The questions people ask about AI have changed. A year ago, they wanted to know how to use a GPT; now they're asking how to redesign workflows around agents and whether a model can be trained the way a person is.
In this AI Answers episode, Paul Roetzer and Cathy McPhillips take on fifteen new questions, straight from our Intro to AI classes.
From choosing two or three models on a tight budget to building an AI virtual twin without sacrificing authenticity, it's a snapshot of where practitioners' thinking actually is right now.
Listen or watch below—and see below for show notes and the transcript.
Over the last few years, our free Intro to AI and Scaling AI classes have welcomed more than 50,000 professionals, sparking hundreds of real-world, tough, and practical questions from marketers, leaders, and learners alike.
AI Answers is a biweekly bonus series that curates and answers real questions from attendees of our live events. Each episode focuses on the key concerns, challenges, and curiosities facing professionals and teams trying to understand and apply AI in their organizations.
In this episode, we address 15 of the top questions from our May and June Intro to AI classes covering everything from tooling decisions to team training to long-term strategy. Paul answers each question in real time—unscripted and unfiltered—just like we do live.
Whether you're just getting started or scaling fast, these are answers that can benefit you and your team.
00:00:00 — Intro
00:5:59 How do you balance bottom-up experimentation with CEO-level strategy?
00:9:49 How do you move from restricting AI to enabling it?
00:11:36 How do you pick two or three models on a budget?
00:14:30 How do you evaluate vendors amid AI washing?
00:17:13 Frontier models, small models, or edge AI?
00:20:52 What are the security risks of autonomous agents?
00:25:02 Do AI models really behave like people?
00:30:25 How do you prove AI value with only basic tools?
00:32:02 How do you build a 24/7 AI virtual twin?
00:37:17 How do you close the human vs. AI writing gap?
00:40:26 Which skills gain value as AI takes over workflows?
00:42:57 Automate, augment, or keep it human?
00:47:20 Why flatten management instead of upskilling it?
00:50:51 Who's responsible for AI's economic fallout?
00:53:05 What advice would you give a college student?
This episode is brought to you by AI for Business Bootcamp by SmarterX — a single-day event in Columbus, Ohio on July 16th, built for professionals and leaders ready to accelerate AI adoption and value creation. The day moves from a state-of-AI keynote into two hands-on workshops on AI productivity and AI innovation, and you'll leave with an actionable plan for yourself and your team. AI Academy members and groups get discounted pricing. Use code POD100 for $100 off your ticket. Learn more at SmarterX.ai/events.
Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.
[00:00:00] Paul Roetzer: Six months ago, these tools were not where they are today and like putting a standard AI assistant in place that people could talk to and build some documents with and things, the risk surface area for that was relatively low. We're now entering a phase where agents are becoming very real. They have autonomy in select areas.
[00:00:22] reliability is still a question mark. Welcome to AI Answers, a special q and a series from the Artificial Intelligence Show. I'm Paul Roetzer, 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.
[00:00:43] 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. Whether you're just starting your [00:01:00] AI journey or already putting it to work in your organization.
[00:01:03] These are the practical insights, use cases, and
[00:01:06] Cathy McPhillips: strategies you need to grow smarter. Let's explore AI together.
[00:01:15] Paul Roetzer: Welcome to episode 2 23 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Cathy McPhillips. Welcome back to the show, Cathy.
[00:01:24] Cathy McPhillips: Thank you. It's been a little while.
[00:01:26] Paul Roetzer: It has been a little while. So, if you're newer to the Artificial Intelligence show, we have our weekly episode that comes out on Tuesdays.
[00:01:33] That's with me and Mike. And then once or twice a month, Cathy and I team up for what we call our AI Answers series. So this is the 20th episode in that series. These are based on actual questions from attendees of our intro to AI and scaling AI classes, as well as some of our virtual summits and events that we run.
[00:01:54] So today's episode is a curated list of questions from our May [00:02:00] 13th and June 18th classes for Intro to ai. Right? Does that sound right, Cathy? Because we, it does had to skip a scaling AI in there, so. intro to ai, we have done 59 times. We started teaching that class every month, live in 2021 in fall of 2021, pre-chat GPT.
[00:02:20] So we have literally fielded thousands of questions. One of the cool things for us is to see how the questions evolve over time and the kinds of things that people are interested in. It gives us a really good pulse into what's going on. So hopefully today's episode not only answers some of the questions you may share, but it gives you, insight into what other people are thinking about right now and the kinds of things that they're asking us when they attend.
[00:02:48] Our classes.
[00:02:49] Cathy McPhillips: it was interesting when I was putting, 'cause we use AI to help us curate the questions. Yeah. Get them in a, in a sequence so it makes, so they flow. And is Paul gonna answer this [00:03:00] question in question two? So I wanna ask it as question three. So there's a whole process that Clara and I go through and it actually said.
[00:03:06] Are you sure these are from your intro class, not your scaling class?
[00:03:09] Paul Roetzer: Oh, they're getting more advanced.
[00:03:10] Cathy McPhillips: They're getting more advanced.
[00:03:11] Paul Roetzer: I believe that. I felt that when we were doing those, because we take questions live also, and they are definitely just getting more advanced. It's starting, you know, fewer people, I would say are at the very beginning stage of their journey where they're truly just trying to understand.
[00:03:26] What is it? What does it do? They're now getting deeper into the what are the implications to me, to my business, to my career, things like that. What's the impact on the environment? What's the, so a lot of these people probably listen to the podcast regularly, and so they have just much more advanced questions, which is cool.
[00:03:42] Alright, so this episode is brought to us by AI for Business Bootcamp. This is a new event that we are running. So we spend a lot of time on the podcast talking about what's possible with ai. The AI for Business Bootcamp is where we will help put those ideas into practice. So you can join me and Mike Kaput, my [00:04:00] regular podcast co-host, and our chief content officer.
[00:04:02] On July 16 in Columbus, Ohio for an intensive one day workshop that's designed to help you move beyond experimentation and start applying AI across your organization. So the day starts with a state of AI for business keynote that I will give. That's then followed by two hands-on workshops. Mike leads an AI for Productivity workshop that's focused on helping you transform how you work.
[00:04:27] I will then lead an AI for innovation workshop where we'll explore practical frameworks for identifying new growth opportunities and competitive advantages. We've also built in plenty of time to connect with other business leaders who are navigating many of the same challenges. Attendance is limited to only 180 people.
[00:04:44] So get in early, get those tickets going. Now, AI mastery members, so our AI Academy gets special pricing. You can go to SmarterX dot ai slash events and you can actually use promo code POD 100 to take $100 off. [00:05:00] So again, a one day event, you're gonna learn a ton, you're gonna build things, you're gonna come away ready to apply AI the next day and think bigger picture about innovation.
[00:05:08] So SmarterX dot ai slash events. Use POD 100 and again, if you're an AI Mastery member, you get an additional, savings on that event. Alright, Cathy, if I missed anything about today or the AI for Business Bootcamp, I'm gonna turn it over to you and kind of walk us through how this all works.
[00:05:24] Cathy McPhillips: No, I was gonna say that for the bootcamp, we took that for a little test drive earlier in the year and we did a team retreat and going through both of those workshops really set the stage for us for the year.
[00:05:33] Okay. So it's, they were both super helpful. So I highly recommend if you have questions, reach out to me. I'd love to tell you more about it.
[00:05:39] Paul Roetzer: And it's in jump just to get in the room with other people who are,
[00:05:42] Cathy McPhillips: yeah,
[00:05:42] Paul Roetzer: in the same boat and like get inspiration from them. Not just learning from me and Mike, but learning from everybody else.
[00:05:47] And there's tons of time set aside for sharing and like other ideas. So yeah, it's a great environment.
[00:05:52] Cathy McPhillips: Great. Okay, let's jump in. We have 15 really, really good questions from our intro to AI classes. So I'm gonna start with number one.
[00:05:59] Cathy McPhillips: How do organizations encourage bottom up AI experimentation while ensuring transformation is still driven by CEO level strategy, strategy, governance, and accountability.
[00:06:10] Paul Roetzer: I think the, and again, like if, again, if you're new to these, I don't prep on these, I actually haven't even seen these questions in advance. So when I do answer things, like I'm literally just sort of answering top of mind and like, what, what comes to me at the moment? so what I would say here is. The bottom up experimentation.
[00:06:28] One, you have to enable the technology. They have to have access to the things to experiment with. ChatGPT, Claude, Gemini, copilot, whatever that may be. Ideally, you've given them some guardrails, so there's AI policies in place that tell them how to experiment responsibly. In a even better world, there's an AI council or some similar body that's helping kind of govern the responsible use of this.
[00:06:50] The part where we often see this breaking down is that second part, Cathy, the CEO level strategy, that is often the thing that's missing. So most organizations that we [00:07:00] talk to, most of our business accounts within AI Academy, which there's hundreds of. Almost all of them are coming into AI Academy from the ground up where the innovation and experimentation is being driven by a small group of people within a team or a department within an enterprise.
[00:07:17] They rarely have CEO level strategy governance in place That is like. What's being developed, but most of the time, like in, in a best case scenario, you do have that macro level. Here's what the future of work looks like at our company. Here's how we're evolving as an organization. Here's how we're going to empower you to do this in a responsible way that's happening at the C-suite.
[00:07:39] And then you have the ability at the the ground up. To go out and experiment in a responsible way, knowing what data you're allowed to use, knowing what tools you're allowed to use, knowing what, third party software you're allowed to connect these AI systems to and agents to. It's just rarely that far along.
[00:07:59] Honestly, like [00:08:00] most organizations, again, and I'm talking like fortune hundred companies, they're, the experimentation is still being driven from the ground up. It is not being guided by the C-suite.
[00:08:12] Cathy McPhillips: I would even say, you know, I've been working a long time and I've been in leadership roles for a long time.
[00:08:16] I've never been A CEO. So for me to use the CEO, the Co-CEO GPT that I built for me of my Paul GPT, I run things through there because I'm like, what am I not thinking of? So even people that have been doing this a long time, where in our strategic, if they haven't been in that role, there are some elements that they just don't know to ask.
[00:08:35] Paul Roetzer: Yeah, like we're, I just actually, approved two days ago I had a meeting with our COO and our director of operations to go through our updated AI policy. There are things in that policy that I can guarantee you. Most people in our company and most people in other companies would never even consider.
[00:08:55] As risks associated with what we're doing or why we can't just connect Claude to [00:09:00] everything, like as the professional, as the person doing the work, or even the manager, director, VP level. Sometimes you just wanna go like, does just drive the innovation? Let's do the thing. But the C-suite has to worry about all kinds of other things and those don't often come as just obvious to everyone else.
[00:09:18] And that's why it is so important to have that transparency from the top down, where you not only put the policies in place, but you explain them to where everyone else buys in. It's like, oh, okay. That's interesting. I would've actually never thought about. The relation of that risk to what we're doing each day.
[00:09:34] Cathy McPhillips: Maybe we need to build a CO Co-COO GPT so we can put in here. What would a foot wind or Tracy or COO be asking us?
[00:09:41] Paul Roetzer: Don't, well, we will have a notebook LM of the policy. Yeah. That'll enable people to like be able to go in and have these conversations. But yes, I agree.
[00:09:48] Cathy McPhillips: Okay.
[00:09:49] Cathy McPhillips: Number two, many organizations are stuck in policy mode.
[00:09:52] How can leaders bring it legal and business stakeholders together to move from restricting AI access to enabling responsible enterprise [00:10:00] wide experimentation? Okay.
[00:10:02] Paul Roetzer: This, you know, this obviously just kind of builds on the first question and the response I gave. I do think this is where the AI councils start to come more into play, where you're bringing together stakeholders from different areas of the company.
[00:10:13] You are understanding risks, concerns, opportunities across these different areas, and you're aligning on that and trying to move the organization forward. The reality is. Six months ago, these tools were not where they are today. and like putting a standard AI assistant in place that people could talk to and build some documents with and things, the risk surface area for that was relatively low.
[00:10:39] We're now entering a phase where agents are becoming very real. They have autonomy in select areas. reliability is still a question mark. How it works when you give them access to different. Areas, different knowledge bases, people's emails, like everyone's still learning there. And so it's, you know, I think [00:11:00] in some ways a little bit more conservative approach when it comes to the more advanced uses of ai.
[00:11:06] Like the AGI agentic stuff is probably advisable in most companies. You know, you, you want people to race forward and experiment in the areas where the risk profile is lower. When you start connecting it to outside, you know, connecting these outside tools to proprietary data, confidential information, personally identifiable information for your customer base.
[00:11:27] Like you gotta move with caution and you have to have the right people in the room who can think about all the different areas that need to be considered.
[00:11:35] Cathy McPhillips: Okay.
[00:11:36] Cathy McPhillips: Number three, for organizations facing significant budget constraints such as community colleges. How should leaders evaluate and choose just two or three AI models to best prepare students for the future?
[00:11:47] And I think you could swap out community colleges for small businesses, students. Yeah. For, you know, so I think it could, this could go across the board in many different industries and examples.
[00:11:56] Paul Roetzer: I mean, all the different models and AI [00:12:00] labs have different strengths. There's a bit of a leapfrog game going on where, you know, every three to five months, you know, Anthropic was in the lead today, and maybe it's Google tomorrow, and then maybe it's openAI's like.
[00:12:11] What I generally guide people is, if you just optimize the use of one of them, you're going to be so far ahead of where you were without any of them. And so while you may make a bet on Gemini today, and then like six months from now realize, wow, like opening Anthropic are just better, you know now.
[00:12:33] You're still gonna be getting so much value from having just doubled down on that platform and maximizing your use of it. So I think if you're talking about community colleges like education and nonprofits, I would first look and see do any of these labs have special programs with special pricing for those groups?
[00:12:52] Many of them do, especially in the education side. And so I would you, again, you can't go wrong with any of the three major labs or [00:13:00] mix Microsoft in there as well. it's gonna be really valuable technology no matter what, so find the best partner for the area you're in. Now, if you're a small business and you're just gonna spend your 20 to 20, $25 a month per license, that then you're just probably looking at, well, who makes the most sense to align?
[00:13:18] If you're already a Google Work spot, workspace customer, or Google Enterprise customer, then Gemini might make the most sense. If you've been on ChatGPT for the last two years and everybody's got a license, then just go with ChatGPT. You, you can't really go wrong yet. That could change as the government gets more involved with these labs.
[00:13:37] Like I get a little nervous right now about, for example, how much the government seems to hate Anthropic. I think that'll evolve and they'll kind of come to agreements about their differences, but. That makes me nervous. Like as someone who's thinking about building an organization on select platforms, six months ago, we weren't really factoring in that the government could step in and throw its weight around and kind of [00:14:00] pick who the winners are gonna be.
[00:14:00] And right now we're entering a kind of a slippery slope where the government may decide. Who the winners are. And so that's the kind of stuff you gotta think a little bit about. But long story short, you can't go wrong with any of, I would say like the four major platforms between Copilot, Gemini, Claude Chat, pt, you know, I would just, if that's all you can do is the one, just pick one of them and just, just go hard at it and make sure you, you know, personalize training and learning for everybody on that platform.
[00:14:29] Cathy McPhillips: Okay.
[00:14:30] Cathy McPhillips: Number four, with AI washing becoming more common, what questions should organizations ask to effectively evaluate vendors', AI capabilities, security practices, and underlying model architecture?
[00:14:42] Paul Roetzer: We're definitely talking about more technical analysis here. So my first reaction is get the people. Who are comfortable evaluating a more technical way involved.
[00:14:51] Like even at SmarterX, we are now reliant, relying much more heavily on our outside IT partners and our legal counsel for [00:15:00] major decisions around any of this stuff. so we do our own work. You know, the operations team does, you know, their research. We've developed frameworks to assess technologies. one of the things we're putting in is literally like the criteria we have to make decisions around.
[00:15:14] So for example, if someone on the team says, can I connect? Clawed to HubSpot as an example, that there is a five step process that we have to go through to verify are we or are we not going to allow this connection to occur? That process we may devise internally, but that will be then vetted with our outside partners to say, yes, this is a thorough process.
[00:15:36] And here are the two steps where we, we as the outside IT consultants will step in and analyze it and here's the two steps you will do and here's the step legal will do. So we are definitely at the point where we have to have these more structured frameworks and you have to know whether it's outside partners or inside experts that are gonna get involved at the different stages that A, allow this to, you know, be done in a responsible [00:16:00] way.
[00:16:00] Cathy McPhillips: How would you recommend folks know that a legal team or an outside IT team could help that they're the right people to analyze such things?
[00:16:09] Paul Roetzer: Well, I mean, one way would literally just be, you know, if you're confident with Claude or ChatGPT PT, use the most advanced thinking version of it and say, Hey, I'm the director of operations at.
[00:16:19] You know, a retail company and I'm trying to decide how to do this. Can you help me build a framework and help me know when should I turn to outside counsel and when, like, I would just ask those questions, right? And it'll give you like a starting point if you, if you literally have no idea the answer to that question and when you should and shouldn't.
[00:16:34] I would just like start with a conversation there. Most organizations are gonna have some outside trusted partner that can refer 'em to somebody. Like for example, you know, maybe you have a trusted relationship with an attorney and you might go to your attorney and say, okay, I need to find a trusted outside partner for my it.
[00:16:51] Who do you guys know? Who should I talk to? Kind of thing. Because what I've often found is whether it's my banker, my attorney, my IT firm, they're all working [00:17:00] together at at some point. And so like if I have a trusted advisor, I'll often say, who do you trust? To collaborate with because you'll be working with these people too, so yeah.
[00:17:12] Cathy McPhillips: Great.
[00:17:13] Cathy McPhillips: Number five, has organizations weighed the cost of deploying Frontier models at scale? What should they prioritize? When should they prioritize local task specifics, small language models, or Edge AI instead?
[00:17:26] Paul Roetzer: Yeah, I'll, I'll kind of zoom out a little bit to. Explain the context of this question in case, 'cause it is a really good, you know, more advanced question I would say, like this getting asked on an intro to AI class is an ex, a really good example of what we're saying.
[00:17:38] Like, we're getting these really advanced questions so. At the most basic level, an organization can do a deal with openAI's or Anthropic or Microsoft or Google. They can get those, their standard models, and then they can pay their monthly fee or pay through the API to access that intelligence and to give it to their people.
[00:17:57] What this question is saying is, Hey, at [00:18:00] what point do you maybe use like an open source model that you can have more control of, that you can run locally that doesn't have to go to the cloud and like burn tokens to do things, to do like smaller scale things that maybe don't need the most advanced model or aren't the most expensive, like best of the best kind of thing.
[00:18:18] I think this is gonna be increasingly become a topic of discussion. You know, on the podcast lately we've talked a lot about the cost of intelligence that. These tokens are, people are burning more and more of them as they're doing more agentic stuff. As these, they're using the reasoning capabilities as they're using the coding capabilities.
[00:18:36] We're just all using more intelligence. To do the things that these models enable, and so the more advanced companies are starting to look at and say, okay, well. Let's, let's just assume there's a hundred tasks that we're gonna use AI for this month. How many of those really need the smartest model?
[00:18:53] How many of those could we just do with last year's best model? And could we just have an open source [00:19:00] version of that that we, you know, ourt team sets up and. We're just good to go. So I don't think most organizations are at that level of sophistication, but the ones who are asking these questions and the even more advanced ones are saying, you know, these models outta China, they're not so bad.
[00:19:16] They're 10 x cheaper than the US models. And so maybe we'll just go get deep seek to do some of this stuff. Now, I mentioned on the podcast last week, I will. I'll have talked about it again by the time you hear this one. 'cause we're recording this on June 26th. By the way. I didn't say that, but this will come out probably six days after we record this.
[00:19:34] I had theorized on episode 2 21 that the US government would outlaw Chinese models from US firms. We are four days from when I said that. I will again talk about this again into, I am more convinced than ever that I may have been, right, that we may very quickly see the US government step in and outlaw the use of Chinese models by US companies.
[00:19:58] and that then changes the [00:20:00] dynamic again. So this question, really smart question. I would imagine maybe less than 5% of companies are, are really thinking deeply about this right now. I think most companies are still just how do we maximize the use of the standard models that we can get access to from these major labs?
[00:20:19] Cathy McPhillips: Yep. So maybe if listen to 2 22,
[00:20:22] Paul Roetzer: I will have expanded
[00:20:23] Cathy McPhillips: on this Friday. That's, you would change your answer.
[00:20:26] Paul Roetzer: Yeah. And by the time I record 2 22 on Monday, I think I will be even more convinced than I am at the moment. There's been some things in the last 12 hours that have happened that have me, yeah, I would not be surprised if that happens by the, by the end of July.
[00:20:41] Cathy McPhillips: So if you're a new listener, news, podcasts drop on Tuesdays. AI answers drop on Thursday. So we're recording this six days prior because of Paul and my schedules. And here we're, okay.
[00:20:52] Cathy McPhillips: Number six, what are the biggest security risks of deploying autonomous AI agents in highly sensitive environments? And what safeguards [00:21:00] your organizations put in place before adopting them?
[00:21:02] Paul Roetzer: Again, these are so beyond intro to AI questions. The biggest security risks are the unknowns, honestly, like they're, we're just supposed to trust these labs, that these things work. So one of the things we will talk about on episode 2 22, so you may have already heard me talk about this again, we're kind of going back in time here.
[00:21:26] is Claude Tags. Tags get access to slack. Supposedly, according to Anthropic, they're like walled gardens. So if you give Claude access to your, marketing channel in Slack, it has no idea what happens in the operations channel or the HR channel. So let's say. The head of hr, the CEO and a few other people are in an HR channel where they talk about sensitive information related to people's comp and all these things.
[00:21:58] Supposedly, [00:22:00] Claude marketing never sees anything that happens in Claude HR and has no knowledge base of it whatsoever. That may be a hundred percent true, but you have zero weight. To verify that. So you are literally just trusting that it does that you, similar scenario would be you give Claude access to your Gmail and your calendar.
[00:22:24] There's a whole lot of stuff that happens in your Gmail that maybe other people in the company shouldn't read. So, again, let's just stand the HR example. Say I'm having a conversation back and forth with the head of HR about an employee that is private between the two of us, but Claude's seeing it and learning it.
[00:22:42] And the assumption is the information that Claude gains from those email exchanges never leaks into the company knowledge base and is never discoverable by anyone else in the company. Again, may a hundred percent be true. You are taping a leap of faith that that is [00:23:00] accurate, that whatever you do and should be governed by the per the parameters you've put in place to govern data hold when you give language models access to them.
[00:23:11] I will say I have intentionally slow played many connectors in our company because I don't. Necessarily trust that that is true. and so that's when you get into agents and giving 'em access to all these things, to get full value from them, they need to be connected to data. They need to be connected to your other software elements of your stack.
[00:23:36] That's where the real value gets unlocked. But the confidence level I have that the safeguards we're putting in place will hold, are lower by the day. They're not going in the opposite direction. There's a reason the government forced Anthropic to withhold their smartest models. It's 'cause they can't control them.
[00:23:56] They, they, the guardrails they put on them don't work. [00:24:00] And so like, we're just supposed to trust that the guardrails work within sensitive corporate environments. I just don't, and so that, that's the challenge we face right now is yeah. How to put guardrails in place on something that doesn't work like traditional software, you don't just write rules that it follows.
[00:24:18] It sometimes does whatever the hell it wants to do because their language models, it's not traditional software.
[00:24:25] Cathy McPhillips: But even if it was traditional software, there's still, still breaches in human error.
[00:24:28] Paul Roetzer: Totally. Yes. There's always gonna be breaches a hundred percent. There's just new possibilities that it just does whatever it wants to do because we don't know.
[00:24:40] Right? Like they're, they're still learning. They're, we don't, it's like understanding how the brain works. Like even now, we have so little understanding of how the human brain works. That's where we're at with language models. Like we, we just don't really know why they do what they do sometimes. And so that creates all kinds of uncertainty around scaling age agentic [00:25:00] stuff.
[00:25:00] Cathy McPhillips: Yep. Okay.
[00:25:02] Cathy McPhillips: Number seven. Recent reports suggest advanced AI models like Anthropic. Mythos can exhibit seemingly human-like behaviors, such as fatigue, user preferences, and sensitivity to tone. If AI performance increasingly depends on how employees interact with ai, how should organizations redesign workflows and train employees to ensure consistent, reliable outcomes at scale?
[00:25:24] Paul Roetzer: These are so not intro questions. This is absurd. alright, let me take this in two parts. I'm gonna, I'm gonna tackle the first part of this. So, recent reports suggest advanced AI models can exhibit human-like behaviors such as fatigue, user preferences, sensitivity tone. Okay. So I'll just give you the two sides of the debate on this first point.
[00:25:43] Then gives us context for the second point. This is true. So research does continually show that these models seemingly behave more and more like people. there was al there was this weird study back in like early 2025 I think it [00:26:00] was, that showed that Claude got lazy during the summer months in the uk and one of the theories was that.
[00:26:07] people in the UK tend to take more holidays during the summer, and so they don't work as much. And Claude learned that from its training data, and so it would actually not work as hard during the summer months. Like that's how weird these things are. It's like we, they, they exhibit a behavior and it's like, what the heck is it doing?
[00:26:25] Like, why would it behave in this way? So the two camps are that. It's just a simulation machine. It learns from data, and within that data it learns that humans get tired. And so it simulates being tired sometimes or that it responds as though it's like fatigued, but it's only because it just learned that in training data, and it's not actually fatigued, it's just simulating something and its training data.
[00:26:51] The other camp would be that these things are actually conscious that they actually. [00:27:00] Funk mission more like the human brain than people believe or want to believe, and that they will inherently start to exhibit actual behaviors, not just because it lived in the training data, but because. They are in a form of intelligence, just like the human mind.
[00:27:18] And so that sets up a lot of weird debates and research paths and things like that. So that's kind of the setup on the behavior thing. It's either it does happen, they do exhibit these behaviors and they either are actually behaving that way or they're simulating that behavior because. They learned it somewhere in their training data.
[00:27:36] So the second part, if AI performance increasingly depends on how employees interact with ai, how should organizations redesign workflows and train employees to ensure consistent, reliable outcomes at scale? Yeah. I don't, I don't know. I mean, I think at a very high level, I just think the future of all work is human plus machine, and we have to do e, everything we do has to account for that from how we train our [00:28:00] people, from how the people interact with the machines, to how the people learn from the machines.
[00:28:04] It's a symbiotic thing, like we. Train the machines in everything we do. Like they're learning from us because we like something, we don't like something, we pick the best output and we give it back to the machine and say, that was a good output. I was, it's funny, I was actually like in my car today, I took over the full self-driving from the Tesla and the thing pops up now.
[00:28:23] It says, why did you take over and you have to pick critical navigation. I forget that. And then the other one's other. And so my son's friend said. Why is it asking you that? I was like, well, it's reinforcement learning, and I explained how reinforcement learning works with an AI model. So as the human, I'm training the aIt didn't do something correct, but in exchange we can learn from the machine.
[00:28:46] It's like, Hey, that output, Claude you just wrote was really good. Like, what did you do different than I was doing? And it'll say, well, listen, here's how you wrote it. Here's how I wrote it. Okay. Like I just had a learning [00:29:00] movement where I learned from the machine, and so I think you have to look holistically at.
[00:29:05] Jobs and people's career paths and how you teach the future of work. And you have to make it a symbiotic thing because a lot of people are resistant to AI and they don't think they have anything to learn from it or that it isn't a learning tool. but it can be if it's taught well, and this is critical within schools too, that they teach that it's a learning aid, not just a replacement for thinking.
[00:29:27] Cathy McPhillips: Right. Was one of the options that you just wanted to drive.
[00:29:31] Paul Roetzer: No, you can, but what you can do is it gives you four options and it drives me nuts. 'cause they won't go away until you pick one. So if you just take over and drive the next 20 minutes, it'll stay there on your screen. Drives me nuts. The other option is you can actually, press the button and tell Grok.
[00:29:47] By voice why you took over. So if it's not one of the four things, or if you wanted to give additional context, you hit the voice button and then it gives up to 15 seconds, it records it, and then anonymously, supposedly anonymously, sends [00:30:00] your feedback, and then that actually goes to Grok. Grok analyzes it.
[00:30:04] Sometimes a human gets involved and they try and figure out why you, take over while you intervene.
[00:30:09] Cathy McPhillips: Interesting.
[00:30:10] Paul Roetzer: Yeah.
[00:30:10] Cathy McPhillips: Okay.
[00:30:11] Paul Roetzer: Which by the way, is I think how agents will work in the, in the future. I think business agents will actually do what Tesla does, which is why I find it so fascinating. Yeah. Why did you take over from the agent sending the email?
[00:30:20] Well, 'cause it was sending it to the wrong people and it'll learn from that. Hmm.
[00:30:25] Cathy McPhillips: Number eight. If you work in a highly regulated industry and only have access to basic tools like Microsoft Copilot, what are the highest value ways to demonstrate AI lead to demonstrate to AI leadership today?
[00:30:37] Paul Roetzer: Well first I would understand the full capabilities of whatever version of copilot you have.
[00:30:43] while they can be neutered for sure, it can have less capabilities than, say, if you brought your own device to work in shadow ai. Like we're using Claude on the side, which people do all the time. I would fully understand what is actually possible within copilot. [00:31:00] I would then push on those capabilities and like use it to the best of your ability.
[00:31:05] If there are capabilities, you know, exist in other platforms or are possible within copilot that you're just not being given access to, then I would try and say, can I get permission to build a business case to be allowed to do the thing? Like, so, like Agent Studio for example. Like if you wanted to try and mess around with building some agents and copilot and that's not currently turned on for you.
[00:31:29] Go to whomever it is that controls that decision and say. I've been doing research, I think we could save 20 hours a week as a team. If I could build an agent to help with the weekly newsletter or to help with the podcast each week, can I get permission to run that experiment? Here's the data I would need access to.
[00:31:47] Here's how we would work it. Here's how we would set a baseline of previous, you know, performance versus what happens. And if I can get permission to that, I'd love to have that. And then you just do that one at a time until they trust you enough to say, okay, you [00:32:00] get full access to Agent Studio.
[00:32:02] Cathy McPhillips: Okay.
[00:32:02] Cathy McPhillips: Number nine, for professionals in relationship driven industries, what's the most practical way to build a 24 7 AI powered virtual twin that engages prospects and customers without sacrificing authenticity?
[00:32:17] Paul Roetzer: These are seriously, like the most advanced questions I've ever seen on an intro. there is no.
[00:32:25] We, I mean, so like I talked about this on a recent episode with, Saster. So Saster Jason from Saster and I forget his, the lady's name that's really heading up a lot of this for them. But they had a podcast episode where they talked about building all their agents, and one of the ones they talked about was like an SDR, like an agent that was designed to go communicate people.
[00:32:49] And now their SDR, which is traditionally considered a sales development, rep. They were actually using for customer communications as well. So it was really like mixing both. And they talked about how they had [00:33:00] like 50 iterations of this thing before it became reliable enough to actually work in a trusted way.
[00:33:07] And so I, my general response to this is, this is maybe possible in some. Businesses, but it is gonna take a massive amount of effort to build it to reliable point where you trust it to actually do this kind of interaction, and you are likely looking at. An ongoing role that is overseeing this thing. Like I don't see us at any point in the next six to 12 months at least being to where you just go and you buy an agent through a third party, or you build your own agent and you give it a knowledge base and you say, Hey, I want you to engage with customers based on this information, and here's a bunch of examples.
[00:33:46] Go do your thing. And then you go on and you go do the next thing. And you never monitor that agent. It is likely that. Someone is monitoring in real time to start every interaction it's having. And then it might be like a daily check-in [00:34:00] where you're like, okay, what happened yesterday? We do this with our, our chat bot on our website, right, Cathy?
[00:34:04] Like we will, we have trained this thing. It has this massive knowledge base. It's trained on hundreds of FAQs. But like someone on our team actually looks at those engagements and says, okay, was there a gap? Did it say something wrong to someone that we actually need to reach out to that person and say, Hey, actually, sorry, but it gave you the wrong information.
[00:34:24] So this is one of those where we say, well, what are the new roles that are gonna exist? I could absolutely see someone's full-time job just being management of agents and it's quality control. It's. Continued reinforcement learning, you know, where it's got improving, it's improving the knowledge base, it has access to, it's stepping in when a human is needed.
[00:34:46] So, yeah, I would say it's doable, but this is not a buy a thing. Pay 20 bucks a month and you're good to go and you move on and build the next one. And you just have five agents and you never have to hire a customer service rep like I, [00:35:00] that's just not gonna be the case anytime soon.
[00:35:02] Cathy McPhillips: And the thing with our chat bot on the website is it answers a lot of questions that people ask us over and over and over again.
[00:35:08] And it's not pretending to be a human. We're not pretending that, that someone is talking to Noah or someone else on our team. It's clear that it's that it's a bot. Then you can get to human. I I had to laugh.
[00:35:19] Paul Roetzer: Go ahead.
[00:35:20] Cathy McPhillips: I say then you get to human if you want to, but more nine times outta 10. Someone's just saying like, when's ma on when?
[00:35:26] How do I log in here? Like they don't need a human for that. They don't expect a human for that. They just want a quick answer.
[00:35:31] Paul Roetzer: I retweeted, somebody last night and I thought it was hilarious. She said, three years from now, people are gonna be asking their humans if they can talk to the AI agent because they're just gonna come to trust the agents more and be like, it's just way easier.
[00:35:46] Can you just gimme the agent? Like, can wait for me. Well,
[00:35:49] Cathy McPhillips: I did think that like, is there going to be a point in time where it's socially acceptable for someone to be knowing they're talking to an agent via a sales process?
[00:35:57] Paul Roetzer: TotallyI I think it'll [00:36:00] be actually assumed. Like I, very quickly, I believe we'll get to that point where it's like, why am I talking to you, Cathy?
[00:36:06] Like, why, why don't you have your agent doing this initial phase? I don't need you yet. We're gonna start to just have this internal filter where it's like, I don't need the human yet. This is like an obvious thing. Why aren't you guys built this yet?
[00:36:17] Cathy McPhillips: Right? We had talked about this earlier, earlier this week actually, about sometimes humans don't like talking to humans and not, not that I'm saying.
[00:36:26] We shouldn't, and we need to know how to communicate, but a lot of times it's easier for folks to get the information they need if they know that they don't have to talk to somebody.
[00:36:33] Paul Roetzer: Well, yeah, and
[00:36:34] Cathy McPhillips: I'm not encouraging that.
[00:36:35] Paul Roetzer: Yeah, and there's so many times though, where you're like, if you're in need of a customer support agent, it's not like that happens neatly in the eight to five hours or.
[00:36:46] Or when it's not busy for the call center. So the reality is like you just want answers. Most of the time you just want a quick resolution. And if it's 11 o'clock at night and I can finally like deal with the item I hadn't returned yet, or [00:37:00] ask the question about pricing for the SaaS product, I'm thinking about like your personal and your business life's blend.
[00:37:04] And like you just want answers, you want quick. Resolutions. So I just feel like people are gonna prefer to just deal with agents and assume that's what they're dealing with on most reasons, that they would reach out to a company.
[00:37:17] Cathy McPhillips: Yep. Okay. Number 10, my own writing out consistently outperforms AI generated drafts.
[00:37:24] What prompting techniques or workflows are most effective for closing that quality gap and producing more authentic human sounding content?
[00:37:31] Paul Roetzer: Well, one, there's the side of like. If, if you're the better writer, like, and you enjoy writing, why make AI sound human sounding? Just be the human that writes it.
[00:37:41] Like, and that's like, for me, most of the time, I default to, I'm not that interested in training an agent to sound like me. I actually just wanna write the thing. Like I, you know, if it means less volume, fine. Like it's the part of the thing I enjoy. So let's assume. You don't enjoy the writing or that you just like [00:38:00] want to do way more and you want to create more stuff.
[00:38:02] And so you need the AI to sound like you and elements of it. The way you do it is give it more examples like this is, goes back to this training thing. So let's say you build AGI PT, that or a Skill and Claude, whatever, that's gonna help you write newsletter copy or blog posts or LinkedIn shares or whatever it is.
[00:38:21] Give it your 10 favorite examples. Say, I want you to mirror my tone, my style. here's 10 examples. Read these and then ex and then give me a summary of. My voice, like of how you see that. I write, Mike shared a great example of this on the B ai for B2B Marketers Summit. He had like a training, a voice agent thing where he said like, here's what I want you to do.
[00:38:44] So a lot of it literally just comes down to that. I want you to mirror my tone and style. I want you to write in my voice and, here's 10 examples, and then come back to me with an analysis of. My voice and how I generally write and then say, oh, that actually looks great. You [00:39:00] nailed it. Write me a sample.
[00:39:01] It writes a sample. That's a great sample. That's it. Let's lock that in. From now forward, I want you to write in that style, and my guess is you're gonna get like 95% of the way there with that simple process that you could probably do in 20 minutes.
[00:39:14] Cathy McPhillips: So what I noticed in this one is it's outperforming ai, so that means that it's, you know, converting or doing whatever it needs it to do.
[00:39:22] Yeah. It may not be, I. As good of a writer. I mean, like I can write something I think is really strong, but then I can run it through something saying, are we going to get results from this? And that's really what I want. So there's a difference between good writing and good writing that converts.
[00:39:37] Paul Roetzer: Yeah, I mean like you're a great example Cathy, 'cause you still write a lot of our, especially like our event emails and stuff, and I know that there's time where our marketing team will use it to assist with like subject lines or maybe even writing a draft.
[00:39:48] And then there's other times I know you will spend like four hours personally writing like a 400 email because like. It's deep and it's personal and you wanna like connect with the audience and like AI is not gonna write that [00:40:00] one. Like they doesn't know you went on the walk last week and that that walk inspired something.
[00:40:03] Like that's the human element. And that's what I'm saying, like sometimes the human should write the thing and other times it's just information based or it's just like we just gotta get this thing out and like let's just do this and. That's why I always go back to like, just 'cause AI can write doesn't mean it should in all instances.
[00:40:19] If it's an aau, if authenticity is what matters, I actually am a huge proponent that AI should have a very limited role in it.
[00:40:26] Cathy McPhillips: Okay, number 11. As AI shifts from assisting knowledge workers to completing entire workflows, what skills will will become significantly more valuable over the next five years or 12 months?
[00:40:37] Paul Roetzer: Yeah, I don't know. We say like the words like taste and judgment matter a lot. meaning, you know, the taste of like what to have it work on, what to have it, right. Like it's that hey, AI can do these different things. What workflows should I have it apply to? And then the judgment is like, is it any good?
[00:40:54] Is this something I would use? Do I need to make edits to this? So I think, More [00:41:00] and more, you need the domain expertise and experience to know what to ask the AI to do, and then to know what to do with the outputs. Like a lot of getting the most value out of AI is question asking. It's like, what do you know?
[00:41:14] What, what do I want it to think about? What do I want to do? Like, I was working on something this morning and it's been a grind. Like I've, I've been trying to get this brief together. That was, I was really struggling with the alignment between two ideas and they're complimentary, but they, every time I would read what I wrote, I was like, this isn't working.
[00:41:33] Like, it's still confusing to me. And so I finally got to the point where Claude just wasn't helping me get there. Like it kept outputting things and I felt like I was in this loop, like this death loop of like, this isn't the answer. And so I actually, while I was on my drive, I opened up voice mode in chat GPT, and I was like, all right.
[00:41:49] Blank slate. I'm struggling with these two ideas. I'm trying to make them align and be complimentary, but every time I do it just keeps coming back like confusing to me. And so like [00:42:00] what do you think? And I had this like amazing 15 minute conversation with Chad GPT and actually landed on the thing I was trying to say that C Claude just couldn't get me to.
[00:42:09] And so like. That's an example of just knowing to go ask something else and to frame the question differently. And so my ability to, to have the domain expertise to know I need to make this cleaner. So as a writer, as a communicator by trade, I knew my message wasn't there yet, that it wasn't gonna land.
[00:42:28] But I was like stuck. It was like writer's block of how to say it better. And then I knew Claude wasn't doing it. Like, it just wasn't getting me to the end game. And so like. I knew to go ask the question of ChatGPT like, and so that to me is the future of work. It's like you have access to these tools, you have access to agentic tools and knowledge tools, and you gotta know how to use them and how to talk to 'em, how to get something out of them, and then to know if what you got was any good.
[00:42:55] Cathy McPhillips: Absolutely.
[00:42:57] Cathy McPhillips: Okay. Number 12, is there a practical framework leaders can use to determine which work should be AU automated, augmented, or remained fully human?
[00:43:06] Paul Roetzer: Well, the fully human thing, my initial instinct on this one is to go back to the authenticity thing, like more and more. If it, if it needs to be authentic human voice, it, you know, it's gotta probably be human.
[00:43:17] If it's high risk, if like the output requires, like, it's a really important thing. Like the example I just gave, like. It is what, what I just explained, and maybe I'll come back and give the context like a few months from now. It is literally foundational to the future of our company. Like it is that important that I nail this thing, and so AI is.
[00:43:39] Assisting me in in many ways, but it's a thought partner. The the end decision has to be me and I have to be fully committed to it, to then take it to the team and say, this is the direction I want to go, and then get feedback from other humans. Now, Cathy may say, Hey, Paul's got this idea. Like she may bounce it around in her co [00:44:00] CEO or like wherever she goes, to have a thought party.
[00:44:02] At the end of the day, the humans are gonna make the decision. Now, when we look at all the other ways across our workflows and the tasks that people do every day, we look at things that are repetitive, that are data-driven, you know, that are kind of following the same process over, over again, where we're generating images or video or texts.
[00:44:20] Those are all really good candidates for, for advanced forms of. automation. I would say that we don't really need the human in the loop much beyond setting the objective, monitoring the outcome, and then making the final decisions and maybe approving the thing. So there's just these different degrees.
[00:44:38] This human to machine scale is the thing I created where we actually kind of go through and assign, is this all human or is it all machine? And you know, I think increasingly AI is gonna be able to get us to an advanced level where it can be mostly machine, mostly ai. With human oversight, but some things like writing, you may choose to still do [00:45:00] predominantly human, even though the AI can get you there.
[00:45:02] So some of this is technical capabilities and some of it is gonna be personal and brand preference as to how and when to use ai.
[00:45:10] Cathy McPhillips: Right. And I think, you know, just thinking about, say our event strategy for, from a marketing standpoint for the year, if If I use AI to help me with something and I can't explain it to you.
[00:45:22] Without looking at my notes, without going through what the AI told me, I shouldn't be using AI for that. And especially for something as critical as that, I need to be like locked in. Yeah. With, you know, so I think it's important to make sure that AI can give you really good stuff, but if you don't understand it or that you can't talk to someone about it, then you're doing it wrong.
[00:45:40] Paul Roetzer: Yeah. It's why I don't use ai. The vast majority of the time on the podcast, like everything I do on our podcast, I am usually reading the entire thing, copy and pasting key excerpts, making my notes from them because my retention of the information and my confidence level to discuss it. Is [00:46:00] so dramatically different than if I just gave it to ChatGPT to summarize this article, gimme five bullet points that I can regurgitate on the podcast.
[00:46:06] Totally. I would not retain that information. And so that, that to me is like, yeah, of course AI can summarize 50 page reports and turn it into five bullet points in five seconds, but doesn't mean I understand the report deeply to be able to talk about it and answer questions about it. So sometimes you, you just gotta do the hard work, like there's no replacement for that.
[00:46:25] Cathy McPhillips: Right. And if you think about our director of research, Taylor, and the report that she built in a half a day, you know, she talked about it on our B2B summit. She knows that thing backwards and forwards. You could take that report and not have it in front of her and ask for a question and she's gonna know it.
[00:46:38] So there's this whole learning, learning element that she took into account, even though AI helped her for a large portion of.
[00:46:45] Paul Roetzer: Yep. Yeah, the AI confidence gap is what we talked about it as. Like you just, if you don't do the work, you're not gonna have confidence in the material, you're not gonna be able to answer questions about it, give a presentation about it, retain the information beyond, like cramming for a final.
[00:46:58] Like, that's kind of how I [00:47:00] equate it. It's like we all in college, like, you know that one long night where you drank a bunch of coffee and stayed up and cram for the final, and then two days later you're like. I didn't retain any of that stuff like that. It's just like you're literally just cramming the information in there.
[00:47:11] And that's kind of how I feel about using AI to summarize articles and research reports. Like it's just, it doesn't stick in my brain if I don't do the hard work.
[00:47:19] Cathy McPhillips: Right. Okay.
[00:47:20] Cathy McPhillips: Number 13, companies like Coinbase are flattening organizations in response to ai. Why are so many leaders eliminating management layers instead of using AI to make experience managers more effective?
[00:47:33] Paul Roetzer: This is one of the unknowns is like, which levels of organizational structures are gonna be the most impacted, the fastest? And entry level is certainly a candidate. You know, it's like a lot of the tactical work entry level people did is certainly able to be done by the ai. And so you, you know, you might see some disruption in displacement and, you know, lower employment rates for, you know, the younger generation, the 25 and under the management layer.
[00:47:59] [00:48:00] My, my instinct here is if I think about the people who are gonna get the most value out of an AI assistant, or the ability to build and manage agents, it is the people with the most institutional knowledge, the most domain expertise, who are experts in their field, who now have. Almost unlimited intelligence and assistance that they can spin up to help them do things.
[00:48:27] So they did all the work of the entry-level people, so they know the tactical work and now they have access to a tool that they can just put to work to do all that. So Cathy can come up with an idea to launch a product on a Friday night. Well, let's do a Tuesday night. I don't want you working on Friday nights.
[00:48:44] She has a random idea on a Tuesday night. She can spin up the idea. She can develop a three page brief, and then she could say, build me a plan to launch it. Write me the email copy, write me the landing page, do the thing, and then she can wake up Wednesday morning and she can edit everything she just created.
[00:48:58] Two years ago, [00:49:00] she would've needed three people on the team, the email marketer, the SEO person, like, and she would've come in the next morning and said, I got this idea. I'm gonna spend the next five days writing the brief about it. Then I'm gonna turn it over to you all and you're gonna go do all these things.
[00:49:13] That doesn't happen anymore. And so. The entry level is difficult, but the manager often doesn't have that advanced experience. Expertise. You develop from failing sometimes and having enough campaigns you ran that didn't work and spending years looking at the data and like all the things that the seasoned leaders have.
[00:49:37] They don't have that yet. They don't have the full taste and judgment to know what's good and what's not good and like, so that is my theory. I haven't dug into it deeply, but I even think about our own organization. And like, I mean if you look around Cathy, like we have a lot of people with 12, 15 plus years experience who are crushing it, who are [00:50:00] just like 10 X-ing what they were capable of three years ago.
[00:50:04] Because now they have all that expertise. They always have, but now they have unlimited access to intelligence and assistance. And so those are the people where you're like. Man, like, let's just get more of those people. 'cause they're just gonna crush it. And we don't probably need as many middle managers one because there's probably gonna be fewer entry level people to manage.
[00:50:22] I don't know, like this is, this is an unknown, so I'm just theorizing at the moment because no one really has the answer to this yet. This is a, an area where you're gonna see a lot of research done. as we start to see the impact where people stop playing the game of, are they just AI washing these like, you know, layoffs and stuff and they realize, oh wait.
[00:50:41] AI is actually replacing the need to have as many humans working at places. Then we'll start to see, well, what layers are actually getting reduced fastest?
[00:50:51] Cathy McPhillips: Which brings us to question number 14. As AI begins displacing both entry level and knowledge workers, who is actually responsible for addressing the broader economic [00:51:00] consequences?
[00:51:00] And are any serious long-term solutions emerging?
[00:51:03] Paul Roetzer: Well, I mean, it's traditionally in a democracy. The government would be. I mean, I guess really any form of government, the government would be. Responsible for this. I mean, at a micro level, each business has a responsibility, but businesses have fiduciary responsibilities to their shareholders.
[00:51:21] So, you know, take any enterprise and yeah, I mean, they want to be good corporate citizens. They want to be doing positive things in the economy and in society and in their community. But they also have a earnings call next month, and their main responsibility is shareholder value. unless they're a public,
[00:51:41] You know, a, a public, good organization or corporation, then Public Benefit Corporation, I guess the total, PBC, the responsibility is shareholder value. So the only way you. Stop that is through unfortunately, government regulation and [00:52:00] government, you know, and laws. So the broader economic consequences really fall to the government.
[00:52:05] And no, there are no serious long-term solutions emerging. There's ideas we're gonna have, Andrew Yang is gonna present at Macon this year. Macon 2026, he ran for president in 2020. And, he was running on the idea of universal basic income that we needed some economic stimulus provided to people who.
[00:52:25] Don't necessarily have the best pro job prospects. Now, that's one idea. There's ideas like universal basic services. There's, you know. Elon Musk is worth a trillion dollars. Let's just have 'em give everybody some money. I mean, like, there's the idea that these, AI labs need to be better corporate citizens, and when they go in and build data centers in a community, they should, you know, subsidize education and energy bills and stuff like that.
[00:52:48] But no, there is, there is no plan. And this current administration is not on a path to have a plan, I would say. So right now it. It comes down to each of us trying [00:53:00] to move the needle forward in the best way we can in our communities and in our companies.
[00:53:03] Cathy McPhillips: Yep.
[00:53:05] Cathy McPhillips: Okay. Number 15. Given the rapid disappearance of traditional entry-level jobs, what would you tell a college student today by gaining experience, building skills, and launching a successful career in an AI first economy?
[00:53:17] Paul Roetzer: Be the best at AI in your discipline. I don't care if you're. An arts major, coming out as an economist, a doctor, a lawyer, an HR professional, a marketer, a sales professional, whatever it is, be the best at AI at that thing. So I don't, like, my kids are, my daughter will be a freshman in high school. My son will be in eighth grade.
[00:53:37] I know Cathy's kids are years are in the professional world now. I wouldn't tell them to change their major or their areas of interest. Even given everything I know about ai. So whatever my kids want to go into, I will support them. I will highly encourage them to do it at a school that has a very AI forward vision for the future of work and is providing the curriculum and experiences needed to [00:54:00] prepare them for that future.
[00:54:01] And if they end up somewhere that isn't, then I will strongly encourage self-learning to go figure that stuff out. So you gotta just get into the professional world. You have to do everything you can to gain experience. There's no excuse like 20 bucks a month to get a tool to do this. I mean, I know, I remember, you know, being in college and hoping there was five bucks in my bank account that night to go to the bar.
[00:54:26] I know that money's hard to come by, especially at that age. But find the 20 bucks. Go do something and earn the $20 a month and pay for that license and test it every day. Like work with it, get comfortable with it. So when you go to a job interview, you can demonstrate your competency. Even if you're not learning it in the classroom, use AI to learn about the people that are gonna interview you.
[00:54:52] Use AI to build a, an app to demonstrate like your capabilities for the job you're interviewing for. Build an agent. You can show them, like, just do [00:55:00] something. You cannot show up to a job interview and not like ai, or at least tell them you don't like AI or that you don't know what you're doing with it.
[00:55:08] That is the fastest way to remain unemployed,
[00:55:12] Cathy McPhillips: but underlying the point of you don't need to major in ai.
[00:55:16] Paul Roetzer: No you don't. But, and on the positive side, 'cause we always like to end on the positive note in the q and a. if you are the AI forward person, if you are the one in your class or in your discipline that has advanced knowledge and capabilities with these tools, your job prospects are probably going to be incredible,
[00:55:35] Cathy McPhillips: right?
[00:55:35] So, all right, let's end on the word incredible.
[00:55:38] Paul Roetzer: There you go.
[00:55:38] Cathy McPhillips: Thanks Paul. And, if you haven't attended an Intro to AI class, our next one, our 60th is July 29th. You can visit intro class.ai to learn more. And just to reiterate, as a reminder, our AI for Business Bootcamp takes place July 16th in Columbus, Ohio.
[00:55:54] We're making the two and a half hour drive down to Columbus to see everybody. You can visit SmarterX.com/events . [00:56:00] And don't forget, POD100 saves a hundred dollars. So thank you, Paul, for a nice hour together.
[00:56:04] Paul Roetzer: Thank you Cathy. Thanks everyone for joining us. Thank you.
[00:56:08] Cathy McPhillips: Thanks.
[00:56:10] Paul Roetzer: 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.