How Quantum & AI Will Shape the World’s Future w/ Jack Hidary | EP #123

In this episode, Jack and Peter discuss LQMs which are the next stage of AI beyond LLMs. LQMs are growing in applications in the biggest parts of the economy. Jack and Peter then talk about the synergy of bringing AI and quantum together for global impact. Recorded on Oct 1st, 2024 Views are my own thoughts; not Financial, Medical, or Legal Advice. 01:57 | The Future of AI and Quantum 31:41 | The Intersection of AI and Brain Imaging 01:04:46 | Decoding the Mysteries of Quantum Jack Hidary is a leading entrepreneur and visionary at the forefront of AI and quantum technology as the CEO of SandboxAQ, raising over $500m in funding. He is the author of forthcoming book AI or Die and the influential textbook; Quantum Computing: An Applied Approach. A serial entrepreneur, Hidary co-founded and led EarthWeb/Dice from inception to IPO, and co-founded Vista Research and sold it to S&P/McGraw-Hill. Jack studied neuroscience at Columbia University and was a Stanley Fellow in Clinical Neuroscience at the NIH where he applied neural networks to brain imaging. Learn more about SandboxAQ: https://www.sandboxaq.com/ ____________ I only endorse products and services I personally use. To see what they are, please support this podcast by checking out our sponsors: Get started with Fountain Life and become the CEO of your health: https://fountainlife.com/peter/ AI-powered precision diagnosis you NEED for a healthy gut: https://www.viome.com/peter Reverse the age of your skin with Oneskin; 30% here: http://oneskin.co/PETER Get real-time feedback on how diet impacts your health with https://join.levelshealth.com/peter/ _____________ Get my new Longevity Practices 2024 book: https://bit.ly/48Hv1j6 I send weekly emails with the latest insights and trends on today’s and tomorrow’s exponential technologies. Stay ahead of the curve, and sign up now: Tech Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Alzheimer's, 40 years of research, nothing to show for that. Parkinson's, a handful of things to do for those patients. Dementia, an epidemic, cancer, nothing to show.

• 0:11Peter Diamandis?

  You can use the power of quantum physics to understand and model molecules.

• 0:18Speaker 00

  Instead of the world of large language models, we've now entered the world, Peter, of large quantitative models, LQMs.

• 0:25Peter Diamandis?

  People feel like the world is going rapidly and disrupting and reinventing today with generative AI.

• 0:32Speaker 00

  This is just the beginning, Peter.

• 0:37Peter Diamandis?

  Everybody, welcome to Moonshots. Today is an extraordinary episode with a dear friend, Jack Hidary. We're talking about the intersection of AI and quantum. Jack's the CEO of an incredible company spun out of Alphabet called Sandbox AQ. He's a Brooklyn boy, a graduate of Columbia, where he studied philosophy, physics, and neuroscience, did his fellowship at the NIH. He and I go back 25 years to when he started Vista Research. Back in 2016, Jack founded the Quantum Group at Alphabet, working with Sergey Brin and Astro Teller at X. And in March of 2022, spun out Sandbox AQ, taking on the role as CEO,

• 1:19Peter Diamandis?

  attracting none other than Eric Schmidt as his chairman, raised a monster round of $500 million in one fell swoop, and he's on a rocket ship ride. It's good to see you, Jack.

• 1:31Speaker 00

  Peter, good to see you, my friend. It's exciting times.

• 1:33Peter Diamandis?

  It surely is. You know, you wrote a textbook called Quantum Computing: An Applied Approach. I mean, who writes-

• 1:40Speaker 00

  Very light reading, very light reading, Peter.

• 1:42Peter Diamandis?

  But more importantly, you wrote this book as well, which I love, AI or Die. It's really a how-to manual for CEOs. I recommend it for everybody. And importantly, you're on my board, the XPRIZE, one of our trustees. Buddy, I want to talk about quantum. I want to talk about the intersection of AI and quantum. I want people to understand why this is so important right now. We've been inundated with large language models and generative AI, and it's changing the world. But it's changing a part of the world, and the rest of the world is about to be transformed and discovered through this intersection of AI and quantum. Sandbox AQ, A for AI, Q for quantum.

• 2:29Peter Diamandis?

  Go. Let's, let's jump in here, buddy.

• 2:31Speaker 00

  Peter, yeah, it's, Peter, these are very exciting times, not only for Sandbox AQ, for you, for me, for XPRIZE, but for the human race. We, as humans, are dealing with so many challenges. The challenges that we often talk about at XPRIZE Visioneering Gatherings and other gatherings of folks that are really concerned about life sciences, the medical challenges that we face as humans. Alzheimer's, forty years of research, nothing to show for that. Parkinson's, a handful of things to do for those patients. Dementia, an epidemic, literally, across the world as we, as our population gets older. In cancer, some success stories, in breast cancer, for example, much higher survival

• 3:16Speaker 00

  rates now, earlier detection, understanding of the multiple subtypes of breast cancer. Yet in other cancers, like pancreatic cancer, nothing to show. GB, glioblastoma, nothing to show. Steve Jobs died of pancreatic cancer, billions in the bank account, nothing to do. Now, years later, still nothing. It's still nothing. So, massive challenge is in the world of medicine, massive challenges in the world of energy. We all hope for a transition to a cleaner, more efficient energy posture for our world, yet we've made halting, halting progress at best towards that. So, a lot of challenge, and the question is, what are the tools at hand, Peter, that

• 4:00Speaker 00

  we can use, that we can marshal to address these challenges? And one of the reasons I started Sandbox AQ is for deep impact at scale. And you and I have always talked about that. Impact is good, but scale is what's critical.

• 4:18Peter Diamandis?

  Impacting the lives of billions of people, you know, the world's biggest problems or the world's biggest business opportunities.

• 4:23Speaker 00

  And that's what Sergey and others had challenged me to take up as, as we got going with Sandbox AQ. And as we look at these two particular toolsets, AI and quantum, initially, Peter, they might seem quite different, like, wow, one comes from computer science and, and inspired by the brain, neural networks are inspired by biological neural networks, okay, that's one interesting tool. And then physics, on the other hand, and now you particularly talk about quantum physics, how does that relate? How are these two things related? Well, actually, Peter, there's a fundamental core nexus, a wormhole, if you would, that brings us together

• 5:01Speaker 00

  between AI and quantum. And that is, that is, both of them are modeling the world around us. Both of them are taking huge swaths of data and compressing them down to manageable units in a way that we can actually leverage them to make a prediction, to have an output that is useful for us in addressing these kinds of challenges. So they seem quite literally worlds apart, AI and quantum, but there's a fundamental core commonality. So let's, let's start, if you

• 5:36Peter Diamandis?

  Yeah, let's, let's jump in with the, with the large language models and generative AI. And, and, and what does that mean? How do you think about the limitations and what it's given us?

• 5:49Speaker 00

  Uh, Peter, let's dig into that. So before large language models, we actually had architectures of neural networks, these artificial representations that are inspired by the brain architecture. Our brains, as we know, have 86 to 100 billion neurons and then trillions, possibly hundreds of trillions of connections known as synapses in the brain, or connections, or weights, or parameters in an artificial neural network. So loosely inspired by the brain, certainly not an exact depiction of what happens in the brain, very loose, but nevertheless, we call it an artificial neural network. And prior to large language models, we had architectures

• 6:30Speaker 00

  such as RNNs, recurrent neural networks. And these had the ability to actually make a pretty good prediction. If you said, "The dog ate the blank," it actually would make a good guess that the dog probably ate the bone, or the homework, but probably not "The dog ate the house," right? Uh, and, and so it was pretty good. The, the main drawback of RNNs is they're super slow, just not fit for purpose. You cannot, uh, be, uh, doing what people do today, which is doing interviews on Zoom and in real time, asking the LLM to help it do an interview as you see people happening right now. But certainly, they showed that it was possible to have this kind of prediction.

• 7:14Speaker 00

  And along came a paper by colleagues at Google. Eight of them wrote a paper in 2017 called Attention is All You Need. And what they realized in this paper, what they demonstrated in this paper is that these new GPU architectures, we could take advantage of the parallelizability. Took me 10 minutes to practice that word. Parallelizability of the GPU architectures. In order to actually get a lot of throughput to actually make this both the training and the inference, both the training on large corpuses of words and then the real time use of those models. That's what we call inference. Both of those could be sped up massively. And so that's in fact, what happened.

• 7:56Speaker 00

  These GPUs, the G in GPU, of course, Peter, for graphics, not meant initially for language models or anything like that. Initially meant to give us beautiful graphics in Doom and you know, all these kinds of things. And Nvidia, people may not realize, is actually a 30 year old company. This is a company, it's a 30 year overnight success. Let's put it that way. And so it's an exciting moment because in 2017, the marriage of these new architectures known as transformers, another way of basically putting these artificial neural neural networks together, combined with the power of these GPUs,

• 8:37Speaker 00

  really led to this revolution that we have with OpenAI and Anthropic and Google Gemini and Meta Lama 3, 3.1, 3.2, and so on and so forth. All this came from some initial work done over many decades, of course. AI is not new. We can go back many, many years. I like to go back to 1943, a paper by McCullough and Pitts, a neuroanatomist and a mathematician. Quite a strange bunch. We don't have time for that today, but maybe another episode of this podcast. We'll talk about McCullough and Pitts. But they realized that when you open up the brain, you don't see a CPU, you don't see a. Memory, you don't see the kind of, you know, architecture that you think of a von Neumann

• 9:21Speaker 00

  kind of computer after Johnny von Neumann, you see something very different. And that's in fact, was the beginning of these kinds of neural network architectures. But fast forward to, we now have these things, languages are now being, language models are being trained. But what is really happening under the hood, Peter? And how does this, you know, Jack, you tell us that it's connected somehow and has some similarity to what's happening in physics? Well, let's look at it. What's happening in a large language model, Peter, is that you take a huge corpus of words, billions of words, the words in Wikipedia and the paragraphs in Reddit and the posts on social media, some true, some false. So it's garbage in, garbage out. But with

• 10:02Speaker 00

  all the garbage, you bring it all in and you present it to these language models and you train them. And what you hope that is happening is what we call generalization or learning. You hope that it hasn't just memorized that entire corpus. If it just memorizes it, well, no learning has actually happened. Similar with a toddler. If we have a toddler and we take them around our neighborhood and we say, hey, let's go into this car, let's go into this bus, let's do this, let's do that. And later a different car comes by, you want that toddler to say, that's a car. It's a different kind of car. It's red, it's not blue, it's larger, not smaller. It's different than the initial car that the kid went in, but the kid knows that it's a car. How does a kid know that?

• 10:45Speaker 00

  That's what learning is about. That's what Eric Kandel won a Nobel Prize for and many others did, to understand what learning, how learning works in the brain. And now, we can replicate that in these neural networks. And so, one way to think about neural networks is a compression algo. What it's doing is it's taking a huge body of stuff, often represented in our world, language in this case, could be images, could be movies even, videos, and it's compressing them down to their essence, the essence of hardness and the essence of bustness. And it's saying, okay, I've seen enough of these and I understand what some of the core elements are. So if you put

• 11:25Speaker 00

  In saying, show me a car driving down the street that's red but upside down. And it's singing a melody from Taylor Swift. We there are now models that actually will do that now because we've taken not only text, but also video and images and and train these models and they've extracted some of the essence of what's happening. And so that's a form of compression. It's a shorthand that's now embedded in the weights of that model, embedded in the parameters of that model. And some of these language models, as you know, Peter, could get up to four or 500 billion parameters, a trillion parameters.

• 12:03Peter Diamandis?

  Yes.

• 12:03Speaker 00

  And it looks like now we'll hit 2 trillion, maybe even 2 and 1/2 trillion parameters in some of the newer models that are coming out right now.

• 12:10Peter Diamandis?

  There are limitations, though, right?

• 12:12Speaker 00

  Yeah.

• 12:12Peter Diamandis?

  There are limitations of what they can do.

• 12:15Speaker 00

  Yeah. So, so, so that's the good news. We found a way to compress all of, say, you know, various languages down into this model. The problem is, there is no equation for the English language. There's no equation for the for Mandarin or for any language. And so the ability of a language model, it's really limited to mixing and matching what it found on the on the Internet. And so, yes, it can make a new essay, but that essay essentially is regurgitating bits and pieces of what came before. That's really what's happening. It's a probabilistic engine that is spitting out stuff that it hopes, and we hope, makes some coherent sense.

• 12:59Speaker 00

  And with enough training and, you know, human feedback, humans in the loop feedback, then we can actually make that happen more often than not. There's still a lot of hallucinations, of course. So we're really limited in terms of, we can't really go beyond what is really in that in that corpus. So, so that's language models. And the question is, what else could we do with this kind of architectures, the architectures of neural networks and in general with artificial intelligence? Well, let's think about the world beyond words. And so, OK, language models, a lot of words, great, easy training set, low hanging fruit. That's really why it started with that. But it turns out, the majority of our world, Peter, is not words, but numbers.

• 13:42Speaker 00

  The majority of our world, if you think about a medicine, a drug, it's described by numbers, by certain configurations of carbons and hydrogen, and maybe we throw in a nitrogen, or throw in some sulfur and things like that, and we make different medicines. We think about biology, that's numbers. We think about physics, we think about battery chemistry to store energy. Those are numbers, right? And there's no amount of training in words that's gonna help you design that next battery, because you need to know about the laws of chemistry and physics. And we need to have the exact nature of those laws, not a guess, not some paragraph in a textbook, but the actual mathematics of this ballgame.

• 14:23Peter Diamandis?

  There are, and I think this was the insight, right, that brought about the creation of Sandbox AQ. There are laws of physics, they go back 100 years. There are some fundamental laws of quantum physics. And we all know Newtonian physics, F = ma, you know, the what we learned to describe the velocity of a cannonball shooting out of a cannon. But there are a series of laws of physics known as the quantum equations. And I just wrote them down, because I wanna discuss them a little bit. Schrodinger's equation, Heisenberg's uncertainty equation, Planck's equation, Bohr's probabilistic interpretation equations.

• 15:08Speaker 00

  Yeah.

• 15:09Peter Diamandis?

  Was the, you know, was the ability of computation to model these equations accurately what brought about the birth of Sandbox AQ?

• 15:21Speaker 00

  It was the realization that we could have the compute. We could bring the future forward, Peter. That's really what you and I have been doing our whole lives, bringing the future forward. And this was a realization that we could bring the future forward to compute these laws at scale, with impact, with deep impact.

• 15:45Speaker 00

  At a scale that would impact billions of people.

• 15:48Peter Diamandis?

  Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love. The company is called Fountain Life and it's a company I started years ago with Tony Robbins and a group of very talented physicians. You know, most of us don't actually know what's going on inside our body. We're all optimists. Until that day where you have a pain in your side, you go to the physician in the emergency room and they say, listen, I'm sorry to tell you this, but you have this stage three or four going on. And you know, it didn't start that morning. It probably was a problem that's been going on for some time, but because we never look, we don't find out. So what we built at Fountain Life

• 16:33Peter Diamandis?

  was the world's most advanced diagnostic centers. We have four across the US today and we're building 20 around the world. These centers give you a full body MRI, a brain, a brain vasculature, an AI-enabled coronary CT looking for soft plaque, a DEXA scan, a GRAIL blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AIs and our physicians to find any disease at the very beginning, when it's solvable. You're going to find out eventually. Might as well find out when you can take action. Fountain Life also has an entire side of therapeutics. We look around the world for the most advanced therapeutics

• 17:17Peter Diamandis?

  that can add 10, 20 healthy years to your life and we provide them to you at our centers. So if this is of interest to you, please go and check it out. Go to fountainlife.com/peter. When Tony and I wrote our New York Times bestseller, Life Force, we had 30,000 people who reached out to us for Fountain Life memberships. If you go to fountainlife.com/peter, we'll put you to the top of the list. Really, it's something that is, for me, one of the most important things I offer my entire family, the CEOs of my companies, my friends. It's a chance to really add decades on to our

• 17:59Peter Diamandis?

  healthy lifespans. Go to fountainlife.com backslash Peter. It's one of the most important things I can offer to you as one of my listeners. All right, let's go back to our episode. People always start in discussions of the quantum equations that affect our world on atomic and molecular level. There's a set of Newtonian equations that drive everything from a car going down the street to, you know, a kid playing ice hockey to rockets flying into space. And those Newtonian equations are are very deterministic. They're very clear. You can write them out and you can send a rocket to the moon back in, you know, 1969 with the computational

• 18:41Peter Diamandis?

  power that is, you know, found on, I don't know, I can't find a computer.

• 18:45Speaker 00

  A basic watch, yeah.

• 18:47Peter Diamandis?

  To describe that. So those, those have governed and limited what we've been able to model thus far, right? We can model large chunks of atoms moving, but not model on a, you know, subatomic or molecular basis. So speak to me about that.

• 19:07Speaker 00

  Yeah, so this is a fundamental point. As you, as you mentioned, you know, back in the 60s, we had the ability to calculate where that rocket would go and literally computers were doing that. In those days, what we call computers are human beings who are computing, right? That was the initial computer was a human being and their job was called a computer, like a lawyer does law, call him a lawyer. These were computers doing, doing that. There were some also actual computing machines helping out on the side. And, and the reason why they could do that with such small amounts of memory and compute is because equations.

• 19:44Speaker 00

  Really are great compression vehicles. If I want to say, hey, that you mentioned a cannonball, Peter. I have that cannonball shooting out of the cannon. It's going to take this parabolic type of pathway as it shoots out, hits its apex and comes down again. And so I could, on the one hand, take lots of notes about, oh, here it is at time zero, time one, time two, time three. Take lots of notes where it is. Or, or instead of all that data, I could just summarize it very, very succinctly in an equation where I have the starting parameters. And then I could predict anywhere along the parabola, I can tell you immediately where it is and where it's going to be. So that equation with some of those starting conditions plugged in and the variables gives me a very succinct.

• 20:30Speaker 00

  I've compressed a huge amount of data into a small number of bits of information. Back to Shannon, back to Claude Shannon, what he taught us in 1948 in his landmark journal article about Shannon entropy, using the word entropy in a novel way, not in exactly the traditional way we used it in physics, but in a way that said, what is the surprise factor we have in looking at this body of information? If you have random numbers, you know, kind of in a grayscale image, there's no compression possible. It's random, therefore there's no pattern. But when you have a parabolic pathway of a cannonball, oh, Isaac Newton says, I can tell you

• 21:14Speaker 00

  all about that information in a very, very succinct form. And we do that all the time in the Newtonian world, again, to send rockets up, to understand the dynamics of cars, even hydrodynamics and fluid dynamics with airplanes and testing in wind tunnels or even virtual wind tunnels, how the airflow will happen over that curved shape of the wing. Very complex dynamics, but that's still in what we call the classical world, the pre-quantum world. And so now, Peter, you bring up all these interesting equations. And now we're able to say, hey, we did this on the macro scale, on the classical scale. But how about those electrons?

• 21:59Speaker 00

  How about those photons? How about those molecules? At these scales. We've got to use different equations and that's what the quantum greats gave us. Heisenberg and Schrodinger and even Einstein. 1905 paper, the one he got the Nobel Prize for, Peter, was not relativity. It was the photoelectric effect in 1905, part of his honest Mirabalis, his miracle year, that he had one of those papers. And it was inspired, people may not realize, by the way, a little side fun, science note here, in the Moonshot Podcast. Why did Einstein, here's a fun question for everyone out there, science nerds and geeks alike. Why did Einstein write these four plus ones, actually five papers in one year, on seemingly

• 22:42Speaker 00

  a disparate set of topics? I'll leave it to the end, we'll come back at the end and we'll find out why that is the case. But he did write the photoelectric paper and that led to his Nobel Prize, but it was building on Max Planck, 1901, 1900, gives the talk in the Prussian Academy of Sciences, 1901 publishes the paper and really forever changes the world because what Max Planck realizes to resolve some of the key crises happening in physics at the time. I know people feel the tension that we feel right now, the crises of the late, of the

• 23:19Speaker 00

  late 1800s in physics, the ultraviolet catastrophe, the so many, it's actually four or five different crises happening at the same time in physics. It turns out all of those could be explained by the fact that we were still wed to a Newtonian way of thinking and when it came to the subatomic world, we needed to actually abandon that and move into a new regime and have a different view on how the world worked. And Max Planck would kick that off with a sense of how black body radiation working was again, one of the key crises at the time. Einstein, a young Einstein in his twenties, read that paper and then wrote his paper

• 24:03Speaker 00

  in 1905 with homage, explicit homage to Max Planck, his senior, and saying that Max Planck explained that for black body radiation, I, Albert Einstein. Einstein will explain it for a photon, a packet of light. And, and this ushered in along then with Schrodinger and Dirac and Heisenberg and of course Niels Bohr. All of these greats who each won Nobel prizes. Helped us understand the dynamics of how things work fundamental to our universe. It's not, people often say, oh, use quantum only describes things at the smallest of scale. Well, yes and no. It's describing things at every scale.

• 24:45Speaker 00

  It's just that we don't have to go through the trouble of using the quantum equations. When you have something the size of a rocket ship. It does describe the rocket ship. In fact, because that's what's happening is the rocket ship is made up of all these little atoms and electrons. So let's move away if we could, maybe one thing we could also do in this podcast is we'll help society to move away from the phraseology of. Oh, quantum only describes things at the small scale. Actually, it describes everything, everything is quantum. But they're particularly useful when we're thinking about the small scale.

• 25:16Peter Diamandis?

  And we can generalize with Newton's laws every time, every place.

• 25:19Speaker 00

  That's right, that's right. We can approximate using Newton's laws. Exactly, exactly correct. So now coming back to your fundamental question. So we talked about how neural networks compress a lot of stuff in our world. Down to a, a much smaller format so we can manage it, manipulate it and make some output of a generative AI. For example, in language or in images or things like that. But now let's turn to physics. Physics does the same thing. As we talked about, Newton laws can do that, say for a Prabha Lo or rocket ship going to orbit or going around the moon. But now we could also compress something even more fundamental. We can say, what is the behavior of that electron? And let's talk now about valence electrons.

• 26:03Speaker 00

  I know it's bringing back nightmares for our listeners into, into high school chemistry. But, um, but the valence electrons are the ones on the outer edge. And those are the ones we really are concerned about. When we want to make a new drug, Peter, as you well know, as a doctor, when we want to make a new drug and we want to say, what molecule? would fit into that target in the body to give an example to our listeners today. If God forbid, someone has melanoma, someone has non-small cell lung cancer, bladder cancer, a variety of cancers, we now have a new class of cancer drugs that go take us beyond the horrific regime of chemotherapy and radiation. They take us into immunotherapy, the ability to use our

• 26:48Speaker 00

  own immune system to fight these cancers. And the key to that happening is a molecule, actually a synthetic antibody, not an antibody created by our own internal adaptive immune system, but one that we synthesize in the world. And this antibody doesn't have the function that normally antibodies have of helping us directly to fight a particular disease or pathogen. What this does is it locks in to a particular receptor known as the PD1 receptor on a T cell, on an immune cell. It locks into the T cells PD1 receptor and protects that receptor from being hit by a tumor,

• 27:33Speaker 00

  by a ligand, by a molecule coming out of the tumor that normally shuts down T cells and puts them to sleep like hypnosis around the cancer. And this protects it like the Romans had their nice shields. Imagine now the T cells armed with this nice shield and goes into battle. But to develop that molecule that would fit lock and key into that T cells PD1 receptor, we have to have ultimately an understanding of how those electrons at the outer edge of that antibody and the electrons in the outer edge of the PD1 receptor, how they interact. And now for the first time, just in the last two, three years, we now have that ability.

• 28:19Peter Diamandis?

  Let me have you pause here one second because it's important for people to realize, I mean, we do all of our work in, in the world of bits, but we are physically individuals and living in a world of atoms. And when you want to start looking at the functionality on a cellular surface, or in a chemical reaction, or in a new battery chemistry, those are all atoms. And we've been able to model them in classic computation thus far, right? But it's

• 28:53Peter Diamandis?

  It's approximations and it's massively computer-heavy. So are you talking about being able to get to a deeper level of fundamental modeling than we've ever been able to do with our computers today? Because we have had, you know, DeepMind with AlphaFold and AlphaFold 3 and most lately it was AlphaProdeo being able to help us predict new proteins. But that's using classical computer models versus the work that you're doing today. Can you differentiate those two?

• 29:27Speaker 00

  Sure, that's correct. So when we look at what's been done before in trying to model biological systems as an example, a lot of good work has been done, but unfortunately it did not involve the physics itself. And so people would look at libraries of proteins and what's fundamental to proteins as we know is their conformation, the way that they're folded like an origami. And when you have a string of amino acids, the building blocks, the Lego blocks of proteins and you string them together, they're going to fold in a certain way. And that folding is fundamental to the use, to the application of that protein. In fact, when we have a misfolded protein, that's a whole nother ballgame and that leads to diseases of all kinds.

• 30:12Peter Diamandis?

  Alzheimer's.

• 30:13Speaker 00

  Right. Stan Prusner, who won the Nobel Prize and is at UCSF, an incredible body of work, showed how misfolded proteins can lead to complete disaster in the brain and other organs as well. So when we think about the prediction of folding based on a string of amino acids, one can just look at lots of examples and based on those examples, train a neural net on what would happen. And that's in fact, you know, how a number of the methods that we use out there do that, like AlphaFold and others. But now we have the ability, Peter, to go beyond that. Because while AlphaFold does a very, very good job, it doesn't actually get down to the very specific ways in which it will, that protein will interact.

• 30:57Speaker 00

  Looking at the structure of the protein, the folding is only the first step. We must now get to the dynamics of the protein. How will it act on other things? How will other things act on it? And again, we come back to electrons and electrons are described...

• 31:14Speaker 00

  By quantum mechanics. And so if we want to understand how an electron on one molecule would interact with the electron on the receptor on the target, we've got to get down to that level. And that's the level now, finally, that we at Sandbox AQ have been able to model things at. And that is a big breakthrough. That means that we can have-

• 31:34Peter Diamandis?

  Yeah, that I mean, that is, that is amazing because that applies across all material science, all biology. What was that moment in time? So, I mean, when you joined Alphabet to head this division, what did you have this in mind already? Or was this sort of something that unfolded as the computational power came online? I mean, help me understand that moment of creation. I'm just super curious.

• 31:59Speaker 00

  My, my first area of focus was actually in AI. As you know, I was applying AI decades ago now to brain imaging, as you opened up with, and specifically to FMRI's, to dynamic brain imaging. Dynamic brain imaging, as readers may, as listeners may know, is not the same as just a static CT image, you know, just to put it on a lightbox, let's take a look at it. You're talking about gigabytes and gigabytes and gigabytes of data taken over a period of time.

• 32:30Peter Diamandis?

  Looking at blood flow of your-

• 32:31Speaker 00

  Right. Looking at blood flow, for example, where I'll give somebody a test and ask them to move their finger. If they're moving this left finger here, index finger, this the exact spot is roughly about right here in my brain, right now, moving this finger this minute, right now.

• 32:48Peter Diamandis?

  I can see your homunculus right now.

• 32:50Speaker 00

  Yeah, right there. It's right there. As we're speaking and listening to each other, as we all know, we're using Broca's area here, number 44, to speak. And then I listen to you, Peter, I'm losing Wernicke's area back here. And, and so, you know, when these, when we look at these MRI images, the human eye can only see so much. And so, my team and I began to train neural networks, primitive ones at the time, but neural networks nonetheless, to see if we could glean more information from these medical images. And sure enough, we were able to do that. And that shows the robust- of these this idea of a brain inspired neural network that even with a very, very primitive compute we had at the time, building computers ourselves, literally by hand,

• 33:35Speaker 00

  we could, we could actually make that, make that work. Fast forward to today. What, one thing I realized is with AI, yes, language would be important, but the quantitative world, Peter, would actually be as if not more important. The majority of our world is quantitative in nature. The majority of our world is governed by numbers. And so rather than spend a lot of time developing large language models, we became very interested in the quantitative models. And that took a number of forms, but then realizing, of course, from the background of physics, that we needed to figure out a way to take these quantum equations that you were just discussing, Schrodinger's equation, Heisenberg's formulation, all these interesting equations,

• 34:19Speaker 00

  we needed to, we had to do that at scale. And the conventional wisdom at the time, Peter, was that we would need a quantum computer to do that. We'd have to wait two or three decades to get a quantum computer, and specifically, a quantum computer that was fault tolerant, that was error corrected. Right now,

• 34:38Peter Diamandis?

  Low enough error rate, yes.

• 34:40Speaker 00

  Exactly. You and I are speaking from computers right now that are error corrected. There are literally mistakes that pop up in computers. How does that happen? Well, actually Muons, cosmic rays, can actually hit your computer and cause a bit to flip. And so we have various error correction schemes in our phones, in our laptops, in our computers, in our watches, that allow for error correction for transistors, for bits, zero and one type bits. It's not that hard, because, you know, you can take, you can take a vote if you want to of multiple bits and over represent the bit you want with many bits. We have, I mean, bits are so cheap to make, why not have lots and lots of extra transistors? But in the world of quantum computers, it's not

• 35:22Speaker 00

  that simple. These are very, very sensitive devices, which is one reason why, by the way, you can flip the script and make, instead of a quantum computer, you could turn it to a quantum sensor. Maybe that, we'll, we'll leave that to Another time in this in this podcast to talk about. But but basically, quantum computers are very sensitive to perturbation from the outside world. And so they do need this this error correction. And that is hard. That error correction is hard, Peter. And so we knew at the time that it would take years and years before we'd have an error corrected quantum computer.

• 35:57Peter Diamandis?

  Let me pause you here one second because it's a really important distinction here for everybody to understand what Sandbox AQ is doing. Because you're a software company, you're not building quantum computers. And they're a multitude, there are dozens of companies building quantum computers. And we could talk about what the horizon for those are.

• 36:14Speaker 00

  Yes.

• 36:15Peter Diamandis?

  But the important point to make here is that you can use the power of quantum physics, of the equations to understand and model molecules and such without having quantum computers.

• 36:29Speaker 00

  That's correct.

• 36:30Peter Diamandis?

  The computational power, yeah.

• 36:32Speaker 00

  And by the way, Peter, when quantum computers one day do get scaled and do get, and we encourage, and we have relationships with more than a dozen of the quantum computing hardware companies out there. It'll add more fuel to our fire.

• 36:44Peter Diamandis?

  Yeah, and of course, and Google is one of the leaders. I was just at Hartmut's lab, seeing their beautiful golden chandeliers.

• 36:50Speaker 00

  Beautiful work, yes.

• 36:51Peter Diamandis?

  And they just announced some some some great progress on reducing the error rates in quantum computers. And that's all fantastic. But I think the point here is that the same computational power in those GPUs that gave us a large language models, you've been able to build algorithms that you can use on those GPUs to approximate or to solve these, you know, these classical equations of Schrödinger and Niels Bohr and Planck and Heisenberg to help you model the actual world of atoms and electrons and ions today without quantum computers.

• 37:30Speaker 00

  That's correct.

• 37:31Peter Diamandis?

  Is going to give us incredible insights into the physical world. Across health, materials, environment, everything.

• 37:38Speaker 00

  Peter, not, not will get is giving us, is giving us right now, right now. This is happening in real time. And that's what's so exciting. And when you look at, again, coming back to the fundamental idea of information, right? Of what does it mean to take a part of the world and represent it in an equation? In a dynamics, in a modeling, in a simulation? You're talking about, again, it's very similar to what we did with language. We talked about large language models. We took a corpus of billions of words and we compressed it down and embedded that information into the weights, into the weights of a neural network. Again, we didn't memorize those words. That's not what we did there.

• 38:23Speaker 00

  We embedded information into a space, into an information space that encodes all that stuff. We're doing something similar here. We're taking the dynamics of a certain molecule and we're describing it in a much shorter way, using these equations given to us over a hundred years ago by the quantum greats. And that allows us then to make predictions, very precise predictions about, okay, Peter, let's say you work at a large pharma company and you say, Jack, I heard you have this wonderful platform. I'll give you a molecule that we're thinking about that might hit this particular target in the body. Maybe it hits glioblastoma as an example, this brain cancer, horrific disease.

• 39:09Speaker 00

  And we'll take that, we'll make a digital twin of that, Peter, and we'll make 10 million, 100 million, maybe a billion permutations on that drug. We'll add a methyl group that is adding a carbon and a few, a few hydrogens. We'll add a nitrogen, we'll add an amin group, we'll add this, we'll take away that. Each one will be a slight variation on the theme that you initially started with.

• 39:32Peter Diamandis?

  These are AI simulations in quantum.

• 39:36Speaker 00

  That's right. So first there, we take the quantum equations, we run those and that becomes the data set. So we're generating our data set and that's what we use to train the AI.

• 39:47Peter Diamandis?

  Okay, let's, let's pause.

• 39:48Speaker 00

  So that's a fundamental point. That's a fundamental point.

• 39:51Peter Diamandis?

  That is a fundamental point. The, the, if you were trying to discover these molecules that are useful in cancer, Alzheimer's and so forth. Trying to get those with a large language model, the data doesn't exist in the corpus of data that the large language models have been crawling.

• 40:09Speaker 00

  That's right. It's outside the data set.

• 40:10Peter Diamandis?

  And so there's no, it's impossible for them to discover it if they didn't have the data in the first place.

• 40:16Speaker 00

  That's correct.

• 40:16Peter Diamandis?

  And so you've got to generate the data that these, you know, these quantum models can then assimilate and generalize. So let's double down on that for me.

• 40:29Speaker 00

  Yeah, let's double click on that. So basically what's happening here is instead of the world of large language models, we've now entered the world, Peter, of large quantitative models, LQMs. And LQMs are about starting with equations to generate data. That is one, that's the most efficient way to generate data and the most accurate way to generate data is with the equations themselves.

• 40:53Peter Diamandis?

  The equations are the bedrock of the universe. They are the fundamentals. They're upon which everything is built and created.

• 40:58Speaker 00

  That's correct. The fact that humans, by the way, just taking a step back, and the fact that human beings have uncovered the quantum equations of the universe is stupendous, deserves a moment of silence.

• 41:13Peter Diamandis?

  Yeah.

• 41:13Speaker 00

  Okay, moment observed. And, and so this is absolutely incredible. And by the way, as many of our viewers may know, the quantum equations are not one of, they're the most tested set of equations that we've ever had in the corpus of science.

• 41:32Peter Diamandis?

  And they were so, and the people wanted to doubt them so much. They were so much.

• 41:37Speaker 00

  Even Einstein. Einstein hated them. He hated them.

• 41:40Peter Diamandis?

  God doesn't play dice.

• 41:42Speaker 00

  He hated them. He railed against them until his death in the 1955 because although he was one of the creators of it, he couldn't comprehend, given a classical view that he held on to, how this world could even be described by these equations. Ironically, one of his best known papers, 1935, known as EPR, Einstein, Podolsky, Rosen, the three authors of it. Was an attempt to derail quantum mechanics as a science. It ended up becoming a cornerstone of the science describing the phenomenon of entanglement. But but coming back to your key question, Peter, which is what's happening now in the world of

• 42:23Speaker 00

  quantitative AI, rather than having to use a corpus of data on the outside world, which contains a huge amount of garbage and false info and good info all mixed together, like in the language world. We start with the pristine equations themselves. We take a theme and make variations on that theme. We take a molecule, make variations on it. We take a battery chemistry, which we're dealing with ions now. And ions, again, are subject to the quantum laws. And we're saying, OK, this is a lithium ion battery. But you know, we've been stuck with lithium ion chemistry, Peter, for forty plus years. Right? And we actually need to kind of start moving beyond that. Need to think about what other chemistries would give us batteries that may be cheaper, maybe more lightweight.

• 43:07Speaker 00

  The heaviest thing in a car, an electric vehicle, is the battery. But batteries actually are going way beyond just electric vehicles. The the bigger market, the bigger application for batteries is not in cars. It's stationary. It's in every building in the world needs ultimately have an energy storage system that accepts electrons when they're cheaply available and then uses them when they're in demand. And that arbitrage, that day trader like arbitrage of buy when low and use when high, right? That is going to impact the the world of energy beyond anything we've ever seen.

• 43:45Peter Diamandis?

  I'll tell you, the one I'm waiting for is room temperature superconducting. That's what I want quantum to deliver us.

• 43:51Speaker 00

  Yes, well, this that's the kind of modeling we now are beginning to embark on. So when we think about the drugs that we need, the medicines that we need, we think about diagnostics, biomarkers that we want to have. Right now, as our audience may know, there is no marker in the world for the progression of Parkinson's. There's a marker that tells you whether you have Parkinson's or not, not very helpful, since it's probably very obvious. But in terms of whether it's progressing. Or you've halted it with some treatment, there is no. So we need new biomarkers. We need treatments. We need better battery chemistry. We need cheaper solar energy, as cheap as it's become. The fact now that the underlying substrate of solar silicon is competing with the semiconductor industry.

• 44:35Speaker 00

  It does not bode well for the inexpensive nature of solar.

• 44:40Peter Diamandis?

  But perovskite's coming.

• 44:41Speaker 00

  Perovskites are exciting, but what's the problem? Perovskites, Peter, they're not stable. To be bankable, to be financeable, solar panels need to be have a 25 year guarantee, a 25 year life. Shelf stable, roof stable. In fact, perovskite technology only takes us out about a year in terms of stability. And so there's a need to actually model that at the quantum level.

• 45:04Peter Diamandis?

  There is a company, Paranovo, I'll tell you about it sometime soon, that's doing a heck of a lot better than that.

• 45:09Speaker 00

  We hope so. We want that to happen. We want that future.

• 45:12Peter Diamandis?

  Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love. Company is called Fountain Life and it's a company I started years ago with Tony Robbins and a group of very talented physicians. You know, most of us don't actually know what's going on inside our body. We're all optimists. Until that day where you have a pain in your side, you go to the physician in the emergency room and they say, listen, I'm sorry to tell you this, but you have this stage three or four going on. And you know, it didn't start that morning. It probably was a problem that's been going on for some time, but because we never look, we don't find out.

• 45:54Peter Diamandis?

  So what we built at Fountain Life was the world's most advanced diagnostic centers. We have four across the US today and we're building 20 around the world. These centers give you a full body MRI, a brain, a brain vasculature, an AI-enabled coronary CT looking for soft plaque, DEXA scan, a GRAIL blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AIs and our physicians to find any disease at the very beginning.

• 46:30Peter Diamandis?

  When it's solvable, you're gonna find out eventually. Might as well find out when you can take action. Fountain Life also has an entire side of therapeutics. We look around the world for the most advanced therapeutics that can add 10, 20 healthy years to your life, and we provide them to you at our centers. So if this is of interest to you, please go and check it out. Go to fountainlife.com/peter. When Tony and I wrote our New York Times bestseller, Life Force, we had 30,000 people who reached out to us for Fountain Life memberships. If you go to fountainlife.com/peter, we'll put you to the top of the list. Really, it's something that is, for me, one of the most important things I offer my entire

• 47:15Peter Diamandis?

  family, the CEOs of my companies, my friends. It's a chance to really add decades on to our healthy lifespans. Go to fountainlife.com/peter. It's one of the most important things I can offer to you as one of my listeners. All right, let's go back to our episode.

• 47:32Speaker 00

  So, so back to the core question that you have. The fundamental breakthrough now of realizing that what's happening in the world of physics, in this case, quantum mechanics, is that we're summarizing essentially a massive dynamics in the universe with these core equations. We're taking a molecule that has infinite degrees of freedom. It can move in any way, form, or manner. There's anything that can happen to it. And then we're focusing like a laser on the business end of that molecule. We're not going to model every electron in that model, in that molecule. We're going to limit ourselves to the valence electrons, that is, the electrons. And within the valence electrons, we're going to limit ourselves to the business end

• 48:15Speaker 00

  of the molecule that might be hitting the actual target that we're going for in the body. By constraining ourselves down to that portion of the problem, we can make it tractable in today's GPU-based computers.

• 48:30Peter Diamandis?

  When did this become, when did this become possible, Jack? When did it become possible for you to do this with the compute and the algorithms?

• 48:38Speaker 00

  We first had the breakthrough. Through, we had the first breakthrough exactly three years ago, just three years ago. Yeah. So just three years ago is when we realized this is going to change the world. This is something that's going to fundamentally change how we do things. And again, while most of the world was focused on language, and again, God bless the applications for college students writing essays an hour before the deadline for language models. But we realized that this was going to be a fundamental change in one, how we thought about AI and how we thought about the use of quantum in the real world. We've always had quantum in textbooks. We, we, we have many quantum innovations. The MRI machine is a quantum in nature.

• 49:19Speaker 00

  The, uh, the laser is quantum in nature, lots of quantum.

• 49:24Peter Diamandis?

  We've, we've used them, but we haven't been able to really predict and utilize them.

• 49:30Speaker 00

  At scale, yeah. At scale on any arbitrary quantum system. And that's now where we've come to. And that means that the world now has a superpower now, a new superpower. Humans have a superpower. This generation of humans is the first to have the superpower to do this at scale on real world types of systems. Every quantum textbook in the world usually has a chapter one, two, and three describing the equations. You just rattled off Peter, and then has a chapter five or six that says, let's use it on an actual case. And what is the case? One hydrogen atom, one proton, one electron.

• 50:10Speaker 00

  Now, um, as far as I know, hard to cure cancer with just a hy- an atom of hydrogen. And so we got to get to real world system, and that's what happened in the last number of years. Our team and I, we've worked on real molecules from labs in UCSF Nobel Prize winning labs. We've worked on molecules from large pharma companies, spin outs, all kinds of folks working on battery chemistry with a company called Nirvonyx, a public company that does battery chemistry, working on new materials for the US Army. The US Army wants to lightweight, uh, the tanks. Car companies, uh, that may announce soon want to lightweight their vehicles so that they're more fuel. Efficient. Well, that that's new material science. We need new materials to make that happen.

• 50:55Speaker 00

  Materials are made of atoms. And those atoms have those electrons that we talked about. And so we've got to fundamentally rethink now.

• 51:04Peter Diamandis?

  By the way, I think material sciences is like the most underappreciated area of technology, right? Everything that is new and breakthrough, I bow down to material scientists and the work that they do. And this notion of the materials genome, right? The idea that we understand the fundamentals of certain limited number, like, you know, a fraction of a fraction of one percent of materials that are possible.

• 51:30Speaker 00

  Right.

• 51:30Peter Diamandis?

  And we use them, but given what the work that you're doing, we're able to expand this understanding that will head towards, you know, fundamental across every industry is going to be transformed by this.

• 51:46Speaker 00

  Yeah. And see, Peter, you get into a fundamental point here, which is the compute we're talking about now, both AI, quantum, this quantitative AI we're talking about. It's not just about doing things faster. Often people write, oh, they're doing things faster. And by the way, faster is good. I mean, yes, let's get to the medicine faster. That's great. But here's the more fundamental point. We're actually exploring a bigger landscape. We're able, in the case of medicine, to explore a bigger biochemical space than can otherwise be explored. In the case of material science, explore a bigger space. If you're looking at batteries, battery chemistry, there's about nineteen elements of the periodic table

• 52:27Speaker 00

  that you could, in different combinations, build a battery from for the electrolyte, for the anode, cathode, for the membrane, all these core four elements. And so we actually can now start to explore a much bigger space. If we were limited before, as we were, to building prototypes by hand and testing each one, how many could you possibly build, right? Even if you're Willy Wonka and have Oompa Loompas around, you're limited to the number of batteries you could possibly prototype. But now, now that we have the actual quantum equations in the system and you could run it at scale, you could explore a much bigger material science landscape.

• 53:07Peter Diamandis?

  Gone are the local minima and maxima that we've been living with.

• 53:10Speaker 00

  Yeah, we were really in a cul-de-sac, to use a more suburban term for a minimum or maximum, but yes, yes, exactly right.

• 53:19Peter Diamandis?

  So let's talk about what the implications of this are. Our favorite subject, you and I both love health and longevity. And, you know, reminding people the way that we've discovered drugs in the past. We'd go into the Amazon, we'd chop down bushes and trees and dig up dirt and we'd crush them up and we'd try and find unique molecules and we'd test them molecule at a time. And that led to today's devastating drug industry, which is riddled with failures. You know, what do you, what do you hold is the average drug development time and cost? A decade and, and $3 billion?

• 53:56Speaker 00

  Exactly. Right now, it's about seven to 10 years of pre-clinical work. That is developing first a target. You got to start with a target in the body. What are you going for? What, you got to validate that target and then you've got to drug that target. You got to design a drug that fits like lock and key into that target. That's about eight to 10 years. Then you go into clinic, even with, and God bless, the FDA has actually done a pretty good job trying to compress down the phase one, phase two, phase three trials. You often could do a pivotal phase two now where the phase two becomes the, in a sense, like the phase three.

• 54:31Peter Diamandis?

  Sufficient, sufficient data to give you an approval by the FDA.

• 54:34Speaker 00

  To get out there. There's breakthrough path, you know, pathways for drugs now, particularly for orphan drugs and, sorry, sorry, particularly for orphan diseases and, and rare diseases. And, and so when we think about the time it takes, eight to 10 years pre-clinical, four, five years minimum in clinic. And here's the kicker, here's the most sobering of the statistics. 90% failure today in clinic. 90 out of 100 drugs that go into clinical phase one trial, phase two, phase three, never see the light of day, never come out again.

• 55:09Peter Diamandis?

  You know what's equally sobering for me? When you get prescribed a drug because you have a particular problem, chronic disease, whatever it might be, you expect that that drug works for you. But do you happen to know what percentage of people that drug words prescribed for them actually works?

• 55:28Speaker 00

  Please.

• 55:28Peter Diamandis?

  It's like 20%.

• 55:30Speaker 00

  Wow.

• 55:30Peter Diamandis?

  The, the fact of the matter is the FDA is making sure it's not harmful, right? That's the results we get out of a phase one, phase two side of the equation. But the FDA is approving a drug if it helps a sufficient number of people, not everybody.

• 55:46Speaker 00

  Yeah.

• 55:46Peter Diamandis?

  And so you, you know, I think it's, it's insane. But the hope now is not a drug that statistically might work for enough people for the FDA to approve it, but I want a drug that works specifically for me. I want a drug that is coded for my molecular design and genetics and so forth.

• 56:06Speaker 00

  And Peter, this is now the precipice of where we're going because, because of that two and a half, three, three and a half billion dollars per drug program, drug companies, biotech companies have not had the ability to do more than just one big cannon shot and just hope it works and hope it goes to enough population to then amortize and pay back the cost of that.

• 56:30Peter Diamandis?

  Which is why the drug costs are so extraordinarily expensive.

• 56:33Speaker 00

  Exactly. Cause also the successes, the few successes have to pay for all the failures. But now let's talk about a world, let's talk about a world where it costs one tenth the amount to make a drug, 300 million instead of three billion. Let's say it costs half the time to make that drug. Let's say we take the rate of success in clinical trial from 10% to 50, 60%. This is all possible now. And so if we look at that world, then a biotech company, a lab, a set of researchers can say, you know what? We can actually now make a drug that's targeted to this very specific sliver of the demographic

• 57:13Speaker 00

  of the population that has this particular genome sequencing or other characteristics. And we could start to get to that future that you just described that says, instead of having one size fits all kind of drugs that end up not fitting many people at all, we can now start to really. Understand how we can make these more targeted drugs that really match well with key cohorts out there in the population.

• 57:40Peter Diamandis?

  Now, we've been doing that in part, right? So we've got companies like in Silicon Medicine. We've got Alpha Prodeo, but again, they're using classical computers and AI large, you know, modeling. How far are you from using, you know, the equations of quantum physics through the power of Sandbox AQ to help companies with this?

• 58:05Speaker 00

  Well, Peter, two fundamental parts of that question. We're doing it right now and fundamental to our success and our velocity in this is that we are not a biotech company. That's fundamental. When you look at the landscape and you say, who else is using computation to help with drug design? Actually, there's quite a few companies using computation. But here's the thing, Peter, they're all biotech companies. They're all companies themselves who have the burden, the overhead of trying to bring a drug to market with pipelines and and clinical expenses and all that overhead, not just of money, but of time and management attention. We do none of that. We focus just on doing this advanced

• 58:47Speaker 00

  set of calculations in partnership with labs and with drug companies.

• 58:52Peter Diamandis?

  So you're a software company that is supporting a multitude of different industries?

• 58:57Speaker 00

  Right, exactly. And so that allows us to now have the world-class talent that can do this. We have quantum physicists on board, chemists, medicinal chemists, biologists, doctors, physicians advising us. We have all these specialties all coming together, AI specialists of all kinds, modelers of all kinds, mathematicians in them in our midst, coming together to make this happen. That is not something that a drug company can really do because they've really got to hit their bottom line, which is making the final drug. We focus on working with dozens and ultimately hundreds and thousands of drug companies in the same way that Oracle is a database used by every vertical out there.

• 59:41Speaker 00

  Ultimately, our platform will be used by many, many verticals out there today. It's being used by the biopharma community. It's being used by the chemicals community, by Dow Chemical, by others. And then we'll start moving into material science, batteries, energy, all these different areas as well. So business model really counts. And when you want to focus and say, Peter, what are you going to do? Be the best at in the world? What are you going to be the best at in the world? What is your company going to say? This is our territory. This is where we're going to really focus. You have to make a choice. You can't be all things to all people. And, and so we made the choice to be best at this kind of computation, this software.

• 1:00:24Speaker 00

  And our software runs today on the GPUs and it's architected. So that it could also run on the quantum computers of tomorrow. And that is the future we're heading to, Peter. We're heading to a meshed hybrid cloud world that meshes CPU as a basic controller. GPU workhorse, absolute workhorse with QPU, quantum processing unit. And bringing those together is a core part of that future.

• 1:00:52Peter Diamandis?

  Real quick, I've been getting the most unusual compliments lately on my skin. Truth is, I use a lotion every morning and every night religiously called OneSkin. It was developed by four PhD women who determined a 10 amino acid sequence. That is a senolytic that kills senile cells in your skin. And this literally reverses the age of your skin. And I think it's one of the most incredible products. I use it all the time. If you're interested, check out the show notes. I've asked my team to link to it below. All right, let's get back to the episode. Jack talked about the future of, of clinical trials here because I think, you know, I've heard you.

• 1:01:33Peter Diamandis?

  Sort of visionary this notion of running in silico human trials that drops the cost, not by 50%, but you know, basically a thousand fold eventually so that you know when you introduce this drug into humans because it's run in quantum models, because it's run in simulation that you know it's going to work. Just like the first time, you know, the, the team at SpaceX launched the dragon falcon combination to the space station, they didn't kind of hope it would actually get there and dock accurately. They'd run the simulation so many times in such accuracy that they knew it was going to work. So is that the future for drug development?

• 1:02:15Speaker 00

  Yeah, we still have to do the clinical trials. There's no way around that. But as you pointed out, we can go into the exam with the answers, right? That's very exciting. And, and so if we could go in right now, the 90% failure, what does that tell us, Peter? The 90% failure in clinical trials today, it tells us.

• 1:02:35Peter Diamandis?

  And by the way, this is, this is having gone through phase one and phase two.

• 1:02:39Speaker 00

  Yes.

• 1:02:40Peter Diamandis?

  And failing in phase three, which is insane, right? So you've had enough of a success in phase one and phase two, and you know.

• 1:02:48Speaker 00

  To show some safety and efficacy.

• 1:02:50Peter Diamandis?

  But then at the end of the day, it didn't help enough people.

• 1:02:54Speaker 00

  Exactly. So, so given that 90%, what that tells you is that we have the ability to add a lot of value here, right? You know, we're not talking about some optimized system.

• 1:03:05Peter Diamandis?

  Can I still invest in the company, buddy?

• 1:03:07Speaker 00

  Exactly. There's a lot of value to be created here. If you look at highly optimized systems in our world, those are different stories, right? If you look at, you know, a sterling engine, one of the most efficient, you know, things on this planet, not a lot of value you can add to a sterling engine, right? But if you look at the, the clinical trials that you're pointing to, this is where we can add a lot of value by modeling exactly what's going to happen and adding to that model every year, more and more of the variables. And so initially looking at those valence electrons and how they're going to hit there, and then looking at maybe. Maybe some dynamics, if it's a protein, let's say you're talking about a small molecule, a less than a thousand daltons with carbon, say being 12 daltons. So small molecules, as you know,

• 1:03:52Speaker 00

  Peter, but to share with our listeners are things like aspirin or Tylenol or things like that are small molecules. But when they interface with a much larger beast like a biologic such as a protein or an antibody or things like this, then there's a lot of stuff going on. And so we're now adding functionality to the system that allows us to do that kind of stuff, protein against protein, small molecule with proteins. This is complex stuff and it's getting more and more capable every few months. And so this is the kind of work that will ultimately lead us to a much better sense of the answer before we walk into that clinical trial. Let me emphasize, we'll still need the clinical trial because we do want to have that final real world confirmation,

• 1:04:37Speaker 00

  but we'll have so much information and modeling before that that will move up that success rate very, very dramatically.

• 1:04:45Peter Diamandis?

  You know, I need to dive into quantum computing a little bit with you because while, while you're on a rocket ship, when we add quantum computing to your rocket ship, it becomes a warp drive and you're a starship all of a sudden, not just interplanetary, you're going to the stars. So quantum computers have been around for a little bit. And as you've said, we've got dozens of companies and they're they're qubits, they're equivalent of their bits are atoms or photons or ions, lots of different approaches. Can we talk about where they are today in your estimate and where they would need to get to, to be functional for Sandbox AQ to use?

• 1:05:30Speaker 00

  Yeah, great question. And you talk about they've been around for a little while. Let's be more specific. Paul Benioff, yes, a cousin of Mark Benioff, Paul Benioff, want to give some kudos out to Paul Benioff. He is the one who's had the first paper describing a quantum computer. That was in 1979. He's passed away recently, just in the last few years, but I want to give credit out there because often histories of quantum computing gloss over him and talk about Richard Feynman. Yes, Feynman did popularize the idea of what a quantum computer can do. And we owe that to Feynman for helping get the idea out. But I do want to give some credit to where credit is due with Paul Benioff for having kicked us off.

• 1:06:14Speaker 00

  And one interesting history of science question is Johnny von Neumann. We mentioned it before in von Neumann architectures. Johnny von Neumann, being at the nexus of computing and physics. Why wasn't he the one to conceive of a quantum computer? It's an interesting question. Maybe another session we'll come back to that.

• 1:06:33Peter Diamandis?

  We're still gonna come back to Einstein's five papers.

• 1:06:36Speaker 00

  Yes, we're gonna come back to that as well. So we have many, it's good to give answers on this podcast, but also to plant questions with the audience as well.

• 1:06:46Speaker 00

  So.

• 1:06:47Peter Diamandis?

  1979.

• 1:06:48Speaker 00

  So back to quantum computers now. So you ask about the different modalities of quantum computers. Yes, indeed, there's seven major ways to build a quantum computer. And as you pointed out, we can build them either with natural qubits or synthetic qubits. A natural qubit, an example there would be a neutral atom. That's one of the dark horses in this race, one that hasn't got as much attention, but is scaling very rapidly. This is where you take a neutral atom, not an ion, so it's not an ion trap computer, but it's neutral. It's not, doesn't have any charge. You manipulate it with lasers.

• 1:07:25Speaker 00

  Several people won Nobel prizes, such as Steve Chu and others, for showing us how to manipulate various entities with lasers.

• 1:07:34Peter Diamandis?

  We get them down to near absolute zero.

• 1:07:36Speaker 00

  Exactly. We use those techniques to set the atom, which now becomes the qubit, into a certain state. And again, let's remember that quantum bits or short qubits can be in the state of zero or one, just like the transistor could be in zero one. A bit can be a zero one, but they can also be in. Superpositions and combinations of zero and one, and that gives us an infinite palette to draw from. And that qubit, we can say, is going to be some part zero, some part one, or a third this, two thirds that. We can have different combinations.

• 1:08:13Peter Diamandis?

  Everything in between.

• 1:08:15Speaker 00

  And everything in between. And so, we represent these qubits in very different ways than just the normal transistor. And so that neutral atom, we can manipulate into one of those states. We could read that state, and then we can operate on that state. And that's what's critical to a quantum computer, the ability to initiate a state on a qubit, operate a set of operations on those states, and then at the very end, read it out at the very end. And so those are critical things in a quantum computer. And now, in fact, neutral atom quantum computers are scaling faster than almost every other kind of quantum computer. Doesn't mean the other ones are out, but advantages to neutral atoms are

• 1:08:57Speaker 00

  that they're basically room temperature, easy to transport, quite compact, and you're starting with a compartment of gas, let's say rubidium as an example, where you already have hundreds of millions of these neutral atoms in the actual container. And to do things that are useful with quantum computers, we generally know that we're going to need to do the error correction. Let's use, for the sake of this conversation, a ratio of 1,000 to 1, 1,000 physical qubits to 1 error-corrected or logical qubit, right? 1,000 to 1 ratio.

• 1:09:35Peter Diamandis?

  Really, really important for folks to recognize, because you hear about all these quantum computers that have 100 qubits, 127 qubits.

• 1:09:42Speaker 00

  Those are physical qubits, exactly, Peter, right.

• 1:09:44Peter Diamandis?

  And they're not the functional error-corrected qubits.

• 1:09:48Speaker 00

  That's correct.

• 1:09:49Peter Diamandis?

  And when you, so, so where are we today in this, in this race?

• 1:09:53Speaker 00

  So today, we're at, you know, a few hundred of these physical qubits. Really, some papers have claimed to make one logical qubit, but we're really not at the point of having a set of logical qubits, of error-corrected qubits, that we can really manipulate at this time. Now, that will change very rapidly. If you look at the photonics side, using photonics, that's another promising approach exemplified by SciQuantum.

• 1:10:22Speaker 00

  Both in California and Australia and also Photonic, a company in Canada, as well as a number of labs working on photonics. The Chinese, by the way, are making good progress as well in photonics. Pan Jianwei, the leader of the quantum program in China, himself is a photonics-oriented physicist. And so that was the first quantum computer that he started to build.

• 1:10:49Peter Diamandis?

  My favorite science fiction, my favorite science fiction stories are always about, you know, massive quantum computers buried under Beijing that brought about AI, superintelligence and.

• 1:10:57Speaker 00

  Yeah, yeah. Well, there is, there are quantum computers deep, deep inside these universities that are run by Pan Jianwei, but they're not in Beijing. They're about two-hour fast train ride from Shanghai in a, in a different place. But yes, they are, they are there. But in any case, so you have photonics and one of the advantages that the photonics people will tell you is that we can mass produce these qubits using silicon photonics. We can use some of the same techniques of the semiconductor industry. In fact, SciQuantum uses Global Foundries, uses one of the big fabs out there to mass produce this by hopefully the millions. And so if you want to do something like

• 1:11:37Speaker 00

  crack RSA, right? Let's say you want to crack the encryption protocols that are used throughout the world, that are the bedrock of our economy, the reason why we have.

• 1:11:47Peter Diamandis?

  You want to crack my Bitcoin wallet?

• 1:11:49Speaker 00

  Yes, Peter, that's what we want to do. We want to crack right in and, and break the chain. And, and in fact, you want to do that with either RSA, which has been around since 1978, since R, S and A, Ravesh Shamir and Edelman gave us RSA. If you want to crack ECC, elliptic curve cryptography, so blockchain, you mentioned blockchain, Bitcoin, you know, Ethereum, these are all based on either RSA or, or ECC. If you want to crack those, but more even bigger than just blockchain, every ATM transaction, every wire transfer, every e-commerce using a credit card on Amazon, every single transaction, every WhatsApp.

• 1:12:32Speaker 00

  You're on WhatsApp, it says encrypted end to end on the WhatsApp messages there. What is that encryption? That is RSA and ECC. So if you want to correct that, estimates are we'll need roughly 5,000 or so logical qubits. Maybe we can get away with 4,200, but let's just say 5,000 error corrected qubits, which using our thousand to one ratio, Peter, let's go back then and say 5 million physical qubits. So we're at a few hundred physical qubits today and we're going to need 5 million of these things.

• 1:13:03Peter Diamandis?

  So Jack, I know you're a betting man and you predict the future. Actually, you implement and create the future. When are we going to get there? When do you think we're going to have actual quantum computers that you're going to want to use at Sandbox AQ?

• 1:13:18Speaker 00

  I would say that.

• 1:13:19Peter Diamandis?

  Min max.

• 1:13:20Speaker 00

  Yeah, I would say that by year 2029, uh, which is very, you know, only five years from now.

• 1:13:27Peter Diamandis?

  It's crazy.

• 1:13:27Speaker 00

  We'll start having the building blocks of about a thousand to 5,000 physical qubits in like these modular Lego blocks. And then what'll start to happen is people will daisy chain these blocks up using fiber optic connection and modulators that allow the physical instantiation of those qubits inside the block to be coordinated with a quantum state without collapse, without observation, without collapse with the quantum state in the adjacent Lego block. When you could start to daisy chain them all together and then thus create a mega computer made of lots of these Lego blocks.

• 1:14:12Speaker 00

  Let's say, for example, they each had a thousand of these, um, physical qubits. And then I got a thousand of these Lego blocks together. Now I've got a million qubits and therefore I have a thousand logical qubits.

• 1:14:26Peter Diamandis?

  And so. Amazing. Now, of course, 2029 is when Ray's predicting, you know, whatever AGI is. And of course, the reality is, you know, we're going to be using some variations of digital supercomputing to help us build these quantum computers. And then they'll, you know, those digital supercomputer or digital AIs, super AIs will become resident on these.

• 1:14:50Peter Diamandis?

  It's an exciting five years ahead.

• 1:14:51Speaker 00

  And Peter, just to finish, so that'll be like, that'll take us about five years. That'll take another two, three years of the engineering to put all that together, make it error corrected, bring it all in. So let's talk about the year, maybe 2031, 2032. I think it's going to be a very critical year. Yeah.

• 1:15:11Peter Diamandis?

  Insane. And, and.

• 1:15:13Speaker 00

  And Peter, I think we should note, I think we should note that while we're talking about quantum computers, there's whole worlds of quantum technology to take us beyond computing. Quantum sensing is one of those critical things. Quantum sensors are here today. We don't need error correction. We don't need millions of qubits. They're here today. They're flying on planes right now, helping us to navigate when GPS is jammed, when GPS is denied by countries and by bad actors. They're right now being tested in hospitals to diagnose how your heart is, given the magnetic field of the heart, that is magnetocardiography, MCG, versus ECG.

• 1:15:51Peter Diamandis?

  So these are, these are all the areas that Sandbox AQ is pioneering.

• 1:15:56Speaker 00

  That's right.

• 1:15:56Peter Diamandis?

  Which we're going to need to come back and.

• 1:15:59Speaker 00

  We'll come back to it in another podcast.

• 1:16:01Peter Diamandis?

  Absolutely, because it's, it's extraordinary. I mean, quantum computers at this level in the next five, six years,

• 1:16:10Peter Diamandis?

  changed the game. I mean, people feel like the world is going rapidly and disrupting and reinventing today with generative AI.

• 1:16:20Speaker 00

  This is just the beginning, Peter. Just the beginning.

• 1:16:23Peter Diamandis?

  This is a, this is a super exponential on steroids. I have no, I have enough, I don't have enough superlatives to explain how fast.

• 1:16:29Speaker 00

  Peter, let, let's come back, if we could, in summary to one of the core points. I hope we can have viewers and listeners take away from our conversation today, which is information. Information, not in a generic sense, but in a very specific, technical, understanding of that word. The way that Claude Shannon understood that word, the way we understand it in a field we call QIS, quantum information sciences. The way we understand information now in neural networks, where we're taking a large body of data. And we're representing it by a smaller amount of bits.

• 1:17:07Peter Diamandis?

  Learning.

• 1:17:07Speaker 00

  Right? And that learning, that generalization leads to information that represents that larger dataset that we started with in the first place. The same in physics, where we can take dynamics in the world, be it Newtonian with the rocket ship, quantum, electrons, molecules, and we could take very complex behavior and dynamics and summarize it in a small number of pieces of information called equations, called dynamics. And this fundamental ability of humans to

• 1:17:46Speaker 00

  search and look for summarization, conciseness, compactness, compression. This is fundamental to the breakthroughs that we're now seeing both in the AI world and we are now seeing in the application of quantum physics for the first time at scale in compute on GPUs that we've never seen before. This fundamental insight that information is the building block of our universe. This fundamental insight that information in this sense of Claude Shannon's entropy of information, and we can then quantize that into the quantum theory of information. Maybe in another podcast, we'll have time to talk about that. This

• 1:18:30Speaker 00

  is fundamental to the human race will completely revolutionize our existence on this planet and hopefully other planets as well.

• 1:18:40Peter Diamandis?

  Beautifully put. Before we break away here, I have two questions. The first, simulation theory, yes or no? Are we living in a simulation, buddy?

• 1:18:54Speaker 00

  Um, let me say this, that if we are in a simulation, then the

• 1:19:01Speaker 00

  the beings who created the simulation, kudos to them. They've done a pretty good job. I'll put it that way.

• 1:19:07Peter Diamandis?

  Okay.

• 1:19:08Speaker 00

  And that we should also answer the Einstein question.

• 1:19:11Peter Diamandis?

  That's the second question.

• 1:19:12Speaker 00

  Okay, there you go. So it turns out Einstein, as a young man, read a book by Henri Poincaré, uh, the mathematician and physicist, that in that book, Poincaré put out a series of challenges, challenges about Brownian motion, challenges about light, uh, challenges about how the world works. And it turns out that four or five of those challenges are the ones that Einstein decided to tackle as a twenty-something, sitting in Bern, being a third-class patent clerk at the patent office, the only job he could get due to his friend's dad who got him the job.

• 1:19:54Speaker 00

  This is what he decided to tackle, but he was inspired by this book. And for some reason, histories of Einstein often gloss over why he wrote this on subject matters that seem to have nothing to do with each other in that year of 1905. So we have Henri Poincaré. So just like David Hilbert did in the year 1900, Henri Poincaré in his book, very often contributions to our society can take the form of not just the answers, but the questions. David Hilbert put out a challenge in the year 1900 of key mathematical problems, some of which still vex us today. The Clay Mathematics Prizes, more recently, do the same thing, but updated them

• 1:20:38Speaker 00

  for our mathematics of the last few decades. Henri Poincaré put that challenge out there, and a young man called Einstein took up that challenge. So I leave the listeners with this, Peter. What are the questions we want to pose to ourselves as challenges to our colleagues around the world, to young people today, to our kids, to the next generation? Let's focus on the questions and not just the answers.

• 1:21:05Peter Diamandis?

  I love it. Ladies and gentlemen, none other than Jack Hidary. Jack, you are an extraordinary entrepreneur. I am so blessed to call you a friend. Thank you for all the work that you do.

• 1:21:16Speaker 00

  Thank you, my friend. Thank you, Peter. Great to see you. Love you.

• 1:21:18Peter Diamandis?

  Yeah, great to see you. Love you too. And let's do this again real soon.

• 1:21:22Speaker 00

  Take care.