#246: AI Answers - Live from MAICON 2026: AGI, AI Costs, and the End of GPTs

Welcome to AI Answers

0:00So 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, OK, like these people do these 20 tasks and I'm going to take Claude Opus 5.5 or 6.1 Astra from OpenAI. And I'm going to give that AI those same 20 tasks. Is it on par with like an average person on your team?

0:36Welcome to AI Answers, a special Q&A series from the Artificial Intelligence Show. I'm Paul Reitzer, founder and CEO of SmarterX and Marketing AI Institute. Every time we 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. But we never have enough time to get to all of them. So we created the AI Answers series to address more of these questions and share real-time insights into the topics and challenges professionals like you are facing.

1:08Whether you're just starting your AI journey or already putting it to work in your organization, these are the practical insights, use cases, and strategies you need to grow smarter. Let's explore AI together.

Live at Maycon 2026

1:21Welcome to episode 246 of the Artificial Intelligence Show. I'm your host, Paul Reitzer, along with my co-host, Mike Foote. We are, for the second time in history, I think, sitting at the same table doing this. It's weird. Is this the second time? I think so. I think the last Macon. So we are live at, well, not for you when you're listening to this, but we are recording this live at Macon 2026. It is October 14th, about 3-ish in the afternoon. We just heard an insane keynote from Kevin Roos, who just dropped the AGI Chronicles, a new book you absolutely have to go check out.

1:57He literally just left the main stage. He's going to be signing books here shortly. So my brain is kind of like on overload, Mike. 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, an AI for CMO Summit, and workshops yesterday. Yes. And then... Opening night party. We did an opening night party at the Rock Hall, which apparently people shut down the Rock Hall. I don't know if anybody that's here live with us was part of that group, but apparently some of our attendees stayed past their welcome.

2:29But it's the Rock Hall, you know? You've got to 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. 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 exhibit hall.

3:00And we're going to go through like an AI Answers format, it sounds like, Mike. Yep. You're filling in for Kathy on the AI Answers. I am. I am. I feel like Kathy needs to sub in here, but yeah, I'll do my best to live up to her. And this is dropping Thursday the 15th, right? It sounds like it is happening tomorrow. Okay, so you're going to be maybe listening to this while we are here at the final day of Macon 2026. All right, with that, I'm going to stop talking and turn it over to Mike and he'll get a guide us through. Awesome. So thanks, Paul. I'm glad to have everyone in the live audience here that folks can't see at home, but super cool to be here doing this for Macon.

3:36Um, like Paul, you mentioned we're doing AI Answers and what we've had is our audience submit questions. People are attending Macon submit questions. Uh, Claire and our team did a great job pulling those together. So I took a look at them and selected a handful that I think would be awesome to chat through. And I definitely have not seen these because honestly, I wasn't even sure what we were doing when I came up here. Yeah. So this is unfiltered.

Standout insights from the conference

4:02Let's go. All right. So to kick things off, this first question is for both of us, actually. So the question goes, Paul and Mike, what has been the standout insight or concept you've picked up at Macon so far that has challenged your thinking or has confirmed a hunch you have about the direction of AI in the next couple of years? You're going first. 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.

4:38So I have gotten to sit now in our council meeting with some very advanced, uh, marketing and AI leaders. I've taught a workshop to an incredible audience, but many different levels of AI literacy in the group. And then just hearing how people are approaching AI in their sessions on stage, hearing where companies are at. I was interviewing Craig Karma's in at Good Karma Sports this morning on stage. CEO. 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 get to bring them along to the next level and what that looks like when it comes to AI education.

5:25So 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. I think that kind of hurts my brain right now. Um, yeah, I don't know. Like I, there's one really cool thing I've seen, which is I've had conversations, uh, at least three of them where it was someone who was here last year, who was trying to figure out how to kind of get the transformation going for the, say their marketing team.

6:00Who is now either in the head of AI transformation for their organization or about to become. And so what it's shown me is in these, a lot of big companies, 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. Then when everyone else, including the C-suite realizes, we don't know what we're doing, who's doing it.

6:30It 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. Um, Ty, good karma. 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. Um, 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 impact. Not, I don't think about it as like the impact we've had.

7:02It'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 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. Um, you know, as a, as a business world, not saying we smarter X, but we have this ability to, to reimagine like what we can do. And the people who are proactive are going to be on the frontiers of it and have really cool opportunities.

7:34Yeah, that's a really good point. And it 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. Okay. Next question.

Avoiding burnout and information overload

7:48How 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 of stay sane?

8:02Yeah. I mean, I think like for me, I've become pretty disciplined just about my not non AI time. Like, 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. So, and, and so I monitor that all the time. A lot of times on walks or going to the gym or in the car, I'm, I'm listening to podcasts and you just never know when you're going to hear that one episode that triggers something.

8:39It's either like, I have to have that person as a speaker at Macon. 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 a mic and be like, we got to talk about this. Like this, 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 just, you know, pay attention at all times. But then I don't have any hesitation to be like, I'm just done for today. Like I'm going to go home and I'm going to like walk up and meet my daughter from school, or I'm going to just spend Saturday with my family and I'm not going to open the computer.

9:14And I think I've just gotten at peace with myself that we're so productive all the time. I mean, you and I is a good example, like when you truly infuse AI into your life and into your work, you're just insanely productive. And I think you have to just accept that sometimes there's, you've made enough of an impact. And like, it's okay to, to, to, 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 follow to not do a thing.

9:46It, it, 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 in the podcast sandbox. Like, it's just like your, your mind like is wired to just, what did I miss today? What's important to talk about? Yeah. You know, I talked to someone last night and this morning about kind of the concept of running your own race. Right. And I try to remind myself of that to avoid feeling overwhelmed.

10:16Like someone was telling me this elaborate story, really great about how they had changed their career, their business, uh, by applying AI to their particular industry. They were seen as like an authority in their field now on it and we're teaching around it, speaking around it. And they were like, yeah, but like, that's just in my little corner of the world. And they're this, I won't name any specifics. It was a huge industry. It was like a 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.

10:48And I was like, all that matters is like, did you transform your business, your life, your work? It doesn't, you don't get extra points for how many different types of agents. It's amazing if you're 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 it. You get extra points for the outcomes. And it's like, you're not a header behind if you're getting the outcomes. Yeah. And I, I mean, I guess it's some weird way we're like getting into the loss of the year, but like mine was the mindset. And I think that is like, as an entrepreneur, I used to deal with this all the time.

11:20Like people are like, well, how many employees do you have? What's your revenue runway? It's like, well, I don't know. Like, why don't we talk to each about how we're doing and like what the struggles are? Like, 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. And it's like, well, what do we care? Like, we're both entrepreneurs. Let's just help each other. And I feel like AI is 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. Yeah, exactly. But it's like, you don't, you don't have to feel that way to your point.

11:50It's just like, but is it helping you? Is what you're 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 on the podcast before, like you're doing all kinds of stuff with agents. Like I have no idea what you're doing. Like, I wouldn't even know how to do the things you're explaining. And like, that's cool because that's your thing. And like, I'm doing my thing and it works out really well. Right.

Translating frameworks into daily work

12:11All right.

Translating frameworks into daily work

12:12This next question, the listener said, I keep coming back to the idea that how we learn is not how we live. 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 I've been like struggling with. How do you like, you can give someone the plan on paper, even in a company.

12:42How does it actually get translated to their work? Like, how do you see that balance? 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, like that's not personalization. Personalization is 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 in their careers, things like that.

13:22So, yeah, I don't know. 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. Kind of related to that. It was funny. I was having a conversation with our friend Jeff Culleton, who's helping sub in. He's everywhere on the make-up stage. And it reminds me of just how personalized AI can be because we're both similar in the sense 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.

14:00And I felt so seen because 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 going to look completely opposite to someone like, I would suspect, like Ashley, who runs our events, who's extremely detail-oriented. 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 it. 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 journey is getting this hyper-personalized to how you work, how you live your life, how you approach your work and your job.

14:35It's critical. In my workshop, the AI co-executive, we were building an AI assistant and that was one of the elements. 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 to you. 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 a specific way, talk to you in a specific way. But increasingly, you can tailor how you want it to communicate. 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 going to hurt my feelings.

15:06Like, 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. 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. All right, next up.

Navigating the messy middle of adoption

15:23Someone asked, AI models can already do impressive work. But in my experience, the blockers are everything around them. Permissions, usage limits, organizational trust, etc. 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?

15:57That's like 95% of people probably. 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, legal is 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 going to take to actually get people to drive adoption where they have, you know, a great example we've talked about recently. We have Claude, and we recently connected it to our CRM system. So, until Claude was connected to our CRM, it was dramatically limiting what we could do with it.

16:30As 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'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. You have to have buy-in from the leadership so that if Mike were to come to me or, you know, someone else in the other day to say, hey, really, I have this idea. I think it can generate millions of dollars or make this impact on our AI Academy members, but I'm not allowed to do this.

17:01Like, IT is not letting me. You have to be able to go to someone who can say, all right, 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 for 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. We should do this. And I'm 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.

17:35We have, like, 48 hours. And then legal figures out how to, like, make it work.

Planning for rapid model breakthroughs

17:41I'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 when a new breakthrough could change what's possible next month?

17:54Yeah, I don't know. I mean, I think, like, it's kind of like making a staffing decision when the model might be able to do what that person does in 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. 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.

18:27And so I think that that's really the best way to do it is 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 open AI has experienced this recently. I think the best example is GPT is going away. 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 GPTs, spent the last two years running hackathons to build GPTs.

19:04And now we have two months to figure out, what do we do? The entire marketing team now runs on GPTs. So that's a good example. And I think maybe we can use that as a retrospective to go back and say, okay, if 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 going to keep changing the names of the things. They're going to 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.

19:34How 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. And I was like, what is it? Like it came out like a week ago or two weeks ago. I haven'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 with people. It's like, oh, okay, well, that's pretty cool. That might change the way we do something. So I don't know. For us, it's about this dynamic learning idea. Like we're just going to push a lot more into like regular weekly, monthly cadence of what is new in these models and platforms.

20:11What does it mean to us? How does it affect our work? And then we're going to try and bring that to people through our AI Academy and make it more like real-time learning. 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 get insights from it the same as they would as like a consultant or advisor or whatever.

20:52My point here is that it doesn't matter tomorrow if everything about Gemini changes. That concept 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. Which I created in like 2018. And we still teach based on it. And it's still extremely relevant. That kind of thing is also really invaluable, I think. Teaching framework. Yeah, and a really good example of this is, so Mike's doing this Gemini, what do we call them, Gemini for Business, Gemini for Network? Yeah, Gemini for Business, yeah.

21:23And so last week, Google holds an event like a week, not even a week ago from, and what do they call it, Gemini, what's the new agent harness thing? It's technically just called Gemini, which if this is confusing to you, join the club. It's like a harness now or something. It's like kind of an agent to harness a bit. Yeah, so they rebranded Gemini as something else. And so that was six days before Mike's workshop or four days before it. So I'm messaging Mike like, hey, you're going to change the workshop based on this? And then during your workshop yesterday, if I'm not mistaken, they released Argon model.

21:56The Gemini for Argon became like, well, so it's like, 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 going to teach in the workshop.

22:09All right, this next question is fascinating, very relevant, I think, to several people listening.

Future costs of frontier models

22:15What, 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 go public and they have to start turning a profit, are we going to see them start jacking up prices? How's that going to work? 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 popular. Is Fable, I don't know what Fable version we're on.

22:45I think we're on Opus 5. All right, so let's say Opus 5.5 is it. It's been a busy week.

22:51It's going to cost 10x less in 12 months. And, and so at some point, like you're going to say as an organization, like, okay, this model is 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 have been ecstatic. And I'm like, great, like, we'll just ride with this. We'll build an entire company around this model. And I'm sure that model costs 5x less than it did when it first came out. Um, cause now they're on whatever, Sonnet 5.0 something. So I think the, the vast majority of use cases for businesses, it's just going to get cheaper as long as you're willing to use the previous state-of-the-art model.

23:33And so that's going to, the competitive nature of these labs is going to force that to happen. They're not going to just keep making it more expensive. So what they're going to 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. They just need to infuse agents and get everyone in companies using these things. So if you think about how, I mean, Anthropic and OpenAI are both going to IPO at somewhere between $1 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 it into their workflow every day.

24:13So the amount of untapped market potential for these models is so vast that even if you're paying 10x less per token to do it, you're, 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, OpenAI models are going to be comparable. So they have to accept that and they have to, like, think about what the business model is going to be. Anthropics IPO is going to be a really interesting look into what really is the business model.

24:47Like, what is sustainable here? What are the moats that these companies have? Somewhat related, too. It's been interesting, especially this year at Macon. Everyone is obviously using things like Muse or GrokBot or Instinct. Yeah, Docs. And I realize we're still in the time where the model powering that matters. But half the conversation, someone's like, well, GrokBot did this or that. I don't even know what model Grok is on. I think they're using Opus now. I think they're using Anthropics models. The point is, I don't know. Yeah. And so it's more like if you, it's almost 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.

25:23It's just like my agent works. Yeah, I'm fairly confident that, like, this time next year at Macon, the average user's experience, you will not be choosing your model. Like, 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 co-pilot. Here's your 12 models you can choose from when you're writing your emails or building your proposals or doing whatever. They're going to be like, okay, like, I'll just use whatever it's turned on to.

25:56I don't even know. Like, sometimes I'll go into ChatGPT and it's like 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 Band-Aid off. My guess is, I don't know if Google's going to do it first. Like, somebody's going to do it first where you just don't know what you're using and they're going to route it based on your prompt.

What AI can versus should do

26:21So, 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. 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? Yeah, 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.

26:52It's like when I prepare for the podcast, it's why I actually read every article, listen to every podcast, watch every video myself 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. Like, 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.

27:25So, I think some of it is just like, well, why are you doing the work in the first place? 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, 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, they're not saying they're saying, like, I don't think Paul actually wrote this page. One, my name's not even on it, so you don't even know that I'm the one who created it.

27:55Two, the whole purpose of that is to provide information to someone if they have a question about how the product works. It is not to hear my personal opinion or our perspective on something. 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.

28:31Turn it into a product FAQ page as though you were writing a best-in-class, like, SaaS product FAQ page and make it super approachable tone. Boom. 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 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 book that I'm assuming you wrote, that you put thought into this.

29:04Like, 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 it's important that organizations develop these frameworks and teach people on these frameworks because, especially with younger employees, they're not going to 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, man, Claude's better at writing these than me. So, you just have Claude write it and you hand it in. And then the boss says, like, come on in. Let's talk about this document you just created. Like, I don't agree with these three points.

29:34Why did you pick this? And the person's like, I have no idea. Claude wrote it. Like, I don't know why we picked it. You've lost the point. The point was to do the work. The point was to, like, be able to stand behind something you've submitted. It's funny you mention that example. Someone we know who I will 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 immediately this 20-page document was very clearly immediately out of the mouth of Claude and, like, not touched at all by a human.

30:08And 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? And then they couldn't do it. And they were like, all right, I need you to go redo this. And they're like, which page? All of them. You know? So, the point is, this stuff is happening already. I don't know where that line is, but clearly some line needs to be on. It has to be established. And it has to be part of the culture. Yes.

30:37Okay. Next one.

Defining AGI for regular workers

30:40What 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? So, going back to how we started this, Kevin Roos' new book, The AGI Chronicles, since I just sat through his talk, 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, OpenAI in particular uses.

31:12And he was actually saying that Ilya Sutskiver, who was a co-founder of OpenAI, used to go around to people's desks and just say, like, Yoda, like, feel the AGI. Like, and that was it. And so, Kevin interviewed Ilya, and he's 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 know, you see that you're zoomed in on Earth, like someone on a beach or whatever.

31:43And 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. 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 is when the model is roughly the equivalent of the B players on your team. It's kind of like the simplest, most tangible way I've thought about it recently. 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 going to take Claude Opus 5.5 or 6.1 Astro from OpenAI, and I'm going to give that AI those same 20 tasks.

32:28Is it on par with, like, an average person on your team? 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 you 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. So, I don't, I mean, Kevin showed, like, the different definitions from OpenAI and Anthropic, which is, like, a data center of geniuses, is, like, Dario's infamous way of explaining it.

33:08He showed Google DeepMind, 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? And then the next level of, you know, artificial superintelligence is, is it better than your best worker? Like, is it pretty much just superhuman? And I'm, I said 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 using the model in an intelligent way, like, you know how to work with these models.

33:43The models we're going to have in 12 months are, are going to be A 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, 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 AGI. Like, this is so obviously superintelligence. And yet here we are just debating a term that's become, you know, basically meaning.

34:16Yeah, it seems like that yardstick is just moving too, because like, I even think about going on a walk and using voice mode specifically with codecs, voice mode alone, but like voice mode with like codecs, where my computer back home is creating files, doing stuff for me on the internet. People will 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'm working on that. Great. Okay, it's all done. What's next? Like, if you sent that recording to somebody 20 years ago, they would be like, this is in sci-fi movies this way.

34:53Like, it feels like we're basically there. Well, and the key there is like, you did that for a work product, and it nailed it. 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, 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. 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.

35:24Okay, next question.

Can AI solve broader societal problems

35:27If 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? 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? Like, I, the labs think the answer is yes. Like, the labs believe that if we just get to the end game, I call it the end game, it might be the beginning of the game.

36:02But like, if we get to the point where we truly have super intelligence, then it'll fix all of this. So, if we go back like five years ago, all the big AI labs, the cloud companies, the tech companies, everywhere is going to be like net zero emissions. And so, it was all about like, we're going to save the environment, we're going to 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 going to build super intelligence.

36:34And then by 2030, it'll actually figure out how to fix all the environmental stuff that we're going to 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, once it gets good enough. So, the mathematics stuff that it's been solving is in part because you can prove if it's right or not. So, there is like an obvious, did this work or did this not, in terms of having an answer. It's why you can train it in games a lot of times, because there's a score, there's like a way to keep track.

37:06When you try and solve something messy like, what do we do with economic policy and what do we do with jobs? There is no like, we can work really hard and throw 100,000 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 going to 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.

37:40That doesn't solve the fact that I also now have no purpose in my life. Like, uh, 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 society. And that's why you have other ideas like universal basic services. 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.

38:12Right. 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, um, to own that problem. And to say, we are going to proactively look for solutions. We are not in a current political climate that wants to admit that AI has any impact on jobs at all. Um, David Sachs, who's, you know, kind of a, um, a voice of the administration unofficially these days, just go look at his X feed from the last four months and see how many times he's blasted people for implying that AI will take jobs.

38:52So it, and that is the, that is the position largely of the administration. So at least in the United States, we are unwilling to acknowledge that it could go wrong, um, 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. And that's what worries me is like, we're just, we're not even 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.

39:31And that's just lacking from politicians and from AI lab leaders. They're not even offering real ideas. It's just, it'll just be abundant and it'll be awesome. And we'll see what happens when we get there. Okay. 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? Legally none. So, I mean, if you think about employment law and the contracts we all sign when we work for organizations, if, if you create something, that intellectual property belongs to your employer.

40:05So, if you're an AI forward, you know, director within 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, um, you own none of it when you walk out the door. 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?

40:39Like, if they decide, hey, Paul Mike, it was great having you humans, but like, we're actually going to spin up some digital versions of you guys. Don't give people ideas. Yeah, I know. We already have the baby versions of us. I don't remember what episode that was. Yeah, this is, I'm already, I already know this is going to result in something. Yes. Yeah. Sorry. Um, but we've addressed this actually in our contracts. So, you know, again, it's, it's about being AI forward and thinking this stuff through. And this is why you need your attorneys. It's why you need to have AI forward leaders who understand to ask these questions.

41:10Um, 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? 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? 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, uh, co-pilot shop to Claude, vice versa, et cetera.

41:43I 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. And my experience has been, and Mike, you can jump in on this one. And 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 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.

42:13Like that doesn't really matter. And that's the biggest concern. So if I was an AI forward employee that I was making a move somewhere else, and I, let's say I was really used to Claude and now I was going to have to use co-pilot. I would, I would be more interested in like, what, what am I going to be allowed to do with it? Do I have a limitation on my tokens? Because once you're used to using these tools as part of your work, that's going to be the biggest problem is if you now would go to a company that limits your ability to use these tools. Um, I think it's going to be a lot more of the other way where people who are trying to

42:45be the innovators within organizations who are using Claude on their personal account at nights and weekends, um, they're going to jump ship and go to a company that's going to welcome them in and allow them to use those tools. Yeah, I think that's exactly it. It's less 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 become, 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

43:19you're excited about your work. So giving that up is more than just something like a comp consideration or a career. Also, it's like 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. Yeah, I mean, if I was interviewing for a job, I would say, 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? How are those kind of taught within the culture?

43:50What tools will I have access to? Are there limits? Do you have a connect? I'm looking for a job in marketing. Is your AI connected to your CRM or like, do you guys not, do you all that off? Those are the things I would be asking as an AI forward practitioner. It's like, I would want to know, am I going to be able to do the job you're asking me to do? All right, we have time for maybe one more question here.

Replacing custom GPTs and wrap up

44:12Now, 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 Mekon. Someone said, I trained people how to create GPTs in chat GPT. Now that GPTs are gone or going away, I don't know what to use now. Instead, what do you suggest as a replacement? So what they're suggesting are plugins for replacements to GPTs. My experience is that does not work. Like I don't, it was a one-off experience and it was not good.

44:43So it's certainly not as intuitive as I think they intended it to be. I think it depends on what your goal with it is. So if it is something you want to publicly share, then you're probably moving more in the direction of something like sites or something. I don't know. If it's an internal tool that you're going to use as an AI assistant, you might be building it as a skill or a project. 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 going to need to audit any GPTs you built and said,

45:15what is it used for? Who uses it? 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. Like I, you don't know. Yeah, it's interesting. It's almost like you start to see, you start to see a decision tree kind of manifest here. Unfortunately. Yeah, right. It's interesting. When I talk to people, it's like, if you're just using a GPT for yourself and you're not that far along and you just want like repeatable instructions,

45:46go learn how to build skills and talk with AI about how to do it. That's going to become important in the future. But in the meantime, just put the instructions into a project and use that yourself. It's going to be roughly the same thing. It's not perfect, but it's the closest 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 going to require a lot of iteration anyway. And then, yeah, if you're getting into, you need people publicly to use this, good luck. Because that's a huge thing that they've pulled the rug out from under you.

46:17Maybe it's an app. Maybe it's projects you can share with your team, but then they can also edit the instructions. So it's like, you don't want that. Right. So who knows where that is? 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 GPTs. And it's like, oh, this is brilliant. Like, I get it. I see it. And now it's just like, just blow it up. Like the one thing that was super intuitive. Yeah. Yeah. All right, 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.

46:50I have tons of speaking to do at Maycon. But it's been an awesome event so far. Yeah. Thanks for doing this. Yeah. Thanks, everyone who's been here. I know we have a bunch of podcast listeners here. We're actually, when this is airing, we're probably going to be in our live lunch that we talked about on the podcast. Yeah. So we've got a bunch of podcast listeners that joined us. We're going to do a kind of exclusive lunch Q&A with them tomorrow, which will be today for you all. 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 at Maycon 2027, which I would imagine we'll be announcing the dates for if we haven't already.

47:21Did we announce the dates? I don't know. I don't think so. Okay. I won't announce them yet. Yeah, don't do it. I'm pretty sure it's next October, though. All right. Thanks, everyone. Thanks, Paul. Thanks for listening to AI Answers. To keep learning, visit smarterx.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. That's it for now. Continue exploring and keep asking great questions about AI.

47:51Bye-bye.