Episode Transcript
[00:00:00] Jeff Wilser: Here's the fun part. To prove that we are human, to get this free money, we need to, like, use biometric devices to show we're human and not some, some fake AI to show, hey, I'm the real Jeff Dylan and not the fake A.I. jeff Dillon. And the way to do that is to stare into this, like, orb. Stare your eyeball into the orb. So, as Jeff, I actually flew to Berlin to the headquarters of the company that's making the world's headquarters, and I stared into this orb to prove I'm human and eligible for this future income.
[00:00:46] Jeff Dillon: Welcome to another episode of the Signal.
Today's guest is a keynote speaker, an AI strategist and a journalist who spent years writing for the New York Times, Wired, Fast Company, and GQ before turning that same reporter's instinct toward AI. He's the founder of the Curious Edge, where he runs Hands on AI workshops for leadership teams, Fortune 500 companies and organizations that need a practical roadmap instead of a buzzword. He hosts the podcast AI Curious, named by Inc. Magazine as one of the best ways to get AI savvy. Whereas he's had more than 100 long form conversations with CEOs, scientists and strategists shaping what comes next, including a memorable one with a business school dean about the existential pressure AI is putting on universities themselves, he's spoken at Davos during the World Economic Forum and on global stages including Web Summit and Humanx. He's also the author of eight books published by HarperCollins and Penguin. Please welcome the Jeff Wilser.
Jeff, it is great to have you on the show. Thanks for being here, Jeff.
[00:02:00] Jeff Wilser: Great to be here. I'm excited. Thank you.
[00:02:02] Jeff Dillon: Well, you've had a career that runs through intel, the gap, journalism, newsrooms, and now AI strategy. Was there a moment when you realized that this common thread in all of it was making complicated things clear to people?
[00:02:20] Jeff Wilser: Yes. Thank you. It's funny, I thought for a long time there was utterly no rhyme or reason to what I'm doing, because to your point, on the surface, a lot of different things. And even as a journalist and book author, my books have range from firefighters to health trends to Alexander Hamilton to exploration articles ranging from AI to blockchain to mental health and education and so on. And for a while I was like, oh my God, this is so scattered. And I did realize at one point that, oh, the actual surface topics might be different, but the approach was the same through virtually all of this, of following my curiosity and trying to make opaque and complex topics clear and that's what I've done.
Ranging from again, revolutionary era, US history now through AI, which is a lot of fun in that I'm in constant learning mode.
[00:03:29] Jeff Dillon: Right.
[00:03:29] Jeff Wilser: I'm in constant awesome. Like I want to learn a new thing, I to want to understand it. And now I want to help others and help organizations and leaders understand it also. So it's been a lot of fun.
[00:03:39] Jeff Dillon: Yeah. One thing I really appreciate about your work is that you cut through a lot of the hype. You know, AI conversations can get so focused on what the technology can do and we forget to ask what it shouldn't be doing. I hosted more than 100 conversations with AI leaders myself, and I think one thing that keeps coming up is that the more capable these systems become, the more valuably distinct human qualities seem to be. So it made me smile when I heard one of your favorite lines. You said AI can't help you become a better tennis player or make your dog Mojo any cuter. What's the most surprising human problem you've come across that AI just can't touch?
[00:04:22] Jeff Wilser: Yeah. Well, first off, Mojo needs no help being cuter. He is maxing out cuteness, and so he's doing fine there. But you're right with tennis. Like, look, you can ask ChatGPT or Claude all you want about hitting a top spin, but nothing will replace going out there again and again, again and getting the reps in. But to get to the, the real kind of core of your question, I think that ultimately it's the interpersonal connection and eyeball to eyeball in person, human empathy that AI can absolutely not replace. And that's especially true in education.
[00:05:02] Jeff Dillon: Right.
[00:05:02] Jeff Wilser: I think, yes. I'm sure we'll get into this in a bit. There's all kinds of potential and possibility, and I'm sure your listeners have heard a lot of possible things they can do. But the trust and bonds that a teacher has with a student, that's not something easily replaceable. And similarly, just other. A lot of what I do is connect the dots across different industries. I think another example is in real life, like hospitality.
[00:05:32] Jeff Dillon: Right.
[00:05:32] Jeff Wilser: My wife works in the food industry at a wholesale level with restaurants. So I spend a lot of time in the restaurant world. And thankfully, in my eyes, we are not going to replace breaking bread with our human friends and family and colleagues. Right. And I think, I think this all steers us to doubling down more on what makes us human and not just outsource that part of our lives to AI and the tech.
[00:06:03] Jeff Dillon: Yeah, yeah, yeah. It's an important reminder, I think, because people really get swept up in the capabilities and they lose sight in the people that are, that are using them. And one thing I've always really admired about great journalists is that they're trained to be curious, skeptical, and willing to really challenge assumptions. And in a field that's moving so fast as AI, that's a pretty valuable perspective. Before AI was the story, you were a journalist writing for the New York Times, Wired, gq. How does a journalist's instinct for finding the real story change the way you interview AI builders compared to how a typical tech analyst would approach it?
[00:06:46] Jeff Wilser: Yeah, I think, I mean, I can't speak exactly how a tech analyst would, but my approach is often pretty holistic and I come at things intentionally with a bit of a outsider perspective and come at it with humility of, hey, I don't, you know. And one thing I'll ask people from years and years and years, why is this interesting? Why should I care, right. And not presume that because, oh, this model is, you know, trained on 1 trillion data set, whatever. Like, I don't presume that matters or is important. I kind of want to start with like this clean slate of curiosity and ask why it matters. And also, as you've already done, Jeff, kind of get into the humanity of this too. Like, not just this narrow scope of how can this one system be improved or how can we optimize by 14% or slash this. But okay, what does that mean to the actual human beings in the room? What would that mean for students?
What does that mean for the exhausted professor buried in paperwork? Right. So as a, and like yourself, as a profession, career long storyteller, I also try and tease out the, the stories behind this. Not just the benchmarks or the numbers, but take a more holistic approach.
[00:08:11] Jeff Dillon: Yeah. One of the reasons I love hosting podcasts is that you can spend hours researching a guest, but every once in a while they say something that's completely changes, you know, my, how I think about a topic with more than a hundred conversations you've had, you know, under your belt, you've had front row seat to how AI thinking has evolved in real time.
I'm curious if there was a moment when something changed for you, challenged your assumptions. What's the belief about AI you held a couple years ago that a guest completely dismantled for you?
[00:08:48] Jeff Wilser: Yeah, that's a, that's a great question. I think that to be honest, the one that is most I think shook me, as the kids might have said, like five years ago, is Actually, not on the podcast or for a profile I wrote a couple years ago, actually, with Sam Altman. And I interviewed Sam Altman. Of course, everyone knows him as the CEO of OpenAI, but he also has something of a side hustle, co founding a company that's gotten a lot less press. Originally called worldcoin, now renamed to Worlds. And the idea, and bear with me here, the idea, it's a little wild.
Altman's thought is that eventually AI will lead to AGI, or artificial General intelligence, and get so powerful that it might do a lot of the work for us that could create this additional abundance and wealth. And so just in case there's this transition period of folks losing jobs, we should have some form of. Of universal basic income. And this is coming from him, not from me. So we're going to all then receive this economic largesse from the AI's wealth. But here's the fun part. To prove that we are human, to get this free money, we need to, like, use biometric devices to show we're human and not some. Some fake AI to show, hey, I'm the real Jeff Dylan and not the fake AI Jeff Dylan. And the way to do that is to stare into this, like, orb. Stare your eyeball into the orb. So, as Jeff, I actually flew to Berlin to the headquarters of the company that's making the world's headquarters, and I stared into this orb to prove I'm human and eligible for this future income. Right. So what long winded way of saying this conversation, I think, showed me just how great of an unknown there is in a future and just how wild things might get. And I am not. This is not a value judgment either way. I'm not saying I think we should all get our eyeballs staring into orbs, but I think that as much concern and hype as there is now, that's like, to me, in my mind, almost maximum hype. There's so much hype out there about how AI will be so transformative, we'll all get free money and we'll need to stare into orbs to prove we're human.
[00:11:32] Jeff Dillon: Yeah.
[00:11:32] Jeff Wilser: So that really gave me a sense of how. How wild this thing could get in the future.
[00:11:36] Jeff Dillon: I just keep thinking you should start and move your career into writing black Mirror episodes with these stories totally like this. Yeah, but a couple comments on that. Yeah, I did hear about that. I didn't tie it to World. I didn't know that was Sam Altwin. But I know about the Orb I identification kind of project out there. It Makes me think, though, with a ubi, a universal basic income, there's a lot of talk about that would go right back to Sam Altman's big company. Like they would pay the tax to pay this ubi. So I'll be interested to see where the money comes from because there's a lot of people talking about that it might have to happen that way. Just. Yeah, crazy world.
[00:12:12] Jeff Wilser: And I think that it almost speaks back to kind of challenging assumptions. There is no forecaster, no prognosticator who really knows how this will play out. And after interviewing so many experts in this, like, there's literally that the people at the tip of a spear building it, they can't really agree on what's going to happen or when it's going to happen, which is kind of wild. And there's not much precedent for that. So kind of my, my approach to it is, hey, let's actually not get swept up in all of that either, you know, frothy euphoria or kind of doomsday apocalypse stuff. Instead, focus on. All right, since we don't really. We can't peer into the crystal ball in five years. What's useful and how to make AI useful for ourselves, our company, our university and so on.
[00:13:04] Jeff Dillon: Another example that's not higher ed yet. We'll get there, but it's. Matthew McConaughey has been talking a lot about how it's going to affect actors out there in movies. And I think we all know there's probably been a lot more AI in movies already that we don't really realize yet. But as far as the likeness of an actor, he's proposing go out there and own your identity. Make sure you own your identity. Make sure that trademark it. What do you have to do? He's not sure how you do this yet, but, you know, because what you're start getting offers, offers to say, hey, we want to use your likeness in a movie. Well, I'm not really available, but yeah, I'll let you use my AI for this much.
[00:13:39] Jeff Wilser: Yeah.
[00:13:40] Jeff Dillon: He's saying it might be okay, you know, depending on the movie or the script, but they're really going to have to protect themselves and like sell their clones, I guess, in movies. So it's kind of a crazy world we're thinking of.
[00:13:49] Jeff Wilser: I mean, this is my clone, right? You know, the real Jeff Wilster is on a beach somewhere, right? This is the clone right here on the, on the podcast. No, but Jeff, you're right, it is, it is mind boggling of how this might play. Out. And on a more serious note, I do think that's it is important for I am not a AI Kool Aid drinker. I'm obviously my podcast called AI Curious. I help companies figure out AI strategy. Of course I believe in the upside, but I also think it has to be done fairly. And I think for actors. Back to Matthew McConaughey, I think it's important that actors get appropriately compensated. And if they are having their likeness being used, that needs to have their consents and they should be benefiting from that as well. Right. So it's just none of these are kind of easy frameworks to figure out. I'm optimistic for it to an extent, if it could happen in a fair way that treats actors and creatives with dignity and kind of gives them the compensation they're due.
[00:14:51] Jeff Dillon: I think one of the biggest takeaways from these conversations is the people that are getting the most value from AI aren't chasing models or features. They're being really intentional about solving real business problems. And in higher ed, I hear a lot of talk about doing AI, but far fewer conversations about what success actually looks like for enrollment, student success, or operations. And that's where I'd love to get a little practical. Your philosophy is AI to empower, not eliminate. When you sit down with a, with an academic leader in enrollment or on the. Even on the administrative side of the house, what does it actually look like in practice versus how it gets used as a. As a talking point?
[00:15:33] Jeff Wilser: Sure. I first like to strip away the hype and the anxiety by really focusing to your point, Jeff, on what's useful.
[00:15:41] Jeff Dillon: Right.
[00:15:41] Jeff Wilser: And not to get enchanted with shiny object tools that are likely going to be obsolete next Tuesday.
[00:15:49] Jeff Dillon: Right.
[00:15:49] Jeff Wilser: So instead I actually.
One thing that stuck with me back to your question about, you know, guests that have challenged my thinking. When I spoke to IBM's chief scientist, Dr. Richard Paris, he said when he talks to companies and there's thousand scientists who report to him, they're focused on just one simple question, what is useful? And so I use that North Star coupled with curiosity. So with this kind of humble, curious mindset, asking what's useful? And to get to your question of in practice, what's it look like? I actually have something called the Curiosity Canvas where I guide leaders, educators, project managers, and so on to thinking about very simply doing exercises. Like the first one is what I call the drudgery dump. Think about what is the most boring, tedious work you do. And everyone has work like that. Right. And literally make a list of all the stuff you don't like doing, it should pass what I call the Netflix test. The kind of thing that you can do on a Tuesday evening at home while watching Netflix if you're doing work. Also, it's probably not very high value work. Right. And so kind of bucket out all of us, work for you and your teams or your team's teams, and then think, okay, what can AI be useful for automating some of the stuff you don't like doing. And then that inevitably frees up time to do more stuff that you do enjoy doing. And that can mean spending more time actually face to face with students, it could be more time with colleagues, it could be more time brainstorming growth initiatives. Right, so that's what I mean about empowering is not, I think, too many companies make the mistake of chop, chop, chop and using AI to cut.
My mission in life now is to help leaders figure out how to use AI, not to cut, but to grow to do more.
[00:18:01] Jeff Dillon: Yeah, I really like that distinction.
You know, strategy only matters if we turn it into action. I've sat through plenty of presentations where everyone leaves excited, but six months later, nothing's really changed. And I think what caught me about your workshops is that they don't end with inspiration, they end with a plan. And so, you know, you run these AI workshops that end with a 90 day roadmap instead of a slide deck. If a university's marketing or admissions teams called you tomorrow, what would the first 30 days of that roadmap really look like?
[00:18:35] Jeff Wilser: Yeah, so I encourage folks to actually brainstorm, use cases that they can do, like tomorrow. So, like focus on kind of the both the easy win stuff, the low hanging fruit, and then also what are the bolder, more ambitious, perhaps agentic workflows? And then what do you need to do tomorrow, two weeks from now, where you kind of proof of life, I call it in two weeks. So you're right, Jeff. If you just go to a workshop and like, yay, we're all excited, then folks, forget about it. I found what's most effective is when companies have some champions, some team of evangelists who really want to own this, and you actually have accountability sessions planned. You say, cool, tomorrow we're going to do this one small thing thing. In two weeks we're going to do this thing.
[00:19:26] Jeff Dillon: Right.
[00:19:26] Jeff Wilser: And this sounds so basic in meat and potatoes, but I view that as a feature, not a bug. Too often companies think of AI as this mystical, magical thing, but in some ways it's also just a project.
And most leaders have plenty of experience project management. And so when you kind of give it the guardrails and actual like almost like tame it, like, okay, let's not get swept away in the hype and the magic and Sam Altman magical money orb staring your eye thing. Forget that stuff and focus on. All right, just like any projects, me and my team of four, we're going to meet on two Tuesdays. For now, we're going to share what our wins are and we're also going to keep track of a pilot. We're going to say, all right, maybe there's a low hanging fruit thing that we want to see some. Put some points in the board right away. And then we also say, okay, what if we.
How can we build some magentic solution to help the infrastructure between the students and faculty? How can we automate some of the tasks that take us a long time to do?
[00:20:40] Jeff Dillon: Right?
[00:20:41] Jeff Wilser: And like, what do we need to build to actually stand that agent up?
What files do we need to create? What are the key stakeholders to get involved and actually building a plan together? That's what it looks like.
[00:20:53] Jeff Dillon: Yeah, I agree. You know, as you were talking, I had to look this company up because I forgot who it was. But it was Ikea. And this is back to your enhanced versus eliminate kind of strategy. They recently did not lay off 8,500 customer service employees where other companies are kind of doing that, but they, they implemented an AI chatbot to handle routine calls and took those 8,500 people to retrain them to become premier interior design advisors and ultimately generated 1.3 billion euros in new revenue. So it's a great example of, it's
[00:21:29] Jeff Wilser: a great example of. And it's one of my favorite things is to help teams figure out, okay, not just how to use AI to like automate tasks. Yeah, yes, that's part of it, a big part. But also crucially, to brainstorm, what else could you be doing if you had AI Right? One way to think about it in a thought experiment I often invoke is let's imagine you had a goal. Let's say the CFO told you you need to double revenue, but keep headcount for flat. What would you do or imagine? Let's imagine for a moment you had double the staff. Just, just for a second, if you had double the staff, what could you do? And use that as a way to think about how AI could help. Like one more concrete example for higher ed when Georgia States, I believe, had a chatbot to, over the summer to answer students questions or incoming students and admissions that there was not happening already.
[00:22:30] Jeff Dillon: Right.
[00:22:30] Jeff Wilser: But they use a chatbot to kind of handle the kind of, like, most obvious questions. And summer melts slashed from, I believe, 19% to 9%. Don't quote me on those numbers. But directionally, the idea was, hey, we're going to have resources we didn't currently have. We now have, thanks to AI, we're getting more questions answered. That is bringing us more students acceptance the next year.
That is not taking away anyone's job. It's not saying, okay, we're going to take from humans to robots. It's saying, here's a thing that wasn't happening at all. And now we're using AI to do this. That's empowering, not eliminating.
[00:23:10] Jeff Dillon: Yeah, I think these roadmaps are really what campus leaders are craving now because it takes AI from something abstract to something they can really execute. But at the same time, many institutions are living with an interesting contradiction. They're experimenting with AI at this incredible pace, but they're also questioning what it means for the future of higher ed itself. And you had a fascinating conversation that really captured the attention. You had the dean of a business school on your show talking about AI as an existential threat to universities, but also 90% faculty adoption inside the same building. How do those two things coexist in the same institution at the same time?
[00:23:55] Jeff Wilser: Yeah, it's a great question, referring to Dean Marchik of Kogod School of Business at American University. And I've spoken to him a couple of times, and it is fascinating, and in some ways, it kind of gets at the core tension every university faces, certainly, but also in a way, every company.
And what's interesting about what Dean Marchuk did is you kind of transform COGOD into the first AI native business school is how one publication referred to it and encouraged professors to inject AI into the curriculum to actually have more interactive experiences. And it's been talked about a lot in theory, and it was cool to see them do it in practice.
He had one professor who was extraordinarily AI skeptical.
They started out skeptical. He got so into this that eventually he shifted a lot of his curriculum from textbooks into students interacting with AI to actually, like, actively learn. Right. And this is very different from saying, hey, AI, do my homework. It's saying, hey, let's have it once. A classic Socratic dialogue going back 2,000 years to, like, ask questions to help me learn. Right. So they use that as a driver. But to get to the other half of the coin, the Wild thing is, yes, the dean also thought that for reasons I'm sure you cover a ton on this podcast, Universities have a ton of really serious existential questions. And hard. There's stubborn cost, financial math. Every university is dealing with, AI comes along. And if there are in the future students who decide, well, the hell with this, I can either rack up a mountain of debt, or I can learn a lot of this stuff through AI right now, do I think the majority or all students will think that way? No, of course not. Am I personally vouching for that? Obviously, universities have a ton more to offer, especially including the social elements, the human interaction. We. I, of course, having gone to college for four years, I would never swap that experience for just an awesome binge session with Claude. Right.
So I don't think it's that simple. But there will be this additional pressure that universities have to confront with competition in the form of who knows what it looks like. But AI fueled alternatives, right? And so I think, for my money, the strategy is not to bury our heads in the sand, say, oh, yeah, it's terrible. I don't want to deal with this, but instead say, okay, there is additional competition, There are additional challenges.
So how can we use AI smartly to be more competitive, to survive and thrive, as opposed to the risk of the AI alternatives nipping at our heels in the future?
[00:27:11] Jeff Dillon: Yeah, this tension, especially in higher ed, I'm seeing schools that they realize they can't wait, but they also don't want to move too fast.
I think sometimes the best lessons come from the schools or organizations that got it wrong the first time without naming names. I'm curious if there's an example that really stands out in all your workshops or meetings with leadership teams.
[00:27:32] Jeff Wilser: I'm actually happy to name a name.
I'm happy to name a name. It's not. Not from. It's an example that I share as a cautionary tale in workshops, and that is using AI for AI's sake. And I first saw this as a very, very visceral example. To me, I live in Denver, Colorado. I went to a hockey game and the Colorado Avalanche. And, you know, in the timeouts between the periods, they have the jumbotron, right? We've all seen the jumbotron. They have, like, the Kiss Cam and things like that. Well, in one of these moments on the jumbotron, they had a big thing called, like, ABS AI Nights. And I was thinking, oh, my God, like, my heart sank. I'm like, what is this?
And what they did, Jeff, is the camera would Pan to different people in the audience, and it would, like, transform their heads to things like a dragon or some cartoon character. Right. It almost reminded me of, like, kind of very basic Snapchat filters. And I could just visualize. I could just visualize maybe the CMO of the Colorado Avalanche at some conference room saying to his or her team, hey, guys, AI is big right now. You know what people want when they come to the game? They want AI. I'm thinking, no, when people go to the hockey game, you know what they want? They want to watch hockey.
So I think a lot of companies are getting so swept up in AI hoopla that they feel like they have to do it. As you're saying, they move fast, and they might, like, paint with a coat of, like, AI paints in hopes that will, like, excite people, and it could easily backfire. And so back to higher ed. I don't think there's any reason to lean in to, like, brand. Ooh, we have these AI solutions. Like, AI should be a tool that solves actual problems or gives new capabilities we didn't have before. It shouldn't be this shiny coat of paint because people get turned off very fast.
[00:29:49] Jeff Dillon: Yeah. I think of when you think of AI for AI sake and, like, wasted money and time, and I think of some of the super bowl ads. I think, oh, yeah, there's a Svetka vodka ad and Alexa Plus. And like, I just was like, gosh, someone actually, like, it sat in a room and, like, they all agreed to this, like, amazing.
[00:30:10] Jeff Wilser: But there's a lot of capital sloshing around in the AI world. A lot of money that people are trying to spend.
[00:30:17] Jeff Dillon: Well, I have one final question to wrap this up. You know, for students graduating into this job market, and honestly, for the staff that already are inside, you know, higher ed institutions, what's a skill you tell them to double down on just because, you know, I can't replicate it?
[00:30:34] Jeff Wilser: Yeah. This is going to come across as extraordinarily biased, but I think for me, the North Star that applies to virtually everyone, at virtually every level, from freshman students to dean of the university, is curiosity. And treating curiosity not as an afterthought, but as a muscle, and using it all the time and following your curiosity. And I think that the beauty of that for me is there's no phase of your AI journey where curiosity will not be useful and where you have stopped learning.
[00:31:16] Jeff Dillon: Right.
[00:31:17] Jeff Wilser: Even the very. The people at the absolute frontier, the tip of the spear, building AI, they don't know where it's going next. They're all still curious, right? Jeff Bezos was once asked what's the most important attribute for a CEO. His response? Curiosity. And that is not something like the tools. By the time this podcast is done recording, they're going to have probably dropped a new model.
[00:31:42] Jeff Dillon: Right.
[00:31:43] Jeff Wilser: The tools are constantly going to change and actual like focusing on just the technicals. Sure. I'm not saying ignore technical stuff, don't ignore engineering, but I am absolutely confidence that that is one skill that will never in one trait that will never go out of style again. That is very biased. My podcast is literally called AI Curious, but it's called that for a reason.
[00:32:09] Jeff Dillon: Yeah, well thanks Jeff. It's been a terrific conversation. I appreciate that you've brought a lot of clarity to topic that's really filled with hype and headlines. We will put links to connect with Jeff and listen to his AI Curious podcast and learn more about the Curious Edge in the show notes. So thank you Jeff. That was really fun.
[00:32:27] Jeff Wilser: Thanks so much Jeff. Really enjoyed it.
[00:32:30] Jeff Dillon: That's a wrap of this episode of the Signal. If today's conversation sparked a new idea or challenged your thinking, that's exactly the point. This show is about cutting through the noise and helping you see what's actually shaping higher ed right now. Please subscribe so you never miss an episode. And if you found this valuable, leave us a quick review. You it helps more higher ed leaders find the Signal. For deeper ed tech insights, news and trends delivered monthly, subscribe to the Signal monthly newsletter at edtechconnect. Com.
Thanks for listening. We'll see you next time.
