[The AI Show Episode 246]: AI Answers - Live from MAICON 2026: AGI, AI Costs, and the End of GPTs

Paul Roetzer has a simple, uncomfortable test for AGI: line up the 20 tasks a person on your team actually does, hand those same tasks to a frontier model, and ask whether it performs at the level of an average team member. By that measure, he argues, we quietly crossed the line a year ago — and the models a year from now will be at A-player level across nearly every department.

Recorded live in the podcast studio at MAICON 2026, this AI Answers episode has Paul and Mike Kaput taking audience-submitted questions on what AGI really means for ordinary work, how to keep up without burning out, and why the hardest part of adoption is everything that surrounds the model.

Listen or watch below—and see below for show notes and the transcript.

Watch the Video

What Is AI Answers?

AI Answers is a bonus series that curates and answers real questions from attendees of our live events and classes. Each episode focuses on the key concerns, challenges, and curiosities facing professionals and teams trying to understand and apply AI in their organizations.

This one is special: we recorded it live at MAICON 2026, in the podcast studio overlooking the exhibit hall, with questions submitted by attendees in the room. Mike Kaput steps in for Cathy to host the Q&A, and he and Paul work through 12 of the top questions from the audience—covering everything from tooling decisions and team training to AI costs, AGI, and long-term strategy. No script, no filter, answered in real time in front of a live crowd.

Whether you’re just getting started or scaling fast, these are answers that can benefit you and your team.

Timestamps

00:00:00 — Intro

00:04:03 What MAICON insight has challenged your thinking about where AI is headed?

00:07:46 How do you keep up with AI without burning out?

00:12:09 How do you go from following a demo to using AI in your own work?

00:15:21 How do you get AI adopted in the messy middle of an organization?

00:17:41 How do you build an AI roadmap you can stick to as models change?

00:22:09 Will Claude and ChatGPT get more expensive after the IPOs?

00:26:23 What can AI do versus what should AI do?

00:30:35 What does AGI actually look like for a regular person?

00:35:25 Can AI solve the unemployment it creates?

00:39:42 Can you take your AI work with you from job to job?

00:41:19 Do you have to readjust when you switch AI models at a new job?

00:44:08 What should you use now that GPTs are going away?

00:46:42 Closing thoughts and a look ahead to MAICON 2027

This week’s episode is brought to you by MAICON, our 7th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16.

For more information on MAICON and to register for this year’s conference, visit www.MAICON.ai.

Read the Transcription

Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.

[00:00:00] Paul Roetzer: So I think tangibly for AGI, the way I think about it is when the model is roughly the equivalent of the B players on your team. So if we're talking to marketers and you look across a team of people on your staff and you say, okay, like these people do these 20 tasks, and I'm gonna take Claude Opus 5.5 or 6.1 Astra from openAI.

[00:00:26] And I'm gonna give that AI those same 20 tasks. Is it on par with like an average person on your team?

[00:00:36] Mike Kaput : Welcome to AI Answers, a special Q&A series from the Artificial Intelligence Show. I'm Paul Roetzer, founder and CEO of SmarterX and Marketing AI Institute. Every time we

[00:00:46] Paul Roetzer: host our live virtual events and online classes, we get dozens of great questions from business leaders and practitioners who are navigating this fast moving world of ai.

[00:00:56] But we never have enough time to get to all of them. So we created the [00:01:00] AI Answers Series to address more of these questions and share real time insights into the topics and challenges professionals like you are facing. Whether you're just starting your AI journey or already putting it to work in your organization.

[00:01:13] These are the practical insights, use cases, and strategies you need to grow smarter. Let's explore AI together.

[00:01:25] Paul Roetzer: Welcome to episode 2 46 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Mike Put, we are for the second time in history, I think, sitting at the same table doing this. It's weird. Is this, is this the second time?

[00:01:36] Mike Kaput : I think so,

[00:01:37] Paul Roetzer: yeah. I

[00:01:37] Mike Kaput : think the last MAICON was,

[00:01:38] Paul Roetzer: so we are live at, well not for you when you're listening to this, but we are recording this live at MAICON 2026.

[00:01:45] It is. October 14th, about three-ish in the afternoon. We just heard an insane keynote from Kevin Roose who just dropped the AGI Chronicles, a new book you absolutely have to go check out. he literally just left the main stage. He's gonna be signing [00:02:00] books here shortly. So my brain is kind of like on overload.

[00:02:03] Mike. I we're on, I mean, it's technically day one, I guess, for attendees, but this is day three for us. We did an AI council meeting on Monday.

[00:02:12] Mike Kaput : Yep.

[00:02:13] Paul Roetzer: An AI for CMO Summit. And workshops Yesterday.

[00:02:16] Mike Kaput : Yes.

[00:02:17] Paul Roetzer: And then

[00:02:17] Mike Kaput : opening night party,

[00:02:19] Paul Roetzer: we did an opening night party at the rock hall, which apparently people shut down the rock hall.

[00:02:23] I dunno if anybody that's here live with us was part of that group, but apparently some of our attendees stayed past their welcome. But it's the rock hall, you know? You gotta be a little rebellious. yeah, so we are here. It's been a pretty incredible week, but again, it's day one, but it feels like, I've been here for at least a week right now.

[00:02:42] We still have a bunch of amazing talks coming. We have Andrew Yang is actually about to go on stage in an hour or so. I have to leave here and go Welcome Andrew to the stage. so yeah, a ton going on, but we figured, hey, what the hell, let's record a live podcast while we're sitting here. So we are in this amazing new podcast studio we have overlooking the [00:03:00] exhibit hall.

[00:03:00] And we're gonna go through like an AI answers format, it sounds like Mike.

[00:03:04] Mike Kaput : Yep.

[00:03:04] Paul Roetzer: You're filling in for Cathy on the AI answers front.

[00:03:06] Mike Kaput : I am. I am I feel like Cathy needs to sub in here, but yeah, I'll do my best to live up to her.

[00:03:12] Paul Roetzer: And this is dropping

[00:03:13] Mike Kaput : tomorrow, Thursday

[00:03:14] Paul Roetzer: the 15th right?

[00:03:15] Mike Kaput : It sounds like it is happening tomorrow.

[00:03:17] Paul Roetzer: Yeah. Okay. So you're gonna be maybe listening to this while we are here at the final day of MAICON 2026. Alright, with that I'm gonna stop talking and turn it over to Mike and he'll get to guide us through.

[00:03:27] Mike Kaput : Awesome. So thanks Paul. Glad to have everyone in the live audience here that folks can't see at home, but super cool to be here doing this from.

[00:03:36] like Paul, you mentioned we're doing AI answers and what we've had is our audience submit questions, people are attending MAICON, submit questions. Claire and our team did a great job pulling those together, so I took a look at 'em and selected a handful that I think would be awesome to chat through.

[00:03:54] So,

[00:03:55] Paul Roetzer: and I definitely have not seen these because honestly, I wasn't even sure what we were doing when I came up here.

[00:03:59] Mike Kaput : [00:04:00] Yeah, this is,

[00:04:00] Paul Roetzer: so, this is unfiltered.

[00:04:02] Paul Roetzer: Let's go.

[00:04:03] Mike Kaput : All right. So.

[00:04:04] What MAICON insight has challenged your thinking about where AI is headed?

[00:04:04] Mike Kaput : To kick things off, this first question is for both of us actually. Okay. So the question goes, Paul and Mike, what has been the standout insight or concept you've picked up at MAICON so far that has challenged your thinking or has confirmed a hunch you have about the direction of AI in the next couple years?

[00:04:24] Paul Roetzer: You going first?

[00:04:24] Mike Kaput : I'll go. I can go first if you want. So, so far I have been. I would say I've challenged my thinking about what folks. Need from AI education. So I have gotten to sit now in our council meeting with some very advanced, marketing and AI leaders. I've taught a workshop to an incredible audience, but many different levels of AI literacy in the group.

[00:04:53] And then. Just hearing how people are approaching AI in their sessions on stage, hearing where [00:05:00] companies are at. I was interviewing Craig Karma's in at Good Karma Sports this morning on stage CEO of Good Karma, and hearing just the diversity of Journeys makes me challenge a lot of my own thinking about how we as an organization at SmarterX need to be meeting people where they are and how to actually.

[00:05:20] Get to bring them along to the next level and what that looks like when it comes to AI education. So I don't know if I'm overturning anything I thought before, but I'm thinking a lot deeper about, I don't know if it's just a beginner, intermediate, advanced journey. I'm thinking about a lot more nuance, a lot more granularity of what the AI education journey looks like.

[00:05:41] I think

[00:05:42] Paul Roetzer: interesting. That kind of hurts my brain right now.

[00:05:44] Mike Kaput : Um.

[00:05:46] Paul Roetzer: Yeah, I don't know. Like I, there's one really cool thing I've seen, which is I've had conversations, at least three of them where it was someone who was here last year who was trying to figure out how to kinda get the transformation going for, say, their [00:06:00] marketing team, who is now either in the head of AI transformation for their organization or about to become.

[00:06:07] And so what it's shown me is in these, a lot of these big companies, and we talked about this idea of like, you just become the change agent, like be the person who's proactive. And what we're actually starting to hear is it's working. It's like the people, whether you're in marketing or ops or whatever, who just goes and figures this out.

[00:06:24] Then when everyone else, including the C-Suite, realizes we don't know what we're doing, who's doing it? It doesn't matter. If you were a marketing associate two years ago, it's like you're the one that actually knows it. You're now in charge of this and let's go find five other people like you. Ty, good karma.

[00:06:41] They told that story kind of around Ty, where it's like he heard Craig say, Hey, we're going all in on ai. And Ty's like, all right, let's go. And all of a sudden Ty's like in this role where he's having a massive impact across the organization. so I think that was kind of cool just to like, you know, every year we come back and we hear these stories about the [00:07:00] impact, not, I don't think about it as like the impact we've had, it's the impact the community has had where people get together, they collaborate, they get inspired by each other, they go to all these talks.

[00:07:09] And then they leave and they go do something and a year back later they come back and it's like, yeah, like I'm in this dream job. Like we created this role for me. And so I think it just, it's encouraging because it shows that we really can create these new career paths, you know, as a, as a business world.

[00:07:25] Not saying we SmarterX, but. We have this ability to, to reimagine like what we can do and the people who are proactive are gonna be on the frontiers of it and have really cool opportunities.

[00:07:34] Mike Kaput : Yeah, that's a really good point. I makes me think of several people Yeah. I've talked to who are just in these roles that like frankly do not exist on a job board right now that are incredible opportunities.

[00:07:46] How do you keep up with AI without burning out?

[00:07:46] Mike Kaput : Next question. How do you keep up with AI without feeling overwhelmed? How do you do it without burning out? Are there any specific tips or strategies you use? Any boundaries you have to draw to kind [00:08:00] of stay sane?

[00:08:02] Paul Roetzer: Yeah. I mean, I think, like for me, I, I've become pretty disciplined just about my not non-AI time, like.

[00:08:12] I definitely spend a lot of like ambient time thinking about AI scrolling. You know, my AI feed on X is where the vast majority of my information comes from, for better or for worse, but it's like a highly curated list of probably four or 500 people. and so I monitor that all the time, a lot of times on walks or gonna the gym or in the car.

[00:08:34] I'm, I'm listening to podcasts and you just never know when you're gonna hear that one episode that triggers something. It's either like. I have to have that person as a speaker at MAICON. Like I'm always on the lookout for those kinds of things. Or it's just like reinforcing something that, you know, I'll send to Mike and be like, we gotta talk about this like this.

[00:08:51] It's just time to deal with this big topic. So I feel like part of my like role right now is to try and like connect the dots and [00:09:00] just, you know, pay attention all times. But then I don't have any. Hesitation to be like, I'm just done for the day. Like, I'm gonna go home and I'm gonna like walk up and meet my daughter from school.

[00:09:10] Or I'm gonna, just spend Saturday with my family and I'm not gonna open the computer. And I think I've just gotten at peace with myself that we're so productive all the time. I mean, you and I as a good example, like when you truly infuse AI into your life and into your work, you're, you're just insanely productive.

[00:09:27] And I think you have to just accept that. Sometimes there's, you've made enough of an impact.

[00:09:33] Mike Kaput : Yeah.

[00:09:33] Paul Roetzer: And like it's okay to, to, to decon, you know, disconnect for a little while. So I would say it's, it's much more of a mindset and a mental thing than it is like some, here's the exact like, workflow I followed and not do a thing.

[00:09:46] it really is just that mental permission for yourself to be okay. Like, I've learned enough, I've done enough. Now that being said, like, I'm exhausted last night, like I'm, you know, on my deathbed this morning and. I'm still scanning X and putting things [00:10:00] in the podcast sandbox. Like, it's just like your, your mind like is wired to just, what did I miss today?

[00:10:04] What's important to talk about?

[00:10:05] Mike Kaput : Yeah. You know, I talked with someone last night and this morning about kind of the concept of running your own race. Right. And I tried to remind myself of that to avoid feeling overwhelmed. Like someone was telling me this elaborate story really great about how they had.

[00:10:20] Changed their career, their business, by applying AI to their particular industry. They were seen as like an authority in their field now on it. And were teaching around it, speaking around it. And they were like, yeah, but like, that's just in my little corner of the world. And there this, I won't name any specifics.

[00:10:36] It was a huge industry. It was like a sign significant industry. And they were like, oh yeah, but the speakers here are doing this with that, you know, they're using muse or instinct or dots or whatever the flavor of the week is. And I was like. All that matters is like, did you transform your business, your life, your work?

[00:10:54] It doesn't, you don't get extra points for how many different types of agents, agents you have running. It's amazing if you're [00:11:00] doing that, if you're geeked out about it, use a million of them. But like I have to remind myself of that too, is like, you don't get extra points for you. You get extra points for the outcomes, and it's like you're not a header behind if you're getting the outcomes.

[00:11:11] You,

[00:11:11] Paul Roetzer: yeah, and I mean, I guess it's, in a weird way, we're like getting into the philosophy here, but like. Mine was the mindset. And I think that is like, as an entrepreneur, I used to deal with this all the time. Like people are like, well, how many employees do you have? What's your revenue runway? It's like, well, I don't know.

[00:11:24] Like, why don't we talk to each about how we're doing and like what the struggles are. Like I, why does it matter? Like why do we measure my success as an entrepreneur just purely on how many employees I've gotten to hire? Like, but that was the thing, it was always this like, you felt like someone was trying to compare your ability to grow faster.

[00:11:38] And it's like, what? What do we care? Like we're both entrepreneurs, let's just help each other. And I feel like AI's in a similar boat, it's like. I'm not using Muse. I haven't connected to all my stuff. And so you almost feel like inferior.

[00:11:47] Mike Kaput : Yeah, exactly. Right.

[00:11:48] Paul Roetzer: Right. But it's like you don, you don't have to feel that way to your point.

[00:11:50] It's just like, well, but is it helping you? Is what you are doing making a difference in your business? Don't worry about what they're doing. Like go to a talk if you get inspired, cool. But like I've said that I probably swear like you're doing [00:12:00] all kinds of stuff with agents. Like, I have no idea what you're doing.

[00:12:02] Like I don't, I wouldn't even know how to do the things you're explaining and like, that's cool 'cause that's your thing. And like I'm doing my thing and it works out really well.

[00:12:09] Mike Kaput : Right.

[00:12:11] How do you go from following a demo to using AI in your own work?

[00:12:11] Mike Kaput : All right. This next question, the listener said, I keep coming back to the idea that how we learn is not how we live.

[00:12:19] We can all be taught the same methodology or SOP, but how it's applied differs from person to person. So with ai, how do you get someone from this stage of like, I can follow the demo or framework or steps to, I can actually figure out how to use this in my own work? I feel like teaching workshops is like a hundred percent what I've been like struggling with.

[00:12:39] How do you like. You can give someone the plan on paper or even in a company, how does it actually get translated to their work? Like how do you see that balance?

[00:12:48] Paul Roetzer: Yeah. This is why I talk a lot about the personalized journeys, like, and not just, Hey, here's 10 courses you should take, and we suggest starting with these five.

[00:12:57] Like, that's not personalization. Personalization is [00:13:00] like, well, where are you at today with your understanding? What is your like sentiment towards ai? Do you hate it? Like, are you afraid of it taking your job? And so. From an organizational perspective, until we understand where people are really at, it's hard to diagnose or prescribe what a journey should look like for them and how we should adapt to their behaviors, their goals, and their careers, things like that.

[00:13:22] So, yeah, I don't know. That's, that's why I think like the personalization becomes so critical and so few organizations are doing it really well right now. but that's like, we're trying to enable that a little bit better, I guess.

[00:13:34] Mike Kaput : Kind of related to that. It was funny, I was having a conversation with our friend Jeff Culliton, who is, who's everywhere by sub.

[00:13:40] He's everywhere in the makeup on stage. and it reminds me of just how personalized AI can be because we are, we're both similar in the sense of where it's hilarious how bad of detail oriented people we are. So we both have given Claude and Codex rules about how to talk to us and work with us because otherwise it can be a disaster.[00:14:00]

[00:14:00] And I was. Felt so seen. 'cause Jeff was like, yeah, I've got this whole working doc with Claude about all the things I'm bad at. And I was like, so do I. This is great. My point here, being that mine is gonna look completely opposite to someone like I would suspect, like Ashlee, who runs our events, who's extremely detail oriented.

[00:14:16] So the way you even work with ai, there's no one size fits all, is kind of the point. It's so personalized and you have the beauty of you can now personalize these tools to exactly how your brain works. So it's kind of what they're getting at. It's like the personalized learning journeys, getting this hyper-personalized to how you work, how you live your life, how you approach your work in your job, that it's critical

[00:14:36] Paul Roetzer: in, in my workshop, the AI co-executive, we were building an AI assistant, and that was one of the elements.

[00:14:42] There's like 13 components to it, and one of the items within it was the behavior and style. Like tell it how you want it to talk. And it's a really good point because these things come out with some inherent traits that are given to it by the AI labs. They're trained to be specific way, talked to in a specific way, but increasingly you can tailor how you want it to [00:15:00] communicate.

[00:15:00] And so some people may need it to be stern with them. It's like, listen, just be super direct with me. And other people are like, you're gonna hurt my feelings. Like just give it to me a little bit softer. Like explain it in a different way. Or, I like to see it, you know, from a visual perspective versus text.

[00:15:12] So build it in charts for me. And so I think even that, like the personalization of how we experience the AI systems becomes really important, for sure.

[00:15:21] Mike Kaput : All right.

[00:15:22] How do you get AI adopted in the messy middle of an organization?

[00:15:22] Mike Kaput : Next up, someone asks, AI models can already do impressive work, but in my experience, the blockers are everything around them. Permissions, usage limits, organizational trust, et cetera.

[00:15:35] All these stories I hear are about either a, CEO going all in or a small business owner who can accept the risks and like move fast and get around some of these organizational challenges. But the question is, what about everyone Who's in that messy middle? How do you get capable AI adopted inside an ordinary organization when things like access budgets and trusts still have to be worked out.

[00:15:57] That's like 95% of people [00:16:00] probably.

[00:16:00] Paul Roetzer: Yeah, I think this is, when we talk about the whole change management idea around this, all of that has to be factored in. So if it is a blocker or legal as a blocker, procurement is a blocker, hr, you know, whatever. You have to have all those parties at the table. You have to understand what is it gonna take to actually get people to, to drive adoption where they have, you know, a great example we talked about recently is we have Claude and we recently connected it to our CRM system.

[00:16:24] So until Claude was connected to our CRM, it was dramatically limiting what we could do with it. As soon as we gave permission to do that, it opened up thousands probably of use cases almost immediately for marketing, sales, customer success. And so that's why I, I'm just a big believer, like it has to be driven from a change management perspective and it has to be driven from the top.

[00:16:47] you have to have buy-in from the leadership so that if Mike were to come to me or. You know, someone else just say, Hey, really I have this idea. I think it can generate millions of dollars or make this impact on our AI Academy [00:17:00] members, but I'm not allowed to do this. Like it is not letting me, you have to be able to go to someone who can say, alright.

[00:17:06] It just step aside, like, this is ridiculous. This is an enormously valuable thing. We have enough guardrails in place, we have to accept a little bit of risk. We're just going to do this. So for us, we're a small enough entity where obviously it's a CEO driven environment where AI. That like you and I can just have a chat, like hang up from the podcast and be like, Hey, we got this idea.

[00:17:27] We should do this. And I'll be like, all right, hold on. I'll message Tracy and tell her. And then Tracy, our COO will contact legal and say, Paul's going to do this. We have like 48 hours. And then legal figures out how to like make it work.

[00:17:41] How do you build an AI roadmap you can stick to as models change?

[00:17:41] Mike Kaput : I'm very curious about your answer to this next one. How do you make an AI transformation plan or roadmap that you can actually stick to?

[00:17:50] When a new breakthrough could change what's possible next month?

[00:17:54] Paul Roetzer: Yeah, I don't know. I mean, I think like it's kinda like making a staffing decision when the model might be able to [00:18:00] do what that person does. You know, a month from now, I think the best we can all do is have some sense of. I used to call these like AGI Horizon teams.

[00:18:08] Like I remember in like 2023, I was talking with a bunch of big SaaS companies and I told them like, build an AGI Horizons team. Like build a team whose job it is to look at the next 12 to 18 months of model advancements and say, here's what we think they're going to be capable of doing, and here's the trickle down of what that means to our staffing, to our product, to the services we offer.

[00:18:28] And so I think that that's really the best way to do it is you, you just have to have some. Reasonable conviction about what the near term future looks like by staying close to these model advancements and then have an organization that is flexible based on it. Now, we've all experienced, well, anybody who is building around openAI's has experienced this recently.

[00:18:47] I think the best example is GPTs going away. We, we were having this conversation in our AI for CMO Summit, and I know even during the council meeting on Monday, because there were people in the room who had built their marketing departments around [00:19:00] gpt. Spent the last two years running hackathons to build GPTs, and now we have two months to figure out what do we do.

[00:19:08] The entire marketing team now runs on gpt, so that's a good example. And I think maybe we can use that as a retrospective to go back and say, okay, as we weren't ready for that kind of change, what do we have to do now moving forward to be ready for that kind of change? Because they're gonna keep changing the names of the things.

[00:19:25] They're gonna keep introducing new tools like dots and. You know, maybe it comes down to having evals of like, when they make a change, here's the ways we're using it. How does this new model affect it? What are these new user interfaces like? I was talking to somebody last night, one of the parties, who was using sites from openAI's, and I was like, what is it?

[00:19:44] Like I, it came out like a week ago or two weeks ago, I hadn't even tried it. And it's just like, instead of asking it to build an HTML file, which is something we commonly do, is like, Hey, turn this into an HTML file. You can publish the HTML file and share it with people. It's like, oh, okay. Well that's pretty cool.

[00:19:58] That might change the way we do [00:20:00] something. So, I don't know. For us, it's about this dynamic learning idea. Like we're just gonna, we're gonna push a lot more into like regular, weekly, monthly cadence of what is new in these models and platforms. What does it mean to us? How does it affect our work? And then we're gonna try and bring that to people through our AI Academy and make it more like real time learning.

[00:20:18] Mike Kaput : Yeah. One thing that was really cool too, that I tried to do in my workshop on Google Gemini, but you could apply this to any model, is there was a whole segment on. Using this and approaching this more as a true strategic thought partner and going through like a measured kind of process driven query or conversation with the tool because so many people just write a few words or don't converse back and forth, or don't have a strategic discussion with it, with really smart questions and inza get insights from it the same as they would as like a consultant or advisor, whatever.

[00:20:52] My point here is that it doesn't matter tomorrow. If everything about Gemini changes that concept. [00:21:00] Is really, really important. So even focusing also on some of these more evergreen strategies like our use case model, the problem based model innovation model,

[00:21:08] Paul Roetzer: which I created in like 2018.

[00:21:10] Mike Kaput : Yeah. And we

[00:21:11] Paul Roetzer: still teach based

[00:21:11] Mike Kaput : on it and it's still extremely relevant.

[00:21:14] That kind of thing is also really invaluable. I think

[00:21:16] Paul Roetzer: teaching framework. Yeah. And a really good example of this is, so Mike's doing this Gemini, what do we call 'em? Gemini for business. Gemini for word

[00:21:21] Mike Kaput : Yeah. Gemini for business. Yeah.

[00:21:23] Paul Roetzer: And so last week, Google holds an event like a week, not even a week ago from today.

[00:21:27] We already, and it, what did they call it? Gemini. What's the new agent harness thing?

[00:21:31] Mike Kaput : It's technically just called Gemini, which if this is confusing to you, join the club. 

[00:21:36] Paul Roetzer: It's like harness now or something

[00:21:36] Mike Kaput : It's like kind of an agent harness a bit.

[00:21:39] Paul Roetzer: Yeah. So they rebranded Gemini as something else, and so that was.

[00:21:44] Six days before Mike's workshop, or four days before it. So I'm, I'm messaging Mike, like, Hey, you gonna change the workshop based on this? And then during your workshop yesterday, if I'm not mistaken, they released Argonne model. Yes. The Gemini four Argonne became like, so it's like [00:22:00] even in a real time learning environment where Mike's doing a live workshop, you have in this four day span, two things that could affect what he's gonna teach in the workshop.

[00:22:09] Mike Kaput : All right.

[00:22:09] Will Claude and ChatGPT get more expensive after the IPOs?

[00:22:09] Mike Kaput : This next question is fascinating, very relevant, I think, to several people listening. What, if anything, is going to happen to the cost? Of Claude or ChatGPT or insert whatever tool here. But those two, especially once Anthropic and openAI's go public and they have to start turning a profit, are we gonna see them start jacking up prices?

[00:22:33] no. How's that gonna work?

[00:22:35] Paul Roetzer: They can't, the price of the, so the frontier model today. So take like an Opus 5.5. I think is that, that's probably their most power is Fable. I don't know if Fable version we're on, I

[00:22:45] Mike Kaput : think we're in Opus five

[00:22:46] Paul Roetzer: all. So let's say Opus five is, it don't, it's been a busy week.

[00:22:49] it's gonna cost 10 x less in 12 months. and so at some point, like you're gonna say as an organization, like, okay, this model is [00:23:00] good enough. Like I said this publicly on the podcast, like I thought sonnet 4.6, like if you told me this is the last model I'm ever going to have. I would've been ecstatic.

[00:23:09] And I'm like, great. Like we'll just ride with this. We'll build the entire company around this model. And I'm sure that model costs five x less than it did when it first came out. 'cause now they're on whatever sonnet, five point something. So I think the vast majority of use cases for businesses, it's just gonna get cheaper as long as you're willing to use the previous state of the art model.

[00:23:33] And so that's gonna, the competitive nature of these labs is going to force that to happen. They're not gonna just keep making it more expensive. So what they're gonna do is they have to sell the more advanced model to the companies that need the really, really advanced reasoning. But the key for them is they actually don't care.

[00:23:51] They just need to infuse agents and get everyone in companies using these things. So if you think about how. I mean, Anthropic and Open [00:24:00] Air are both gonna IPO. It's somewhere between one and $2 trillion, and yet when you go around to any major enterprise, there are almost zero of them that have full scale adoption of these tools to where everyone is infusing into their workflow every day.

[00:24:13] So the amount of untapped market potential for these models is so vast that even if you're paying 10 x less per token to do it, you are. You almost have an infinite need for tokens as you expand the use of it across the company. I don't think they care. Like, and plus overweight models are gonna be comparable, so they have to accept that and they have to like think about what the business model is going to be.

[00:24:41] Andros IPO is gonna be a really interesting look into what really is the business model. Yeah, right. Like what is sustainable here? What are the moats that these companies have

[00:24:50] Mike Kaput : Somewhat related too. It's been interesting, especially this year at MAICON. Everyone is obviously using things like Muse or Grok.

[00:24:58] Bott. Oring, [00:25:00] yeah. Dots, whatever. And I realize we're still in the time where the model powering that matters, but half the conversation, someone's like, well, Grok Bott did this or that. I don't even know what Model Grok is on the model.

[00:25:12] Paul Roetzer: I think they're using Opus now.

[00:25:13] Mike Kaput : The point is, I don't know.

[00:25:15] Paul Roetzer: Yeah.

[00:25:16] Mike Kaput : And so it's more like if you, it's almost. It could possibly, at least at the personal agent level, going to kind of get abstracted away where it's like, you kind of don't care. It's just like my agent works and

[00:25:25] Paul Roetzer: stuff. Yeah. I'm fairly confident that like this time next year at MAICON, the average user's experience, you will not be choosing your model.

[00:25:35] Like I I think they've known for a long time that model choice is basically a developer thing, and that if you give this to any director, VP at any normal company and say, Hey, here's your copilot. Here's your 12 models you can choose from when you're writing your emails or building your proposals or doing whatever.

[00:25:52] They're be like, okay, like I'll just use whatever it's turned onto. I don't even know, like sometimes I'll go into ChatGPT and it's like [00:26:00] 6.1 sole light, medium high. Like I don't even know the difference anymore. So I don't know. I think the model choice is just like an artifact of, you know, four years ago that for some reason they haven't just ripped the bandaid off.

[00:26:13] I, my guess is. I dunno if Google's gonna do it first. Like somebody's gonna do it first where you, you just don't know what you're using and they're gonna route it based on your prompt.

[00:26:23] What can AI do versus what should AI do?

[00:26:23] Mike Kaput : This next question is phrased, how should we be thinking about what AI can do versus what AI should do? So for instance, this is relevant to you.

[00:26:34] Just because AI can write a book doesn't mean it should write a book. How do you decide what's worth doing yourself, even when AI could do it for you?

[00:26:43] Paul Roetzer: Yeah, I don't, I don't know that there's like one answer to this. So the writing example is always a good one that I use because like for me, writing is thinking, it's how I process information.

[00:26:52] It's like when I prepare for the podcast, it's why I actually read every article. I listen to every podcast, watch every video myself. [00:27:00] Because doing the work itself is the whole point of it. Like my ability to sit here and just answer whatever question you throw is because for like. 15 years I've been studying the field and reading all these things and doing the work.

[00:27:13] If I'd spent 15 years asking Claude had it existed to just summarize everything for me and then spit out some bullet points that I could sit here and regurgitate to you, like you've lost the point of doing the work. So I think some of it is just like, well, why are you doing the work in the first place?

[00:27:29] Is the purpose literally just to create an output that's like information gathering? So I think I used the example in my workshop of. for AI score, which we just, you know, pub put into public beta this week, I needed to create the product FAQ page. Nobody cares if, like, I actually wrote the product FAQ page, like from an authenticity perspective.

[00:27:49] They're not sitting there saying like, I don't think Paul actually wrote this page. My one, my name's not even on it, so you don't even know that I'm the only one created it. Two, the whole purpose of that is to provide information to someone [00:28:00] if they have a question about how the product works. It is not to hear my personal opinion or our perspective on something.

[00:28:06] So in that instance it's like that's an obvious use case. It's like, and it's how I did it. I literally went into Claude, my seven month project I've had with IT building AI score and said, take all seven months of context of our work together developing this technology. Take the public facing marketing page that we already created together, and here's 80 questions the team asked me about how the product works and what things people might want.

[00:28:30] Mike Kaput : Yeah.

[00:28:31] Paul Roetzer: Turn it into a product FAQ page as though you are writing a best in class like SaaS product, FAQ page and make it super approachable. Tone, boom and nailed it like, and it in like 10 to 30 seconds wrote something that I wouldn't have been able to write in 10 hours and so. I think it's just like getting good at that judgment of when is something fine to just use ai?

[00:28:53] Because the expectation is I'm just trying to get information and facts and something from a brand, for example, versus I'm reading a [00:29:00] book that I'm assuming you wrote, that you put thought into this, like these are actually your words. Then the expectation is that it's authentic and so you know, again, there's no perfect answer, but I think.

[00:29:12] It's important that organizations develop these frameworks and teach people on these frameworks, because especially with younger employees, they're not gonna have that judgment yet of when is it okay to turn this in? So my boss asked me to come up with a strategic document, and I was like, yeah, claude's better right than these, than me.

[00:29:27] So you just have Claude write it and you hand it in and the boss says like, come on in, let's talk about this document. Just created like, I don't agree with these three points. Why did you pick this? And the person's like, I have no idea. Claude wrote it. Like, I don't, I don't know why we picked it. You've lost the point.

[00:29:40] The point was to do the work. The point was to like be able to stand behind something you've submitted.

[00:29:44] Mike Kaput : It's funny you mention that example. someone we know who leave unnamed was telling me and laughing as they told me a story about how one of their employees did exactly that thing, except they didn't even get to the interview question because this person knew [00:30:00] immediately.

[00:30:00] This 20 page document was very clearly immediately outta the mouth of Claude and like not touched at all by a human. And so they said, Hey, give me five bullet points about these 20 pages. And they were like, about what? And like literally anything in it, any single thing. And they were like, well, what do you mean?

[00:30:18] And then they, they couldn't do it. And they were like, all right, they need you to go redo this. And they're like, which page? All of them, you know? So this is the point is this stuff is happening already. I don't know where that line is, but clearly some line needs

[00:30:32] Paul Roetzer: to be, it has to be established and it has to be part of the culture.

[00:30:35] Mike Kaput : Yes. Okay, next one.

[00:30:40] What does AGI actually look like for a regular person?

[00:30:40] Mike Kaput : What does AGI artificial general intelligence really look like to the regular person? Some people say, we have AGI now, but AI still struggles with tons of basic things. Like, what would AGI actually look like in an ordinary person's life? Like, how would we even know the difference?

[00:30:56] Paul Roetzer: So going, going back to how we started this Kevin Roose's, new book, [00:31:00] the AGI Chronicles, since I just sat through his talk.

[00:31:03] He actually went through this pretty entertaining thing about feel the AGI and what the hell is it actually? So feel the AGI is sort of like you openAI's in particular uses. And he was actually saying that Ilya Sr, who was a co-founder of openAI's, used to go around to people's desks and just say like, Yoda, like, like feel the AGI.

[00:31:21] Like, and that was it. And so Kevin interviewed Ilya and he is like, what do you mean when you say feel the AGI? Like what is AGI and how would we know if we're feeling the AGI? And so he told this hilarious story about like Ilya, who's basically like a philosopher. When he answers questions, he's like, oh, you know, it's kind of like when you, you know, you see that, you zoomed in on earth, like someone on a beach or whatever, and then all of a sudden you're like zoomed out to the universe and you hear Bach playing in the background and then he, like, that was the answer.

[00:31:49] And Kevin's like, I don't know what the hell that means. Like, what are you talking about? So I think tangibly for AGI, the way I think about it [00:32:00] is when. The model is roughly the equivalent of the B players on your team. It's kinda like the simplest, most tangible I, I've, I've thought about it recently.

[00:32:09] So if we're talking to marketers and you look across a team of people on your staff and you say, okay, like these people do these 20 tasks, and I'm gonna take Claude Opus 5.5 or 6.1 Astra from openAI's, and I'm gonna give that AI those same 20 tasks. Is it on par with like an average person on your team?

[00:32:34] Under that definition? We had AGI like a year ago. Like there are very few tasks that most of us do where it's not already like B player level in terms of quality, which means might need some edits, might need a second draft. You might need to iterate on the prompts, but like it's generally capable of creating the work product that an average person across pretty much any department would create.

[00:32:58] So I don't, [00:33:00] I mean, Kevin showed like the different definitions from openAI's and Anthropic, which is like a data center of geniuses is like Dario's infamous way of explaining it. He showed Google Deep Mind and it like no one agrees. But that's how I always thought about it, is the most tangible is equated to workers and it's like, is it on par with your average worker?

[00:33:18] And then the next level of, you know, artificial super intelligence is, is it better than your best worker? Like is it pretty much just superhuman? And I, I'm, I sat on stage I think this morning in my keynote. I'm kind of under the assumption that if you just fast forward 12 months, just assume the model, that you've given it the right data, that you're, you're, you're using the model in an intelligent way, like you know how to work with these models.

[00:33:43] The models we're gonna have in 12 months are, are gonna be eight player level and beyond. It pretty much every task you would do in any department of a company. And so like I have a hard time accepting that that isn't. AGI in terms of what it was originally meant to be like. [00:34:00] I think if you rewound 10, 20 years ago and you gave AI researchers who were thinking about AGI, then today's models, they would be like, this is beyond a, like, this is so obviously super intelligence, and yet here we are just debating.

[00:34:14] A term that's become, you know, basically meaning.

[00:34:16] Mike Kaput : Yeah. It seems like that yardstick is just moving too. Yeah, because like, I even think about going on a walk and using voice mode specifically with Codex voice mode alone, but like voice mode with like codex where my computer back home is creating files, doing stuff for me on the internet.

[00:34:34] People would probably say this about personal agents too, but like. Being able to talk and say, go do these three things for me. Do this to that, this to this slide deck. Go send this email. It's like, Hey, I am working on that. Great. Okay. It's all done. What's next? Like if you sent that recording to somebody 20 years ago, right?

[00:34:50] They would be like, this is in sci-fi movies this way. Like it feels like we're basically there.

[00:34:55] Paul Roetzer: Well, and the key there is like you did that for a work product and it nailed it. Yeah. [00:35:00] But then like two minutes later you're like, oh no, I forgot to do my taxes. Like, could you go in and like pull everything I need out of these files and fill out the PDF for me and have it ready for me when I get home and, oh, I forgot I was supposed to send an anniversary gift to my wife.

[00:35:14] Could you like go? How is that not like what we initially thought AGI would be. It's a single system that can now do basically anything you ask it to do.

[00:35:25] Mike Kaput : Okay.

[00:35:26] Can AI solve the unemployment it creates?

[00:35:26] Mike Kaput : Next question. If AI can solve the world's most advanced math problems, like it's been recently doing with the Millennium Prize, why can't it solve also the problem of unemployment caused by ai?

[00:35:39] Yeah. So, and this is a big question, but obviously we cannot just click a button and solve unemployment, but the point here is, could AI help us solve problems like this, that it might also end up creating

[00:35:51] Paul Roetzer: I the labs? Think the answer is yes. Like the labs believe that if we just get to the end [00:36:00] game, call it the end game, it might be the beginning of the game, but like if we get to the point where we truly have super intelligence, then it'll fix all of this.

[00:36:08] So if we go back like five years ago, all the big AI labs, the cloud companies, the tech companies, everyone's gonna be like net zero emissions. And so it was all about like, we're gonna save the environment. We're gonna like get rid of all the carbon that we're putting out into the world. At some point they're like, wait a second, we actually have to build like 10,000 data centers that are actually going to increase emissions, but don't worry about it because we're gonna build super intelligence.

[00:36:34] And then by 2030 it'll actually figure out how to fix all the environmental stuff that we're gonna create. And so a lot of what they're doing is truly, it'll figure it out for us once we throw enough compute at it and once it gets good enough. So the mathematics stuff that it's been solving is in part because.

[00:36:52] Yeah, you can prove if it's right or not. So there is like an obvious, did this work or did this not? in [00:37:00] terms of having an answer, it's why you can train it in games a lot of times because there's, there's a score. There's like a way to keep track when you try and solve something messy. Like what do we do with economic policy and what do we do with jobs?

[00:37:12] There is no, like, we can work really hard and throw a hundred thousand GPUs at it for a week and get to the right answer. We don't know what the right answer is. We still actually have to test it and put it out into society and see if it works. So like Andrew Yang today at the closing keynote, I think he's gonna talk about some of the things around like universal basic income or universal high income as one way that we can address the fact that people might not have jobs.

[00:37:39] that doesn't solve the fact that I also now have no purpose in my life. Like, okay, thanks for my $5,000 a month, but like. I still have to go home and look at my family and be like, I don't have, I can't find work for two years. Like, so it's just a really messy thing that that covers a lot of areas of [00:38:00] society and that's why you have other ideas like universal basic services.

[00:38:03] It's like, okay, let's provide healthcare to people too. And so there's a lack of really good ideas that are worth testing other than let's just give people money to be alive.

[00:38:12] Mike Kaput : Right?

[00:38:13] Paul Roetzer: So I think that one, you need the will from the government. To want to solve this problem, to accept that there is an emerging problem, to own that problem and to say we are going to proactively look for solutions.

[00:38:30] We are not in a current political climate that wants to admit that AI has any impact on jobs at all. David Sacks, who's, you know, kind of a, a voice of the administration unofficially these days. Just go look at his XV from the last four months and see how many times he's blasted people for implying that AI will take jobs.

[00:38:52] So it, and that is the, that is the position largely of the administration. So at least in the United States, we are unwilling [00:39:00] to acknowledge that it could go wrong, because it would cost votes. So it's just like, first you have to have the will to do it. Then you actually have to bring the minds together who are willing to ask the hard questions to start trying to solve it.

[00:39:17] And that's what worries me is like we're just, we're not even out, out of the starting gate doing that. And it's why I wanted Andrew here, because he's one of the few people, whether you agree with his position or not, who has actually taken a position of, here's something we could try. And that's just lacking from politicians and from AI lab leaders.

[00:39:34] They're not even offering real ideas. It's just, it'll just be abundant and it'll be awesome and we will see what happens when we get there.

[00:39:42] Mike Kaput : Okay.

[00:39:43] Can you take your AI work with you from job to job?

[00:39:43] Mike Kaput : Next up, how, as people progress through their career, how do you see them traveling with ai? Like how much of what they do with ai, will they be able to take from position to position?

[00:39:54] Paul Roetzer: Legally None. So, I mean, if you think about employment law and the [00:40:00] contracts we all sign when we work for organizations, if, if you create something that intellectual property belongs to your employer. So if you're an AI forward, you know, director within a, a company and you build or you're an orchestrator, like you're building all this stuff, you're devising all these strategies and you build all these agents and you know, all these workflows and everything and, you own none of it when you walk out the door.

[00:40:23] So. That's, it's tricky, but that also exists within like your likeness when you're at that company. So, I mean, we could do this with you and I, it's like who owns our likeness to, if they wanted to turn it into like avatars, like if they decide, Hey Paul, Mike, it was great having you humans, but like. We're actually gonna spin up some digital versions of you guys and

[00:40:46] Mike Kaput : we get people ideas.

[00:40:47] Don.

[00:40:48] Paul Roetzer: Yeah, I know we already have the baby versions of us, so I don't know what

[00:40:50] Mike Kaput : episode that was, but yeah, this is, I'm already, I already know this is gonna result in something.

[00:40:54] Paul Roetzer: Yes. Yeah, sorry. but we've addressed this actually in our contracts, [00:41:00] so, you know, again, it's, it's about being a Ford and thinking this stuff through, and this is why you need your attorneys.

[00:41:06] It's why you need to have AI forward leaders who understand to ask these questions. Yeah, it's, it's, it's messy, but the short answer is nothing. Nothing you create for a company do you own when you leave that company?

[00:41:19] Do you have to readjust when you switch AI models at a new job?

[00:41:19] Mike Kaput : You know, somewhat related to this was a follow on question here of like, have you seen companies consider how to retrain or integrate people who come from a company using different models?

[00:41:30] Like is there a big adjustment, I've never really totally thought about this, but I could see where this is coming from. Is there a big adjustment for employees coming from. copilot shop to Claude, vice Versa, et cetera.

[00:41:43] Paul Roetzer: I don't know. I haven't heard of that. I mean, I think it would just fall generally under the overall training as a whole.

[00:41:49] And my experience has been, and I, Mike, you can jump in on this one if you know how to talk to AI assistants and if you know how to build AI agents, if you're that far along, you can [00:42:00] go model the model or AI system, AI system pretty easily. Like it's still prompting the same now. If you're stuck in a, like a neutered version of co-pilot within an enterprise that's turned off all like the most valuable features, you're screwed either way.

[00:42:13] Like that doesn't really matter. And that's the biggest concern. So if I was an AI Ford employee that I was making a move somewhere else, and I, let's say I was really used to Claude and I was gonna have to use co-pilot, I would, I would be more interested in like, what, what am I gonna be allowed to do with it?

[00:42:30] Do I have a limitation on my tokens? Because once you're used to using these tools as part of your work. That's gonna be the biggest problem is if you now go to a company that limits your ability to use these tools. I think it's gonna be a lot more of the other way where people who are trying to be the innovators within organizations who are using Quad on their personal count at nights and weekends.

[00:42:52] they're gonna jump ship and go to a company that's gonna welcome them in and allow them to use those tools.

[00:42:57] Mike Kaput : Yeah, I think that's exactly it. It's less [00:43:00] about the nuances of each model, most of the time and what you're actually allowed to do versus not allowed to do. I think for especially some top talent out there is going to be become.

[00:43:09] If not one of the top thing, because we're very close to, if you're using these tools and embedded with them day in, day out, you can't live without them for one, but two, it's like the reason you're excited Yeah. About your work. So giving that up is more than just something like a comp consideration or a career.

[00:43:28] Also, it's like a, a limiting career move. I would argue with how fast things move. You're hamstringing yourself if you can't. Do enough, whatever that looks like for you.

[00:43:39] Paul Roetzer: Yeah. I mean, if I was interviewing for a job, I would say, can, can you share your AI policy with me? Or at least like the general frameworks of what's allowed, do you have responsible AI principles?

[00:43:47] How are those kind of taught within the culture? What tools will I have access to? Are there limits? Do you have a connect? I'm, I'm, I'm looking for a job in marketing. Your AI connected to your CRM or like, do you guys not, do you wall that [00:44:00] off? Those are the things I would be asking as an AI forward practitioner, right?

[00:44:03] It's like I would wanna know, am I gonna be able to do the job you're asking me to do?

[00:44:08] What should you use now that GPTs are going away?

[00:44:08] Mike Kaput : All right. We have time for maybe one more question here. Now I want to kind of come back to this because we did talk about it a little bit on the podcast, but it's come up so many times already At MAICON, someone said, I trained people how to create GPTs in ChatGPTs.

[00:44:24] Now that GPTs are gone or going away, I don't know what to use now. Instead, what do you suggest as a replacement?

[00:44:33] Paul Roetzer: So what they're suggesting are plugins for replacements to GPTs. My experience is that does not work. Like I don't, I, it was a, a one off experience and it was not good. so it's certainly not as intuitive as I think they intended it to be.

[00:44:46] Um. I think it depends on what your goal with it is. So if it is something you wanna publicly share, then you're probably moving more in the direction of something like sites or so, I don't know. Yes.

[00:44:56] Mike Kaput : Yeah,

[00:44:56] Paul Roetzer: yeah. if it's an internal tool that you're gonna use as an AI [00:45:00] assistant, you might be building it as a skill or a project.

[00:45:02] if it's a repetitive action, you might be turning it into an agent. Like I think we're just moving into the realm where it just depends on what the actual usage is, and you're probably gonna need to audit any gpt you've built instead. What is it used for? Who uses it?

[00:45:18] Mike Kaput : Yep.

[00:45:18] Paul Roetzer: and then map it to what is the best thing to turn it into based on what OpenAI is currently offering that they might change in three months.

[00:45:27] Like I, you don't know. Yeah.

[00:45:28] Mike Kaput : Yeah. It's interesting. It's almost like you start to see, you start to see a decision tree kind of

[00:45:34] Paul Roetzer: Yeah.

[00:45:34] Mike Kaput : Manifest here, unfortunately. Yeah, right. It's interesting, I, when I talk to people, it's like. If you're just using GPTs for yourself and you're not that far along and you just want like repeatable instructions, go learn how to build skills and talk with AI about how to do it, that's gonna become important in the future.

[00:45:51] But in the meantime, just put the instructions into a project. Yeah. And use that yourself. It's gonna be roughly the same thing. It's not perfect, but it's the closest [00:46:00] analog skills is the ideally, I think, the path you start moving down and they're not hard to build. They're hard to get right. But that's gonna require a lot of iteration anyway.

[00:46:09] And then, yeah, if you're getting into, you need people publicly to use this good luck. 'cause that's a huge thing that they've pulled the rug out from under you. Maybe it's an app, maybe it's projects you could share with your team, but then they can also edit the instructions. So it's like you don't want that.

[00:46:24] Paul Roetzer: Right.

[00:46:25] Mike Kaput : So who knows where that hit?

[00:46:26] Paul Roetzer: Yeah. It's like they took a really simple, intuitive thing that anybody could be taught. And we've taught it, like we ran workshops teaching people who are just starting an AI, how to build gpt. And it's like, oh, this is brilliant. Like I get it. And now it's just like, just blow it up like the one thing that was super into it.

[00:46:41] Mike Kaput : Yeah.

[00:46:41] Paul Roetzer: Yeah.

[00:46:42] Mike Kaput : Alright, Paul, that comes to the end of our time, end of our questions. Appreciate you being game to answer all these, especially I'm sure you're getting tons of questions, have tons of speaking to do at MAICON, but it's been awesome events so far. Yeah. Thanks for doing this.

[00:46:55] Paul Roetzer: Yeah, thanks everyone who's been here.

[00:46:56] I know we have a bunch of podcast listeners here. We're actually, when this is airing, [00:47:00] we're, we're probably gonna be in our live lunch that we talked about on the podcast. Yeah, I know. We got a bunch of podcast listeners that have joined us. We're gonna do, uh. Kind of an exclusive lunch, q and a with them tomorrow, which will be today for you all.

[00:47:12] But yeah, thanks everybody for listening. Thanks for being here. If you're here and if you're not here, we'd love to see you MAICON 2027, which I would imagine we'll be announcing the dates for if we haven't already. Did we announce the dates?

[00:47:22] Mike Kaput : I don't know

[00:47:22] Paul Roetzer: I don't think so. Okay. I won't announce 'em yet. I know.

[00:47:26] I'm pretty sure it's next October though. Alright. Thanks everyone.

[00:47:28] Mike Kaput : Thanks Paul.

[00:47:30] Paul Roetzer: Thanks for listening to AI Answers to Keep Learning. Visit SmarterX dot ai where you'll find on-demand courses, upcoming classes, and practical resources to guide your AI journey. And if you've got a question for a future episode, we'd love to hear it.

[00:47:47] That's it for now. Continue exploring and keep asking great questions about ai.