26 Nov 2025 · 26m · Andrew Ng, DJ Patil, Rana el Kaliouby, Bob Safian · 24 quoted facts
Andrew Ng argues that AI's real value now comes from applying it to concrete business workflows rather than waiting for artificial general intelligence. He says America's lead is at risk from weaker science funding, tighter immigration, dependence on Taiwan for chips, energy shortages, and underinvestment in open models. He also says learning to code matters more, not less, in the AI era.
The central tension is between the excitement inside Silicon Valley and the distrust many people elsewhere feel toward AI. Ng says the industry has not yet won public trust, and that fear of job loss could slow adoption. His answer is to spread the tools and skills widely, so that many kinds of workers become far more productive, and to keep building.
Ng compares a single prompt to writing an essay in one pass with no backspace. Agentic workflows have the AI outline, research, draft and critique, which takes longer but works better for medical, legal, tariff and coding tasks.
Ng says marketers, HR staff, analysts and finance staff who can code are already outperforming colleagues who cannot. He calls advice to skip learning to code some of the worst career advice ever given.
Ng says AI does best when the needed information is text and the AI can be given the same context a human would have. A written standard operating procedure that can be turned into a multi-step workflow is a good sign.
Ng lists immigration limits, reduced science funding, reliance on TSMC in Taiwan, and energy permitting for data centers. He says disruption in Taiwan could hurt the US more than China if China becomes more independent.
Ng says China has pulled ahead in releasing open-weight models that anyone can download. He cites a statistic that cumulative adoption of Chinese ones may have passed US ones, though US closed models are still better.
It means having an AI work through a task in steps, such as outlining, researching, drafting and critiquing, instead of answering in one go. Andrew Ng says he promoted the word to stop debates over what counts as a true 'agent'.
Ng says yes: people who can tell computers exactly what they want will be more effective, and he expects colleges to treat not knowing how to create software as odd. He suggests learning to code with AI's help.
Ng suggests checking whether the task is mostly text, whether you can feed the AI the same context a human would have, and whether the process can be written as a standard operating procedure (SOP). Images and voice make it harder.
He points to tougher immigration for students, cuts to science funding, dependence on Taiwan's TSMC for chips, and energy and permitting limits for data centers. He also thinks the US is underinvesting in open models.
He says people like call center and fast food workers fear losing their jobs, and politicians sometimes tell them AI will take them. He thinks the tech industry must show that AI benefits everyone through training and better tools.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
DJ Patil said Andrew Ng's early online machine learning course led to Coursera and that it has helped over 10 million students.
“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 it's helped over 10 million students.”
DJ Patil said Andrew Ng started the Google Brain project along with Jeff Dean, Greg Corrado and Rajat Monga.
“Along with Jeff Dean, Greg, uh, Corrado, and uh, Rajat Monga, you started uh the Google uh DeepMind, uh, not the deep”
DJ Patil said Andrew Ng was one of the first people to advocate using GPUs for deep learning.
“he was one of the first people to advocate and using GPUs for deep learning.”
Andrew Ng said that almost two years before the talk he saw a debate over whether software counted as an agent.
“almost two years ago, I saw this rising trend in AI that a lot of people are excited about. But within the tech community, there's all this debate where some people write software and says an agent, others say, no, that's not an agent”
Andrew Ng said he ran an unpublicized campaign to get people to adopt the word 'agentic'.
“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 agentic.”
Andrew Ng said marketers later applied the word 'agentic' to everything, which helped the movement take off.
“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.”
Andrew Ng described agentic workflows as having an AI iterate through outlining, researching, drafting and critiquing.
“With agentic workflow, the idea is we can ask the AI to take a more iterative approach and say, first write an outline, then do some research and write the first draft, then critique it.”
Andrew Ng said agentic workflows work much better for tasks such as medical advice, legal advice, tariff compliance and writing code.
“from medical advice, legal advice, tariff compliance, writing code for a lot of different things, these agentic workflows work much better.”
Andrew Ng said a task that can be written as a standard operating procedure may be a good candidate for a multi-step agentic workflow.
“if you can write a standard operating procedure, like an SOP, that also may be a sign is we're seeing if you can codify the SOP in a multi-step agentic workflow.”
Andrew Ng said advice earlier in the year that people should not learn to code because AI will automate it will be seen as some of the worst career advice ever given.
“I think we look back on that as some of the worst career advice ever given.”
Andrew Ng said non-engineers in Silicon Valley teams who know how to code are starting to outperform those who do not.
“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.”
Andrew Ng said his children, aged four and six, occasionally use tablets with him present.
“my kids four and six, um, they do use uh uh tablets occasionally, but I'm there with them, right?”
Andrew Ng characterized the previous US administration's AI safety thinking as safety theater driven by lobbyists seeking regulatory capture.
“There was really safety theater driven by lobbyists, um, fear-mongering to try to create regulatory capture, anti-open source types of regulations, right?”
Andrew Ng said he worries American competitiveness will suffer if high-skill immigration is made harder.
“I really worry about American competitiveness if we make it harder for high skill immigration.”
Andrew Ng said he came to the United States as an undergraduate student at about seventeen years old.
“When I came to the United States as an undergrad student, I think I was like seventeen years old.”
Andrew Ng said China's recent ban on certain Nvidia chip imports signals movement toward independence from TSMC in Taiwan.
“it's been interesting to see China recently banned certain imports of Nvidia chips, which is a strong signal that China is moving toward independence from TSMC in Taiwan”
Andrew Ng described a data center as a machine that turns electricity into intelligence, or tokens, and said energy is a bottleneck.
“when you build a data center, that's a machine to turn electricity into intelligence, or turn electricity into, um, tokens.”
Andrew Ng said the mobile ecosystem has two gatekeepers, Android and iOS, and hoped AI will not end up with few gatekeepers.
“the mobile ecosystem is kind of uninteresting is because there are two gatekeepers, Android and iOS.”
Andrew Ng said China has pulled ahead of the US in releasing open-weight models, which anyone can download and use for free.
“China has really pulled ahead of the US. in releasing open weight models. These are models anyone in the world can download and use for free.”
Andrew Ng said he saw a statistic suggesting cumulative adoption of Chinese open-weight models may have surpassed that of US open-weight models.
“I think I saw a stat showing that cumulative adoption of Chinese open weight models, I think, is about to surpass or may already have surpassed cumulative adoption of US. open weight models.”
Andrew Ng said European regulators told him they wanted to lead in regulating AI, which he does not think creates competitive advantage.
“for a while, over the last few years, visiting some European regulators, I heard things like, we want to be leaders in regulating AI.”
Andrew Ng said people in Silicon Valley may underestimate how much distrust of AI exists across the United States.
“I think many of us may underestimate the distrust that a lot of people across the nation have for AI.”
Andrew Ng said he likes Claude Code for coding and is increasingly using OpenAI's Codex.
“I love, uh, for coding, I love cloud code and open increasingly using, um, open AI, uh, codex as well.”
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Ng explains how he promoted the term to end arguments about what counts as an agent, and how marketers then spread it.
“let's just call it all agentic and then stop arguing and get on with the work.”
Ng contrasts one-shot prompting with iterative outline, research, draft and critique, and says AI should be applied to real workflows rather than waiting for AGI.
“first write an outline, then do some research and write the first draft, then critique it.”
Ng offers rules of thumb: text-based work, enough context, and a codifiable SOP make success more likely.
“if you can write a standard operating procedure, like an SOP, that also may be a sign”
Ng argues coding skill is becoming as basic as web search and spreads across roles beyond engineering.
“I think we look back on that as some of the worst career advice ever given.”
Ng describes supervising his own children's tablet use and admits financial incentives can push companies toward harmful products.
“I wish I knew how to solve the problem of human incentives, I guess.”
Ng lists risks including immigration, science funding, TSMC reliance and energy permitting.
“I'm really worried about US. national competitiveness in AI.”
Ng warns against a few gatekeepers, says China leads in open-weight releases, and criticizes Europe's regulation-first stance.
“China has really pulled ahead of the US. in releasing open weight models.”
Ng says distrust of AI outside Silicon Valley is underestimated, then closes with a call to build.
“I worry that, um, we have not yet won the trust of a lot of people in this country.”
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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 DeepLearning 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 GreatPoint 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 can't wait to share it with you. Let's jump in. I'm Rana El Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.
Please give a warm hand to Andrew Ng. All right. Okay, so uh I was I was told there was a lot of heart in this audience. Yeah, there's heart in this audience. And now we go to AI, cause none of us heard any of this, right? About AI. But but here's the important thing. Here's here's the really important thing. 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. I 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 of of Andrew, he was one of the first people to advocate and using GPUs for deep learning. He wrote Yeah, should the
should have bought Nvidia stock,
Yeah, I I was gonna say, Jensen, d how many Jess does Jensen owe Yeah, I you, right?
don't know.
I mean, it's like, what size, right?
Yeah, happy for the guy, though.
Uh, did he ever give you anything?
I don't think he gave me a few GPUs, 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 it's helped over 10 million students. Is that correct? 10 million?
Yeah, yeah, thank you. Yeah.
Incredible accomplishment. Along with Jeff Dean, Greg, uh, Corrado, and uh, Rajat Monga, you started uh the Google uh DeepMind, uh, not the deep
Google brain.
Google brain, thank you, the Google Brain project. Um, much, much more. Uh, but some of the things that you're doing right now, you have a fund uh and a 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 column 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 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 are excited about. But within the tech community, there's all this debate where some people write software and says an agent, others say, no, that's not an agent, it's an agent, not an agent. I thought this is a waste of time. 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 agentic. 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 wanna do this in four stages. Uh, let's first talk about today. And and talking about today in 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 this amazing agentic AI capabilities and map it to real business workflows. I think we've Let's been doing that for actually, what like a year.
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 and this gives 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 to think and without ever using backspace. So humans don't do our best writing like that, and neither does AI. With agentic workflow, the idea is we can ask the AI to take a more iterative approach and say, first write an outline, then do some research and write the first draft, then critique it.
And so the iterative workflow takes much longer, but for a lot of tasks from medical advice, legal advice, tariff compliance, 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 that way for AGI, that'll solve all the problems. I'm not a fan of this. This way for AGI, you know, that feels hypey to me.
A lot of the work that's very valuable that you're doing today is to take the technology and what may be possible in the next six to twelve months and just go do valuable stuff with it.
And and what do you? What's where? Where? Sometimes the analogy I think of is 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 80% solution, other times it knocks out of the park. 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 it has 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? We 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 is we're 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.
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Let's switch gears and go to education because you've 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. And I I want to start with, 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 really 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 that AI will automate it.
I think we look back on that as some of the worst career advice ever given. 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 clearer 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 you 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 education 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 and 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, uh, when I was when should 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 are also a lot of stuff that we would not let a young child use.
So my kids four and six, um, they do use uh uh tablets occasionally, but I'm there with them, right? When they're using it, I'm kind of not using as a babysitter, but, you know, having them do educational things or doing weird things when 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 full things? Just like I don't le let my books kids read certain highly inappropriate books for their age, I don't let them do certain highly inappropriate things for their age. So, but it is a challenge. Give 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 gonna be helpful? Or what's that conversation like? Because these are a lot of people have taken your classes that are actually doing some of the behaviors that I think as parents, we find really problematic.
You know, th 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 the billions of dollars at stake to still always do the right thing. It it 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 of the incentives are big enough.
Um, I wish I knew how to solve the problem of human incentives, I guess.
Great. Well, uh, well, let's switch to something easier. Uh, US. policy. Speaking of children, statements that former US. chief data scientists get to make. Um, 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 were working with international technology companies. And so given some of y 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 the their words, woke systems. We've seen also executive orders and policies trying to accelerate the adoption of AI. And so if you had five minutes with the president, and you, what advice would you give them to both a responsibly unleash the power of AI to benefit all Americans, and B ensure national competitiveness? What would that be?
Yeah, I'm really worried about US. national competitiveness in AI. I think, um, some things that the current administration has done well, I think the previous administration had some AI safety types of thinking. There was really safety theater driven by lobbyists, um, fear-mongering to try to create regulatory capture, anti-open source types of regulations, right? So if you don't want to compete with open source, make up a bunch of stuff about the dangers of AI to try to get stifling licensing, whatever passed.
So I think current administration, you know, seems to have very low patience for that. That's good. Um, things that worry me. I think that, um, both of us are immigrants. Um, I think that
Are students have been immigrants?
um, a lot of students are immigrants. I really worry about American competitiveness if we make it harder for high skill immigration. But frankly, not just high skill immigration. When I came to the United States as an undergrad student, I think I was like seventeen years old. I was frankly pretty clueless. I don't think I was high school at all when the US. let me in. So letting students come to the US. to then grow up and hopefully be higher skill, um, I really worry about that.
I think defunding of science, uh, the the decrease investments in science and AI and other things. Universities do have issues, right? Let let's let's be candid. We we there are things we could fix with universities, but I think that, um, diminishing the ability of this country to execute science, I really worry about that. Um, yeah, and then I think, um, in terms of national policy, I also worry about, um, our reliance on, uh, uh, TSMC. Uh, I I think TSMC Arizona's Chipmaker?
Yeah, chipmaker, sorry. Um, it's been interesting to see China recently banned certain imports of Nvidia chips, which is a strong signal that China is moving toward independence from TSMC in Taiwan at a moment where the US. is becoming still heavily reliant on Taiwan manufacturing. One of the implications of this is if anything were to happen in Taiwan, either a natural disaster or a man-made event, um, disruption to the Taiwan semiconductor ecosystem could end up hurting the US.
much more than it hurts China, if China becomes, you know, more independent of Taiwan manufacturing than the US. So I think semiconductors, and then lastly, uh, I think that AI semiconductor is a bottleneck. The other big bottleneck, which you read about in the news, is totally true. It is energy. But when you build a data center, that's a machine to turn electricity into intelligence, or turn electricity into, um, tokens. And so the constraints I I have so many friends are hung up in permitting.
Can we build the power plants here? And then you you think you have the permits, but then there's, you know, local objections or whatever, which are maybe maybe valid, but I think we're that the energy capacity is the other bottleneck that I really worry about.
when you think about China specifically, uh, uh, let's just take China and Europe. So two very radically different approaches in how they think about AI regulation, only the same. It's, you know, and then you sort of watch the US. trying to navigate this for competitiveness. One of the things that I'm I'm curious about from your your lens is this idea of both the open source models and do people build off of the main trunk of AI, which is US.
based primarily right now, but increasingly new models out of China don't it a little bit out of Europe. What's the right strategy here? What's a strategy that you believe is best and optimal for the world for AI? Is it kind of like a central trunk of AI with branches, or is it many different trees in the forest with people with a federation of different models, approaches, techniques?
I think we need multiple branches because otherwise, um, one of the reasons why, uh, make an analogy, mobile phones, uh, the the mobile ecosystem is kind of uninteresting is because there are two gatekeepers, Android and iOS. And unless they let you do certain things, you're just not allowed to experiment. So I hope the AI will not end up with a small number of gatekeepers that can limit innovation. What has happened is over the past year, two years, China has really pulled ahead of the US.
in releasing open weight models. These are models anyone in the world can download and use for free. And I think I saw a stat showing that cumulative adoption of Chinese open weight models, I think, is about to surpass or may already have surpassed cumulative adoption of US. open weight models. The US. closed models are still better, but open weight models are a key part of the AI supply chain and people are using them. Um, I think there's a problem that we aren't investing enough as a nation in that.
And then you asked about Europe. Honestly, I I think, um, love Europe. I wish Europe would wake up and get going faster. Um, for a while, over the last few years, visiting some European regulators, I heard things like, we want to be leaders in regulating AI. You know, I don't think that's how you gain competitive advantage.
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I want to turn to the future in this remaining couple minutes and really talk about the future of AI, but I want to talk about it through the cutting edge next generation of students that you see, the entrepreneurs. What are the problems that you see them gravitating to? What are the things, the hopes, the dreams, and specifically, what does that tell you about how the next twenty four months look?
So we're in Silicon Valley, where most of us love AI. I love AI, love what I do. I think make the world better. I think many of us may underestimate the distrust that a lot of people across the nation have for AI. And I feel urgency to kind of get our act together, um, to make sure that we can tell a compelling narrative to explain why AI is actually really good for the world. Um, it turns out we are all getting excited about productivity improvements, but when a con context, uh, a context center worker is scared of losing their job, or a fast food worker hears a politician say, yep, guess what, these AI people, they're gonna make your job
go away. That creates a lot of fear and distrust of AI that we don't really see here in Silicon Valley. Um, so I think to win people over, we need to make sure technology genuinely benefits everyone writ large. And I think there is a path to that. AI can make individuals much more effective and much more productive. Um, but to get the tools evaded by everyone, teach everyone to use it, does the upskilling, improve the tools?
And really, you know, I I feel like with a concept of a ten x engineer, I think with AI, we can have ten x marketers, ten x analysts, ten x finance professionals. But to actually make that happen, it feels like there's a lot of work ahead of us. And I worry that, um, we have not yet won the trust of a lot of people in this country.
What's your favorite way that you use AI today?
Gosh, uh, maybe I'll share one that is not widely known. I use
It's the part where everyone's as a like, go on,
no, yeah, I I I use AI as a brainstorming companion much more than even my friends know. Uh, and it and and the trick is, it turns out AI do you use is very
one model? Do you use multiple models? multiple models. Uh, oh, Asking for a friend?
yeah, I see, yeah, yeah, actually, as my my I use a actually, I love, uh, for coding, I love cloud code and open increasingly using, um, open AI, uh, codex as well. But for brainstorming, I use multiple models. It it turns out the trick is AI is very smart, but getting context in is difficult. And so when brainstorming, I find that a lot of it is not let me say some stuff then give me ideas. It's making sure you have an extended conversation where the conversation is give me three ideas
Voice or text?
or give feedback. Uh, either one. When I'm driving voice, uh, and when I'm sitting, I I I find that when I'm driving, I um talk to AI quite a lot. And then I'll say, summarize it for me, you know, and I'll send it to my team and just get work done when I'm driving.
In the final ten seconds, what's a problem that you would wish people would focus on more using AI?
Actually, go and build stuff. I think every one of you. This is a wonderful time to build. So if there's one thing you take away from what I believe in, just go and build stuff. There's so much cool stuff you can now build that just was not possible before. So build, build, build.
I think that is a perfect way to end on build, build, build. Andrew Ng, ladies and gentlemen, thank you for your work, Andrew. Thank you for your research. Thank you for being here.
Andrew and DJ's conversation shows how AI is an ecosystem. For the US. to win the AI race, it will take a multifaceted approach. It's chips and AI infrastructure, yes, but it's also talent. We must continue to attract top global talent and offer opportunities to scholars and innovators from all around the world to build a successful life and career here. You can find the full video of this and more from the summit stage at the Masters of Scale YouTube channel.
Pioneers of AI is a Wait What original. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa, and our associate producer is Jordan Smart. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pew. Original music by Brian Holliday. And our head of podcasts is Lital Molad. You can join the conversation on LinkedIn, Instagram, TikTok, YouTube, and X. Just search for at Pioneers of AI. Thanks so much for listening.