to be the operating system of society and business. And I feel like that's what's actually starting to happen is like it really is just everything operates on top of the AI layer. Welcome to the Artificial Intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Raitzer. I'm the founder and CEO of SmartRx and marketing AI institute and I'm your host. Each week I'm joined by my co host and Marketing AI Institute Chief Content Officer Mike Caput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career. Join us as we accelerate AI literacy for all. Welcome to episode 168 of the Artificial Intelligence Show. I'm your host Paul Raitzer along with my co host Mike Kaput. It is. We're recording this on September 22nd, 9am Sounds like there's some stuff going on this week. So again, as always, if something happens after this, we'll get to it next week. There's been a lot of chatter about some releases coming this week and even Sam Altman confirming they're definitely entering a release season at OpenAI. So lots to do. I don't know you, Mike, but I put Notebook LM on overdrive this weekend getting ready for this podcast. There was a lot of in depth research papers specifically related to the economy. So that's actually going to be the opening topic. We're going to kind of dive into some of those papers, but there was no less than like five papers that I went through on Sunday night. So yeah, NotebookLM was my friend. Give me executive summaries extracting key findings. If you don't use NotebookLM for that stuff, it's awesome. And even separately I've been using it for flashcards and quizzes like experimenting with some of those other features. So yeah, this wasn't intended to be a promotion for NotebookLM, but I needed it to get ready for today. All right, so today's episode is brought to us by AI Academy by Smarter X. We've been talking a lot about this lately. Each episode we try and give you a little preview of some of the things we're doing. So today we wanted to touch on our gen AI app series. This is brand new as of August 19th and we rolled out this next generation of AI Academy. So the Gen App series is weekly product and feature reviews. So we do like these 15 to 20 minute reviews. They're meant to be shorts, something you can pick up real quick and kind of learn about a feature or a product. So these drop every Friday morning. Claire on our team and Mike on our team. You know Mike put who you're familiar with have been creating those. We're actually expanding our plans for that. I'd love to do more of these. I think they're incredibly valuable. I love them. So I thought I'd just let Mike give a little overview here of what we've dropped so far. So again, these are in our AI Academy by SmartRx. You can buy individual course series or you can buy an AI Mastery membership which is a 12 month month membership. These are AI Mastery member only things that we feature. So Mike give us a quick rundown of what we've released so far and maybe a little preview of what's coming up in the Genai app series.
- [03:05]
B
Yeah, for sure, Paul. So every week since launch we've released one of the Genai app series. We've dropped one on GPT5, one on Gemini Deep Research and ChatGPT Deep Research. There's one on Google NotebookLM which is relevant to what we just talked about. Claire did one on Google's Nano Banana, that's the kind of nickname of their image editor. And then we've also got one that just came out on GPTs in general within ChatGPT and how to get the most out of those. And what's really cool is we've got a ton of awesome video, image and audio tools coming up, mostly courtesy of Claire. Like we've got in the next few weeks here, Google Image and 4 Google VO3. Hey Gen OpenAI's image generator and then video generation from Pika and audio generation from Suno. So those are all once a week coming up here.
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A
Yeah, it's, it's, it's a fun series. And so this was like a big part of the shift in AI Academy was this regular creation of content. It's not just a collection of courses that you know, over time, you know, we have to kind of re record. So the plan is for the major courses we'll redo those probably every 12 months. But this plus our AI Academy live keeps the content fresh every week in a really digestible way. So you can go to Academy SmartRx AI, learn more about that. And I think Pod 100 Mike, I believe is for the AI Mastery membership. You can get a hundred dollars off the Mastery membership. All right. And then it's also brought to us by Macon. This is coming up really fast. Anyone who follows along on the podcast knows we are now, I think when this drops. We're 21 days out from Macon. I've been teasing the main stage. We haven't really made these announcements. If you're subscribed to our emails, you've probably seen the announcements, but there's some of these that actually got signed the weekend. So this is kind of hot off the presses stuff. So this is our sixth annual marketing conference, Macon. So October 14th to the 16th in Cleveland, dozens of sessions. I think we have over 40 speakers. You can go to Macon AI that's M A I C O N AI to learn more for the main stage. This is the thing that I'm mainly responsible for. I, I used to oversee like the entire agenda. Now Tracy and Ashley and our team take the lead on kind of building out a lot of that. And my focus is on sort of these 10 main sessions for the main stage, envisioning what those should be and then and kind of recruiting those speakers. So for these sessions, because it's everyone all together. So 1500 plus this year at the event and everybody will be in the main room together. So I like to broaden the focus to more macro issues such as AI advancement on the future of work and jobs, the economy, educational system, society, science. So that's the gist of these is that they really kind of broaden your mind about the impact of AI and what's possible. So I'm going to give you a quick rundown of what those are. We have one more to announce that's not included in this, but this is nine of the ten. So I'm leading off with the move 37 moment for knowledge workers. I'm very excited about that. I actually started really diving into that this past weekend. That's followed by becoming an AI Driven leader with Jeff woods, who's the CEO and founder of AI Leadership and the author of the AI Driven Leader, which is an awesome book. We have Thrival Skills for the Age of AI with Pat Yang, Pradat Jan Predet, Chief academic officer for code.org that's going to be incredible. That's about the impact on AI and education. Empowering teams in the age of AI How McDonald's is building an AI ready workforce with Michelle Gansley, Chief Data and analytics officer for McDonald's we have the future of AI marketing. A Silicon Valley insider's perspective with Jeremiah Oyang, a VC investor, founder of Llama Lounge Events and Blitzscaling Ventures. One I'm extremely excited about the human side of AI Inside the leading AI labs with Xiao Ma, who's a software engineer at Google DeepMind and Angela Pham, who is a UX content design lead manager of Genai at Meta. And that's going to be moderated by a former communications executive for Meta, Google and Anthropic, who's kind of seen it from the inside. I'm really excited about that. We should be able to announce her as well this week. Then we have backstage with Big Tech AI Truths from the Front Lines of Innovation with Alex Kantrowitz. He's a reporter and founder of Big Technology, author of Always Day One, and a CNBC contributor. Mike is going to be moderating that one. That's going to be awesome. Alex has interviewed all of the big names in AI and so we're going to kind of hear what Alex is seeing and hearing from the front lines of that one. We just locked in last week that I'm extremely excited about. This is one of the ones I targeted for the main stage. From the beginning, I wanted like, AI and video or film. So we have the rise of the AI filmmaker from concept to 250 million views in 60 days with PJ Asetoro, the CEO. AI filmmaker in genre. AI. This is going to be incredible. He did the calls, the ad for the NBA Finals. It's going to be awesome. He's going to do a walkthrough of, like, how to build an AI film. And then the closing keynote is reimagining what's possible. A conversation with Dr. Brian Keating, who is a cosmologist, professor, best selling author of Losing the Nobel Prize, and host of into the Impossible podcast. And I'm going to actually lead that conversation. That is. I don't know which one. I can't even pick which one I'm most excited about. Like, that. I want to watch all of them. But I can't wait for that conversation with Dr. Keating. I love the podcast and I've followed his work for years, so it's going to be incredible. So that's the lineup for the main stage. I said there's one more to go, but if you go to the Mekong AI you can see the full agenda and check out all the speakers. Yeah, it's going to be awesome. So Macon AI and same thing. You can use pod 100 for 100 off registration there. Got three weeks to go, so we'd love to see you in Cleveland.
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B
All right, Paul, we have a packed agenda this week, so we are going to kick things off with talking about AI and the economy because AI's economic impact is starting to show in maybe everything from central bank policy to corporate restructuring. So a few kind of interwoven stories here. The Fed just cut interest rates for the first time since 2024, citing growing uncertainty around unemployment and inflation. And meanwhile, AI driven disruption is ramping up across industries. We had a story this past week from Fiverr, whose CEO we've talked about in the past, that they laid off 30% of their workforce to become what they call an AI first company. They're streamlining management and restructuring around leaner, faster teams. In some other news, Zoom's CEO predicted that we're going to shift to a three day work week because AI will soon make five days of work unnecessary. At the same time, Anthropic's latest economic index says that AI capabilities are poised to transform productivity. But adoption right now is uneven. It's often clustered by income and geography. DeepMind released a virtual Agent Economies paper which envisions an ecosystem where autonomous AIs create value with minimal human input. And on top of it all, 40 top economists are calling on the Labor Department in the US to track AI's effect on jobs in real time. Now, Paul, I'm going to turn this over to you here because you dedicated quite a bit of space and thought in your weekly Exec AI Insider newsletter from SmartRx to unpacking all these different signals we're seeing right now, like what dots are you connecting here that we should be paying attention to as business leaders and professionals?
- [10:40]
A
This was a, it was interesting. So the background here, if people aren't familiar with kind of our process. So throughout the week we keep a sandbox of links and resources, research papers, articles, tweets, anything that we could potentially talk about on the show. And then I go through on. Usually I try and do it on Fridays, it never works. So usually on Saturday mornings I wake up like 6:37am and I write the newsletter. So it usually takes about two hours. Mike. Thanks to Mike, he gets it ready to go and sends it for us. And so at about 8:30 on Saturday morning, I messaged Mike. I was like, this is not gonna be ready at 9:00'. Clock. Like, I'm sorry. So what happened was we had close to 50 links in this week's sandbox. And as I'm going through Saturday morning to figure out what the editorial is going to be. So I always lead off with an editorial. It's, I don't know, it's usually like 500 words or something like that. And then I just Do a kind of a preview of some of like, usually it's about five to seven topics that we're going to talk about in the podcast this week. And so as I was going through these 50 or so links, I just saw this trend like, you know, jumps out on the page of AI and the economy because a bunch of these were all related to it. And it was, it was across everything. It was, as you were outlining Mike, it was job stuff, it was interest rates that they weren't saying AI. But that was kind of the undertone of what was going on there. It was research papers, it was, I don't know, it was just like this crazy collection of everything. You're like all of this happened in like a seven day span. Like something is going on bigger here from an economic perspective. And so that's what I ended up writing the editorial about. It just took way longer than normal to write it. So I think I finally got Mike the newsletter, like 10am or something like that. So I'm just going to kind of wander through these a little bit and maybe add a little context as we're going because it's even hard honestly to figure out the full story here yet. But what I think everyone will realize is we are entering this definite trend where there's way more conversation about AI's impact on the economy. So Mike, you let off with the Fiverr thing, you know, so Fiverr, if you're not familiar, they operate a self service digital marketplace where freelancers can connect with businesses or individuals requiring digital services like graphic design, editing and programming. So they sort of have an inside track on kind of what people are hiring people for. So the quote from the. I think this was the CEO. Yeah, we are launching a transformation for Fiverr to turn Fiverr into an AI first company that's leaner, faster with a modern AI focused tech infrastructure, a smaller team, each with substantially greater productivity and far fewer management layers. So that was in a letter to employees that was shared and then they commented in basic. I mean they didn't deny this at all. It was like, yes, we are getting smaller. AI first in their world means fewer people prioritizing tech over people, which is what I've always said. It's like there's nothing wrong with the AI first movement at all, but the implications are it's tech over people and that's exactly what's going to happen. So even things like this could end up giving that perception to AI first if it wasn't already there. So Google DeepMind, this was this Was a dense one. This is a notebook LM research paper for me for sure. So Virtual Agent Economies was the name of this paper. So again, like, if you're really into this stuff, go read these things individually. If you just want a little bit better, drop them into NotebookLM and just ask for kind of like simpler summaries because some of these get kind of dense. But. So this paper starts off with current technological trajectories, could potentially lead to a global economy in which autonomous AI agents interact with one another to generate economic value independently of human labor. The paper concludes certain domains may always require active human decision making for a variety of reasons. For example, human preference, culture, risk sensitivity, et cetera. However, the rapid increase in AI agent performance coupled with the development of scalable AI safety oversight frameworks and guardrails is likely to result in an increasing number of use cases for autonomous agents. Autonomous or semi autonomous AI agents may potentially be able to achieve more faster, adding substantial value to society. This will not come without significant challenges requiring alignment and coordination not only of individual agents, but, but perhaps more importantly, the alignment and coordination of agent networks across various scales. Okay, I'm going to, I'm going to pause on this one, Mike, because we'll get into the next in a second. But there's a couple of things going on here. So one is the labs themselves see a near future where autonomy becomes far more reliable. So right now, AI agents we have today aren't always super reliable. We've talked about like with Agent 3 recently. We talked about like the runtime and how they can. Most of that is related to coding and software development, basically. So this isn't like out into the real world of all the other industries doing legal work and marketing work and sales work and customer success work, things like that. But what they're seeing is the increased capabilities on coding that they're then translating to say, okay, this is going to be a problem. Like as these agents become more reliable, the other thing it's addressing right up front is these agents interacting with each other. So it's no longer like human sending its agent to go do something. Agent does the thing, comes back and the human's like super involved. It's human, maybe tells the agent, here's what I want to achieve, here's what I want the outcome to be, here's what I purchase whatever, and then the agent goes and does something. But it starts interacting with the brand's agents and the humans just like get extracted from the loop, except the very beginning and very end. And this can be in commerce, it can be information consumption, it can be anything. And so this is very transformative and also potentially very disruptive. And so I think what we're starting to see, the reason all these articles we're kind of talking about, research reports we're talking about are coming at the same time, is all the labs are seeing the same trajectory of agentic capabilities and they're now investing the resources to figure out what does this all mean. So, Mike, before I move on to the next one, any thoughts from you on those so far?
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B
I was going to ask you about that because my distinct impression, which I was going to mention is, look, maybe I'm just superstitious, but all these stories coming at the same time just really made me feel like, what do they know that I don't know? Or what conversations have been had behind closed doors? It does feel like this is in response to something. And so the agentic piece of it makes perfect sense.
- [17:13]
A
Yeah. And that's why even like the subject line for the newsletter, you know, I was trying to think like, what would I even say here? And honestly, like, I just went with the simple the AI economy. Because that was what started jumping out to me is like, that's what this is like. We're now seeing the development of this entirely AI powered. Like it's I used to talk about years ago. I would say AI was eventually going to be the operating system of society and business. And I feel like that's what's actually starting to happen is like it really is just everything operates on top of the AI layer. So the next one was, and this was an article from the Information, it said how Anthropic and OpenAI are developing AI co workers. This is a good read for people. The information we often cite as a source, I want to say it's like 300 a year for a subscription to the Information. If you love this stuff, It's a great 300 to spend. They have incredible articles and sources, so this one becomes more tangible. I found this one really intriguing. So it said, the AI models are being taught how to use everything from Salesforce customer relationship software to Zende Desks customer support and Cerner's health records app. The idea is to teach AI how to handle some of the complicated tasks white collar workers do. This training isn't like anything AI models have done before. Researchers give the AI fake versions of apps to play around with and hire specialists in various subjects to show the models how to use the apps. Such techniques aren't cheap. Anthropic Leaders, for instance, have privately discussed spending 1 billion over the next year on these cloned enterprise apps, otherwise known as reinforcement learning environments or gyms. OpenAI plans to spend around a billion this year in data related costs, which includes paying for human experts and reinforcement learning or RL gyms, rising to 8 billion in 2030. One reason for the cost increases the cost of hiring human experts. So they cited a company called Label Box, one of a half dozen prominent firms that provide expertise experts to AI developers like OpenAI. So these are people that are the companies that hire experts in different industries to train the models to do the jobs of the people in those industries. So again, zoom out people who, who don't think that these labs and these VC funded startups want to replace humans. You're completely missing what's happening. They absolutely intend for them to do that. And the way you do that is you train them to use the software we all use and to have the domain expert we all have. And so the cost of that, so they're literally hiring like mathematicians and consultants and doctors and like to train these models to do what they do in the software that they do it in. So Turing, a firm that helps companies such as Google Anthropic improve their model, says it has built more than 1,000 RL gyms, including copies of Airbnb, Zendesk and Microsoft Excel in the recent months. Turing's rivals, including Scale Surge, Merkur, which stick with that name for a minute, we're come back back to that one. And Invisible Technologies have also begun to offer RL environment services, including providing human experts who come up with tasks to run in these apps. And then the last piece I'll say here is as the AI models have gotten better data labeling firms have gone through hiring student. From hiring students pursuing master's degrees and doctorates to working with professionals with multiple years of experience in niche fields where they're actually doing complete real world tasks using specific application. So if you weren't aware this was happening, here you go. Now, if you want to know how significant this is and how it's shifting the economy, Merkur, one of the companies I just mentioned that does this, their CEO Brendan Foody tweeted an article last week called the economy will become an RL environment Machine, Reinforcement learning, the gyms, basically. So building on this concept, he also tweeted Merkur scaled from 1 million to 500 million revenue run rate in the last 17 months, according to him, quote, making it the fastest growing company of all time. Merkur is an AI powered talent acquisition platform that automates the hiring process by matching candidates with job opportunities, facilitating recruitment for companies. One trend he says is driving the meter of growth is the economy becoming this RL environment machine. He says reinforcement learning is becoming so effective that agents can hill climb any benchmark, meaning pursue any benchmark. But humans need to define the rewards to automate everything. While everyone fears job loss, we're creating a new category of knowledge work. Faster than any time in history. The future of work will converge on training agents. We're paying out over this. Still quoting him. We're paying out over 1 million per day to people in our marketplace and hiring experts rapidly across nearly every domain. Software engineers, doctors, lawyers, consultants, bankers. And many more meaningful million dollars per day for people like you and me to train models to do what we do. So again, Mike, I'll pause there for a second. We've been explaining this for years, that this is where this was going. We now actually see the data and it's just, this wasn't intentional. It's like again, connecting the dots. As you go throughout the resources of the week. You're like, oh, wait a second, the information says this. Oh, I had tagged this tweet from this guy from Merkur and now all of a sudden it's like, oh, it's all happening. Like you're seeing the pieces come together. Yeah.
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B
I think it was maybe easier before to ignore this trend as you're kind of really getting your hands dirty with AI and saying, okay, like some features aren't there yet or some tools aren't as advanced as people would want to admit or want to sell you on. This could not be more clear now. You really, really. I just would implore anybody listening if you haven't really taken a close look at the writing on the wall just yet, this is your wake up call to do so. You're probably still far ahead of the curve, but this is going to be the story, I feel like in the next several years.
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A
Yeah. And like this idea of the economy will become an RL environment machine. I get that like everybody in every field, people are willing to pay for expertise to train the models. But what happens when they've been trained right? Like, what happens when they're genius level at your job and then what do you do? So I think there's going to be a window where people are going to be able to make a ton of money. It's almost like, it's almost like the uber economy.
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B
Yeah.
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A
For AI, like it'll exist for a while until the autonomous vehicles Take the, the role of the people driving the cars. And that's basically what this is. You're going to have this window where there's like tons of money is going to be made, companies are going to be built on this, people's careers. May be this, you may be an AI trainer for the next five to seven years and like make great money. Maybe you can make a couple hundred thousand dollars a year. Just train an AI in these models. Then what? Right, that's my big thing. Which then leads us to what will AI look like in 2030? So this is an Epoch AI report. It's 119 pages. Another great notebook, LM Use Case. This one was commissioned by Google DeepMind. So it provides a comprehensive forecast of AI development by 2030. So it basically looks at the scaling laws and say, okay, are they going to continue? And if so, what does that mean? The report argues that the exponential growth in training and inference compute, which again is when you and I use the models, will continue requiring investments potentially reaching hundreds of billions of dollars, which is what we've talked about. The training of these models is going to cost billions or hundreds of billions of dollars. The byproduct of these investments could be a significant increase in net productivity and GDP across the economy worth trillions of dollars. That then led to the, the other one from last week, the Anthropic economic index. Understanding AI's effect on the economy. This is a 48 page report as we've talked about before. I think this is the third edition of their report. CLAUDE is dominantly used for coding, so you always have to look at their uses and understand that their user base is heavy coding. So they don't have the totality of coverage across the world of diversity of use cases. But we'll actually get into that in OpenAI's research report in the second main topic. So in the anthropic one they said existing capabilities of CLAUDE and other frontier AI systems are already poised to transform economics economic activity. Given how broadly applicable the technology is, rapidly advancing AI capabilities only reinforce the conclusion that immense change is on the horizon. And yet early AI adoption is strikingly uneven. Usage currently clusters in small sets of tasks with strong geographic variation that is highly correlated with income. So this gets into some much larger societal issues. People with money, people in privileged areas are the ones that are benefiting most. And this is the first study that breaks this into like regions. They say AI is being used to fully or directively automate more and more tasks over time. In just nine months we saw directive automation jump from 27% to 39% of all conversations. For enterprise customers, that figure is 77%. So this is a really important distinction. They say users are entrusting Claude with more autonomy. Directive, quote unquote, conversations where users delegate complete tasks to Claude jumped from 27% to 39%. That's 12 basis points which is, I don't know, quick math, 40% ish or whatever increase not insignificant. So again, the difference between 27% and 39% isn't 12%, it's 12 percentage points, which is much closer to like 40%. So there's a major jump in a very short time where people actually relying on these things to do the work for them. So they then break everything into automation versus augmentation. And so I kind of like where they're going here. Again, their data is somewhat limited because it's predominant coding, but I like the direction and I like the fact that they're really committed to these economic impact studies. And then that leads to Mike, which you had highlighted the leading economists of 40 of them signing a letter calling for the Department of labor data on AI's impact on jobs. Good luck. Like the, the current administration is hiding every relevant piece of data that would show any of this. So this is great that the leading economists are calling for this. They're not going to get it like any, any indication. So this is where we have to rely on these like private companies and third parties to do the research. The US government is not going to tell you if they're seeing trendline data that says AI is going to take jobs. It's not coming from the Senate administration. So again, regardless of political views, if you're new to the podcast, we, we keep this as middle of the line as humanly possible. I don't care who is in office, which side is in office. All we're going to talk about is the implications on AI. And I'm going to tell you right now, this administration is not going to tell you if they see an impact of AI on jobs. So you have to look at the third party studies to see what's actually going on. All right, I will stop there because I know we're going to kind of work into the OpenAI one, but you, you make the call. Mike, if there's anything else you want to talk about in this one before we go to open.
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B
Now that was an awesome connecting the dots here. I think it's super valuable. Just one kind of final point here and I don't want to be too down in the dumps. But I think you have to, as a professional, kind of in charge of your own journey and your own success, like, I do think you kind of. I applaud very much the company is putting out more research. This is all needed. But like, you kind of have to sit back and realize whether it's the government or the company building this stuff, like, nobody is coming to save you on this issue. And I realize that can be really depressing in some ways. But I also think the sooner we kind of get to that as individuals trying to kind of make our way in the world and navigate this stuff, I think the sooner you get to that idea, the more you can at least start empowering yourself using these tools, using the research out there, the education being put out by us, by plenty of others to kind of chart a path forward. Because if you think to your point about the administration, if you think the data is coming, the policies are coming to fix this or to address this fast enough. A. I think that's deeply unlikely.
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A
Yeah. And I, I do think, you know, when I look at this over time, I, I just see a very extended Runway for people who are highly competent with AI tools and have domain expertise. Like, I, I just feel like the diffusion of this technology is still going to take years. No matter how advanced the tech gets, no matter how good these models get, People who work in corporations know nothing happens fast, nothing happens overnight. So there are some companies that are going to be disrupted very quickly a lot in the tech space and stuff like that. Disruption is coming. But you becoming AI literate, you being highly competent in using these tools, being the one in your company that can apply it, you have a wonderful Runway ahead of you and to be more valuable in your company. And I think over time, maybe we do figure it out and maybe the cliff doesn't happen as fast as sometimes we think it might. And maybe new jobs do emerge and like, maybe we solve all of this. But sitting on the sidelines and waiting is not the answer. You will get disrupted as a company or as an individual. So your best chance is just stay on the forefront of this stuff and like, connect the dots before everybody else. And I feel like that's always been true in business. The more proactive you are, the more intrinsically motivated you are, the more knowledge you have, the more power you have and the more leverage you have in your own career. And I feel like that's going to continue to be true. So, yeah, it's, yeah, like that lens of kind of hope and optimism around this while also being realistic that we're running out of time to get started. If you're not started yet.
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B
Yeah, it's extremely exciting. Yeah. I mean, I feel like roughly I kind of, in my head I'm just like, there's a golden opportunity decade at least right now with this stuff, doing everything you just said, despite, you know, my how starkly I frame this. I think there's a huge upside. I'm excited.
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A
Yep.
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B
All right, let's get into our second main topic here, which is a study released by OpenAI. They have released what they call the largest study to date of how people are using ChatGPT. So in it, researchers looked at 1.5 million ChatGPT user messages between May 2024 and June 2025. And they used AI to label what each message was kind of being used for. And they found that Most usage, over 70% is now non work related. People use it for writing information, seeking, tutoring, advice, translation, creative brainstorming. And they kind of break this down into three big categories of usage. The three biggest ones are what the researchers call first practical guidance, which is basically like getting advice, writing and then seeking information. And these three broad categories account for nearly 80% of all usage. However, work usage is rising too, especially among educated users in professional roles. And in that area, writing tasks dominate. But AI is increasingly being used as a decision support tool, not just to execute tasks. The study actually classifies most work use as what they call doing so things like generating content or asking where ChatGPT acts more like an advisor. Now, an interesting surprise here is only 4% of the messages they looked at are about coding and even fewer are about relationships, two kind of big topics of AI usage. So Paul, I'm curious, like what surprised you if anything about this data? Definitely thought that last point was interesting.
- [32:39]
A
Again, another good one to analyze. Throw into notebook is 64 pages long. They dropped this, I think the day after anthropics maybe. But again, both labs working on big economic data, big research studies simultaneously. The one thing Mike I I will note is so this was done in partnership with National Bureau of Economic Research, a working paper by OpenAI's economic research team. It covers consumer plans only. So when we're talking about the use of commercial or, you know, work versus personal, that's only for these consumer plans. It does not, based on my interpretation of what they mean by consumer plans, only does not mean team and enterprise accounts. So that's extracting the millions of users who have team or enterprise accounts. And we're only Looking at individual plans here, so the fact that even then 30% are still work related. Right. Like so 70%. That makes sense. Like it's a personal account. One thing I thought was interesting, Mike, was the age breakdown. So they said nearly half or 46% of all messages sent by adult users were from users 18 to 25. Wow. So that's one that you. Again, when we zoom out and we think about the impact on society, the impact on the economy and the future of work, the people who are getting the value from these tools are the younger generation, it's the generation we've talked about recently that have 13% unemployment, like 22 to 25. And so the people with the capabilities are really starting to centralize in that young age group. And that's going to be that AI native generation. Like they won't have known education or life without an AI assistant on call. And that's fascinating. Like I think the downstream impacts of that. The other one I would just mention, there was an excerpt where it said a key way that value is created is through decision support. ChatGPT helps improve judgment and productivity, especially in knowledge intensive jobs. And as people discover these and other benefits, usage deepens with user cohorts increasing their activity over time through improved models and new use case discovery. The one thing I thought about here, Mike, that just keeps coming back, every conversation I have, whether it's, you know, basketball on Thursday nights or just meeting with friends who are in corporations or talking at, you know, when I go out and do presentations, meeting with executives, the lack of awareness and usage of reasoning models is, is shocking to me. Still like people who have never done a deep research project. Yeah, nine times out of ten when I'm talking to somebody like, oh yeah, because they know I do AI stuff. So everybody always talks to me about AI stuff. It's like, oh yeah, I'm using ChatGPT all the time at work. I was like, have you used deep research yet? No, what's that? Every time. And I'm like, dude, you gotta go do it. Like, and I'll give them like, hey, run this prompt. Like you have to try it. It'll change your perspective on all of this. So this, this research is really fascinating and this, the, the contextual stuff you and I hear all the time, Mike, about how little people still know about what these things are capable of. It's part of why I love the Gen app series we've been talking about. It's like, get that stuff to people in like bite sized bits like notebook lm I'm not intentionally like you know, talking about notebook non stop this but that's the kind of thing like you go in and use that you try the guided learning. I said this to some parents so I think I've mentioned this. I got paid basketball on Thursday nights and so it's all a bunch of dads from my kids school. And so I was sitting on the bench like talking with you guys like have you tried guided learning with your kids? No. No. What's that? I mean it changes the way you help your kids learn. And so there's all these incredible features that I think help people connect the dots of the value these tools can create. And so you know to watch this data over time is, is really interesting but I think we're kind of hitting that escape velocity point where I mean OpenAI is what 700 million. So about 10% of the world uses ChatGPT on a monthly basis. The question is are they just using it to do quick prompts and they don't know about image generation and you know, reasoning models and guided learning and all these other capabilities? My guess is yeah. And that's what kind of the data is showing. It's like the vast majority then they say the vast majority of use cases is for writing. Like it's the dominant use case. It's the most obvious thing to use them for. And so I think once we catch up a society of all the other capabilities, I think Google's doing a really good job with Gemini right now to surface those things. Like when you go into Gemini now they're kind of showing you prompts to help you use Nano Banana Chat gbt. We talked last week about like the hundred sample prompts for college students. Like I think the labs know it. They need to make the uses more tangible for people so they understand the value they can create faster. And I think we're heading that way.
- [37:08]
B
Yeah, the we barely are even scratching the surface on using these things as true like cognitive copilots like we've discussed many times, using a reasoning model for deep strategic work is like gaining a multi, you know, double digit IQ bump. And I wonder what the effects of that will just be once people, if people are doing that at scale. You know one final thing that just jumped out to me is they actually wrote in this report quote, this widening adoption underscores our belief that access to AI should be treated as a basic right. And like honestly I like can't disagree with that at this point. I feel like it's roughly where Internet is for me. As well. Like you have to have access to this to do anything.
- [37:50]
A
Which is interesting because it ties back to the anthropic thing that looks at the disparity of income levels and access. Yeah, I agree. I mean, it is truly like a utility. It's like electricity, you know, it's intelligence is the new electricity. In essence. Like, everybody's got access, running water, you know, electricity, intelligence like that. I think that's how the labs think about it. And it's probably a good mindset. What I was saying at the beginning, like, AI is truly the underlying, underlying operating system to society. I think that's how it becomes. And anyone who doesn't have access to that intelligence on demand, whether it's for helping your kids at school or helping you with work, or helping you find a job, or helping you plan your life, like it is going to change the way we do everything, the way we buy, the way we consume information, the way we build companies. And yeah, access is fundamental, I think. Yeah.
- [38:37]
B
Just flagging for our future selves. There's at least a couple keynote ideas in there. What you just said.
- [38:43]
A
Yeah, I don't know what I just.
- [38:44]
B
Said, but intelligence as the new electricity, you know, There you go. All right. Our third big topic this week is a talk given by Replit CEO Amjad Massad, in which he said the future of business is being rewritten thanks to AI in some really unique and interesting ways. So he gave this recent presentation at Y Combinator's AI Story Startup School event. And in it, he envisioned a world where anyone can build software just by speaking, with no coding required and AI agents doing all the heavy lifting. In this type of future, the role of an engineer, a software engineer, for instance, shifts from technical execution to high leverage thinking. And as we discussed last week, Replit's own Agent 3 is already working autonomously for multiple hours, writing code, deploying software and testing features. As a result, Mossad predicts traditional SaaS will go to zero because every individual will spin up personalized apps or agents on demand. And he says this will also have widespread effects on how companies are structured at a fundamental level. He sees eventually organizational hierarchies being replaced by fluid networks of generalists collaborating with autonomous tools. Now, Paul, that last bit is kind of the money quote here, money idea. And I know you have unpacked that a bit on LinkedIn and you've been thinking about that idea a lot.
- [40:03]
A
Yeah. So I don't remember what day this was that I listened to this, but, you know, we obviously talked last Monday Came out on Tuesday, episode 167, about Agent 3 and their pursuit of autonomy and how they felt they'd get 10x increase in the autonomy of their agents and their ability to do like 200 minutes of run time without human intervention. And so that was like on my mind. And the way I do my podcast listening is I'll, I'll go through and like, you know, once a week I'll scan all the latest episodes, I'll save ones to listen to, and then depending on how much time I have, whether it's in the car or out for a run, I try and listen to one that I can like consume as much as possible, like in that, that short segment. And so I listened to this one on the way into work and it's like 32 minutes long, but one and a half speed. You know, you get through it pretty quick. And so I, I got to the office and I was like, damn it, like, I, I shouldn't listen to that because I had a lot to do that day. And my mind was just sort of racing with what he had said. And it reinforced a lot of the things we'd heard about and we've even talked about on this podcast. But yeah, the four things I called out in the LinkedIn post was and on. His vision for the future of business is ideas of the greatest resource. So pairing with knowing how to use AI to bring them to life. So again, these things are going to be capable of basically everything. If we just assume the scaling laws to be true, we look out, you know, two, three to five years, you have to assume they're going to be genius level at every profession, every domain, and they're going to be able to execute most of the tasks that humans do. So the greatest resource, he's saying, is the ideas of what to use them to do it. So if we assume we all have access to this genius level intelligence on demand for any, anything we want to do, what do you do with it? And so, I mean, you could look at deep research right now as like a prelude to that. You and I know deep research is capable of it. I could come up with 15 things to do every day with it if we had the capacity to do it like all day. I think about things I could be doing with it. I think that's what happens here. It's like, okay, we have the ability, but how many people actually understand it? So this idea that moving forward, ideas become the greatest resource, which requires experience and expertise, which bodes really well for senior level people who can Envision the application of this intelligence to their businesses. The second everyone is empowered to create apps and software in real time to solve problems and increase productivity using language, no coding needed. I don't think people comprehend the significance of that. Just anything you want to spin up, you can literally just go and say, hey, build this thing for me. Build this app, build this software, do this thing more efficiently for me. The one you mentioned, org charts look more like fluid networks versus rigid structures. As someone who is actively hiring and planning to scale very quickly our business, this is the one that I lay in bed thinking about. Like, I don't want to structure the business in a traditional rigid way when we have the opportunity to reimagine this as an AI native company. And so I want to get that right and I want to like, position us to be able to adapt quickly and not be stuck in some traditional org charge structure. And then the fourth optimal teams are collections of generalists working with agents to achieve a mission. I love that idea and similar concept. It's like, do we really hire salespeople and customer success people and marketing people? And like, do you do it in this traditional way or are you just hiring really intelligent people with great ideas and the ability to work with agents who can maybe work cross functionally? Like, I don't know the answer. And that's why I loved this podcast. Is it actually, like, forced me to step out and given where we are as a company ourselves, just think about what would I do if I could do anything, if I could reimagine how to build a company, what does it look like? And to me, as you were saying earlier, Mike, that the optimism and the excitement of this moment like to get to do that, to not have, in our case, thousands of employees that it might impact, but where we're a smaller team and we can try and do this right from the ground up. I don't know that there's ever been a better time to build a company when, when you think about it in this way, we're still planning on hiring a ton of people. Like, we are a people first company. Like, we want to hire a bunch of people, but I'd rather hire 50 or 100 versus a thousand. Like, if I can do this the way we want to go and pursue our mission of accelerating eye literacy and transformation and not need a thousand people to do it, that'd be amazing. Like, I'd love to do it. I want those 50 to 100. I don't want more than I have to have as an entrepreneur and as a CEO, and I think, like, I, I know executives at major companies with thousands of employees who are thinking the way I'm thinking, but they have a bunch of legacy staff and technology. And that's why I'm convinced disruption is coming. Like, they have to get where we're trying to go, but they have to first get through what already exists to do it. So that's the idea of AI native versus AI emergent companies that we often talk about. Way, way easier to be an AI native company right now to be the ones building smarter from the ground up.
- [44:44]
B
Yeah, it's interesting to think about. I don't have any of the answers either at this stage, but you can almost see at least one possible pathway forward where when you go to hire someone in this kind of AI native organization, it's like, I don't even know if I'd care or even look at their background. It's like, just get me in the room with an hour with them for an hour to kind of. It's like you're your vibe hiring almost, but you're kind of testing that.
- [45:10]
A
Yeah, we're doing it right now. We've made a couple hires like this already where I just wanted people who I trusted, who I knew were highly intelligent, who are adaptable, who are resilient, and who could solve problems and bring ideas to the table. And it's like, I don't even, like, I'll create a new role for you. Like, just like, that's the kind of people I want. And I think that, you know, it's a change for businesses, but it's also change for professionals. Like people who've maybe built their career and become successful in one track. Like, well, I was a marketer, I was a salesperson. Or like, yeah, but you don't have to be like, you can be anything now like that. And I think that's a. Again, to wrap your mind around that and then how do you hire for that and how do you train people for that when it is this gen? But I'm, I've always. Mike, you know, we worked together at my agency all those years. I always hired generalists. Like, I was a huge believer of like, companies hire us to transform their businesses, grow their businesses. You need diverse knowledge across a number of areas. You can't just be a marketer and be a consultant. Like, you have to have diverse knowledge. So I'm very comfortable in the building a business around generalist idea. It's what I did. But that's not a comfortable thing for People who have made their careers in one specific department or domain. Yeah.
- [46:17]
B
Topic for another time. But kind of interesting as we have these conversations to just see like all these almost serendipitous things that you've learned in your past businesses and career that becomes super, super relevant in the age of AI and build. It's like, almost like if you're meant to build this type of business. Right?
- [46:35]
A
Yeah. And I would say, like, just, again, I don't, I don't talk about the personal stuff too much, but like, this is, it's, it's wildly exciting, it's wildly frustrating because, like, there's a part of me when I listen to this podcast, it's like, I just want to go, I want to go hide for five days and solve this. Like, because you hear something, sometimes you're like, oh, I can figure this out, but I need brain power and I need quiet and like, you just want to, like, leave and go on a retreat for five days and do nothing else but figure this out. But then the reality smacks you. It's like, oh, wait, Macon's in three weeks, I have four presentations. We're hiring a bunch of people. I need to make hiring decisions. Like, and so reality comes back to where, like, we all have jobs, we all have other things we have to do, but stuff like, this is what I want to do. Like, I want to do all of it, but like, I love trying to figure this stuff out and I think the answers are there. And I think in part it's because we now have reasoning models that you can work with as a business leader to like bounce things around at midnight when you're thinking about it, which we didn't used to have. So, yeah, with my co CEO, GPT and me, I think we could do things like this. I just, I have to find the time to do it. And I think we all struggle with the same challenge.
- [47:46]
B
Yeah, for sure. All right, let's dive into this week's rapid fire topic. First up, Meta has launched their Ray Ban display glasses. This is a wearable that features a built in screen and it's paired with a neural wristband for gesture control. So these are not just camera glasses. The right lens now shows text messages, video calls, turn by turn, directions and visual answers from Meta's AI assistant. Instead of tapping the frame, you control these glasses by subtly moving your fingers, thanks to the EMG powered Meta neural band that reads your muscle signals. Mark Zuckerberg calls this the next step towards super intelligence, positioning the glasses as the start of a Larger platform ship shift, and they ship September 30th in two colors and cost $799. So, Paul, we've talked about wearables in different formats, like, plenty of times, but there's a good reason for this. Like, they do seem like necessary, but as of right now, immature or even missing piece of kind of AI being truly everywhere.
- [48:51]
A
Right?
- [48:51]
B
Not just on your phone or in voice mode or whatever.
- [48:54]
A
Yeah, I mean, I think Apple's gonna make a play here. Google's gonna make a play, I think OpenAI. While they're saying it's not glasses, their initial one, they also are looking at all user interfaces of the future. So I think we just have to come to grips with whether you like these things or not. They're probably gonna be a pretty commonplace thing, you know, in the coming years in society. I will say, just on a separate note, if you've ever given a presentation, especially a live demo, and it didn't work, go watch Zuckerberg. Like, it wasn't as bad as what he went through. It was so painful there. Two live demos just crashed and burned and Zuckerberg ended up on stage for a minute trying to get a live demo to work where one of his executives was calling him and he was going to like, you know, FaceTime or whatever they call it with. With Meta. Just brutal. And after the fact, I did see one Meta executive say the problem was when he was saying, hey, Meta or whatever, it was activating every meta device in the auditorium. And so it was like crashing the system. And that's why it wouldn't work. But it's so uncomfortable. Zuckerberg did as best of a job as you can do, where he's probably thinking, like, how many people my firing when I get off stage. You can see every, like, thought going through his head while it's happening. Yeah, yeah. Again, like, glasses, wearables, the form factor, tbd. Like, I feel like a lot of them do feel like glasses is a play. I think that Apple will probably input cameras into AirPods eventually. I think Apple believes AirPods are actually maybe even a bigger play for this. Always on, always aware of your surroundings, where you can get a 360 view from multiple cameras in your AirPods. I think they'll make a big play maybe next year around that. So, yeah, wearables are going to be a thing. I think it might take five to seven years maybe before there's a clear winner as to which form factor it is. Or maybe it ends up being a collection of form factors. But the Thing I am confident is by 20002030 just assume everything you do, everything you say is always being recorded by someone. Like if you go to a conference, especially a tech conference, like everybody's going to be recording everything. Probably already are in some cases. So again, like it or not, just be prepared for a future where this is standard through different devices.
- [51:08]
B
Yeah, we also, we can't talk about on stage fails. This close to Macon, that was making me extremely anxious.
- [51:14]
A
You have to connect that dot. I wasn't even thinking about that. I'm not doing any live demos right.
- [51:19]
B
Right now. Just even just hearing about it, I'm like, I feel so bad. I mean, obviously it's your job to make sure this works, but I just feel bad no matter who it is.
- [51:26]
A
Actually, now you're saying that I will probably be doing some live demos.
- [51:29]
B
Oh no.
- [51:31]
A
Always have plan B. Yeah.
- [51:34]
B
All right, next up, AI has aced one of the toughest coding competitions in the world. Both Google DeepMind's Gemini 2.5 and OpenAI's reasoning models have competed in the 20 ICPC World Finals, which is basically the Olympics of collegiate software programming. They delivered stunning performances. Gemini solved 10 of the 12 problems that they have to complete in the five hours of the competition, including one no human team crack. OpenAI solved all 12 and this is a perfect score that would have secured it first place if it was actually a competitor in the competition. What's interesting about this is that these problems they give during this competition, they demand things like abstract reasoning, multi step logic and creative solutions under strict time limits. So you know, Both Google and OpenAI are saying, look, this is more than just like a party trick when it comes to coding or when it comes to math. It is signaling this general purpose reasoning that AIs are now able to do at or above elite human level in high pressure problem solving. So this could translate, they say, into all sorts of breakthroughs in fields like drug discovery, chip design and logistics. Because all these problems are like algorithmic. So this definitely seems like a noteworthy milestone for me. I realize it's like it's a kind of one competition, but it just really feels like even more, if I'm a, if I'm a programmer or a coder of any type, I'm looking over my shoulder what AI can do now.
- [53:05]
A
Yeah, yeah, you can't. I get that it's like abstract and maybe these are like competitions you've never heard of and things like that. These are a huge deal within like Silicon Valley, within the tech world, within AI world. These math competitions are massive.
- [53:19]
B
Yeah.
- [53:20]
A
But to the average person, it's like, I, I have no idea what, what I would say is, like, as you explain this, I kind of think about this. So for a long time, decades, the pursuit of language was like, like the cornerstone of AI development because they, they believed that, that language was fundamental to human intelligence. So understanding and creating language was needed to break through and build general intelligence. That's what the transformer moment, moment in 2017 gave us, was that the architecture to now pursue what ended up becoming the language models we see today. Math and reasoning is sort of the next frontier to solve what they think to solve, to truly reach human level general intelligence. And so math, these breakthroughs, these advancements are fundamental stepping stones to building the agentic AI that we talk about and that they all envision and to building the kind of economic impact we let off with. These are fundamental steps to doing that. And so, yeah, I would not sleep on these kinds of breakthroughs. They can pass by real fast. And, you know, you're never going to hear about this stuff in mainstream media. I don't think. Again, I don't watch too much mainstream media, read too much. But this is not stuff that the average person thinks about or cares about. But when you look back three years, five years from now on, the milestones toward AGI and beyond, things like this probably appear on that timeline, I guess, is a good way to explain.
- [54:55]
B
All right, next up, a new Pew Research report finds that Americans are far more concerned than excited about AI's growing presence in daily life. Half of US adults they surveyed say they're worried about the impact AI will have, compared to just 10% who are more excited about the impact it'll have. And the bigger fears here are that AI will erode people's ability to think creatively and form meaningful relationships. Only 16% of people in this research surveyed believe it will actually improve creativity. However, at the same time, most Americans are open to using AI for everyday tasks, especially in areas like weather prediction, fraud detection, and drug development. But when it comes to personal decisions that they asked about, like faith or matchmaking, most say AI should play no role at all. Interestingly, 76% of people said it was really important to know whether content was created by a human or AI. But more than half admit they can't reliably tell the difference. So, Paul, it's always good to see some solid data from a reputable polling outfit about AI attitudes, like what jumped out to you here in these responses.
- [56:05]
A
Yeah, it's a great Point Mike. We talk a lot about research on this podcast, and we always encourage people, drill into the methodology, figure out, how was it done? Who. Who was part of it, what did they ask? So this is over 5,000 adults from June 9 to June 15. So recency is great. Large sample size done, as you mentioned, by Pew Research, there's very few, if any, more reputable research arms, so that's all great. But I was curious. It's like, okay, so we're, we're sharing all these findings about AI from who? Like, who are the 5,000 adults? And I get that it represents, you know, the U.S. base, and it does. It's like a broad spectrum. But I actually was. I wanted to see the questions, and so I wanted to see how are they defining AI? So if we're assuming that a large portion of US Citizens don't know what AI is, other than maybe what they've seen in ads or heard about or seen in movies, how relevant are the rest of the responses? Is kind of what I'm getting at. If. If you're asking people who don't even know what it is. So I found the questions they asked, which Pew does a great job of, like, download the report, view the questions. So it's super clear how they did this. The first question is, AI is designed to learn tasks that humans typically do, for instance, recognizing speech or pictures. So assume it's your grandma, your mom, your brother, like your co worker who knows nothing about AI. That's the question they were asked. And your choices are a lot, a little nothing at all, don't know, or just refuse to answer it. So right off the bat, nothing at all. I know nothing at all about this AI you're explaining as recognizing speech or pictures. That's a. A pretty broad and incomplete explanation of what AI is. If someone is like, I don't even know what you're talking about. But okay, recognizing that's. Now What I think AI is is kind of what I'm getting at. 5. 5% said nothing at all. 48% said a little. So 53% of people surveyed under the definition of AI as recognizing speech or pictures doesn't know anything about it.
- [58:04]
B
Yeah.
- [58:05]
A
So you then have to segment the responses by like, okay, the only people whose answers I even care about are the 47% of people who actually claim to have read a lot about it. Because now we're going to get, like, a little bit better sample. And again, to Pew's credit, they break this stuff down 10 ways to tomorrow. Like, there's 46 pages, tons of data. They go through all the charts. So I would say it's, it's really interesting stuff. There were definitely some points that jumped out to me. You know, the creativity One you mentioned, 76% say it's extremely or very important to be able to tell if pictures, videos and texts were made by AI like that. You don't need to know what AI is to know that that should be true. So there is some good data in here. The attitudes. 50% say they're more concerned than excited about the increased use of AI in their daily lives. So good stuff. Just always go into it before you start throwing clickbait up and things like that. Know a little bit more about the research to make sure that you know what you're sharing. But this is a super legitimate study, lots to be learned from it. Very digestible. This is not a highly dense research like some of the other ones we talked about today. So I would say it's a good read and it's a good resource for people who want to understand where we're at. I would imagine there are a lot of politicians reading this research trying to gauge again, how much does AI play into the midterms next year?
- [59:25]
B
I was going to literally say, I would bet a substantial amount of money there is some private polling in the field trying to figure out what the wedge issues are here and how people feel about them, you know.
- [59:35]
A
Yep.
- [59:36]
B
All right, so next up, the lawsuits against AI companies keep coming. So periodically we're going to dedicate a rapid fire segment to recapping some of the lawsuits that are happening and their significance. So this week we have two new ones that have been filed against AI companies. First, Disney, Warner Brothers, Discovery and NBCUniversal are suing the Chinese AI company Minimax for what they call massive scale piracy of copyrighted characters. So this is filed in federal court and it alleges that Mini, Minimax's image and video generator, which is called Helluo AI, routinely produces high quality content featuring Hollywood ip. So, for instance, one prompt they said can generate videos of Disney, Marvel, DC characters, all with Minimax's branding on it without any licensing or permission. And the studios also claim Minimax ignored multiple cease and desists and they continue to profit from this kind of theft. So they're seeking damages and an injunction to halt what they argue is blatant copyright infringement. Second, Penske Media, which is the publisher behind Rolling Stone, Billboard and Variety, has filed a landmark lawsuit against Google, accusing the tech giant of using its journalism to power AI generated search results without permission. The suit actually targets Google's AI overviews, saying that they scrape and summarize Penske content while siphoning away traffic, subscriptions and ad revenue. Penske says Google effectively forces publishers to hand over content in exchange for visibility. It is the first major US publisher to sue over this issue specifically. So, Paul, another lawsuit against AI video generation companies. Curious how you see this playing out just because it seems so blatant and obvious these tools have been trained on this stuff. And then would also just love your thoughts overall on the AI overviews.
- [61:25]
A
Lossy. Yeah, I don't know. I mean, again, there's going to be dozens, if not hundreds of these. There probably already are dozens of them. I think we just get the one surface that we see in the media. But yeah, I mean, like we've talked about before, they did they, they obviously trained on this stuff. There was one. I was just trying to find it real quick because I don't think I put the link in for this week. We'll touch on it next week. But Sora, I think a new version of Sora is right around the corner. Video Generation from OpenAI, maybe this week, certainly soon. And it was blatantly trained on all this stuff. Like all of it, we know, took these things. I have no idea how this plays out in the courts. We know a bunch of, you know, penalties are going to be paid whether, I don't imagine they're going to eventually have to like, retrain the models. Like, I don't think anything like completely disruptive to the industry happens, but there has to be a reckoning at some point and there has to be a resetting of the values that go into building these models by these companies. Right now. It is this, they did it. So we're going to do it. And that's basically how this started in 2022. Like, well, they did it like, like, we gotta keep up. I mean, we have quotes from internal memos at Meta that said this exact thing. Like, we, we know this is what happened. So I, I don't know. I mean, we'll just, like you said, we'll kind of keep following the space. But it's, it's challenging and everybody wants to just do a deal. I think, like at this point just like get their money from these labs and accept that they've done it and try and, you know, get some revenue out of it or get some, some fees paid. I don't know. Know, it's, it's a very complicated space and I just think it's going to take years to play through all this.
- [63:02]
B
Next up, Reid Hoffman, the co founder of LinkedIn, has, in a new thread on X, says he has, quote, voice pilled. And if you've ever spoken to ChatGPT instead of typing, you might be voice pilled too. So he just kind of coined this term in this viral thread on X where he argues that the next major leap in AI interaction will necessarily come from bigger models. It'll come from how we engage with them, voice input. He says it's faster, more natural and more flexible than a keyboard. You can do things like fumble, rephrase, ramble, and today's models will keep up with you. This makes voice an ideal way to interact with AI, especially for creative or exploratory tasks, because you're not compressing your thoughts to fit a text box. You're more like thinking out loud. So he thinks that voice will reshape hardware and even office design. And it may also make AI more accessible, lowering the barrier to AI for people across cultures and literacy levels. So soon enough, here is if you've ever experimented with voice mode and how much it's gotten better, talking to machines may just feel more human than typing ever has. And Paul, I mean, I genuinely have to say I think this reflects my own experience. I'm not like coming down on one side or the other, but my gosh, I've gotten so much done using voice mode. I mean, with the caveat, like, it craps out like five times on my morning commute every morning. But it is really incredible what it. That it fundamentally unlocks some different stuff, I think.
- [64:29]
A
Yeah, I'm with you on the, you know, it stops working. Probably you and I drive the same route to work.
- [64:37]
B
Where it drops exactly where.
- [64:39]
A
Yeah, on the way there and on the way back. I think that's my biggest problem with it at the moment is that you don't know when it stops listening. And if you get going, like you're in the flow and you say a bunch of stuff and then you're like, like, hello, are you there? And nothing. You're like, oh, man, every. All those thoughts are gone. I think that's solved with local running models, you know, so let's say Apple solves it by having a small language model on device and it doesn't have to be connected to anything, don't have to be a WI fi, cell signal, things like that. And then you, you know, it's working. And the whole idea is hands free, eyes free. Like, you know, when I'm having these Conversations. Well, if eyes are free, I don't know if it's transcribing what I'm saying or not. So I think for me, that has to be solved. I get so frustrated when those thoughts are just gone because it dropped and you didn't know it. So I think that is an infinitely solvable thing, though. I, as a non AI developer, know how to solve that. I don't know technically how to do it, but it seems like an obvious way to solve it is just to have it on the local device, whether it's your glasses, your AirPods, your phone, whatever it is. It's just. Just the model lives there, not up in the cloud. So, Yeah, I. I don't know. I. I do think, like, at the office, though, it'd be kind of weird if, like, everywhere I walked, everyone's just talking to their machines all the time. I do, like, there's some nuances that I think. I don't know. Like, I think I'm still gonna type. Like, I. I don't think everything's just going to become Voice because you always get that. You walk by, like, people are talking on their AirPods and you don't know that's what's happening. You stop and look at them like, oh, they weren't talking to me, they were talking to someone else. That would get weird if everyone in the office is just like, constantly talking to their machines and no one knows if they're on a call or what they're doing.
- [66:22]
B
Right, Right.
- [66:23]
A
Yeah.
- [66:24]
B
And I will say, too, I'm running an AI productivity workshop at Macon and Voice is not the focus of it, but that's definitely one strategy we're going to cover. It's like exploring these different modalities that help you get more out of AI tools, because you could see a very near future where you're not only typing and prompting and working with AI, but also you've got Voice fired up just to log any thought or work through any issues, things like that.
- [66:49]
A
Yep.
- [66:50]
B
All right, Paul, to wrap things up, I'm going to run through some quick AI product and funding updates to kind of bring us home here.
- [66:56]
A
Sounds good.
- [66:57]
B
So, first up, Elon Musk's AI startup Xai, has raised over $10 billion, according to Bloomberg. This gives it a $200 billion valuation and makes it one of the most valuable private companies on Earth. Google has given its Chrome browser a major AI upgrade. Gemini, Google's flagship AI model, is now deeply embedded into Chrome. So you can ask it to summarize a web page coordinate across tabs, schedule a meeting, or even pull up a YouTube video, all without leaving your browser Figure the robotics startup building general purpose humanoid robots just raised over a billion dollars. At a $39 billion valuation, this puts it among the most highly valued robotics companies ever. This money will help them scale up manufacturing, expand robot deployments, and grow the AI system that powers its bots. OpenAI has launched GPT5 Codex, a specialized version of GPT5 designed to act as a true coding teammate. Can independently work on complex software tasks, refactor code bases, debug and even review pull requests with a level of depth that rivals senior engineers. Luma is a startup behind a tool called Ray3, which is the world's first video generation model, they say with native reasoning and studio grade HDR. So Ray 3 generates photorealistic 4K HDR video with real world physics, preserved anatomy, complex motion and even interactive lighting. But what really sets it apart is this reasoning. Ray 3 can think in visuals and language, interpret sketch annotations and follow complex directions.
- [68:36]
A
And then one more mic to add that I saw on Sunday night, Sam Altman tweeted Over the next few weeks we are launching some new compute intensive offerings which would mean video image reasoning would be the three things that jump immediately to mind as compute intensive. Because of the associated costs, some features will initially only be available to Pro subscribers which 200amonth, Mike. Is that right?
- [68:59]
B
200Amonth, yeah, yep.
- [69:01]
A
And some new products will have additional fees that'd be interesting if you pay your 200amonth and you pay additional fees on top of it.
- [69:07]
B
Yeah.
- [69:08]
A
Our intention remains to drive the cost of intelligence down as aggressively as we can and make our services widely available. And we are confident we will get there over time. But we also want to learn what's possible when we throw a lot of compute at today's model costs at interesting new ideas. So stay tuned. It's going to be a busy September October for OpenAI and I would imagine they are not the only ones. But I think we're going to see video image reasoning, maybe some new audio stuff from people, but those would be the things to watch for as we and agentic would be the other thing, extended runtime of agents, kind of like we saw with Agent 3. That would be the other other compute intensive. So agents video image reasoning. That's the things I would expect in some forms from OpenAI. Yeah, that'll be fascinating. So stay tuned. It's going to be a busy period here as we enter the fall.
- [70:03]
B
Awesome Paul. Well, thank you for as always breaking everything down for us this week and super packed, super exciting week.
- [70:10]
A
Yeah thank you Mike and thanks everyone for listening. We'll be back with you next week again. Check out Macon AI if you want to join us in Cleveland October 14th to the 16th and Academy SmarterX AI if you want to jump in on AI Mastery membership and start checking out these Gen AI app reviews, course series and certifications and all the AI Academy live stuff that's going to be coming this fall. So thanks everyone. We will talk with you again soon. Thanks for listening to the Artificial intelligence show. Visit SmarterX AI to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in person events, taken online AI courses and earned professional certificates from our AI Academy and engaged in the Marketing AI Institute Slack community. Until next time, stay curious and explore AI.