Silicon Valley Girl: AI, Tech and Career Growth - Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think

Episode Date: August 28, 2026

Andrew Ng co-founded Coursera, built the founding team at Google Brain, and has taught roughly 8 million people AI.In this episode, he pushes back on the AI job panic making the rounds this year, walks through the actual math on how much of a job AI can automate, and explains why he thinks universities are still teaching students for a job market from 2022. He also gets specific about what he looks for when hiring marketers, recruiters, and ops people now — and admits that despite building his career on AI education companies, AI is currently terrible for learning. 📩 Follow my Newsletter: https://siliconvalleygirl.beehiiv.com/p/your-first-autonomous-agent-in-20-min-5429?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=first-agent ⁠⁠⁠⁠𝕏 : ⁠⁠⁠https://x.com/siliconvalleymm⁠⁠⁠🔗 Instagram: ⁠⁠⁠⁠https://www.instagram.com/siliconvalleygirl/⁠⁠⁠💼 LinkedIn: ⁠⁠⁠https://www.linkedin.com/in/marinamogilko⁠⁠⁠📌 My Companies & Products: ⁠⁠⁠https://Marinamogilko.co

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

People talk to me about data centers and job loss. Maybe AI could do 30, 40% of many jobs. And what that means is, well, that 60% that the human does has become even more valuable. What about loss of human control over AI? I think about something else that we can't control. I think you're one of the voices in AI who comes with a huge background and machine learning and teaching AI. And also your positive voice. Because this is something that I've been seeing, especially this summer, how

Poorly as a 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? 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 registry capture. It turns out that one of the most valuable things in AI right now is a giant AI models, giant large language models that some places have trained. But if you spend billions of

dollars training a model, it's 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 styming the other teams, be it researchers or other companies, they want to just give away open way to open source models that anyone could use much cheaper. Unfortunately, fear-mongering works. When you go and 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 making 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. And unless we get the truth about AI out there, which is that it's fantastic benefit with some problems,

but not nearly the degree to which they're blown up to be, it will hurt individuals. I'm going to read out some of the problems that people are highlighting. Job loss and inequality. What do you think? The job apocalypse, the job apocalypse, this idea that AI will take over 50% of jobs, people will be out of the work,

writing in the streets. That's just not going to 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 have superintelligence or we'll have artificial general intelligence or whatever and can do all the stuff that humans do. I wish AI work better, which is not good enough to make AI do everything a human does. And if you look at the analysis of jobs, economists, like my friend Eric Brinnovson

at Stanford, Antimache at MIT, economists have analyzed many people's jobs, and by break it down into individual tasks, and maybe AI could do 30, 40% of many jobs. And what that means is, well, that 60% that the human does has become even more valuable because it's called an economic complement

to the 30, 40% is 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, most majority of jobs to replace people. Of all the different professions, the one that's most affected by AI now is software engineering, because AI is actually fantastic in writing code. And what we see is that the number of job openings in software engineering is up, contrary to what the doom fearmonger would say. 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, is someone still write code like this 2002 before chat GPT, they're in trouble. They need new skills. Don't do stuff that 30, 40%

the AI can automate. You've got to start doing that. Let AI do that. But then gain your skills to do the other 60, 70% that AI cannot do. What would your advice

be to new graduates? Because I talked to Eric on this podcast and 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 18 to 25 who just graduated. What would be your advice to those people who don't have the expertise maybe to strategize in their job yet? They can only do manual work that AI can do as well. So one real challenge for Fresh College Dress is that the university system is slow to adapt. And so, you know, I love academia. I think we should all support academia 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 skills, then create new courses, get curriculum committee approval or whatever, get the faculty send it 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 2022, when we shouldn't even be teaching them for the jobs of 2006, we should be teaching them for the jobs of 2028 and beyond. And what this means is the job openings are there. Tons of employers I know just can't find enough skill, you know, people at any level of seniority.

But it turns out in my office right now, we have a lot of interns. There are current college students, fresh college grad, we're also at 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 to do the things that I could do. But then also, lean in to doing the things that humans can do the AI can't for a long time. So there's plenty of work for people to do. But so my advice to fresh college grads or the people currently in college is by all means work-hounding classes, you know, get good grades, learn from the instructors. But to the extent that there's still additional skills, the university has not yet adapted

to teaching, then find other ways to learn online, be it from Coursera or deep learning.ailles or Udemy or other places where you can gain the more cutting-ish skills, especially AI skills, that universities have not yet worked in the curricula. I want to say one other thing. It turns out if look at the skill map changes, one of the most important changes is it's so much easy 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

is 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 marketers, recruiters, HR professionals, operations specialists, I think if they learn to build with AI, they're really just too much more, whatever the job is.

How do you, by the way, measure the increase in productivity when you deploy AI? Do you have like a KPI in your company? I wish that a simple answer. I find that the business outcome of AI is more a function of the business than the function of the AI. For some, it may be increased, you know, a customer growth in retention, or maybe a faster to serve customers, or it would be increased accuracy in some tasks. So the KPI tend to be related to the business rather than the AI. So you can't directly measure just AI.

Because it's an interesting thing to do because we've been deploying AI actively in my company. And I think for me as a media company is probably the amount of views, the output. It's just interesting how, yeah, it's just interesting how different people measure. Even revenue, like if you're becoming more effective with how you make money. Actually, say more. How are you using AI in your business? Oh, my God. 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 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 40,

40 meaning tier 1, 30 meaning tier 3, et cetera. And then we have another one that analyzes every single podcast and gives me tips on how to ask questions. Same for Instagram, same for LinkedIn. It has my tone of voice, personal dossier, my business strategy. So whenever it writes something, it knows all the facts about me, how I sound. Every social media is run by 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 have a bot that scans all of our chats. But I really want AI to be able to learn continuously from this feedback to just know my taste better. There's one thing to see a lot in AI, which is it turns out for AI's data scientists or

AI's brainstorming partner, it often comes up with, you know, one or two good ideas, two or three mediocre ones, and, like, 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 job apocalypse 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, our large business is not anytime soon is because humans have a massive context advantage compared

to AI. We know so much that, you know, from a years of experience that we talk to customer, we saw the funny facial expression that told us, ah, they don't like this. Or we talk to business or, you know, our manager said, hey, blah, blah, blah, really care about this. So it turns out that almost all humans, what maybe all humans, just know a lot of stuff that the plumbing does not exist, and I don't think it exists for the foreseeable future for AI to get. I know sometimes people talk about the importance of human judgment or human tastes, and

sometimes you wonder, all right, what is taste? Is this fuzzy thing? But to me, the technical thing that underlies why humans have better judgment and better tastes than AI is this context advantage. And because this is a long-term advantage, like no one's going to solve this 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.

And doesn't this make education even more important? Because educationals gives us context. Because it's another thing I'm hearing about AI, like you won't need education because all the information is at your fingertips. You just ask chat GPT. But when you say context 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. I'm going to say something that may be controversial.

I don't have said this publicly, but I think it's true, which is frankly, AI models are terrible for learning. I know people think 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 a study is now backup as well, so we also have numbers, but the data is very clear.

Students score higher on homeworks when they use AI. Yay, higher homework schools. 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. There's tons of facts.

Web search is a wonderful tool. It's 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 gets work done. But human retention is much worse. It's just so clear that LMs, 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 are also AI model over the last six months or whatever. Building some project, how does this front-end, backend 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 AI again. So data is really clear. We should stop thinking of AI as hopeful 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. But you're building a company helping solve that, right? Because the one-to-one tutoring with AI is that where you just announced with 100 million. an investment from Grisera. Yes. So I'm excited about leading a new organization called Learn Vector

that is focused on building new learning experiences that is much more one-to-one than one-to-many. So 15 years ago, I was privileged to participate in the online causes 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 a lot more to show by early next year. When think about human skill development, I feel like because AI has so heavily impacted software engineering, what we see happening in the job market for software engineering is a harbinger or is a fourth-runner of what we'll see in other disciplines as well. And in software engineering, 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've seen the early signs of in other disciplines as well, for example, in software engineering, you know, most developers, like front-end, backend 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, coordinate marketing campaigns with AI help, they can now become more of a full cycle marketing, take on a broader scope. And I'm seeing, frankly, sources 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. 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 monitoring is 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, well, AI, do all this and everything I learned 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, we make people wonder if they will even be relevant,

and it makes people not lean in to gain these skills. They'll put them in a much better position. So I see very clearly that these fear-mongering messages are distorting how many people, including high school students, college students, fresh rats, think about the economy. And frankly, making people give up. one of the worst things we've been doing in this era when people that lean in will thrive.

Andrew has taught over 8 million people AI. He started teaching machine learning online back in 2011, years before the current AI boom. Now one of the companies his 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 to 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? You know, I don't know what's the best major. There are awful lot of great majors. It's like I kind of feel like what's the best job. in the world is like what's the best major in the world.

Something that you love, right? 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 your mind. I see so many opportunities 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 teams were working on the 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 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. Well, 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. 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 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. 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, engineers and others in large companies, that told me that their manager tells them to stay in their swim lane.

They'll say, oh, I have this creative idea, but the manager has no, initially to focus on this 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, 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.

Talk to customers would drive a lot more value than the more hierarchical silo organizations. Yeah, 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, marketer, knowledge worker, advanced with AI.

What are you looking for when you're interviewing this person? I'm pretty sure my team's ahead of the curve. All of my marketers know how to code. So as part of how I interview marketers, we asked them what they've built. And if they have not built any software. If it's a dashboard, is it good or bad? Like, is it too basic or? A dashboard, again, my team's pretty, you know, somewhat ahead of the curve.

But it's all of my-it-to-hear like that. All of my markets have built much more sophisticated things than dashboards. Like what to me? Oh, I feel like, I don't know. The other day, one of our, someone in the marketing team was talking about the tools that he had built. to when he's considering writing an article on something, it will crawl the web, find related work, has a custom desktop app, actually build a desktop app that runs on this map,

to highlight related articles for him, then you can chat to the whole system, navigate 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. Now, even on my team, I think that market is ahead of the curve. But that's great to hear any other interesting use cases that will inspire people to build something similar. Let's see. Maybe my finance team uses AI extensively. So I think one of my CFOs realized that her team was spending hours every week, clicking a few documents, open this, copy paste this number here.

And so she started building automation scripts that runs on a routine. that automatically opens files, checks was in there, checks for consistency, highlights for our team, if there's something they need to be paying attention to, if a new document that showed up. So I find that rather than waiting around for an engineer to do the work for them, the team's ability to kind of not just build dashboards, but build kind of data management infrastructures. They can adjust data, alert them as something is happening.

I think my finance and marketing teams are doing that. Oh, my recruiting team, 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, 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 way you can do.

We do the same. We start with something basic, build it ourselves. Then we hit the wall and engineer comes in. We build it further. 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 not worried about running out of, frankly, all my friends were so busy,

we think, boy, how could we run out of engineering jobs? 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 we talk about 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? This is complicated. I think AI privacy is a complex area. And it depends a lot on the company

that you are sharing your data with. So, for example, I trust all the hypers to really 100% follow their terms of service and to do what they say. My person, I'm not giving legal business advice, but I'd be shocked if, you know, you know, the largest hypers published the terms of service with some privacy notice, and if they reach that, because that would be not the culture, it'd be so damaging of the long-term business model. Now, that's on the largest hyperscalor side. If you look at the AI company's 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, so you pop up,

hey, we change the terms of service, retain your data, or train your data, and if you aren't paying attention and click the wrong button, then they 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 sense of information. So then I tend to be very careful with the businesses that I just don't feel their culture and the DNA and frankly, the long-term business model is as tied to protecting individual user privacy than the hyperscalators. And I see businesses, you know, get this as well. For For example, one of my teams, AISPIRE, we work with very large corporations, including banks with incredibly sensitive financial data. And as you can imagine, AI Aspire and our clients do not willingly share, you know, really sensitive.

Often a material non-public information, right, NMPI, with frontier labs without really careful thinking about the guardrails and privacy. So I think it's complicated. So trusting hyperscale, but also another thing that you can do, you can download an open source model and just run it on your computer and then it just stays on your computer. Yes, I think, yes. It turns out a lot of banks will actually run the things in virtual private call or on-prems. So they never even leave their control. But I think for individuals, it's true, for the very sense of things, I sometimes run a local model. And it's been interesting with the open-way models.

Some of the latest open-way models are approaching frontier capabilities. and that are, you know, actually small enough. They're actually really good models now that can run on. Yeah, the one from Meta, right, the recent one? Oh, yes, Metamuse is a good model, and I'm thinking also the latest version of Quinn is also very good. But I think, frankly, these models change every other week. So I think the best practice is to not get stuck on one,

but they keep on trying new models. So basically, when there is a situation that you don't trust anyone, you run a local model, and this is how you keep your data safe. I do trust the hypers, but sometimes for, you know, literally an MPI material and public information that I won't even send to that I just can't even send that to the call

so that I'll either do it manually without AI help or if I really need to use AI then you know really carefully only use the local model. Interesting. Okay this is this is an interesting one. Okay, what about loss of human control over AI?

Because I've talked to, I talked to Joshua Benja who is very negative when it comes to free AI without any regulation. And he painted me some very scary pictures of AI taking over control because basically the whole scenario is we can't control something that's smarter than us. And if AI gets smarter and smarter, where do we end up? What do you think about that? I think about something else that we can't control, which is 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 of AI. No one can perfectly control AI because it generates tokens or outputs a little bit random, so we don't really know what exactly will do. But as we run them, and there's been a small number of mishaps, 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 what's wrong, and then to shape it to make sure we can control it well enough that it behaves responsibly and safely. And to this day, we can't perfectly control any airplane. And we will never perfectly control AI either. But I think we are certainly controlling them well enough that this loss of control doesn't feel like science. It feels like science fiction. Yeah, yeah.

What about deep fakes? Deepfakes are a problem. Well, one of the most disgusting things I've ever seen or heard of is non-consensual, intimate deep fake imagery. I'm really glad that, you know, U.S. Congress has been moving to, right, let's pause laws, get rid of that. Penalties for that. I think there's some really problematic uses of AI

that we should outlaw, heavily penalized, let's just get rid of that. What do you think about children and social connection when it comes to AI with kids using more of AI? Because 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,

He's like, ask Chad GPT. And like, who's that person? I'm like, I don't know. Ask Chad Jifety. Like, she thinks Chad Jifety knows everything. What would you say about, you know, kids' future with AI? First, I think kids have a bright future.

It's such an exciting time to be 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, I think social media has probably been blame a bit more than it deserves, but it does deserve blame. 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, I have a five-year-old and a seven-year-old, when I teach them math, they're so young enough that I can basically, you know, not let them use a calculator, can say, how do you multiply these numbers? 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. But in 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 have my daughter learn to type. And I'm hoping that she's actually getting pretty decent now for a seven-year-old. Oh, so she's having already. Oh yeah. She actually typed all the lowercase letters.

She's a little bit, you know, not, her shift uppercase letters a little bit, not quite there. But I think that this unlocks, you know, responsible adult supervised use of online tools. And I think it's really tricky, you know. I think adults supervise 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, to do funny things. Yeah, has to be the right incentive when it comes to AI. Okay, you mentioned, 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 people who want to build? 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, 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 with some problem and I have some idea for building some AI thing to automate it.

Last weekend, I had, really, I was using a front-tier model to analyze a lot of our key business metrics because I didn't have time to do it myself, but it was kind of measuring, you know, de-landized key business metrics and I didn't have time to go find a data scientist to go work me on it.

So I just see a variety of front-tier models, being really careful on their data attention policies. I did not use models with data attention policies I don't like in order analyze data. And then I find that what's the type of 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 bottle neck. And so people, you know, founders, engineers, product managers that can talk to customers get a sense for the taste of judgment on what to build, and then build with AI and iterate quickly,

I think there's just a ton of exciting things. And you've been starting so many companies. You're like, 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? It turns out building a company is still really, really hard.

And so there's a lot to be said for a single-threaded leadership or someone that's fully focused on just one thing. I find that over a weekend, I can often build an unwrapper, build a simple application, but I wish it was that easy to build a large company. I find that building something meaningful often takes either real technical depth and or deep customer insight and integration of customers.

And yes, we can now use AI to code something in a few hours, but that's a small piece of the puzzle. So spending time understanding the technical complexity and building the really complex software that takes us like months, 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 what to build. 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 still seems important for building a business. My last question,

I know we don't have much time, but I wanted to ask about AG. just because people use this word so much. And some people say, I think Jansan Huang said, we already reached AGI. You said it's decades away. What's the one criteria when you're going to say we reach AGI? So different people say we reach AGI at different times because of different definitions of AGI.

The definition I'm most familiar with is AI that could do any intellectual task that the human can. But so the human brain can take, say, five years to study and do a PhD thesis or a, And so can AI write a PhD thesis? Or a human can learn to drive a truck through a dense rainforest with 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 for what feels to me decades. I hope it's only decades. Maybe you'll turn out to be longer.

So that's what I think for that definition of AI or AGI is still very far away. But it turns out because of economic incentives, I think Open-A-N Microsoft had an agreement. There's actually been renegotiated now, so that's gone away. But Open AI had an economic incentive to try to declare reaching AGI earlier. And so it turns out that if you come over with other definitions of AI, depending on how far you lower the bar, then you could totally have reached AGI already or even 30 years ago,

depending on how you want to define it. Yeah, true. Andrew, thank you so much for this. conversation very applicable. I like when you watch something and then you go and you measure yourself against what people are doing with AI, look at your process and maybe expand it. So thank you so much for showing what your team is doing. And thank you for your insights. Yeah, I think given the huge benefits of AI to come, I hope whoever's watching this is motivated to really go learn AI, apply it, and even to go build some things.