Searching for Fit: The Impacts of AI in Higher Ed

When an AI based tool can empty my inbox for me. Right. I think that's the functional Turing test that matters when we're thinking about transformation in the knowledge works sphere. A language model by itself can't do this. A language model can digest an email. It can in some sense capture semantically what's being said in that email. So it could resummarize it, it could put it in different languages or words. It can pull out the main but to act on an email, this requires things like forward looking planning, predicting the future. It requires you to have a functional model of other people and other people's minds that's more explicit than what's just implicitly captured in language models.

That was New York Times bestselling author and computer science professor Cal Newport, who writes extensively about the intersection of technology and work. And today he joins us to talk about an issue that's on all of our minds artificial intelligence. That's on this episode of Future youe.

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I'm Michael Horn. And I'm Jeff Salinga. So, Michael, we've obviously covered the topic of AI a few times on the show, but the subject and its impact on higher ed is so big and so important that we're returning to it in this episode and the next episode as we lead off season eight of Future youe. In the episodes that are being sponsored exclusively by College vine, which you'll hear more about during the break, is bringing AI to the college recruitment process in a new way. Now, in this first episode of 2 on AI, I kind of view it really as helping us set the larger picture. Right. I think for many who work in higher ed, probably, heck any of us, we're kind of heads down in our work. And when we look up to see the larger context of AI, it's probably a bit dizzying and probably a lot confusing. So where does AI fit into the landscape of other technological revolutions? Right. What's its impact now and in the future going to be on work? And what are the implications for higher ed? Kind of broadly speaking, yeah, Jeff.

And to help us answer those questions and put all of this into broader Perspective. We're thrilled to welcome Cal Newport to the show. As Jeff, you mentioned at the top, Cal is a computer scientist and a best selling author. He's a professor at Georgetown University, writes often about the intersections of technology work and the quest to find depth in our distracted world. And he wrote, I will tell you, Jeff, one of the best early pieces in the New Yorker, helping people understand just how ChatGPT really worked with simple one off scaled down examples to help us all really imagine its actual capabilities. With all that as background, you can see why we thought Cal was the perfect guest to help us out here. So Cal, welcome to Future You.

Yeah, thanks for having me. I've been looking forward to this. To actually talk shop about the world of universities, like so often I'm talking about sort of corporate knowledge workshops. It's like, yes, we can get into my bread and butter here.

Well, it's like doubling up on your expertise. I think so. But let's start with where you've been writing some for the New Yorker and so forth, which is really put the development of AI and perhaps large language models specifically into some historical context for those in higher ed. You know, if we think of the big developments, right, that have changed higher ed, we could go all the way, of course, back to the printing press. But then there's the Land Grant act in the United States, the GI Bill, and if we're thinking of technology, of course, the Internet. Where does AI fit into all of that?

Well, we don't know yet. And I think what's important is that we recognize that two things are true of the same time when it comes to AI. Like one, we recognize that the new generation of generative AI models are really doing something amazing. The leap they took in terms of semantic comprehension and semantically coherent text generation, these were major leaps and I think everyone accepts that. That's why it caught so much attention. On the other hand, however, we know it's amazing. We don't yet know what the impacts that are going to be and we don't know if in the short term that's going to be amazing or not. In other words, we can be amazed by the technology and yet be pretty patient about grounding our reactions to the actual developments on the ground, which I think now we are really beginning to realize are slower than people are prognosticating, are less dramatic than people are prognosticating in the short term. That probably AI is going to have to go through the same relatively long product cycle of other transformative technologies before it really has its High impact hits. There is this thought of just maybe chatgpt by itself will change the world. But no, this picture is probably going to be more like it was with the early web. It still took a decade for that transformative technology to be filtered through the experimentation of countless companies and failures and products and fits and adjustments until it really became that dominant role in our life. So I'm kind of prefacing everything I'm going to say about AI on that reality is that we have to. I'm trying to ground my reactions to what's happening, not what could happen. And so right now we don't fully know what the scope of impact is going to be. We can, we can lay out the scale here, right? We have on the far skeptic side of, you know, it'll be maybe even less than the Internet. Maybe it'll be like word processors or when we started using word processors at college. And then we have the other side which would be sort of more of like the Ethan Mollick position, which is like college as we know it is done. I think he today, the day we're recording, he was re emphasizing his homework Apocalypse Frame, I think in his newsletter, like how can we ever give an assignment to anyone ever again? Everything has changed. So we have I think a pretty big scale scale to calibrate this on and I'm probably somewhere in the middle on that now.

Gotcha well, so before we go into prognostication for you personally, I'm just curious, is the fact that it's rolling out in terms of ultimate implications slower than maybe the hype might have suggested it would to some folks? In your view, is that the technology limitation or is it really the organizations, the business models, the people write themselves and how they interact or incorporate the technology into their workflows?

I think it's actually product fit, right? So I think we have the underlying technology. What happens when you make transformer based language models this big that has all this potential but that there is going to be a non trivial cycle required to try to figure out how to actually best integrate this into people's lives. So the current form factor for this technology is a chat box. You go to a website and there's a chat box, you can talk to it. This technology accessible through that form factor is not having the disruption that some people thought. So I think what's actually going to unlock bigger disruption, I don't think it's necessarily it's being held back that its organizational barriers is preventing it from having the impact it could I actually think it's the product form factor to figure out the right form factor there. How do we integrate this properly into tools in a way that's going to have a big impact. That just takes experimentation. That's going to take a lot of VC funding, it's going to take a lot of failed companies. We have to have our equivalent of the web's early days webvan. Right. We're going to have to have our AOL Time Warner mergers. That doesn't work out, there's going to be some big AI company is going to have that go up in flames after they burn through $100 billion in capital. I just think it's going to take a few more years than we thought to start to get the real disruption.

That's a useful reminder and also history lesson for those tuning in. And before we get into higher ed itself, just looking at the knowledge economy more generally because obviously you think about the use of AI in lots of different industries. I'm just curious for you to prognosticate. You said you're sort of in between the two poles of the skeptics on the one hand, the Ethan Mollicks on the other. At the moment, what's your best guess or thinking on 15 years from now how it has changed knowledge work.

I mean the, the Turing updated Turing test I care about is when an AI based tool can empty my inbox for me. Right. I think that's the functional Turing test that matters. When we're thinking about transformation in the knowledge works sphere. In order to do that, there's a lot of things that AI is going to have to be able to do. I wrote a New Yorker piece about this last spring. A language model by itself can't do this. A language model can digest an email. It can in some sense capture semantically what's being said in that email. So it could resummarize it, it could put it in different languages or words, it can pull out the main points. But to act on an email, this requires things like forward looking planning, predicting the future. It requires you to have a functional model of other people and other people's minds that's more explicit than what's just implicitly captured in language models. And so I think what that's going to require, this is what I wrote about in the New Yorker was multimodal systems, where you have a language model to deal with the language. You're going to have a theory of mind model, you're going to have a future prediction model kind of based on the way we play games with computers now to try to simulate the impact of various decisions to find the right one. All of these are going to work together. That I think is going to be pretty interesting. So if you have an AI that could answer an email, you now have a functional assistant that is digital. That I think is transformative. Because of course, a big part of my writing about digital knowledge work is the degree to which we have added too many total things on the plate of knowledge workers and too much of a diversity of things on the plate of knowledge workers. And the human brain is suffering. So I really am closely following those type of movements. Like, how do we get closer to a functional assistant AI? The language model piece was a big problem and they solved it. So now we have to focus on the other pieces, which I actually think are easier. It's just a matter of people actually starting to do that work.

Well, Cal, since my kids make fun of me for my inbox when they see the number on my phone, which I won't even say how embarrassing it is, that idea of emptying my inbox, I'm looking forward to that day, that's for sure. So I want to talk a little bit more about how these changes might impact or should impact higher ed. And let's start with one I'm getting a lot of these days from college leaders is should we be explicitly teaching students about AI?

Yeah. So my take on this, and again, this gets me pushed back, is I say not yet because I don't think this first generation form factor is going to be the way five years from now that we're interacting with these tools. Like, I'm in this interesting place on this debate where maybe it's not on the scale, it's orthogonal to it, or I agree in some sense with the Ethan Mollicks of the world that there might be huge impacts coming. Like, we've made a leap here that enables big impacts, but I disagree on the timescale and what's actually required for that to happen. I think there's a more complicated path to get to those impacts than just simply, let's make the parameter count larger, give OpenAI another year, let's get the GPT5, and suddenly everything's going to be solved by a language model. I think that path is more complicated, but by the way, a lot of computer scientists feel the same way. It's why you tend to see less of the chatgpt by itself is going to change the world. So if you started teaching right now, and I have been thinking about this I'm on Georgetown's University wide Task Force for Pedagogical implications of AI. I've been speaking about it. I'm working on a big piece on it right now. If you started teaching students tomorrow, prompt crafting relevant to, Prompt engineering relevant to like the current generation of chat models. I just don't think that's, that's not probably going to be the relevant skill for using AI two years from now. So I have a. Let's, let's wait a little bit when it comes to training. On the other side though, we can't avoid the fact that like, these tools are having intersections already with academia, that they are being deployed in certain instances in ways that like, we can't ignore. So it's in this interesting situation where we have to react to it right now. We can't ignore it, but we're not quite ready yet to say, let's reorient around these tools and make sure that we're teaching you well how to use them. Because I don't think we know what using these tools well looks like. For example, I think the chat interface is something that's going to be seen as just an early transitional step to the way that we're ultimately going to use these things. We're in this weird place. We cannot ignore it as universities, but we're not ready yet to say, great, let's codify and teach a new generation how to use these things yet. Because what these things are, is changing too rapidly.

Okay, so we're in this weird place, maybe not yet, but as you know, colleges, maybe not Georgetown, but a lot of colleges have become very transactional in nature, right? Students are voting with their feet around majors. You know, they're majoring in things that they see particularly lead to jobs. You know, a lot of colleges are talking about teaching students skills rather than broadly educating them. You know, there's a lot more talk about skills than majors on a lot of college campuses these days. So are there specific skills that colleges should be teaching students so they can better leverage AI in their work or when they're ready to. So how can we think about this? You've written the fact that AI might not totally kill jobs like some have thought, but are there other implications on the careers colleges should be aware of, but perhaps aren't thinking about? Because again, maybe beyond the top 25, top 50 most selective colleges in this, in this country, in the U.S. most colleges and a lot of students are looking at the college experience as very transactional. And so they want to make sure they're getting the skills that they need to get a job on the other side.

So my theory there is you have to ground that instruction with the ground truth of the actual commercial world, but then be willing to move rather quickly. So in other words, the university shouldn't get out ahead and say, we could imagine it would be useful in the future in this type of job to be good at ChatGPT. So we're going to teach you how to do it. It needs to actually. How is this tool being used in this job? Okay, now we can move fast and go back and actually teach about it.

I'm laughing because as you know, colleges don't necessarily move fast, right?

We don't, but okay, this is very relevant in computer science, right? I'm a computer scientist. So there's this sense of these models are good at producing code. So how do we teach this or how should we be teaching this? Or what's relevant? And really the right answer is how are companies right now, this month using language models in their development? That's what we should be teaching, not a prediction of in the future. We think most of the code will be produced by these models. And so we should then in the intro courses have them using these models to produce the code. It's like, no, we should say, what are they actually doing? Are they heavily making use of Microsoft Copilot? How are they making use of Microsoft Copilot? We should teach them how to do exactly that. So I'm a big believer in reacting to the grounded realities of AI right now as opposed to potentials or predictions. I think this is actually like the biggest rift we're having in sort of small scale policymaking is whether we're reacting to grounded realities. Okay, this tool is now being used in this way here. What do we want to do about that? How do we prepare for that? How do we react to that versus I could imagine this tool being used in this way. Now let's react to that hypothetical. So much, I think of the discussion has been reacting to the hypotheticals and even some of the grounding I think is being. I just think we have to be careful. That same newsletter I was reading this morning, for example, and I respect Ethan's Mollik's work a lot, by the way. I really liked his book and we're at the same publisher, but there was like a throwaway line where he's like, look, these tools are now used universally basically among students. And he cites a survey. But you look at the survey, right? And he was saying, yeah, 82% of students are using this. Well, actually, the survey said 18% of students had never used it. So. Yeah, he's right in the sense that 82% of the students surveyed had used ChatGPT before. But really, the bar in there you cared about is use once a week or more. That was actually pretty small. Right. And I think there was a. So there's, you know, it matters. Right. So it's not true right now that college students are universally now using this, and this is how they're handling all their assignments. They might be soon and we should keep an eye on it. But it's. The grounding is. It's so tempting to jump to the hypothetical that's interesting and then react to that, because that's more interesting than, like, when I talk to developers and they're using Microsoft Copilot as they program, but it's like, look, the code's a little sloppy, but honestly, here's the ground truth of how in the first six months of this product, people or programmers are using this. They're using it as like an advanced documentation instead of looking through a manual or Googling. What's the function name for what I'm trying to do here? And Copilot's great. Like, oh, I know what you mean. It's this, and here's the parameters. Right. Or can you write me a little bit of code that uses this so they can see how to call it and then they write their own code? Like, it's not as exciting as. Programming is dead. Right. Writing is dead. But. So that's been my big advocacy right now is like, we have to react to grounded realities because there's too many hypotheticals possible and we'll get drowned in it.

Yeah. Okay, so, Cal, that's the technology side of this, but how about the other side of this? The kind of the legal, ethical, social, and moral ramifications and kind of the critical thinking skills that will be required to kind of navigate this world. It's still hypothetical. I get it. The hypothetical world. But how should higher education be talking and educating students about those implications? Again, hypothetical, but how should we be thinking about that side of it?

Yeah, I think writing is something I've been looking into, and I think this is like a good case study probably for these issues. How do we think about, for example, text produced by a model from your prompting vs. Text you produce from scratch vs. Text that you got from somewhere else? And where does that actually fall ontologically on the sort of authorship spectrum there? I think that's an interesting question. What does it mean if. Which is by the way is how most students who are using this to write right now are for the most part for non trivial assignments, aren't producing and submitting text. They're interacting with the model. They're also producing bad text and then they're editing it into good text. So how do we feel about that? I think that for example, is actually a new pedagogical activity that didn't exist before. It's easier to edit bad text just from a cognitive functioning perspective than facing the blank page. So how do we think about that? Those are the big questions, I think now. I think we have some historical cases here that are relevant. I think that by far the biggest comparable disruption really was the Internet plus a usable way of accessing the Internet. Basically. Google, we have just through familiarity memory hold a little bit how disruptive that was. I was a student back when that happened. But it made for, I mean a lot of changes happened here. You could get access to text. We didn't really have to worry in writing as much about plagiarism because it was like you're copying text out of a textbook or someone's professional academic book that they turned their dissertation into. No one's going to believe the sophomore wrote that. But now you could have access to lots and lots of text. It was a big crisis in computer science because you could Google code. It really became a whole thing where you would. Professors got good at being like, you clearly copied and pasted this code in because of the way we never had to worry about that before when I was studying computer science and man, I wish this was still the case now that I'm a computer science professor. The exercises in the back of the book is how you practice. Professors would hone the problems in the back of the book over like a generation. These things had gone through years and years of polishing and adjustments and they were fantastic problems. And then you get like one year into the Google universe and you can just find all the sample solutions and like it was the end of being able to use problems from the back of the book. Right. It changed the way we talked about plagiarism. It introduced the notion of plagiarism to computer science, which didn't exist before. It changed the way we had to create assignments. This seems comparable to me, like, I mean I think this is like Google arrival in the university take two at least this early stage.

But your point is that computer scientists are not worse off today for that, right? They're. They're better. They're better off for it in some ways. Right. Because of that advancement. And is that kind of the same thing you potentially see for writing, for example?

Yeah, that'll be interesting to see. Okay, so let's use those two cases. Google made computer scientists better, that's for sure. Because actually what happened is it was a stack overflow effect. So stack overflow is an online bulletin board where people post questions about code and other people answer questions about code. The rhythm of being a computer programmer became you Google about whatever. How do I write this function, how do I do whatever? And it will find a stack overflow page where someone is given that code. That became how computer scientists learn new programming languages. That's how computer scientists learned how to use new features or libraries. It really made things more efficient on the writing side. Google introduced new problems as well because you could now plagiarize, et cetera. I don't think it made people better writers. I think it made research easier for sure, but it didn't make people better writers. And then the other secondary effects of the Internet writ large, in particular the distractions, et cetera, algorithmic content curation probably made people worse writers. So it's interesting you had these two parallel academic fields, let's say computer science and writing. Academic activities both heavily disrupted by the Internet. One, we ended up like, I'm more flexible and better because of this technology. The other, no one would say people are faster writers or better writers or the quality of writing has improved. And so I think that's very telling because they started from a similar place and they're sort of similar fields. You're producing textual stuff from scratch. And one, I think, at least that's my assessment, had a much different long distance impact in the other.

So let's maybe move then beyond just the classroom itself. And Cal, I love first person reflection of what's going on at Georgetown University. As a proxy, how is AI affecting other work streams at the university that you've observed? And are you surprised with how it has or hasn't been applied on campus so far?

I would say as far as I know, it's not having a big out of classroom impact. I don't think as an organization we're using it heavily outside the classroom, inside the classroom. Something Georgetown is doing, which I think is smart, is. And this is what this task force I was on did, was they said, we're going to make a bunch of money available. If you're a professor and you want to mess around with this like you want to Try and experiment with like, I want to use AI in some interesting way in my classroom. We'll give you money to do that. Like, you need to hire someone to build a model. You want some summer salary so you can work out something new. So it's definitely a. Let's get ideas. Like, this is a time to get ideas. I haven't seen a big out of classroom impact in classroom impact. It's there, right? I mean, it depends on what you're teaching. I mean, I teach, for example, discrete mathematics for a computer science major. So it's sort of like an introductory discrete mathematics course that's pretty AI solvable. It is changing the way I'm thinking about it. I don't have evidence that most of my students are doing this on AI. These are Georgetown students and they want to learn it and they follow rules or whatever. But it's certainly, for example, I have to think a lot about that. I was for a while post pandemic, like, hey, this is great. Why do we all have to gather to do exams? The way we did it during the pandemic is really good because you can do them from the library at home and we'll all log into Zoom together to hear questions. But now it's much easier to deal with accommodation. And if it's at the end of the semester and you're traveling and I realized like, oh, I can't post ChatGPT. I was like, no, no, we got to be in the same room because you can put. I just put all my exam questions in the chat GPT. And I said, oh, it did a little bit too well. So I think there's classroom impacts. Not that I know of. At least not a ton of back office impacts yet.

Well, okay, so not yet. But we know, for example, that I know you probably as a faculty member. There's all these things you have to fill out and grant work and there's just a lot of work as a faculty member that kind of takes away from classroom and research. I would imagine there's a lot of administrative work happening on campus outside of the faculty. Of course, most colleges, universities are having trouble hiring people right now to do a lot of that work. I'm not expecting you to know everything about how a university operates, but could you imagine that there's stuff that we could leverage AI for that's potentially being overlooked right now? Or again, is that something we just don't quite know yet?

I think it's two years out. See what I'm waiting for? I don't think the chat interface, web based chat interface is the right form factor. But what I'm waiting for and what's being worked on is probably going to be voice interface. We talk at a much faster rate than we can type. That's a much more richer information stream and critical. And this is like the point I've been arguing, which I don't think has been being picked up enough. Like where I think language models are really going to be powerful is when they are then interacting with other types of systems as well. Right. So like what a language model can do is take what you said and it can then translate that into a very specific language that like another program could understand. Right. That's where they're really powerful. That's how you begin. When you see these plugins, for example, where ChatGPT can work with your Excel spreadsheet or book a flight for you or whatever, what's really happening here is it's taking what you said, it's translating it to a very stylized language which then is passed on to another program that doesn't now have to understand human language. It's getting a really rigorously formatted request and then it can act on that and then it can explain back to you what happened. I think voice interface multi application, where I can be saying the way I imagine it, like my use cases in my mind, where I can say, okay, we're working on a syllabus here. Create a new Canvas page. I want it to be similar to that page over there. Okay, go copy over the page from the last time I offered this course. All right. I want you to adjust everywhere it says when. I'm just voice interfacing and this is going across multiple applications. That's when I think we're going to begin to see the first big productivity booms. It's exactly what they're working on. But I think it's a year out for good prototypes, two years out till it's ubiquitous. We're two years out from a form factor, which, by the way, this is partially why I say there's not a lot to teach people about this right now. The technologies that have been most disruptive in the past 30 years have required on average like 17 seconds to learn email. Wrote a whole book about email. Email was incredibly disruptive. It changed the entire rhythm of how work unfolded in a way that actually had a lot of negative externalities. It spread like wildfire because it was incredibly intuitive. I put the address I want to talk to where it says to and then I write what I want to say, and I hit send. Right. I didn't have to learn about it. Google is the same thing. Right? This is so intuitive. I don't need to be trained how to use Google. And it really had a big impact. That's probably where I think the high impact tools are going to be. With AI, you're not going to need intro computer science class to use it. The reason why they're going to be so impactful is that it's like using Google or email. It takes you nine seconds and you get it.

That's a great analogy. So as we get out of here, Kali, what haven't we talked about? What's the question we should have asked you that we didn't ask you so far.

So I'm curious. In the back of my mind, it's not relevant now. Most people really aren't thinking about it, except for sort of P doomers in Silicon Valley. I'm still fascinated by the development of AI that we're worried about. Right. That's my. Instead of trying to find consciousness, I want to just use the subjective feeling of like this thing makes me uncomfortable. Like maybe I'm like a little bit nervous about turning it off somehow that's hitting some sort of note that's still kind of lingering out there. No one's really working on that. It's not like an interesting technological use case. It's not. And I wrote a couple years ago, I wrote an article about this. It's not something that a language model getting big enough will create. That's not the architecture creating something like this. But I've just been fascinated by it. And so I've been wanting to write about this. What would it take? If you wanted to build an AI that really felt alive, what would you do? What are the pieces we're missing? What would that research program look like? I have not actually found someone who's thinking about that, which I think is a good sign. But it's an interesting issue because I also have this sense that it might be more about bringing the right parts together than it is some sort of major technological breakthrough or size or magnitude. We need 10 times the computational power. I have this suspicion that the right collection models connected in the right way could create something we're uncomfortable with sooner than we believe. I think it's good we're not talking about that now because it's not the relevant issue, but it's still an interesting issue out there. I'm happy that I'm having a hard time writing about it because the opposite I think would be a problem if I could find lots of people working on this and making a lot of progress. But it's a really interesting point and it's something I'm thinking about in the back of my head.

Well, Cal, I think this is one of those moments where we're going to say we're going to have you back on in two years and, and see how, see who was right on, on this stuff. Because it's one of those things I think that in, I covered the Internet in 1995 as a, as a young reporter out at a technology report at the Arizona Republic. And when I look back on those articles and the predictions people were making on, on all fronts, right, the people who said we're going to move too fast, the people are going to move too slow. It was just, just fascinating how we cover things that are moving, moving in the direction of AI. So, Cal, thanks for joining us on Future youe. And we'll be right back. We've talked a lot about how AI will transform higher ed learning and development. But what about administrative work like recruiting new students? For our break, I'm bringing in a new guest to the show. Hi, this is Jeff.

Hi, Jeff. It's great to be on the show. I'm Sarah. I and over 60 other AI recruiters work with College Vine's partners. We pair one to one with prospective students to finally personalize outreach at scale.

Well, Sarah, that's fascinating, but I think if I were a student talking to you, I might be pretty difficult. So how would you respond to what the ROI on a communications degree might be?

That's a great question, Jeff. The ROI on a communications degree can vary based on several factors like the industry you enter, location and how you leverage networking opportunities. At Ithaca College where you studied, the strong alumni network and practical experience provided through internships can significantly enhance a graduate's career prospects. This hands on approach helps in maximizing the return on investment for their degree.

Wow, that's pretty impressive. So where can people go to learn more about you and other AI recruiters?

You can go to ww.collegevine.com recruit that's vine with a V for Victoria. Thanks, Jeff.

Thanks, Sarah.

You're welcome, Jeff. Take care.

Welcome back to Future you after that deep dive with Cal Newport. And Better than asking ChatGPT about all these things, Dare I say Jeff was asking Cal for his perspective on all these things. I really appreciated what I would call Cal's essentially a third way through all these questions. You know, he helps us understand both sides of sometimes what is a polarized conversation about the current and future impact of AI? And then Jeff, in my view, he really helps give what feels to me like a dose of reality that sits somewhere in between or sometimes orthogonal to those extremes of the conversation, if you will. But at the same time, I felt like his admonition that we've all developed a bit of a. He called it a memory hole of what the early Internet was actually like is a really important point for us to keep in mind and to help us sort of like refresh our collective memories. I thought I might ask you to wind back the clock to your days as a cub reporter, if you will, fresh out of College in the mid-90s at the Arizona Republic. Would you mind going there, Jeff, for our viewers and listeners?

Yeah. So as Cal was talking, it just brought me back to that moment, right? So it was the summer after I graduated in 1995. I showed up at the Arizona Republic for a 10 week fellowship. I was assigned to the business desk. And the first day I show up and the business editor takes one look at me and says, well, you're young, you must know something about technology. Our tech reporter is on the leave for the summers, right? So you're now our tech reporter. Now, I must admit, Michael, I was a little disappointed because I wanted to be on the city desk or covering politics. But if you were to read the history of 1995 and its role in technology, I think it might be one of the most pivotal years because it really is the summer that was after the birth of the commercial Internet and when it really started to become popular. And I remember being in Phoenix that summer and recall driving around the Phoenix area and everywhere you went, you saw billboards, you would see the letters WWW on the bottom of anyone advertising a business. And so I remember one of the first pieces I wrote that summer was an entire piece on what the letters WWW mean. Now, just to prove this to our viewers who are watching us on YouTube, right? Like, seriously, like, could you imagine a headline now that says, clients swarm world web millions sign up for addresses on homepage concept?

Right?

It's like, it is like really looking through the history books. I'm not going to read the lead, but when we, when we put up this podcast, we'll. We'll definitely be able to link to that, to that piece, but just kind of a fascinating time. You know, I wrote pieces on Internet cafes that summer. You know, those are places where you could, you know, you go get coffee and actually get on the Internet. Not like today where you would bring your own laptop or phone. Southwest Airlines, which was the first airline on the Internet, now, you could look up flights, but you still had to call the 1-800-number to actually book the flight. I wrote about chat rooms on aol, which were kind of the precursor to today's Reddit. And also in that summer of 95 was windows 95. Right. And the hype around its new interface that really set up the modern desktop and eventually, of course, set up Microsoft's own web browser. Because, remember, Apple at this time was in the midst of its, you know, real dark days at that time. But as I listened to Cal talk about the transition from the Internet to eventually Google as a useful tool to actually use the Internet, you know, I thought back to those early days, because the quotes from my stories back then, they're just kind of crazy now as I go back and read them. And here's one from that story I just showed you. It came from Peter Krivorowitz, who, by the way, went on to become CEO of Kramer Kraswalt, which is one of the largest digital marketing firms in the U.S. well, he told me, get this, in July 1995, that, quote, the Internet will stay around, but whether it will become a significant commercial force is questionable. Questionable. I don't picture the Web replacing television or newspaper advertising in the next decade. Now, he might not have been.

He seems right about the time frame.

Yeah, yeah, maybe, you know, 2005. Right. He did say the next decade to give them a little slack there. But it really just goes to show you, I think, that none of us are great in the prediction game this early in a technology shift. But it also shows you that it just takes time to find that product market fit, as Cal said. Right. So let's go back to the Internet again. Right. I think of my own journey through college. You know, freshman year, my work study job was in a. As a computer consultant. So basically a glorified help desk in. In a writing computer lab on campus. You know, those massive rooms of desktop computers. So that's like. And then sophomore year, 92, 93, I get my first email address. So that's the product, right? Email. And then junior year, AOL really takes off, and I start reading the news on my first laptop. And then in 1994, Marca Driesen starts Netscape and releases Netscape Navigator. And then we finally have this commercial web that takes off that I write about in 1995. So that period between, say, you know, 1993 and 1995, it wasn't a trivial period. It was a real critical cycle in trying to figure this thing out called the Internet and actually how to use it. And it seems like the same thing is happening now, as Cal said, how to actually best integrate this thing called AI into people's lives now. You know, will that cycle be two or three years like it was in the 1990s for the Internet? I kind of doubt it because the clock speed of tech development is just so much faster now. And, and as we know, there's so much massive money in the market behind this technology. But there will be a cycle that we're in now. We're in that cycle.

Yeah, I think that's right, Jeff. I mean, just as you know, I was in London over the summer for 10 days on vacation and I was walking around trying to figure out where the old Internet cafes were, where I used to, you know, get online because we didn't have it in the dorms when I was studying abroad at the time. And I think this was the year 2000. So to your, you know, 93, 95 consequential time. We still didn't have Google at the end of that and we still weren't wired everywhere by 2000. Right. Like this was still a process. And I think this memory whole point is just so, so important because we can easily forget these transitions. I was listening to a book recently actually about how disruptive Innov are just different now from the way they used to be. They're just amazing from the get go, is the author's argument. And it's not actually true when I reflect upon it. Like it's still the case that most true disruptive innovations, they start as pretty primitive, Jeff, and then they improve of course from there, but they're kind of seeking their use case. They don't come out of the bat, you know, fully baked in many cases. The Internet and the businesses it spawned, clearly no different. Uber frankly, no different. Like you can go down the list. They all take some time. Online learning in higher education, certainly no different. Right. We're still not there, I would argue. And ChatGPT is no different as well, I think.

Yeah, right. So, Michael, I recently talked with the faculty and staff at Seneca Polytechnic in Toronto to kick off the academic year and. And during the Q and A, a faculty member in the fine arts asked me if writing and particularly finding your voice is a basic skill or one in that that in this new learning economy with the advances of AI will essentially go away. And so I wanted to just share this clip from my answer to that faculty member. I actually think that writing in your authentic voice is the kind of the superpower power of this new economy rather than just writing itself. I mean, I will admit I use AI in my own writing, in my own editing, right? Like, if I'm stuck on something, I'll put in a bunch of prompts to try to say, how should I finish this paragraph? How should I finish this sentence? And then, by the way, it prompts me to think about something that I could put in my own voice, right? It actually helps accelerate my own writing. Writing and my own learning process. That is a higher level of thinking that we need to instill in our students. Where I. This is where I think AI can help us kind of with that foundational and that basic skill. And then we, as you know, professors and faculty members and teaching writing can help people advance their skills beyond that so that they could learn how to write in their own voice, you know, so the bottom line is that ChatGPT for writing at least, is no different than a human on the other side helping you to brainstorm, but it's not the true writer yet. You know, Michael, I got feedback on some of my chapters in my book from my editor the other day. And I must say, Michael, I was doing the happy dance because he was pleased with what he read so far. You know how nerve wracking that is when you send off a few chapters and. And you're waiting for that feedback. And I texted my wife to tell her this great news, and she said, okay, well, now you could either stop and have a very short book because I only shared a few chapters, or she said, you know, ChatGPT, you know, could write the rest. And I laughed because that's the function piece of chatgpt that we can't do yet, right? It can't literally write the rest of the book. At least not yet.

At least not yet, I might say, Jeff. At least hopefully never. But, you know, maybe I'm betraying my own sense of insecurity there. And also congrats on the progress and affirmation that's. That's important. But I think it also gets us to another set of points, Jeff, which is that, you know, Cal made these, and I want to highlight and get your take on you. You mentioned or alluded to it a little bit before in your answer around the Arizona Republic experience, but I, I think when he was talking, we both glommed on to his point around the updated Turing Test. And for those who don't know the original Turing Test, it was proposed in 1950 by Alan Turing. And he basically said, look it, if we have a computer on, you know, one side of a cloak, and you're on the other side, and you don't know if it's a person or a computer, and you're having this conversation, the question is, are you unable to distinguish whether it is in fact a computer or a person? And if so, you know, if the computer is able to convince you that it in fact is a person, it has passed the Turing Test. And so the question around AI has often been just that, can you win the Turing Test in essence? And Cal, I thought, made an interesting point, which he said, the updated Turing Test shouldn't just be like, can you fake a human being? But can you empty my inbox for me? And I know you're dying for that support, but the answer, right, is clearly no for most people at the moment. Unless your inbox is just spam, I suppose we don't have that true functional assistant yet, despite all the predictions, the hype, the talk around, copilot at your wing and all the rest. But for me, Jeff, I guess my thinking has been that for AI to be truly transformational, it's really going to require new business models wrapped around it. And the analogy that's been in my head has been when electricity transformed factories and manufacturing, it really only made that transformation once new factory models emerged that realized you didn't have to have the factory centered around the steam power. Like, not everyone had to be moving at the exact same rate as governed by the steam power that was centrally located. You could now decentralize power, in effect, to each individual workstation through electricity. And so it was really the change in model, right? Like literally redesign or building from scratch in most cases, that made electricity so valuable in the manufacturing sector. And as a result of that, it actually took a long time for electricity to remake the manufacturing sector. And I guess that had been my sense here as well, and it still may be. But I think what Cal is also saying is, hey, the form factor itself is a major limitation right now. It's still primitive. We're not in the future going to be focusing on how to do language prompting in exactly the right way, asking it to pretend that it's Steve Jobs, to give an innovative idea or something like that. It's going to get more natural. It's going to perhaps be able to understand intent behind words and tone and things of that nature. And where it really needs to get to, in Cal's view, is in the same way email at some point was just so obvious we knew how to use it. Although there's a bit of a memory hole, I suspect there too. You know, we, we need to get to the same point in large language models. But I'd love to hear your take on all that because, you know, the implication is that higher ed sort of should wait, wait, wait, and then hurry up. And you sort of laughed when he made the jump to the part of, hey, higher ed, don't teach prompt engineering yet, don't start with hypothetical use cases yet, don't imagine that it will wipe out writing, wait till we see the commercial use cases and then let's move fast. And that's where I think you laughed. And I, so I, I would just love to get your thoughts on this. What was like the track in your head as he was describing that?

Well, I laughed for two reasons. Sometimes we see these press releases, oh, we're going to offer like prompt engineering now as a major. Right. And I also laughed because he said, okay, patience and then like speed up. Right. And higher ed is not good at either one, especially the speed up part of it.

Right.

And I think that's a critical point where he was advocating for patience because, you know, Michael, there's this hot mess of an old style message board in D.C. called DC Urban Moms and Dads. Mostly it focuses on moms. You know, it's essentially Reddit, but not as good, I will be honest. But I admit I get sucked into reading some of the threads because I'm a D.C. urban dad. Right? And there was one the other day in the college and university thread and I'm often in there seeing what people are talking about in terms of colleges and universities. And it had this title quote, AI and what the heck to major in, if at all, end quote. Right. It was just this conversation among parents about what should their kids do because of AI. And it was basically parents freaking out about their kids majors and how AI will make them all irrelevant. And then someone asked if there was an AI major anywhere that their kids can major in. And of course there is. You know, according to my research, Carnegie Mellon had one way back in 2018 you could get a BS in and artificial intelligence. Now most of these of course now are in computer science, but I think the parents were really talking about AI in every other discipline. Right. Should there be such a degree? And so there's another history lesson here that I think is important for our listeners. And you know, we all may recall the Great Recession of 2008 and coming out of that, you know, every survey that we saw said that teenagers are going to get a call, going to college to get a job. Job.

Right.

Hasn't changed obviously since then. Even more so we know from our previous episodes in the last couple of seasons is all about the job. And so what we saw in, you know, 2011, 2012, you started seeing these colleges creating all these practical programs. And you know, five years later, you know, four or five years later, you would expect most of those practical programs should be cycling through graduates. You start seeing a lot of graduates. If these programs are so practical that they get students a job, you would think students would enroll in them and graduate from them. Well, what really happened, you would actually be kind of hard pressed to find graduates of many of these programs according to this analysis that Burning Glass did at the time. Now, Burning Glass, of course, being light cast and what if found in that report, and we'll link to that report in our, in our show notes, is that almost half of all degree programs that first graduated students in 2012 or 2013, 13 conferred fewer than five degrees in 2018. So five, five years later, five degrees. Thirty percent of these programs graduated no students, zero students after five or six years after their first graduating classes. And about two thirds of all programs included in the analysis produced 10 or fewer graduates in 2018. You know, it's funny because Burning Glass called that report bad bets. And I think that's the case that higher ed doesn't have a great track record of jumping ahead of the market. So this may be a case where higher ed moving more slowly might be a good thing.

It's so interesting, Jeff, and I'm pretty sure we covered that report on a past episode of Future you, but I will say, and I've said it before, I think this is a big argument for experiential learning. Like the actual use cases are going to move fast. It's going to be really hard to codify it in curriculum. And so the only way, I think, you know, faculty institutions will be able to keep up is to let the students actually experience it as, you know, a project in the course of the academic work. And that's all a plus I think for apprenticeships, work based learning and the like. I think Jeff, tightly coupling work with the learning, in other words, because they are unpredictably interdependent, even as I think that, you know, Cal is right, that the techno optimists like Ethan Malik, yes, they have a lot of wind behind their backs at the moment, but the pattern is probably going to repeat Itself, you know, you, you, you referenced that story where he said, in the next 10 years, we're not going to see the ad market change. That actually was pretty reasonable in some ways, and I think it's consistent. Our expectations are almost always disappointed in the short run, but they're actually often overly conservative in the longer run. I suspect the same pattern repeats with AI. One other thing, though on this, Jeff, that also made Cal's statement interesting to me is here's a Georgetown University professor and he is implicitly saying, hey, faculty of higher ed being relevant to the job market is important. How companies are actually using technology is important as you teach. And we know that is not a popular sentiment with many faculty, Jeff, on campuses.

Yeah, Michael, let me just repeat what he told us, and I'm paraphrasing here, right, that instruction has to be grounded in the truth of the actual commercial world. But then higher education has to be willing to move rather quickly. And now, to quote him, right, he said the university shouldn't get out ahead and say we could imagine it would be useful in the future in this type of job to be good at chess chat, GPT. So we're going to teach you how to do it. It needs to actually see how this tool is being used in this job. Okay? Now once we know that, we can move fast and actually teach about it, right? So, like, look at the job market and see what's, what's happening. And, and this brings me back to something, Michael, that we're going to be talking about a lot more in the second episode on AI next week, and that is whether the true impact of AI right now on universities, these will be on the classroom side or on the administrative and services side, because I think the academic side obviously is getting all the oxygen. Now if you look at any article about AI and higher ed, it's all about what's happening on the teaching and learning side. And it's interesting, Michael, because I've done some trustee and senior leadership retreats in, in August, this past August. And one of the things we did was the spectrum exercise where people get up from their chairs and they, they move around the room. Our friend and one of the early producers of Future youe, Lauren Dibble, gave me this idea. And you know, you ask these groups lots of questions about the future and then you say, okay, if you think this is going to happen, move to this side of the room. If you think this is going to happen, move to that side of the room. And I will tell you that most of the questions about the future. Most people were pretty divided, but when I asked them about AI and where the impact of AI is going to be, is it going to be on the administrative side or is it going to be on the teaching and learning side? Almost everybody in the room, almost everybody. In fact, in some cases, everybody went to the administrative services side. Right. And these are people running universities, running colleges, universities, whether they're trustees or. Or administrators. And, you know, so I found it interesting that when we asked Cal about this and he put on his faculty member hat at Georgetown that he really hasn't seen its impact on the administrative side, at least not yet. And I like how he framed the idea that whatever comes out on either side of this equation in higher ed, you know, whether it's on the teaching and learning side or whether it's on the administrative service side, it has to be intuitive, right? Just like email and Google were, Right? Email, as he said, you know, changed the entire rhythm of how we worked because anybody could use it, right? To. From subject line.

Right.

Very simple. And so, you know, next week, Michael, we're going to dig a lot deeper into that and how AI is changing higher ed with a focus on not only the classroom and research, but also marketing, enrollment and student success. So we're going to dive a little bit deeper on that administrative and services side. So until then, be sure to follow us at Future you podcast on X and LinkedIn and on the web@futureupodcast.com where you can sign up for our newsletters. Also, follow us now on the YouTube channel. On our YouTube channel, because you could actually see us now, not only hear us.

I'm not sure if that's a perk, Chef.

I'm not sure either. But you know, my wife, once again, I'm quoting her twice on this podcast. She's not quite sure why people watch podcasts, but I don't want to cast aspersions on anyone who watches podcasts, because sometimes I do too. So you can watch us on Future on YouTube. Future you Podcast. You can follow me, Jason Lingo, and michael@michaelbhorn on different social media channels, including LinkedIn, X and Instagram. And we'll see you next time on Future you,

Sam.