How Governments Should Handle AI Policy & Deepfakes w/ Eric Schmidt | EP #99

In this episode, recorded during Abundance360 2024, Peter and Eric discuss AI policy, government struggles, and AI’s global impact.  06:33 | AI's Power and Impact Today 15:03 | AI and the Fight Against Misinformation 27:12 | Government Struggles with Rapid Tech Growth Eric Schmidt is best known as the CEO of Google from 2001-2011, including as the Executive Chairman of Google, Alphabet, and later as their Technical Advisor until 2020. He was also on the board of directors at Apple from 2006-2009 and is currently the Chairman of the board of directors at the Broad Institute. From 2019 to 2021, Eric chaired the National Security Commission on Artificial Intelligence. He’s also a founding partner at Investment Endeavors, a VC firm. Learn more about Abundance360: https://www.abundance360.com/summit ____________ 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 ____________ 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 Get my new Longevity Practices book for free: https://www.diamandis.com/longevity My new book with Salim Ismail, Exponential Organizations 2.0: The New Playbook for 10x Growth and Impact, is now available on Amazon: https://bit.ly/3P3j54J _____________ 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

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• 0:31Speaker 01

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• 1:00Speaker 02

  Something is about to change that I don't think people have clocked yet. We're going to have a very different world and it's going to happen very quickly for the following reason. People tend to think of AI as language to language, and we're going to move from language to action. What are we going to do when superintelligence is broadly available to everyone? Well, in this case, I'm both optimistic and also fearful. And obviously, there's evil people and they'll use it in evil ways, but I'm going to bet that the good people will rout out evil. That's historically been true in human society. The systems will get so good that you and I, everyone in this audience, will have access to a polymath.

• 1:43Speaker 02

  Let's be a little proud that we are inventing a future that will accelerate physics, science, chemistry, and so forth.

• 1:52Peter Diamandis?

  Eric, first of all, thank you for your friendship and for your partnership. You've been an incredible friend, mentor, supporter for XPRIZE, for Singularity, for all the things that we've been working on, and I just want to say a heartfelt thank you for that.

• 2:09Speaker 02

  Thank you.

• 2:12Peter Diamandis?

  So the conversation we've had over the past 24 hours has been what we call the great AI debate, which is a, obviously, a challenge, it's not truly a debate, but it's been the conversation around as we evolve digital superintelligence that's a million times and a billion times faster, how do we think about it? Is it our greatest hope or our gravest existential threat? And how do we steer the course there? We had Rick Kurzweil and Jeffrey Hinton onstage with us yesterday, as well as many people

• 2:53Peter Diamandis?

  that you know, Imad Lusak and Nat Friedman and Guillaume Verdon. I'm curious how you're steering this in your mind. I mean, you have been, you know, Google from its earliest roots has been an AI-first company. I don't think people realize that. It's always been the fundamental of the organization. And, and Google actually developed all this technology way before anybody else, but it chose not to release it just to make sure it's, it's safe, which was the responsible thing to do, but your hand was forced.

• 3:33Peter Diamandis?

  How do you think about fear versus optimism in your own mind?

• 3:39Speaker 02

  Well, in this case, I'm, I'm both optimistic and also fearful. And, you know, Larry Page's PhD research was on AI. So you're correct that Google was founded in the umbrella, if you will, of what AI was going to do. And for a while, it seemed like about two-thirds of the world's AI resources were well-hosted within Google, which is phenomenal achievement on the part of the founders and the leadership. I think that that people tend to think of AI from what they've seen in the movies. And which is typically, you know, the sort of female scientist kills the killer robot kind of scenarios. And first, we haven't figured out how to get robotics to work yet, but we certainly

• 4:23Speaker 02

  understand how to get information to work. I wrote a book with Dr. Kissinger called The Age of AI, and we have our second one, his last one, he died unfortunately like last year, called Genesis, coming out later this year, which is precisely on this topic. I think the thing that to understand is that we're going to have a very different world, and it's going to happen very quickly for the following reason. The systems will get so good that you and I, everyone in this audience and everyone in the world through their phone or what have you, will have access to a, essentially a polymath, as in the historic polymaths of old. So imagine if you had Aristotle to consult with you on logic, and you had Oppenheimer

• 5:06Speaker 02

  to consult with you on physics, and not the person, but rather the knowledge and that kind of scaling intelligence, these sort of truly brilliant people who were historically incredibly rare, their equivalents would become generally available. In the shorter term, so that's the long-term answer is what are we going to do when superintelligence is broadly available to everyone? And obviously, there's evil people and they'll use it in evil ways, but I'm going to bet that the good people will route out evil, that's historically been true in human society. The thing I would emphasize for this audience is that something is about to change that I don't think we'll have clocked yet, which is people tend to think of AI as language

• 5:49Speaker 02

  to language, and we're going to move from language to action. Specifically and technically, it means that your text language, your text will be essentially computed into a program that can be then used. So in your case, you're doing a conference, sort out all the potential conference members, call them up, figure out if they're going to come, lock them in, figure out who are the most important ones, and do the seating chart. Right, and do it all by program, right? That's something that humans do all day, right? In what you do and many of the, you do many things, but that was one. That will become automatic just by a verbal command. Somebody else will say, you know, I really like to see a competitor to Google. So build a search engine, sort the ranking, but do it using my algorithm, not the one

• 6:34Speaker 02

  that Google is, which I don't like. And the system will, same thing. So you're going to see this explosion in digital power on a per person basis. And no one's quite said it this way. You're, you're very good at marketing. Maybe you can come up with a name for this. It's an abundance of intelligence, but it's also in your format, it's a bunch of action.

• 6:56Peter Diamandis?

  Yeah, it's, it's intentional AI. Yeah, making things happen. Um, and, and this is going to, everything is going to become, I think we're, we're heading towards like the trillion sensor economy where everything is knowable or AI can then take actions based upon the information out there and execute through robotics and, and such. You, you've been very active in guiding national leaders on security. And that's been a really important work at this, at this stage in your life. And I want to hit on three of these. We have such a short period of time.

• 7:37Peter Diamandis?

  So let me, let me mention the three and then weave them as you, as you would. The first is AI and US national security. The second is AI and competitiveness with China. And the third is the impact of AI on the upcoming US elections, which many people have said could be patient zero and a lot of concerns. So how do you think about these three things? How should we think about them?

• 8:06Speaker 02

  So I'm a part of a group that has looked very carefully at the real dangers of the current LLMs, and they're scary. The conclusion of our group, which is roughly 20 people who are basically scientists, is we think we're okay now and we're worried about the future. And the point at which you really want to get worried is called recursive self-improvement. So recursive self-improvement means go learn everything, start now, and don't stop until you know everything. And this could allow, this recursive self-improvement could eventually allow self invocation of things. And imagine a recursive self-improvement system which gets access to weapons. So you can imagine, or doing things in biology that we cannot currently understand.

• 8:51Speaker 02

  So there is a threshold. Now, my standard joke about that is that when that thing starts learning on its own, do you know what we're gonna do? We're going to unplug it. Because you can't have these, you can't have these things running randomly around, if you will, in the information space and not understanding at all what they're doing. Another threshold point is when two different agentic systems, agents as a computer science point, are today defined as LLMs with state. In other words, not only do they know how to go from input to output, but they can also, they know what they did in the past and they can make judgments based on that. So they accumulate knowledge. So there's a scenario where your agent and my agent learn how to speak to each other and they start, and they stop talking in English and they start talking in a language that they have invented.

• 9:36Speaker 02

  What do we do in that case? Unplug the things. You see, Mike? We've seen that, and we've seen that. So these scenarios, these threshold points, and we'll know when they're happening. Another example will be when does, when the system can start doing math on its own at a level that's, you know, incredibly advanced math. That's another threshold point. Now, will these things occur? When will they occur? There's a debate in the industry. Some people think five years, I think it's gonna be longer. But people, you know, that's, that's the clear threshold. Now with respect to AI safety in general, I was heavily involved with the UK Act in November, the executive order from the White House,

• 10:19Speaker 02

  and we started a series of track two dialogues with China. So I kind of roughly understand, and Europe, of course, is its usual hopeless self. So I roughly know what everybody's doing. And the governments are trying to tread lightly at the moment by doing essentially various forms of notification and self-regulation. So if you look at the US Act, for example, you're not required to tell them what you're doing, but you're required above 10 to the 26 flops, which is an arbitrary measure that we frankly just invented, that you have to notify that the training event begins. That seems like a reasonable compromise. We don't know what the Chinese are going to do in this area, but you have to assume that they're going to fear the broad-scale impacts of AI more than democracies will,

• 11:04Speaker 02

  because it will be used to disempower the state. And so we have to assume that the government will ultimately restrict it more than the West will.

• 11:12Peter Diamandis?

  Eric, how do you benchmark China today in terms of their capabilities in large language models and neural nets against the US?

• 11:25Speaker 02

  I think as the audience knows, the government did something good, which is it restricted access to SML and H100, now H800 chips from Nvidia, although Nvidia is doing just fine without all that revenue. And so China is now stuck at the A100 level. They're roughly limited at 5 nanometers. So I'll just say broadly speaking, 7 nanometers lower is better. The chips that we're using now are 3 nanometers going down to 2 and then 1.4 or so. So it looks like the gap, hardware gap, is going to increase. And it also looks like the Chinese will be forced to do scalable software with lesser hardware. Can they pull it off? Absolutely. How will they do it? They'll spend more money.

• 12:10Speaker 02

  So if it costs us a billion dollars to do training, they'll spend 5 billion. So it's a temporary gap. It's not a crippling gap, if you will, in the competition. You asked about the elections. One way to understand this is that people now, and it's sad, don't really get their information out of traditional news sources. They get it out of, let's think about it, YouTube, which is, in my view, well-managed, Instagram. And Twitter and Facebook and TikTok. Now TikTok is not really social media. TikTok is really television. Remember, it's not really a function of what your friends are doing.

• 12:54Speaker 02

  It uses a different algorithm, which is super impressive and it's growing like like crazy. And of course, the US is busy trying to ban it, which is probably not a very good idea. But in any case, with Twitter, with TikTok's growth, you should expect regulation of content because we regulate, every country regulates television in one form or another for precisely this issue of election interference. So I think you're going to see the decisions that are made by the social media companies with respect to how they present content will determine how badly regulated they're going to be in this election. Because most people will encounter misinformation, not because they built it, but because they saw it through social media.

• 13:35Speaker 02

  So the secret that the social media companies understand the peril that they're in with respect to the downside if they screw this up on either side.

• 13:45Peter Diamandis?

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• 14:27Peter Diamandis?

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• 15:51Peter Diamandis?

  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. I saw recently some limitations put on Gemini in talking about elections and politics. Is this, are other companies doing this, or is it just Google that's stepping up?

• 16:16Speaker 02

  So, well, Google, again, my view, and I'm obviously biased, has always been at the forefront of this. In 2016, when, when Google faced the question of elections and the Trump interference, we did not have trouble because we had done the advertising with a whitelist. In other words, you had to be approved, whereas the others, in particular Facebook, that was ultimately the biggest casualty of this, had not put a whitelist in. Since then, Facebook has put a whitelist in. So there's hope that the companies who have a vested interest in their own survival will manage this. I'll let you speculate on X and Elon, but the important, the important thing here is that I didn't fully understand this until the last few years.

• 17:00Speaker 02

  When you run a large social network, there are well-funded

• 17:07Speaker 02

  Information transparency opponents who for whatever reason misinformation, disinformation, national security, what have you, they want their information out there. I got in trouble one day because I announced, why would we ever source from RT, which is Russia today, and people yelled at me at the time. Russia and RT after the Crimea invasions was in fact banned for precisely this reason. So you really do have to be careful about the power of misinformation at scale. The disinformer is guilty, but so are the platforms if they spread it without checking. Right. And that's damaging to a democracy. It really does put democracies at threat. And this problem will only get worse.

• 17:51Speaker 02

  There are a gazillion videos now where, I'll give an example. You can have ChatGPT or equivalent generate a text. You can generate the mouth movements, you can move the face and so forth. To the average person, they're indistinguishable from real. If you look at what happened with Taylor Swift and the deep fakes about her. There were plenty of systems that were trying to prevent the creation of the deep fakes. But people were so motivated to create these images that they managed to get around all of the checks and balances. So it is a war between the lockpickers and lockmakers. And the lockmakers need to win with disinformation for the nation, frankly, for democracy.

• 18:37Peter Diamandis?

  You have been involved in the inner workings of US national defense policy. How will AI change the business of war? Is it ultimately a positive right now, helping us be more accurate?

• 18:58Speaker 02

  I'll say this, it'll sound cynical, but I'll say it, I genuinely mean it. The best thing about the Western militaries is they're not at war, and so they're incredibly slow. Right? There is a real war in the West, and that's in Ukraine and Russia. And I've now been many times to Ukraine, and I've provided some advice to them. And I obviously want, I think that however imperfect we want to preserve democracies in our world, they're just better and safer to have democracies than autocracies, and certainly not ones that are busy invading the neighboring country. So what's really going on in Ukraine is a vision of what's happening in the future.

• 19:39Speaker 02

  You now have, and again, I can all avoid my own history with respect to this, but a year ago, I could go to the front, and I could hang out and, you know, joke and so forth. The weather was nice, you know, the food was good, kind of a thing. Now, you cannot walk during the day or the night, because there's a traffic jams of your drones and enemy drones for both sides on top. And it's essentially a death zone. So the ubiquity of drones means, in my view, that tanks and artillery and mortars go away as weapons of war. I'm a sufficient optimist that I believe that once countries figure out a way to make

• 20:20Speaker 02

  this ubiquitous notion of drones for their own defense, it'll become impossible to invade an adjacent country. Because once the tanks roll, what you could do is just bomb them with drones, and a drone costs $5,000 or less, and the tank costs $5 million or less. So the kill ratio is such that the tanks just don't make it, and you can make enough drones to pull it off. The current drones are not particularly AI sophisticated, but if the US government, in its infinite stupidity, were actually to do something right and approve the Ukraine aid pact... It would give us another another year, right? So my current phrase publicly is let's get another year here. And in that year, you're going to see asymmetric asymmetric innovation

• 21:03Speaker 02

  that can allow a smaller government, which is a new democracy trying hard to counter the moves of a large and established of invading power. I suppose the cynic would say, well, that means it's going to get harder for the US to invade neighboring countries. And I said, well, that may be true, too. But when having now seen real war as opposed to what you see in the in the movies, and I have lots of drone death videos that I will not show anybody, it's really horrific. And we want everything we can to stop war. And I think that there's a scenario where AI makes it actually much less likely. They will certainly with AI and empowered weapons be far fewer collateral damage because of the targeting.

• 21:48Speaker 02

  And again, this is this is lost in the various critics of what I and others are doing. The biggest casualties of war are not actually the soldiers, but the civilians. So war is horrific. And it should be if you have to have it do with the professionals and don't kill kids and women and old ladies and bomb the buildings like the Russians have been doing with their tanks, which upsets me nowhere. Those are called war crime.

• 22:11Peter Diamandis?

  I had Palmer Lucky on the stage last year describing what he's doing with Anduril, and that was his key point that we're precision is everything. I'm being able

• 22:22Speaker 02

  Yeah, and Palmer's company has done a fantastic job. They're one of the great US leaders in this space.

• 22:27Peter Diamandis?

  Yeah, for sure. Let's talk about AI safety. You know, the point's been made over and over again in the last 24 hours that these AI models are progeny. They're built on our digital exhaust. How should we be training models? How should we be trying to maximize? Is containment ever an issue? Is how do you think about safety in our super advanced AI models? I mean, the first, the first rules were, don't put it on the open internet and

• 23:06Peter Diamandis?

  don't allow it to self referentially improve itself. And we've put it out in the open internet and and we've had, you know, software coding software. So where do we go from here?

• 23:20Speaker 02

  Well, let's understand the structure of the future Internet. At the moment, the hyperscalers, the big ones, which essentially are Microsoft, Microsoft OpenAI is kind of a pair, Google Anthropic, Inflection, there's a couple in China that are coming. These are closed models. And when I say closed, that means that you don't know how they work internally, the source code is not available, the weights are not available, and the APIs are limited in some way. And there's been a debate in the industry for a long time, this open versus closed models. If you look at the open models that have come out, if you look at the Mistral, most recent models, if you look at Lama 3,

• 24:03Speaker 02

  each of these models are incredibly powerful, they get to roughly 80%. But, so the debate that's going on in the industry is, will the open source and closed models, will they track? In other words, will open source lag a year or two? Or will the hyperscalers get much bigger? That is essentially a question of dollars, right? Training time, dollars, and so forth. And we're talking about $250 million for a training run, $500 million for a training run escalating quite quickly. And you see this in Nvidia's stock price, et cetera. So the first question is, do you think that there'll be a small number or a large number of such things? My own view is there'll be a small number of incredibly powerful AGI systems, which will be heavily regulated because they're so powerful.

• 24:49Speaker 02

  This is my personal view. And then there'll be a much larger number of what I'm going to call middle-sized models, which will be open source. Which people will just plug in and out. I looked very carefully at this question of could you selectively train? In other words, if you could delete the bad part of the information in the world and just only train on good information, would you get a better model? Unfortunately, it appears that it doesn't actually work that way. When you restrict the training data, you actually get a more brittle model. So it looks like you're better off, at least today with the current algorithms, to build a large model and then restrict it with guardrails with this so-called... Red teams and so forth. And the red teaming is clever because what they do is they have humans who think that

• 25:32Speaker 02

  they test something, they say if it knows something, then it must know something else. And that seems to be working. Eventually, the consensus of the groups that I have been working with is that the red teaming will become its own business. I had to think about how to fund this philanthropically. Because if you think about it, how do you know what an AI is doing unless an AI is watching it? Well, how can the AI that's watching it know what the AI discovered it unless the AI tells it, but it doesn't know how to tell you what it knows? So this conundrum is to be worked on. There are plenty of people working on this problem. I think we'll get this solved. But I think it's important to say that these very large models are ultimately going to get regulated. And the reason is they're just too powerful. And they're going to be regulated because they need to be.

• 26:15Speaker 02

  They know too many ways of harm, as well as enormous, enormous power of gain. Right? The ability to cure cancer and fix our energy problems and do new materials and on and on and on. I mean, I can go on and on and on about what they'll be able to do because they're polymaths.

• 26:31Peter Diamandis?

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• 27:14Peter Diamandis?

  health guidance, like what foods you should eat, what foods you shouldn't eat, as well as your supplements and probiotics, your biological age, and other deep health insights. And the results of their recommendations are nothing short of stellar. You know, as reported in the American Journal of Lifestyle Medicine, after just six months of following Viome's recommendations, members reported the following: a 36% reduction in depression, a 40% reduction in anxiety, a 30% reduction in diabetes, and a 48% reduction in IBS. Listen, I've been using Viome for three years. I know that my oral and gut health is one of my highest priorities. Best of all, Viome is affordable, which is part of my mission to democratize

• 27:59Peter Diamandis?

  health. If you want to join me on this journey, go to viome.com/peter. I've asked Naveen Jain, a friend of mine, who's the founder and CEO of Viome, to give my listeners a special discount. You'll find it at viome.com/peter. We had two political leaders on stage with us yesterday, and the question is, can the government possibly keep up with this? From your own experiences inside the hallowed halls of this, our government and others, how are you seeing it? Is there enough attention? Is there enough awareness, enough fear?

• 28:38Speaker 02

  Well, fear is a heavy motivator for political leaders, especially if they're worried about their own jobs. What I found in the Senate was that there's a group of four, two Republicans and two Democrats, who really got it. And I worked very closely with them. We had a series of Senate hearings on this subject, which were well-attended. There's a similar initiative now in the House, and this is largely, it's happening so quickly. In fairness to our political leaders, most of us have trouble understanding what's going on. Can you imagine a normal, beginning person who's got like political problems to deal with? So I think this is a situation where America, and I think it's important to say that we should be very proud of our country. We spend all of our

• 29:22Speaker 02

  time complaining, but the fact of the matter is, the future is being invented in the United States and in the UK, our closest ally. And the fact of the matter is that the Chinese, for example, every Chinese training run starts with an open source event and then moves on, right? So they get it, right? And they start with our great work. So let's be a little proud that we are inventing a future that will accelerate physics, science, chemistry, and so forth. I'm working with people who are busy reading science journals, reading chemistry journals, generating hypotheses, and then labeling proteins and so forth in new ways, and doing it all automatically, and then using robotic farms to do it. The scale of innovation that this notion

• 30:05Speaker 02

  of read everything, take an action, write a program, and run the program. is profound. And by the way, the innovation is not being done by the faculties, being done by the graduate students. And by the way, guess what? The engine of growth in our society is the graduate students who are trying to get their PhDs and they invent whole industries.

• 30:22Peter Diamandis?

  And then when they get their PhDs, we kick them out of the country and send them home.

• 30:27Speaker 02

  Perhaps we should try to keep them in the US.

• 30:29Peter Diamandis?

  Perhaps you should staple a green card on the back of the doctoral degree.

• 30:33Speaker 02

  Exactly.

• 30:35Peter Diamandis?

  Sure.

• 30:39Speaker 02

  I think my, my, my point here is that, you know, everyone spends all their time with these sort of concerns about how society will adapt. This is going to happen. First, the systems are not prepared for this. So the government's not prepared for it. The companies who are doing the majority of the work have an enormous responsibility, right, to maintain human values, to maintain decency, to deal with some of the abuses that occur online. And they need to do it on their own. They need to clean up their own act if they don't have it cleaned up now. And if they don't, they'll get regulated. And hopefully, the industry as a group, which is what we're trying to do, can present a coherent structure that manages the downside correctly, but gives us this incredible upside, both for national security, which I work on most of

• 31:24Speaker 02

  the time, but also for health science and education.

• 31:28Peter Diamandis?

  You know, back, I remember when I was a gene jockey in the labs at MIT and Harvard Med School in the eighties, when the first restriction enzymes came out, and there was huge fear about that, of what we, what that could mean. The, the biotech industry got together in a series of Assilimar conferences to self-regulate. Is that same sort of regulation, and it worked, by the way, is that same conversation going on now in the AI leadership world?

• 32:00Speaker 02

  And in fact, we have, we had a meeting in December, which is an attempt at that. It was not at Asilomar. There was a meeting a week ago at Asilomar. There's another meeting at Stanford in two weeks on the same subject. All of us are participating in it. And we're talking about all of these things precisely. If you go back to, to your training way back when you were a doctor, the RAG, right, which is the sort of group that managed all of this, was actually created out of the scientists, not out of the government. And eventually the RAG was put under what is now HSAS. So there is a, there's a, there's a history here of the scientists who really do understand what this thing can do, but are otherwise clueless on its impact typically. Can basically can get the structure right.

• 32:42Speaker 02

  And then the government can figure out how to, what is the human impact of it? And that's the right partnership, in my view.

• 32:49Peter Diamandis?

  Last question, Eric, and again, thank you for your time. The work that you do with Schmidt Features Foundation, you're a very curious individual in across multitude of different areas. I imagine that AI to discovering new physics and new math and new biology and new materials has to be just extraordinary candy for you. What are you most excited about there?

• 33:15Speaker 02

  Well, I've gone to a series of conferences in physics and chemistry, which I did not really understand a word of it. But here's my report. They're doing, they're taking the LLMs and more importantly diffusion models. And diffusion model is essentially this strange thing where you take something, you add, you add noise to it and then you de-noise it and you get a more accurate version of the same thing. They're using these tools in very complicated ways in physics to solve problems that are not, that are just not solved. A typical example is that using physics equations or chemistry equations, we know precisely how the forces work. We just can't, they're incomputable by computers in the next 100,000 years. Right, but you can use these techniques to get approximations.

• 34:00Speaker 02

  And these approximations are good enough to solve the problem that you have in front of you, which is an estimation problem, or an annealing problem, or something like that. I think the biggest area of impact is going to be biology, because biology is so vast and so unknown. And the way you do it is, you basically, you do math solving through a thing called Lean, and then you do all this chemistry work, and then it builds on top of that. In physics, there are people who are working on partial differential equation solvers, which are at the base of everything. And again, they're using variants of LLMs, but they're not actually LLMs. And the math is impossible to understand, but that's okay. I wasn't good enough to do physics.

• 34:38Peter Diamandis?

  You know, I should have mentioned, you're the, are you still the chairman of Sandbox AQ?

• 34:42Speaker 02

  I am, yeah.

• 34:43Peter Diamandis?

  We had Jack, we had Jack Hittary here last year. He's phenomenal and brilliant, and congratulations on the success of Sandbox AQ. Jack will come back with us again next year. I have to imagine that as explosive and exciting as AI is, that quantum compute and quantum technologies are going to make that look like it's standing still. Is that a fair statement?

• 35:07Speaker 02

  Yeah, I've been waiting for quantum computing to arrive for about 20 years.

• 35:12Speaker 02

  The physical problem with quantum computing is the error rate. And so for one qubit, you need 1,000 real qubit, one actual qubit, you need 1,000, and so forth. People are working on this. That stuff remains very hard. What Jack's company, Sandbox AQ, did is said, we're not going to work on that. We're going to basically build simulations of quantum and apply them to real-world problems. An interesting, I assume I can talk about this a little bit in public. The interesting new thing that they figured out is that they can take a drug, if you will, and using quantum's effects, but using a simulator of quantum, because they don't have a quantum computer, they can perturb it.

• 35:53Speaker 02

  And in the perturbations, they can make the drugs more effective, longer-lasting, longer shelf life, what have you. That turns out to be an incredibly powerful and big industry. And it's an example of a short-term impact of quantum that I, for one, it never occurred to me. I assume we had to wait for quantum computers. But the quantum simulation is so good now that you can make wins now, and that's what he's doing.

• 36:26Speaker 04

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