Andrew Ng Debunks AI Fear-Mongering and Reveals Future Skills

There's been a lot of misinformation about AI. >> This is Andrew. He co-founded Google

Brain and Corsera. 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

monongering around AI to try to get regulations passed. This drum beat of fear-based messaging has skewed [music]

societal 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 in machine learning and teaching AI and also you're a positive voice because

this is something that I've been seeing especially this summer how poor 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? 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 regret 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 have trained. But if you spend billions of dollars training a model 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 stying 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 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 cherrypick cases of AI, you know, making a misstep and making 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 drum beat of

fear-based messaging has skewed societal 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 TV which they're blown up to

be, it will um 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 or job apocalypse,

this idea that AI will take over 50% of jobs, people will be out of work, riding in the streets, that's just not going to

happen. With every wave of technology, including AI, [gasps] 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 super intelligence or we have artificial general

intelligence or whatever and can do all the stuff that humans do. I wish AI work 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 Eric

Brennoffson 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 30 40% of many jobs and what that means is well that 60% that a human

does has become even more valuable because it's called an economic complement to the 30 40% 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 one that's most affected by AI now is software engineering because AI is actually

fantastic at writing code. And [clears throat] 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 is 2022 before chai GPT they're in trouble they need new skills like don't don't do

stuff that that 30 40% AI can automate you got to stop 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? Cuz I talked to Eric um on

this podcast 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 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? uh they can only do manual work that AI can do as well.

>> 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 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 2022 when we shouldn't even be teaching them for the jobs of 2026.

We should be teaching them for the jobs of 2028 and beyond. And what this means is the job openings are there. Uh tons

of employees I know just can't find enough skilled, you know, 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 had 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 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 for 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 um by all means work hard in classes. you know, get good grades,

learn from the instructors, but to the extent that there's still additional skills that the university has not yet

adapted to teaching, then find other ways to learn online, be it from Corsera or Deep Learning.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 curriculara. >> 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 chat GBT prompt engineering playbook. And the idea is very simple.

in 7 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 20 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. Links in the description. Thanks to Hopspot for sponsoring this video. And now back

to our conversation with Andrew. >> I want to say one other thing. Um it turns out one if if 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 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 whatever their job row is. >> 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?

>> I wish 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 customer growth and retention or maybe faster to serve

customers or increase accuracy in some tasks. So the KPIs tend to be related to the business rather than the AI. M so

you can't like directly measure just AI because it's 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 it's probably the amount of views the output [snorts] uh

it's just interesting how yeah it's just interesting how different people measure even revenue like if you're becoming

more effective >> uh with um how you make money >> actually same 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 [snorts] 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 40. 40 meaning tier one, 30 meaning tier three, etc. And

then we have another one that analyzes every single podcast and gives me tips on how to ask questions.

>> Oh wow. >> 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.

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 from this feedback

to just know my taste better. There's one thing I see a lot in AI which is um it turns out for AI as data scientists

or AI brainstorming partner it often comes up with you know one or two good ideas two or three mediocre ones and

like 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 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 of large business 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 talked to customer we saw the funny facial expression that told us ah they don't like this or we talked 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 Some people 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 taste than AI is this context advantage and because this is a

long-term advantage like no one's going to 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 complimenting AI >> and doesn't this make education even

more important because education gives us context because it's another another thing I'm hearing about AI like you

won't need education because all the information is at your fingertips. You just ask Chad 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 know 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 a study now back up 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 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 has tons of facts web search is a wonderful tool has tons of facts but it turns out that when you ask AI to do

work for you you're 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 MS 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 good for learning but even for myself there's so many things on the AI model over the last you know 6 months or

whatever like I don't know [gasps] building some project how does this front end backend component work

whatever give me the answer get the job done it was fantastic but 6 months later I don't remember the answer when I need

to redo that front end backend component I ask AI again so data is really clear we should stop thinking of AI as helpful

for learning at least the vast majority of ways that the vast majority majority of your people are using AI models

today. It's absolutely terrible for learning. >> But you're building a company helping

solve that, right? Because the one onetoone tutoring with AI is that where you just announced with a 100 million

investment from Corsera. >> Yes. So I'm excited about leading a new organization called Learn Vector that is

focused on um building new learning experiences that is much more onetoone than one to many. So you know 15 years

ago I I was privileged to participate in the 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

onetoone 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 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 as 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'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 hub 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 that did marketing coordination uh coordinating marketing campaigns with AI help they can now

become more of a full cycle marketing take on a broader scope and I'm seeing you know frankly sources in recruiting

become more full cycle do endto-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 of 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, they'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'll be 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 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 a 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 is 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? >> Oh, 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 get older

though, she may change your 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 AI

engineing 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 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 positive 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 talked to a lot of people is 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, but the manager says, "No, I need you 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 the

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.

>> 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?

>> 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 ask them what they've built and uh 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 probably,

you know, somewhat ahead of the curve, but uh >> was good to hear like that.

>> All of my marketers have built much specific things in >> 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, actually built a desktop app that runs on his Mac to um highlight related articles for him. then you 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 trolling 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 >> but that's that's great to hear any

other interesting use cases uh that will inspire people to build something similar

>> let's see maybe u uh my finance team uses AI extensively So I think uh uh my um 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 to her team if there's something uh if there's

something they need to be paying attention 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 a not just build dashboards but

build kind of a data management infrastructures. They can ingest data, alert them if something's happening.

>> 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 that are building very sophisticated

tools for recruiting. And this is actually the other trend. I think marketers, recruiters, HR professional,

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. >> We do the same. We we start with something basic, build it ourselves,

then we hit the wall, an 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, you know, not worried about running out of, [laughter]

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 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?

>> This is complicated. I think AI and 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 their

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 with some privacy notice and if they breach that

because that would be not the culture be so damaging of the long-term business model now that's on the largest

hyperscaler side if you look at 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

changed the terms of service to retain your data or train your data and if you're 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 sensitive information. So then I tend to be very careful with the

businesses that I just don't feel that 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, AI 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 I often material nonpublic information, right? NPI uh with Frontier with with Frontier Labs

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

>> So trusting hypers scale, but also u 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, right? >> Yes. I think yes I it turns out a lot of

banks will actually run um the things in uh you know a virtual private car or on prem 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 way models some of the latest open way models are approaching

frontier capability and that are you know actually small enough they're actually really good models now they can

run on it >> 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 Quen 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 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 hyperscalers but

sometimes for you know literally NMPI material nonpublic information that I won't even send to that I just can't

even send that to the cloud. So that uh I'll either do it manually without AI help or if I really need to use AI then

you know really carefully only use a local model. >> Interesting. Okay. This is this is this

is an interesting one. Okay. What about loss of human control over AI? Because I've talked to I talked to Yoshua Benja

who is very um negative when it comes to open 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? 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 candly in the early days of developing airplanes, some airplanes crash 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 that 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 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 circus 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 um we are certainly controlling them well enough

that this loss of control doesn't feel like science science >> fiction yeah what about deep fakes

>> deep fakes 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 US Congress has been moving Right. Let's

pass laws. Get rid of that. Penalties for that. I'm just I think there's some really problematic uses of AI that we

should outlaw, heavily penalize. 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 5 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 Jajiv Viti like she

thinks Jaji knows everything. What would you say about you know kids future with AI?

>> First I think kids have a bright future. It's just such an exciting time to be child to grow up in this environment

with tools that none of us ever had before. [gasps] 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 5-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 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. >> Oh, she's Oh, yeah. She actually typed all the lowercase letters. she's a

little bit fit, you know, not her shift uppercase letter is a little bit not quite there. But I think that this um

unlocks, you know, responsible adult supervised use of online tools and I think 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 [gasps] say social media, right, to do funny things.

>> Yeah. has to be the right incentive when it comes to AI. Okay. [snorts] 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

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, um, [clears throat]

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, we 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 because I didn't have time to do it myself, but it was kind of measuring, you know, deandized

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 variety of

frontier models being really careful on their uh data retention policies. 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 happen 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 bottleneck 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 that's just a ton of exciting things to do >> 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 it, you know, over a weekend I can often build an Elm wrap, 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. 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 custom 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. 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

Hang 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 a human can.

But so the human brain can take say five years to study and do a PhD thesis or 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 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 you turn out to be longer. So that's why

I think for that definition of AI or AGI, AGI is still very far away. >> But um it turns out because of you know

economic incentives, I think open Microsoft had an agreement that's actually been renegotiated now. So

that's gone away. But open AI had an 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.

>> Yeah true Andrew thank you so much for this positive conversation very applicable. I like when u 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. Yeah, I

think given the huge benefits of AI to come, I hope whoever was watching this is motivated to really go learn AI,

apply it, um, and and even to go build some