AGI Is Here: AI Legend Peter Norvig on Why it Doesn't Matter Anymore

Are we chasing the wrong goal with Artificial General Intelligence, and missing the breakthroughs that matter now On this episode of Digital Disruption, we’re joined by former research director at Google and AI legend, Peter Norvig. Peter is an American computer scientist and a Distinguished Education Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). He is also a researcher at Google, where he previously served as Director of Research and led the company’s core search algorithms group. Before joining Google, Norvig headed NASA Ames Research Center’s Computational Sciences Division, where he served as NASA’s senior computer scientist and received the NASA Exceptional Achievement Award in 2001. He is best known as the co-author, alongside Stuart J. Russell, of Artificial Intelligence: A Modern Approach — the world’s most widely used textbook in the field of artificial intelligence. Peter sits down with Geoff to separate facts from fiction about where AI is really headed. He explains why the hype around Artificial General Intelligence (AGI) misses the point, how today’s models are already “general,” and what truly matters most: making AI safer, more reliable, and human-centered. He discusses the rapid evolution of generative models, the risks of misinformation, AI safety, open-source regulation, and the balance between democratizing AI and containing powerful systems. This conversation explores the impact of AI on jobs, education, cybersecurity, and global inequality, and how organizations can adapt, not by chasing hype, but by aligning AI to business and societal goals. If you want to understand where AI actually stands, beyond the headlines, this is the conversation you need to hear. In this episode: 00:00 Intro 01:00 How AI evolved since Artificial Intelligence: A Modern Approach 03:00 Is AGI already here? Norvig’s take on general intelligence 06:00 The surprising progress in large language models 08:00 Evolution vs. revolution 10:00 Making AI safer and more reliable 12:00 Lessons from social media and unintended consequences 15:00 The real AI risks: misinformation and misuse 18:00 Inside Stanford’s Human-Centered AI Institute 20:00 Regulation, policy, and the role of government 22:00 Why AI may need an Underwriters Laboratory moment 24:00 Will there be one “winner” in the AI race? 26:00 The open-source dilemma: freedom vs. safety 28:00 Can AI improve cybersecurity more than it harms it? 30:00 “Teach Yourself Programming in 10 Years” in the AI age 33:00 The speed paradox: learning vs. automation 36:00 How AI might (finally) change productivity 38:00 Global economics, China, and leapfrog technologies 42:00 The job market: faster disruption and inequality 45:00 The social safety net and future of full-time work 48:00 Winners, losers, and redistributing value in the AI era 50:00 How CEOs should really approach AI strategy 52:00 Why hiring a “PhD in AI” isn’t the answer 54:00 The democratization of AI for small businesses 56:00 The future of IT and enterprise functions 57:00 Advice for staying relevant as a technologist 59:00 A realistic optimism for AI’s future Connect with Peter: LinkedIn: https://www.linkedin.com/in/pnorvig/ Visit our website: https://www.infotech.com/?utm_source=youtube&utm_medium=social&utm_campaign=podcastFollow us on YouTube: https://www.youtube.com/@InfoTechRG

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Geoff Nielson?

  Hey, everyone. I'm super excited to be sitting down with AI legend Peter Norvig. Peter is a former research director at Google, an AI fellow at Stanford, and the author of the most important text on AI of the past 30 years, "Artificial Intelligence: A Modern Approach." What's so special about Peter is that he's sat at the forefront of AI research and teaching for over three decades. So he hasn't just pushed the technology forward, but educated an entire generation of AI leaders. I want to ask him how close we are to unlocking the true potential of AI, what he's most worried about, and what we need to do to build the future we want. Let's find out.

• 0:40Geoff Nielson?

  Peter, you're the author of the preeminent, if I can call it that, textbook in the AI space, "Artificial Intelligence: A Modern Approach", which has recently turned 30. And so this is an area that you've been thinking about since at least 1995, I'm sure a lot longer. As you think about where the technology was then, where it is today, one of the things I'm hearing a lot about these days is a lot of hype around, "Oh, we're only one to two years out from artificial intelligence reaching its final form or being AGI and having this full potential." Do you believe that? How far has this technology come since you wrote the first edition of this book? And how close are we to the modern version

• 1:25Geoff Nielson?

  actually achieving what is the complete promise of this technology?

• 1:31Speaker 01

  Yeah. So we have seen amazing progress in the last couple of years. I do think it's ironic that 30 years ago, we titled this book "A Modern Approach", and we kept the same title. So I don't know how it can be modern, both 30 years ago and today. And it does seem like textbooks seem obsolete now because they come out on a cycle of several years, and AI is advancing on the cycle of several weeks. And so it is exciting what's been happening the last couple of years. I think unanticipated by most, certainly unanticipated by me.

• 2:09Speaker 01

  And just this idea that scaling up in data and processing power and with a few very clever ideas for algorithms made such a difference. So I think that's really different. In terms of AGI, I think, you know, I don't really like the term. I think there's, there's no clear definition of it. Everybody has a different idea of what it was, what it is. Depending on what it is, achieving it will vary by five, six, seven orders of magnitude.

• 2:46Speaker 01

  And I guess I feel like there's not going to be a moment when we say AGI is here. I don't believe in sort of this hard takeoff idea. I think it'll get better and we'll just get used to it. And I think past technologies have been like that. Right? So if we had all of a sudden gone from the days when if you wanted to learn something, you had to drive to the library, to the days where you have a machine in your pocket that gives you access to all the world's information. If that had happened in one day, people would said, oh, this is an incredible singularity in transformation. But it happened gradually and we just got used to it. And so I think it'll be the same with AI. It'll get better and better.

• 3:31Speaker 01

  There won't be one point when we say this is the transition. It'll just do more and more. Now, Blaise Agaray Arca and I wrote this article a year or so ago in which we said AGI is already here. What we meant by that was not that the machines we have now are perfect. They're certainly flawed in many, many, many ways. But if you take the G seriously, we made a transition in, say, 2022 or so of going from writing programs that were specific.

• 4:09Speaker 01

  A program to play Go, a program to recognize images and so on, two programs that are general. So ChatGPT and the like can do lots of things that the inventors never realized. And and we liken that to the invention of the computer, you know, maybe going back, say, to the Eniac in in nineteen forty five or or von Neumann's Maniac a couple of years later, where they were a hundred percent general. Now, they're terrible computers by today's standards. They're big and clunky and slow and have no memory and slow processing speed. But if you have a a conditional statement, a branching statement and a sequential statement,

• 4:54Speaker 01

  and you can read and write memory, then you're a hundred percent general. You're as general as a Turing machine. You can't get more general than that. And so in that sense, we now have programs that are general. We write them. They can do things we didn't think of before.

• 5:10Speaker 01

  So that's general. They're imperfect and they'll get better and and we'll play with that technology. But I don't see having AGI as the focus as being that helpful right now. I'd rather focus on how can we make them better? How can we make them more reliable? How can we make them safer? What else can they do?

• 5:31Geoff Nielson?

  I I appreciate that that distinction. And so with that in mind, as we look at, you know, generative AI and tools like, you know, L L Ms, what's your reaction to that? You know, what's what's your reaction to you use the word imperfect and and these sort of, you know, imperfect chatbots? And maybe that's something that will never go away and something that we have to get used to. But it's it sounds like you're leaning toward acknowledging that they are

• 5:56Geoff Nielson?

  AGI in the way that we would have described it ten or twenty years ago.

• 6:01Speaker 01

  Yeah, I think that's right. So, you know, just as I was saying, with the phone in your pocket, if we had gone in nineteen ninety and, you know, said, all of a sudden, here's a ChatGPT of today. I think everybody would say, OK, AGI is here. You know, this is an incredible leap. This is AGI. But because we got it gradually, there's been resistance to that.

• 6:26Geoff Nielson?

  That, that, yeah, that, that makes sense. And so, I mean, if you look at the, the progress and you mentioned that you yourself were surprised by this, what specifically did you find, you know, maybe, maybe most surprising or, you know, was something that you didn't see coming?

• 6:42Speaker 01

  Yeah. So, you know, you said Stuart and I started writing the book in 1995, but I went to grad school in AI in 1980 and my topic was natural language processing. And the way I look at it, I said, well, there's two problems. One is there's written words on the page and we have to figure out what they mean or, or how to process them. And we saw that as a, a problem in linguistics. We had to figure out what's the syntax of this language? What's the definitions of these words? How do they relate to each other? And we said, okay, we think we have a handle on how to do that. But then we said, then there's a lot more that's going on up here in the head. And we said, ah, I don't know how to do that.

• 7:26Speaker 01

  That's going to be the hard part, right? We're going to get the linguistics part right. But then what, you know, how do you react to a sentence? What does it mean? How does it relate to another sentence? That's going to be hard and that's going to have to figure out what's going on up in the head. And then we built these L L Ms by saying, the thing we're going to put in the head is a very sort of broad priors that have the capability to pay attention and learn, but not much else. Otherwise, it's kind of a blank slate. And then we're just going to push billions of words past it. And it worked. And I think nobody really anticipated that that would work. We, we thought there was going to be a, have to be a lot more going on and figuring out

• 8:08Speaker 01

  how thinking works and not just passing a lot of words past it.

• 8:12Geoff Nielson?

  Do you see that? So I, I'm glad you said that and it's, it's aligned with kind of my thinking and some of the thinking I've heard from other, you know, kind of AI leaders in this space. The approach of, you know, passing a lot of words through this tool, do you see that as kind of a continued trajectory to where we need to get to in terms of the next generation of, you know, the phrase that comes to mind is AI as agents or agentic AI, which is something, you know, you, you and Stuart have been talking about for a long time?

• 8:40Speaker 01

  Yeah. I guess. You know, you can look at the picture and react in different ways. Some people say, well, we have to tear it all down and start over again. And some people say, well, we just have to continue to to make it better. I think it's interesting that Jan LaKoon is on the tear it all down. And I'm on the let's continue to make it better. But other than that broad philosophy, I think we're very aligned in our views. So we both say, here's some things that are missing. We have to be able to do reasoning better in certain ways. We have to have a better connection to the physical world and so on. And I see that as as evolution and he sees it as revolution.

• 9:23Geoff Nielson?

  It makes sense. And I think a useful distinction there to say, OK, well, we're we're still broadly talking about the same thing. So when you look out over the horizon about where this is going and, you know, there's no shortage of benefits or challenges on each side. What's sort of top of mind for you in terms of what we need to get right next? And how is that influencing, you know, where you're spending your time and mental energy?

• 9:49Speaker 01

  Yeah.

• 9:52Speaker 01

  So we want to build tools that are useful and don't make dangerous mistakes. So I think we're on the right track. So a language model by itself seems like it's not all that useful, right? And so there's a very specific mathematical definition of what a language model is. It's a probability distribution over the next words, given the previous words or the context. And just having that doesn't seem that useful. And, you know, when we had the first chatbots, it was like you give it a prompt and then it says, OK, what's the next word I'm going to spit out? And then what's the word after that? And then what's the word after that?

• 10:38Speaker 01

  To me, that didn't seem like artificial intelligence. That seemed more like artificial politician, right? They're really good at spitting out one word after another without pausing to think. But now we have these models that do. More reflection. So rather than just saying, what's the next word I'm going to spit out? It says, let me try 10 different lines of approach, see where they go,

• 11:03Speaker 01

  criticize them, compare them, vote. And you know, maybe in the future, run some experiments, look something up, see how it relates. And only when I've done all that, now I'll start responding. And that seems much more like intelligence. And we're starting to see those kinds of approaches. But we got to figure out how to do it better.

• 11:28Geoff Nielson?

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• 11:56Geoff Nielson?

  So coming back to the, you know, the, I guess, the concern piece, you know, you mentioned, we have to make sure that things don't go too off the rails with these technologies. What do you see as some of the, I guess, more and less realistic risks for this technology in the next handful of years?

• 12:13Speaker 01

  Yeah. So I think there's a lot of issues, right? And I think we don't have the best record with that, right? So, you know, as an industry, we invented social media. We saw some of the advantages of bringing people together. But I think we didn't do a good job of foreseeing all the problems of of addiction and misinformation and so on.

• 12:39Speaker 01

  And I think anytime you have a powerful tool, it can be used for good or for bad. And, you know, and maybe it's a flaw of the tech industry that. They tend to be maybe more idealistic and less connected to the real world and optimistic and see the good uses and don't defend enough against the bad uses. I think we're in a pretty good place in AI because right from the start, there's been all this talk of AI safety. You know, I wish we had done a better job of that with other technologies, right? So when the internal combustion engine was invented, that was a great thing for humankind, provide all these services and transportation and get food to people.

• 13:22Speaker 01

  But we didn't really foresee all the effects of pollution and so on. But here, sort of right from the start, it seems like AI is concerned with that. I'm worried about misinformation type issues to some degree, I guess. A large part of it, I feel like, well, we've already got that. We've already got cheap labor that can generate junk and push it out there. And the bottleneck doesn't really seem to be creation of the junk. The bottleneck is building up the networks that can get it propagated to others. So I don't, I don't see AI as fundamentally changing it. It's just another tool that the bad guys can use.

• 14:07Speaker 01

  So that's an issue. I worry about sort of empowering bad actors to have more powerful technology, right? So, you know, a lot of work has gone into, if you ask a chatbot, how do I make a pathogen that will kill a billion people? It's supposed to say, no, I won't do that for you. But we have open source models that, that allow you to get around that. And fortunately, Anthropic and others have done this research and said, well, kind of the things you need to know aren't really out there. And so neither the search engines nor the chatbots really have access to that.

• 14:52Speaker 01

  So that's a little comforting. But you can easily imagine. You know, and now I'm in the future saying, huh, that's an interesting question. I don't know the answer to that.

• 15:05Speaker 01

  But from my knowledge of biochemistry in general, here's some ideas. You could try this and maybe it could guess right. So that would be a bad thing. And I think we're, we're seeing this in general, not just with AI. We're seeing sort of the, the dumbing down or the cheapening of the ability to impose your will on the world. Right? So 50 years ago, if you wanted to impose your will, you kind of needed a big aircraft carrier group that you would send off to threaten other people. Now, a much cheaper set of drones can be as effective.

• 15:44Speaker 01

  So technology has made the ability of smaller groups to do more attacks. And that's a danger and AI helps, helps make that even more powerful. I'm also worried about income inequality. And again, that's not uniquely an AI problem. It's an inherent problem in digital technologies where the cost of reproduction is, is near zero. And that tends to concentrate wealth in the hands of a few. And I think that's dangerous for society.

• 16:21Geoff Nielson?

  I, I think that's well said. And, you know, as I process, you know, sort of both sides of what you said there, starting with, yes, we seem better positioned than with a lot of new technologies to actually be investing in and caring about the safety. However, there are, you know, a number of varied risks that we can't, you know, necessarily get around. Certainly compared to some of the people I speak to here, it sounds like on balance, you're gently optimistic about this and our ability to, to get our arms around this versus, you know, like some of your peers being in a, and, and by the way, the one that comes to mind is, you know, Jeffrey Hinton about, you know, we're completely screwed here. Is that kind of gentle optimism? Is that, is that reflective, would you say, of where you're at?

• 17:05Speaker 01

  I think so. I mean, I think there's real dangers and I think bad things are going to happen. But I think overall the, the good will, will outweigh the bad. And, and Jeff's got his sense, you know, set of concerns. There's certainly. People like Eliud Gudkowski or even farther out on the on the danger side.

• 17:25Geoff Nielson?

  I understand you're doing some work now with the Human Centered AI Institute.

• 17:29Speaker 01

  Yeah.

• 17:29Geoff Nielson?

  I got that right. Good. And, you know, working with with folks over there like Fei-Fei Li. What what what sort of mandate does that institute have? And, you know, is there work over there being done that you think is helping to address some of these challenges?

• 17:42Speaker 01

  Yeah. Right. So this is an institute at Stanford founded by Fei-Fei Li and some colleagues looking at how AI affects society and and how we can make it work for people. And so, you know, there's also this design school at Stanford. And so I see the HAI as kind of a continuation of that. How do we do design of products that use AI that will be useful for people? Will augment them rather than replace them. It will be fair and unbiased and will be easy to use.

• 18:26Speaker 01

  So so that's what the sort of the charter of the institute is. I'm teaching a class there. The institute does a lot of policy type work of. So we recently did a boot camp for congressional aides and tried to teach them. Here's where the current state of AI is. Here's the kinds of things you might be worried about, both in terms of promises and threats and in terms of what possible legislative role Congress can play. So I think that kind of more, not really advocacy, is more education is is part of the role of HAI.

• 19:11Geoff Nielson?

  What kind of role on that note do you see Congress potentially playing?

• 19:17Speaker 01

  I think most of the issues could be already covered by existing law, right? So most of the time, when you do something bad, it's the fact that you did something bad, not how you did it, right? So we have rules that murder is bad, and we don't have specific rules for murder with a particular technology.

• 19:44Speaker 01

  Although, that being the case, there are places where we we we single out specific technologies. So we do say that if you use a gun in certain cases, that changes the nature of the crime. So I think, you know, government has a has a role to say, how are we going to use this in a way that doesn't take advantage of people? How are we going to share those benefits across? And how are we going to let it grow while also keeping it under control?

• 20:19Speaker 01

  And I kind of feel like there's a lot of players involved, and I don't want to put all the the emphasis on government. So so, yes, they have a role to play. But in my experience, government tends to be reactive to speed that's slower than the speed that AI is going. So I worry about that. So I think other players are important, too. So self governance is important, and all the big AI companies have their AI policies. And and from my experience, it's taken pretty seriously. So, you know, internal to a company, they say, what can we try doing this?

• 21:01Speaker 01

  Well, no, we can't, because we have to clear this first because of our policies. And I think that works well.

• 21:09Speaker 01

  I think there might be a role for professional societies. We haven't had that before in computing. All right, so I get to call myself a computer scientist and, you know, and I have some degrees and some experience. But I don't have any, anything official and anybody could just say, all right. I'm a computer scientist or I'm a software engineer and I'm gonna release some software. And they let you do it. It's great. Uh, in other fields, they don't do that. I couldn't go out tomorrow and say, you know what? I'm gonna call myself a civil engineer and I'm gonna go build a bridge. They don't let you do that. You need to be certified in order to, to, uh, to do those kinds of things. I don't want to slow down the software industry, but I think there might be a role to say.

• 21:50Speaker 01

  If you get to a certain level of power of these models, maybe there should be some certification of the engineers involved. And then finally, I think, uh, external third parties are play, can play an important role. So I actually joined an AI safety board with Underwriters Laboratory. And I thought they were interesting because the last time we had a technology that the public thought was gonna kill everybody. It was electricity. And everybody was worried they were gonna get electrocuted. And you could, you know, you can see some of these vintage cartoons, uh, showing death and destruction. And then Underwriters Laboratory came along and said, we're gonna put a little sticker on your toaster or your microwave and it means it's not gonna kill you.

• 22:35Speaker 01

  And consumers trusted that and because consumers trusted it. Companies voluntarily submitted themselves for certification. And that seemed like a good thing. And I think maybe these, uh, third-party nonprofits can be more agile than a government can in, in setting regulations.

• 22:53Geoff Nielson?

  The, um, the comparison to electricity is a really interesting one. And, uh, you know, I, I won't drain the story here, but I'm sure you're. As familiar, if not more familiar than most, about the story of alternating current and direct current and Edison and Tesla. And it's funny, I've never reflected on it before, but it seems like you could draw some parallels with those competing standards with some of the big players in AI and some of the narratives they have about their competitors right now and how safe or unsafe their models are.

• 23:24Speaker 01

  A little bit, although, you know, it isn't to the point where we have to say, you know, we have to choose one standard that everybody's going to be hooked up to.

• 23:32Geoff Nielson?

  Well, that's where I was going with this. Is one of the narratives is like, oh, this is an arms race and one person will get there and everybody else will lose out. Do you see it as being winner take all or do you see it being more of kind of a long tail of different models and technologies that are more fit for purpose?

• 23:48Speaker 01

  Yeah, I see it as not being winner take all, but I see it as a few winners take most. So I guess it's two issues. What is, you know, some of these futurists or single itarians are saying, well, there's this hard takeoff scenario where, you know, one team figures out the magic so that its model doubles in a week and then it doubles again in a day and then it doubles again in an hour and then the rest of the world is left behind. I don't really believe that. You know, so I think so far we've seen parity among the top groups and I think we'll continue to see that.

• 24:29Speaker 01

  So, so I don't worry so much about one dominating because they have a technology that leaves everybody else behind. If you'd asked me three or four years ago, I would have said, well, there's going to be a very small number of players because there's only a few providers who have enough data centers to build these really huge models. And I think those few companies will do really well, but I don't think they're going to capture everything for two reasons. One, we've seen these much smaller models become very capable and we've seen more demand for kind of privacy of saying, people saying, well, I want something on premises because I don't want my data or my queries to go outside.

• 25:13Speaker 01

  So I think, you know, yes, the big companies are going to capture a lot of market, but there's going to be lots of other ones as well.

• 25:23Geoff Nielson?

  There's an interesting tension in my mind between fully democratizing some of this technology versus keeping the really important stuff contained within a few different companies. And I think I've heard you say before that one of the pieces that concerns you the most is open source AI and what a bad actor or an organization could do with some of these models if there isn't enough safety or regulation there. Where does that balance out in your mind? Like when we have to balance it being broader versus more contained? What is, I don't know, how would you answer that question in terms of where we should draw the line?

• 26:02Speaker 01

  I mean, I guess my feeling is it doesn't matter what I think because the cat's out of the bag. And I think you were right that I was hesitant. Again, I mentioned Yann LeCun, he's really pushing hard for these open models. I was saying, you know, wait a minute, maybe it'd be good if somebody's making a query to do something terrible that it gets logged somewhere. And I guess another person I can mention that I've seen the shift in is my colleague Eric Schmidt, who was very adamant of saying we can't have open models because of the threat from bad actors, you know, two or three years ago.

• 26:42Speaker 01

  And now he's switched and said, it's too late. These models are powerful enough. If the bad actors want to use them, they can create them. So we might as well harvest the good of the open models because the bad guys have got them anyways. And I think that's right. I think there's nothing you can do about that now.

• 27:03Geoff Nielson?

  So what are the implications there? Given that the cat is out of the bag, what do we need to do, I guess, as, you know, calling it an industry is a bit strange, but as kind of an ecosystem to reap the most benefits or at least mitigate our risk?

• 27:19Speaker 01

  I guess what we got to do is say, beware that here's another attack vector, right? And I think sometimes in some areas, we've done a really bad job. So in terms of cybersecurity, as an industry, we haven't really focused on that. And there are a lot of losses.

• 27:40Speaker 01

  Bad guys coming in and stealing data and extorting and so on.

• 27:45Speaker 01

  And we've we've accepted that trade off. We've said, we want the industry to move fast. We want to provide you with all these tools and we'll do that without guaranteeing that they're safe and we'll accept those losses. And maybe that was a good choice. But I think now maybe we can think a little bit harder to say, well, if there's more powerful attacks, maybe we should build our systems into a more reliable standard. And I guess I'm kind of optimistic there. So I'm not an expert in cybersecurity, but I talk to the people who are. And they kind of feel like, yes, this will be good tools for the attackers. But they think maybe it's a better tool for the defenders.

• 28:28Speaker 01

  The reason the attackers are successful is because we build software that's just full of holes. And if we can have, and you know, the systems we build are too complex for a human to understand. But it seems like maybe an AI system could understand it. And we could ask the AI, you know, here's this million lines of software, analyze where the holes are and tell me how to fix it. And if we can do that, then the attackers have a much harder job.

• 28:59Geoff Nielson?

  Right, so it's almost like AI can raise the tide for all ships there. And you know, that it's it's an interesting perspective and it's one I appreciate because I feel like there's so much, there's so much fear these days of, well, every, every year in all things cyber is going to be riskier than the last. And you're saying, well, maybe, maybe that doesn't have to be the case.

• 29:19Speaker 01

  Yeah, I think we can make things more secure.

• 29:23Geoff Nielson?

  So, yeah, you know, kind of adjacent to that, one of the undercurrents of all of this thematically is just speed and everything is accelerating. It's going faster and faster and who knows what model will be out tomorrow that's not out today.

• 29:40Geoff Nielson?

  And I think about that in relation to, you know, an article that you wrote a number of years ago, the teach yourself programming in 10 years, which was, you know, in some way a response to the, this, you know, addiction to speed and just everything is now, now, now. Do you, you know, given when that article was written and where we're at, do you still hold on to those, you know, principles? Or do you think something has fundamentally changed?

• 30:05Speaker 01

  So yes and no, right? So I still think if you want to really understand a field and software engineering could be one example of them, but in any field, you do have to put in that work. And you know, it may not take exactly 10 years, but you're going to have to put in a lot of time and you're going to have to study and you're going to have to deeply understand things. On the other hand, there's a lot of things you can do without that deep level of understanding. And now it seems like programming is one of them. So I have no issue with the many people, maybe the majority of people saying, you know, it'd be really cool if I had a piece of software that did this.

• 30:49Speaker 01

  Let me chat with this chat box and build something that seems to work. Let's go. I think that's going to be fine. I do see kind of a generational schism on this, right? So this happened a couple of times with me. I'm working with a younger colleague at work and there's, we discover there's, hey, here's this new software package. Seems to do what we need. Great, you know, let's figure it out. And so I sit down and, you know, I'm reading through the manuals and I'm taking some time and I have questions. And then my colleague comes back in and says, okay, I'm done. Let's go. And I say, what do you mean you're done? And I said, well, I figured out, you know, we call this method A and then we take this result and then we call B and then we have it. And that's the answer.

• 31:34Speaker 01

  Let's move on to the next problem. And I say, but you know, I don't understand this package. How does it do X, Y, and Z? And they say, no idea. But I know this will give me the right answer. And I feel like there is this trade off of sometimes it's really important to deeply understand something or else you're going to get bitten later, you know, sort of you're building up technical debt if you don't understand what's going on. Other times, completely understanding them is not important and just getting the right answer is important. And it's hard to make that trade off. And I feel like in these kinds of instances,

• 32:14Speaker 01

  Neither of us is making a rational trade off, right? So I like to study and understand because that's what I was used to. And my younger colleague likes to go fast because that's what they were used to. And, you know, maybe one of us was right, maybe the other was right, maybe the the right place is somewhere in the middle. But it's hard to get the experience to know, right? You only got so much time. What do you want? What are you going to understand completely? And what are you gonna just accept and and move on? And say, if I get the right answer, it's okay, even if I don't understand it. And I think I think it's really hard to get that right.

• 32:53Geoff Nielson?

  And it makes sense. It's a, yeah, it's a tricky balance. And when is good enough, good enough? So when I think about that, when we talk about computer science as a discipline, you know, we've, we've already talked about everything from it's easier and faster than ever to, well, maybe we need to start thinking about credentializing this more for the safety of the greater good. In your perfect world, how would you like to see, you know, AI and some of these, you know, advancements in technology? Where would you like to see them take computer science that's going to be most beneficial for everybody?

• 33:29Speaker 01

  Yeah. So I think that's really interesting. You know, we're starting to see some advances. There already been some AI assisted discoveries in computer science and math. There's been announcements recently from Terrence Tao, you know, one of our foremost mathematicians and others of saying, you know, here's AI assisted proof that that I came up with. And Tao has said, these systems seem to be at the level of a not incompetent grad student. So that's kind of promising and, you know, maybe soon they'll be at a pretty good grad student. So they can do things that we couldn't do before.

• 34:12Speaker 01

  And and I feel like it's kind of the first time we've had a technology like this. Because all our other tools have been, if I can formalize something, then the tool can help me work through this, these formal calculations. Once I've done the hard work of of describing what I wanted to do. And it feels like the current AI systems are the first ones that can help us in that process of saying. I want to go from a messy semi-understanding to something that actually works without having to do all the formalization myself. Right? And so, you know, we've been burned by that in the past. Right?

• 34:55Speaker 01

  So you go back in the 1980s and there are all these predictions of saying, oh, you know, worker productivity is going to go up so much because we have PCs now. And, you know, we have spreadsheets. So, you know, 90% of the accountants will go away because it will all be automated. Well, that didn't happen. And, and why didn't it happen? Well, it's true that if you want to add up a long column of numbers, a spreadsheet is a great tool. But most of what the accountant was doing was not adding up the numbers. That was a very small part of their job. A big part of their job was knowing what numbers go where. And here's this expense and what column does it go in and so on.

• 35:39Speaker 01

  And we couldn't automate that part. And so therefore productivity did not go up that much. But the AI systems we have now, it seems like the first tool that maybe they can do that messy kind of thing.

• 35:52Geoff Nielson?

  So, so is the implication there? You know, I, I want to tease this out a little bit more because there's certainly, these days it feels like there's a lot riding on, you know, the productivity is going to skyrocket across all these organizations. And, you know, on the one hand, there's not so fast it hasn't before. And on the other hand, there's, well, this time may be different. What, what's kind of your prediction? Like, is this again, just gradually over time it'll trickle out? Or, or what will the productivity gains, if any, look like?

• 36:22Speaker 01

  I think it'll be gradual. You know, if you look at GDP by year, well, first we have, you know, tens of thousands of years where it was pretty flat.

• 36:35Speaker 01

  But then we got the industrial revolution and it started to go up. And if you look at like GDP in the US over the last hundred years. It's kind of pretty steady line with a little wiggles in it. And you can really only see two events in the last hundred years. And that's the Great Depression and World War II. And everything else is, there's a little dip, but then it goes back to the trend line. So it feels like technology is giving us faster progress than than we had pre-technology. But no one has been kind of instrumental. You know, and you would have, you know, as a computer scientist, maybe I thought, well, when you get PCs in offices, that would make a big difference.

• 37:20Speaker 01

  You can't see that on the on the chart of GDP. And maybe GDP is measuring the wrong thing in certain ways, but but basically you can't see that much difference. Now, another chart that I think is interesting to look at is look comparing China to the US. So if you compare China to the US in the, say, in the 90s or so, they were up at like 10% GDP growth. And you know, we're two or three. And I think what was happening is they were kind of reaping these technology benefits all at once. Right? So we kind of slowly said, well, computers are coming in, the 70s, 80s and 90s.

• 38:03Speaker 01

  China was not deploying any of that technology. And then they kind of did it all at once, and that gave them about a 10% GDP annual growth. So I think you could see a case that AI could do a similar kind of thing.

• 38:19Speaker 01

  Now, I was at this meeting of economists and AI people, and they took a poll of what do you think GDP is going to be? I think it was 20 years from now. I forget the exact number. And and the median was about 10%. But the range was 1,000% to the complete destruction of civilization. So we got some wide error bars on that.

• 38:45Geoff Nielson?

  That's a very generous way of putting it. So let's stay on the China piece for a minute. And what can happen when you have these more open source technologies? And when you can have these kind of leapfrog technologies, I guess, that help China bend the curve upwards? And so I'll ask the question broadly, but you know, what's, I guess, your concern level? Or the concern level that you think the average American or Westerner should have? Both in terms of the overall health of the economy as well as the health of the job market?

• 39:20Speaker 01

  Yeah.

• 39:20Geoff Nielson?

  Given that, you know, these technologies are being increasingly available outside the walled gardens of the West?

• 39:28Speaker 01

  Yeah. Yeah, we're certainly seeing shocks to the job market. I think mostly what we're seeing so far, I think, has not that much to do with AI. I think it has more to do with sort of the recovery from COVID. We had a big up and down and that is still reverberating from that. And so I see, you know, students at Stanford come to me and say, well, I didn't get a job offer yet. What's happening? And, you know, in the past, they would come and say, well, I got a job offer from each of the top six companies and from these four startups. How do I choose between them? Right?

• 40:13Speaker 01

  So that's a big difference. So far, everyone eventually gets a job. So the difference is, you know, rather than getting 10 job offers, they're getting one and it's taking a little bit longer to do that. And I think that's because, you know, during COVID, everybody was online. Companies over hired. Now we're coming out of that and there's some threats to the economy and companies are cutting back. I don't think we've seen the full effects of AI yet.

• 40:48Speaker 01

  Certainly, you do see it in small places. I was just talking to someone who said, well, we used to have an artist on staff to like make up logos and PowerPoint slides and so on, and now we don't have that. We've seen that kind of thing before, right? So there used to be, you went into a typical office in the Mad Men 1950s era, and there'd be a lot of people whose job was typing. And now we have much less of that. And most people are expected to do their own typing. And so now maybe doing art, well, people will be doing it on their own with the help of these tools.

• 41:32Speaker 01

  So I don't think that's a huge effect on the economy. I guess there will still be plenty of jobs. It might be harder to find some of them. But I think the main issue will be the speed of the disruption. And, you know, so we've seen this disruption before. It used to be the majority of Americans were farmers, and now we went to only a couple percent are farmers. But that happened over generations.

• 42:01Speaker 01

  And now we're seeing changes happen over months or years.

• 42:07Speaker 01

  And that may be too fast for people to adapt to, right? It was okay to say, well, my grandparents and my parents and I were all farmers, but it looks like my kids are gonna go off to college and take a job in the city. Good for them. And it's much harder to say, I had one job and I was laid off by AI, and then another, and then another, and then another, and now I'm pretty pissed. So I think we're gonna have to deal with that. There've been a lot of these proposals for some kind of universal basic income or something like that. I don't know exactly what the right thing is, but I think we do need better kinds of social safety nets, because there will be this kind of disruption.

• 42:49Geoff Nielson?

  So it sounds like you're concerned then that this isn't just just part of the cycle and reverberation, as you said, from COVID, but that the we may see at least in the medium term, a lower or I call it a higher unemployment floor before we get back to whatever the new version of farmers is. Is that fair?

• 43:11Speaker 01

  Yeah, I mean, I don't know if about total employment or unemployment, but I think there'll be more disruption, right? So, you know, it may be that you're employed, but you may have to move from job to job faster. So, you know, you could see the economy going up, but everybody feeling worse because they're nervous. Right? And, you know, I kind of feel like, you know, we we had this invention of the full time job. And I see that as kind of a mutual insurance policy, right? So why do we get

• 43:51Speaker 01

  insurance? Well, you know, the expected value of insurance has to be negative for the individual and positive for the insurer. But the value to the individual is to even out the ups and downs. And I see, you know, a full time job is like that. Right? It's probably not the case that the most value I could provide to the world would be staying at one company permanently. And probably if I split my my work between multiple companies, that would be more effective for the world as a whole. But it would be a cost on me to have to go out and find these gigs and never know where my next paycheck was coming

• 44:32Speaker 01

  from. And so we accept these sort of suboptimal use of resources to have this steadiness and even things out. And if we're going to start losing that steadiness, we're going to need some other some type of insurance or guilds or UBI or something to make people feel more secure.

• 44:55Geoff Nielson?

  It's an interesting point, and as you were talking about it, I was thinking, I really like the analogy, and I guess for the employer, there are benefits to the employer as well for full-time labor. I'm thinking about trust and switching costs in terms of hiring costs and firing costs, and if you just bring in all these fractional people, how useful is that? But it does sound like, in this world where it's a combination of people plus machines, if I can broadly call them that, if I can just kind of throw that back at you, it sounds like when you think about the future of work, you see it as less rigid, more flexible, if I can choose an optimistic word, but not necessarily in a

• 45:39Geoff Nielson?

  way that's fully beneficial for the employee. Is that fair, or how would you call it that?

• 45:46Speaker 01

  I think that's right. So I think less rigid, I think, is an important part of it, and I think this kind of communication that AI enables is an important part of that, right? So why do we have hierarchical structure? Because it was impossible to have all N squared interactions, and so we have the interactions flow up and down through a hierarchy, and now there's only order N instead of order N squared. So that was important. But if we have these AI systems that can route the right information to the right person, then we need less hierarchical structure. And so that's a possibility. But if that

• 46:30Speaker 01

  hierarchical structure means now you have a job and tomorrow you don't, then that's stress on the individual.

• 46:39Geoff Nielson?

  Well, and I want to come back to the question of social nets and universal basic income, because there's an implication there, and I'm not coming at one way or the other on it right now, but there's an implication that the economics of artificial intelligence are creating a world where there's more value capture, if I can call it that, by the firms, and then there's got to be some sort of redistribution of wealth that helps the people at the bottom who are struggling. Is that the world you envision in terms of winners and losers, or would you paint it differently?

• 47:18Speaker 01

  Yeah, so I do think that in general, automation allows firms to capture more value. And then I think we want to, you know, sort of figure out where that, who gets credit for that value. And, you know, and there are a lot of cases we're working through now and things like copyright law. I'm not sure copyright is exactly the right thing to be worried about, but, you know, who deserves credit for this stuff that we've built up? And how do we allocate that fairly?

• 47:58Geoff Nielson?

  Right. So maybe let's zoom out for a minute and who deserves credit is such a, it's such a big, difficult question. And so I'll ask maybe a slightly easier one, which is just broadly, who do you see as being the winners and losers of this disruption?

• 48:15Speaker 01

  I guess, you know, the losers are people who had a safe position now that was protected by various kinds of moats and, and now we'll see challengers come in. And then the winners will be those that are agile enough to, to exploit that, to use the technologies and see opportunities.

• 48:37Geoff Nielson?

  So if you, and I don't know if you've been in this position, but if you had a CEO or a CTO come knocking on your door and say, Peter, I'm really interested in AI, in, you know, just basically the advancement of some of these technologies and how my firm can capitalize on this and what we need to, to get ahead, either in our industry or in terms of, you know, modernizing our organization, what advice would you give them and what would you tell them to watch out for?

• 49:07Speaker 01

  Yeah, so I do a lot of that, right? So one, one of the roles at, at HAI is educating companies as well. So, so we've done a bunch of that. Also at Google for Startups, we mentor startup companies, usually a little bit more technologically savvy, but are asking some of those questions. I look at it as saying, let's not think of AI as being unique. Let's think of what are your market opportunities? What are the tools you have to address that? How can you make your organization more efficient? What are your goals that you're trying to achieve? And if you can lay that out clearly, then we can start looking at saying,

• 49:50Speaker 01

  here's a place where AI can play a role within that workflow.

• 49:56Geoff Nielson?

  I like that, and it makes a lot of sense to me. And, you know, in some ways is seeing tools as tools, I guess, versus seeing them as being the all end alls. Are there any views or assumptions you're seeing people coming to you with as patterns about AI that you're like, that is just totally wrong headed? Or I guess if I can ask that another way, are there particular flavors to the hype that you're saying like, just look out for that, ignore that, that's going to put you on the wrong track?

• 50:24Speaker 01

  Yeah, so I think we've seen, you know, pretty fast evolution in sort of the public eye. If you go back like three years, every article about AI had a picture of the Terminator robot with glowing red eyes, right? And it was, robots are going to kill us all. Now we're seeing less of that. Now the robots are only going to steal your job. They're not going to kill you.

• 50:46Speaker 01

  One thing I see a lot, you know, so talking to the CTOs and so on, you know, they come to me and say, well, I need to hire a PhD in AI, but I can't get any because Google and Meta already hired them all. And my colleague, Cassie Gassaroff, has a great analogy. She says, that's like saying, well, I'm the owner of a restaurant and I need to hire a PhD in stove design. And the answer is no, you don't need that. What you need is a chef who will tell you what stove to buy, understands how to operate the stove and knows what the customers want to eat and can make that.

• 51:29Speaker 01

  And so I think that is a flaw that some people see sort of the cutting edge of AI research and saying, you know, every company has to be doing that. Instead of saying, every company should learn how to use the appropriate tools and fit it into what they're trying to do.

• 51:49Geoff Nielson?

  I really like that and I'm chuckling too because I just spoke with I just spoke with Cassie very recently as well and we had a great she's fantastic. We had a great conversation.

• 51:57Speaker 01

  Yeah, she's great.

• 51:59Geoff Nielson?

  Yeah, she was a lot of fun. So just just taking that that analogy to its logical end though, if we think about who the chefs are in this AI world, are those still technologists? Are they business people? Are they somewhere in between? What, you know, skill setter role should we be looking at if we're CTO, CIO, someone like that?

• 52:18Speaker 01

  Yeah, so I think that's really interesting, right? So certainly, uh, somebody who's savvy in technology can do more faster. But I think a really exciting thing is for the non-technologist to be able to get ahead. Right? And so I think particularly in the small business, right? So say, you know, you want to automate your your workflow in your business. If you're a big company, you have an internal IT team and you give them projects. If you're a medium company, you hire sales force and it's more expensive and you pay these consultants and they do it for you. If you're a tiny company, there's nothing you can do. Right? You you don't have any programmers on staff. It's too expensive to afford that.

• 53:02Speaker 01

  You can't afford Salesforce or any other competitors, so you're stuck. But it does feel like now, I'm in that small company. We've only got two salespeople. They can sit down together and they can prompt and say, here's what we do on a daily basis, and they can build something that will automate their workflow. And that was never possible before, right? Before, you had to be a real programmer to do that. Now, it seems like you can have pretty good luck, uh, doing that kind of thing. And this is the first time we've seen that sort of possibility.

• 53:38Geoff Nielson?

  Yeah, and it seems like a real boon. I mean, frankly, not just any organization, but to a lot of different roles. If everybody's, if what everybody can do with technology increases, right? Everybody's more kind of enabled individually.

• 53:51Speaker 01

  Yeah.

• 53:52Geoff Nielson?

  You know, on that note and staying at kind of, you know, you talk about, you know, enterprise IT and we're talking about CTOs. One of the big trends we've certainly seen, I probably can say over the last couple decades or even beyond, is just the proliferation and the sprawl of, you know, corporate technology functions, corporate IT. And suddenly, we have to add a whole, you know, data governance function and a whole cybersecurity function and a whole infrastructure function. There's, you know, in my mind, sort of competing futures of whether as technology becomes more central, these functions continue to grow because suddenly we need a new layer of AI people, or they shrink because we've said we can disintermediate. And now the business people are their own technology experts or somewhere in between.

• 54:37Geoff Nielson?

  What do you see as being kind of the the future of this function?

• 54:40Speaker 01

  I I'm hopeful that it can shrink. And we build our tools and we make them better over time. Right? So, you know, if I think back 30 years and I was programming in C, it was easy for me to make lots of mistakes, right? I could pass the wrong type into a function and you wouldn't complain. Now I get a nice compile time error, said you made a mistake. Here's how to fix it. And so I think we're building tools that can do a better job of of helping us along the way. And I think with AI, it'll be possible to do that at kind of the the business level

• 55:22Speaker 01

  as well as just the compiler of code level. So that suggests that that maybe there'll be less of this, right? That, you know, and, you know, and I hear this at Google. You know, I hear from my friends who've been here for decades saying, oh, you know, it's so much more complicated now. Used to be we just launched a product. And now we have to go through the security review and the privacy review and these five other reviews. And it takes so long. And, you know, one of my reactions is, well, well, how much longer does it take? And they'd say, you know, like five times longer. It's terrible. And then I'd say, well, you know,

• 56:07Speaker 01

  how many more users do you reach on day one? They say, oh, well, like 100 times more users. So I'm saying, well, so you're saying it's 20 times more efficient per user. And I think that's one way of looking at it. And I think we can we can get to the point where maybe the AI is automating more of this. Right? Where there's just sort of company wide policies and you write code and it works with those policies rather than having to have human teams reviewing everything.

• 56:38Geoff Nielson?

  Right. So there's potentially you can get a lot more done. You can be a lot more efficient. And I want to continue down that path again, because it's interesting to me that there is still a happy story and a concerning story. The happy story is, you know, we're more efficient than ever. We're able to do more with less. The concerning story is, and you know, you touched on this earlier, Peter, but if you're Joe technologist or Jane computer scientist, well, maybe, maybe the enterprise just doesn't need you anymore because they're, they're running a leaner function. And so if you're someone who finds themselves in this computer science field and is worried about being a winner

• 57:18Geoff Nielson?

  in this space versus being disrupted, let's say, what's kind of your guidance for these people to make sure that they're, that they're still relevant and able to, you know, continue to, to be, you know, valued, you know, members of the team?

• 57:33Speaker 01

  Yeah, I guess what you want to do to be valued is to understand what the goals are of your organization and fit into that and use your knowledge to do that. And I think we had this period where, you know, people were told, well, if you become a computer science major, then you automatically get a good, high paying job because there's this scarcity of the ability to write code that will compile. And, and now maybe that's not the scarcity. And instead, the scarcity is to be able to understand the business need, understand the environment in which you're operating, and design something that will solve that need.

• 58:17Geoff Nielson?

  I like that framing of it. And I think, I think you and I are fairly aligned there. There's another, you know, there's another idea that's been floating around. And I'm curious what you think of it, because we've, we're having this conversation in the context of one organization, one enterprise. There's this sort of seductive idea of AI being able to enable You know, a one person billion dollar shop now.

• 58:41Speaker 01

  Yeah.

• 58:41Geoff Nielson?

  And whether we take it to that extreme or we say, you know, if you're a computer scientist, you can now be an army of one. You don't have to work for, you know, Google or, you know, name your big tech company. You could be working for yourself and, you know, now have the tools in your toolkit to do an awful lot more. Do you buy that future? Is that aligned with your view of, you know, more, more flexible or more, um, you know, kind of bit work? Or is, is that kind of fanciful thinking?

• 59:14Speaker 01

  Certainly, one person can do a lot more.

• 59:18Speaker 01

  But there's a lot to get done that one person just doesn't know. And it's not clear to me if the AI is going to know all those things. Right? So, you know, why do companies have thousands or hundreds of thousands of people? It's because there are all these exceptions, right? And you got to operate in all these countries and they have different rules and so on. So I remember, uh, Google bought this airline travel company called ITA.

• 59:50Speaker 01

  And they, you know, I was sort of doing due diligence on that. And, and one of the guys on in the company was a colleague I had worked with before. And I remember him telling me, he said, look, I'll talk honestly. Google's got a bunch of smart programmers. You could build what we build, but it's going to take you a really long time. You'd get 90% of it done right away. And then there's all these exceptions, right? So, you know, there's all these weird time zones and there's all these weird fees and, uh, and regulations that different airlines have and different countries have and so on. And we've done all that and, and it's not written down anywhere. You have to discover it. So, uh, you know,

• 1:00:34Speaker 01

  the value of our company is that we've done this long tail of exceptions and we figured it out. And there's no easy way to figure that out. Uh, and I think there's a lot of that going on. And so. It's gonna be easy for one person to write something that will solve the easy part of the problem. The rest of it, you know, maybe some of it can get done by AI if it's written down somewhere. But a lot of it isn't written down anywhere. And, you know, I think we're still a generation away, right? So you could imagine that could be solved by an agent that goes out and discovers all these exceptions by taking actions in the world. We don't have anything close to that yet.

• 1:01:17Speaker 01

  So I think, you know, for the next number of years, uh, there's still gonna be value in having a lot of people that know a lot of these sort of exceptional things.

• 1:01:28Geoff Nielson?

  Right. No, I, I really like that is it sounds like it's basically a parable for the current state of anyone working with AI in an enterprise capacity is sure, it can get done 90%, but there's an awful lot of asterisks that you still need all these people to do.

• 1:01:44Speaker 01

  Yeah, I remember, uh, I just reread, uh, Joel Spolsky's article on, on why you shouldn't rewrite your software. Right? Cause he says, you know, you look at it and you say, oh, here's this mess of stuff that seems obsolete and not worth it. And 90% of it is not worth it and you could throw out, but 10% of it solves a problem that you don't understand and you've never seen before, but is still a problem. And you don't know which, which of that 10% is important and which 90% is junk.

• 1:02:19Geoff Nielson?

  Oh, that makes, it, it makes complete sense. It's, it's interesting and it certainly resonates with me.

• 1:02:26Geoff Nielson?

  Peter, I wanted to talk about something completely different, but you know, while I have you, I wanted to ask you, you know, I, I've heard kind of through the grapevine that, that her is your favorite movie about AI. And, you know, it's certainly a movie that, I love the movie as well. And it's, it's a movie that when I first saw, I remember walking out of the theater and just like, it's just like, I need to think a lot more about that. And certainly it seems like the world has moved in that direction. Do you see this as being a reality that we're in right now or we're about to be in right now? And, you know, what?

• 1:02:57Speaker 01

  Yeah.

• 1:02:58Geoff Nielson?

  Do you worry that, do you worry that tech companies are seeing something like that and saying, yeah, we should, we should build that versus taking it as a cautionary tale?

• 1:03:07Speaker 01

  Yeah. I think yes, right? So, and you know, anytime somebody writes a cautionary tale, there'll be some tech leader that says, yeah, I want that. And I think we already have that, right? So there's lots of people whose boyfriend or girlfriend is a computer that they chat with. And so we're already there to some extent. I don't know if hers is my favorite movie, but you might have seen, right? So I did this event where we had a screening of her, and then we had a Q and A afterwards. And I remember one of the questions was, what other science fiction movie does this remind you about?

• 1:03:48Speaker 01

  And my answer was, well, not a science fiction movie, but it really reminds me of Life of Brian, because both of them are about faith, right? So in Life of Brian, here's this schmuck, and everybody wants to believe he's the Messiah. And in her, here's this piece of software, and the protagonist wants to believe this is my girlfriend. And I think we're just built that way, right? So humans want to do that, right? And so, you know, talk about can we build this interactive entity that people will think of as a, as a real person and companion? Well, we did that decades ago.

• 1:04:32Speaker 01

  My daughter loved her teddy bear, and it was not very interactive. And yet she loved it completely. And so we just want to, you know, sort of inject our feelings onto, onto other people, onto other machines, onto other pieces of software. And I think that's just the way humans are built, and I think her touched on some of that.

• 1:04:56Geoff Nielson?

  Yeah. So it's in our nature. It's not something we can avoid, or, you know, there's something to be done about. It's just something we need to understand and, you know, live with, it sounds like.

• 1:05:05Speaker 01

  Yeah. I mean, I think we, we do want to worry that, you know, there's lots of things that can give you pleasure in moderation and can lead to bad results when you're overly addicted to them. And we could see this as certainly being in that range.

• 1:05:22Geoff Nielson?

  Peter, I wanted to say a big thank you for being on the program today. This has been really interesting and really insightful, and I really appreciate your time.

• 1:05:30Speaker 01

  Yeah, great talking with you, Jeff. Thank you.

• 1:05:34Geoff Nielson?

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