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

There's been a lot of misinformation about AI.

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

Maybe AI could do 30 to 40% of many jobs. And what that means is, well, that 60% that the human does has become even more valuable.

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

There's been a lot of misinformation about AI, and the root cause of a lot of this is an unfortunate attempt that started two, three years ago of, I think, PR and regulatory capture. It turns out that 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, 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 stymieing the other teams, be it researchers or other companies that want to just give away open-weight or 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 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 drumbeat 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. And unless we get the truth about AI out there, which is that it's fantastic benefit with some problems, but not nearly to the degree to which they're blown up to be, it will hurt individuals.

The job apocalypse—this idea that AI will take over 50% of jobs, people will be out of work, rioting 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 shift. And so AI is changing job professions. But boy, AI just doesn't work well enough. I know that a handful of businesses want to hype up AI to say we have superintelligence or we have artificial general intelligence or whatever and can do all the stuff that humans do. I wish AI worked better. We're just not good enough to make AI do everything a human does. And if you look at the analysis of jobs, economists like my friend Erik Brynjolfsson at Stanford, Andrew McAfee at MIT—economists have analyzed many people's jobs by breaking it down into individual tasks. And maybe AI could do 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 what we see is that the number of job openings in software engineering is up, contrary to what 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 writes code like it's 2022 before ChatGPT, they're in trouble. They need new skills. Don't do stuff that the 30, 40% AI can automate. You've got to stop doing that, let AI do that, but then gain your skills to do the other 60, 70% that AI cannot do.

One real challenge for fresh college grads is that the university system is slow to adapt. 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, 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. Tons of employers I know just can't find enough skilled people at any level of seniority. It turns out in my office right now, we have a lot of interns who are current college students, fresh college grads. 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 lean into doing the things that humans can do that AI can't for a long time. So there's plenty of work for people to do. My advice to fresh college grads or the people currently in college is, by all means, work hard in classes, 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, find other ways to learn online, be it from Coursera or DeepLearning.AI or Udemy or other places where you can gain the more cutting-edge skills, especially AI skills that universities have not yet worked into curricula.

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

I wish there was 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 some, it may be increased customer growth and retention, or maybe faster to serve customers, or increased accuracy in some tasks. So the KPIs tend to be related to the business rather than the AI.

Actually, how are you using AI in your business?

Oh, wow.

There's one thing I see a lot in AI, which is it turns out for AI as data scientists or AI brainstorming partner, it often comes up with 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 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 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 of large business anytime soon is because humans have a massive context advantage compared to AI. We know so much from our years of experience: that we talked to a customer, we saw the funny facial expression that told us they don't like this, or we talked to business, or our manager said, "Hey, 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 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 in a few years—this is why we just need a lot more humans with that judgment and taste to keep on complementing AI.

I'm going to say something that may be controversial. I don't know if I've said this publicly, but I think it's true, which is, frankly, AI models are terrible for learning. 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 studies now back this up as well, so we also have numbers—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 they let AI do the work for them. More and more studies are coming out to back this up now. I think people think, "Oh, it turns out..." 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 LLMs, as they are most commonly used, are terrible for learning. I'm not saying there's no way to use it in a way that is good for learning; I think there are ways to use it that are good for learning. But even for myself, there are so many things on the AI model over the last six months, like building some project, "How does this front-end, back-end component work? 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 helpful for learning, at least the vast majority of ways that the vast majority of people are using AI models today. It's absolutely terrible for learning.

Yes. So I'm excited about leading a new organization called LearnVector that is focused on building new learning experiences that are much more one-to-one than one-to-many. 15 years ago, 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 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 we 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, as a forerunner 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. What I've seen the early signs of in other disciplines as well: for example, in software engineering, most front-end or back-end developers have now become full-stack developers because, with AI help, you could take on broader scope. I'm seeing early signs of this in other disciplines as well where, for example, someone in marketing that did marketing coordination, coordinating marketing campaigns, with AI help they can now become more of a full-cycle marketer, take on a broader scope. And I'm seeing sourcers in recruiting become more full-cycle, do end-to-end recruiting. So the good news and bad news is, for people to step up to these broader roles, you do need to learn AI skills, but it's not just learning AI—you also need to learn these other skills, 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 big need for people to gain new skills. But when they do, which is both AI skills and 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 worry about the fear-mongering is I got an email from someone that was about to enter college. He emailed me saying, "Hey, Andrew, taking online courses, but I'm really struggling with what I should major in 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, we make people wonder if they will even be relevant, and it makes people not lean in to gain these skills that will 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 grads, think about the economy. And frankly, making people give up is one of the worst things we will be doing in this era when people that lean in will thrive.

You know, I don't know what's the best major. There are an 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?"

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, though, she may change her 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 team's been working on is an AI engineering skills map to try to map out 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 accelerating into an era where people don't just sit around and wait for their boss to tell them what to do.

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 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 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 encourages people to learn AI, build fast responsibly, and talk to customers will drive a lot more value than the more hierarchical, siloed organizations.