One of the most important skills for the future is the ability to tell a computer exactly what you want so they can do it for you.

And for the foreseeable future, people that know the language of computers, be able to understand coding, will do that much more effectively than people that don't.

So I know that even, you know, earlier this year there were some leaders that were advising others not to learn to code on the grounds AI will automate it.

I think we look back on that as some of the worst career advice ever given.

>> Hi there, listeners. Today we are sharing another conversation from this year's Masters of Scale Summit.

Andrew Ng is a true AI pioneer. He's co-founder of Coursera and Deep Learning AI and managing partner at AI Fund, a studio that incubates new AI companies.

He was joined on stage by DJ Patil, former chief data scientist under the Obama administration and general partner at Great Point Ventures.

It's a dynamic discussion about the current state of AI, how future generations should approach the technology, and how America can stay competitive in the global AI race.

I've known Andrew for a long time. He's been talking and working on AI long before it was cool.

I remember actually sitting down with Andrew when we were coming up with these ideas around data science and these things and he was talking about AI was like, "Hey, we're still in the middle of winter for AI." But he's he's he's been on this for the incredible journey.

But to just give you some of the highlights uh of of Andrew, he was one of the first people to advocate in using GPUs for deep learning.

He wrote >> He should should have bought Nvidia stock. >> I was going to say Jensen, how many jets does Jensen owe you?

>> I don't know. Like I mean it's like what size, right?

>> Happy for the guys. >> Exactly. Uh did he ever give you anything?

That was nice. >> A few GPUs. You got some GPUs out of it. All right. Um you wrote the uh the first major online course on machine learning and AI which led then to Coursera.

At the time it was radical thinking to actually teach an online course and has helped over 10 million students.

Is that correct? 10 million. >> Yeah. Yeah. Thank you. Yeah.

>> Incredible accomplishment. Along with Jeff Dean, Greg Corrado and Rajat Manga, you started uh the Google uh DeepMind not the deep >> Google Brain.

>> Google Brain. Thank you. The Google Brain Project.

>> Much, much more. Uh, but some of the things that you're doing right now, you have a fund uh, and an and uh, a studio investing, building with uh, on AI, and you also have some of the most seminal, cited papers in AI today.

>> I see, I have to admit, I probably have highly cited papers. I I don't track my citation count as often as I used to.

>> Exactly. You leave that for everybody else or the AI systems. But we're also something that you don't know about.

Yeah. How many people have heard about Agentic systems these days? Yeah, maybe I should ask it the other way around just to like highlight people.

Guess where Agentic came from? This is a guy. It's a little known fact that uh, Andrew was actually the guy who really came up with with Agentic.

Actually, let's start with that. What's the story behind Agentic?

So like, um, almost two years ago, I saw this rising trend in AI that a lot of people were excited about, but within the tech community, there was all this debate where some people would write software and say it's an agent.

Others say no, that's not an agent, it's an agent, not an agent.

Why don't we, instead of having a binary agent, not agent, let's just call it all agentic and then stop arguing and get on with the work.

And so I actually kind of ran a campaign that I didn't publicize, but I did it anyway to try to get more people to just adopt the word "agent." What I didn't realize was a few months later, a bunch of marketers would get a hold of this word and slap it as a sticker on everything in sight, and that helped the movement take off.

But even though the hype's gone like that, I think the real value is really growing rapidly too.

So that's been exciting. >> Well, let's let's stay on that. So so I want to do this in four stages.

Uh let's first talk about today and and talking about today and the state of AI through the lens of one of the OGs.

What is your honest take of what AI can and can't do, especially through this lens of agentic?

Because I think we're all struggling with all the marketing and buzz out there of what's real and what's not.

>> A lot of work that lies ahead is to take these amazing agentic AI capabilities and map it to real business workflows.

I think we've been doing that. What is an agentic?

>> Like, let's ground us there. >> So a lot of us use AI, uh large language models by prompting it and asking it to write an output.

That's a bit like going to a human or in this case an AI and saying, please write an essay by just typing it out from the first word to the last word all in one go without stopping.

the thinker without ever using backspace. So humans don't do our best writing like that, and neither does AI.

With an agentic workflow, the idea is we can ask an AI to take a more iterative approach and say, first write an outline, then do some web research, then write a first draft and critique it.

And so the iterative workflow takes much longer, but for a lot of tasks from medical advice, legal advice, tariff compliance, or writing code, for a lot of different things, these agentic workflows work much better.

But there's still a lot of work ahead of us.

I know that some people say, "Oh, don't worry about it.

Wait for AGI. That'll solve all the problems." I'm not a fan of this, "Let's wait for AGI." You know, that feels hype-y to me.

A lot of the work that's very valuable to doing today is to take the technology and what may be possible in the next 6 to 12 months and just go do valuable stuff with it.

Sometimes the analogy I think of is I'm using these systems because I'm trying to build, uh, you know, I'm trying to deploy AI to really help senior citizens in their healthcare journey, different things.

And sometimes I think, am I on thick ice? Am I on thin ice with what the systems can and can't do? I suspect a lot of people out there are wondering, does it work for this problem or is it fragile?

Sometimes we just get to an 80% solution.

Other times it knocks out of the product. Sometimes we're incredibly disappointed. How do you as Andrew, who's building companies, advising so many people, think about this?

>> Yeah, it is tough. I think, um, the closer a task is to only text processing, and if you have the plumbing to get all the information hopefully in text that people need to do a task, the easier it is for AI to do it.

If you need to feed in images, voice conversations, it's not impossible, but it gets harder.

And then one question I often ask is, um, humans know a lot of stuff. We just have a lot of context.

And so, do we have the data plumbing to get the AI system similar context as a human would needed to do that task?

And then I think, um, for a lot of multi-step processes, if you can write a standard operating procedure like an SOP, that also may be a sign it's worth seeing if you can codify the SOP in a multi-step agentic workflow.

So it's hard to determine what can and cannot be done, but I think these are maybe some suggestions for rating what's more or less likely to succeed.

Well, let's switch gears and go to education, uh, and how we're because you've changed, you really changed and transformed academia through Coursera.

Your current online courses have, I, I can't even keep track of how many views and how many people are taking your courses.

I'm sure every parent is asking you what their kid should do to be prepared for AI.

Should kids still learn to code? Is CS a thing? Is data science a thing? Or is that a bad idea?

>> Yeah. So, um, one of the most important skills for the future is the ability to tell a computer exactly what you want so they can do it for you.

And for the foreseeable future, people that know the language of computers, be able to understand coding will be able to do that much more effectively than people that don't.

So, I know that even you know earlier this year there were some leaders that were advising others not to learn to code on the grounds AI will automate it.

I'm already seeing, um, on my teams, a lot of Silicon Valley teams, not just the software engineers, but the marketers, HR professionals, the analysts, the finance professionals, the ones that know how to code, they're starting to run circles around the ones that don't.

So, um, if your kid intends to be a software engineer, you know, have them learn to code with AI. And even if they don't, it is becoming clear that in the future, I think we need a lot more not just users of software, but creators of software.

And so rather than your kid growing up and asking, is there an app for that? I want them to say, I built an app for that.

And with AI assistance, coding is much easier than it used to. So don't code by hand, get AI to do it for you and people that do that will be more powerful and more effective than people that don't.

So that's one challenge that the system has to go through as well.

There's this new skill, just like today. I can't imagine, you know, if you go through college without learning how to do web search, right?

You know, that's kind of weird. It limits your job prospects.

In the future, I think if you go through college, you come out not knowing how to create software, we'll go, "Oh, that's kind of weird.

It will limit the prospects of what someone can do." >> When we think about uh um when should a kid get access to to this technology, what does that look like?

And specifically through the lens of some of the lessons I think we've really started to struggle with of around social media.

Surgeon General previously, Vivek Murthy, really highlighted the challenges that have happening around that. When is too early in your idea?

When is it the appropriate time for someone to really start having access to AI to make sure they're truly AI native and get the maximum benefits of this technology?

>> I know, I think it's difficult, so I I I feel like, you know, when I was mentioning kids get access to books, we think really, really young, but there are also some books that are clearly inappropriate for a two-year-old.

And I think one of the challenges with technology is, I think there are apps that are just fine for a very young child to use, but there's also a lot of stuff that we would not let a young child use.

Um, they do use uh, uh, tablets occasionally, but I'm there with them, right, when they're using it. And kind of I'm not using it as a babysitter, but you know, having them do educational things or doing weird things and we talk about it.

So I think, um, the medium has changed from other things to tech, but I think the challenge is what are the business incentives for companies to do or not create certain experiences for the kids.

And as parents, how can we, um, have guardrails or curate the whole for things, just like I don't let my kids read certain highly inappropriate books for their age.

I don't let them do certain highly inappropriate things for their age.

So, but this is a challenge given the incentives of, you know, certain types of companies to do things that we as parents may not want them to.

>> Do you ever talk to some of those companies or groups and you're like, "Hey, knock it off.

That's not going to be helpful." Or what's that conversation like?

Because these are a lot of people who've taken your classes that are actually doing some of the behaviors that I think as parents we find really problematic.

>> You know, the the 99% of engineers and business people in Silicon Valley want to do the right thing, right?

The people doing these, frankly, they're, you know, our friends, maybe some people in this room right now.

I think everyone kind of wants to do the right thing. And, um, I wish we could find a way when there are billions of dollars at stake to still always do the right thing.

It is a real problem and we do see a small number of people that will sometimes do not quite the right thing when the financial incentives or some incentives are big enough.

Um, I wish I knew how to solve the problem of human incentives.

I guess. >> Great. Uh, well, let's switch to something easier here.

Um, US policy. You were early in advocating both the, you know, as we mentioned, the policies of GPU, but also, you know, you were one of the first people I saw that really were working with international technology companies.

And so, given some of the place where you sit and you get to see across the landscape, you've seen we've seen this like whipsawing on GPUs from federal policy.

We've seen executive orders talking about what they would describe in their words, woke systems.

We've seen also executive orders and policies trying to accelerate the adoption of AI.

Andrew Ng on America's standing in the AI race (with DJ Patil) | Masters of Scale Summit 2025

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