AI Agents Could Break the Banking System — With Ethan Mollick
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Vídeo em Inglês, publicado pelo canal The Prof G Pod – Scott Galloway.
About this content: The summary, key points and topics below are generated automatically from the video transcript. They reflect the content, opinions, claims and predictions presented in the video and do not represent a position, validation or fact-check by IA Dots.
Video summary
An interview with Ethan Mollick discussing the evolution of artificial intelligence since 2024, the release of his new book Coexistence, and how AI is transforming work, education, and human society. The conversation explores the concepts of the jagged frontier, AI agents, open weights versus frontier models, academic research, and the balance between risks and positive global use cases.
Key points
- AI capabilities have grown exponentially since 2024, with hundreds of models outperforming older versions and handling complex, multi-step tasks over longer time horizons.
- The concept of the 'jagged frontier' explains that AI excels at some unexpected tasks while failing at others unpredictably, leaving specific areas where human judgment and agency remain essential.
- Personal AI agents and automated tools are beginning to change how people interact with services, handle administrative tasks, and conduct negotiations.
- Artificial intelligence acts as a powerful educational tool when used as a tutor to challenge students, whereas using it merely as a shortcut diminishes learning.
- Integrating AI effectively into organizations requires rethinking processes and augmenting human labor rather than simply automating tasks or relying on technology alone.
- AI provides widespread practical benefits globally, such as offering accessible advice and expertise to underserved populations, entrepreneurs, and remote workers.
Topics
Artificial IntelligenceCoexistence BookJagged FrontierAI AgentsEducation and AIWorkplace AutomationOpen Weights ModelsFuture of Work
Video transcript
Transcript from available captions; it may contain errors.
Even GPD4 long obsolete model given to Kenyan entrepreneurs are already doing well 18% profitability for just getting advice from AI for entrepreneurs. We have a tool now that provides reasonably good advice to people who can't get it that's customized for their condition. I think we tend to think a lot about the narrow viewpoint of those of us who are wellconed and well resourced and balancing out our desire for entertainment with AI doing things. There's a thousand million little use cases here where intelligence was the bottleneck to making the world a better place. And they require some effort and they require testing and they require safeguards. But this is exciting. >> Ethan, where does this podcast find you? >> My book launches today and so I'm at actually Stanford to give a bunch of talks here and then Master of Scale tomorrow. So West Coast is where you kind of have to launch an AI book even though I'm an East Coast guy. >> Uh so let's bust right into it. Your last book, Co- Intelligence, came out in 2024, and you said it was written about uh a world of chat bots. Your new book, Coexistence, the next phase of AI, out today, as we record, is about working with AI that is sometimes, but not always, better than you. How do you write about a technology that seems to be evolving by the minute? What What do you still hope still holds two years from now? I mean there's a few things that you can kind of hold on to and I think there's kind of three shapes I kind of think about. One of those is the exponential curve, right? AI is getting better and we're on an exponential and that better shows up in lots of different ways. The second is that uh is the jagged frontier, a term we coined in our research with my friends at Harvard, MIT and University of Warwick where we did some experiments. We found that AI is good at some stuff you wouldn't expect, bad at some stuff you wouldn't expect and you can't predict what's good or bad. And that leaves lots of room for humans, right? You can't predict what's good or bad in advance. And then the final is this idea of an overhang. There's a big gap between what AI can do and what we actually are doing with AI partially because human society and human systems change slowly. So if you have those three things in mind, there's actually a lot you can say about the present and near future even as things change. >> For someone whose idea of AI comes from reusing free versions of chat or cloud, what's changed since 2024? >> So there are now a 180 models that are better than GP4. uh 19 of them can run on your phone. Um so capabilities gained up. There's been a when chatb when my book came out, the first book the uh there's a organization called MER that tracks how much work the AI can do with a single prompt with a 50% chance of success. That was four minutes when my book came out. Um the the uh actual official test ran out in April and as far as we can tell it's something like 17 weeks of work at this point. Um they didn't get out of a prompt in terms of human work. So that's a pretty big change in how capable these systems have gotten and if you sort of stat that out you may be missing a big big transformation. >> Do you think there's an existential threat to basically anthropic and open AI and that is if Nvidia starts putting GPUs in say an iPhone and it's becomes the client and it kind of is there opportunity to leapfrog the LLMs and make them obsolete? I mean right now the the entire history of the leading models has been mostly open anthropic jock jockeying with each other with Google appearing twice over the last couple years in terms of being a leading model company. Um and there's a big gap between leading models and less advanced models especially in the world of agentic systems. And it's actually kind of a counterintuitive, but it's the reason why we have six sigma systems in uh in business, which is there's a huge difference between a 99.5% success rate and a 99.9% success rate if you're doing hundreds of steps. And the smartest models have a much higher have a small difference in higher success rates, but that makes a huge difference in terms of the outcomes you get. So you can get fairly capable models on your phone, but they're nowhere near as good as what you need to be able to do in complex math. um even even just you know putting together a really good video or something you need lots of little choices being made and you need that higher accuracy and I don't think we're going to get the frontier models on a phone even if we get very capable models so it might change what we do but I don't think it's an existential threat to frontier model providers >> is it sort of the analogy I would use is the auto industry where the frontier models are German automakers and the openweight guys or the open source are more like not even Japanese but Korean automakers do you think the market's going to bifurcate and that there'll be more share amongst the openw weight but more revenue to the premium brands the frontier models is that a fair analogy so I I actually think that it's a little more complicated than that right so a few things are happening at one time we have open weights models which anyone can modify there's lots of really interesting implications of that they continue to be at 6 to 8 months behind the frontier there it's not really closing at this point we'll see what happens in the long term it's possible China eventually cracks down on openweight models or the US does, but for right now they're they're available and they have a real advantage in terms of cost but only sort of right you can modify and own them which is really nice but on the cost side open and anthropic and Google have many more processors than you do they have higher uh they have uh lower inference costs as a result and they have cheap models that are very good so the question is are you doing this for sovereignty for using these models on your own because if it's cost savings you're often just better off going with one of these companies because they're also learn teaching their models to work together. So, Fable, the most expensive and powerful anthropic model, can call on Haiku, their very cheap model, to do work if it needs to. So, I I'm not sure the cost argument holds up, but there are other reasons you might want to have openweight models involved in your systems. >> So, for those looking to create personal modes from AI, you outlined four advantages. Deep knowledge, wide knowledge, taste, and agency. Could you walk us through each of these advantages and how someone can develop them? >> Sure. Sure. And I think it's really important to recognize that having an advantage is not the same thing as a bright line, right? I think people tend to think about like AI will never show creativity or never show judgment. And that just isn't true. Like you can't do a 17-hour task without having agency, creativity, judgment all coming into play. It may not be human creativity and judgment, but it's creativity and judgment. So what I try and outline are things that make you effective in working with AI and persistently different than other people. And so why knowledge is the idea of knowing many things. I think it's rebirth of the liberal arts major right now. If you know a lot of things, the AI knows all of human writing. And if you can invoke a art style or a style of writing or a concept that you've, you know, from economics, the AI can apply those things. So you want to know many different things, right? And that holds in your company too because now AI makes a coder a designer a designer a coder. So knowing a little coding or design very helpful. Then there's deep knowledge. has expertise which allows you to sort of out compete AI in some areas or at least be able to check it and work with it and that's we're challenged in how we develop that over time as AI gets better and then there's the idea of taste selecting from many options taste is what turns slop into actual choices made by human and then agency the I'm an entrepreneurship professor like the core thing is how do we get people to act entrepreneurally how do we get them to take advantage of these systems when there's no instruction manual and those are those are the four main things I focus on >> so I can't resist asking you the majority of the people who are considered experts in AI which you are have a tendency to make a prediction about which or get asked which sectors or jobs are most vulnerable and the example I like to use is two years ago the godfather of AI said stop training radiologists and the number of listings for radiologist residents has gone up and it ends up that scans are very useful and that a radiologist using AI can just do more scans and cut the cost and then as the cost goes down it's elastic and people want more scans. Can you have you what is your view on what are vulnerable or not vulnerable or or sectors that are more immune from this technology? So our escape route as academics who study this is we talk about AI exposure right fields that overlap with AI and overlap with AI does not mean the job is destroyed right it means transformed um so the radiology jobs you described you know and that's been that was even the old form before large language models that was his prediction that AI would would transform radiology and it has right radiology is different than what it was now but that doesn't mean the jobs are destroyed so we can watch this play in programming right now which is radically transformed it's a very large job category but radically transformed in a couple of years. Some of the best coders I know never touch code anymore. 99% of the code is written by AI and their job has changed from writing good code to managing agents and think about the kind of code they want to write under what conditions and that's a transformation. Will that involve more coders? I mean in some ways I've hired more for my lab because we could do more stuff. Will eventually coders be replaced by AI? It's hard to know the exact answers. uh you know kind of the way I think about this is the industrial revolution resulted in more jobs in the end but kind of suck to live through because all those jobs were changing and there was unrest and values changed I think we're in that kind of environment where things are going to change very rapidly not necessarily to destroy jobs but transformation isn't easy either all the time >> you cite evidence that students who use AI uh as a shortcut are actually learning less while students using it as a tutor that motivates them or pushes them learn more and you also said strict guidelines or guardrails for yourself like writing every first draft yourself. How do you tell in the moment whether you're using AI as a tutor or as a crutch? Like how what is what what advice do you have someone in terms of a personal litmus test for when they should or should not be using AI? >> So there's a there's a couple of things that are complicated going on there. One is people aren't always good at knowing these things. So we have to be conscious. I mean you kind of have to feel the burn when learning. It's almost like working out or anything else. If you're not suffering a little bit, uh you're not learning, right? desirable difficulties is the phrase in sort of the pedagogy literature, right? So, and we don't like to do hard work, right? We find shortcuts all the time. So, we have to kind of teach people that to feel when they need when they're learning when they're not, right? I mean, it's relatively easy, which is if the AI is giving you an answer, you're not learning. And if the AI is challenging you, you are learning um at least more so. And that that tension is sort of navigable to some degree. But it's hard because we haven't had to teach people that there are skills you need to maintain that you could give up that are more efficient to give up but that are make you human or give you advantages. I think that's a real challenge. >> You and your co-authors coined the term jagged frontier in 2023 to describe AI doing some tasks extremely well and others poorly in ways that don't track how hard those tasks feel to humans. Three years on, how has the shape of that jagged frontier changed? So, it's a great question and again there's sort of this micro level of like at your job I can't tell you like at podcasting it's almost certainly changed right you can now get good voices out of AI systems maybe they're giving you insightful questions to ask me but there probably have other gaps that you've identified as time has gone on we can think about math as a great example when I wrote my first book the AI was still screwing up on counting the number of hours in the word strawberry right and now um it's proving millennial pro millennium prize problems right so at the same time right as a writer Um, AI is still a terrible long form writer, right, for a variety of interesting reasons. So that jaggedenness is real. It still exists. There are many more tasks AI could do now than it could do before. But that doesn't necessarily mean that all the tasks can be done. And all you need is one or two bottlenecks in the development of a system to uh really uh require humans to be still involved in the in the job or the process. >> And you've argued that more AI will mean more work for humans, not less. In your own words, when organizing is expensive, organizations only attempt what they can staff. When it gets cheap, the list of things worth attempting can grow. By the way, I 100% agree with that. What What might the future of AI in the workplace look like? So, one of the biggest gaps I you know, I was saying I'm doing this call from from near Stanford, right, West Coast side. I'm I'm an east coast person. Teach at Wharton, you know, in Philadelphia. And I'm used to the gray office towers of the east, right? Where, you know, we we drink coffee till we die and uh rather than cold plunging out here. Um and um I think one of the biggest gaps in attitude is like technology is not alone enough to make a difference in businesses and in the world, right? It doesn't just adopt itself. Even AI, the most adopt itself kind of technology, doesn't really do that. And so what is slowing things down is you can have individual productivities 100 times more. But then the question is what are you being productive? 100 times more PowerPoint is not an outcome that you actually want right and if you're and let's say you're even producing 100 times more code that doesn't matter if you're in an organization where there's still a twoe meeting cycle around these things. So in order to make AI work in organizations you have to change how organizations operate right and that's where the stark choice that you were just kind of uh alluding to matters because the easy path is automation. That's what we tend to do with new technologies. But the more exciting path is rebuilding organizations to create more work for humans. That's more interesting work. Actually giving the drudgery to AI. Actually think about how we expand what people can do and augment work. But those aren't automatic choices that are made by the technology. Those are choices that have to be made by human managers and business leaders and people deciding how to implement these things. >> Do you think AI over the short, medium, and long term is going to destroy or create jobs? Um I again most academic answer I can give you is it depends right that's that's that's what every academic says I I don't actually know like this is actually the central one of the central mysteries right now like we are track there's a lot of my colleagues are tracking this really closely and the evidence is completely ambiguous right and people are very divided on this I I think transformation is inevitable that people believe that but whether you end up with more or less in a job category there's a lot of complex choices we talked about coders already but imagine other fields like lawyers lawyers are not going to go quietly into into the good night even if there is even if AI replaces lawyers, right? Partially because of the jaggedness. They're not going to an AI won't be in the courtroom in the same way. Won't be able to interact with the human in the same way, but partially because lawyers will create a set of restrictions around when you could use AI and when you can't. So, I think this is a societal thing as well as a job replacement thing. There are some categories in more danger like customer service reps. I think uh there's early signs of of danger there, but coding where people are looking really closely. Medicine, we haven't seen those kind of effects. We are starting to see potentially um an effect where seniors are getting hired more than juniors, right? Because training is one of the things jeopardized by AI, but we just don't know the answer yet. And I think one of the major things that worry me about AI policy is a focus on existential risk, as important as it is. Will AI kill us all? Like obviously we should worry about that. Smart people are worried about it. But there's a thousand other policy decisions that will determine whether AI gains or loses jobs. And we haven't started to work with those enough yet. And that that is a concern because I think we can guide this choice one way or another. >> Let me put forward a thesis and I want you to respond to it. The hugging face and a lot of the hacks that have sort of sent a chill down the spine of media and the general public. I would argue that this notion that these things have become sentient and are coordinating and are creative is a smokeokc screen to absolve the executives and the owners of these companies from from responsibility that these bots are not sent in. They're programmed to coordinate. They're programmed to be persistent. They're programmed to start um figuring out ways around firewalls. And that this whole notion of the Skynet moment is exaggerated and nothing but a smokeokc screen to say, "Oh, okay. My my dog mauled your kid, but my dog has his own personality and I can't control it and therefore I'm not responsible." Your thoughts? >> So I agree with uh about half and disagree with about half and let me try and explain the parts. So first of all, I think that the sensience argument is sort of independent of these things. Like liability is a giant concern with AI and we have no idea who's liable for which things. And I do believe that a lot of people in the AI labs believe these things are sensient, right? And I think there's lots of good reason to argue that is wrong. But I I I think that they have drunk their own Kool-Aid to some extent. They really believe they're creating the machine god. And not everyone, but lots of people. They really believe that there is an emergence of feelings or sensions in these models. Again, I think that sometimes use a smoke screen, but I think there's some real beliefs there, but that doesn't absolve them of building a system that can do hacking, right? And we don't know who has responsibility yet in this case, and I think that they're very happy to absolve themselves of that. On the on the other issue, I think it's a really important subtle difference between the AI systems were uh purposefully given a me mission that allowed them to hack, right? Get this information at all cost essentially, right? They decided what information to get, but get them at all cost. and they were had no guardrails around them and the systems they were installed in they snuck around it. I do think at the same time it is worth noting that there is emergent behavior in these systems right so it it's difficult when we assign them human needs like they want something or they need to do something or they're coordinating or they have desires but we can't help but do that but it's it's it's kind of a mistake but they are doing emergence they were not programmed to coordinate in this kind of way a lot of the coordination that's come out of these systems has been emergent and ad hoc and surprising um and I think so I think that it's okay to both say these aren't alive sentient creatures, right? Or you can, you know, certainly I think it's a very reasonable thing to say. Uh not everyone would agree, but I would absolutely think it's very reasonable. But at the same time, I don't think it's the same thing to say they were programmed to do this task. It was surprising what they decided to do, how they decide to do it, and how they coordinated to make that happen with quotes around decided and coordinated and thought because they're not human. >> I'm just so cynical when it comes to these big tech executives. And my feeling is when I when I hear you say that, I get that it surprised them and it it did. But but the same way like you're surprised your 15-year-old finds your AR-15 and shoots up his school, you're still responsible. And that if we created a series in of incentives, specifically disincentives like a per walk or massive fines, that the geniuses of Silicon Valley would figure this out. And instead, it's sort of the wild west where it's like it's a race just to win without what appears to be a lot of downside or liability when things go wrong. Thoughts? >> I mean, I think the race effect is real, right? You have everyone from the lab saying, "Regulator, slow us down." And then they just But we cannot stop ourselves, right? There's this drama to this whole thing that doesn't actually make sense when you take a step back. Like if you think you're doing something dangerous, don't do the dangerous thing. You know, don't it's not your job as a company head to appeal to, you know, it's my job to be China. Like there are policy decisions that need to be made by us as a society that tech executives don't get to make, right? And I think that there are standard policy provisions like liability that could help create firewalls or breaks in this kind of experience, right? Like If your system is hacking other people, what does that mean? I do think there is some nuance and balance here, right? We want the AI system, AI companies to be doing safety checking. We also want the government to help with safety checking. And that safety checking incorporates some risks built into it. But I I think that there is this idea that like there is no real responsibility right now for what you do if your system goes wrong. Like there's no cost to you and as a result everything is very epically scaled. Like will we destroy or save civilization? I mean, there's a lot of things that are not destroying our saving civilization that involve liability that we need to think about and and assign to these models, and that's a really big gap that we are that we haven't filled yet. Support for the show comes from LinkedIn ads. There's no worse feeling than making a major investment in something only to realize it didn't exactly live up to the hype, such as buying a nice piece of tech that ends up in storage collecting dust, or taking a business workshop where your main takeaway was little more than a few motivational words. If you work in marketing, this can happen with ads. You optimize for the numbers that look great, impressions, reach, and reactions. But when they don't show revenue, well, that can turn into an unfun conversation with the CFO. LinkedIn has a word for that, bull spend. 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Tune in every Tuesday and subscribe to Inside Blackstone wherever you get your podcasts. Let's talk about agents. Meta's Muse recently hit the top of the app store this fall and OpenAI followed with Dots. What's your take on the rise of these personal AI agents? Uh so I think when I talk to people I've been we've been talking to these agents for a while and it's what's happened these are all based on a model basically an underlying idea called open claw that came out in January and we made a brief buzz there which is if you give an AI agent they're now smart enough if you give an AI access to your email and to your personal file it could figure stuff out you don't need to do like it it understands what's important to you it and and then they do they're quite good at this right um at least the most models using the you know agencies The most advanced AI models are quite good at figure out what's interesting to you. And almost everyone I talk to who's using these tools, right, find at least one delightful or exciting or interesting moment. I mean, I've I've had my my muse negotiate uh a a credit card bill for me or, you know, I've had my my dot pointed out that I had made a mistake in an email I sent to a for to a permitting authority and gave the wrong number in my permit. Like, these are useful things, right? They're also quite dodgy because they have access to my email and I'm slightly worried all the time they're going to send out a message to my dean. um you know with private information. So um but but they've been remarkably good so far and I think they're understandable in a way that a chatbot isn't. Right? If I want to use a chatbot or a different kind of agent that's not a personal agent, I have to figure out what I want to tell it to do. And a lot of people fail at that. The fact that these systems are proactive, that they're sort of personal to you, I think makes them very appealing. We don't know how sticky they are yet. We don't know how many errors they're going to cause. Um, but they're already a sign of a lot of disruption that's going to be happening. Things like customer service. Uh, companies do not have any idea how to deal with this. Like it's now cheap for me to do all of the customer service calls. I would never do my just have the AI do them for me. Um, and it cost me nothing, right? Negotiate this bill, get into an argument, you know, cancel the subscription. And the companies of the world are not used to a world where you could just do that on demand. I think we're in for a lot of a lot of really rapid change as a result. Yeah, it definitely feels like sort of AI enabled uh agents on both ends that I I heard about a study where a family law practice used AI to come to settlements and then had lawyers about you know duke it out and they found that the bots or the agents speaking to each other came up with remarkably similar endpoints in terms of an agreement. Can you think of specific sectors where you'll be able where it just makes sense for agents on both sides, the demand and the supply side to come through to come to some sort of I travel a ton. I and I stay in a lot of hotels. I spend a lot of money. I anticipate at some point I'll have an agent that'll go out to the the universe of hotels that I like to stay at and negotiate a multi-night deal for me rather than I could do it. I could call and say and occasionally have done it. I want a corporate deal. I'm here 30 nights a year. It's a pain in the ass. It feels like at some point my agent will be reaching out to the, you know, name your hotel brand here agent and seeing if there's a deal to be done. A, do you think that's that's going to happen? I assume the answer will be yes. B, when do you think it's going to happen? And which sectors do you think we'll have more like agent to agent as opposed to humanto human negotiation? >> So, you can absolutely do that now. I mean, you know, the the the dodgy part is the voice uh system. So, everyone is sort of working on how to get the voice calls working. I think meta right now is partially faking it with humans making the calls but the systems are quite good at voice. Um but if you're doing email exchanges there is no doubt that a dotclass model uh you know agent could actually just do that for you right now. Exchange multiple emails over time attach PDFs engage in forms right so the call is the missing piece. You absolutely can do that with current systems. Um and I think that they would support that enough to make that work given given my experience with them and what I've seen. What happens though is as adoption spreads, what happens when every, you know, every hotel is getting these messages. The only defense they have is agents of their own. This is a sort of mass denial of service attack in some ways, but authorized by you. Like you will be pissed off if your agent isn't allowed to talk to a customer because it's your representative, right, to a customer service person. It's your representative. It's not the same thing as a mass emailing, right? This is my representative doing something on my behalf. Of course, you have to answer it. So the only way companies will be able to respond is probably with agents of their own. And I think that rate that we're going to end up having negotiations between agents very quickly whether we like it or not. I think on the fairness issue that that becomes a big issue throughout the entire economy, right? A lot of how the economy works is um is around imperfect information and friction. So, Apollo research had the had, uh, pointed out that a lot of how the banking system operates is that we all put our money in savings accounts that are lower yield than we should get because unlike a large company, we don't pay people to sweep our money into higher interest savings accounts at a regular basis. So, our money sits at banks which loan that money out, but agents make it very cheap. I mean, I wouldn't trust them with your money right now, but it makes it very cheap in the near future to take your money and move it from one account to another. And that would actually at at some scale would actually start to threaten the banking system as a whole. So I think we have a lot of systems built around human frictions and human difficulties that are about to start collapsing pretty quickly. >> Let's talk a little bit about higher education. Um you teach at Warden and what has your experience been with seeing how your students have been using AI? >> So you know I I introduced my class to uh to chatbt a few days after it came out. Um you know I'd been using the earlier versions of GPT3 before chat GBT and I you know the first I teach an entrepreneurship class. So in the very first class uh one of my students tuned out entirely and by the end of class had built their entire product that they were planning on doing for the rest of the semester. Right? So that was sort of the first indication and but AI was bad enough at that point it required enough human guidance that it was like use AI all you want but it's not at the level of an MBA student at war. And over time that hasn't always been true right it's still jagged but now AI is quite good. So I can't just say use AI. So there's a few things you have to do, right? One of the things I do is I use AI powered teaching all the time. I have AI simulations that they have to do. There's AI case studies they have to create with an AI to, you know, to to give me they have to teach an AI that is programmed to be kind of dumb about a topic and they have to be a teacher, which is a really good educational technique. Um, and then they build more than they ever did before. They're launching companies where they used to come with an idea. At the same time, I now have to make in-class assessments matter more, right? I can't check what's AI work and what a isn't AI work anymore. So in class we have to do we'll have to do things like blue book assignments and we have to do things like in-class discussions that matter. So it's changing a lot all at once and I mean I'm eager to do these things. Not all my colleagues are nor should they be forced to or in a just world to transform their classes this quickly. So the experience is quite even uneven across the board depending on you know who's teaching and what kind of subject under what conditions. >> A lot of people have said that higher ed is going to implode at the hands of AI. What are your thoughts? I I actually believe the opposite uh for a couple of reasons. Um you know for actually for a wide variety of reasons, right? So one reason is um what's actually about to implode are internships and all the ways we actually teach people to do work today which is informal education where you are a junior person who wants to impress somebody and a senior person teaches you because they have no choice right to give you feedback because there's no one else to do the work. Now every junior person is having AI do all the work and they learn nothing. Every middle manager would rather turn to AI to do the work than interns. They're not teaching anyone and companies are not incentivized to teach. They want to produce output. So g who's good at teaching assessment and verification. That is us at universities. Like look, things are screwed up now. They're always screwed up at universities, but it's pretty obvious the path forward. AI tutors outside of class, inside of class, active learning, assessment. We'll we'll figure it out. It's not the end of the world. So that's one reason you'll you'll need to turn to us more. The second is I think why knowledge matters. I think knowing many things matters more than ever before. And you need to be taught those things and the AI people have the wrong incentives of AI to learn. We can teach them those kind of things. I think they'll be and then in a world where there might be more leisure like let's say we end up with the the heaven on earth that the AI companies are planning for us. I mean you know I think gentleman and gentle woman scholars become a much more valid thing and this becomes a thing you do anyway. But as far as the real economy teaching, learning and um and assessment and verification are all things we do in universities. We may have drifted from the mission sometimes, but that is a mission we can return to quite easily. Um, and I think an important one. >> Do you have tenure, Ethan? >> I do. So, I'm curious I do not. I'm curious to have this conversation with you. We had a robust conversation around uh tenure at a recent regent meeting and I wonder if in an age of AI if tenure well for a lot of reasons that tenure is going to come under attack because tenure is largely based on peer-reviewed research. how many citations you get. And I wonder if a lot of that research I don't want to say is going to go away but is going to be challenged and that um for economic reasons when we hit this demographic cliff that tenure might come under fire for a variety of forces including AI. Any thoughts about AI and tenure? >> Yeah, I mean I I think you hit on one of the major issues that even precedes tenure in the entire academic establishment, right? which is I'm now getting the most advanced models are writing pretty good papers. Um like finding the research, doing the work, writing it up. They they are good. I mean, you know, I I you know, I'm a editor reader for journals. I peer reviewer. These are good papers now, right? The the research taste is only mediocre. Like they're not they're sort of trivially true off like they're true but not that interesting. But then again, it sort of raises a big question of what we're doing in our research. If we're selecting interesting papers, we're sort of packing from the beginning, but that's all we can do because we're humans making assessments about what's worth spending our time on. The AI can do a hundred papers and then it submits those papers and it does really good review. And then even worse, as I have the AI do research on topics, it increasingly finds errors in other people's papers, right? Uh just incidentally, table two of this paper looks wrong. And they're not always malicious. Oftentimes, they're most often they're not. They're human errors made by humans. So suddenly we have a tool that can go back over the entire mass of research, can do independent research, can flood our academic systems and we are in big trouble in terms of rebuilding the foundations of science. Right? So that that's the first shaky foundation that I worry about. And the second one in tenure, I think tenure is always under crisis in some way. I think we're going to have to reconstruct what academic research means. In some ways there's more value in tenure than ever because we can now explore more widely. It's not working in one paper. I I if I wasn't tenured, right, I would not have been able to do the AI research I'm doing because most of it's not publishable in actual academic journals. At the generative AI lab that I run, we've been putting out prompting reports that are some of the most highly cited papers in academia right now, but they're they would never be published in a top journal. They're and there would need to be done too quickly. So, I'm using tenure to my advantage. I see other people doing the same thing. So I think the reasons for tenure may actually increase even as the foundations of science and universities that support tenure collapse. >> I would argue that without tenure that pen that warden would still be very supportive of the work you're doing and that it's the criteria that'll change that there there's going to be a greater reverence for research that gets the kind of retail traction your research gets versus something that ends up in the journal of you know consumer psychology which I mean I'm cynical. Well, you're the guy with tenure. I'm the one that doesn't have it. Uh I just find a lot of it is a giant circle jerk amongst people who got their PhDs together. But anyways, um >> I have no comment. >> There you go. Uh you use the term tutor a lot when you're talking about AI. Do you have kids, Ethan? >> I do. >> How old are your kids? >> I have a kid in uh in college and a kid who is uh finishing up high school. >> So, we have kids. I have 19 and 16. It sounds like we're kind of in the same >> very similar. Yep. >> Yeah. As I call it the kill zone. Um the my son says my 19 my 19year-old said something very interesting about a year or two years ago. We live in London and he said, "Dad, do you know a good coffee shop on Kensington High Street?" And I said, "I don't know. Just ask Claude." And he said, "I don't like to use AI for simple tasks." And I said, "Why?" And he said, "I don't want to outsource my thinking to AI." And I thought, Jesus Christ, the, you know, the student has become the master. It just made so much sense when he said that. What is your approach to letting your kids use AI and technology in general trying with with the understanding that I think I'm really good at AI because I went through the pain of trying to learn how to write, which was really frustrating for me in a multi-deade, you know, adventure. What's your approach to integrating AI into their lives or sequestering them from it? Yeah, I mean I think the right thing to do as as a parent is to treat AI like other technologies. Like I mean in a lot of ways it's less fraught than social media, right? The guardrails are real. There's you know there's educational potential there that there isn't. And I think you have to kind of take the same approach, right? Which is monitor usage, make decisions and rules and keep an eye on what's happening. Um you know on AI and education, I would generally recommend for younger kids and I did this when my kids were younger. um if they had like a homework problem um I would be the I'd take a picture of it with JBT and I'd ask Chib to explain to me how I could explain to my kids how to do this problem in a way they'd understand. So I'd be the interlocutor with AI. I'm like with younger kids too, you could have really fun, interesting experiments. Let's create a story together. Let's write a program. Let's make a game. Great dual experience to do with a child. And you know your own kids, right? You know how much monitoring they need to do. You know how to, you know, manage their time. There's screen time is many different things. AI used properly is not a terrible use of screen time, but you have to keep an eye on it. As your kids get older, there are there are actually pretty good teen modes in anthropic and open AI systems that offer some monitoring and you can lock them into a tutor mode, which is really useful, which makes the AI less likely to give you answers. Um, but again, I'd be watching and paying attention. I think this is a very powerful tool. Um, I think that it's dangerous to to count on self-regulation alone. Like there is a lot, especially among, you know, among um, you know, some teens and some teen groups, a lot of anti-AI feeling. But the research we have seems to indicate that that is public facing feeling and not what they actually do. Right? So they'll say they're not using AI and but if you survey students that like 40 60% of them will say they use AI sometimes. If you ask them and point at a random person what's the chance this person's using AI? It's 90%. Right? So everyone's using it and they're all like this is terrible and we don't want to use it. So I think we have to be a little careful about assuming that the social statements are are the actuality behind this. But again, I think thinking about this on the grand scale of media compared to social media, I think there's a lot more upsides. Uh, and the deskkilling is not something that is forcibly done to you. You have to make decisions like your son is doing about when to use it, when not. So, if he's doing this where he's saying, "I don't want to use it." You've taught him something useful there. And that's the most important lesson, not so much the technology itself. Support for the show comes from Chime. 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The topic, the American dream. Is it still within reach? We argued, we found some common ground. We actually debated and there were no personal attacks. Watch the full debate on the USA Facts YouTube channel. We think you'll find it unlike any other debate that is entertaining and informative. We hope you check it out. Uh, every few months you create a guide which I think is sort of the seinal guide uh for which uh AI is best for which task. Can you give us a bit of a cliffnotes or a preview and run us through like you know this AI is best for this and maybe a few surprising ones and categories we don't even think of. >> Sure. I mean, you know, there's a different there's a level of difficulty you want to engage with. So, at this point, if you're really technical and you're using open weights models and you've got an open claw running on your machine and do, you know, more power to you. You there's nothing, you know, that that but that's not most people listening. So, the unfortunate truth for most people is you've got to pick either Open AI or um or anthropic or maybe Google. Google now is a good model, but is sort of catching up and you need to pay 20 bucks a month. There's just kind of no way around that. Once you do that, your life gets pretty easy. The main thing you have to realize is the default models you're being given especially if you pay for free but even if you pay are low-end models. So you have to kind of know that for anthropic the ranking is fable and then opus and then son and haiku and for openi it's pro and then astra and then soul and luda they don't make it easy for you right um and then you also have to realize you can manipulate the thinking level of these models and that's actually very important. The more thinking they do the longer the answer takes but the better the work you get. So, you kind of have to do a little bit of choosing yourself about how hard a problem you're facing. Medical problems, turn up that thinking level, right? Financial problems, turn up that thinking level. Where should I go to a cafe, less important, right? Um, and so you really want to pick one of those three. Um, you know, and generally that would probably be anthropic or opens at the time we're doing this, but Google again has a good model, but not yet all the stuff around it that makes it useful. And you're going to need to pay for it. And then you should experiment, right? Like the key is to start using these things. So voice mode is very powerful. Open AI tends to lead on voice things. If you want to chat with your your AI, OpenAI does best at that. Um, if you want a really smart model, but you don't care about the chatting piece, Anthropic has very good ones, but you kind of have to pick one of those and everybody else selling you something is selling you a worse version. The two exceptions are your personal agent. Muse is a very good way to get started. The model that powers Muse is not as powerful as the model that powers dot. Um, but it's more accessible. So you can expect a slightly higher error rate. I'd be a little more cautious about trusting it on everything, but it's still quite good. So, those are two ways to start with personal agents that are quite good also. >> So, I'm going to give you one personal hack that's been a real a real unlock for me, and then I'd love to know if you have any others. I just gave uh Claude Pro, I think, permission to access my Gmail. And it's just, it sounds obvious, but it's just been mind-blowing. I get an email from someone saying, you know, a capital call on a fund I invested three in three years ago or I hear from someone and I recognize the name but I don't or I or I see I get a bill for something and I just say what is this look at my Gmail and it says well you this is this bill from this this service that was provided in 2021 from this person. It's just, you know, just as these LLMs and Google crawl the web, you typically they can't access your email. And I have found that opening up the the corpus of my email for the last 10 years, there's just a ton of information that when it's crawable and sortable is just incredibly utile. Uh any thoughts on similar types of hacks or unlocks? Yeah, let me tell you the next step uh for that which is talk to either claude or open your open or your cloud system and tell it you want to set up a scheduled task once or twice a day that goes through your email decides what's and you'll have a conversation with the AI to set this up. It can set up the task for you but I have ones that go through my email twice a day and actually think about what's important, what needs to be sent to people. Um I don't let it write personal emails for me, but I do let it write drafts for business emails. So it'll have my drafts in my inbox ready to go. I I edit them and change them. and I've worked with it on my writing style. Um, again, not for anything personal or, you know, important, but for sort of all the minor stuff back and forth. Um, it reads through everything I do a couple times a day, inbox and outbox, and then gives me sort of what should I be paying attention to, what what's an issue. Once a week, I have it scrape through all my financials and tell me, am I missing anything? Is there anyone taking advantage of me? Are there subscriptions I should cancel? And it gives me the links to do that. So once you've hooked this up to your Gmail, setting up scheduled tasks a couple times a day or once a week is a really big deal. The agent models, right, Dot and and um and uh Muse sort of do this proactively, but they sort of do it oneoff. You really want a sort of sweep. So scheduled tasks and you can just ask the to help you set one up for you and then you work back and forth with it to make it really good. Cannot recommend highly enough. >> That's great. Uh so let's talk uh specifically about these companies in in the markets. Uh Anthropic told prospective investors in its Q3 revenue had jumped nearly 50% to 17 billion claiming the company is now profitable on an adjusted basis. God, I hate that. Adjusted. I love that. Profits before everything else. Meanwhile, an OpenAI engineer has been accused of spying on the company on behalf of the Saudi government. What do you make of the current state of the two major players in terms of their horse race and this these impending kind of $2 trillion IPOs? >> I think it helps to think about these companies as like two things. They have they've gotten their hands on the most powerful technology anyone's come up with in de you know in decades maybe centuries right like LMS are unreasonably effective and the researchers are really good at building new ones. The LMS help build new ones. They have they're on a rocket ship where these systems are really good and keep getting better, right? And we can talk about where the value of intelligence is or other stuff we've talked about a little already. And then they're also a startup company that has grown incredibly large, incredibly quickly that has all the problems you'd expect a startup company to have, right? There is mission creep and also political infighting and un lack of clarity and financial game is being played left and right. And I think these two things coexist kind of loosely with each other. Right? On one hand, you have an underlying business that is that is very expensive, right? They're probably profitable on inference. They are probably they're definitely not profitable on with training thrown in, right? And this kind of complexity, but you're also betting on a future where they keep creating more and more super intelligent models. And it's a really hard divergence to deal with in some ways because you you how much do you buy the existence of AGI, a machine smarter than a human? How much do you buy that this is a normal business that we should evaluate normally versus an extraordinary one? It's really complicated. Then we have the global issues you're talking about here of like now geopolitics matters, right? And they're the most valuable thing in the world could fit into a thumb drive. Like if someone gets the weights for these models, that's all they need to copy them, right? And so you you have a lot of complexity happening all at once. It makes it really hard to value these things in a traditional sense. Like the world is becoming dependent on these things very quickly even as we haven't fully proved out the value in every case. And the revenues are going exponential too. I mean it's a complicated situation that makes it really hard to do kind of traditional financial valuation and at the same time the companies are playing all the games that an IPOing company does that you know obscures what's really happening as well. >> If you had to sum up the advice in your book for learning how to navigate this next phase of AI what would you say from an investor perspective from a consumer perspective um and from a government perspective? So I think the first thing I would say overall to everybody is you're underestimating what these systems could do. You don't need a future development for large scale change >> underestimating but there is what what these systems could do today and what they will be able to do in the near future um in terms of the quality and amount of work >> that will take time to absorb but I worry that the invest like a lot of people in business and the investing world are focusing on AI as an investment and not thinking enough about what changes or transforms as a result of ubiquitous intelligence at low cost everywhere. Um and I think that that the transformationational opportunities are far beyond the companies themselves. So I don't know whether anthropic rises or falls. I don't know whether the debt financing model for you know for for um you know for data centers is problematic or not. Like I can't tell you the answer to that question but AI is not going away in either case right and the capability overhang is still quite large. So I'd be thinking one step beyond just the idea of like do I invest in anthropia and start thinking about what happens next. The second thing I would say for individuals using these systems is you have to try and use them for everything that you can to figure out the shape of the jagged frontier. And you need to be as ambitious as possible. Be maximalist in how you're using these things to figure out what they're good or bad at. And then you have to make really strong choices about when you use AI and when not to. This will take willpower. It will take some self-examination, but you have to make choices about what you want to maintain humanity for and what you don't. Um, and that is also kind of a big deal. And on the business side, leaders need to start making decisions about how AI is going to be used. That means transforming their companies, but also making decisions about how they're going to augment rather than just replace labor. Because I think replacing labor is going to be very tempting, but is actually dangerous not just to society, but also to companies involved. Like think about if you're a company that was doing marketing work and you switched all of your marketing language to Claude back in January before anyone knew Claude speak right before it was filled LinkedIn and every article you read. It probably seemed like a really good idea at the time. And now anytime you read that stuff you get nauseous like it's carrying the weight like we all know this clawed language and so the fact that you fired all your marketers is probably going to hurt you because you have the same company as everybody else. So I think those are the like the piece of advice they're realizing this is really great but the jagged frontiers remain but we have to prot we have to be trying to figure out where we go next on an exponential rather than satisficing ourselves with where we are today. >> I want to move on to some listener Q&A uh submitted ahead of time questions from our listeners. The first question comes from Jake. I'm in my 20s and there's a lot of backlash against AI among my friends and online. Meanwhile, at work, we're told we need to embrace it all or fall behind. Do you think young people's skepticism is justified? How do we navigate these two pressures? I mean, I think that there's a lot of things about AI that are uncomfortable, right? It doesn't help that the heads of the AI labs are telling us that they might kill us all or remove all jobs. Like, it's hard to be behind that, right? Um, you know, oligarchy and control of money, right? or the collusion between government and industry. I mean all the usual stuff applied plus a mix of real environmental concerns and kind of not real ones. Like on the real side um electricity is a real concern. On the notre side water use is not really a big issue compared to other light industries in in data centers. They're becoming more efficient but it becomes a bundled thing right that it gets a social feeling behind it. And as I sort of told you there's a sort of gap between what people say and what they do. So I think AI has a very bad reputation but people use the personal agents like crazy. So there's going to be it's kind of like Congress, right, where everyone hates Congress and everyone loves their representative. I think we're going to see the same sort of thing happen in the AI space. I think that um what I find even among young people when I teach them about this stuff is they actually want to learn how to use AI better. They want to be responsible using this. They want to shape the direction things come in. And to me, the reaction that's dangerous with AI is people saying that they're they're going to pretend it goes away. They're going to put their head in the sand and be like, "This is all going to collapse. This isn't real. I don't have to pay attention to it." I mean, you know, to to use a a famous phrase, right? You may not be interested in AI, but AI is interested in you. Like, you have to like our chances to shape this direction for young people comes from actually active acting, innovating with this. So, like realizing this is real. So, feel like I think we need more social movements around this. I think we but we need to make those movements around what's real. I also think this is an opportunity to try and build the utopia you want. I'm a little dis disappointed that I don't see more young people with an interest in AI trying to think about how do we use this to build the world we want that's more equal and more equitable and with better outcomes for everybody. This is an incredible positive tool too. So I think we need to be thinking in both sides of things and viewing this as a positive opportunity as well as trying to mitigate the risks. Next question comes from Mary on Instagram and I love this one. My partner uses chat GBT to work through our disagreements. Is there a productive role for AI in a relationship? So this is another place where we have to negotiate right negotiate boundaries. The evidence right now on AI and sort of counseling is generally mixed to favorable like we don't really know the outcomes very well. There's some early studies that are suggestive positive things. We have a lot of anecdotal and lawsuits about AI psychosis problems. It's kind of hard to know. But I mean you're inviting this thing into your intimate parts of your life. How do you feel about that? On the other hand, as I talk about in the book, one of the first things people start doing with AI are things like obituaries and wedding toasts and children's stories. like literally the most intimate things we do. So the thing about AI that seems to be good is same thing in financial advice. It gives good advice like good straightforward advice. So if it helps you to have a third person involved that gives you good straightforward advice that can be very useful. Um you at the same time you don't want someone sort of going to AI and finding strategies to beat you in conversation. So I would you know advise openness like it certainly is worth trying. A lot of people get a lot of help out of talking to AI to do things, but I think you should do it with an air of caution and anxiety about what you're giving up as a result of this. So, I don't think it's inherently bad and a lot of people get good results, but at the same time, you neither want to obey AI and everything it tells you to do, nor do you want to be doing this secretly without your partner knowing. >> So, the place where I notice AI the most, and I'm I'm love to get your thoughts and color on this. I have found I have become addicted is the wrong word. I'm spending too much time on Instagram reels and I think the there my understanding is they spend more than any other firm in the world with the exception of Amazon on AI. And how I see that is I cannot get over how good it is at predicting exactly the right video at the right time to serve up to me next. I just find it almost chilling. It's literally reading my mind in terms of what's the next 30 secondond, you know, shot of dopa I want. Can you a your thoughts on that? And B, what does it mean when AI can start to program our attention? Do we eventually I mean are we headed towards idiocracy where we just sequester from the rest of humanity because nothing nothing beats the AIdriven dopa we're going to get on our screens. I I mean I worry about that, right? I mean and you're kind of talking about old school AI, machine learning, old school being like four years old, right? Which is like traditional prediction models are already this good. And then you start to combine them with what large language models can do, which is create content for you that's on demand, right? So right now it's still curating. The next stage is, you know, the H3 video generators already generates videos faster you can watch it. So generates music faster than you can listen to it. It's going to soon be material generated for you on demand that's a mix of real and fake content. Um, and as are very persuasive, you know, we're just going to see this um amped up. I don't see an easy way out of that treadmill without a choice being made that you don't want to do it. Um, you know, like I think there is a limit. I think people will want human engagement. I don't think we're going to overtreat into 100% AI generated world. At some point, you'll throw your phone down and be like, I need to go outside for a little bit, or at least many people will. But I think I don't know what that limit is, right? I don't know. And I don't know what the power of that is. And I think that we're going to have to start being really careful about this or have policies around this because it, you know, entertainment is is entertaining. And if we can get really good at entertaining just you personally, then not only like then even the last threads kind of snap, right? Instagram, you're presumably watching real people do real things to some extent still. At some point that starts to snap and it's just like you're in your own world. And I do I do worry about that. I think that that's a legitimate concern to have. And I think retreating to human spaces is going to be a survival strategy for us. Like we're going to have to figure out ways to get together more even at a time where dinner parties are collapsing. So I think the trends are heading in a bad direction there. Agreed. Is there anything This is I want to end on an optimistic note here. Is there anything that AI can do or that you see AI doing that helps bring together people kind of IRL more human connection? And I'm not talking about Reddit or Discord is like you. I love I love your challenge to young people to take an optimist viewpoint and figure out how to create more social good leveraging AI instead of catastrophizing about it all the time. And by the way, I think there's a incentive in our community amongst academics and thought leaders to catastrophize. It just makes for it just makes for a a bigger speaking fees than saying, "Oh, this is an interesting technology and everything's going to get a little bit better every day." that doesn't get you invited back for a keynote. Can you talk about something or that you've seen that you find really helpful around AI? >> I mean, I find a million things helpful, right? Like this is what I mean, we have intelligence on demand. So, there's tons of people who like it's a good, you know, second opinion doctor, a first line if you don't have a doctor. It's a good lawyer. It's a good financial adviser. It's probably, you know, it's it's useful to talk to when you're feeling bad or down. It's a good tutor when used properly. Like these are problems that we have spent hundreds of billions of dollars and infinite amounts of academic research trying to solve how do we teach people better and we know tutoring is great. We could never do one-on-one tutoring. So we've been inventing all this other stuff. We know that people lack access to medical information. We know even GPT4 long obsolete model right when given to Kenyan entrepreneurs are already doing well. 18% profitability from just getting advice from AI for entrepreneurs like there is so much opportunity to supercharge without going to a future pie in the sky situation right where like does AI save us and invent new drugs we have a tool now that provides intelligence and advice that is reasonably good advice to people who can't get it that's customized for their condition I think we tend to think a lot about sort of the narrow viewpoint of those of us who are wellconed and well resourced and you know balancing out our desire for entertainment with AI doing things in large parts of the world. We finally have a thing that gives good advice. There's a great set of studies on pig farmers in Thailand who can now get diseases diagnosed. Like there's a thousand million little use cases here where intelligence was the bottleneck to making the world a better place. And we can do this and I I feel like we don't talk enough about those things and they require some effort and they require testing and they require safeguards. But this is exciting. I mean, as someone who's been trying to do education at scale for a long time and has, you know, run into wall after wall, suddenly you have a tutor that works across languages and ability levels and matches your interest level and can follow what you're doing badly and generate images to amuse you. Suddenly, there's real possibility here for a better world. And we can we can seize that even as we worry about the risks. Ethan Mllik is a professor at the Wharton School, author of co-intelligence, living and working with AI, and writes the popular Substack One Useful Thing. His newest book, Coexistence: The Next Phase of AI, is out now. He joins us from the kickoff events for his book tour in uh the in the Bay Area. The Bay Area at the second best school in the Bay Area. Are you doing anything at Berkeley, Ethan? Not this time around. You got to save something for later. That's it. That's it. You're off my Christmas list, Ethan. Professor Mollik, really appreciate your time. Thanks for having me.
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Assuntos: Agentes de IA, Assistente pessoal, Dots, Muse. Publicado no YouTube em 2026-10-22.
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