0:03 So, Reed, education, which a lot of people I don't I don't know how many know how active you are in education, education philanthropy, charter schools, et cetera. What are you doing in that space? >> Uh I'm on the board of Khan Academy. Um and generally I've been working for last 25 years uh to try to find ways to make, in particular, US schools, but some international uh work better um and have better outcomes for kids. And I would say after a billion dollars in 25 years, um we're down below where I started. So, uh it's a hard problem. >> And what do you, you know, we heard about the the risks of edtech, um but you are investing in in edtech, you're giving to edtech. What are your thoughts? What What's the tradeoff there? What Why do you continue to to potentially believe there? >> You know, in the 18th century, most factories uh were steam engine driven. So, you have a big steam engine rotating uh rods and pulleys and belts and drove the factory, very efficient. And then electricity came in and they replaced 1:05 the steam engine with an electric engine um thinking that it was going to help a lot and productivity didn't increase at all and they were puzzled. And then they realized here the limiting factor is the power distribution system, all these spinning rods and mechanical power. And they ripped that out and just put in small electric motors for each device. Then they could move things around, fit them in better cuz it wasn't aligned to spinning. Uh they could have variable speed, turn different motors on and off, uh and then um productivity increased dramatically. So, it's a classic uh 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 2:05 teaching to, is the fundamental friction in our mass education system. Uh and the theory is if each of us had an individual human tutor, um so imagine you go to school, um you have your normal social activities, but when it comes to learning, uh you get an individual who's going to sit down with you, and um and then they could do Khan Academy, or they could do Color Book, or whatever's appropriate, uh that learning would be massively increased. Um and there was a a famous study uh 40 years ago, Bloom, uh that documented this. Uh and now a friend of mine, uh 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, um where uh 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, 3:07 but it seems to be distracting. It seems to to correlate with 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. Totally agree with uh all his zip up uh um phones and don't use phones. And and I think you guys clarified uh he likes uh offline tablets. That's fine. It's the internet that's the the problem, not the physical device. And so I think there's lots of ways to uh cater to the concerns that he correctly expresses, which is uh letting kids go wild on the internet under 16 is 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? And they can use some technology if they want. Uh but then obviously that's cost prohibitive cuz it's about $100,000 per kid per year. Um so then the the the hard challenge becomes how do we use AI to approximate that human and provide everyone an 4:08 individualized education as AI gets better. Current AI is not good enough to do that. But you know, in 3 years we've gone from, you know, chat GPT 3.5 and barely um being able to do high school math to, you know, just incredible intelligence. And um you know, that's likely to just continue to double, double, double um 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? Uh 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 it maybe looks embodied in some way, but there are you imagining there's no human teacher? Do 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 uh three big purposes. One is create good citizens. Another is give economic opportunity to the kids. And then the other is 5:09 socialization. Um social emotional learning, uh how to work with other people, adults outside of your family. So only in the first part is really where uh online is really good. And what we want to do is have teachers be able to focus on social emotional learning. Um they become uh you know, really helping maturity, interpersonal skills, uh values clarification, all those kind of higher level things. Um and then we've got to figure out in the AI age, um you know, how do we enhance that role of creating good citizens? Okay, cuz one of our uh ways to come together is to have, you know, a 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 to 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 that mission to get attention also. >> And and I just want to double click on that and make sure maybe we have a 6:09 common vision or maybe it's divergent. You you still see a major role for the human teacher and the human classroom. You just see that role shifting. >> But you're going sort of. Okay. So uh most teachers today, um 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 the student. So that part is the social emotional. But in terms of transferring information, what educators uh cynically call sage on a stage, it's eliminating sage on a stage as a teaching modality. Okay? And so it's really just focused on the individual. What would education be if there was no mass teaching? >> And to be fair, you know, if you go to an ed school, if you went to an ed school 20 years ago, this is what they were preaching. Differentiated instruction, active learning, don't be sage on the stage, have a Socratic discussion, do a simulation, have So it's really potentially, and this is what I say cuz I get this question a lot, the teacher I think moves up the 7:10 value chain and is able to facilitate and drive a lot of that active learning, which is better 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. But if you have incredible software, it's a pretty inexpensive to make it globally available. >> No, that's right. I I mean, you know, we we talk a lot about it at the Khan Academy board that the technology can raise the the safety net, raise the floor. We've have seen stories of young women in Afghanistan using Khan Academy. One of them is at MIT now. I mean, some 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 your other board you're on isn't obviously very related to this, Anthropic. I I actually I'm just curious what what, 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 um pressures from the White House, how your new model 8:13 might break all software. Um What Tell us what you can. >> Yeah, it's a lot like your board meetings, you know. >> [laughter] >> Talking about uh 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. Um it's that we're successful the human humanity. How do we get into the age of AI successfully crossing through sort of this uh portal. And they recognize it's going to be very challenging um and that they're very dedicated to having that happen in rolling out AI and having the incredible beneficial outcomes, whether that's the Waymo's self-driving, whether that's uh gene editing, um whether that's curing cancer. You know, 10 20 years from now, uh it's very likely we'll have pretty abundant energy. Um we will have uh amazing health outcomes. I mean, so much positive outcome from the AI infusion into science. Um and I would say Anthropic's very serious about 9:14 helping us manage or avoid most of the downside. >> And And how have y'all pulled that off in closed doors? And I I've been in some of those closed doors where peop- people are genuinely afraid of more than 10% chance that this could be an existential threat to humanity. It does seem that Anthropic somehow is is proving it to be very responsible um or that that's what we appears to be and at the same time moving very fast. The hyper speed. It feels like almost every few weeks there's something new and it and it's very tangible in what it might do for work. How are y'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 know, you're probably going to see them go, you know, certain ones are the lead in certain areas over time and you know, frankly, it's good for the country if you know, we have three really successful models to to choose from. Um, and then how do they balance it? Um, you know, case by case. Uh, so I think each one have to see, you know, how accelerated is the learning, how 10:15 powerful is it? What are the downside scenarios? What are the new possibilities it can do? >> And I'm curious about Anthropic itself. I I had a chance to visit there a couple of weeks ago and you know, I I take pride that, you know, Khan Academy were super nimble and we're innovating etc. and we're obviously trying to leverage AI for 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 that co-work was what was it a week or two that that it was essentially built primarily by the AI itself. What will an organization look like in the future? I think y'all 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 might be relevant for that class of company, but broadly across the economy, I think everyone is figuring out, you know, it's a a bigger version of the internet wave where all companies had to, you know, do things and we used to talk about our AOL 11:14 keywords, you know, and crazy stuff like that, right? Which was the phasing in. Um, 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, uh, you know, how to you 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? How just just as a thinker, how is Anthropic thinking about it? >> Well, look, if you look over the last 200 years, there've been a bunch of dislocations, but they were in, you know, happened slowly and over one part of the economy. Um and so the danger is, you know, are there multiple that happen in multiple fields? If these happen slowly, then people are able to find other roles. Um so it depends on how fast this all comes. Um and again, there's I would say the 12:14 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 um changes your views and assumptions. So, let's look at 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, that's really pretty tiny, okay, 20 years later. So, these things take a long time to actually mature and diffuse. Another example is Geoffrey Hinton won the Nobel Prize for his work on neural nets and really the father of AI. And in 2016, so 10 years ago, he said, "Stop training radiologists now because in 5 years, 2021, there would be no need for them." So, what's happened instead is um as radiology got AI boosted, the 13:16 cost came down, you can walk into an MRI center in the US for $300 and get an MRI now, and so docs started ordering them more and using them more and insurance covered them more. 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 have climbed to 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 the 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 these AI 14:17 labs are talking about not 20 years, they're talking about next year. They're talking about 2 years, you're going to have a data center of, you know, superintelligent geniuses that can do our work. Do you Do you think they're wrong, or do you think it's a probability? And even if it's a 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 Is that not a concern, or should we start doing something about that? >> Um if the AI really gets incredible in a very short amount of time, like starts writing itself and self-improving, >> Isn't it writing 90% of itself right now? >> Um 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, you know, reinforcement learning. You know, when it invents something completely new as the 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 15:18 thing, we talk a whole 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. Um 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, um 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, you know, a glorious and harmonious society. And I don't mean, you know, UBI. That's got a a bad taste to it, but it's uh a partial sharing of the rewards, which is again what happened with the Alaska fund and and the Norwegian fund. >> Makes sense. Well, Reed Hastings, thank 16:19 you so much. And thanks everyone. Accelerating possibilities. >> [applause]