Is in the waiting room. Chris Alczyk from Cadence. Chris, how you doing? Great. Hey, Jordy. Hey, John. Thanks for having me. Thanks for having me. Great to meet you. Welcome to the show. Introduce yourself, tell us what you're building, and then we'll talk about the round. Sure. Chris Allcheck, founder at Cadence, we are building clinical AI to automate the treatment of chronic disease. We just announced our series C last week and super excited to be on the show. How much did you raise? Start there. Start at the gong. What did you raise? We raised $100 million. Congratulations. 10 mole, 9 figs. Talk about. Yeah. When did you start the company? What's been the progress to date? What got you to this round? Yeah. So company is five years old. I was privileged to grow up in a family of doctors, and I'm married to a doctor, too. I saw how frustrating it is to know what treatment would actually make a patient healthier, but not have a system to be able to do it. And we knew that we could automate the treatment of the most common chronic diseases. Heart failure, hypertension, diabetes. And so we set out to build this technology over the last five years. We thought it would take 10 years to get to real automation. And we're five years in, and it's going a lot faster than we ever expected. We have the privilege of managing 100,000 patients now nearly every day, with a lot of the leading hospital systems in the country, and preventing strokes and heart attacks and helping people get healthier. So it's been super exciting. Okay, so pick a condition and then walk me through exactly how the product works for a patient and for their care provider. Yeah. So let's take heart failure, because that's a super important one. Eight million seniors in the US with heart failure. Those seniors are in and out of the hospital at a super high rate, costing the US government, which insures these people, about $50 billion a year. So pre cadence, less than 10% of these patients in the country are on the right drugs. Getting to the right drugs expands lifespan by five to seven years on average. So we've got 90% of people with heart failure in the U.S. probably your families, my families, our aunts, our uncles, people we know who are living five to seven years shorter lives because they're not on the right drugs. And it's not because they don't have amazing cardiologists or amazing primary care doctors. It's because to get a patient on the right drugs, you need to be adjusting their medications, often five to seven times in a year. And you need to be looking at their heart rate and their blood pressure as you're doing it, and their weight. And so with Cadence, the physician orders Cadence. Cadence gets the patient cellular connected blood pressure cuff, a scale, devices that give us their vitals remotely at home. The patient starts taking their vitals. We have their full medical records, their labs, vitals, allergies, symptoms, everything. And we're using AI to figure out, is this patient on the right drugs? If they're not on the right drugs, let's prescribe new medications, adjust current dosages, remove old medications. And we do that with all in an automated fashion with humans in the loop making the final decision on these med changes so the physician actually doesn't have to do work. The Cadence team and the Cadence agents are doing the work on behalf of the physician. So that's number one. Number two is we're getting their blood pressure and heart rate and weight on a daily basis. So if a patient has a blood pressure of 200 and it's Saturday night at 9pm, we have a voice agent that calls the patient within 2 1/2 minutes. Elect symptoms. If they're symptomatic, then we're figuring out, do they need to go to the hospital, can we change their meds at home, or do we need them to see their cardiologist on Monday morning? We're catching about 20 strokes a week right now before the patients know that they're having a stroke just off of these agents doing symptom triage, plus the data we have. So that's number two. And then number three is we're then coaching the patient on diet, exercise, med adherence, all the little things that require a lot of support on a daily basis. Our average patient is 75 years old to sort of keep them on their care plan. And we had patient in rural North Carolina who, with heart failure, was in and out of the hospital three times before getting on Cadence in the last six months. Got him on Cadence, got him stabilized, got him to the right meds, and he was playing golf again for the first time in three years, in his mid-70s, which is like, you know, that's what we're trying to do here. You gotta be like 100 times louder with what you're doing, because I think white pill. Yeah, it's a total white pill. And actually delivering a lot of the potential that people have talked about around the technology broadly for a long time. I would love some more information just getting me up to speed on the state of the medical devices for monitoring vitals. You Mentioned an Internet connected or cellular connected blood pressure cuff. Is there significant transition from the consumer medical devices, the Apple watches, the Fitbits, are those relevant or for these patients, are they getting a separate suite of medical devices for vital monitoring? Yeah, it's one of the exciting places of the next five years. So today it's a separate suite. These are FDA cleared devices that give you blood pressure in a medically accurate way or blood glucose, cgm, et cetera. So we're using medical devices today. Hopefully if the wearables and various Apple watches of the world get to medical grade accuracy or get the data in a way that we can use it, then we'll be able to use those. But today you couldn't use those devices to make clinical decisions. That is part of the exciting place here is we're managing 100,000 patients today. There's easily 10 million patients in the US who could benefit from this, if not 20 or 30 million. And we've just got more data, more sensors going out via wearables. And we need a clinical intelligence layer who can actually again take clinical action based off these data and these signals and turn it into longer, healthier lives for patients. Okay, John, nominative determinism here. Alternative checkup. Okay. Yeah, I like it, I'll check. I think we missed a C in the last name. We need to update the chiron. But I want to know more about the devices. Let me. So you mentioned blood pressure monitoring, blood glucose monitoring, those I've been aware of since I was a kid. You go into the doctor's office, they put, maybe they do it manually. So I understand that we're on the track of Internet connected, more regular testing and vital monitoring. But is there a new maybe in the last decade metric that doctors are monitoring? Is there a new, is there a new number that's popping up and proving to be indicative of health performance or drug dosage? We're not there yet. In terms of HRV or hemodynamics with heart failure, how effectively is your blood, is your heart pumping? How much fluid retention do you have? We're actually starting to get closer. So Cadence is testing a bunch of devices that measure these alternative metrics and, and then we're comparing them to the standard clinical of care, but just off of blood pressure. If you take that one right now, most patients, you get it four times a year if you go to the doctor four times a year. If you or me, you get it once a year when you go to the doctor once a year. We're getting it on average 22 days a month for patients. And so the level of clinical insight you get from 22 days of data versus four times a year is pretty dramatic. So I would say a big part of this is turning what was previously episodic clinical infrastructure into an everyday 24. 7 experience for patients. And just then and there you could take likely $100 billion out of US healthcare costs just on a very conservative basis. Today Cadence saves Medicare about $2.7 million per week by preventing avoidable hospitalizations. And we're still very small scale relative to what this can become. Yeah. What is the key to scaling? Do you need to work with health care? I like this dynamic. You say something incredible, I say a joke, John asks a serious question, and we could just go around like this, we could just go around like this forever. But, but I, but I love that. I love the focus on, on savings. It's incredible. Sorry. Go to market distribution. How do we 10x that? How do we 100x that? Are we going to insurance providers, insurers, hospital hospitals, individual doctors, individual patients? Like, what are the key funnel steps for you? Yeah, so key funnel, step number one is how many health systems are you working with? Hospital systems are you working with? So we work with 21 of the leaders in the country today from we announced actually Duke and Texas Health last week. We work with some of the largest health systems in every state Orwell in Michigan. So how do we go from 21 hospital systems to 100 hospital systems? So that's step number one. Step number two is effectively working with those physicians and their patients. You know, Cadence is a full end to end clinical solution. So we are working directly with physicians, working directly with patients. Our AI agents are interacting with both. So that's sort of step two. And then step three is continuing to work with payers. So today we work with two of the largest payers in the country. We worked very closely with CMS and the US government to ensure that there's positive ROI for payers. So those are the sort of big three expansion, expansion motions for us. We're only at 3% of the eligible patients within the hospital, the hospital health systems that we are today. So as this becomes the standard of care in the US this should hopefully be able to help a lot of people. Amazing. Jordan, Anything else? Incredible. I have one last question. Can you talk about the General Catalyst Partnership? They're an investor, but they also own a hospital network. I don't know if that deal's been completed. Has that been helpful? Are you the synergy that we were hearing about when that news initially broke. Walk me through that. Yes. So General Catalyst acquired a non for profit hospital system called Summa Health that closed earlier this year. It's a really exciting testing ground for new technologies inside of important community health systems. And Summa Health is both the provider in their community as well as one of the big payers in their community. So they can benefit from these kinds of services multiple different ways. And it's one of several examples of really fast modernization of US healthcare that's happening right now with AI. I think people think of health care as a laggard industry that's always slow to adopt technology. And when you look at AI, it's definitely one of the leaders in adoption of AI today. And then on Cadence's side, what we're really excited about is a lot of AI has been pointed towards automating back office tasks, billing, rev cycle call centers, et cetera. We're actually using AI to deliver clinical care. And so it's not about AI to replace people, it's about AI to make people healthier, which I think can and should become one of the most important applications of AI over the next 10 years. Yeah. Awesome. Well, thank you so much for taking the time to come talk. Thank you for doing this and thank you for everything you're doing. Yeah. Very important work. Appreciate you guys having me back on soon. Can't wait to talk to you next time. We'll see you. Cheers.

Anyway, let's bring in Chad Rigetti from Rigetti Computing and Segaldry. Chad, how are you doing? What's going on? I'm doing great. How are you guys doing? We're doing fantastic. Thank you so much for taking the time to come chat with us. I would love to start a little bit with your background and your journey. Of course we're going to talk about the company today, but if you could give us a little bit of an overview of your journey in Silicon Valley, I think that might be informative. There's a lot to talk about there and of course it relates to what you're doing today. You bet. Yeah. Great to be here, guys. I got interested in quantum computing when I was a senior in college and did a PhD in this field and spent about three years at IBM Research in the early days helping build up the quantum computing team there and then started my own company. That was Rigetti Computing. In 2014, I was introduced to Sam Altman and he said, we had coffee and he said, hey, well you should do yc. And I said, well, what's yc? And. And so he explained to me what Y Combinator was. And that was the first batch after Sam had taken over YC in 2014 and he brought in a bunch of hard tech companies into Y Combinator for the first time. And so I got to be a part of this incredible group of companies including Helion, oclo, which is now public. Ginkgo Bioworks. Ginkgo Bioworks, yeah. Boom. Was a couple batches after me, but there was this cohort summer 20. But yeah, so anyways, it was a fantastic experience. Ended up running Rigetti for about 10 years. Years. We took it public and in early 2022 through a SPAC transaction or the third quantum company, I think, to go public. And so that was an incredible journey. And you know, so I've been in quantum computing, I usually say my entire adult life and in Silicon Valley for a big part of that, but it's just a really fascinating mix and there's incredible people working in this area. There's incredible technology that's being developed and it's going to. It's going to change. Change the relationship between artificial intelligence and computing infrastructure. And that's we're working on at Sign. Yeah, the journey of going public. All the market gyrations is being a public company less predictable than Venture and being private because there's still the whims of the private market. Whether you're in the hot category. That year, venture investors are scrambling to get, you know, their position built up in a particular category. But the public markets seem like even harder to read on because you have retail investors and the stocks up and down and things can reprice on a minute to minute basis. What was it like psychologically transitioning from private company to public company? I think either can work. And there's a right answer for different companies. And you got to ask yourself the question what you're trying to achieve, is it liquidity for your early investors? Is it a primarily a capital raising activity? Is it to provide, you know, have, have liquidity for your early employees? For example, to some companies where you've got a 10 year exercise window for your options and you know, zooming out in the rigetti kind of taking public journey. That was a point in Silicon Valley when quantum computing was growing in commercial maturation and the technology was maturing. But a lot of the capital in the markets at that point had migrated for deep tech companies particularly, just wasn't available in the private market. So when you look at 2020 to 2022, most of that capital was actually sitting, you know, a lot of it was sitting in SPAC trusts on the public markets and, and they were in those SPACs were hungry to cut a deal. And so a lot of companies ended up going public during this wave simply because the founders, the executive teams were making the decision that that gave them the best chance of capitalizing the business going forward. And I think there's a right answer for different things. And now in the past month or so Quantinuum has gone public via ipo. A tremendous company that's made great progress. And so the quantum, you know, the public markets for quantum computing have reached a point of maturity. There's analysts that deeply understand the technology that are writing about covering different companies. It's a, you know, it's a very, very interesting marketplace. And then in terms of what it's like and the decisions that different companies have to make, I think the key thing is to take a long term perspective on what you're trying to accomplish and what kind of business are you trying to build? What kind of cap table do you want to build and what strategy best suits, you know, is best going to help you, help you achieve that. Yeah. What kind of feedback did you get in the early days around naming the company after yourself? I've been surprised that more. Yeah, there's so many generic names in the startup world now. That's like the Blank Company of San Francisco or things like that or you know, all the Neolabs have like the same sounding names. It'll be like Advanced Superintelligence. And then there was a big boom in like lys, like friendly bit ly, musical ly. There were tons of companies that were dot ly for a while. And I only know one other. I can only think of one other company. Chris Amadon's company has Amazon Heavy Industries. It's rare, but I'm sure, I'm sure people thought you were a little crazy back then. Well, quantum was a different thing back then. Look, I think there's two quantum companies that don't have a Q in their name. And I started both of them. One is Rigetti and the other Sigiltry, which is what, you know, what I'm focused on. But I will tell you, when you think about advice for founders, when you think about naming something and advice is worth what you pay for it, but think of a name that can become iconic. And if you. That means it's got to sound very fresh and new and different. And if every other corn company has a cue in it, maybe you try avoiding that. That's what led me to Sigildry. Siglitry. I love this name. It's from a Patrick Rothfuss novel. And here's an American writer or this incredible novel called Name of the Wind that came out in mid 2000, 2010 or so. Anyway, so Sigildry is we're building quantum accelerated. Quantum accelerated AI servers for the data center to bring quantum technologies directly into the data center to act as a co processor for the GPU or XPU pods that have become the unit of compute in AI infrastructure today. And we're based in Ann Arbor in San Francisco. Our hardware development is here in Ann Arbor, Michigan, where it is hot and humid today. And. And our AI research team is right there in downtown San Francisco. So what actually needs to happen? What is the path to, I would imagine, like, cheaper tokens. Is that the pitch? Like, one day the tokens will be cheaper and we need to do X, Y and Z to get there. What's X, Y and Z? You need to. Well, first of all, quantum hardware is going to address a lot of different computational challenges today, right? So quantum computers were able to solve problems that are impossible. They're very challenging to solve with any form of classical computing, no matter what scale it reaches. So signaldry, we're focused on applying that capability specifically to some of the computational challenges in AI to reduce the power and reduce the costs associated with training and deploying these models at a very large scale. What needs to happen to get there? Well, you have to build a quantum computer that meets the specific requirements for AI workloads. And the strategy that we're taking at Signaldry is we are very focused on deeply understanding what those challenges are, what needs to happen in, inside the data center to reduce the, to bring these algorithms that can have a different kind of scaling complexity class than classical algorithms for AI training and inference, and then understanding what kind of quantum hardware is needed to run those. And what we found is there's a set of requirements that you need to meet that probably are never going to be met by single modality hardware. What do I mean by that? In quantum computing and quantum hardware, there's different kinds of qubit technologies that you can use to, to instantiate the qubits. So there's supermarketing qubits. That's what I did my PhD in and what my first company was based on. That's what IBM is focused on and largely Google has been focused on. But there's also trapped ions. Quantinium and Ionq are doing trapped ions and a long list of other companies. There's photonics, there's now neutral atoms, there's spin qubits and semiconductors. There's all these different hardware substrates that people are using to pursue and to build quantum computers based on those. And what we're doing at sigldry is stepping up a layer and saying from a computer architecture perspective, you know, modern computers aren't built out of one, aren't built out of one physical kind of bit. There's not just one transistor type that makes up these computers that we're using today or the computers that are used to train large scale models and deploy them. There's a plethora of different physical technologies that are used to build these computer systems. And so at signaldry, we're looking across all the different quantum modalities and hardware types and architecting computer systems to meet the requirements of AI to based on the maturing path that all these different hardware modalities are on. And that allows us to build systems that are specifically tailored to AI that we believe are going to be able to meet the requirements of bringing quantum into the AI data center at scale. How important is simulation at this point? Are you at a place where you can run this, like basically run the code of the future in simulation to understand like run it on a classical computer, not see the performance gains, but at least understand that when the computer, when the quantum system is available there will be a cost savings? Yeah, we've been able to do that, largely speaking. And you can do simulations of something computer system or a jet or anything, and varying levels of physical fidelity and detail. The simulation we've been able to do so far indicate that we expect a level of, you know, several orders of magnitude potential speed up for key training tasks. Right. So this is not a factor of two or a factor of five increase that we're targeting with quantum acceleration inside the data center. It's several orders of magnitude, you know, when, when all the pieces come together. But that simulation you talked about is a really, really important and powerful part of designing a computer system. You can't simulate all the, all the logic of a quantum computer because that would require a quantum computer itself, kind of by definition. But you can do load profiling, you, you can do traces, you can understand how that's going to be distributed across classical and quantum hardware and also simulate all the networking transactions in between. And so that's the kind of simulation driven design approach we're taking. Yeah, I guess what specifically in training benefits from quantum computing? Because the example that everyone goes to in terms of quantum computing, you know, novel, novel algorithms that actually have potential to do something that a classical computer can't do. It's like Shor's algorithm cryptography usually. But when people think about training AI, they usually just think a bunch of matrix multiplication. Is there some different path that you plan on taking? Or do you think you can operate at sort of a hardware agnostic layer, much like we're seeing leading AI firms get off of cuda. Like, is there a world where you get off of classical. But by and large it's the same training paradigm. It's really interesting. I think the answer is both. So our starting point is we're looking at ways that you can insert quantum algorithms and quantum computing capability into the existing paradigm, the existing workflow for training and deploying very large models, frontier models at scale. And that means you're looking for an insertion point from quantum algorithm where the data in, the data out allow you to then take a step that would take maybe a day or two classically and compress that down to hours or minutes and do that throughout the workflow. The challenge is that quantum computing provides an exponential, the possibility for exponential speed up with the right algorithm. But it also has this issue with data in and data out. So it's classical data in which is can't be exponential in size and classical data out. And so the less you do that, that translation between the quantum part and the classical part, it's going to end up working better. So asymptotically where we're, where we're heading is more quantum native models. Models that are designed in the first place to leverage a quantum computing capability tightly integrated with your classical infrastructure. But where you're not, where you're probably not going to see is fully quantum based models that don't include a substantial amount of classical compute as well. This isn't going to replace all the AMD or Nvidia infrastructure in the data center. It's going to augment it. And our business model and our focus and our product strategy is to build a quantum accelerated AI server that sits next to the POD and acts as an accelerator for the XPU or the GPU POD in the data center and drive towards very high attach rate of ideally one to one in the data center infrastructure of the future. And that's what's going to allow you to then run, accelerate the current paradigm, but also use it as a substrate to design new kinds of models that will fundamentally be better and more efficient. More efficient from a time perspective, from a cost perspective, from an energy perspective. But also these models are just in a way, just a representation of the computer hardware that they're based on. And what's easy and hard from a computing and communication perspective on the hardware translates into the model capability. And with quantum, you have a fundamentally new resource in the data center that's going to allow new model capabilities to be developed and brought to market. How are you thinking about timelines with the new company? Do you think there's, I imagine with the business right now is entirely more of technical risk than execution risk. Is that the right way to think about it? There's a lot of hardcore research that needs to be done understanding, you know, the feasibility of, of, of the approach and, and what kind of, kind of like conversations are you having with you know, potential partners, if at all right now versus you know, about, about kind of like the near term application or are you, you know, our conversations like 2030s and beyond kind of thing? Yeah, we're targeting, we're talking to customers now. We've got several active conversations. I think partnerships and early engagement with customers is a big part of our strategy. The reason that's important is because the challenges of really bringing a new compute capability into the AI data center are substantial and you got to be working with customers out of the gate to really understand those requirements, what moves the needle for them as an organization. And so that's what we're, that's what we're doing and that's what we're focused on in terms of timing. It's a fantastic time to start a company like this. The underlying hardware has made such tremendous progress in the past 10, 15 years. And the market is, you know, with the amount of investment that's being made in AI infrastructure, there is clearly a recognition that we need a new approach to drive down the cost per token, to drive down the energy associated with these very large scale data center projects to make it fundamentally more efficient. And Quantum promises a, you know, a more efficient way of translating Watts into intelligence. That's what this enables and unlocks in the long term. And to me, this is in many ways a better idea than putting stuff, stuff in space because ultimately, yes, space gives you a lower, you know, cheaper access to energy and it gives you a better way to, to dissipate that heat. But, but you got to put it into space. And that takes a lot of fossil fuels. That takes a ton of energy in the first place. And it doesn't actually change the computational complexity of the computer hardware that you're running. Why don't the Power challenge Quantum can unlock much more than that. Yeah, it's a good point. Why don't you think Elon has made a real run at Quantum? I think the answer is that Quantum is at the, at this interface of deep science and engineering. And a lot of what needs to happen over the next three to five years to bring this technology to market at scale is engineering risk. But it is quantum engineering risk. And it's not vanilla. Not that it's easy. Not that any of the purely classical stuff is easy. It's not vanilla rocket science. It's not vanilla rocket science and it's not vanilla fab at scale. Right. And so if you look at the leaders in quantum computing hardware, it's not necessarily the Intels of the world, incredible company that has propelled humanity forward for half a century, but they're not the leaders in Quantum, because Quantum is a new form of engineering. And I wouldn't characterize it as science risk. I think for Quantum, a lot of that is behind us, even though there's tremendous work to be done. But there is a lot of quantum engineering risk. And that's an area where I think you need to see companies that are quantum specific bring the technology forward. And at that point, I think that all the big labs are going to need to lean in with Quantum. Yeah, when, when do you think there will be a flip around sentiment from around, around Quantum? It feels right now like at least in our corner of the Internet. There's so much FUD around Quantum and obviously financials. Like. Yeah, yeah, yeah. So that's, that's what I want to know, though. Like, is, is there like, like, you know, rewind 10 years if somebody said AI, there was a very, very small percentage of people that were, like, incredibly excited about it and, you know, deeply involved and could see the trend line and could see that we would get to this point. I mean, Sam was talking about, like, people becoming best friends with a chat bot. I think in like 2015 or something. Like 2014 was like losing money. It wasn't like making revenue. Yeah, that was even before that. Yeah, no, I know. Well, well before that. And so. But then eventually it flipped and it's really hard to, you know, there's a lot of people that are AI bears and they talk about, like, overinvestment, but they can't deny the value of the products. Right. Like, they're fundamentally pretty useful. Right. And you could argue that they're, you know. Well, some bears can, but yes, yes, some bears would still figure out a way to argue that it's that they're not useful. But I imagine with both of your companies, you're predicting that within the next five year, there's a flip. But what do you think is the first driver of that? Where maybe the average person in Silicon Valley actually starts to say, hey, I wasn't taking Quantum seriously enough. There's a few things that need to happen. I think the FUD is real because the companies that are succeeding and doing well in this space, you can't tell by looking at their financials. You can't put on your kind of growth investor hat and say, yeah, this is going to be a tremendous company. And look at the metrics. It doesn't work like that. You got to be able to analyze and look at these companies and value them based on their ability to buy down technical risk over, over time and the progress that they've made towards that. So it just creates a lot of uncertainty because it's a challenging task and it's subject to a lot of, you know, discussion and debate. But nonetheless, I think there are clear, there is clearly tremendous momentum and progress in the space now. What's going to change it? I don't know. My bet is when we have quantum computers in the data center running production workloads and that you don't have to say, hey, that's a quantum computer for someone to care, you care, because it's a more efficient way of generating the, you know, the answers you need or, you know, training the model or deploying the model for inference. And that's when quantum is really going to become a mainstream category, is when you don't have to talk about the fact that it's quantum anymore. And I think in a large part this is what we're trying to achieve with Signal Tree, right? The goal is that to take quantum computing and to obfuscate it underneath the hood of a classical computing system or underneath all the rest of the infrastructure that's already there and to not ask the end user to be programming it and writing code for it. That's all going to be done with AI anyway. And so that is just a better, it's a better way to train your model and you know, you need this thing or else it's going to take you too long and your customers aren't going to be happy with the quality of the outputs they're getting. That to me is a big inflection point. And I think that can happen in the next five to seven years. I think that can. But there's this whole march that needs to happen to take the technology from one proof point to then, you know, all the cost engineering that needs to happen, the reliability engineering, and that's going to be the really fun journey for quantum computing over the next decade is to get to that point where we're selling hundreds or thousands of units a year. But that's the journey we're on. And that's the march that quantum technology has been on for a good one two decades now. And then this is probably very obvious to somebody that is focused on quantum, but not to me just because I don't follow it closely. But why a new company? It feels like quantum, as you've explained. It feels very obvious to apply it to data center build out. And you said it could be like a meaningful inflection point for the technology overall. Why was a new company necessary and why did you take this approach? Well, high level, I think all the different quantum hardware modalities have made tremendous progress. And the right way to build quantum computers for AI is multimodality. That is a fundamentally new approach and it ultimately is going to, in my opinion, be very obvious. In retrospect, it's going to work better. But it is, it's such a fresh idea. It's got to be baked into your strategy, the DNA of your company and then all the different quantum hardware companies that were out there before Sigildry basically started with a thesis which was we've got the best qubit and so we're going to scale this qubit type up and see how far we can get by scaling it up. And that's why you have so much doctrine and like kind of organizational belief around a particular qubit choice. But in reality, you know, customers are buying a computer, they're not buying a, you know, the, the physical device or your, your Qubit technology. And so at Single Dream, what we're doing is working backwards from the market application, from the AI workload as the, as the use case, and using that to drive the specification of a system that can then be built from folding in whatever technologies are needed to meet those requirements. Requirements. It's just such a totally different approach to quantum hardware. It's got to be a new company. And that's, that's, that's Signal Tree technologies. That's the approach that we're taking. I think that that is ultimately what's going to unlock this new market, you know, this market application of AI. The other reason is you said it's obvious, but it's actually not obvious at all to most people in Quantum that Quantum is going to be useful for AI. And in fact it's not even a consensus view right now. And the reason for that is because quantum algorithms themselves are still in this very, this phase of discovery and development. And obviously AI is going to help with that eventually as well to an extent. But Quantum, you know, when you interview a set of leaders from across the quantum hardware industry, the, the, you know, the, the median answer you're going to get for what the applications of Quantum is going to be is you're going to use it for quantum chemistry, you're going to use it for optimization problems, things like that. Then applications to Frontier AI is a new area that is, is just being developed now because it requires a development and extension of what current algorithms can do and then new algorithms altogether specifically for that. That's what we're tackling at Sigljry is that kind of Quantum AI native research lab. Right. Or a Frontier AI lab. That's Quantum native. And then we're doing that alongside developing our own quantum hardware. Thank you. Before, before we jump, I didn't get you mentioned kind of the history behind the name, but what is the significance of Sigildry in the novel that you mentioned? Well, you guys got to read the novel for one. It's absolutely incredible. And, but the other thing is singletry is basically a discipline in the book that is learned at university and you know, and it basically amounts to you inscribe runes on a particular object, and by doing that, you can imbue that object with properties that it wouldn't otherwise have, or you can govern, like heat and light, flow and things like that. It's also a discipline where it's got a quantitative angle to it, and if you do it wrong, you can blow things up. So it's got this. This mix of kind of coding and hardware, then a mysterious kind of angle of controlling things from a distance by how you do this, these inscriptions. So it's a really amazing concept, a little bit of magic. Amazing. Thank you so much for taking the time. Have a great rest of your day. Cheers.

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