Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think

1. 0:00 – 2:12

   Why AI Fear-Mongering Started

   1. ANAndrew Ng0:00

      There's been a lot of misinformation about AI.

   2. MMMarina Mogilko0:03

      This is Andrew. He co-founded Google Brain and Coursera. His machine learning course has reached millions of learners, and he is one of the most influential voices in AI today.

   3. ANAndrew Ng0:13

      So a handful of leading AI companies have been very loud voices, fear-mongering around AI to try to get regulations passed. This drumbeat of fear-based messaging has skewed societal's perception to be really negative on AI.

   4. MMMarina Mogilko0:26

      People talk to me about data centers and job loss.

   5. ANAndrew Ng0:30

      Maybe AI could do thirty, forty percent of many jobs, and what that means is, well, that sixty percent that a human does has become even more valuable.

   6. MMMarina Mogilko0:38

      What about loss of human control over AI?

   7. ANAndrew Ng0:42

      I think about something else that we can't control.

   8. MMMarina Mogilko0:46

      I think you're one of the voices in AI who comes with a huge background in machine learning and teaching AI, and also y-you're a positive voice. 'Cause this is something that I've been seeing, especially this summer, how polarized the society has become, especially on social media. When I'm posting about AI and people talk to me about data centers and job loss. Why do you think this wave started recently? What do you think the causes are?

   9. ANAndrew Ng1:13

      There's been a lot of misinformation about AI, and the root cause of a lot of this is an unfortunate attempt that started two, three years ago of, I think, PR and regulatory capture. It turns out that the most-- one of the most valuable things in AI right now is the giant AI models, giant large language models that some companies have trained. But if you spend billions of dollars training a model, it is really inconvenient if someone else trains a model and wants to give it to anyone in the world to use for free. So a handful of leading AI companies, I think as you know, have been very loud voices, fear-mongering around AI to try to get regulations passed to create an unfair playing field that favors incumbents, so that we all have to pay a high toll for use of AI while stymying the other teams, be it researchers or other companies that want to just give away, open way to open source models that anyone could use much cheaper. Unfortunately, fear-mongering works, um, when you go and

2. 2:12 – 4:26

   The "Job Apocalypse" Myth

   1. ANAndrew Ng2:12

      say AI is like nuclear weapons, which is an analogy that has no basis in fact. What do they have to even do with each other? Or when you go around and cherry-pick cases of AI, you know, making a misstep and make it much bigger than it is, or even spread misinformation about how AI uses-- data centers uses a lot more water than the actual reality. This drumbeat of fear-based messaging has skewed societal's perception to be really negative on AI, which is unfortunate because this is slowing down American adoption in AI. This is making America less competitive. Um, and unless we get the truth about AI out there, which is that it's fantastic benefit with some problems, but not nearly the dev- which they're blown up to be, it will, um, hurt individuals.

   2. MMMarina Mogilko2:58

      I'm gonna read out some of the problems that people are highlighting, job loss and inequality. What do you think?

   3. ANAndrew Ng3:04

      The Job Apocalypse or Jobpocalypse, this idea that AI will take over fifty percent of jobs, people will be out of work, rotting in the streets, that's just not gonna happen. With every wave of technology, including AI, the skills we need to do great work shifts. And so AI is changing job professions. But boy, I wish AI were-- AI just doesn't work well enough. I know that handful of businesses want to hype up AI to say, "We'll have super intelligence, or we'll have artificial general intelligence or whatever, and can do all the stuff that humans do." I wish AI worked better. We're just not good enough to make AI do everything a human does. And if you look at the analysis of jobs, economists, uh, like my friend Erik Brynjolfsson at Stanford, Andy McAfee at MIT, um, economists have analyzed many people's jobs and by break it down into individual tasks, and maybe AI could do, you know, thirty, forty percent of many jobs. And what that means is, well, that sixty percent that a human does has become even more valuable because it's called a economic complement to the thirty, forty percent that's now cheaper. And so what will happen is people that use AI, maybe people that use AI will replace people that don't use AI. But AI is not in a position for the vast majority of jobs to replace people. Of all the different professions, the

3. 4:26 – 11:59

   Advice for New Grads in the AI Era

   1. ANAndrew Ng4:26

      one that's most affected by AI now is software engineering, because AI is actually fantastic at writing code. And what we see is that the number of job openings in software engineering is up, contrary to what, you know, the doom fear-mongerers would say, right? AI is not actually able to replace software engineers, and all the good software engineers I know are busier than ever. Now, the flip side of it is, if someone still write code like it's two thousand twenty-two before ChatGPT, they're in trouble. They need new skills. Like, don't, don't do stuff that, that thirty, forty percent AI can automate. You've got to stop doing that. Let AI do that, but then gain your skills to do the other sixty, seventy percent that AI cannot do.

   2. MMMarina Mogilko5:07

      What would y-your advice be to new graduates? 'Cause, uh, I talked to Eric, um, on this podcast-

   3. ANAndrew Ng5:12

      Oh, cool. Yes

   4. MMMarina Mogilko5:12

      ... and, uh, he was talking about that there's not really a lot of impact on the job market except for, I think he mentioned people from eighteen to twenty-five who just graduated. What would be your advice to those people who don't have the expertise maybe to strategize in their job yet? Uh, they can only do manual work that AI can do as well.

   5. ANAndrew Ng5:31

      So one real challenge, um, uh, for fresh college grads is that the university system is slow to adapt. And so, um, you know, I love academia. I think we should all support academia and universities. And when AI comes and transforms the way software is written, universities often take, like, a year or two for the faculty to master the skills, then create new courses, get curriculum committee approval, whatever, get the faculty senate to vote. It just takes years, and that speed of change in academia is very poorly matched to the speed of change in AI. So sadly, many universities are still teaching students to be ready for the jobs of two thousand and twenty two, when we shouldn't even be teaching them for the jobs of two thousand and twenty six. We should be teaching them for the jobs of two thousand and twenty-eight and beyond. And what this means is the job openings are there. Uh, tons of employers I know just can't find enough skill, you know, uh, uh, people at any level of seniority. But, um, it turns out in my office right now, we have a lot of interns. There are current college students, fresh college grad. We also have one high school intern, and they're amazing and productive, but the key is they're all very AI native. They all use AI tools-

   6. MMMarina Mogilko6:43

      Mm-hmm

   7. ANAndrew Ng6:44

      ... to do the things AI could do, but then also, you know, lean in to doing the things that, uh, humans can do that AI can't for the f- for, for a long time. So there's plenty of work for people to do. But so my advice to fresh college grads or to people currently in college is, um, by all means, work hard in classes, you know, get good grades, learn from the instructors. But to the extent that there are still additional skills the university has not yet adapted to teaching, then find other ways to learn online, be it from Coursera or DeepLearning.AI or Udemy or other places where you can gain the more cutting-edge skills, especially AI skills, that, um, universities have not yet worked in the curricula.

   8. MMMarina Mogilko7:24

      Quick pause, because what Andrew just said about workflows connects to something that HubSpot just put out for free. The thing is, the real opportunity right now is not just in the models themselves, but in what you build on top of them, how you turn AI from a chat window into workflows that actually do work for you. And the good news is you don't have to start by building a full agent. You can start much simpler with better prompts. HubSpot just released the advanced ChatGPT prompt engineering playbook, and the idea is very simple. In seven days, it helps you move from getting generic AI answers to building prompts that give you much more consistent and useful options. Because we've all had this moment, you type in one vague sentence, get something mediocre back, and then spend the next twenty minutes fixing it yourself. This playbook is basically built for that exact problem, and it walks you through a single progression. First, you learn how to structure prompts with things like role, context, format, and parameters. Then it gets more advanced. Few shot examples, chain of thought reasoning, more precise prompt structures, and ways to make outputs more reliable. What I like is that it doesn't stop at individual prompts. It also shows you how to build reusable AI personas, modular prompt components, and eventually your own signature prompt engineering system. So instead of starting from scratch every time, you're building a repeatable way to work with AI, whether you're creating content, analyzing data, or making business decisions. The playbook is free. Link's in the description. Thanks to HubSpot for sponsoring this video. And now back to our conversation with Andrew.

   9. ANAndrew Ng9:00

      I wanna say one other thing. Um, it turns out one... If, if you look at the skill map changes, one of the most important changes is, um, it's so much easier to build with AI than before. When something becomes much easier, a lot more people should do it. And so now, not only should professional software engineers build software with AI, it's becoming much easier for everyone to build with AI. And people that embrace that and do so will be more productive and will accomplish more, and I think have more fun than the ones that don't. And AI lets you build really fast. So for people that are not just software engineers, but you know, marketers, recruiters, uh, HR professionals, operations specialists, I think if they learn to build with AI, uh, they'll really just do much more-

   10. MMMarina Mogilko9:48

       Yeah

   11. ANAndrew Ng9:48

       ... whatever their job role is.

   12. MMMarina Mogilko9:50

       How do you, by the way, measure, uh, the increase in productivity, uh, when you deploy AI? Do you have like a KPI in your company?

   13. ANAndrew Ng9:58

       I wish there's a simple answer. I find that the business outcome of AI is more a function of the business than a function of the AI. For, for some, it may be, um, increase, you know, uh, uh, customer growth and retention, or it may be, uh, faster to serve customers, or it would be increased accuracy in some tasks. So the KPIs tend to be related to the business rather than the AI.

   14. MMMarina Mogilko10:18

       Mm. So you can't, like, directly measure just AI. 'Cause it's, it's an interesting thing to do, 'cause we've been deploying AI actively in my company, and I think for me as a media company, it's probably the amount of views, the output.

   15. ANAndrew Ng10:29

       Oh, yeah.

   16. MMMarina Mogilko10:29

       Uh, it's just interesting-

   17. ANAndrew Ng10:30

       The business metrics, yeah

   18. MMMarina Mogilko10:30

       ... how... Yeah.

   19. ANAndrew Ng10:31

       Yeah.

   20. MMMarina Mogilko10:31

       It's just interesting how different people measure... Even revenue, like if you're becoming more effective-

   21. ANAndrew Ng10:35

       I see

   22. MMMarina Mogilko10:36

       ... uh, with, um, how you make money.

   23. ANAndrew Ng10:37

       Actually, say more. How are you using AI in your business?

   24. MMMarina Mogilko10:39

       Oh my God. Uh, I have the... So first of all, we have Claude for all of us, and we have certain projects for every social media that we're on. So for example, for this podcast, we have a project that's called Guests, and it knows all the analytics from previous guests, and it has certain criteria on which we rank every single person who comes to the podcast, whether he's, he or she's cited, whether they have a certain opinion on AI, whether they've been active with AI in their company, or if they're a recent founder in AI. So it gives them different weights, and it comes up with a grade based out of forty. Forty meaning tier one, thirty meaning tier three, et cetera. And then we have another one that analyzes every single podcast and gives me tips on how to ask questions.

   25. ANAndrew Ng11:23

       Oh, wow.

   26. MMMarina Mogilko11:23

       Same for Instagram, same for LinkedIn. It has my tone of voice, personal dossier, my business strategy. So it, whenever it writes something, it knows all the facts about me, how I sound. Every social media is run by a person, so a person makes a strategic call. And by the way, if you can give me feedback on this, if I can improve. So what I'm working on right now is closing the loop, because sometimes they send me a text and I'm like, "Oh, we need to change this, this, and that." But that happens in a chat, in Telegram, and we have this feedback. We, we have a bot that scans all of our chats. But I really want AI to be able to learn continuously

4. 11:59 – 13:56

   Why AI Is Bad for Learning

   1. MMMarina Mogilko11:59

      from this feedback to just know my taste better.

   2. ANAndrew Ng12:02

      There's one thing I see a lot in AI, which is, um, it turns out for AI as data scientist or AI as brainstorming partner, it often comes up with, you know, one or two good ideas, two or three mediocre ones, and like S-- you know, four atrocious ones. And sometimes you wonder, how could my AI have thought, you know, like, that could even be a plausible idea? And to me, this relates to the jobpocalypse point of view, which is that for a long time, humans, you, me, everyone watching this, will have a significant context advantage over AI, which is that you know something that's incredibly obvious to you that, you know, that was an awful idea, but the AI did not. And it turns out that one of the reasons why AI will not replace our jobs or whatever, or a large percentage of them anytime soon, is because humans have a massive context advantage compared to AI. We know so much that, you know, from our years of experience that we talk to customer, we saw the funny facial expression that tell us, "Ah, they don't like this." Or we talk to business, or, you know, our manager said, "Hey, blah, blah, blah, I really care about this." And so it turns out that almost all humans, well, maybe all humans, just know a lot of stuff that the plumbing does not exist, and I don't think exists for the foreseeable future for AI to get. I know sometimes people talk about the importance of human judgment or human taste, and sometimes you wonder, all right, what is taste? It's this fuzzy thing. But to me, the technical thing that underlies why humans have better judgment and better taste than AI is this context advantage. And because this is a long-term advantage, like, no one's gonna solve this, you know, in a few years. This is why we just need a lot more humans with that judgment and taste to keep on complementing the AI.

   3. MMMarina Mogilko13:43

      And doesn't this make education even more important? Because education is-- gives us context. 'Cause this is another, another thing I'm hearing about AI, like, you won't need education 'cause all the information is at your fingertips. You just ask ChatGPT. But when you say context

5. 13:56 – 14:55

   Inside LearnVector: Andrew's $100M Bet

   1. MMMarina Mogilko13:56

      and taste, for me, that's years of acquiring knowledge and learning from the best and seeing how they perform versus just asking a chat.

   2. ANAndrew Ng14:04

      I'm going to say something that may be controversial. I don't know if I've said this publicly, but I think it's true, which is frankly, AI models are terrible for learning. Um, I know people think AI is, AI is wonderful at getting things done. Use it all the time, love it. But all the data that's coming out is that when, say, college students use AI, we know this, it's just the studies now back it up as well, so we also have numbers, but the data's very clear. Students score higher on homeworks when they use AI. Yay, higher homework scores. But retention, their long-term performance is much worse because their AI do the work for them. More and more studies are coming out to back this up now that I think people think, "Oh, it turns out," you know, I think Wikipedia is a wonderful tool. It has tons of facts. Uh, web search is

6. 14:55 – 23:57

   What to Study If AI Scares You

   1. ANAndrew Ng14:55

      a wonderful tool. It has tons of facts. But it turns out that when you ask AI to do work for you, you are cognitive offloading to AI, which is great because that's how society moves forward and it gets work done. But human retention is much worse. It's just so clear that LLMs, as they are most commonly used, are terrible for learning. I'm not saying there's no way to use it in a way that is good for learning. I think there are ways to use that are good for learning. But even for myself, there's so many things that I ask the AI model over the last, you know, six months or whatever, like, I don't know, building some project, how does this front-end, back-end component work, whatever. Give me the answer, get the job done. It was fantastic. But six months later, I don't remember the answer. When I need to redo that front-end, back-end component, I ask the AI again. So data's really clear. We should stop thinking of AI as helpful for learning. At least the vast majority of ways that the vast majority of people are using AI models today is absolutely terrible for learning.

   2. MMMarina Mogilko15:53

      But you're building a company helping solve that, right? 'Cause the one, one-to-one tutoring with AI, is that what you just announced with a hundred million investment from Coursera?

   3. ANAndrew Ng16:02

      Yes. So I'm excited about leading a new organization called LearnVector that is focused on, um, building new learning experiences that is much more one-to-one than one-to-many. So, you know, fifteen years ago, I, I was privileged to participate in the o- online courses movement that I think changed the way a lot of people learn. But that was, and still remains, largely a one-to-many experience where everyone, you know, kind of watches the same video, which is actually okay. It actually works well. But the technology now exists to create much more personalized, customized one-to-one experiences. And so our team is working hard on that. I think we'll have, uh, a lot more to show by early next year. One thing about human skill development, I, I feel like because AI has so heavily impacted software engineering, um, what we see happening in the job market for software engineering is a harbinger or is a forerunner of what we'll see in other disciplines as well. And in software engineering, um, people need to learn new skills, but when they do, they are thriving and creating more value and frankly, getting raises and doing even more exciting projects. And what I'm seeing the early signs of in other disciplines as well, for example, in software engineering, you know, most developers, like front-end, back-end developers, have now become full stack developers. Because of AI help, you could take on broader scope. I'm seeing early signs of this in other disciplines as well, where, for example, someone that in marketing, they did marketing coordination, uh, coordinating marketing campaigns. With AI help, they can now become more of a full cycle marketer-

   4. MMMarina Mogilko17:32

      Yeah

   5. ANAndrew Ng17:32

      ... and take on a broader scope. And I'm seeing, you know, frankly, sourcers in recruiting become more full cycle, do end-to-end recruiting. So now the good news and bad news is for people to step up to these broader roles, you do need to learn AI skills, but also it's not just learning AI. You also need to learn these other skills, uh, like how do you do the other parts of marketing or recruiting or software engineering or AI engineering. So I think this actually creates a heavy need, a big need for people to gain new skills. But when they do, which is both AI skills, but also disciplinary skills, then they can do much more, hopefully have more fun, work on more exciting projects, hopefully get paid more as well. And one reason I kind of worry about the fear-mongering is, um, I got an email from someone that was about to enter college, and, you know, he emailed me saying, "Hey, Andrew, taking online courses, but I'm really struggling with what I should major in college because In four years, won't AI do all this and everything I learn will be obsolete? And the answer is no, of course, it won't all be obsolete. But when we keep on pushing these fear messages, uh, we make people wonder if they will even be relevant, and it makes people not lean in to gain these skills that'll put them in much better position. So I see very clearly that these fear-mongering messages are distorting how many people, including, you know, high school students, college students, fresh grads, think about the economy. And frankly, making people give up is one of the worst things we could be doing in this era when people that lean in will thrive.

   6. MMMarina Mogilko19:03

      Andrew has taught over eight million people AI. He started teaching machine learning online back in two thousand eleven, years before the current AI boom. Now, one of the companies he's building is focused on AI agents. From the way that it sounds, it can still feel way too technical. So I put together a step-by-step guide to building your first AI agent with no coding required. It walks you through what to automate, how to set it up, and how to make it actually useful. It is in my newsletter this week. The newsletter is called Future Proof. It's free. Link is in the description. What would you reply back to that email that somebody sent you? What would you say is the best major to study now to thrive in AI era? Do you think it's like going deep into niche or just broader computer science so that you can acquire AI skills really fast?

   7. ANAndrew Ng19:50

      You know, I don't know what's the best major. There are an awful lot of great majors. It's, it's like, um, I kind of feel like what's the best job in the world is like what's the best major in the world.

   8. MMMarina Mogilko19:58

      Oh, something that you love, right?

   9. ANAndrew Ng20:00

      Yeah. My daughter wants to be an astronaut. I don't know if she can major in becoming an astronaut. I have to think about that. When she gets older, she may change her mind. I see so many opportunities, um, across, across so many job roles. It all seems very exciting to me. But do learn AI, do learn to build with AI. The other thing that my team's been working on an AI engineering skills map to try to map out, you know, the most important skills for AI engineering. One thing that I felt intuitively, but I was surprised to see it show up in the data, was that a lot more job descriptions seem to be saying that they want people that demonstrate a very high sense of agency. Because it turns out, with AI, there are a lot more opportunities for individuals to spot problems and go build something or do something to go solve it. So I think we're really evolving what we've long been evolving, but we're accelerating past the era where people sit around and wait for their boss to tell them what to do.

   10. MMMarina Mogilko20:50

       This is what I've been feeling a lot, especially when we started doing remote work. I want people to be entrepreneurs within their niche. Like, if you're d- helping me with LinkedIn, you're an entrepreneur there. You can hire more contractors, you can deploy different tools. You make the strategic decision whether this topic is good or not. Shall we proceed with it? I really think, and tell me if you agree with me, we're moving into that job market where everyone is kind of independent in their workplace.

   11. ANAndrew Ng21:15

       I think people will have much more autonomy and creativity. So I agree with that, and I'd even go one step further, which is I talk to a lot of people, you know, engineers and others in large companies, that tell me that their manager tells them to stay in their swim lane. They'll say, "Oh, I have this creative idea up," but the manager says, "No, I need you to focus on just one thing," frankly, often because their manager's career depends on it. But I feel like the number of opportunities for people to spot things outside their swim lane, um, and then in a responsible way, explore how to get it done, that feels very exciting to me. And I think that in the future, the businesses that set up a culture that encourage people to learn AI, build fast, responsibly, um, talk to customers, would drive a lot more value than the more hierarchical silo organizations.

   12. MMMarina Mogilko22:03

       Yeah, it, it starts with hiring the right people and then nurturing this in your organization. When you say learn how to use AI and become proficient with AI, can you give me some benchmarks? Like of a person who's like, say, a marketer, knowledge worker, advanced with AI, what are you looking for when you're interviewing this person?

   13. ANAndrew Ng22:20

       I'm pretty sure my team's ahead of the curve, although my marketers know how to code. So as part of how I interview marketers, we ask them what they've built, and, uh, if they have not built any software-

   14. MMMarina Mogilko22:31

       If it's a dashboard, is it good or bad? Like is it too basic or-

   15. ANAndrew Ng22:34

       It's okay. A dashboard's... Again, my team's pretty, you know, somewhat ahead of the curve.

   16. MMMarina Mogilko22:38

       Mm-hmm.

   17. ANAndrew Ng22:38

       But, uh, all of my-

   18. MMMarina Mogilko22:39

       Well, that's good to hear like that

   19. ANAndrew Ng22:40

       ... all of my marketers have built much more sophisticated things than dashboards.

   20. MMMarina Mogilko22:42

       Like what? To me.

   21. ANAndrew Ng22:43

       Oh, I, I feel like, um, I don't know. The other day, uh, one of our-- someone on the marketing team was talking about the tools that he had built to, uh, when he's considering writing an article on something, it will, um, crawl the web, find related work, has a custom desktop app. He actually built a desktop app that runs on his Mac to, um, highlight related articles for him, that he can chat to the whole system, navigate, you know, the thing he's writing, as well as the related work, and he had a large dashboard for trawling the internet to highlight to him exciting things that are popping up. Um, now, even on my team, I think that marketers is ahead of the curve.

   22. MMMarina Mogilko23:19

       But that's, that's great to hear. Any other interesting use cases, uh, that will inspire people to build something similar?

   23. ANAndrew Ng23:26

       Let's see. Maybe, uh, uh, my finance team uses AI extensively.

   24. MMMarina Mogilko23:32

       Mm.

   25. ANAndrew Ng23:32

       So I think, uh, uh, my, um, o- one of my CFOs, uh, realized that, you know, her team was spending hours every week clicking through documents, open this, copy paste this number here. And so, um, she started building, uh, automation scripts that runs on a routine that, um, automatically opens files, checks what's in there, checks for consistency, highlights her team if there's something, uh, if there's something they need to be paying attention

7. 23:57 – 27:07

   Is Your Financial Data Safe With AI?

   1. ANAndrew Ng23:57

      to, if a new document has showed up. So I find that rather than waiting around for an engineer to do the work for them, the team's ability to, to kind of, uh, not just build dashboards, but build kind of a- Data management infrastructures-

   2. MMMarina Mogilko24:11

      Mm-hmm

   3. ANAndrew Ng24:11

      ... they can adjust data, alert them if something's happening.

   4. MMMarina Mogilko24:14

      Yeah.

   5. ANAndrew Ng24:14

      I think, uh, my finance and marketing teams are doing that. Oh, my recruiting team, um, well, we actually have recruiting engineers, which are really professional engineers that sit in a recruiting team. They're building very sophisticated tools for recruiting, and this is actually the other trend. I think marketers, recruiters, HR professionals, ops people should all learn AI. But the other thing is, when you take an engineer and embed them in these teams, then that further accelerates what you can do.

   6. MMMarina Mogilko24:38

      We do the same. We, we start with something basic, build it ourselves, then we hit the wall, [laughs] an engineer comes in, we build it further.

   7. ANAndrew Ng24:45

      Frankly, when you look at not just software engineers, but recruiting engineers, marketing engineers, HR engineers, I think there's so much valuable engineering work that can now be done. I'm just, you know, not worried about running out of... Frankly, all my friends were so busy, we think, "Boy, how could we run out of engineering jobs?"

   8. MMMarina Mogilko25:01

      Yeah, yeah. There are so many cool ideas you can experiment on. But you touched up on something that is actually one of the fears. When, when we talk about, like, financial information, how much you're giving to AI. So I gave my Perplexity permission to scan my Fidelity account so it can track my portfolio, tell me when to rebalance. It doesn't do anything on my behalf, but it has access. Do you think there is any problem with that?

   9. ANAndrew Ng25:23

      This is complicated. I think AI privacy is a complex area. Um, and it depends a lot on the company that you are sharing your data with. So for example, I trust all the hyperscalers to really 100%, you know, follow the terms of service and to do what they say. My personal opinion, not, not giving legal business advice, but I'd be shocked if, you know, the largest hyperscalers publish the terms of service or some privacy notice, and if they breach that. Because that would be not the culture, that'd be so damaging of the long-term business model. Now, that's on the largest hyperscaler side. If you look at AI company side, there's been, you know, at least one company that I won't name that seems to occasionally change the terms of service. And if you're using it, you go to the website, say pop up, "Hey, we changed the terms of service to retain your data or train your data." And if you're not paying attention and click the wrong button, then they've suddenly gave themselves permission to access your data in a way that I'm not that comfortable with. I feel like I handle, you know, some sensitive information, so then I tend to be very careful with the businesses that I just don't feel the culture, and the DNA, and frankly, the long-term business model is as tied to protecting individual user privacy than the hyperscalers. And, um, I see businesses, you know, get this as well. For example, one of my teams at Aspire, we work with very large corporations, including banks with incredibly sensitive financial data. And as you can imagine, AI Aspire and our, and our clients do not willy-nilly share, you know, really sensitive, um, o- often, uh, material non-public information, right, NMPI, uh, with Frontier, with, with, uh, Frontier

8. 27:07 – 30:44

   Can Anyone Actually Control AI?

   1. ANAndrew Ng27:07

      labs without really careful thinking about the guardrails and privacy. So I think it's complicated.

   2. MMMarina Mogilko27:12

      So trusting hyperscalers, but also, uh, another thing that you can do, you can download an open source model and just-

   3. ANAndrew Ng27:17

      Right

   4. MMMarina Mogilko27:17

      ... run it on your computer, and then it just stays on your computer, right?

   5. ANAndrew Ng27:21

      Yeah. Yes. I think, yes. I, I, it, it turns out a lot of banks will actually run, um, the things in, uh, you know, like a virtual private cloud or on-prem.

   6. MMMarina Mogilko27:28

      Yeah.

   7. ANAndrew Ng27:28

      So they, so it never even leaves their control. But I think for individuals, it's true. For, for the really sensitive things, um, uh, I sometimes run a local model, and it's been interesting with the open weight models. Some of the latest open weight models are approaching frontier capability and that are, you know, actually small enough. They're actually really good models now that can run on-

   8. MMMarina Mogilko27:47

      Yeah, the one from Meta, right? The recent one.

   9. ANAndrew Ng27:49

      Oh, yes. Meta Muse is a good model, and I'm thinking also the latest version of Qwen-

   10. MMMarina Mogilko27:52

       Mm

   11. ANAndrew Ng27:52

       ... is also very good. But I think frankly, these models change every other week, so I think the best-

   12. MMMarina Mogilko27:57

       It's true

   13. ANAndrew Ng27:57

       ... practice is to not get stuck on one, but to keep on-

   14. MMMarina Mogilko28:00

       Yeah

   15. ANAndrew Ng28:00

       ... trying new models.

   16. MMMarina Mogilko28:01

       So basically, when there's a situation that you don't trust anyone, you run a local model, and this is how you keep your data safe.

   17. ANAndrew Ng28:08

       I do trust the hyperscalers, but sometimes for, you know, literally NMPI, material non-public information-

   18. MMMarina Mogilko28:15

       Mm

   19. ANAndrew Ng28:15

       ... that I won't even send to that. I just can't-

   20. MMMarina Mogilko28:17

       Mm

   21. ANAndrew Ng28:17

       ... even send that to the cloud.

   22. MMMarina Mogilko28:18

       Mm-hmm.

   23. ANAndrew Ng28:18

       So that, uh, I'll either do it manually without AI help, or if I really need to use AI, then, you know-

   24. MMMarina Mogilko28:25

       Yeah

   25. ANAndrew Ng28:25

       ... really carefully only-

   26. MMMarina Mogilko28:26

       Yeah

   27. ANAndrew Ng28:26

       ... use a local model.

   28. MMMarina Mogilko28:27

       Interesting. Okay, this is, this is, this is an interesting one. Okay. What about loss of human control over AI? 'Cause I've talked to, I talked to Yoshua Bengio, who is very, um, negative when it comes to open, uh, free AI without any regulation, and he painted me some very scary pictures of AI taking over control because we, basically the, the whole scenario is we can't control something that's smarter than us, and if AI gets smarter and smarter, where, where do we end up? What do you think about that?

   29. ANAndrew Ng29:00

       I think about something else that we can't control, which is, um, airplanes. No one can build an airplane that you can fly perfectly. Winds will buffet it around. And then candidly, in the early days of developing airplanes, some airplanes crashed and people died, and it was tragic and awful. But through the early lessons learned, we then learned to control airplanes better and better so that today, you know, we can mostly get in an airplane and not fear too much for our lives. And it's really like that too, with AI. No one can perfectly control AI because it generates tokens or outputs. They're a little bit random, so we don't really know what exactly it'll do. But as we run them, and you know, there's been a small number of mishaps un- which is unfortunate, and some number of mishaps have done some real damage. But the way we engineer almost any system, from an airplane, to electric circuits, to now AI, is carefully grow their capabilities so that we can have a controlled environment in which to measure-

   30. MMMarina Mogilko30:00

       Mm
9. 30:44 – 33:07

   Raising Kids in the AI Era

   1. ANAndrew Ng30:44

      heavily penalize. Let's just get rid of that.

   2. MMMarina Mogilko30:46

      What do you think about children and social connection when it comes to AI, with kids using more of AI? 'Cause we've seen social media, how, you know, there are people who are dumb scrolling all day. And my daughter, who is five years old now, whenever I don't have an answer, she's like, "Ask ChatGPT." I'm like, "Who's that person?" I'm like, "I don't know." "Ask ChatGPT." And, like, she thinks [laughs] ChatGPT knows everything. What would you say about, you know, kids' future with AI?

   3. ANAndrew Ng31:15

      First, I think kids have a bright future. It's such an exciting time to be a child, to grow up in this environment with tools that none of us ever had before. At the same time, we've seen that social media, um... I think social media has probably been blamed a bit more than it deserves, but it does deserve blame. Uh, has kind of not been great for kids. I actually worry a lot about... It's a wonderful tool, but AI damaging learning is something I worry a lot about. So it turns out, um, I have a five-year-old and a seven-year-old. When I teach them math, they're still young enough that I can basically, you know, not let them use a calculator. Can say, "How do you multiply these numbers?"

   4. MMMarina Mogilko31:53

      Mm-hmm.

   5. ANAndrew Ng31:53

      And I don't give them a calculator and practice that with them. But as they're a little bit older, I worry a lot about students using cognitive offloading to AI in a way that damages the long-term learning and retention. Um, but then at the same time... Oh, I actually built an app. I did not like any of the, you know, free online learning to type types of things. So I actually built my own to, um, have my daughter learn to type, and I'm hoping that... She's actually getting pretty decent now for a seven-year-old.

   6. MMMarina Mogilko32:20

      Oh, so she's seven already?

   7. ANAndrew Ng32:22

      Oh, yeah. She can actually type all the lowercase letters. She's still a bit, you know, not... Her, her shift uppercase letters is a little bit not quite there.

   8. MMMarina Mogilko32:30

      Mm-hmm.

   9. ANAndrew Ng32:30

      But I think that this, um, unlocks, you know, responsible adult supervised use of online tools, and I think it, it's really tricky, you know. I think, um, adult supervised use of digital tools seems a great thing for kids, but too many adults don't have time to supervise the use of the tools, and then the incentives of, say, social media, right-

   10. MMMarina Mogilko32:51

       Mm-hmm

   11. ANAndrew Ng32:51

       ... to do funny things.

   12. MMMarina Mogilko32:53

       Yeah. Has to be the right incentive when it comes to AI. Okay. You mentioned... We, we talked about the fears. We talked about how you can improve your work with AI. Can you name some of the biggest opportunities in AI in 2026 for

10. 33:07 – 36:11

    Best AI Opportunities to Build in 2027

    1. MMMarina Mogilko33:07

       people who want to build?

    2. ANAndrew Ng33:08

       For an individual that wants to build, I don't think it's one size fits all. But because the cost of building has plummeted, um, I encourage people to learn AI, build fast, and talk to customers. I find myself building things, I don't know, every week, every weekend, because I or someone on our team will have some problem, and I have some idea for building some AI thing to automate it. Last weekend, I had... Really, I was using a frontier model to analyze a lot of our key business metrics, 'cause I didn't have time to do it myself, but it was kind of measuring, you know, devandized key business metrics, and I didn't have time to go find a data scientist to go work me on it. So I just did a variety of frontier models, being really careful on their, uh, data retention pr- policies. So I did not use models with data retention policies I don't like, uh, in order to analyze data. But... And then I find that, um, what's happened with AI is the cost of building has plummeted, and so the challenge is shifting to deciding what to build, which I was calling, which I've been calling the product management bottleneck. And so people, you know, founders, engineers, product managers that can talk to customers, get a sense for the taste or judgment on what to build, and then build with AI and iterate quickly, I think there's just a ton of exciting things to do.

    3. MMMarina Mogilko34:31

       And you've been starting so many companies. You're like... Y- When I looked at your portfolio, do you think for beginners, when you said you built something during the weekend, how do you decide what to focus on? Or you can pursue multiple ideas because of AI now, and you can just be, you know, playing in different companies at the same time?

    4. ANAndrew Ng34:47

       It turns out building a company is still really, really hard. And so there's a lot to be said for a single thread of leadership or someone that's fully focused on just one thing. I find that, you know, over a weekend, I can often build a little wrapper, build a simple application. But I wish it was that easy to build a large company. Um, I find that building something meaningful often takes either real technical depth, uh, and/or deep customer insight and integration with customers. And yes, we can now, you know, use AI to code something in a few hours, but that's a small piece of the puzzle.

    5. MMMarina Mogilko35:22

       Yeah.

    6. ANAndrew Ng35:22

       So spending time understanding the technical complexity and building the really complex software, that takes us, like, months, you know, maybe years. Or having that deep customer insight to decide what to build, that also just takes a lot. Talking to people, reading facial expressions, surveys, doing that over and over until we figure out what to build. Um, and so I think sometimes there's a lot of value to sampling widely, but then having that focus for an individual to go really deep in a couple sectors, that, that still seems important for building a business.

    7. MMMarina Mogilko35:51

       My last question. I know it's... We don't have much time, but I wanted to ask you about AGI, just because people use this word so much, and some people say, I think Jensen Huang said, "We already reached AGI." You said it's decades away. What's the one criteria when you're gonna say we reached AGI?

    8. ANAndrew Ng36:08

       So different people say we reach AGI at different times

11. 36:11 – 37:50

    When Will We Actually Reach AGI?

    1. ANAndrew Ng36:11

       because of different definitions of AGI. The definition I'm most familiar with is AI that could do any intellectual task that a human can. But, so the human brain can take, say, five years to study and do a PhD thesis, uh, or... And so can AI write a PhD thesis? Or a human can learn to drive a truck through a dense rainforest with, you know, tens of minutes of practice. So when can AI do that, to drive a new environment with tens of minutes of practice? It feels like there's a long list of these things that AI cannot do, uh, for what feels to me decades. I hope it's only decades. Maybe it would turn out to be longer. So that's what I think for that definition of AI or AGI. AGI is still very far away.

    2. MMMarina Mogilko36:54

       Still really far away.

    3. ANAndrew Ng36:55

       But, um, it turns out because of, you know, economic incentives, I think OpenAI and Microsoft had an agreement that's actually been renegotiated now, so that's gone away. But OpenAI had the economic incentive to try to declare reaching AGI earlier. Uh, and so it turns out that if you come up with other definitions of AI, depending on how far you lower the bar, then you could totally have reached AGI, you know, already or even 30 years ago, depending on how you want to define it.

    4. MMMarina Mogilko37:21

       Yeah. True. Andrew, thank you so much for this positive conversation. Very applicable. I like when, um, you watch something, and then you go and you measure yourself against what people are doing with AI, look at your process, and, uh, maybe expand it. So thank you so much for showing what your team is doing, and thank you for your insights.

    5. ANAndrew Ng37:40

       Yeah. I think given the huge benefits of AI to come, I hope whoever is watching this is motivated to really go learn AI, apply it, um, and, and even to go build some things.

Episode duration: 37:50