TED Talks Daily - Why AI is an even bigger deal than you think | Reed Hastings

Episode Date: July 20, 2026

Netflix cofounder and Anthropic board member Reed Hastings joins TED's Sal Khan to give a look inside the race to build AI. Hear his take on how the technology could accelerate education — including the possibility of an AI tutor for every student — and reshape the economy faster than anyone expects. Hosted on Acast. See acast.com/privacy for more information.

You're listening to TED Talks Daily, where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hugh. Entrepreneur and philanthropist, Reed Hastings, is probably best known as the co-founder of Netflix. But a big part of his life's work has actually been in education. For decades, he's funded schools, supported education nonprofits, and served on the board of Khan Academy. Even after all that, he thinks we're still up against the same fundamental problems in education. I've been working for the last 25 years to try to find ways to make schools work better and have better outcomes for kids.

And I would say after a billion dollars in 25 years, we're down below where I started. So it's a hard problem. In this conversation with Sal Khan, Ted's Vision Steward and the CEO of Khan Academy, the two discuss why the classroom model itself may be part of the challenge, especially when one teacher is trying to reach a whole room of kids who are all learning at different speeds. For Reed, this led him to AI, which opens up the possibility

of something more individualized and not as a tool to replace teachers, but to change what they're able to focus on and what kind of support students might be able to get. I would say the biggest uncertainty for everybody is how fast is the AI getting better and how fast will it be getting better going forward. And then that changes your views and assumptions.

Reid, who recently joined the board of Anthropic, Talks about what AI could mean, not just for education, but for work, jobs, and the future we're preparing kids to enter. It's coming up right after a short break. And now our TED Conversation of the Day. So, Reid, education, which a lot of people, I don't know how many know how active you are in education, education philanthropy, charter schools, etc. What are you doing in that space? Well, on board of Khan Academy. And generally, I've been working for the last 25 years.

to try to find ways to make, in particular, U.S. schools, with some international work better and have better outcomes for kids. And I would say after a billion dollars in 25 years, we're down below where I started. So it's a hard problem. And what are you, you know, we heard about the risks of ed tech, but you are investing in ed tech, you're giving to ed tech. What are your thoughts?

What's the tradeoff there? Why do you continue to potentially believe? You know, in the 18th century, most factories were steam engine driven. They have a big steam engine rotating rods and pulleys and belts and drove the factory, very efficient. And then electricity came in and they replaced the steam engine with an electric engine, thinking that it was going to help a lot, and productivity didn't increase at all. And they were puzzled. And then they realized he had a limiting factor as the power distribution system, all these spinning rods and mechanics.

and they rip that out and just put in small electric motors for each device. Then they could move things around, fit them in better, because it wasn't aligned to the spinning. They could have variable speed, turn different motors on and off, and then productivity increased dramatically. So that's a classic kind of economic, you know, surprise lesson. And it's always stuck with me because I think that's what we're seeing, which is we keep doing things to improve classroom education,

but the fundamental power distribution is 25 kids stuck at the same level. And that the friction that that creates, which is, you know, roughly a third of the kids are behind, a third of the kids are bored and above, and a third you're teaching to, is the fundamental friction in our mass education system. And the theory is, if each of us had an individual human tutor, so imagine you go to school,

You have your normal social activities, but when it comes to learning, you get an individual who's going to sit down with you, and then they could do Khan Academy or they could do color book or whatever is appropriate. That learning would be massively increased. And there was a famous study 40 years ago, Bloom, that documented this. And now a friend of mine, Ben Summers, is redoing that study, but at much bigger scale. And I think what we're going to see, is the key to much more learning, where middle school kids know all of the high school curriculum, high school knows all of the college, much better outcomes will be individualized education. How do you square that with what Jonathan Haidt said?

Look, these screens, I mean, at least it's a correlation. We don't know causal yet, but it seems to be distracting. It seems to correlate with some test scores going down. Is it for you a little bit of it? Is it an all or nothing type of thing? I'm a huge fan of Jonathan. I totally agree with. all his zip-up phones and don't use phones.

And I think you guys clarified, he likes offline tablets. That's fine. It's the Internet that's the problem, not the physical device. And so I think there's lots of ways to cater to the concerns that he correctly expresses, which is letting kids go wild on the Internet under 16 is not great. But you don't have to do that to be able to do individualized tutoring. So the individualized tutoring we're doing is with humans, okay?

They can use some technology if they want, but then obviously that's cost prohibitive because it's about $100,000 per kid per year. So then the hard challenge becomes, how do we use AI to approximate that human and provide everyone an individualized education as AI gets better? Current AI is not good enough to do that,

But, you know, in three years, we've gone from, you know, chat GPD 3, 5, and barely being able to do high school math to, you know, just incredible intelligence. And, you know, that's likely to just continue to double, double, double, and get better and better and better. So there is a world where the AI, I think, will be able to match and beat the human individual tutor. And what does that world look like? let's just say it's in 10 years. Are you imagining that you're just socializing and then you go to this AI tutor that even maybe looks embodied in some way,

but are you imagining there's no human teacher? What do you think happens to that role, that profession? What about the adult humans in the classroom? Well, let's think about schools. So three big purposes. One is create good citizens. Another is give economic opportunity to the kids, and then the other is socialization,

social-emotional learning, how to work with other people, adults outside of your family. So only in the first part is really where online is really good. And what we want to do is have teachers be able to focus on social, emotional learning. They become really helping maturity, interpersonal skills, values, clarification, all those kind of higher-level things. And then we've got to figure out, in the AI age, you know, how do we enhance that role of creating good citizens? Okay, because one of the one of of our ways to come together is to have, you know, a tighter idea of who we are as a country.

And, you know, the K-12 systems be able to take that for granted for the last 100 or 200 years, maybe post-Civil War, you know, because society was working well. But if we're going to go into a period of stress, it's really important for that mission to get attention also. And I just want to double-click on that and make sure maybe we have a common vision or maybe it's divergent. You still see a major role for the human team. and the human classroom.

You just see that role shifting, potentially going, sort of, okay. So most teachers today, their Pride Center is teaching and connecting and, you know, understanding the material. Okay, some part of that is really connecting on a personal level

with a student, so that part is the social emotional. But in terms of transferring information, what educators cynically call sage on a stage, it's eliminating sage on a stage as a teaching modality. And so it's really just focused on the individual. What would education be if there was no mass teaching? And to be fair, if you go to an ed school, if we went to an ed school 20 years ago, this is what they were preaching, differentiated instruction, active learning,

don't be stays on the stage, have a So it's really potentially, and this is what I say, because I get this question a lot, the teacher, I think, moves up the value chain and is able to facilitate and drive a lot of of that active learning, which is better for everyone. I think it's more fun for the teacher. Right, the positive side of it. And the other part is, once you can do a lot of this in software, you can do it globally. So it's really hard to scale up the teacher force. If you have incredible software, it's pretty inexpensive to make it globally available. No, that's right. I mean, we talk a lot about it at the Khan Academy Board that the technology can raise the safety net,

the floor. We've seen stories of young women in Afghanistan using Khan Academy. One of them's at MIT now. I mean, it's amazing things. But we see also in the classroom, most students need that human element, arguably all of them do, ideally, if they have it. Switching gears a little bit because your other board you're on isn't obviously very related to this, anthropic. Actually, I'm just curious, you know, you could do anything, what made you join that board? what's it like at those board meetings when, I'm assuming y'all talk about pressures from the White House, how your new model might break all software. Tell us what you can.

Yeah, it's a lot like your board meetings, you know, talking about the software and what it can do and how it needs to get better. So the mission of the company is very clear. It's not maximizing profits. It's that we're successful, humanity. how do we get into the age of AI successfully, crossing through sort of this portal? And they recognize it's going to be very challenging and they're very dedicated to having that happens

in rolling out AI and having the incredible beneficial outcomes, whether that's the Waymo's Health Thriving, whether that's gene editing, whether that's curing cancer. You know, 10, 20 years from now, it's very likely we'll have pretty abundant energy. we will have amazing health outcomes, I mean, so much positive outcome from the AI infusion into science.

And I would say Anthropics are very serious about helping us manage or avoid most of the downside. And how do you all pull that off in closed doors? And I've been in some of those closed doors where people are genuinely afraid, a more than 10% chance that this could be an existential threat to humanity. It does seem that Anthropics somehow, is proving it to be very responsible,

or that's what it appears to be, and at the same time, moving very fast, hyper speed. It feels like almost every few weeks. There's something new, and it's very tangible in what it might do for work. How are you all balancing that at Anthropic instead of just saying, go, go, go? You know, I think all of the big models

are improving rapidly, and you're probably going to see them go, you know, certain months I'll have to lead in certain areas over time, and, you know, frankly, it's good for the country We have three really successful models to choose from. And then how do they balance it? You know, case by case. So I think each one have to see, you know,

how accelerated is learning, how powerful is it? What are the downside scenarios? What is the new possibilities it can do? And I'm curious about Anthropic itself. I had a chance to visit there a couple of weeks ago. And, you know, I take pride that, you know, Khan Academy, we're super nimble and we're innovating, et cetera, and we're obviously trying to leverage AI for social good as much as possible.

When I visited there, I tangibly felt that they were pioneering completely new ways of running an organization, new ways of developing product. I think it was something like. Co-work was, what was it, a week or two, that it was essentially built primarily by the AI itself. What will an organization look like in the future? I think you'll have a pretty good crystal ball there. Yeah, I don't know that most companies will come to look like Anthropic. I think it really depends on your industry. If you happen to be a pure software company, then it might be relevant for that classic company. But broadly across the economy, I think everyone is figuring out, you know, it's a bigger version of the Internet wave where all companies had to, you know, do things.

And we used to talk about our AOL keyword, you know, and crazy stuff like that, right? Which was the phasing in. This is a lot bigger and more intense, but it's sort of a larger version of that same thing, which is all companies around the world, organizations, governments, militaries, are scrambling to figure out, you know, how to use AI well. I guess related to that, people are talking about it with software engineering, people are talking about call centers. I have a friend who has one of his startups has a call center in the Philippines. They're going to lay off 80%.

That's 7% of that country's GDP. How are you thinking about jobs? Just as a thinker, how is anthropic thinking about it? Well, look, if you look over the last 200 years, there have been a bunch of dislocations, but they were in, you know, happened slowly, and over one part of the economy. And so the danger is,

are there multiple that happen in multiple fields? If these happen slowly, then people are able to find other roles. So it depends on how fast this all comes. And again, I would say the biggest uncertainty for everybody is how fast is the AI getting better and how fast will it be getting better going forward?

And then that changes your views and assumptions. So let's look at it. self-driving cars. I mean, you know, we thought 20 years ago it was going to be pretty fast, and it's 20 years later from when it started 2007 in the DARPA Grand Challenge, and we're like, what, 0.1% of all miles, maybe 0.001%. I mean, it's really pretty tiny, okay, 20 years later. So these things take a long time to actually mature and diffuse. Another example is Jeffrey Hinton won the Nobel Prize for his work on NER. and really the father of AI. And in 2016, so 10 years ago, he said, stop training radiologists now

because in five years, 2021, there would be no need for them. So what's happened instead is as radiology got AI boosted, the cost came down. You can walk into an MRI center in the US for $300 and get an MRI now. And so Doc started ordering them more and using them more, and insurance covered them more. And so the number of the number of the number. And so the number of scans has gone way up. And guess what? We have a shortage in radiologists. We have 35,000. We need about 40,000. Wages acclimed at close to $500,000 a year. So even the best intentioned people in the field, okay, can prophecy disaster in radiology and have it be not accurate. So again, and it's just a timing thing because in 20 years, I'm confident Hinton will be right. Okay, so just think

There's two examples there, which is a lot of this stuff may not happen right away, but it's still probably going to happen in a long time, 20 years. Some of it might happen in a short time, so you want to be ready for it. And that makes sense, although something does feel different about this time. And all the people leading these AI labs are talking about not 20 years. They're talking about next year. They're talking about two years. You're going to have a data center of super-intelligence.

intelligent geniuses that can do our work. Do you think they're wrong, or do you think it's a probability? And even if it's a 10% chance that they're right, are you worried that this can lead to political polarization? What happened in globalization can now happen, what happened in 30 years could happen in two or three. Is that not a concern, or should we start doing something about that? If the AI really gets incredible in a very short amount of time,

like starts writing itself and self-improving, all of that. Isn't it writing 90% of itself right now? You know, again, lines of code is a tricky measure. You know, when it's invented a new type, so of learning, you know, so there's reinforcement learning. You know, when it invents something completely new, then you can say that. But so there are cases where it's pretty fast, and so I think it's important to say we should be ready.

Now, here's the thing. We talk a bunch about unemployment, what it will do. Every other time we've had big unemployment, it's been a recession. and so the stock market's down and government tax revenues are down. This time, if AI is successful in the way we think it will, I think we're going to see high productivity, high GDP growth, high stock market, and high unemployment.

So we'll have money to do things. And so think of it like the Alaska Fund, which is oil, or the Norwegian sovereign fund. We need some ideas like that. What are we going to do with all these huge tax revenues that are going to be able to come in with big growth. And if we've got a fund, which is for the benefit of all citizens, then we may, in fact, be able to have a path to a glorious and harmonious society.

And I don't mean, you know, UBI. That's got a bad taint to it, but it's a partial sharing of the rewards, which is, again, what happened with the Alaska Fund and the Norwegian Fund. Makes sense. Well, Reed Hastings, thank you so much. And thanks to everyone. That was Reed Hastings in conversation with Sal Khan,

at TED 2026. If you're curious about TED's curation, visit TED.com slash curation guidelines. And that's it for today. Ted Talks Daily is a podcast from TED. This episode was fact-checked by the TED research team

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Christopher Fazy Bogin, Valentina Bohanini, Ban Ban-Ban-Chang, Brian Green and Lainey Lott. Learn more at podcasts. dot ted.com. I am Elise Hu. I'll be back tomorrow

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