The Professor Who Required His Students Use ChatGPT In Class — With Ethan Mollick

21:51Experimenting with companies is expensive. Experimenting with your own jobs is super cheap, right? You try a chat, it doesn't work. Try it again, it doesn't work. Maybe you give up. You try, it starts to be interesting. You keep going. The trial and error is cheap for your own tasks, expensive for other people's. So it's very easy to adopt to the individual level. The benefits are huge individually as long as no one knows you're using it. And I talk to people all the time who outsource 90 % of their work and just don't tell anyone about it. What happens when people find out? Well, so I think that's the real question.

22:25If you're a smart company, you're going to be working really hard to make sure the answer to that is they get richly rewarded, right? Because what I want to do is find out what everybody's been experimenting on. All these companies are used to doing experiments, to doing change from the top down, as you said. They're used to having a boss do things. They have an innovation group. They hire McKinsey. That's not the way that this works. There's no reason McKinsey knows more about how to automate some mid-level manager's job than they do. So I need to get that middle-level manager willing to talk about what they're doing, to share that information with me.

22:54And to do that, I need to make sure that middle manager doesn't feel like they're going to get fired. I need to make sure that mid-level manager knows they'll get rewarded, that it won't fire other people because they've ratted out this capability. I want to make sure that people feel incentivized to be part of the team. So this is where having a hostile work environment where you hate the boss, but are grinding away at work. I'm never going to share anything. If I'm for a company where I'm all in on that company, maybe that's a little bit different. Right. And so it sort of goes to this question of like, all right, so what's going to happen to my job?

23:22It's like, actually, if you can take this, these tools and make yourself more productive, you become more valuable to the company. And I think that like one of the companies really never talk about like, ah, like, you know, we wish we did the same thing with less people. They always talk about, we wish we did more. we just have a labor constraint or a cost constraint. And this could potentially flip that. Is that how you see it? Well, I mean, there's lots of different constraints, right? Like what you might be, lots of companies also fire people, right? Because I mean, let's be realistic. Like if you get a productivity gain, that's what people tend to do, right?

23:54Is like, or they view that there's overhead, they slash it. But if you can figure out ways to turn that extra overhang of people and productivity into positive growth, your company will be much more successful, right? And if you're firing people left and right, when they become more efficient, they're going to stop becoming more efficient because they know what happens. So you have to embrace the idea that there might be some short-term inefficiency to get this long-term gain. But it does tweak the employment contract just a bit, don't you think? Because you get paid to do a job. The contract is basically, you're doing this job.

24:28This is what I think someone on your level is able to do. then you find a way to be you know much more efficient and then all of a sudden it's like you know it it really does you know say basically as the technology has shifted everything there and it's no longer like it's it's almost a i don't want to say dishonest but it's a completely different way of approaching the job if someone can do it like shouldn't shouldn't i mean yeah okay you're shaking your head no i'm agreeing with you actually i i think i think i i i think that you're right that this is completely shattering but every i mean this gpt a general purpose technology breaks things right like this is like this is like old systems are going to get broken everywhere and we have to reconsider them what does it mean to work with an ai like what how much am i a free agent working with an ai to help a company how much does my employment contract matter if i get all my work done in an hour and a half do i get the extra seven and a half hours i'm you know seven hours on my own like or six and a half hours on my own if you're a banker 16 hours on my own.

25:26Like what happens, right? We don't know. What happens when like a lot of our systems at work are built around human limitations. They're built around enforcing limits on human, like keeping everyone operating at the same pace, making sure you're gearing the machine. What happens when that breaks? We don't know. Like this is, you know, it's very funny. People talk about the singularity, right? Is this thing that is like the AI gets smarter than us and murders us all. And I'm definitely think, you know, that seems worth worrying about, But it's also, the original meaning is it's like a mathematical point that we can't predict what happens afterwards.

25:59That's already going to happen at work. I don't know 100 % what the future of work is going to look like. I don't know 100 % what the future of education looks like. Because we're just assuming that everything stays the way it is, right? What happens is work gets more and more easy to automate with AI. What kinds of categories of work change? Like, we're barely scratching the surface. Let's go through a scenario that sort of hints to what you've been discussing. So I am a manager. somebody comes to me and says, listen, I read Ethan's sub stack and I'm confessing I'm using AI. You shouldn't fire me.

26:28Here's the article. This is why, by the way, I'm doing a hundred percent of my work in 10 % of the time. So does the, what is the man, what is the next step for the manager there? Do they say we actually need you to do 10 times the work in a hundred percent of the time, or do they say, yep, that's good. Teach us how to do it. And we're going to have basically give everybody, put everybody on the two day work week. So there's a whole bunch of options, right? I I mean, no, I'm serious. So one option is something we already know works really well, which is let's job craft this. People are more motivated to do better jobs when they go through job crafting, which means working together with managers to figure out what their job is.

27:03So you could say, hey, what outsourced stuff have you done? The 10 % that's left, do you find that engaging? Is that what you really want to do? Because a lot of the early work of AI is about freeing us from drudgery, which is kind of the good, it's ominous in the longer term, but the short term, it looks pretty good, right? Like my job is a bundle of tasks that includes many things like philotic expense reports. If AI does that, yes. Like that's the best thing in the universe. I hate expense reports. Right. And so you could ask this person, what have you outsourced? How do we get you to do more of that 10 %?

27:29Maybe we're giving you a raise because you're doing 10 times the work now. Maybe we're giving you a million dollar bonus because we can spread that idea across the entire company. We're saving that much. Extravagant rewards, rebalancing work towards what people want to do. Right. Thinking about what systems we can use to support that person. It becomes part of a conversation. Exactly. It's interesting. So I know we kind of danced around this question earlier about jobs, but I mean, like, so we don't know exactly what's going to happen there, but there has to be some sort of impact, don't you think?

28:02I mean, there's going to be, right? I mean, and again, general purpose technology, it's going to be a huge, like a huge generalized impact, right? It's going to affect everything in all kinds of different ways. So some jobs are going to change and disappear, right? And like when the telephone system was moved to a digital telephone system, it resulted in a giant wave of change. A lot of people lost their jobs. A lot of things ended up happening very quickly. But most times when technology change happens, people get better jobs and higher paying jobs and the nature of work changes. But we tend to look back on those historically as not in the moment, which is where we're going through now.

28:41So there's gonna be disruption everywhere in the nature of jobs. Yeah, and it's also, there's something that you hinted at that I think we should expand upon, which is that taking away the drudgery is really nice in the short term, but ominous in the long term. I mean, after reading your work, I didn't really fully think about how this could potentially change like the nature of meaning that people get out of their work. And you had this one line that's just amazing. You're talking about, I think, letters of recommendations. And you start to say that we can create documents mostly with AI that get sent to AI-powered inboxes where the recipients respond mostly with AI.

29:18Even worse, we still create the reports by hand, but realize that no human is actually reading them. We can view the destruction of busy work as freeing. and we do not yet have to start our, oh, this is about setting our time on fire as a signal. But like, let's go back to this idea of the AI's community. I mean, this is one of these things where like, you know, it seems like maybe one day instead of us getting on the podcast, my AI will interview your AI and it will be listened to by AI users who will summarize it for people. What is that? How does that change our society? How does that change work?

29:52I mean, we have to reconstruct meaning, right? This is the biggest crisis. I think that everything else is secondary. I am sending a report. As a mail manager, the report that I would write about my employees' work, even if no one reads it, was valuable because it means that I checked the work, right? And then my boss looked at this and saw that I was doing this and is like, okay, the systems of monitoring are in place. Now I hit a button in Google Docs. It creates an automated report that looks plausible. I send it over Gmail with a fake message to my boss, who then hits on a reply and gets a message back.

30:28We've just taken the intellectual meaning, but also the reason for doing this work, and it's disappeared entirely. We have to reconstruct this. This means our work systems were built. The reason we have org charts is because in the 1850s, somebody wanted to make sure we had org charts. early railroad barons, wanted to build a structure that would let them control vast geographic train networks from the top. So they built systems that look like train networks that do that. Henry Ford realized that if he hired low-paid employees, he couldn't monitor and control them and what they were doing. But if they did one task over and over again, he could keep an eye on it.

31:07So thus assembly lines. Agile development is all about the idea that we could track bugs and changes in the internet, but we still need stand-up meetings to coordinate work. All of those things are going to change as a result of AI being able to coordinate, to interact, to change, you know, and that's going to change work. The companies that figure this out first are going to win. It's almost a chicken and egg thing, right? Where it's like, okay, the AI is changing work, but it's also commentary on how much of our work is meaningless, don't you think? I mean, you, in your essay, write about how, and this is what I was getting at with setting the time on fire, which is a concept that you've talked about.

31:41But basically, people are asked to write letters of recommendation, not because they're actually going to read it, but just to kind of show that the professor would put the time in to recommend the student. And it's a signal. And it just seems that if you can automate this and have AIs talking to AI, so much of the system of work today is kind of garbage, don't you think? Yeah. I mean, look, work is broken in lots of ways. Literally, as a management scholar, one of the dominant methods of understanding how organizations work is called the garbage pail method, which is just like stuff happens, right?

32:13Like people meet each other, it all gets tossed together and stuff happens. AI can help a lot, right? It can help free us from drudgery, from bureaucracy. But there's a lot of people who work in bureaucracy. There's a lot of people who, you know, there are reasons part of those systems exist and part of them just grew that way. And, you know, and we have to reconstruct all of how that works. As you were saying, the problem with the letter of recommendation is not just that I can push a button and write it, but that the letter of recommendation that I push the button and write is going to be better than the letter of recommendation I spend an hour writing, because the AI will read all the documentation, all the material, and be able to produce something that's much better.

32:44So the problem is I turn in the old-fashioned school, properly done, morally correct letter of recommendation, and I'm actually hurting my students' chance of getting the job compared to pushing the button. And we don't have things built around that. I mean, Do I just send the prompt I would have sent? Like where I say, I would have told the AI, I like this person, they're a good job, give them a good recommendation. Or what do I do? We don't know. But that's one minor system out of so many that's going to be broken. It does seem, so there's been this question of like whether AI will automate all of our work and I can keep coming back to it.

33:19But it does seem at a certain point, like the further we get into this, we could effectively just like set it and forget it with our economy. And maybe I'm kind of delusional, But it does seem like we'll still make work for ourselves. Like, why don't we just like have the robots kind of take care of our basic needs and just kind of live? Well, I mean, but that, of course, is the bet, right? We do like we're coming back to the billion dollar question, the trillion dollar question, actually, I guess, or what are the companies? What, like seven trillion, 12 trillion? I don't remember off the top of my head.

33:47That's the question, right? The question is, first of all, how good does this get, right? Like, there's too much obsession. not that it's wrong about like whether we build artificial general intelligence and a machine god totally get it i'm glad people are worried about it they absolutely should be government should be worried about it we should all have concerns about you know whether the machine god will save or kill us but like there's a lot of steps between there and now and the real salient question for technology is is this as good as it's going to get i think the answer is probably no will change be linear or exponential in the future that we don't know if change is exponential then maybe the robots take caring of everything is something that happens the next five or ten years If not, if it just keeps getting better, we have a gradual squeeze.

34:28If you're not in the top 10 % of workers in your task, you're probably, AI will replace you. Then top 8%, top 7%. So I think the question about what happens with our leisure time, what happens with work, depends, like right now, if AI were to pause where it is today, I think it would be 10 years of us absorbing the impact, but I think we'd be fine, right? Like it clearly needs oversight. It does not, there are very few categories of workers that completely replaces. It tends to do drudge work, right? I think we'd be fine. Now, what happens if it gets 10 times better? I don't know. Then we start to think about how work gets automated and changed in much more profound ways.

35:07We have abundant resources on this planet and in the modern world that we've developed, but we still fight over them. Like, it just seems so ridiculous in some ways that we're getting this technology and we're still going to fight over them. I mean, it's going to help. I mean, the question is whether it makes us fight worse. you know is is the is a real question right right i mean look it is hard it's hard when new technologies come around everyone gets utopian and uh and cataclysmic at the same time right and so this is the like but this is something quantifiably different the question is just whether it keeps going right where we have it right now it is a complete change in technology and it's very exciting and it will be a huge revolution on par with the internet or maybe steam power.

35:51But if it gets 20 times better, then it starts to get really weird, right? Because then we actually have, you know, and if it starts to be able to outperform humans at every task, that starts to get very strange. And we don't know what happens then. We don't know what the limits are and we don't know the outcomes. And that's where like the entire sort of system modifies. Right. Because if you talk about like, all right, the technology gets good enough that you only need the top 10 % of workers and 90 % you don't need, well, you still need workers. and you're not at like maximum production and that drives inequality in society and destabilizes it.

36:23That's what you mean by the system shifting. Potentially, right? I mean, at the very least, you need government intervention to make things happen. But, you know, again, if we look at the history of work, change will be slower than we think, generally. And people generally tend to get better jobs. We just don't know if this is the end of those rules, right? The historical rule has been technological change every other time it's happened has resulted in generally an increase in GDP, an increase in quality of work. There's been some exceptions. Will that keep happening is the big question. Ethan Malik is here with us.

36:58He is a Wharton professor and he writes the One Useful Thing newsletter on Substack. On the back end of this break, we're going to talk about a few new models and a bit of a lightning round and take some questions that popped up on Twitter and threads over the past couple of days. All right, back right after this. These days, it feels like every dollar should be working a little harder, but figuring out where to put your cash can be confusing. That's where Wealthfront comes in. Wealthfront is a tech-driven financial platform built to help you grow your savings into long-term wealth. Their high-yield cash account through program banks offers a 3.5 % APY on your uninvested cash as of November 7, 2025.

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38:08Wealthfront brokerage is not a bank. Rate is subject to change. Promo terms and conditions apply. For more information, see the episode description. Capital One's tech team isn't just talking about multi-agentic AI. They already deployed one. It's called Chat Concierge, and it's simplifying car shopping. Using self-reflection and layered reasoning with live API checks, it doesn't just help buyers find a car they love. It helps schedule a test drive, get pre-approved for financing, and estimate trade in value. Advanced, intuitive, and deployed. That's how they stack. That's technology at Capital One.

38:51And we're back here with Ethan Malik. He's a professor at the Wharton School of University of Pennsylvania. He writes the great one useful thing on Substack. It's a great newsletter. We've been talking a lot about his most recent pieces here. Let's do a quick lightning round. So, Ethan, you've been experimenting with some of the bots that have been coming out from the non-OpenAI, non-Microsoft groups. So I'm going to ask you about your experience with two of them, first of all. So what is your experience with Claude from Anthropic and how do you find it different from OpenAI? So Claude was developed by people at OpenAI and they said they left because they were worried about OpenAI's risks it was taking with its AI.

39:30So it's supposed to be a friendlier and less harmful AI. They just released Claude version two, which is somewhere between GPT 3.5 and GPT 4. So it's a really good model, probably the second best model out there right now. And one of its really great capabilities that it has in the short term over anyone else is it has 100 ,000 token context window, which basically means it can hold a book, a short book in memory. And that means you can upload PDFs to it and it's very good at working with documents. So it's very good at summarizing and annotating at combining documents. I find it very useful for those purposes.

40:04Because it's safer, it also is a little bit preachier, so it can be a little bit more annoying to work with. But it's a very powerful model. And if you're working with documents, I'd strongly recommend trying it. Yeah, you have this chart of breaking down all the different models. And I think for Claude, it's like, good, but a bit too preachy. Exactly. They can be that way. Well, and I don't really mind some of the others because without guardrails, AI gets really bad really quickly. But Claude could get a little over the top. You mentioned that anthropic researchers are dreading what they're building, which is really strange, right?

40:36It's like they're going ahead. This is covered in the New York Times article, but they're going ahead and they're really freaking out about the nature of AI. And this is a question I ask sometimes, but it's just so unbelievable to me that some of the people with the biggest concerns about AI are like, man, this stuff can really destroy society. Let's build the next new model. What's happening there? There are three theories, right? Theory number one is it's all cynicism. I don't think that's the case. I mean, some people are being cynical and marketing it, right? Or paying lips, but that's one option.

41:06The second is that they are genuinely worried, but not that worried, right? So they have to say that they believe in possible doom, but they really think it's going to be okay, which a lot of researchers do. And a lot of researchers are also very worried. The third option is to take what they're saying at face value, which is that they actually think that if they don't build a safe version, people will build the dangerous version. That's always a little bit weird to start an arms race that like, if we don't burn down the village first, someone else will burn it down is sort of a disturbing viewpoint.

41:37But that may be a serious kind of approach, right? But they may think that they could shape the future that way. So it's some combination of those things, right? I do believe talking to people at AI labs, many of them are true believers. They really believe they're building super intelligence. Whether or not they're right or wrong, I don't think anyone really knows. What do you think about this Pi bot? It's a Reid Hoffman's bot, right? It's supposed to be a very friendly bot. I started talking to it a little bit. It can even like play like, it gives you an option to be like a therapy role, which was interesting.

42:08I was like, I'm about to confess my deep, dark secrets. And it's like, your secrets are safe with me. What's up with Pi? Pi has gone from driving me absolutely insane to intriguing me, right? So like it is, it vomits emojis. It tries to be a friend really hard. It will not do work to save your life. It's to save its life, which is something I push really hard on. But on the other hand, it is pretty good at chat. It's pretty good at adapting to you in the way AIs are. Pretty good at keeping chat going. If you have not tried the app, I strongly recommend it because there's the ability to get on a call with it, which is near real-time conversation back and forth, which is pretty nuts.

42:44So I admire the attempt to create an AI that doesn't do the thing the other chatbots do. Is there a market for it? Is it an open question? Is it like, is this good or bad for the world? Like high engagement bots are kind of a high risk also in their own weird way. So like it can be friendly, but it takes away time. But I've been increasingly impressed by its ability to be interesting to talk to at the very minimum. Yeah, it's definitely fun to speak with. Do you think we need all these? I mean, there's so many. There's ChatGPT, there's Claude, BingBot, Pi, Character AI. Like this stuff is going to consolidate eventually.

43:19it has to, right? I don't know. I don't know. I mean, I think the models could get specialized too, right? I mean, if we're talking about electricity providers or railway systems, having more than, I don't know how much consolidation there is. And I actually don't think for large language models, there aren't that many contenders, right? And the question, of course, is, is there a flywheel? Once you've built a large language model, right? Does that LLM give you an advantage? Does that help you code the next model? And so there's only a few companies that have significantly big large language models so far.

43:52And so OpenAI, which has Microsoft stuff built on top of it, there is Claude and Anthropic. There is the company behind Pi and there's Google with its bard. Those are the models. Elon Musk's announced a new model he's building, but it's going to be years of training to get that thing up and running. So the question is, are there really going to be that many? And will they specialize the way Pi is the conversation bot, AI is the document, you know, Claude is the document bot, and GPT is your general purpose tool. Right. Yeah, that's interesting. That's an interesting possibility. Do you think that prompt engineering is going to be a new job?

44:33I think that's kind of a ridiculous idea. I think everybody's going to learn how to do this. What's your perspective? I agree with you. And not only is everyone going to learn to do it, it's just going to be do it for you. If you already use something like, I mean, go back to Pi, you never prompt it once. It keeps a conversation going. If you ask GPT what I should ask, it will tell you what to ask. If you use an AI art system like MidJourney, it went from like when I did MidJourney prompts a year ago, those prompts would be these elaborate invocation spells, code of Chrome, art station seven. We just didn't know what we were doing.

45:07We'd throw everything into it to get a good picture. And now you just say, show me the thing. And it shows you the thing. I think that prompting is going to get easier. I don't know anyone who works in an AI lab who doesn't think that prompting is just going to get easier to the point where prompt engineering is kind of silly. Yeah, I agree. What are the US AI efforts look like in comparison to China? As an academic who studies this, I'm curious what you think. I'm not an expert on Chinese AI policy. The US is typical US stuff, which is laissez-faire while a thousand different court cases resolve themselves.

45:38And there are Senate hearings. It is going to be a slow process to figure out what all this means. Japan, for example, has said pre-training. The training data is, can it be protected by copyright, which is interesting. The U.S. hasn't taken a position on this. China, the early evidence seems to be a desire to regulate these systems. But on the other hand, national militaries are probably spinning up these. So I think regulatory systems are going to be really interesting. I think Europe is really being very careful and we'll see what happens with that. I mean, I think there's a lot of change happening all at once.

46:09Code Interpreter is just something that you've picked up on and different plugins that can help you make sense of data. What's your perspective on those? I think that that's some of the most exciting stuff out there, right? So yeah, say more about it, yeah. So Code Interpreter lets an AI, lets GPT, both take in data, give you data to download and also run its own Python. It gives it a Jupyter notebook, essentially, to do its own work in. And it turns out, increasing evidence suggests that when you give AI tools, it becomes much more capable. So given the ability to use tools, it first of all solves all the problems that have made AI really annoying to work with, right?

46:46So if you've ever tried to use AI to work with language, that is a hard thing to do, right? It doesn't understand sentences and paragraphs the way we do, right? If you ask it to count the number of words in a sentence, it can't do that. but code interpreter will write a little program to do that it'll solve math problems that it couldn't solve i write a little program and it starts to do really complex stuff like it will you throw data at it and it will apply theory and it will i give it a paper i give it a you know i did a little experiment where i um gave it the nba playoff data and had it find interesting stuff and it found all these cool hypotheses and then graphed it and then i threw in some work by um by tufty who's a famous graphic uh design infographic design person and said apply these skills to make the graph better.

47:29And it did. Like there's a really powerful democratization of coding and analytics that is already starting to happen. Do you have to know how to code to use them? I don't. I can't code in Python to save my life, but I do know stats. So I can look at the statistical outcomes, but no, I don't need to know code to do it. We got some questions from social media. So one person asked, how is accuracy necessary in terms of like, let me just read the actual question. Taka, ask him about the necessity of accuracy, whether it's a core need for enterprise use cases for LLMs. So should tools like Code Interpreter, here we go again, give companies greater confidence that LLMs won't hallucinate as often and provide reliably accurate outputs?

48:14So this idea of hallucination is a real problem, right? Yeah. I would argue that it's probably not as big a problem as people think. So first of all, hallucination rates are dropping. So we don't have a lot of comparative data, but there was a really cool study where they gave the variety of AIs the neuroscience board qualifying exam, neurosurgery board qualifying exam. And unsurprisingly, GPT passed the flying colors, but they tracked the hallucination rates. Bard hallucinated 44 % of the answers, GPT 3.5, chat GPT 22%, and GPT 4 only 2%. So hallucination rates do seem to be dropping. And then when you attach to code interpreter or data or PDF, it drops further.

48:52But hallucination is a genuine problem, which is why right now there's a huge advantage in that expertise thing we talked about. If I'm an expert, I can check over the results and fix problems. And I do need to do that with code interpreter. So I think that it's less of an issue. It favors experts. And also, there's a lot of jobs where accuracy isn't the number one thing. If you're doing marketing writing, it's not that big a deal. Even if you're doing customer service, a lot of the interaction, if there's small errors, they don't make as much of a difference as getting the big things right. So the other question is how accurate you need to be.

49:21For some applications, AI is totally out because it hallucinates, but for a lot more than you think, it works. There's some concern that people are, we're going to develop brain chips with AI inside them and attach them to our brains. And we could like have our data sucked out and stuff like that. What do you think about that? That feels that in a world where we're living in science fiction, that still feels too science fictional right now. So I don't think involuntary brain chips are in the cards and physical world and biology are much harder than software. So I think let's not get ahead of ourselves too much, I think.

49:55Would you get a brain chip if you could? No. I mean, no one's getting generation one brain chip. I mean, you know, and also like, you know, I think that, and I think when you look at what the actual brain development, you know, the chip stuff is, it's not telepathically communicating with AI. I think we need to be really careful about, you know, even a world where a lot of hype is coming true, there's still hucksterism going on. So there is no telepathic brain chip in the near future. We don't know how the, you know, we don't know how LLMs work. We don't know how humans brains work either. Like it's, we know how they work, LLMs work technically, but we don't know all the details of, you know, why a particular decision is made.

50:25I think we're going to be a long ways off from connecting AI directly to our brains. Yeah. Are there models outside of LLMs that you're looking at or interested in? I mean, so LLMs underlie a bunch of other things, right? So large language model and the transformer technology that powers it is what is powering art-based AI dialogue and, you know, podcast-creating AIs, you know, all of these things. So like the large language model technology is the transformer technology and attention mechanism that's at the heart of this technology are what's driving all of these sets of tech changes all at once.

51:00Okay. Last question for you. What are you looking forward to in the fall when students come back and how are things going to be different in terms of the way that you teach? Because you basically had to deal with this mid-stream this year. What does next year look like? I'm excited. I mean, I think like it's great to take the burden. It has allowed me to reimagine how teaching works in really exciting ways that I think will benefit my students and make things interesting. But that's for me. I think a lot of other people are terrified and or putting their heads in the sand or they try AI a bit or they've heard of AI.

51:30I mean, I talk to teachers all the time, we've heard of it, but haven't really tried it. Like, I think that we're in for sort of a mess. Yeah. Okay. I can't wait to watch. I hope we can keep in touch. Ethan Malik, thanks so much for joining. Thanks for having me. Thanks for being here. Thank you, everybody, for listening. Thank you, Nate Guatney, for handling the audio. LinkedIn for having me as part of your podcast network. And all you listeners, if you enjoyed, please hit five stars on Spotify or podcast. First time here, if you hit subscribe, that would be awesome. Thanks again to Ethan. I've been looking forward to this conversation for a long time and it delivered all the way through.

51:58So really appreciate you being here. All right, we'll see you next time on Big Technology Podcast.