Moonshots with Peter Diamandis
Why AGI Is Close but Not Here Yet | Ray Kurzweil | EP #261
03 Jun 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What predictions does Ray Kurzweil make about AGI?
We just saw a story where Demis Hassabis, who you know, said 50-50 whether we need another breakthrough to get to AGI. What do you think? Well, I think we need two things. So we've made a 75,000 million trillion fold increase over this 75 years. But AGI will happen by 2029. The large language models have only been effective for the last six months.
We're being really affected by the exponential growth. A year ago, large language models were okay. Now they're really very effective and we're really going to be able to feel that in the future. If you could send a message back in time to the 1960s or 1970s for how to avoid plateaus and just speed up progress toward the singularity, what message would you send back in time?
I think we have to consider
Now that's a moonshot, ladies and gentlemen.
It's a pleasure to invite everybody to an afternoon with an extraordinary man, Ray Kurzweil, with my moonshot mates, my co-author Stephen. Ray, you've been a mentor, a business partner, a co-author for me personally, and just an incredible guide for many of us.
Ray Kurzweil is called the relentless genius by the Wall Street Journal, the ultimate thinking machine by Forbes, the rightful heir to Thomas Edison by Inc. Magazine. PBS named him as one of the 16 revolutionaries who made America.
He invented the first CCD flatbed scanner, the first print to speech reading machine for the blind, the first text to speech synthesizer, and the first music synthesizer capable of recreating a grand piano. He's a National Inventors Hall of Fame inductee, a National Medal of Technology recipient, a Grammy Award winner. He holds 21 honorary doctorates and has been honored by three U.S.
presidents. He's authored five national bestsellers, including Singularity is Near and How to Create a Mind. He proposed the concept of pattern recognition theory of mind, arguing that human neocortex is composed of roughly 300 million hierarchical pattern processors. That theory became his engineering blueprint. In 2012, he got his first job.
As the director of engineering at Google with a singular mission, teach machines to understand human language, not just match keywords, but grasp meaning and context. His team helped build the knowledge graph and advanced semantic search so that when you typed Apple, Google finally understood whether you meant the fruit or tech company. His arrival helped trigger Google's massive AI talent grab.
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Chapter 2: What are the two key breakthroughs needed to achieve AGI?
Okay, so it's about 20 years. Yeah, well, let's see. It's 2009, guys. 2009, okay, well, the journalist here is telling the story here. Well, no, it was before that, because we launched Singularity in 2009. I think we met in 2007, and the Singularity's New York came out in 2005, right?
Yeah.
I think so, 2005. I remember I took The Singularity is Near, which is quite a thick book. I took it backpacking in Chile. And I read the book, making notes in the margins. And I had started with Bob Richards and Todd Hawley, something called the International Space University. back in 1987 with the founding conference. And when I read your book, it changed my world.
How many folks here in the room did the Singularity of New York change your world, right? I mean, amazing. And I said, there needs to be a university that teaches this stuff. Because all universities, you go down a very narrow niche, you become a hyper-super specialist, and no place could you learn a broad version. I pitched you over a lunch, and then off to the races. It was a dinner, wasn't it?
Chapter 3: How has AI evolved in the past year according to Kurzweil?
Yeah, it was a dinner. It was true. Yeah. Can you hear me? Yeah. Yeah. Well, I said yes right away because I make important decisions very quickly. Yeah, it was. And we had our founding conference at which Salim attended. I got invited there by NASA.
I'd set up a relationship between Yahoo and NASA, and somehow I'd never heard of the Singularity or XPRIZE or any of that. Walked in, top of my head lifted off, and I asked a few too many questions.
Yeah, and we made him our executive director, little to his knowledge.
Yes. Yes.
I remember having a board meeting. I said, hey, come to the board meeting tomorrow morning. I was like, oh, and you'd ask me, how much spare time do I have? And I said, I've got a day, day and a half a week. I'm building a startup as is needed in Silicon Valley.
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Chapter 4: What is the significance of the year 2029 in AI development?
And you said to the board, all right, we have our inaugural board meeting. We've formed the board. We need an executive director. You've all read Salim's bio. He's agreed to do it for 50% of his time. And Ray said, I seconded the motion. Boom, all of a sudden it's ratified. I remember hanging up the phone and my wife said, how was the phone call? And I said, I think I'm a dean.
I don't know how that happened.
So there was that.
That's typical for startups.
There you go. So empty seat, warm bum.
By the way, I know that you're busy and sometimes these episodes run long and you don't have time to listen to the whole episode, or if on occasion you miss an episode. I now put out a Moonshot summary on Substack, which includes a link to all the stories that we cover. The weekly recap covers what I and the mates had to say, what we think is most important, and what we're most excited about.
And it's free. You can subscribe at diamandis.com slash metatrends. That's diamandis.com slash metatrends. All right, now back to the episode. So, Ray, one of the questions I'd love to ask, we just saw a story where Demis Hassabis, who you know, said he doesn't, that 50-50, whether we need another breakthrough, a fundamental breakthrough in AI to get to AGI. What do you think?
Well, I think we need two things for this to understand physics. Like, it doesn't really understand physics. It can infer that from the wording, but it really doesn't understand how different types of things would interact. Google has announced a project to do that. That, I think, will take, I think, until 2029. And then robotics is behind large language models.
I mean, large language models can basically understand everything. But I've got to clean up after dinner. That's my assignment from my wife. And robotics doesn't understand that. If I... Like this one, I need to put something in the refrigerator. This one needs to be washed away. Like everything's a little bit different.
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Chapter 5: How can society prepare for the impact of AGI?
less expensive. People can't spend $100,000 to have a robot clean up after dinner. So I think that will come about 2029. It's not there today. Those two things, I think, need some additional work, but we know what needs to be done. Let's let's go around the horn here with the mates, and then we're going to be opening up for yourselves in a little bit. Dave, you want to kick us off?
Well, I got to tell you, your very first book was such a life-changing moment for me to read it. Age of Spiritual Machines, which one?
It was the first one. It was... Singularity is Near.
Yeah, Singularity is Near, I think it was the title of it. It was the one where he invented the term singularity, which a lot of people in AI had been thinking about for a long, long time, but nobody had crystallized it into a term. And now the topic of are we in the singularity is gonna come up constantly until it's in the rear view mirror.
But it was world changing for me because I had read a lot of analysis from Danny Ellis, I'm sure you know Danny, who built the connection machine. trying to predict how much compute will it take for us to crack open AI. And it's not an easy problem at all.
Because you could simulate maybe a million parameter neural net at the time, and then a million became 10 million, became a billion, and now we're at a trillion, or I guess 10 trillion now.
Nobody had really put pen to paper and said, I'm going to put dates on this and I'm going to put curves on this. Because, you know, to plan your life and to plan a business, you need to have some prediction. And it's more needed now, I think, than ever before. And you were the first person to say, you know what, I'm going to name it. And I'm going to predict an exact date.
And I swear, when you predict the future, as you and Peter do, if you're 99% right and 1% wrong, everybody likes to jump on that 1%. But as a service to the world, making those predictions is just a blessing, because then you can build your life and your career around the future and not around the past.
Yeah, well, after the singularities near came out, Stanford had a conference basically to examine whether my prediction was correct or not. And several hundred experts came from around the world and they agreed that this would happen, that human level AI would happen. Yeah. But they figured it would be 100 years, not 30 years. Yeah. And what I was predicting was AGI would happen by 2029.
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Chapter 6: Why is the concept of consciousness important in AI discussions?
People would have slightly different definitions of AGI. So there'd be a three-year period where people would predict AGI is here. And that would start three years earlier, like 2026. And indeed, we're having people predicting AGI is here already. going through 2029, when we really will be very confident that AGI is here. So that was my prediction back in 1999, actually. Yeah.
The thing that really blows my mind about those predictions, aside from AGI being pretty much right on the number predicted, what, 25, 30 years in advance? In 1999. In 1999 to, I guess, 2029. Yeah, so 30 years in advance. right on the number. It's just nutty. But a lot of those predictions are based on compute and the availability of compute and Moore's law continuing.
And then somewhere in there, carbon nanotubes or some future compute substrate needs to exist. But what we've done instead is just hammer the transistor on silicon to death and stretch it into massive data centers and stay right on your curve. Yeah. Well, if I can show my curve. Yeah, sure. Can we go ahead to the end of the, yeah. No, not this one. Let me back up a second.
So we got, here we go, this one here. Yeah. I mean, in 1939, we were actually increasing relay-based computers. And the exponential growth of relay-based computers is the same as it is today. And NVIDIA and other people are not looking back and say, well, we want to match the exponential growth of relay-based 70 years ago. But they are. The exponential growth has been pretty much the same.
This is basically a straight line. And this is an increase of 75 quad trillion. So it's 75,000 trillion fold increase in the hardware. We're also making advances in the software. conservative estimates that we've made about a million fold increase over the 70 years. So this thing and the overall increase in computation is equal to the hardware times the software.
So we've made a 75,000 million trillion fold increase over the over this 75 years. That's why we didn't have large language models 70 years ago or even three years ago. Yeah, they were effective. It's actually large language models have only been effective for the last six months. Like a year ago, it really wasn't usable.
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Chapter 7: What ethical considerations arise with the advancement of AI?
You know what's amazing to me, Ray, is the raging debate going all the way back to sort of 1980s, 1990s when I was working on AI. Raging debate on whether a parameter in a neural net has anything to do with a synapse in a brain. Because you could count the synapses, you could count the nerve cells, and you could say, OK, there's, what, 300 billion or so.
And then you count the- Roughly 100 billion.
About 100 billion.
100 trillion synapses.
Yeah, and about 10,000 to 10,000 synapses per cell. So you can center on about 100 trillion synapses might make a human brain. But then there's all this debate on, well, a synapse could have anything from quantum effects going on.
It could be like, it could take entire years to simulate. Who knows? It's also going to land on pretty much exact, within an order of magnitude, exact parity. A synapse a parameter in artificial neural net, the IQ coming out the other side is one for one. I mean, no reason to believe that the algorithm is quite different.
The rate at which synapse will form in the human brain is about 200 calculations per second. Yeah, it's very, very slow, very slow. But every every synapse is operating simultaneously. So it's massive power parallelism. so i mean uh back when computers actually did one thing at a time
I predicted that we really need to increase the parallelism, which we've done, but we don't have every single synapse happening at the same time, but we have maybe a million to one parallelism, and that's given us the power that we have today. Yeah, you saw it coming. Steven, do you want to lob a question for Ray here?
Sure. God, there's so many. What I really sort of want to know, like, I remember our very first conversation, and you said, I asked you, you know, how do you think of yourself? And you told me you think of yourself more as an artist and a creative than you did as a technologist and an inventor.
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Chapter 8: How does Ray Kurzweil envision the future of human and AI collaboration?
And I said, you know, I didn't know how to put these together, but if I could actually figure out how to put these together, I could create anything. And they thought I was very imaginative at that time. Totally sparked a memory, too. I remember really, really clearly, everybody who had a band here on campus wanted a Kurzweil keyboard.
It was like, you know, the coolest rock keyboard you could have. And then I heard about this AI research futurist guy named Ray Kurzweil. And I'm reading this material. I'm like, there's no way these are the same guy. I had no idea that it was the same Kurzweil until, actually, I think it was like years later. Ray, I had a chance to read it.
I had a chance to read your autobiography and draft, which is amazing, quite the life that you lived and that of your grandparents and parents. When is that going to come out? February. February. Oh, yeah. And your name for the book? Have you picked one? My Exponential Life. Oh, nice. Cool. Nice. Salim, let's go to you next. Yeah. Can't wait to read that.
So, Ray, I've heard you speak, I think, 62 times.
But who's counting? What's very annoying? Does that count today? It counts today.
I have one today. What's very annoying is that I don't think I've ever not learned something, which is really annoying. And I remember one of my favorites was we were having a late night conversation with one of the classes of singularity and the inevitable question about consciousness came up. And you said, language is a really thin pipe to discuss concepts as rich as that.
That was such a brilliant framing of that discussion, and it comes up always in our conversations here. I want to kind of ask you a language question. You talk a lot about computers or AI being smarter than humans, and my beef is, what do we mean by smarter? And I was wondering if you could drill down a little bit on what do you mean by that, because it's not just processes per second, et cetera.
There's a lot more to it. How do you frame or define or subdivide smarter? I mean, AI is already smarter than most humans. And it can actually do research that's much better than we can do. Because it can actually look, let's say, at a...
something that might be a medicine and can actually consider a billion possibilities and test each one and actually test it with fidelity and decide which one of the billion is actually the medicine. Humans can't do that. Maybe we can consider a few. That's how we've actually come up with all the medicines. People consider a few things that they've had experience with.
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