In this conversation from theCUBE’s NYSE Studio, John Furrier welcomes Jack Hidary, chief executive officer of SandboxAQ, for a deep dive into how AI is moving beyond language and into the physical world. Hidary outlines how his team built a billion-dollar platform around proprietary data, physics-based models and quantum-informed algorithms to tackle real-world problems in medicine, energy and cybersecurity. More than a theory, it's applied science meeting scalable engineering.
The interview explores SandboxAQ’s release of AQAffinity, its work with NVIDIA and OpenFold, and why the future belongs to large quantitative models, not just LLMs. Hidary unpacks the growing threat of quantum computing to cryptography and how his team’s approach to embedded R&D is setting a new standard. With a culture driven by “deep impact at scale” and over 45,000 applicants in the last year alone, SandboxAQ is writing the next chapter of the AI era – one molecule, one model, one breakthrough at a time.
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Jack Hidary, SandboxAQ
In this conversation from theCUBE’s NYSE Studio, John Furrier welcomes Jack Hidary, chief executive officer of SandboxAQ, for a deep dive into how AI is moving beyond language and into the physical world. Hidary outlines how his team built a billion-dollar platform around proprietary data, physics-based models and quantum-informed algorithms to tackle real-world problems in medicine, energy and cybersecurity. More than a theory, it's applied science meeting scalable engineering.
The interview explores SandboxAQ’s release of AQAffinity, its work with NVIDIA and OpenFold, and why the future belongs to large quantitative models, not just LLMs. Hidary unpacks the growing threat of quantum computing to cryptography and how his team’s approach to embedded R&D is setting a new standard. With a culture driven by “deep impact at scale” and over 45,000 applicants in the last year alone, SandboxAQ is writing the next chapter of the AI era – one molecule, one model, one breakthrough at a time.
>> Welcome back to theCUBE everyone here at the New York Stock Exchange CUBE Studio, part of our NYSE wired program at CUBE Original. Of course, we got our Palo Alto studios connecting Silicon Valley and Wall Street. We speak both languages as the tech and the capital markets come together. The Wired and theCUBE community are doing its part to share. We got a great mixture of expert on theCUBE here, an AI leader, Jack Hidary, CEO of SandboxAQ. I'm looking at my notes here. Jack, welcome to theCUBE. You had a billion dollars raised in two rounds spinning out of Alphabet SandboxAQ. So this is not Y Combinator.
Jack Hidary
>> No. No.
John Furrier
>> This is like real deal, Google-
Jack Hidary
>> We were nurtured inside of Alphabet and my big thanks to Sergey, to so many people, Astro Teller, Sergey and Ruth and many others at Alphabet for nurturing us in our early years. But the intention always was to spin out in order to really grow a large global company and thank God we're on the way.
John Furrier
>> People don't give Sergey and the team enough credit what they're doing with Alphabet, how they structured it. They fostered the innovation research, commercialization, but yet fun. They do wild moonshot projects, but they do an amazing job.
Jack Hidary
>> When Ruth Porat came over and created a lot more discipline in the company as CFO initially at Alphabet, and also they realized, Larry and Sergey realized that, "Hey, we really want to expand and do more stuff here." And Waymo is an example. At that time, it was called Chauffeur. That was the code name for it, Chauffeur. I was lucky enough to be right there and see, really drive in some of the super early autonomous cars. They were not as smooth as they are today. And to see Waymo so successful today, John, is amazing. They just hit half a million paid rides a week just a few months ago. And I think they're on track to hit a million a week, I think by end of year.
John Furrier
>> Finally got into Palo Alto, Mountain View. I'm in San Francisco, dominate. I took one just recently. It's fun in Palo Alto driving on Alma Street.
Jack Hidary
>> Yeah, it's amazing there.
John Furrier
>> You also authored the book AI or Die and a textbook in Quantum Computing: An Applied Approach, the title.
Jack Hidary
>> And AI or Die, John, just because it's literally that is the choice that people have today. It is for your businesses. Either you embrace this stuff or you're not going to exist. And the AI we're talking about here, John, is not just the AI for the digital world. And I'm sure you've had lots of great conversations here at this table about AI like large language models such as ChatGPT and Gemini and Anthropic. Great models, really a lot of applications in marketing, customer service, legal, for example, is today's news. But where we focus on at Sandbox is AI for the real world. And so when you want to take AI and make a new medicine for cancer, a new treatment for Alzheimer's. When you want a new battery chemistry to store energy, when you want to do new risk modeling, right? We're here in the New York Stock Exchange right now looking at the options floor. And if you want to do those quantitative models, a language model and something trained on cat pictures and videos isn't really your best friend. You want something trained on numbers and equations, and that's where we shine.
John Furrier
>> Well, I want to dig into it, but I want to just share with you and get your reaction because I know you run the same agreement, AI for the real world. The two hottest things we're covering and we're getting data on is AI and cryptocurrency, the crypto industry, which includes a lot of the things around quantum that you guys are doing.
Jack Hidary
>> That's right.
John Furrier
>> The number one trend is the convergence of physical and digital.
Jack Hidary
>> That's right.
John Furrier
>> As first party citizens, all the data interacting, real world assets on chain.
Jack Hidary
>> That's right.
John Furrier
>> The number one topic that's driving the financial stack here in New York. In AI, Jensen, energy-
Jack Hidary
>> Let's talk about AI for the physical world. We're talking about AI for the real world. That is the new big, big territory for AI. So of course, low hanging fruit a number of years ago when you wanted to train an AI model, the transformer model was the words of the internet, right? The images, the words of the internet. That made a lot of sense. And now, we're seeing the fruits of that labor. ChatGPT, Gemini, Anthropic. These are very powerful, very, very useful tools for productivity, for customer service, marketing. But when it comes to the core of the Fortune 500, the core product development, when you want to make that car, you want to make that spaceship, you want to make that battery, you want to make that drug, that takes a new kind of AI. That's AI for the real world.
John Furrier
>> Yeah. And I like how you bring in the progression. And I want to come back to this because I want to zoom out for the folks watching. And we talked about this on our CUBE Pod last Friday. If you look at the generation that we grew up in, the internet was upon us, that came from a lot of the research. Things were on paper. Then the internet came, then the web, then you had mobile and cloud. That generation in the computer industry was a magical time. Okay, that's over. That was wave, call it wave, big wave one.
Jack Hidary
>> Yeah.
John Furrier
>> Now you got transformer, some of the stuff early days in AI and then kind of where we are today, but yet there's a whole path to full autonomy. Take me through your vision on that because I totally agree with AI or die. It's a mandate. It's a pound the table, get with the program where you're going to be extinct.
Jack Hidary
>> Absolutely.
John Furrier
>> But take me through the path of where we are today, because as we get the physical world on with AI, that's going to change and open up use cases we can't fathom. Unbelievable. So take me through the vision to get autonomous. This is a similar wave. So paper was transformer. Internet now feels like ChatGPT, LLMs, but as we start to see agents and the path to physical and autonomy, autonomous things, not just cars, what's your vision on what that path looks like?
Jack Hidary
>> Well, exactly, John. Very good analogy to talk about the big wave one that we all experience over a number of decades, because each one of those movements within that big wave led to the next one. If you think about it, people listening to this today may remember dial-up modem. Okay? The crazy sounds. "Beep, bop, ba, ba, ba." Okay. That was a dial-up modem. Well, when we're in the era of dial-up modems, you really couldn't have YouTube because it wasn't practical to upload and download all those videos. Once broadband and really almost universal broadband came to the US, to Europe, to other places, that was then the birth of YouTube. Google, of course, bought YouTube and then grew it even bigger. And we have the kind of video world that we have today, be it TikTok, be it others as well. So each wave really opens up the opportunity for the next wave. And that's happening now in this like-
John Furrier
>> What is that? What's the sequence? Where are we now? What's that wave?
Jack Hidary
>> Right. So the first one was the web itself, Tim Berners-Lee is a friend and informal advisor to us, along with Rosemary Berners-Lee as well, who's an official advisor for us. And I joke with Tim, I said, "Tim, congrats for creating the web. You actually ended up creating the largest training set in human history." And that's really what 30 years of the web gave us. He gave us this massive training set, and that's great in terms of the large language models. And kudos also to Google. 2017, the paper, Attention is All You Need, which was the paper that defined a new way of architecting neural networks inspired by the brain and having to do that. I really first started using neural networks in medicine when I was working in science at the NIH, bringing neural network ideas to neuroimaging, to brain imaging. So it has a long enrich history there. But now, let's look fast-forward. Okay. So with the low hanging fruit is the worldwide web, download it, make your transformer model, make it work. But the next wave now is really new proprietary data sets, data sets that don't exist on the internet. These are special data sets for biochemistry, for catalysts. Viewers may not know this, but almost everything that you're seeing and touching on this set and in your homes or office is watching this now comes from a catalyst. This laptop you have here could not be made except for these compounds, cold catalysts, which drive a reaction forward quickly with low energy and are not used up in that reaction. It turns out it's really hard, John, to make a catalyst, just like it's hard to make a new drug. AI for chemistry that we have pioneered is now doing that in record time. So this is a very exciting moment now where we're moving from just words as the data set to now quantitative data and equations as the data set. And that's opening up a whole new world of AI that we didn't have before.
John Furrier
>> Okay. So first of all, explain how that works because I think that's one, exciting and motivating for anyone watching like, okay, it's going to get better, but it's still hard problems to solve.
Jack Hidary
>> Yeah.
John Furrier
>> So take me through and scope the magnitude of how you do it, what's it take? If there's no data, do you make the data? Do you train it? Is it off synthetic data? How's it going on?
Jack Hidary
>> So let's start with biopharma, because that's something affects all of us. We all want to see new medicines for cancers. We want to see new medicines for neurodegen diseases. Unfortunately, most everyone viewing this, their family will be impacted at some point by a neurodegen disease, could be Alzheimer's, could be dementia. Unfortunately, this is going to become almost a pandemic as the population ages. And so you want to make a medicine. Today, the traditional way of doing that, it takes 10, 15, 20 years, John, to get to that understanding of what that medicine could be. And then you got to go into clinical trials. And before the advent of AI, we're seeing about 85% failure, John, in clinical trials. So what can AI do about that? Well, what we can do is start to make really good simulations first of the chemical world in a computer. And we could do so with the laws of physics itself. And that is something that we always dreamed of. I dreamed of it more than 20 years ago, okay, in science, but now it's happening. Now, we and others have worked on this and now we have computers that have high fidelity between what we imagine in the computer. And then when we synthesize it, what we see in x-ray crystallography, what we see in cryo electron microscopy, we now see high fidelity between these two. So this is an exciting moment where for the first time we can harness in silico to go for the real world.
John Furrier
>> Do you guys develop your own data sets?
Jack Hidary
>> We have to. Yes, we absolutely do. We don't use any third-party data in terms of downloading data from the internet or things like that. The first thing we have to do is we have to run lots of experiments. We contract labs in different parts of the world to run thousands and thousands of robotic experiments, things with robot hands, and that is something that you can do out there in the world. We own that data, we take that in, and then we add to it synthetic data based on the laws of physics and chemistry itself. That creates a dataset that's unique, that's proprietary to us. And the next step is we can train models on those. And those models are not large language models, because again, we're not dealing with language here. Those are new kind of models. Those are LQMs, large quantitative models, John.
John Furrier
>> Yeah. So maths involved and data that's not available.
Jack Hidary
>> That's right.
John Furrier
>> And the Q for quantitative and just quantitative, is there other non-quantitative?
Jack Hidary
>> Well, so we have A for AI.
John Furrier
>> Or it's all math, I guess.
Jack Hidary
>> Yes. We have A for AI. We have the Q for quantitative, but also quantum, because the techniques we're using, we're not running them on a quantum computer because those are not ready yet. We're running them on GPUs and other types of accelerated chips, but we have to use quantum equations. We have to use the equations of quantum physics to analyze and understand the relationship between this molecule and that molecule. That's a quantum issue. And the same now, John, we can go beyond biopharma into batteries, into materials, into oil and gas and those sectors as well.
John Furrier
>> When I last saw when I was in DC for NVIDIA's special GTC in-
Jack Hidary
>> The special GTC in October. Yeah.
John Furrier
>> I asked Jensen about AI factories and I kind of teased out and he kind of laid out the dots. I connected it. There'll be microfactories. There's going to be edge and certainly core, but he also answered the question around how one of the trends he was pointing out is because he always asked the quantum question, but he wanted to address, he says, the GPUs and systems that we're using with Omniverse are actually being used to solve quantum computing. And that was not obvious to most of the analysts, but-
Jack Hidary
>> Well, here's a proof point, John.
John Furrier
>> Explain, because this is a very important, because the horsepower is helping that. You have stuff like data simulations. I'm sure you got something going on similar with compute.
Jack Hidary
>> NVIDIA is not only investor, but also one of our partners. And with NVIDIA and other partners, we announced just a few days ago, AQAffinity, right? AQAffinity is a software platform that we opened up to the world and is now, we believe, the most advanced platform for determining both the structure of those proteins that we want to target in the body, but also the binding affinity between a potential drug and that target protein. So it's doing two things at the same time. 5, 10 years ago, the conventional wisdom, John, is that we would never be able to do that on GPUs.
John Furrier
>> Yeah. Supercomputing was a terminal that was basically a PC server.
Jack Hidary
>> Out of 1000. Yes, exactly. But we pulled it off last week and we're really proud of that and really proud of our partners. OpenFold3, OpenFold is our partner. It's a consortium of top academics, of Bayer, of many other pharma companies. And of course with NVIDIA, our partner as well, we use the DGX Cloud via GCP in this case, and this is what allowed us to have this massive breakthrough. So this builds on work that we've done with NVIDIA on the SAIR data set, on other data sets. You have to build the really good precision data first. You cannot use random data.
John Furrier
>> Okay. So are you sharing your data set with people?
Jack Hidary
>> Yeah, we made the dataset public. It's open source right now. It's online right now.
John Furrier
>> Like NVIDIA, they're sharing their libraries.
Jack Hidary
>> Yes, that's right. That's right.
John Furrier
>> You're doing the same.
Jack Hidary
>> In fact, in fact, I'm happy to share that NVIDIA just announced that their DiffDock, which is one of their key models for looking at molecular development of new pharmaceuticals is now improved because of the SAIR dataset that we worked on. It's on their website now.
John Furrier
>> I really want to call that out because I think two reasons. One, people should know that this is practice in the industry.
Jack Hidary
>> That's right.
John Furrier
>> Number two, it's super impressive that you're doing that and you made that a milestone. Scope the alternative from time to completion to hit that proud moment that you just shared. If you didn't have the super computing, the NVIDIA systems and the AI factories, what would that have been like? I mean, even in a super HPC world, even some cloud, we had old cloud or cloud legacy, if you can call it that, modern legacy. What's the difference? Because I think researchers should know that this is now available. It's super inspiring. Share the scope of timeframe.
Jack Hidary
>> And John, it's a great question. In 2016, when we got going inside of Alphabet, the conventional wisdom was that it would be many decades for us to get the kind of compute that we needed to do real world kind of stuff with molecules, with energy, with risk management. And we made a bet that the various chip operations, both inside Alphabet and at NVIDIA and other places would really ramp up on an almost exponential type of-
John Furrier
>> Which it did.
Jack Hidary
>> And that's what happened.
John Furrier
>> It's a good bet. It came home.
Jack Hidary
>> And so that was the bet that we made. And sure enough, around 2022, 2023 is when the crossover point happened, and we started to see we could do real world computation with these kinds of chips.
John Furrier
>> And you're doing it. All right. Now, take me through some of the biology side. There's a lot of concern in biology, chemistry kind of tied together. Pharma, you mentioned. What are some of the cool things you see being enabled right now in terms of the breakthroughs?
Jack Hidary
>> We can see breakthroughs, John, both in new therapeutics. We're working on cancer. We made some announcements with iOncologi for brain cancer. This is one of the areas that has been one of the most difficult for traditional oncology. I think we're going to see breakthroughs there. We've talked about our work with UCSF and Stan Prusiner, Nobel Prize winner. That's in terms of neurodegen diseases, in Alzheimer's, dementia, Parkinson's. Very, very important there as well. So those are more important, but also let's look at the energy sector. The energy sector, we need now more than ever, because we need to power buildings like this and we need to power data centers and we need novel battery chemistries. We need novel ways of producing, storing, and transmitting energy as well. And that's all material science. And so AI for material science, John, is to me, one of the new uncharted territories
John Furrier
>> I have to ask you on the business side, we got the billion dollars in funding, which is ... I like the way you did that. You got great investors, you got the long game view for sure.
Jack Hidary
>> Yes. Long horizon investment. Yeah.
John Furrier
>> Long horizon view. So if you look at some of the successes, the old school mentality on corporate strategy and governance was. You got corp dev, you got your divisions and you got the R&D labs, applied and you got weird R&D.
Jack Hidary
>> Yeah, that's a red alert. Once you see a different R&D lab from the core divisions, red flag on the play.
John Furrier
>> And then the modern version for AI is interesting that bring in almost forward deploying R&D a little bit further in. How are you looking at? Because you must be really leaning in with R&D. All the successful AI companies have a strong bench of R&D research. R&D.
Jack Hidary
>> Yeah.
John Furrier
>> How do you look at the role of research as you start to commercialize all this?
Jack Hidary
>> Yeah. For us, the R&D is part and parcel of the product itself. It is not some separate division out there that we say, "Hey guys, how are you doing over there in the R&D division and what do you have to throw?"
John Furrier
>> And call me when you got a breakthrough.
Jack Hidary
>> Yeah. And that's what the lessons we all learn from Xerox PARC and things like that. Massive breakthroughs, but never made it over the wall to commercialization. And so I think the more modern way of doing things is to embed it all together. We've got more than 125 PhDs in our company, very technical company, PhDs in physics, chemistry, biology, AI, mathematics, cybersecurity. And then we've got about 100 engineers, coding engineers, other kinds of engineers on top of the PhDs who are working together with the PhDs side by side to translate their insights into scalable code. And so that's really what this game is about. What it's about is taking domain knowledge that's specific to particular areas, combining across disciplines, that's really powerful. And then the next step is to use code to make it scalable and usable to a huge number of clients.
John Furrier
>> It's like, remember the old pair programming days, you're like pairing human capital and collectible capital all together so it's like real synergistically cohesive.
Jack Hidary
>> Oh, these are special folks in our company. I'm super proud of what they've been able to accomplish, John. Every time a challenge is put to them by a customer, they overcome that challenge.
John Furrier
>> All right.
Jack Hidary
>> This is really an incredible-
John Furrier
>> So I'm curious about the company culture. Intel has Moore's law, doubling every whatever, which is now some people are saying is dead, but we'll see what it does with that one. I still like Intel personally. I love the company. What's your culture? Do you have a cadence of something? What would be description in your culture, all that horsepower and rocket fuel? What would be the culture like?
Jack Hidary
>> The core four words of our culture are deep impact at scale. The ways on why people come to work every single day, what drives people, what drives me is having significant impact in the world, but at scale, right? And that's what's key now. Right now, because of AI, relatively small teams can have global impact. You've seen that with us, you've seen that with many other companies as well. You don't have to have 10,000 people in your company to have very significant impact. So deep impact at scale is number one. Number two, a learning culture. I love being inside of our company, because every day I'm learning so much. I'm learning for the people around us. We hire people who are way more knowledgeable in so many areas than I am. And so I'm learning a lot. And what I hear from everyone else is they're learning a lot too in this beehive of learning. So I think those are two of the hallmarks that we have there. And also bar racing. This is something that we learn from a number of other company cultures. When you're hiring somebody, it's very easy to fall into the trap of hiring somebody that you know or hiring somebody who knows a friend and so on and so forth. And the bar raiser is somebody who comes in-
John Furrier
>> Amazon does that.
Jack Hidary
>> That's right. Amazon is one of the examples. They come in from not your team, some other team, sometimes it's our legal counsel, sometimes it's somebody else, and they make sure to ask the hard devil's advocate questions on that hiring committee. And actually, we've turned down 45 people just on that.
John Furrier
>> And Amazon, the bar raiser is a veto vote.
Jack Hidary
>> Yes, yes, yes, yes. We do the same thing here. And so it's a very powerful tool. Interesting stat, John. We're about 250 people or so in our company. If you go to our website, we have about 20 or so open positions, 25 own positions. How many people have applied to our company in 2025, past 12 months? I asked the team that question, I was expecting maybe a few thousand people had applied. The answer is more than 45,000 people apply to SandboxAQ in the last 12 months. And because people I think want to have impact.
John Furrier
>> Most companies want your scraps.
Jack Hidary
>> Yeah. People want to have that impact, John.
John Furrier
>> Yeah. And I think a culture like that sets the bar. And it's like when you were a kid, you had to be this tall to ride the roller coaster. You got to have a certain level, raise that bar. All right, cool. First of all, I could go on, but my final question before we get into some of the security things I'm interested in is, as physical AI comes out and becomes more prevalent, like Google and Alpha, you saw Waymo come from scratch. That's a great example. People can view that, "Oh, autonomous cars." But there's a lot more than that.
Jack Hidary
>> Yeah.
John Furrier
>> Explain your vision on physical AI, computer vision, full convergence. What does that look like?
Jack Hidary
>> Yeah. As we bring the world, the physical world, the human world and AI together, this is going to yield incredible benefits for humanity and it's going to take us more than a decade from today to really play this out as a whole industry. Let's take self-driving and autonomous cars to begin with. These are really robotics. That's really what this is. It's not really a car, it's robotics. People mistake Tesla as a car company. It's not a car company. It's a robotics company. And Elon now is making even more clear moves to signal that where he's at least temporarily putting a couple of models on hold to really focus on Optimus and focus on that. But ultimately, what the robotaxi is, it is a robot. And that's what Waymo is as well. It's taken 17, 18 years to get to this stage with Waymo. And now you see that really the scale and also the experience. You've had now the pleasure of being in a Waymo.
John Furrier
>> Many times.
Jack Hidary
>> And that little screen in the back, I think is what gives me at least, and a lot of other people, that peace of mind, showing you what the computer sees. And so that transparency into the mind of AI, this is going to be a hallmark going forward for the integration of AI and human life. We need to see what the AI sees. That gives us more comfort.
John Furrier
>> And Spotify is good too. You see just music on there.
Jack Hidary
>> Yeah, that's right.
John Furrier
>> It knows your name.
Jack Hidary
>> Yeah, exactly.
John Furrier
>> All right.
Jack Hidary
>> So to me, that's one area that integrates vision, integrates human experience, integrates obviously mapping the world. And you have some interesting startups now. You have Fei-Fei Li, one of our advisors started a company called World Labs that's helping to make these world models. Yann LeCun, one of our investors has gone out now, left Meta and is raising money now and doing very well for AMI. AMI is the name of his new company. Again, a very interesting role model company based on JEPA, which is a fascinating approach and I think it's going to have tremendous progress as well. So you're beginning to see the wave of companies post LLM, and this is a very exciting area.
John Furrier
>> I love that post LLM view and I think it's going to be one of those moments where you start to see the progression that you were talking about earlier.
Jack Hidary
>> That's right.
John Furrier
>> All right. Let's talk about the quantum side of the Q. You got the quantitative, which is physical things that aren't in LLMs. Quantum computing is, on one hand, it's just fantasy. In other words, it's a nightmare for some. There's work being done. I've interviewed Cloudflare and a bunch of other companies that are doing the technical work now of figuring out, "Okay, how do I start thinking migrating my keys?"
Jack Hidary
>> That's right.
John Furrier
>> Because those private keys are on the dark web waiting for ... They've been harvested, they're waiting. So people are scared in some level.
Jack Hidary
>> And this brings us back, John, to cryptocurrency as well. So when you look at the quantum threat Q-Day, we call it as when quantum computers will rise up in terms of their capabilities and scale and error correction and have the ability to run some version of Shor's algorithm from Peter Shor in 1994, but now an updated version that allows it to crack RSA, to crack ECC, to crack the fundamental protocols that we use in this building here, in the stock exchange building and in banking, in telco, in governments around the world, so this is-
John Furrier
>> And Bitcoin.
Jack Hidary
>> Yeah. And when we get to Bitcoin and Ethereum and Vitalik Buterin, the founder of Ethereum just got on stage a few weeks ago and said, "We must migrate now." Right? This is not something you can wait on. You can't wait till last minute on this kind of thing. This has to happen now. So we are seeing some banks starting to move. We're seeing some governments start to move. I do think we need more push from the government. The good news is NIST did its work well. They have great standards now that have been put out there, published out there. We and many others had a lot to do with getting those standards done. Thousands of researchers and academic and industrial participants. So the good news, NIST standards are there for PQC, post quantum cryptography, but now it's time to get out there. But the here and now threat, John, right now, is AI and cyber. So 75%, 80% of-
John Furrier
>> Oh, from a security standpoint?
Jack Hidary
>> Yes, yes.
John Furrier
>> Yes. Okay.
Jack Hidary
>> Exactly. 75%, 80% of all big companies now are using GenAI. The problem, John, is that only 6% of those companies have an AI plan. What we call SPM, secure posture management. AI SPM means that you have a plan and an implementation framework for keeping the use of AI secure. Example, agents. You might say, "Oh, I have agents running around my network. Okay. I'm going to have some productivity." But now, you're also going to have some hackers because hackers know that if they get hold of the password and login of that agent, we call it NHI, non-human identity, they can have the run of the show. And so agents both-
John Furrier
>> And recruit other agents.
Jack Hidary
>> Exactly. So agents, and we're seeing now with Moltbook and things like that. So agents certainly can give us productivity, but also they open the door to hackers having more capability inside the enterprise.
John Furrier
>> And what are the problems with that? Is it because the folks are running too hard and fast, fast and loose, or is it more of there's just so new implementations happening? What's the issue?
Jack Hidary
>> Yeah, the issue is that things are moving really fast. People are excited about this technology. I understand that, but they're bringing it in so, so fast. We have a technology called Active Guard. It just announced that it's being used by the Pentagon after two years of testing. And in mission-critical areas such as the Pentagon, such as banks, such as large companies, such as the stock exchange, it's fundamental. We must have the security and, yes, it is very exciting to have agents, but we must use them in a secure fashion. The good news is we now can do that, but many people start doing the AI work without the security. That's a problem.
John Furrier
>> You can hear the bell for the Mad Money Show being filmed at the same time.
Jack Hidary
>> There you go.
John Furrier
>> Jack, I got to ask you as we close out here. I wish we had more time. We'll certainly do a follow-up. It's a great story. Talk about the origination store a little more detail. When did it really initiate in Google? When was the spin out officially happen? When did that all go down?
Jack Hidary
>> Yeah.
John Furrier
>> And looking back to that and now, what's been the biggest change?
Jack Hidary
>> Yeah. Well, first of all, just one note on the real origin story was I had the vision for this more than two decades ago when I was a scientist at NIH and seeing all the failure around me, people trying out this drug, that drug, clinical trials here and there. I was working in building 10 of NIH, which is where we have the clinical center, the patients, the human patients. And it was a very big privilege, John.
John Furrier
>> So you had an itch, you've been scratching for 20 years.
Jack Hidary
>> Yeah, that's right.
John Furrier
>> Where's the horsepower? Where's the compute?
Jack Hidary
>> Yeah. Where's the compute? Where are the chips? Where are the algos? Okay. All right.
John Furrier
>> By the way, a lot of people are coming out of the woodwork because this AI ... I mean, in the '80s when I got my CS degree, there was AI going on, theory, but there was no like ...
Jack Hidary
>> Yeah. So waiting for a long time, we did show that neural networks would have tremendous power. Even back then, we showed that, but now with the kind of capabilities we have, this is what's exciting. When Google decided to launch Alphabet and recruit folks like myself who had both a deep science experience and, sorry, a deep science and technical background, as well as a business background, I saw that as a really good fit for these kinds of big, big challenges. You can call them moonshots, you can call them global challenges.
John Furrier
>> I think they call them moonshots at the beginning.
Jack Hidary
>> Yeah. Yeah. But these are the kinds of things that we want to do with technology. And so to me, it was a great marriage to come together with the folks at Google and Alphabet.
John Furrier
>> If you don't mind me asking, Jack, explain what the culture and vibe was at that time, because if I remember that time correctly, Sergey still was around. It was more of like an academic.
Jack Hidary
>> Oh, absolutely.
John Furrier
>> There wasn't a lot of pressure, but it was a mandate. It wasn't like people banging you every day, or what was it like? What was the vibe like?
Jack Hidary
>> The blessing was that Sergey gave us all tremendous platform to explore, to experiment. It wasn't pressure to say, "Oh, I need a product in three months." It was about wide-ranging experimentation to think about what is going on and people like Astro Teller and others really allowing that to flow. And Sergey is somebody who's really interested in the next wave of humanity of where things are going.
John Furrier
>> And hard problems too.
Jack Hidary
>> Yeah. Yeah. Physics, AI, these are the core areas that really interest him and also benefiting humanity.
John Furrier
>> So you had to frame what you were bringing to the table in a clean canvas.
Jack Hidary
>> That's right.
John Furrier
>> So you had a clean sheet of paper. So you essentially had to-
Jack Hidary
>> And I knew, John, I knew that these twin engines, AI and quantum will be the keys. I knew that this would be transformative. And big shout out obviously to Eric Schmidt, who also helped us out while I was inside Alphabet and then of course joined us as chairman as we spun out.
John Furrier
>> And he's nerd too, so he gets the big shots. So he understands, okay, it's a big win. It's a classic Silicon Valley, go big or go home.
Jack Hidary
>> That's right.
John Furrier
>> And you're going big.
Jack Hidary
>> Yeah. And it took us a number of years. This is not an overnight kind of thing.
John Furrier
>> Yeah, it's just patience.
Jack Hidary
>> It takes a number of years. And then with the spin out, the whole idea always was to spin out because ultimately, if you want to make a big global platform, you've got to be in-
John Furrier
>> When was the spin out?
Jack Hidary
>> The spin out-
John Furrier
>> Roughly. Not exactly.
Jack Hidary
>> The spin out was mid 2022.
John Furrier
>> Okay. So recently, so on incubation wise, how many years?
Jack Hidary
>> So about five, six years of incubation, and now we've been spun out independent for about three and a half years.
John Furrier
>> Well, it's a super great story and it's great that you're also a distinguished author. Love the title, AI or die, and you're very passionate about the fact that they will die if you don't as a company. And I was doing textbooks too. Jack, congratulations. And again, well done. We'll have more conversations. And again, you're in the heart of the action and also forward actions coming. You're in the line, but you're also watching it. You're skating with the puck.
Jack Hidary
>> Yeah.
John Furrier
>> You're not even going to where the puck is going to be. You're kind of skating with the puck.
Jack Hidary
>> It's a very exciting time, John. And I just encourage our viewers to really jump on your show because you have so many interesting interviews here. Jump on our YouTube channel if I can encourage people to check out the SandboxAQ YouTube channel. It's free. We've got over 100 million views on the channel, and we're not a content company at all. We're just trying to share where things are going. And so we'd love to work more with you, John. Great to be here.
John Furrier
>> Yeah. And people, there's an appetite. And if you're out there, there's so much great content. Sometimes you can not even have to be a PhD to have PhD kind of critical thinking. This is the world opportunity that's in front of us. As the societies come together, physical and digital, it's going to open up more opportunities to affect change, stories, drive movements. This is a great movement. Jack, thank you for what you do and appreciate it.
Jack Hidary
>> John, great to be here.
John Furrier
>> Okay, I'm John Furrier. The AI factories are coming. The mixture of experts are all coming here in theCUBE. The New York Stock Exchange is part of our CUBE's NYSE wired program. Thanks for watching.
>> Welcome back to theCUBE everyone here at the New York Stock Exchange CUBE Studio, part of our NYSE wired program at CUBE Original. Of course, we got our Palo Alto studios connecting Silicon Valley and Wall Street. We speak both languages as the tech and the capital markets come together. The Wired and theCUBE community are doing its part to share. We got a great mixture of expert on theCUBE here, an AI leader, Jack Hidary, CEO of SandboxAQ. I'm looking at my notes here. Jack, welcome to theCUBE. You had a billion dollars raised in two rounds spinning out of Alphabet SandboxAQ. So this is not Y Combinator.
Jack Hidary
>> No. No.
John Furrier
>> This is like real deal, Google-
Jack Hidary
>> We were nurtured inside of Alphabet and my big thanks to Sergey, to so many people, Astro Teller, Sergey and Ruth and many others at Alphabet for nurturing us in our early years. But the intention always was to spin out in order to really grow a large global company and thank God we're on the way.
John Furrier
>> People don't give Sergey and the team enough credit what they're doing with Alphabet, how they structured it. They fostered the innovation research, commercialization, but yet fun. They do wild moonshot projects, but they do an amazing job.
Jack Hidary
>> When Ruth Porat came over and created a lot more discipline in the company as CFO initially at Alphabet, and also they realized, Larry and Sergey realized that, "Hey, we really want to expand and do more stuff here." And Waymo is an example. At that time, it was called Chauffeur. That was the code name for it, Chauffeur. I was lucky enough to be right there and see, really drive in some of the super early autonomous cars. They were not as smooth as they are today. And to see Waymo so successful today, John, is amazing. They just hit half a million paid rides a week just a few months ago. And I think they're on track to hit a million a week, I think by end of year.
John Furrier
>> Finally got into Palo Alto, Mountain View. I'm in San Francisco, dominate. I took one just recently. It's fun in Palo Alto driving on Alma Street.
Jack Hidary
>> Yeah, it's amazing there.
John Furrier
>> You also authored the book AI or Die and a textbook in Quantum Computing: An Applied Approach, the title.
Jack Hidary
>> And AI or Die, John, just because it's literally that is the choice that people have today. It is for your businesses. Either you embrace this stuff or you're not going to exist. And the AI we're talking about here, John, is not just the AI for the digital world. And I'm sure you've had lots of great conversations here at this table about AI like large language models such as ChatGPT and Gemini and Anthropic. Great models, really a lot of applications in marketing, customer service, legal, for example, is today's news. But where we focus on at Sandbox is AI for the real world. And so when you want to take AI and make a new medicine for cancer, a new treatment for Alzheimer's. When you want a new battery chemistry to store energy, when you want to do new risk modeling, right? We're here in the New York Stock Exchange right now looking at the options floor. And if you want to do those quantitative models, a language model and something trained on cat pictures and videos isn't really your best friend. You want something trained on numbers and equations, and that's where we shine.
John Furrier
>> Well, I want to dig into it, but I want to just share with you and get your reaction because I know you run the same agreement, AI for the real world. The two hottest things we're covering and we're getting data on is AI and cryptocurrency, the crypto industry, which includes a lot of the things around quantum that you guys are doing.
Jack Hidary
>> That's right.
John Furrier
>> The number one trend is the convergence of physical and digital.
Jack Hidary
>> That's right.
John Furrier
>> As first party citizens, all the data interacting, real world assets on chain.
Jack Hidary
>> That's right.
John Furrier
>> The number one topic that's driving the financial stack here in New York. In AI, Jensen, energy-
Jack Hidary
>> Let's talk about AI for the physical world. We're talking about AI for the real world. That is the new big, big territory for AI. So of course, low hanging fruit a number of years ago when you wanted to train an AI model, the transformer model was the words of the internet, right? The images, the words of the internet. That made a lot of sense. And now, we're seeing the fruits of that labor. ChatGPT, Gemini, Anthropic. These are very powerful, very, very useful tools for productivity, for customer service, marketing. But when it comes to the core of the Fortune 500, the core product development, when you want to make that car, you want to make that spaceship, you want to make that battery, you want to make that drug, that takes a new kind of AI. That's AI for the real world.
John Furrier
>> Yeah. And I like how you bring in the progression. And I want to come back to this because I want to zoom out for the folks watching. And we talked about this on our CUBE Pod last Friday. If you look at the generation that we grew up in, the internet was upon us, that came from a lot of the research. Things were on paper. Then the internet came, then the web, then you had mobile and cloud. That generation in the computer industry was a magical time. Okay, that's over. That was wave, call it wave, big wave one.
Jack Hidary
>> Yeah.
John Furrier
>> Now you got transformer, some of the stuff early days in AI and then kind of where we are today, but yet there's a whole path to full autonomy. Take me through your vision on that because I totally agree with AI or die. It's a mandate. It's a pound the table, get with the program where you're going to be extinct.
Jack Hidary
>> Absolutely.
John Furrier
>> But take me through the path of where we are today, because as we get the physical world on with AI, that's going to change and open up use cases we can't fathom. Unbelievable. So take me through the vision to get autonomous. This is a similar wave. So paper was transformer. Internet now feels like ChatGPT, LLMs, but as we start to see agents and the path to physical and autonomy, autonomous things, not just cars, what's your vision on what that path looks like?
Jack Hidary
>> Well, exactly, John. Very good analogy to talk about the big wave one that we all experience over a number of decades, because each one of those movements within that big wave led to the next one. If you think about it, people listening to this today may remember dial-up modem. Okay? The crazy sounds. "Beep, bop, ba, ba, ba." Okay. That was a dial-up modem. Well, when we're in the era of dial-up modems, you really couldn't have YouTube because it wasn't practical to upload and download all those videos. Once broadband and really almost universal broadband came to the US, to Europe, to other places, that was then the birth of YouTube. Google, of course, bought YouTube and then grew it even bigger. And we have the kind of video world that we have today, be it TikTok, be it others as well. So each wave really opens up the opportunity for the next wave. And that's happening now in this like-
John Furrier
>> What is that? What's the sequence? Where are we now? What's that wave?
Jack Hidary
>> Right. So the first one was the web itself, Tim Berners-Lee is a friend and informal advisor to us, along with Rosemary Berners-Lee as well, who's an official advisor for us. And I joke with Tim, I said, "Tim, congrats for creating the web. You actually ended up creating the largest training set in human history." And that's really what 30 years of the web gave us. He gave us this massive training set, and that's great in terms of the large language models. And kudos also to Google. 2017, the paper, Attention is All You Need, which was the paper that defined a new way of architecting neural networks inspired by the brain and having to do that. I really first started using neural networks in medicine when I was working in science at the NIH, bringing neural network ideas to neuroimaging, to brain imaging. So it has a long enrich history there. But now, let's look fast-forward. Okay. So with the low hanging fruit is the worldwide web, download it, make your transformer model, make it work. But the next wave now is really new proprietary data sets, data sets that don't exist on the internet. These are special data sets for biochemistry, for catalysts. Viewers may not know this, but almost everything that you're seeing and touching on this set and in your homes or office is watching this now comes from a catalyst. This laptop you have here could not be made except for these compounds, cold catalysts, which drive a reaction forward quickly with low energy and are not used up in that reaction. It turns out it's really hard, John, to make a catalyst, just like it's hard to make a new drug. AI for chemistry that we have pioneered is now doing that in record time. So this is a very exciting moment now where we're moving from just words as the data set to now quantitative data and equations as the data set. And that's opening up a whole new world of AI that we didn't have before.
John Furrier
>> Okay. So first of all, explain how that works because I think that's one, exciting and motivating for anyone watching like, okay, it's going to get better, but it's still hard problems to solve.
Jack Hidary
>> Yeah.
John Furrier
>> So take me through and scope the magnitude of how you do it, what's it take? If there's no data, do you make the data? Do you train it? Is it off synthetic data? How's it going on?
Jack Hidary
>> So let's start with biopharma, because that's something affects all of us. We all want to see new medicines for cancers. We want to see new medicines for neurodegen diseases. Unfortunately, most everyone viewing this, their family will be impacted at some point by a neurodegen disease, could be Alzheimer's, could be dementia. Unfortunately, this is going to become almost a pandemic as the population ages. And so you want to make a medicine. Today, the traditional way of doing that, it takes 10, 15, 20 years, John, to get to that understanding of what that medicine could be. And then you got to go into clinical trials. And before the advent of AI, we're seeing about 85% failure, John, in clinical trials. So what can AI do about that? Well, what we can do is start to make really good simulations first of the chemical world in a computer. And we could do so with the laws of physics itself. And that is something that we always dreamed of. I dreamed of it more than 20 years ago, okay, in science, but now it's happening. Now, we and others have worked on this and now we have computers that have high fidelity between what we imagine in the computer. And then when we synthesize it, what we see in x-ray crystallography, what we see in cryo electron microscopy, we now see high fidelity between these two. So this is an exciting moment where for the first time we can harness in silico to go for the real world.
John Furrier
>> Do you guys develop your own data sets?
Jack Hidary
>> We have to. Yes, we absolutely do. We don't use any third-party data in terms of downloading data from the internet or things like that. The first thing we have to do is we have to run lots of experiments. We contract labs in different parts of the world to run thousands and thousands of robotic experiments, things with robot hands, and that is something that you can do out there in the world. We own that data, we take that in, and then we add to it synthetic data based on the laws of physics and chemistry itself. That creates a dataset that's unique, that's proprietary to us. And the next step is we can train models on those. And those models are not large language models, because again, we're not dealing with language here. Those are new kind of models. Those are LQMs, large quantitative models, John.
John Furrier
>> Yeah. So maths involved and data that's not available.
Jack Hidary
>> That's right.
John Furrier
>> And the Q for quantitative and just quantitative, is there other non-quantitative?
Jack Hidary
>> Well, so we have A for AI.
John Furrier
>> Or it's all math, I guess.
Jack Hidary
>> Yes. We have A for AI. We have the Q for quantitative, but also quantum, because the techniques we're using, we're not running them on a quantum computer because those are not ready yet. We're running them on GPUs and other types of accelerated chips, but we have to use quantum equations. We have to use the equations of quantum physics to analyze and understand the relationship between this molecule and that molecule. That's a quantum issue. And the same now, John, we can go beyond biopharma into batteries, into materials, into oil and gas and those sectors as well.
John Furrier
>> When I last saw when I was in DC for NVIDIA's special GTC in-
Jack Hidary
>> The special GTC in October. Yeah.
John Furrier
>> I asked Jensen about AI factories and I kind of teased out and he kind of laid out the dots. I connected it. There'll be microfactories. There's going to be edge and certainly core, but he also answered the question around how one of the trends he was pointing out is because he always asked the quantum question, but he wanted to address, he says, the GPUs and systems that we're using with Omniverse are actually being used to solve quantum computing. And that was not obvious to most of the analysts, but-
Jack Hidary
>> Well, here's a proof point, John.
John Furrier
>> Explain, because this is a very important, because the horsepower is helping that. You have stuff like data simulations. I'm sure you got something going on similar with compute.
Jack Hidary
>> NVIDIA is not only investor, but also one of our partners. And with NVIDIA and other partners, we announced just a few days ago, AQAffinity, right? AQAffinity is a software platform that we opened up to the world and is now, we believe, the most advanced platform for determining both the structure of those proteins that we want to target in the body, but also the binding affinity between a potential drug and that target protein. So it's doing two things at the same time. 5, 10 years ago, the conventional wisdom, John, is that we would never be able to do that on GPUs.
John Furrier
>> Yeah. Supercomputing was a terminal that was basically a PC server.
Jack Hidary
>> Out of 1000. Yes, exactly. But we pulled it off last week and we're really proud of that and really proud of our partners. OpenFold3, OpenFold is our partner. It's a consortium of top academics, of Bayer, of many other pharma companies. And of course with NVIDIA, our partner as well, we use the DGX Cloud via GCP in this case, and this is what allowed us to have this massive breakthrough. So this builds on work that we've done with NVIDIA on the SAIR data set, on other data sets. You have to build the really good precision data first. You cannot use random data.
John Furrier
>> Okay. So are you sharing your data set with people?
Jack Hidary
>> Yeah, we made the dataset public. It's open source right now. It's online right now.
John Furrier
>> Like NVIDIA, they're sharing their libraries.
Jack Hidary
>> Yes, that's right. That's right.
John Furrier
>> You're doing the same.
Jack Hidary
>> In fact, in fact, I'm happy to share that NVIDIA just announced that their DiffDock, which is one of their key models for looking at molecular development of new pharmaceuticals is now improved because of the SAIR dataset that we worked on. It's on their website now.
John Furrier
>> I really want to call that out because I think two reasons. One, people should know that this is practice in the industry.
Jack Hidary
>> That's right.
John Furrier
>> Number two, it's super impressive that you're doing that and you made that a milestone. Scope the alternative from time to completion to hit that proud moment that you just shared. If you didn't have the super computing, the NVIDIA systems and the AI factories, what would that have been like? I mean, even in a super HPC world, even some cloud, we had old cloud or cloud legacy, if you can call it that, modern legacy. What's the difference? Because I think researchers should know that this is now available. It's super inspiring. Share the scope of timeframe.
Jack Hidary
>> And John, it's a great question. In 2016, when we got going inside of Alphabet, the conventional wisdom was that it would be many decades for us to get the kind of compute that we needed to do real world kind of stuff with molecules, with energy, with risk management. And we made a bet that the various chip operations, both inside Alphabet and at NVIDIA and other places would really ramp up on an almost exponential type of-
John Furrier
>> Which it did.
Jack Hidary
>> And that's what happened.
John Furrier
>> It's a good bet. It came home.
Jack Hidary
>> And so that was the bet that we made. And sure enough, around 2022, 2023 is when the crossover point happened, and we started to see we could do real world computation with these kinds of chips.
John Furrier
>> And you're doing it. All right. Now, take me through some of the biology side. There's a lot of concern in biology, chemistry kind of tied together. Pharma, you mentioned. What are some of the cool things you see being enabled right now in terms of the breakthroughs?
Jack Hidary
>> We can see breakthroughs, John, both in new therapeutics. We're working on cancer. We made some announcements with iOncologi for brain cancer. This is one of the areas that has been one of the most difficult for traditional oncology. I think we're going to see breakthroughs there. We've talked about our work with UCSF and Stan Prusiner, Nobel Prize winner. That's in terms of neurodegen diseases, in Alzheimer's, dementia, Parkinson's. Very, very important there as well. So those are more important, but also let's look at the energy sector. The energy sector, we need now more than ever, because we need to power buildings like this and we need to power data centers and we need novel battery chemistries. We need novel ways of producing, storing, and transmitting energy as well. And that's all material science. And so AI for material science, John, is to me, one of the new uncharted territories
John Furrier
>> I have to ask you on the business side, we got the billion dollars in funding, which is ... I like the way you did that. You got great investors, you got the long game view for sure.
Jack Hidary
>> Yes. Long horizon investment. Yeah.
John Furrier
>> Long horizon view. So if you look at some of the successes, the old school mentality on corporate strategy and governance was. You got corp dev, you got your divisions and you got the R&D labs, applied and you got weird R&D.
Jack Hidary
>> Yeah, that's a red alert. Once you see a different R&D lab from the core divisions, red flag on the play.
John Furrier
>> And then the modern version for AI is interesting that bring in almost forward deploying R&D a little bit further in. How are you looking at? Because you must be really leaning in with R&D. All the successful AI companies have a strong bench of R&D research. R&D.
Jack Hidary
>> Yeah.
John Furrier
>> How do you look at the role of research as you start to commercialize all this?
Jack Hidary
>> Yeah. For us, the R&D is part and parcel of the product itself. It is not some separate division out there that we say, "Hey guys, how are you doing over there in the R&D division and what do you have to throw?"
John Furrier
>> And call me when you got a breakthrough.
Jack Hidary
>> Yeah. And that's what the lessons we all learn from Xerox PARC and things like that. Massive breakthroughs, but never made it over the wall to commercialization. And so I think the more modern way of doing things is to embed it all together. We've got more than 125 PhDs in our company, very technical company, PhDs in physics, chemistry, biology, AI, mathematics, cybersecurity. And then we've got about 100 engineers, coding engineers, other kinds of engineers on top of the PhDs who are working together with the PhDs side by side to translate their insights into scalable code. And so that's really what this game is about. What it's about is taking domain knowledge that's specific to particular areas, combining across disciplines, that's really powerful. And then the next step is to use code to make it scalable and usable to a huge number of clients.
John Furrier
>> It's like, remember the old pair programming days, you're like pairing human capital and collectible capital all together so it's like real synergistically cohesive.
Jack Hidary
>> Oh, these are special folks in our company. I'm super proud of what they've been able to accomplish, John. Every time a challenge is put to them by a customer, they overcome that challenge.
John Furrier
>> All right.
Jack Hidary
>> This is really an incredible-
John Furrier
>> So I'm curious about the company culture. Intel has Moore's law, doubling every whatever, which is now some people are saying is dead, but we'll see what it does with that one. I still like Intel personally. I love the company. What's your culture? Do you have a cadence of something? What would be description in your culture, all that horsepower and rocket fuel? What would be the culture like?
Jack Hidary
>> The core four words of our culture are deep impact at scale. The ways on why people come to work every single day, what drives people, what drives me is having significant impact in the world, but at scale, right? And that's what's key now. Right now, because of AI, relatively small teams can have global impact. You've seen that with us, you've seen that with many other companies as well. You don't have to have 10,000 people in your company to have very significant impact. So deep impact at scale is number one. Number two, a learning culture. I love being inside of our company, because every day I'm learning so much. I'm learning for the people around us. We hire people who are way more knowledgeable in so many areas than I am. And so I'm learning a lot. And what I hear from everyone else is they're learning a lot too in this beehive of learning. So I think those are two of the hallmarks that we have there. And also bar racing. This is something that we learn from a number of other company cultures. When you're hiring somebody, it's very easy to fall into the trap of hiring somebody that you know or hiring somebody who knows a friend and so on and so forth. And the bar raiser is somebody who comes in-
John Furrier
>> Amazon does that.
Jack Hidary
>> That's right. Amazon is one of the examples. They come in from not your team, some other team, sometimes it's our legal counsel, sometimes it's somebody else, and they make sure to ask the hard devil's advocate questions on that hiring committee. And actually, we've turned down 45 people just on that.
John Furrier
>> And Amazon, the bar raiser is a veto vote.
Jack Hidary
>> Yes, yes, yes, yes. We do the same thing here. And so it's a very powerful tool. Interesting stat, John. We're about 250 people or so in our company. If you go to our website, we have about 20 or so open positions, 25 own positions. How many people have applied to our company in 2025, past 12 months? I asked the team that question, I was expecting maybe a few thousand people had applied. The answer is more than 45,000 people apply to SandboxAQ in the last 12 months. And because people I think want to have impact.
John Furrier
>> Most companies want your scraps.
Jack Hidary
>> Yeah. People want to have that impact, John.
John Furrier
>> Yeah. And I think a culture like that sets the bar. And it's like when you were a kid, you had to be this tall to ride the roller coaster. You got to have a certain level, raise that bar. All right, cool. First of all, I could go on, but my final question before we get into some of the security things I'm interested in is, as physical AI comes out and becomes more prevalent, like Google and Alpha, you saw Waymo come from scratch. That's a great example. People can view that, "Oh, autonomous cars." But there's a lot more than that.
Jack Hidary
>> Yeah.
John Furrier
>> Explain your vision on physical AI, computer vision, full convergence. What does that look like?
Jack Hidary
>> Yeah. As we bring the world, the physical world, the human world and AI together, this is going to yield incredible benefits for humanity and it's going to take us more than a decade from today to really play this out as a whole industry. Let's take self-driving and autonomous cars to begin with. These are really robotics. That's really what this is. It's not really a car, it's robotics. People mistake Tesla as a car company. It's not a car company. It's a robotics company. And Elon now is making even more clear moves to signal that where he's at least temporarily putting a couple of models on hold to really focus on Optimus and focus on that. But ultimately, what the robotaxi is, it is a robot. And that's what Waymo is as well. It's taken 17, 18 years to get to this stage with Waymo. And now you see that really the scale and also the experience. You've had now the pleasure of being in a Waymo.
John Furrier
>> Many times.
Jack Hidary
>> And that little screen in the back, I think is what gives me at least, and a lot of other people, that peace of mind, showing you what the computer sees. And so that transparency into the mind of AI, this is going to be a hallmark going forward for the integration of AI and human life. We need to see what the AI sees. That gives us more comfort.
John Furrier
>> And Spotify is good too. You see just music on there.
Jack Hidary
>> Yeah, that's right.
John Furrier
>> It knows your name.
Jack Hidary
>> Yeah, exactly.
John Furrier
>> All right.
Jack Hidary
>> So to me, that's one area that integrates vision, integrates human experience, integrates obviously mapping the world. And you have some interesting startups now. You have Fei-Fei Li, one of our advisors started a company called World Labs that's helping to make these world models. Yann LeCun, one of our investors has gone out now, left Meta and is raising money now and doing very well for AMI. AMI is the name of his new company. Again, a very interesting role model company based on JEPA, which is a fascinating approach and I think it's going to have tremendous progress as well. So you're beginning to see the wave of companies post LLM, and this is a very exciting area.
John Furrier
>> I love that post LLM view and I think it's going to be one of those moments where you start to see the progression that you were talking about earlier.
Jack Hidary
>> That's right.
John Furrier
>> All right. Let's talk about the quantum side of the Q. You got the quantitative, which is physical things that aren't in LLMs. Quantum computing is, on one hand, it's just fantasy. In other words, it's a nightmare for some. There's work being done. I've interviewed Cloudflare and a bunch of other companies that are doing the technical work now of figuring out, "Okay, how do I start thinking migrating my keys?"
Jack Hidary
>> That's right.
John Furrier
>> Because those private keys are on the dark web waiting for ... They've been harvested, they're waiting. So people are scared in some level.
Jack Hidary
>> And this brings us back, John, to cryptocurrency as well. So when you look at the quantum threat Q-Day, we call it as when quantum computers will rise up in terms of their capabilities and scale and error correction and have the ability to run some version of Shor's algorithm from Peter Shor in 1994, but now an updated version that allows it to crack RSA, to crack ECC, to crack the fundamental protocols that we use in this building here, in the stock exchange building and in banking, in telco, in governments around the world, so this is-
John Furrier
>> And Bitcoin.
Jack Hidary
>> Yeah. And when we get to Bitcoin and Ethereum and Vitalik Buterin, the founder of Ethereum just got on stage a few weeks ago and said, "We must migrate now." Right? This is not something you can wait on. You can't wait till last minute on this kind of thing. This has to happen now. So we are seeing some banks starting to move. We're seeing some governments start to move. I do think we need more push from the government. The good news is NIST did its work well. They have great standards now that have been put out there, published out there. We and many others had a lot to do with getting those standards done. Thousands of researchers and academic and industrial participants. So the good news, NIST standards are there for PQC, post quantum cryptography, but now it's time to get out there. But the here and now threat, John, right now, is AI and cyber. So 75%, 80% of-
John Furrier
>> Oh, from a security standpoint?
Jack Hidary
>> Yes, yes.
John Furrier
>> Yes. Okay.
Jack Hidary
>> Exactly. 75%, 80% of all big companies now are using GenAI. The problem, John, is that only 6% of those companies have an AI plan. What we call SPM, secure posture management. AI SPM means that you have a plan and an implementation framework for keeping the use of AI secure. Example, agents. You might say, "Oh, I have agents running around my network. Okay. I'm going to have some productivity." But now, you're also going to have some hackers because hackers know that if they get hold of the password and login of that agent, we call it NHI, non-human identity, they can have the run of the show. And so agents both-
John Furrier
>> And recruit other agents.
Jack Hidary
>> Exactly. So agents, and we're seeing now with Moltbook and things like that. So agents certainly can give us productivity, but also they open the door to hackers having more capability inside the enterprise.
John Furrier
>> And what are the problems with that? Is it because the folks are running too hard and fast, fast and loose, or is it more of there's just so new implementations happening? What's the issue?
Jack Hidary
>> Yeah, the issue is that things are moving really fast. People are excited about this technology. I understand that, but they're bringing it in so, so fast. We have a technology called Active Guard. It just announced that it's being used by the Pentagon after two years of testing. And in mission-critical areas such as the Pentagon, such as banks, such as large companies, such as the stock exchange, it's fundamental. We must have the security and, yes, it is very exciting to have agents, but we must use them in a secure fashion. The good news is we now can do that, but many people start doing the AI work without the security. That's a problem.
John Furrier
>> You can hear the bell for the Mad Money Show being filmed at the same time.
Jack Hidary
>> There you go.
John Furrier
>> Jack, I got to ask you as we close out here. I wish we had more time. We'll certainly do a follow-up. It's a great story. Talk about the origination store a little more detail. When did it really initiate in Google? When was the spin out officially happen? When did that all go down?
Jack Hidary
>> Yeah.
John Furrier
>> And looking back to that and now, what's been the biggest change?
Jack Hidary
>> Yeah. Well, first of all, just one note on the real origin story was I had the vision for this more than two decades ago when I was a scientist at NIH and seeing all the failure around me, people trying out this drug, that drug, clinical trials here and there. I was working in building 10 of NIH, which is where we have the clinical center, the patients, the human patients. And it was a very big privilege, John.
John Furrier
>> So you had an itch, you've been scratching for 20 years.
Jack Hidary
>> Yeah, that's right.
John Furrier
>> Where's the horsepower? Where's the compute?
Jack Hidary
>> Yeah. Where's the compute? Where are the chips? Where are the algos? Okay. All right.
John Furrier
>> By the way, a lot of people are coming out of the woodwork because this AI ... I mean, in the '80s when I got my CS degree, there was AI going on, theory, but there was no like ...
Jack Hidary
>> Yeah. So waiting for a long time, we did show that neural networks would have tremendous power. Even back then, we showed that, but now with the kind of capabilities we have, this is what's exciting. When Google decided to launch Alphabet and recruit folks like myself who had both a deep science experience and, sorry, a deep science and technical background, as well as a business background, I saw that as a really good fit for these kinds of big, big challenges. You can call them moonshots, you can call them global challenges.
John Furrier
>> I think they call them moonshots at the beginning.
Jack Hidary
>> Yeah. Yeah. But these are the kinds of things that we want to do with technology. And so to me, it was a great marriage to come together with the folks at Google and Alphabet.
John Furrier
>> If you don't mind me asking, Jack, explain what the culture and vibe was at that time, because if I remember that time correctly, Sergey still was around. It was more of like an academic.
Jack Hidary
>> Oh, absolutely.
John Furrier
>> There wasn't a lot of pressure, but it was a mandate. It wasn't like people banging you every day, or what was it like? What was the vibe like?
Jack Hidary
>> The blessing was that Sergey gave us all tremendous platform to explore, to experiment. It wasn't pressure to say, "Oh, I need a product in three months." It was about wide-ranging experimentation to think about what is going on and people like Astro Teller and others really allowing that to flow. And Sergey is somebody who's really interested in the next wave of humanity of where things are going.
John Furrier
>> And hard problems too.
Jack Hidary
>> Yeah. Yeah. Physics, AI, these are the core areas that really interest him and also benefiting humanity.
John Furrier
>> So you had to frame what you were bringing to the table in a clean canvas.
Jack Hidary
>> That's right.
John Furrier
>> So you had a clean sheet of paper. So you essentially had to-
Jack Hidary
>> And I knew, John, I knew that these twin engines, AI and quantum will be the keys. I knew that this would be transformative. And big shout out obviously to Eric Schmidt, who also helped us out while I was inside Alphabet and then of course joined us as chairman as we spun out.
John Furrier
>> And he's nerd too, so he gets the big shots. So he understands, okay, it's a big win. It's a classic Silicon Valley, go big or go home.
Jack Hidary
>> That's right.
John Furrier
>> And you're going big.
Jack Hidary
>> Yeah. And it took us a number of years. This is not an overnight kind of thing.
John Furrier
>> Yeah, it's just patience.
Jack Hidary
>> It takes a number of years. And then with the spin out, the whole idea always was to spin out because ultimately, if you want to make a big global platform, you've got to be in-
John Furrier
>> When was the spin out?
Jack Hidary
>> The spin out-
John Furrier
>> Roughly. Not exactly.
Jack Hidary
>> The spin out was mid 2022.
John Furrier
>> Okay. So recently, so on incubation wise, how many years?
Jack Hidary
>> So about five, six years of incubation, and now we've been spun out independent for about three and a half years.
John Furrier
>> Well, it's a super great story and it's great that you're also a distinguished author. Love the title, AI or die, and you're very passionate about the fact that they will die if you don't as a company. And I was doing textbooks too. Jack, congratulations. And again, well done. We'll have more conversations. And again, you're in the heart of the action and also forward actions coming. You're in the line, but you're also watching it. You're skating with the puck.
Jack Hidary
>> Yeah.
John Furrier
>> You're not even going to where the puck is going to be. You're kind of skating with the puck.
Jack Hidary
>> It's a very exciting time, John. And I just encourage our viewers to really jump on your show because you have so many interesting interviews here. Jump on our YouTube channel if I can encourage people to check out the SandboxAQ YouTube channel. It's free. We've got over 100 million views on the channel, and we're not a content company at all. We're just trying to share where things are going. And so we'd love to work more with you, John. Great to be here.
John Furrier
>> Yeah. And people, there's an appetite. And if you're out there, there's so much great content. Sometimes you can not even have to be a PhD to have PhD kind of critical thinking. This is the world opportunity that's in front of us. As the societies come together, physical and digital, it's going to open up more opportunities to affect change, stories, drive movements. This is a great movement. Jack, thank you for what you do and appreciate it.
Jack Hidary
>> John, great to be here.
John Furrier
>> Okay, I'm John Furrier. The AI factories are coming. The mixture of experts are all coming here in theCUBE. The New York Stock Exchange is part of our CUBE's NYSE wired program. Thanks for watching.
John Furrier
