Transcript

Why Peter Norvig Says AI Will Empower Millions—But Raise New, Perilous Risks

From Young and Profiting · Episode page · Watch on YouTube

· 60 min · 10,594 words · 101 timestamped sections

This is the full, timestamped transcript of "Why Peter Norvig Says AI Will Empower Millions—But Raise New, Perilous Risks", an episode of Young and Profiting published December 26, 2025 (60 min, about 10,594 words). It mentions 5 people, works and ideas, including Google LLC, Peter Norvig, AI Vault, Young and Profiting Podcast, Khan Academy.

What is discussed, and when

  1. – 5:00: Google LLC, Peter Norvig, AI Vault, Young and Profiting Podcast
  2. – 10:00: Google LLC
  3. – 15:00: Google LLC
  4. – 40:00: Google LLC, Khan Academy
  5. – 59:35: Google LLC

Notable quotes from "Why Peter Norvig Says AI Will Empower Millions—But Raise New, Perilous Risks"

  • Tools, but also implemented numerous successful AI systems, including the Google search engine.
    about Google LLC · at
  • Way that you think about AI. And by the way, if you're new to the channel and new to Young and Profiting podcast, first off, welcome. You're going to love it here. And…
    about Young and Profiting Podcast · at
  • Like it could work pretty well. So, we had Sal Khan on the show and he as the Khan Academy, he talked a lot about how AI could help education. Do you have any ideas of how AI could support education and students?
    about Khan Academy · at

Who and what is mentioned in this episode?

People

  • Peter Norvig — Person · 2 mentions · first at
    We're diving into the world of human- centered AI with none other than Peter Norvig. He's not only authored major AI textbooks and established software tools, but also implemented numerous successful AI systems, including the.

Companies & orgs

Concepts & topics

  • AI Vault — Concept · 1 mention · first at
    Absolutely. And I think, hey, Yap fam, we're still continuing with the AI Vault series. And by now, I hope you realize that artificial intelligence is no longer some futuristic concept. It's here, and it's reshaping everything. For some, AI.

Full transcript with timestamps

You know, I'm not worried about these uh Terminator scenarios of an AI waking up and saying, "Uh, I think I'll kill all humans today." I guess I'm more worried about a human waking up and saying, "I want to do something bad today." In human- centered AI, the goal is to build systems that do the right thing for everyone. And part of it is saying you want to consider everybody involved. And I think when you do that, you don't end up with good results.

We're diving into the world of human- centered AI with none other than Peter Norvig. He's not only authored major AI textbooks and established software tools, but also implemented numerous successful AI systems, including the Google search engine.

I don't want technology that makes me disappear. I want technology that respects me and let me choose how much the machine is going to be doing and how much I'm going to keep control.

A lot of jobs might get replaced by AI. So, do you feel like AI is going to generate a lot more entrepreneurs and solopreneurs in the future?

Absolutely. And I think, hey, Yap fam, we're still continuing with the AI Vault series. And by now, I hope you realize that artificial intelligence is no longer some futuristic concept. It's here, and it's reshaping everything. For some, AI sparks excitement and limitless possibility. For others, it raises tough questions about ethics, control, and what it means for the future of work. That dilemma is exactly why today's conversation matters. We're diving into the world of human- centered AI with none other than Peter Norvig, a true pioneer who's been at the forefront of AI for decades. He's not only authored major AI textbooks and established software tools, but also implemented numerous successful AI systems, including the Google search engine.

Peter believes that AI shouldn't be about replacing humans, but about amplifying what we can do, making us more capable, more creative, and more efficient. So get ready, yap fam, because this episode will challenge the way that you think about AI. And by the way, if you're new to the channel and new to Young and Profiting podcast, first off, welcome. You're going to love it here. And secondly, make sure you follow and subscribe to the show so you never miss an episode like this. Without further delay, here's my conversation with Peter Norv. Peter, welcome to Young and Profiting Podcast.

Great to be here. Thanks for having me. I'm really looking forward to this conversation. I love talking about AI and I can't wait to pick your brain on that topic, but first I want to talk a little bit about your career journey. Uh, so I learned that you worked at some awesome companies like NASA. You actually worked at Google, but it turns out you started in academia. So I'm curious to understand why did you decide to transition from academia to the corporate world?

Yes. So I've been in a lot of places. Uh, I'm a AI hipster. I was doing it before it was cool. Uh started out, you know, got interested in it as a subject in the 1980s and at that time really the only way to pursue it was uh through academics. So got my PhD

and it was sort of the assumption back then that you get a PhD, you're going to go be a professor. There was much less back and forth between academics and industry than there is today.

So that's the path I took. But then I started to realize, you know, we didn't quite have the word big data back then, but I saw that that's the way things were going. And I saw as a young assistant professor, I couldn't get the resources I needed. You know, you could write a grant proposal, get a little bit of money, get a couple computers and a couple of grad students, but I really couldn't get the resources to do the kind of big projects I wanted to do. And industry was the only way to do that. So, uh, I I set out on that path.

Yeah. I love that. It's so funny that you say like you were doing AI before people knew it was a thing. Uh, for me it was like surprising because I feel like we hear about AI so much, but it turns out that AI has been a thing for decades. Can you talk to us about kind of when you first discovered AI and how long ago that was?

Yeah. So, it's definitely been here sort of right from the start. uh you know so Alan Touring one of the founders of the field wrote writing about it in 1956 sort of foreseeing the uh chat bots that we have today uh but of course we didn't know how to build them back then uh but uh it was definitely part of the vision of where we might go uh so I guess I got interested I was lucky that I had a high school that at that time had a computer class and also had a class in linguistics And I took those two classes and uh talked to the teachers in the classes and say, "Hey, seems like there's some overlap between those two. Can we get computers to understand English?"

And they and they said, "Uh, yeah, that's a great subject, but uh we can't really teach you that. That's kind of beyond what what we know how to do. So, you're on your own pursuing that goal." And and that's more or less what I've been doing since with with some side trips along the way.

[snorts] So I always say that skills are never lost. They're really just transferred. So I'm curious to understand what skills do you feel like were an advantage for you in the corporate world that you took from academia?

Yeah, I I certainly agree with that idea of transfer. I guess the idea of uh being able to tackle a complex problem, being able to move into an area that hadn't been done before. And [snorts] so, you know, academia is all about kind of an invention of the new. And for industry, it's a mix of you want to make successful products, but sometimes in order to do that, you've got to invent something new. and and that's harder to do because you don't you don't know what the demand for it is going to be there. There's nothing to compare to and and yet you have to design a path to say uh we're going to go ahead and build this and we're going to put it out and customers are going to have to get used to it uh because it's not going to be familiar to them.

Yeah. And speaking of building something new, you were responsible for Google search. Uh, and that was a while back uh, when Google really was just not starting off, but there was only 200 employees when you joined them in 2001. So, what was it like working for Google back then?

Yeah, that's right. So, so it was an awesome time. Uh, company was 2001, so it's three years old, 200 people all in one building. uh I came in and and I got the honor of of getting to lead the search team for a while for for about five years. Uh so it was a time when you know it's not like I invented it. Google search was already there but uh they were 3 years old and uh it was really the time when they were trying to uh uh ramp up the advertising business. So a lot of the key people who had built the search team had had moved over to to help build uh the advertising platform and so there was an opening and and I was had just come on board and so I got the opportunity to be a leader of of the search team uh and bring that forward o over the next 5 years. So so that was super exciting uh to be sort of right in the middle of a transformative time in our industry.

Yeah. Yeah. And I think a lot of my listeners, they don't realize that the internet was actually much different before Google. Like Google really changed the way that we use the internet. Can you help people understand what it was like before Google search?

Yeah. So I guess there was a couple of things. First [snorts] of all, there was uh directories and lists of sites. And so I remember from the various early days uh you know 19 93 or so uh and there was a site that was uh uh internet site of the day, right? And so it was just you go there and it says hey look here's a new website that you might not have heard of before and it was like wow you know today 10 new websites joined the web and they picked out a good one. uh and you could sort of keep up that way. But then a year or two later that no longer worked because there were thousands of new sites every day, not just a couple.

Uh and so Yahoo was was one of the first to try to uh to deal with that. [snorts] And they took this, you know, it's not going to be just one person saying, "Here's my favorite site today. It's going to be a company organizing the sites into kind of a directory structure." Uh, and that worked okay when the web was a little bit bigger, but as it continued to grow, that no longer worked. And then we really needed search rather than manually furated lists of uh, directories and so on. [snorts] Uh, but in the early days, the search systems just weren't that good.

uh I guess you know we had some experience uh as a field of doing it used to be called information retrieval rather than search and it was sort of it worked uh the techniques we had at the time work for things like libraries but the problem there was in a library you know everything that was published is like a real book or a real journal article that's already been vetted uh and so the quality is all at a pretty high level on the web that just wasn't true. And so we needed new systems that not only said what's relevant to your query, but also what's the quality of this content and other companies really hadn't done that. Uh and Google said we're going to take this really seriously and we're going to work as hard as we can to solve that problem.

And I think others didn't really see that as an opportunity. Right? So there's a story of uh in the very early days uh people were saying um uh you know here's Google it's rising Yahoo was was far bigger and far better known maybe Yahoo should buy Google and that never happened in part because the Google founders thought they had something more important [snorts] whereas Yahoo said oh yeah you know search that's kind of important we've got a homepage and it's got all this stuff on it and you got to have search on the homepage But you also need like daily comics and horror scope. So why would search be more important than horoscope? Uh, you know, that's sort of how they felt about it. And and Google felt, no, we we think search is really really important and we're going to do an excellent job of it. Uh, and so that was something new that that other people hadn't thought about.

Totally. [snorts] And people who are my age and all these listeners who are tuning in, Google is a verb for us. Google is how we use the internet. But something is changing now with AI. Uh now a lot of us instead of going to Google, we're going to chat GBT and instead of you know putting in a search query and then digging around for information ourselves, we're just asking a question and getting chat GBT to spit out the information. So how do you think AI is going to change search and the way that we use the internet? Yeah, I think there's always been changes and that that's always been true. So, so Google's had a dominant position. Uh, but there's always lots of places that people go to.

Uh, you know, so if you wanted uh breaking news, you went to Twitter. Uh, if you wanted a short explanation of something, you might go to Tik Tok or YouTube to see a video. Uh so there's always going to be lots of ways to access this and we'll see how that changes uh as AI gets better. Right now sometimes it works and sometimes it doesn't. So it's a little bit of a frustrating experience but uh there certainly seems to be a path to say we can have something that's a much better guide to what's out there both in terms of uh answering a question immediately is is is one aspect of rather than saying I'm going to be pointed to a site that has an answer. I can get the answer right away and then also kind of guiding you through and maybe summarizing or or giving you a whole uh learning path. Uh you know so right now you sort of have to make up that path yourself. Uh but I think AI can do a good job of saying you

know where are you now? What do you know? What do you want to know? And we're going to lead you through that.

Yeah. And AI also is just using the information that was inputed into the system, right? So it might not have all the information available that you could potentially find on the internet. Is that right?

Yeah, that's that's certainly true. Right. Depends on what it's trained on and and we're at a point right now where the training of these big AI models is uh very expensive and so it's harder to keep them up to date, right, with with the internet search. if if something new happens, some new uh news is there, it's it's pretty fast of getting that index to making it available. Uh but with the large AI models, it's it's just too expensive to update them uh instantaneously. Uh and so you miss out on the on the newest stuff. Uh but that will change over time and you know, we'll come up with new ways of of getting things out faster and faster. You know, when I when I first

started at Google, you know, there sort of this transition when it started, we said, "Well, we're kind of like a library where you can go to look things up, [snorts] so it's okay that the library catalog only gets updated once a month."

Uh, and now that would seem crazy to say, uh, you're only getting information that's a month old. Uh, but in the earliest days of Google, that was the case. And then, you know, we went to daily and then hourly and then even hourly wasn't fast enough. Yeah. To get faster and faster.

Yeah. It's so interesting how fast technology changes. Hello, young and profits. Can we take a moment and talk about something essential for anybody building a business? I'm talking about your business identity. When you're building your empire, you've got to think about what the world sees and what stays private inside your business. Things like legal documents, security, and especially privacy. I get it. Starting a business can feel overwhelming with all the paperwork. But imagine building your complete business identity in just 10 clicks and 10 minutes. That's where Northwest Registered agent enters the equation with reliable, straightforward support.

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How has AI changed since you wrote that textbook? Yes. So, we did the first edition in '95 uh and we're up to the fourth edition which we did a year or two ago. uh and there definitely are changes and first of all I think we did the book because we saw changes even back in 1995 where in the the earlier days in the 80s and the start of the '90s uh the sort of dominant form of AI was called expert system and what that meant was you build a system by going out and interviewing an expert say an expert doctor [snorts] and ask them in this situation with this patient what would you do and then you try to build a system that would duplicate what the doctor said. [snorts] Uh and it was all built by hand. Uh you know programmers sitting down trying to understand what the doctor said and trying to encode that uh into rules that they would write into the system and it worked to some extent but it was very

brittle and it just often failed to handle problems that were just slightly outside of of what it had anticipated. So in the 1990s there was a big switch away from this expert system handcoded approach towards machine learning approaches where we said rather than telling the system how to do it just show it lots of examples and let it learn by itself. And so we felt like the existing books had missed that change. Uh we wanted to write a book about it. So we did that. Uh but of course things continue to change. And so I guess what can I say about what's changed over the the four editions? I guess one was at the start we felt like uh well AI this is part of computer science and computer science is about algorithms so we're going to show you a bunch of cool algorithms and we did that and then in the the second edition I think we felt more like okay you still got to know all the cool algorithms but if you had a choice

you're probably better off getting better data rather than getting better algorithms so we're going to focus a lot more on on what the data is. [snorts] Uh, and that continued to be more true in the in the third edition. And now I feel like, okay, now we've got plenty of data. We've got plenty of algorithms. You still have to know about them. But really the key to future progress is neither of those. The key is deciding what is it that you want? What is it that you're trying to build? So, we have a great system that says if you give me a bunch of data, I've got an algorithm that can optimize some objective that you're shooting for, but you got to tell me what the objective is. What is it that you're trying to do? And for some tasks, that's easy. You know, if I'm playing chess, it's better to win than to lose. Uh, but in other tasks, that's the whole problem. And so we look at things like we have these systems that

help judges uh make decisions for parole. Uh who gets out on parole and who doesn't. And [snorts]

and you want to parole somebody if they're going to behave well and you want to not parole them if you think they're going to recommit a crime. But of course these systems aren't going to be perfect. They're going to make mistakes. And so the question you have to answer is uh what's the trade-off between those mistakes? you know, how many innocent people [snorts] uh should we jail to prevent uh one guilty person from getting away, right? And so there's this trade-off. You're going to make false positives and false negatives and what's one one worth against another.

And you know we've we've even before there was AI or any kind of automation we've had these kinds of discussions in our societies going back to uh Judge Blackman in England uh more than a century ago who said it's better that uh 10 guilty men go free than that one innocent man uh uh be jailed. Now, I don't think he meant it that literally, like, you know, 10's the boundary and nine's okay and 11 would be bad. Uh, but [snorts] with today's AI systems, you have to specify that, right? So, you have to build the system and and there's got to be an exact number in there of saying where what is the trade-off point. And we're not very good at at understanding how to do that, right? So we've got, you know, we built a software industry and we have 50 years of experience in building debugging tools and so on. So we're pretty good at at [snorts] making reliable software. There are still,

you know, every week you'll see some kind of bug or something, but we're getting pretty good at that. But we don't have a history of tools for saying how do we specify the right objective? What are the trade-offs? uh you know, how important is it to avoid this mistake versus that mistake? [snorts] And so we're we're kind of going by the seat of our pants and trying to figure that out. And so I think that's where a lot of the the focus is now is is how do you decide what you really want?

I [clears throat] want to dig into this a bit because I think it ties in with this idea or the fact that AI is not yet in all instances at human level intelligence, right? And that's not always the goal. I I read [clears throat] some of your work where you said human level intelligence is really not always the goal when it comes to AI. Um so I want to read you a quote from Dr. Feay Lee who came on the podcast episode 285. She's the co-director of the human- centered AI institute which you're also a fellow and it was an awesome conversation and she said the most advanced computer AI algorithm will still play a good chess move when the room is on fire. So she's trying to explain that like AI doesn't have like human level common sense, you know, uh it's still going to play a chess move even when the room is on fire. So let's start here. How do you feel AI stacks up right now against the human brain as a tool?

Yeah. So that's great. And uh Fay is off awesome and I've heard many of her talks where she makes great points like that. [snorts] Let's see. Uh, so I guess I would try to avoid trying to make metrics that are one-dimensional, right? How do how does AI compared to humans? For for a couple reasons. One is uh, you know, I don't want to say the purpose of AI is to replace humans, right? Uh, we already know how to make uh, human intelligence. Uh, my wife and I did it twice, the oldfashioned way. Uh that was awesome. It worked out great. Uh so instead of saying can we make an AI that replaces a human, we should say what kind of tools can we make so that humans and machines together will be more powerful. Right?

What's the right tool? And so we don't want a tool that replaces a human. We want a tool that kind of fills in the the missing pieces. And and we've always had that. There's always been a mix of subhuman and superhuman performance, right? So, my calculator is is much better at me at uh dividing 10-digit integers. Uh so, I rely on it rather than trying to work it out myself. Uh and I think we'll see more of that of saying what are the right tools for people to use. Now, in terms of this uh generality versus general AI versus narrow AI, I think that's really important. and and so there's there's multiple dimensions we want to measure.

It's uh you know so we want to focus on both generality and performance. So how good are these machines and how general are they? So yes, we have fantastic chess playing programs that are are better than the best human chess players and and recently it's also true in go and and we see sort of every week it's true at something else. uh but we haven't done quite as well at at making them good at at being general. Right? So we have these these uh large language models, the chat GPT and Gemini and so on. Uh and they're good at being general, but they're not completely competent yet at at doing that, right? So uh uh they'll surprise you in in both ways. they'll they'll give you an amazingly good answer one time and then the next time they'll give you an amazingly bad answer.

U so they're not reliable yet at being general. Uh and then we have incredible tools that are narrow and so we're kind of looking at this frontier of how can we make things uh both more perform better and more general. And [snorts] so I think you know we'll get to the point where we'll say here's an AI and it can make a chess move and it can also uh operate in the world. But but right now we separate those two things out and we say we're going to have the chess program that only plays chess and then we're going to have the large language models. Uh and it won't be as good at chess but it will be good at at some aspects of figuring out what to do in unusual situations. Could you give us some concrete examples of AI that we might want uh superior human level intelligence versus AI that we wouldn't want to have human level intelligence with?

So I I guess uh you know it's always better for it to for it to be better but sometimes we need that and sometimes we don't. Sometimes we want to make our own decisions. And I guess part of that is I see too much of people saying uh AI is going to be one-dimensional and automation is going to be one-dimensional and and the more the better. And I [snorts] think that's uh the mistake that I'm worried about. And there's a great uh diagram from the uh uh society of automotive engineers of a level of self-driving cars and they define that as as five levels of self-driving and they did a great job of that and that's really useful and now you can say you know where is Whimo or Tesla are they at level two or level three or or what what level are they at and that was useful but the diagram they used to accompany those levels was worrying to me because they've got this diagram and at level one they have this

uh icon of a person behind the car holding on to the steering wheel and then when you get up to level five that person has disappeared and they've just become a a a dot-like outline, right? And so it's like I don't want technology that makes me disappear. I want technology that respects me [snorts] and I don't want this tradeoff to be one-dimensional of if I get more automation then I disappear more. I'd rather have it be two two-dimensional and let me choose. So sometimes I might want to say I've got a self-driving car and I trust it. I just want to go to sleep. It should take over completely.

But sometimes I might want to say it can do all the hard parts but I still want to be in control. I want to be able to say, "Oh, let's turn down that street or go faster or go slower or let's make an unscheduled stop." So, I don't want to say just because I have automation that I've given up control. I want uh me to come first and let me make the choice of how much the machine is going to be doing and how much I'm going to keep control.

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So, like Dr. Lee, you are an advocate for human centered AI. Can you help us understand what that is?

Yeah. Uh, so a couple things. So, so first of all, you know, I'm essentially a software engineer or a programmer at heart. And [snorts] so I look at what are the definitions of these various things. And so software engineering is building systems that do the right thing. But artificial intelligence is also building systems that do the right thing. So what's the difference? And [snorts] I think the difference is that the enemy in software engineering is complexity. We have these programs with the millions of lines. We have to get them right. And the and the enemy in AI is uncertainty.

We don't know what the right answer is. And then in human- centered AI, the goal is to build systems that do the right thing for everyone and do that fairly. Uh, and so that kind of changes how you build these systems. And part of it is saying you want to consider everybody involved. You so you want to consider the users of your system, but you also want to consider the stakeholders and the effect on society as a whole. So we go back to I was talking about this uh aid for judges and deciding who gets uh parole. [snorts] If you took uh a normal software engineering approach, you'd say, well, who's the user? Okay, it's this judge. So I want to make this program be great for them. uh you know I want a a pretty display with graphs and charts and so on and numbers and figures and diagrams so that they can understand everything about the case and make a good decision and yes you want that in human- centered AI but human- centered

AI says well you also got to consider the other stakeholders so what's the effect on the defendant and their family what's the effect on past victims and potential future victims and their family what's the effect on society as a whole of uh mass incarceration or discrimination of various kinds and uh so you're not just serving one user you're you're serving all these different constituencies I mentioned this idea of uh bearing autonomy and and control so not having to give up control if you have have more automation uh and I think there's the aspect that it's uh multiddisciplinary and multicultural And I think too often you see uh companies say, "Okay, I want to build a system." So the engineers will build it uh and get it working and then afterwards we'll kind of tack on this extra stuff to uh make it look better or make it more fair or less biased and so on. And and I think when you do that,

you don't end up with good with good results. You've got to really bring in all these people right from the start. uh and uh both in terms of being aware of of what it means to build a system like this and then also that you know as we were saying before a lot of these problems is deciding what is it that we want what is it that we're trying to optimize and different people have different opinions on that and so if you get a you know homogeneous group of engineers they might all think the same thing and they say great we're agreed we must have the right answer but then you go a little broader to other people from other parts of society and they might say no you know you forgot about this other aspect you're trying to optimize this one thing but that doesn't work for us. So you've got to bring those people in right from the start to understand who all your potential users are and what's fair for all of them.

So one of the things that worries me is that we live in a capitalistic world. So while it's nice to think that people are going to have like a human centered approach with AI, um I do feel like at the end of the day companies are going to do whatever is going to impact their bottom line the best like most positively, right? So what are the ways that you think that um there'll be some guard rails against not using AI in a human- centered way?

Yeah. So that's certainly an issue with capitalism, not specifically for AI at all, right? That's so that's kind of AC across the board.

Um and so uh what do we have to combat that? So so part of it is uh regulations of various kinds so governments can set in and get rules. Part of that is uh pressure from the customers saying here's the kind of company we want. here's the kind of products we want. And part of that would be competition [snorts] of saying, you know, if you build a system that doesn't respect something that users want, somebody else will build one that that's better. Uh, and I think we're in this kind of wild west period now where we don't quite know what the uh bounds are going to be.

And [snorts] so, you know, there's so many of these sets of AI principles now. So the uh all the big companies have have their own sets. Uh I helped put together the Google one. Uh various countries have uh have legislation or or sets of principles. The White House put out their set of AI principles a couple months ago. The professional societies like the uh Association of Computing Machinery has theirs. Uh, I actually joined an AI principles board with Underwriters Laboratory and I thought that was interesting because the last time more than 100 years ago there was a technology and people were worried that it was going to kill everyone and it was electricity and [clears throat] right and so Underwriters Laboratory stepped in and said, "Okay, y'all are worried about getting electrocuted but we're going to put this little UL sticker on your toaster and that means means you're probably not going to die.

And consumers trusted that mark and therefore the companies voluntarily submitted themselves to certification. Uh and I kind of feel like this third party nonprofit certification can be more agile than government uh making uh laws. Uh and so I think that's part of the solution, but I don't think any one part of it can do it all by himself. I think I think we need all those parts. Yeah, very cool. Very interesting. I agree. A third party solution sounds like it could work pretty well. So, we had Sal Khan on the show and he as the Khan Academy, he talked a lot about how AI could help education. Do you have any ideas of how AI could support education and students?

Yeah, [snorts] I think that's awesome. I think the work S is doing has been great uh right from the start. uh and recently over the last year or so with the Kon Migo uh large language model. Uh [snorts] so you know back in 2011 uh Sebastian Throne and I said we want to take advantage of this capability for online education and we put together uh an online course uh about AI. We signed up 100,000 students far more than we ever expected to to sign up. uh and we ran that course. But of course at at that time the leading technology was YouTube.

We' show students a video and then we'd have them answer a question and and we could do a little bit. You know, if they got this wrong answer, we could show them one thing and if they got another wrong answer, we could show them something else. But basically, it was very limited in the flow you could do. And now with these large language models, you have a much better chance to customize the results for the student. both in terms of the learning experience and then I think also in terms of uh the motivation for the student. So that was the one thing we learned in doing the class is that we came in saying well our job is really information. If we can explain things clearly uh then we're done and and we're a success. And we soon realized that that's only part of the job. And really the motivation is more important than the information because if a student drops out, doesn't matter how good our explanations are, if

they're not watching them anymore, it doesn't do any good. Uh and so I I think uh AI has this capability to uh to motivate much better to allow students to do what they're interested in rather than what the teacher says they should be interested in. Uh but we got a ways to go yet and we don't quite know how to do that. Right? So

you can't just plug in a language model and [snorts] hope that it's going to work. So yes, it would be useful, but you have to train it to be a teacher as well as to understand what it's talking about. Uh, and we haven't quite done that yet. We're kind of on the way to doing that. Uh, you know, you look at there's there's a dozen different problems to be solved and we have candidate solutions, but we haven't done it all. Right? So, right now, uh, the language models can be badgered too easily. You say, "Here's a problem." And the student says, "Tell me the answer."

And you know, at first the language model would say, "No, you wouldn't learn anything if I told you the answer."

But then you say, "Tell me the answer, please." And it says, "Oh, okay." Right? And and so we have to teach these things. When is it the right thing to give the student the answer? When is it the right thing to be tough and refuse to do that? When should you say, "Uh, oh, you're right. That's a hard problem. Here's a simpler problem. why don't you try this simpler problem first or to say uh looks like you're getting frustrated why don't we take a break or why don't we go back and do something else that that uh would be more fun for you and so there's all these moves that teachers can take and so doing education well is this combination of of really knowing the subject matter and then really knowing the student and the pedagogical moves you can make and we haven't quite yet built a system that's an expert on both of those. Uh but you know, Khan and others are working on it and and so I think it's a great and exciting

opportunity. Do you feel like some of this learning and training could be applied to the workplace?

Yeah, absolutely. And and some of it, I think, is is easier and and better done uh for uh for workplace training. Uh and I think that's going to be really important. I think, you know, we've built this bizarre system now where we say you should go to a college for four years and then we're going to hand you a piece of paper that says you never have to learn anything again. Uh [snorts] that shouldn't be the way we do things. Uh and you know, there's a value to college. Maybe it doesn't have to be for everybody. Maybe more people could be learning more on the job or learning just in time uh when they need a new skill. [snorts] Um,

so I think uh there's a great opportunity for that. I think that the systems we have right now are kind of better at at shorter subjects anyways, right? So it's hard to put together a class that says, you know, let's do all of biology one or something, but it's easier to say, why don't you get trained on this specific workplace thing, how to operate this machine or how to operate this software and so on. So in some sense we're better at that kind of training than we are at the traditional schooling. Uh so so yeah there's definitely a big opportunity there. The thing that mitigates against it is you know we could spend a lot of uh investment on making the perfect biology one class because there's going to be millions of students that take it. But for some of this on the job training, you know, maybe, you know, I'm in a small company and we do things a specific way and there might be only

five people that need to be trained on it. And so, so right now it's not really cost-effective to say, can I build a system that will do that training? But

totally but that's one of the goals to say can we make it easier for somebody you know who's not an expert programmer not an AI expert to say here's some topic I want to teach and I should be able to go ahead and teach that and and I think that's something that's oddly missing from [snorts] our our our sort of standard playbook right so you look at

you know we have these office suites and what do they give you they give you word processing and and spreadsheets and PowerPoint presentations and and sure that's great. Those are three things that I want. But I think a lot of people want this I want to be able to train somebody on a specific topic more than they want spreadsheets. Uh but we don't have that yet. But you know maybe someday we will. Maybe that'll be a standard tool that would be available to everyone. So, this conversation made me realize that there really is no better time to be an entrepreneur because as we were talking about, a lot of jobs might get replaced by AI. Um, and when you're an entrepreneur, when you own the business, you're sort of in control of all those decisions, and you're the one who might end up benefiting from the cost savings of replacing a human with AI. So do you feel like AI is going to generate a lot more entrepreneurs and

solopreneurs in the future? Absolutely. And I think it's it's a combination. So I think AI is a big part of it. I think uh the internet and access to data was part of it. Uh the uh uh cloud computing was a big part of it. Right? So it used to be, you know, if you were a software engineer, the hardest part was raising money because you had to buy a lot of computers and just to get started, you know.

Now all you need is a a laptops and a Starbucks card [snorts] and you can sit there and start going and then, you know, rent out the cloud computing resources as you need them and pay as you go. uh and so I think AI will have a similar type of effect. Uh you can now start doing things uh much more quickly. You can prototype something and and go to a release product uh much faster. Uh and it'll also make it uh more widely available, right? So, you know, there's a lot of I, you know, so I live in Silicon Valley, so I see all these notices going around of saying uh uh uh looking for a technical co-founder, right? So, there's lots of people that say, "Well, I have an idea, but I'm not enough of a programmer to do it. So, I need somebody else to help me do it." [clears throat]

I think in the future, a lot of those people will be able to do it themselves. Right? So, I had a great example of a of a friend who's a biologist and he said, you know, I'm not a programmer. I can pull some data out of a spreadsheet and make a chart, but I can't do much more than that. But I study uh bird migrations, and I always wanted to have like this interactive map of where the birds are going and play with that. And he said, and I knew a real programmer could do it, but it was way beyond me. But then I I heard about this co-pilot and I start playing around with it and I built the app by myself. And so I think we'll see a lot more of that of people that are, you know, non-technical or semi-technical who previously thought, here's something that's way beyond what I could ever do. I need to find somebody else to do it. Now I can do it myself.

Yeah, I totally agree. And we're we're seeing it first with like the arts. Uh for example, now you can use Dolly and be a graphic designer. you can use GBT and be a writer. So, so many of the the marketing things are already being outsourced by AI. It's only a matter of time where some of these more difficult things like creating an app like you were saying is going to be able to be done with AI.

Absolutely. Cool. So, what are the ways that you advise that entrepreneurs use AI in the workplace right now? Uh I guess so you know you could help build uh prototype systems like that. You can do research. Uh you can ask uh you know give me a summary of this topic. What are the important things? What do I need to know? Uh as you said creating artwork and so on. If that's not a skill you have they can definitely help you do that. uh looking for things that you don't know uh is useful. Uh and so I think just just being aware of of what the possibilities are and and having that as as one of the the things that you can call upon. Uh it's not going to solve everything for you, but it just makes everything go a little bit faster.

[snorts] Yeah. Do you think that AI is going to help accelerate income inequality? Uh I think it's kind of mixed. Uh so you know any kind of uh software any kind of uh goods with zero marginal cost tends to concentrate wealth in the hands of of a few. And so that's definitely something to be worried about. uh with AI we also have this aspect that the very largest models are big and expensive. They require big capital investments. Uh and if you had asked me two years ago, I would have said, "Oh, you know, all the AI is is going to migrate to the the big cloud providers uh because they're going to be the only ones that can build these these uh large state-of-the-art models." Uh but I think we're already going past that, right? So we're now seeing these much smaller open-source models that are almost as good and that uh you know don't impose a a barrier of of huge upfront costs. So I think

there's an opportunity you know yes the big companies uh are going to get bigger because of this but I think there's also this opportunity for the small opportunistic entrepreneur to say here's an opening and I can move much faster than I could before and I can build something and get it done and then have that available. So that's part of it. Then the other part is well what about people who aren't entrepreneurs and we've seen some encouraging research that that says uh AI right now uh does uh alleviate inequality and so there have been studies looking at well you bring AI assistance into like a call center and and it helps the less skilled people more than the than the more skilled people, which kind of makes sense, right? Sort of the the people who are more skilled, they already know all the answers and the people that are less skilled, it brings them up uh almost to the same level. [snorts]

And so I think that's encouraging because that means there's going to be a lot of people who are able to upskill what they do and they'll get higher paying jobs, right? They're not going to found their own company, but they're going to do better because they're going to have better skills.

Yeah, makes a lot of sense. Okay. So, as we close out this interview, let's talk about the future a bit. Um, what scares you the most about AI right now?

Yeah. So, you know, I'm not worried about these uh Terminator scenarios of a of an AI waking up and saying, uh, I think I'll kill all humans today. Uh, so so what am I worried about? Uh, well, I guess I'm more worried about a human waking up and saying, I want to do something bad today. Uh, and so what could that be? Uh, well, uh, misinformation. We've seen a lot of that. Um, and I think it's mixed of how big an effect AI will have on that. I mean, it's already pretty easy to go out and and hire somebody to create fake news and and promulgate it and and the hard part really is getting it to be popular, not to create it in the first place. So, in some sense, maybe AI doesn't make that much difference. It's still just as hard to get it out and and maybe AI can fight against that misinformation. So, I think the jury is still out on that.

But, you know, if you did get to the point where an AI could create new enough about an individual user to say, I'm going to create the fake news that's going to be effective specifically for you. Uh that would be really worrying and and we're not there yet, but but that's something to worry about. Uh I worry about uh the future of uh warfare. Uh so you're seeing these things uh today. We just saw, you know, a tiny little personal size uh drone shot down a Russian helicopter. Uh so you know we we've had half century of or so of mostly a stalemate of saying the big countries have the power to impose themselves on the others but none of them are really going to unilaterally do it on a large way and we have smaller regional conflicts. uh now we may be transitioning into a world where we say the power is not just in the big countries, it's in lots of smaller groups. Uh and that becomes a a more

volatile situation and so there could be more of these smaller regional conflicts and and and more worries for civilians that get caught up in it. Uh so I'm worried about that as well. [snorts] Uh and then uh you know like you said the the income inequality I I think it's a big issue.

Well, let's end on a positive note, I guess. What excites you the most about AI.

So, a big part of it is this opportunity for education that that's where I spent some of my time and and I'm really interested in in that now. So, so I think that can make things better for everyone. Uh just making everyone uh more powerful, more able to do their job, able to get a better job. So that's exciting. I think uh applications in healthcare uh are are a great opportunity and you [snorts] know I I got involved a little bit in trying to have uh better digital health records uh and that really didn't go so far mostly because of bureaucracy and so on but I think we have the opportunity now to do a much better job to invent new treatments new new drugs. You've seen things like uh alpha fold figures out here's how every protein works and you know it used to be you could get a PhD for figuring out how one protein worked and and alpha fo said I I did them all.

So I think this will lead to uh drug discovery lead to healthier lives uh longevity and so on. Uh so so that's a really exciting application. Yeah, it's so interesting to me that AI can do so much good and then there's also such a risk of it doing so much bad, but I feel like any good technology kind of brings that risk along with it.

Yeah, I think I think that's always true, right? If it's a powerful technology, it can do good or bad specific, you know, especially if there are good and bad people trying to to harness that way. Uh and some of it is is intentional bad uses and some of it is unintentional, right? So internal combustion engines did amazing things in terms of uh distributing food worldwide and and making that be available, making transportation be available. But there also these unintended side effects of uh pollution and global warming and and maybe uh some bad effects on the structure of cities and so on. And

you [snorts] know, we would have be a lot better off if it, you know, when cars were first starting to roll out in 1900 if somebody said, "Let's think about these long-term effects." So, so I guess I'm optimistic that there are people now thinking about these effects for AI as we're just starting to roll it out. So, so maybe we'll have a a better outcome. [snorts]

Yeah, I hope so. Well, Peter, thank you so much for joining the show. I end my show with two questions that I ask all of my guests. What is one actionable thing our young and profiters can do today to become more profitable tomorrow?

Uh, I guess keep your eye on what it is that people want. Uh, you know, so I said the problem in AI is figuring out what we want. Uh, I'd worked some with uh with people at Y Combinator and and I still have this t-shirt that says on the back, make something people want. Uh, and very simple advice to entrepreneurs, but but sometimes missed. And and so I think that's true generally and and I think AI can help us do that.

Yeah, it's so true. The number one reason why entrepreneurs and startups fail is because there's no market demand. So make something that people want. Uh and what is your secret to profiting in life? And this can go beyond today's episode topic.

Uh I guess uh you know keep keep around the the people you like and uh be kind to everybody. [snorts]

Love that. Where can everybody learn more about you and everything that you do? Uh you can uh look for me at uh norvig.com or on uh LinkedIn or uh thanks to Google. I'm easy to find.

Awesome. I'll stick all your links in the show notes. Peter, thank you so much for joining us.

Great to join you. Hello. It was so great to connect with Peter and dig into a perspective on AI that feels grounded and deeply human. Peter spent decades at the center of technology, and what stands out to me is how committed he is to creating tools that elevate people rather than overwhelm them. His lens on human- centered AI is a powerful framework for anybody building, leading, or innovating in this new era. Here's a couple takeaways from this conversation. First, clarity matters more than complexity. Peter reminded us that great technology doesn't start with bigger models or fancier algorithms. It starts with defining the right goal.

Entrepreneurs who know exactly what outcome they're aiming for will use AI more effectively than those who are just chasing the trends. Next, human judgment remains irreplaceable. Even as AI becomes more capable, its value depends on the choices we make. Peter emphasized that people will set the objective, interpret the results, and decide what good looks like from AI. For founders, that means leaning into your taste, your creativity, and your intuition. AI can accelerate your work, but it can't choose your mission and it can't replace your human intelligence.

Finally, learning must become a lifelong habit. Peter offered us a refreshing view of education as something that is continuous, adaptive, and personalized. Entrepreneurs that have that mindset in this new era, those who stay curious, update their beliefs quickly, and experiment often will thrive. When you stay adaptable, you stay ahead. All right, Yap fam. If this conversation got your wheels turning, I want to hear from you. So, take a second and share your thoughts on Peter's human- centered approach to AI. Let's keep this dialogue going and build a community that uses AI with intention.

And if you want to follow me on social media, you can find me at Yap with Hala on Instagram or LinkedIn. Just search my name. It's Hala Taha. All right, Yap fam. This is your host, Halataha, aka the podcast princess, signing

Questions about this episode

What is discussed in "Why Peter Norvig Says AI Will Empower Millions—But Raise New, Perilous Risks"?

In order: 0:00 Google LLC, Peter Norvig, AI Vault, Young and Profiting Podcast; 5:00 Google LLC; 10:00 Google LLC; 35:00 Google LLC, Khan Academy; 55:00 Google LLC.

What is mentioned in this Young and Profiting episode?

5 people, works and ideas are mentioned, including Google LLC, Peter Norvig, AI Vault, Young and Profiting Podcast, Khan Academy. Each one on this page links to its own page with every episode that mentions it.

How long is "Why Peter Norvig Says AI Will Empower Millions—But Raise New, Perilous Risks"?

The episode runs 60 min; the transcript is about 10,594 words in 101 timestamped sections.

More from Young and Profiting

About this transcript: the episode's spoken words as captioned on YouTube, split into timestamped sections. Reclip.Me locates every person, work and idea in the text, keeps the time it is first named, and quotes the words around it verbatim. Last updated January 24, 2026.